Maintaining a Reliable World Model using Action-aware Perceptual Anchoring

Reliable perception is essential for robots that interact with the world. But sensors alone are often insufficient to provide this capability, and they are prone to errors due to various conditions in the environment. Furthermore, there is a need for robots to maintain a model of its surroundings even when objects go out of view and are no longer visible. This requires anchoring perceptual information onto symbols that represent the objects in the environment. In this paper, we present a model for action-aware perceptual anchoring that enables robots to track objects in a persistent manner. Our rule-based approach considers inductive biases to perform high-level reasoning over the results from low-level object detection, and it improves the robot's perceptual capability for complex tasks. We evaluate our model against existing baseline models for object permanence and show that it outperforms these on a snitch localisation task using a dataset of 1,371 videos. We also integrate our action-aware perceptual anchoring in the context of a cognitive architecture and demonstrate its benefits in a realistic gearbox assembly task on a Universal Robot.

I. INTRODUCTION
Robots today often interact with objects and other agents in their surroundings. To perform complex tasks, robots should reason about the world before they can plan and execute actions. But the interaction among objects in the environment sometimes limits a robot's visibility, causing it to lose sight of certain objects. This is especially true when using the popular eye-in-hand setup like in our case shown in Fig. 1. To remedy this issue, we therefore need the robot to maintain a reliable world model, with which it can estimate object states when they are no longer visible. This capability, often denoted as object permanence, is a frequently studied topic in developmental psychology that describes the ability to understand that objects continue to exist even when they become unobservable. Psychologists believe that human infants develop full object permanence at around two years of age, when they start to understand that objects still exist even if they have been completely removed from their field of view [1].
In robotics, object permanence is an important concept with practical benefits that augments robots' searching and tracking abilities [2,3], facilitates self-recognition [4,5], and enables belief management and perspective taking when engaging with humans [6,7]. Common approaches consider object permanence as a low-level reasoning problem that heavily relies on the visual perception capabilities [2,4,7]. There are also higher-level approaches that use perceptual anchoring to create and maintain the correspondence between symbols and perceived sensor data referring to the same physical objects [8]. These typically use some form of high-level reasoning to track objects that disappear. For instance, there are heuristic approaches that determine objects being grasped by a hand [6] or track the nearest visible object instead of an unobserved one [9,10]. Others use probabilistic reasoning to infer occluded object states [11,12].
But the performance of these models drops with noisy data or more complex scenarios, like when there is flickering detection of false positives or when objects are no longer detected upon insertion into another object and are subsequently 'carried' (e.g., invisible displacements [13]). For example, when a plug is inserted into a case and is no longer visible, it becomes difficult to infer its position from vision alone, especially when the case is moved. Since we know that the plug has been inserted or attached to the case, we can track the plug based on the position of the case.
In this paper, we define an attachment between two objects to be the state resulting from an action that sticks one object to the other, e.g., insert or screw-on, such that the former becomes a part or an extension of the latter and is physically bound to move together. To reason about attachments, the perceptual system should have knowledge about the agent actions that result in such object states. We propose a perceptual anchoring model that uses agent actions to maintain a reliable world model. By considering actions and their effects (i.e., attachments), the robot can predict imperceivable state changes and estimate the positions of invisible objects.
To start our discussion, we first review some related work (Sec. II). After that, we discuss the fundamental hypotheses of our work and describe our system in detail (Sec. III). We show that this rule-based high-level reasoning approach augments the performance of low-level approaches, by first comparing our system to baseline models on the LA-CATER dataset (Sec. IV) and show that our model outperforms them on a snitch localisation task. We further demonstrate the system on a realistic robotic application with a Universal Robot (UR5) where it performs a collaborative gearbox assembly task (Sec. V). We conclude with a discussion on future work to further improve our model (Sec. VI).

II. RELATED WORK
Object permanence has been addressed previously within the computer vision community, especially for multi-object tracking. Papadourakis and Argyros [2] handle temporally long object occlusions by assigning spatially close objects as occluders to objects. Lang et al. [5] trained a CNN to learn a mapping between the current sensor state and the robot's intended motor actions to predict the next sensor state. It considers joint configuration changes of its own arms that might cause object occlusions and uses the predicted image to replace the occluded part of the object in the current image. Shamsian et al. [9] proposed the OPNet architecture that uses two LSTMs to track the target object's visibility, its occluder that carries it, and the target localisation.
High-level approaches in developmental robotics have previously addressed the perceptual anchoring problem. In Roy et al. [7], the robot maintains a 'mental model' of the environment in order to generate different viewpoints of objects. In Milliez et al. [6], the robot makes position hypotheses on occluded objects such as the human grasping it or placing it inside another object. Persson et al. [11] described a probabilistic reasoning system that infers the state of occluded objects and continue tracking through occluded movements. Blodow et al. [12] uses probabilistic anchoring to identify objects that come back into view. Elfring et al. [14] uses a Multiple Hypothesis Tracker algorithm to improve tracking reliability during occlusion and detect clutter (false positives). Heyer and Gräser [15] define relative anchors to represent relations between two reference anchors and use the robot's (camera) movements to re-anchor them. Nitti et al. [16] implemented a relational particle filter that draws on commonsense world knowledge during tracking, such as when one object is inside another. Mininger and Laird [17] address anchoring errors that occur when associating perceptual data with symbolic representations and how to resolve them. While the above approaches address the same problem of tracking occluded objects, only few address invisible displacements and none make use agent actions to predict attachments. We believe that attachments can significantly improve the high-level reasoning process to complement existing approaches.

III. ACTION-AWARE PERCEPTUAL ANCHORING
We propose a perceptual anchoring model that uses agent actions together with perceptual information to maintain a world model of object anchors, described as a set of predicate symbols  = { 1 , 2 , … }. We believe that highlevel reasoning is necessary to maintain object states that are no longer perceived by low-level object detection and can significantly improve object tracking for complex tasks such as invisible displacements. For this, we consider the following hypotheses that object state changes are: H1. Observable: objects do not move on their own and state changes are only caused by agent actions. Thus, we assume that object positions stay the same as last time they were seen, until they are redetected. H2. Gradual: objects do not 'teleport', so detected objects in consecutive frames are likely to have similar attributes e.g., position and size. H3. Consistent: objects are detected consistently over time, so they are only considered to exist/not exist in the world model if they are present/absent for multiple consecutive cycles. These assumptions allow us to make reliable inferences for the positions of objects that are no longer detected by the perceptual system but are physically still present in the scene. The end-to-end complete pipeline consists of three components ( Fig. 2): A) object detection and segmentation, B) action-aware perceptual anchoring, and C) world state inference and action execution. In the following, we describe each component in more detail.

A. Object detection and segmentation
This component performs the initial object detection and feature extraction. It can be replaced by any object detection module as long as it produces a list of object positions and dimensions. The output is a list of percepts Π = { 1 , 2 , … } each associated with a unique identification label and a set of attributes Φ = { , , } corresponding to the object category, its position and dimension of the bounding box.
can also include other attributes such as colour ( ) that can be used in the anchoring component. Since object detection is an on-going research problem, the assigned labels may not always correspond to the same object in the environment, especially if there are multiple objects of the same category. This will be handled by the perceptual anchoring model in the following Sec. III-B. In our experiments on the LA-CATER dataset (Sec. IV) and demonstration with the UR5 (Sec. V) we used a Faster R-CNN (fine-tuned to the respective domains) to output Φ = { , , , } for each detected object .

B. Object permanence with action-awareness (AAPA)
Our model takes the list of detected objects Π and anchors them to the maintained symbol system . The anchoring process consists of two main sub-components: object alignment and hypothesis reasoning.
1) Object alignment: The first part of our model addresses the alignment problem, i.e., finding the correspondence between objects in two consecutive frames. In our case, we find a mapping between the symbol system  at time and the new object percepts Π +1 in two steps: a) Camera movement alignment: Whenever the robot's camera moves, the previously maintained object positions will not be correct, especially when dealing with eye-inhand robotic systems. Thus, we update all object positions in the current world model  to compensate for the camera movement. Given the respective camera positions at time and +1 as and +1 in a frame of reference, we adjust the position of an object ∈  tô = +|̂ +1 −̂ |, wherê are the camera positions transformed onto the camera frame where is written. This also allows us to track objects that go out of view due to camera movements, based on our observability assumption (H1).
b) Consecutive frame alignment: Using the adjusted positions, we now want to find the optimal alignment between the estimated object states = {̂ ∶ 1 … } and the newly perceived objects Π +1 = { ∶ 1 … }. We define as a cost matrix to measure the dissimilarity between̂ and , where each entry ( ,̂ ) is determined by where denotes a type mismatch constant and (⋅, ⋅) a distance measure such as the 2 -distance. In our implementation we used the Munkres assignment algorithm [18] to find the optimal alignment, but this can be replaced by any other alignment algorithm. We create a new symbol system  +1 = { ∶ = 1 … } with ≤ and elements =̂ for which ( ,̂ ) < , where is a maximum threshold that depends on the reliability of the object detection output. We update the position and size of with Φ , the attributes of and assign a confidence value ∈ [0, 1] which will be used in the hypothesis reasoning component.
2) Hypothesis reasoning: The second part of our model considers objects, that have not been assigned successfully in the alignment process. This includes previously maintained anchorŝ ∈ witĥ ∉  +1 , as well as newly detected objects ∈ Π +1 . Based on our hypotheses on object state changes, we reason about occlusions, potential detection errors and other state changes to decide if their anchors should be maintained.
a) Occlusions and out of view: Objects are considered occluded if their maintained anchor̂ ∈ intersects with a detected object. More formally, if there exists ∈ Π +1 such that the bounding boxes overlap, i.e.,̂ ∩ ≠ ∅. Objects are considered out of view if they have not been assigned a percept and their predicted position̂ is outside of the agent's field of view. In both cases, the anchor is maintained in the world model and̂ is added to  +1 . If an object is removed from the view without an agent action, it is marked as a lost object. b) Object attachments: We define the semantic relation where an object is attached to another object and refer to them as child and parent respectively: if there exists a symbol ∈  +1 and a relation attached( , ), then the child is considered to be attached to the parent and updated to follow . This way, we can track , even when it is not detected, as long as its parent is visible. This relation is inferred from effects of executed agent actions, e.g., for pick-up(hand,obj), the world state changes from is-empty(hand) to holding(hand,obj), from which we can infer attached(obj,hand). Similarly for insert(obj,case) leading to inserted(obj,case) and to attached(obj,case). We assume that an object can only have one parent but one parent can have multiple children. However, more attachment relations can hold at the same time, allowing for an attachment hierarchy e.g., attached(plug,case) and attached(case,hand). In this case, an object anchor (e.g., for plug) will be maintained if it has a higher-order parent that has already been anchored (e.g., hand). In our implementation, we recursively check for attachment relations to maintain anchors for all children.

c) Confidence: Each anchor
∈  is assigned a confidence value ∈ [0, 1]. If the object is first detected, we assign = 0 and for each subsequent cycle where it is successfully aligned, is increased by a fixed increment conf + ∈ (0, 1), where we cap ≤ 1. If the object disappears, but has previously reached an anchoring threshold ≥ ℎ , it will be considered by the hypothesis reasoning component. Otherwise its confidence value will be decreased by a decrement conf − ∈ (0, 1) and when it reaches < 0 it will no longer be maintained in the world model. This allows us to handle false positives or negatives from noisy data, where objects appear or disappear intermittently (H3).

3) World model maintenance:
At each cycle we maintain a complete world model, where those that have not been consistently perceived ( < ℎ ) are not considered for hypothesis reasoning, while the rest ( ≥ ℎ ) are anchored. We can optionally define another confidence threshold ≥ ℎ to filter out objects, that we are confident about, to be considered for the world state inference (discussed in Sec. III-C). These thresholds allow the model to be adjusted to the application scenario that depends on the reliability of the object detection output (e.g., higher ℎ to handle flickering more detection errors).

C. World state inference and action execution
The final component of the pipeline consists of the world state inference. It uses the maintained symbol system  +1 to infer world states  +1 (⊇  +1 ) that additionally include relations between the maintained objects such as spatial relations (e.g., behind( ℎ, )) or object attachments (e.g., attached( ℎ, )) inferred from executed actions (insert( ℎ, )). The world state  +1 is then used for task planning to infer the next executable action. It relies on having correctly anchored objects, as an incorrect world model would yield wrong world states and may not find any executable actions. Finally, the complete world model and executed action are passed as input to the perceptual anchoring model in the subsequent cycle.

IV. EXPERIMENTAL EVALUATION
We perform a quantitative evaluation of our model and benchmark against Shamsian et al. [9] on their LA-CATER (Localisation Annotations) dataset on a snitch localisation task. We compare model performances when using a Faster R-CNN model as the object detector to using perfect perception (ground-truth object locations).

A. Dataset
The LA-CATER dataset [9] was generated from the CATER dataset [19] which contains synthetically rendered videos (10-second, 300-frame) of standard 3D objects (cube, sphere, cylinder, inverted cone) moving in space. The objects can perform four actions (slide, rotate, pick and place, contain), where 'contain' describes a cone moving on top of an object and containing it. The dataset contains 14K videos split into train (9.3K), validate (3.3K), and test (1.3K) datasets. Every video contains a 'snitch', a small golden sphere that is used as the target object for the localisation task. The LA-CATER dataset augments CATER with framelevel annotations and ground-truth locations for the entire video sequence as well as labels for four object permanence subtasks (visible, occluded, contained, carried).

B. Baselines
We compare our model to the neural model OPNet and a programmed heuristic model both proposed in [9] and reran them using the code provided by the authors. The heuristic model switches from tracking the target object to tracking the object located closest to the last known location of the target. OPNet is a deep network architecture that uses two LSTMs as reasoning modules to infer the object to track (if the target is carried or contained) and its exact location. OPNet has been trained and validated on the LA-CATER dataset and outperforms other baseline models, including four 'learned' models and two 'programmed' models, including the heuristic model. As the heuristic model was reported to perform similarly on carried tasks (IoU: OPNet 56.04% vs Heuristic 55.87%), we decided to include it in our evaluation.
Since we work with synthetically generated data, the detected objects can be considered more reliable and contain little to no noise. Therefore, we evaluate our AAPA model with = 6500 for the object alignment threshold, ℎ = 0.1 and confidence values conf + = conf − = 0.1. This means that objects that appear in two consecutive frames are considered for hypothesis reasoning. For object attachments, we use the action annotations from the LA-CATER dataset and trigger an attachment relation when a 'contain' action has been executed.
All models receive bounding box information from the object detector (a Faster R-CNN fine-tuned on the LA-CATER dataset). As misclassified objects can affect the overall performance of the models, we also perform comparisons using Perfect Perception, i.e., we use the ground-truth locations of the object bounding boxes. This allows us to compare only the failures that occur during reasoning.

C. Evaluation Metrics
Similar to [9], we use the mean intersection over union (IoU) of predicted and ground truth bounding boxes as an evaluation metric. However, in order to exclude biases from false predictions, we only compute the metric from the moment, when the snitch is first observed by the object detector, resulting in differing IoU values than reported in [9]. As we are mainly interested in the object location, less in the detected bounding box, performances might differ if predicted bounding box dimensions are wrong. Thus, we also use mean 2 -distances (given in pixels) of the centre points of the predicted bounding boxes as a second performance metric. As a reference, the video size is 360x240 pixel, the ground-truth size of the snitch ranges from 15x16 to 41x41 and of any object in the scene from 8x8 to 117x117.

D. Results
In the following we present results using object detection (OD) and perfect perception (PP) and both mean IoU and L 2 -distances. We compare performances on different task categories (visible, occluded, contained, carried) and discuss common failure cases and their causes.
1) Using object detection (OD): Table I.I shows a summary of the model performances comparing AAPA with OPNet and Heuristic by mean IoU and 2 -distance. AAPA outperforms the baseline models in all categories, except on visible tasks, where it performs similarly (AAPA: 88.67% vs OPNet: 88.98% vs Heuristic: 88.00%). AAPA's performance drops for occluded tasks, mainly due to the snitch moving in occlusion as we assume that objects do not move on their own (H1). Other errors included incorrect prediction of bounding box size for partial occlusions. Similarly, for contained tasks, most of the failures occurred when containment happened after the snitch was moving in occlusion. For carried tasks, AAPA performs a lot better than the other models (AAPA 68.25% vs OPNet's 56.04% vs Heuristic 51.92%). Main failures include moving in occlusion (while being carried) and anchoring errors caused by the threshold parameter in the object alignment. The mean IoU performance is also impacted by the fact that AAPA retains the last detected bounding box size before it became contained, while the ground truth size becomes smaller or larger, when 2) Using perfect perception (PP): When using ground truth bounding boxes instead of the object detector, no detection errors are propagated to the models, allowing us to evaluate their reasoning capabilities. All model performances improved, with AAPA improving the most on carried tasks, outperforming the baseline models in all tasks for both mean IoU and 2 -distance (Table I.II). For visible tasks, AAPA and Heuristic benefit the most from ground truth information, as there is no bounding box alignment error. The main failure cases are caused by moving in occlusion and anchoring errors. The impact of AAPA not adjusting the bounding box size while being carried in 3D space can be seen when comparing mean IoU to 2 -distances: the mean IoU for AAPA is better for occluded tasks (90.53%) compared to carried tasks (82.54%), but the mean 2 -distance shows the opposite: 2.51 for occluded vs 1.94 for carried (lower is better).
3) Discussions: While many deep learning approaches exist that address object permanence, they require large amounts of data and remain a black-box solution. This means that success and failure cases are not necessarily explainable and there is little room to investigate wrong reasoning processes. One of the main benefits of our approach is the transparency of our rule-based model, which makes it human readable. This allows us to investigate failure cases and to fine-tune our model parameters e.g., that anchoring failures occurred when objects moved in occlusion. Other failures were due to objects taking a bigger leap between two consecutive frames: Our model considered them as two different object instances, due to our assumption that object positions stay the same as last time they were seen (H1) and they do not 'teleport' (H2). If we increase , the threshold for consecutive frame alignment, these cases are anchored correctly, but would give a bigger leeway to detection errors, resulting in objects anchored to ghost objects. We experimented with these values and chose a threshold that yielded the maximum overall performance for this task.
The figures next to Table I show the distribution of mean 2 -distances for all 1,371 videos for Heuristic, OPNet, and AAPA using OD. AAPA produces more accurate predictions and with only a few outliers, representing the failures mentioned above. The biggest outlier for AAPA comes from a video, where the snitch was occluded in all frames but one. The baseline models (OPNet and Heuristic) will always produce a prediction for each video frame, even when the snitch is not visible at all. On the other hand, our AAPA model assumes that object state changes are consistent, and only maintains anchors for objects that are detected in multiple consecutive frames. Therefore, in this case, AAPA fails to maintain the anchor for the snitch and returns no prediction of its position, which the evaluation considers as a [0, 0] vector and returns an 2 -distance of | | given by the ground-truth position.

V. REAL-WORLD APPLICATION
We evaluate our model qualitatively and demonstrate its use in real-world applications on a collaborative gearbox assembly task with the Universal Robot UR5, an industrial robotic arm with 6DoF and 5kg payload. The robot is mounted with an Intel RealSense D435 camera, acting as an eye-in-hand system (Fig. 1). AAPA has been implemented as part of ICARUS, a cognitive architecture for modelling human cognition [20], which uses the world model for world state inference and task execution (Fig. 2C). For the object detection ( Fig. 2A) we use a Faster R-CNN that has been fine-tuned to object categories in the gearbox assembly domain (e.g., casing top/base, input subassembly). We found the optimal confidence parameters for this scenario to be A. Gearbox assembly scenario For autonomous navigation and intelligent collaboration, robots must track objects, even if they are no longer perceived by the detection system. For eye-in-hand robotic systems, this is particularly important as the grasping action generally results in losing sight of most surrounding objects. We demonstrate the use of AAPA on an assembly task, where the robot has to manipulate gearbox assembly parts in collaboration with a human operator. The cognitive architecture uses the world model maintained by AAPA to reason about the necessary actions to execute. The collaborative gearbox assembly consists of the following steps: 1) Robot attaches small hub cover onto casing top (case0).
2) Human screws in hub cover plugs.
3) Robot inserts input subassembly to casing base (case1). 4) Human moves casing base to the robot. 5) Robot mounts casing top to casing base.

B. Observations and results
The robot successfully executed the gearbox assembly task in real-time (see Fig. 3). We compare the maintained world state with and without our AAPA model. 1) Unique label assignments: The Faster R-CNN detects objects and assigns unique labels in the format typeN, where is the instance number assigned in order of appearance (e.g., case0, case1). When there are multiple objects of the same type, it can produce incorrect labels as the order of appearance changes or an object disappears from the view. AAPA maintains the unique labels of the anchors with the consecutive frame alignment (Fig. 3b,d).
2) Occlusion and out of view: Once objects are occluded or go out of view, the Faster R-CNN loses track of them, which can lead to failures in the world state inference and action execution. With AAPA, the anchors of these objects are maintained in the world model (Fig. 3b,e).
3) Handling detection errors: When objects are cluttered, stacked, or partially occluded, it sometimes detects the wrong object type or fails to detect them. State-of-the-art object detection models can be very error-prone due to environmental changes such as lighting conditions, or simply when they are viewed from different angles or in close-up when grasping. These models commonly produce flickering ghost objects that appear and disappear within a couple of frames. The robot needs to handle these detection errors correctly, otherwise it can result in an incorrect world state inference and task execution. Our model handles these errors using the confidence value that is updated at each cycle (Fig. 3c).
4) Attachment relations: The assembly task triggers object attachments (i.e., attached(small hub cover0, case0), attached(input subassembly0, case1)). When the robot picks up the casing top (Step 5), it fails to detect the surrounding objects, including the hub cover. However, since it is attached to the casing top, its anchor is maintained and updated to follow the casing top as it moves (Fig. 3e). Similarly, when the robot grasps objects during task execution, it sometimes fails to detect them. Since the pick-up action creates an attachment between the gripper and grasped object, the anchor is maintained and updated consistently.

VI. CONCLUSIONS
We presented an action-aware perceptual anchoring model (AAPA) that can handle object detection errors, out of view, and more complex scenarios such as invisible displacements by considering agent actions. We showed that AAPA outperforms baseline models on the LA-CATER dataset for a snitch localisation task. We demonstrated its use for robotic applications on a real-life gearbox assembly task between a human operator and a UR5. AAPA successfully maintains a reliable world model for the robot to complete the task. Our approach can be used for hierarchical attachments as well as other agent actions that cause attachments (e.g., screwing a plug onto a case). Given a list of agent actions that cause attachment relations, our model recursively checks for these relations and automatically infers the attachment hierarchy irrespective of the number of relations defined. In the future we will focus on using human action recognition in combination with (visual) commonsense reasoning to infer attachment relations automatically from agent actions and object types (e.g., screw action, container objects).


Construction of Student Information Management System Based on Data Mining and Clustering Algorithm

Data mining is a new technology developed in recent years. +rough data mining, people can discover the valuable and potential knowledge hidden behind the data and provide strong support for scientifically making various business decisions. +is paper applies data mining technology to the college student information management system, mines student evaluation information data, uses data mining technology to design student evaluation information modules, and digs out the factors that affect student development and the various relationships between these factors. Predictive assessment of knowledge and personalized teaching decision-making provide the basis. First, the general situation of genetic algorithm and fuzzy genetic algorithm is introduced, and then, an improved genetic fuzzy clustering algorithm is proposed. Compared with traditional clustering algorithm and improved genetic fuzzy clustering algorithm, the effectiveness of the algorithm proposed in this paper is proved. Based on the analysis system development related tools andmethods, in response to the needs of the student informationmanagement system, a simple student information management system is designed and implemented, which provides a platform and data source for the next application of clustering algorithm for performance analysis. Finally, clustering the students’ scores with a clustering algorithm based on fuzzy genetic algorithm, the experimental results show that this method can better analyze the students’ scores and help relevant teachers and departments make decisions.

Introduction
Data mining started with the research of Knowledge Discovery in Database (KDD). It is a key step in the process of knowledge discovery [1]. Its production has its application background. As the world moves towards an information society, human beings ability to collect, organize, and produce information using information technology has also greatly improved, resulting in the creation of tens of thousands of various types of databases [2]. Data mining research does not only come from the accumulation of mountains. e demand for information processing is developed due to the urgent needs of all aspects of social development, and it plays a huge role in scientific research, technological development, production management, market expansion, commercial operations, and government offices. e academic and business circles at home and abroad attach great importance to the research and development of data mining technology and software tools. Data mining is an extremely young and active research field, which combines the latest research results of database technology, artificial intelligence, machine learning, statistics, knowledge engineering, object-oriented methods, information retrieval, high-performance computing, and data visualization. After more than ten years of research, many new concepts and new methods have been produced. Especially in recent years, some basic concepts and methods have become clear, and its research is developing in a more in-depth direction [3].
With the continuous expansion of the scale of education, the number of students has increased sharply, which has put a lot of pressure on student management. e informatization of the student information management system is far from satisfying the demand. erefore, the goal of building a digital campus is proposed, which is to use the Internet. Based on the use of advanced information technology methods and tools, from the environment (including equipment and classrooms), resources (such as diagrams, handouts, courseware, and information), to activities (including teaching, learning, management, service, and office), all digitized data flows on the Internet, and one line of students, disciplines, colleges, student information management, finance, etc. all realize computer management. is digital campus will accumulate a large amount of data. How to mine the laws implicitly in the large amount of data so as to use these laws to guide the work of the school, improve the management of the entire school, and improve management efficiency are an extremely meaningful work [4]. In response to the above problems, we propose applying the data mining method to the student information management system and extracting useful student information through data mining. Data mining is to extract valuable and interesting knowledge from the data of large databases. is knowledge is implicit, unknown in advance, but potentially useful information. Data mining means collecting some facts or observation data. e decision support process of the model is determined. It is an interdisciplinary subject that combines theories and technologies in many fields such as artificial intelligence, database technology, pattern recognition, machine learning, statistics, and data visualization. As a kind of technology, data mining is in the chasm stage of its life cycle. It needs time and energy to research, develop, and mature gradually, and finally, it is accepted by people.
is paper systematically summarizes cluster analysis, one of the key technologies in data mining, conducts indepth research on cluster analysis, introduces the current research hot issue-genetic algorithm optimization into cluster analysis-and proposes a fuzzy genetic algorithm clustering; the main content includes the following aspects: on the basis of a brief introduction to the research background of the subject and the significance of the topic, the current status of education informatization and data mining, related concepts of cluster analysis, and clustering are introduced. is paper introduces the current research status of education informatization and data mining, representative clustering algorithms, introduces traditional clustering based on genetic algorithm and clustering based on fuzzy genetic, and designs and implements a student information management system. Finally, the article applies the hybrid clustering algorithm based on fuzzy genetic algorithm to the analysis of student performance and compares and analyzes the clustering based on genetic algorithm and the clustering based on fuzzy genetic algorithm.

Related Work
e term Knowledge Discovery from Database (KDD) first appeared in the 11th International Joint Artificial Intelligence Conference [5]. After the first International Academic Conference on Knowledge Discovery in Database (KDD) and Data Mining (DM) was held in Canada in 1995, data mining became popular. It is the concept of "knowledge discovery," and the deepening of knowledge discovery and data mining is the product of the combination of artificial intelligence, machine learning, and database [6]. e scale of the KDD International Symposium hosted by the American Association for Artificial Intelligence has grown from the original symposium to the international academic conference.
e research focus has gradually shifted from discovery methods to system applications, focusing on the integration of multiple discovery strategies and technologies, and the integration of multiple disciplines. e domestic research on data mining was a little late and did not form an overall strength. At present, many domestic research institutes and universities are competing to carry out basic theories and applied research on knowledge discovery [7]. ese units include Tsinghua University, the Institute of Computing Technology of the Chinese Academy of Sciences, the ird Research Institute of the Air Force, and the Naval Equipment Demonstration Center. Compared with foreign countries, the research in the field of data mining in our country is still in its infancy [8].
e vast majority of work focuses on the design of local algorithms, and there are few integrated system integration designs. Due to the lack of core technology, data mining is only a preliminary application in some domestic fields, such as banking, finance, and GIS. At present, domestic colleges and universities have not carried out extensive research on data mining in the education system. Zhejiang University uses association rule technology for personnel affairs. e information database is digging, trying to find the factors that affect the development of the discipline, and the relationship between the various elements that affect the development of the discipline. Today, as the network application environment and trends are changing, the campus network construction is transitioning from a 10-Gigabit campus network to a "digital campus network." Digitization of the environment (including equipment and classrooms), resources (such as diagrams, handouts, courseware, and information), and activities (including teaching, learning, management, service, and office), thereby, enhances the efficiency of traditional campuses and expands the traditional function of the campus, ultimately realizing the comprehensive informatization of the education process and achieving the purpose of improving the quality of teaching, scientific research, and management [9]. A new era of education informatization is approaching, and it will gradually enter the "data core era" from the past "basic network core era." Data mining technology will undoubtedly play an increasingly important role in how to find the hidden laws in a large amount of data and then apply these laws to guide the work of the school. e half-life period of an algorithm being state-of-the-art shrinks also with increasing investments in Data Science and with more and more people being interested in the field of Data Science and Machine Learning. Consecutively, this article might be already out of date in a year. But for now, these are leading techniques that help in the progress of creating better and better algorithms. e current research and development directions of cluster analysis are as follows: (1) Research on the scalability of the algorithm: that is, the algorithm should be effective for both small data sets and large data sets. (2) Research on nonnumerical data clustering, which can handle both numerical data and 2 Complexity processing. Nonnumerical data can handle both discrete data and data in continuous domains. (3) Clustering studies of arbitrary shapes can be found. Traditional algorithms using Euclidean distance tend to find spheres with similar density and size clusters, but for other clusters, they may be arbitrary shapes, and it is extremely important to propose an algorithm that can find clusters of arbitrary shapes. (4) Research on algorithms for processing high-dimensional data: many clustering algorithms are good at processing lowdimensional data. In a high-dimensional space, especially considering that such data may be highly skewed and extremely sparse, clustering is extremely difficult. (5) Research on the ability to deal with noisy data: in real applications, most of the data contains, in addition to outliers, unknown data, vacancies, or erroneous data, and some clustering algorithms are sensitive to such data and will lead to lowquality clustering results, so the processing of noise is extremely important. (6) Fuzzy clustering research, such as the clustering of information such as text, image, and sound [10].

Cluster Analysis Based on Fuzzy Genetic Algorithm
Lin et al. [11] propose an attention segmental recurrent neural network (ASRNN) that relies on a hierarchical attention neural semi-Markov conditional random fields (semi-CRF) model for the task of sequence labeling. eir model uses a hierarchical structure to incorporate characterlevel and word-level information and applies an attention mechanism to both levels. is enables their method to differentiate more important information from less important information when constructing the segmental representation. In this paper, the genetic algorithm is a computational model that simulates Darwin's genetic selection and natural elimination of biological evolution. Its ideas are derived from biological genetics and the natural law of survival of the fittest. It is a search algorithm with an iterative process of "survival + detection." e genetic algorithm takes all individuals in a group as the object and uses randomization technology to guide an efficient search of a coded parameter space. Among them, selection, crossover, and mutation constitute the genetic operation of the genetic algorithm; parameter coding, initial group, the five elements of the genetic algorithm, the design of the fitness function, the design of the genetic operation, and the setting of the control parameters constitute the core content of the genetic algorithm [12]. As a new global optimization search algorithm, the genetic algorithm is simple, versatile, and robust. Practical and other outstanding characteristics have been widely used in various fields, achieved good results, and gradually become one of the important intelligent algorithms.

Basic Principles.
Before describing the basic principles, first, use Figure 1 to visually describe the basic process of genetic algorithm.
We are accustomed to refer to the Genetic Algorithm as the traditional GA, which is the process described in figure 1 [13]. Its main steps can be described as follows: (1) Coding: GA first expresses the solution data of the solution space as the genotype string structure data of the genetic space before searching. Different combinations of these string structure data constitute different points. (2) Generation of initial population: N initial string structure data are randomly generated, each string structure data is called an individual, and N individuals constitute a group. GA starts iterating with these N string structure data as the initial point.

Research on Improvement of Genetic Algorithm.
Classification is the most widely used task in data mining. Classification is to make an accurate description or analysis model for each category by analyzing the data in the sample database. e derived model is based on the analysis of the training data set (that is, the data objects whose class labels are known), mining classification rules, and then using the classification rules to classify the records in other databases, to find a concept description of the category, which represents the overall information of this kind of data, that is, the connotation description of the category. e typical methods of establishing classification rules are AQ method, rough set method, genetic classifier, and so on. e connotation description of class is divided into feature Complexity description and discriminative description [15]. Feature description is the description of common features of objects in a class. Discriminant description is the description of the difference between two or more classes. Feature description allows common features among different classes. It is usually represented by rules or decision tree pattern. e schema can map tuples in database to a given category. e predicted value of classification model can be discrete (such as judging whether an animal is amphibian or mammal according to its characteristics) or continuous (such as judging a person's salary range according to their education and work experience).
Fuzzy genetic algorithm refers to the introduction of fuzzy control theory into the genetic algorithm, and the genetic algorithm is closer to the optimal solution in the evolution process through fuzzy adjustment of the relevant parameters of the genetic algorithm.

Chromosome Coding.
In view of the fixed characteristics of the initial cluster centers, we select fixed-length chromosome coding [16]; that is, the length of the chromosome remains unchanged during the genetic process. According to the previous analysis, in FCM clustering based on genetic algorithm, when chromosomes cross and mutate, the value of each cluster center can be regarded as a whole; that is, each cluster center is regarded as the basic unit of chromosome. is representation method can directly use the number of the cluster centers in the sample set to represent the cluster center, and its direct benefit is that it can shorten the length of the chromosome code. e text is coded by symbols; that is, the chromosome code is composed of the numbers of the K cluster centers in the sample set. e representation of chromosomes is Among them, K is the number of clusters, p i � 1, 2, ..., k is the number of the sample corresponding to the i-th cluster center in the sample set, which is a natural number between [1, n], and n is the number of samples.

Population Initialization.
Randomly generate K different natural numbers between [1, n], and concatenate these natural numbers to form a chromosome, where n is the number of samples and c is the number of clusters. If the population size is N, then N chromosomes are generated according to the above method.

Design of Genetic Operators.
e selection operation has a pivotal effect on the performance of the algorithm. In the evolution of the genetic algorithm, we first adopt the optimal preservation strategy to keep the individuals with the highest fitness in the genetic process, so that they do not participate in the cross-mutation operation, and then use the roulette method, which is determined by the probability distribution corresponding to the fitness function. Individuals in the current group are selected, crossed, and mutated to improve the average fitness of the group [17]. Since it is more appropriate to select elite individuals to account for 3%-6% of the population size, the population size of the algorithm in this paper is 30, so we choose to retain one elite individual. e detailed flow of the algorithm is shown in Figure 2. When the genetic operation stops, it is necessary to find the chromosome with the highest fitness in the last generation, and its corresponding cluster center matrix P is the optimal solution obtained by the genetic operation. Take this optimal clustering center matrix as the initial clustering center of the FCM algorithm, execute the FCM algorithm, calculate the optimal fuzzy classification matrix, and then determine the optimal clustering division according to the membership division principle. e specific steps are as follows: (1) Calculate the fitness of each chromosome.

Complexity
(2) Put the most adaptable chromosomes directly into the next generation population. (3) Calculate the selection probability of each individual according to formula (2): where n is the population size, and p is the fitness of individual i. (4) Calculate the cumulative probability P of each body according to where K is the number of times the roulette has been turned, and N is the population size.

Fitness Function Design.
Symbol-encoded chromosome is a chromosome representation method with simple representation and simple genetic operation, and it is easy to understand. At the same time, it can ensure that the search space of cluster centers does not increase with the genetic process, which is conducive to the increase of algorithm efficiency.
For Fuzzy Clustering Algorithm (FCM), the optimal clustering result corresponds to the minimum value of the objective function; that is, the better the clustering effect, the Input maximum number of clusters K = N -1, where K is the number of times the roulette has been turned, and N is the population size.
Put the most adaptable chromosomes directly into the next generation population.  Complexity smaller the number of meshes, and the greater the fitness [18]. erefore, the fitness function of the individual here can be used to calculate the objective function: With the help of FCM, the formula can be defined as

Experimental Simulation and Result Analysis
In order to compare the performance of the traditional fuzzy clustering algorithm and the fuzzy clustering algorithm based on genetic algorithm, we selected sets of standard data; Impact Reporting and Investment Standards (IRIS) data set was selected as the test sample set to compare the convergence speed and optimization degree of each algorithm [19]. e data consists of 120 sample points in a four-dimensional space. e four components of each sample represent the petal length, petal width, sepal length, and sepal width of IRIS. e entire sample set contains three IRIS types: setosa, versicolor, and virginica, each with 50 samples. e first type of IRIS data is well separated from the other two types, and the other two types overlap.
is data is often used as standard test data.
For the traditional FCM clustering algorithm, the cluster centers are randomly selected. We can observe its comprehensive clustering effect through clustering many times. Here, we run it 10 times and observe the clustering results. e experimental data is shown in Figure 3. e data in Figure 3 verifies that the accuracy of the clustering results obtained by the traditional FCM algorithm is not stable enough. It is extremely sensitive to the selected initial cluster centers and thus falls into a local minimum. For example, the result of the third run is not the global optimal solution. e improved FCM algorithm first optimizes the initial clustering center through an improved genetic algorithm and uses the obtained optimal solution as the initial clustering center of the FCM algorithm to start the FCM algorithm. We also run the improved FCM algorithm 10 times and observe the clustering results. e experimental data are shown in Figure 4.
e data in Figure 4 shows that the FCM algorithm optimized by the improved genetic algorithm can also ensure that the results of each convergence are correct and consistent, avoiding the objective function from falling into a local minimum, and the average number of iterations and total running time are higher than those of traditional ones.
From the comparison in Figure 5, it can be seen that the optimized FCM algorithm has a smaller objective function value than FCM algorithm, and the average number of iterations is less. It can be seen that the optimization of the initial center can not only avoid falling into the local minimum, but also speed up FCM. In terms of time, although it takes a lot of time to optimize the initial center with genetic algorithm, it takes much less time to optimize the FCM algorithm than the FCM algorithm, and the time used is within the acceptable range of people. Moreover, if the amount of data processed is large, each iteration of the FCM algorithm will take a long time. At this time, reducing the number of iterations may save more time. From this point of view, the time spent optimizing cluster centers is completely worthwhile.

System Analysis and Design.
Among them, the computing method is based on the data density. By calculating the distance of a group of data, cluster analysis can effectively divide these data into several more dense clusters, and the sum of the distances of the data in each cluster to the cluster center is the smallest. After using the cluster analysis technology, in the student performance evaluation, each cluster is a score group, and the data in the center of each cluster is the central score of the score group. Different clusters divide each score group accordingly and give the central score of different score groups correspondingly. ese central grades are one of the reference standards for grading students' grades. It can be seen from the above that the score division based on cluster analysis is no longer the absolute score division, but the relative score division. erefore, the score evaluation of students is more accurate. e design of the system adopts a structured design method and divides the system requirements into different subfunction modules according to their respective functions.
is design method is not only clear in layers and clear in structure, but also convenient for querying errors during design and debugging, and the preparation of programs is conveniently read [20]. Adopting this design method will bring convenience to future maintenance work, and it is easier to realize the system's added functions and improved functions. e system is divided into four modules: student information management, student status information management, performance information management, and reward and punishment information. Each module is subdivided into small modules to implement related functions [21][22][23][24][25][26][27]. e detailed function design is as follows, shown in Figure 6.
(1) Student information management includes the establishment of freshmen's admission personal file management and the inquiry and modification of school student information. e establishment of freshmen files includes the grades of information such as department information, class information, student ID, name, gender, and age, newly assigned to the students. e query and modification of student information refer to the modification of student information, such as the incorrect registration of instant information such as new students, or the 6 Complexity  Complexity change of student information, such as the change of home address and contact information.
(2) Student status information management mainly refers to the record of student status changes. Student information query modification is mainly to maintain the added student information, including student information modification and student information deletion. e realization of student information maintenance and management is completed by modifying and querying the information in the student basic table [28].
(3) e management of score information includes the registration and query of scores after each test. (4) Reward and punishment information management is mainly to reward students with outstanding learning and punish students with poor performance.

Analysis of Student Performance Based on Fuzzy Genetic
Algorithm Clustering. Student performance is the most important part of the student information database, an important basis for evaluating teaching quality, and an important indicator of evaluating whether students have a good grasp of the knowledge they have learned [29]. erefore, how to evaluate students' grades scientifically, accurately, and fairly is the work that educators have been studying for many years. With the continuous deepening of the reform of the education system, especially after the credit system teaching management system has become the mainstream, the evaluation method of student performance has developed from the single five-point system and the hundred-point system in the past to the more widely used hierarchical system today.
In order to better explain the application effect of the above-mentioned improved algorithm in the student achievement data mining system, 180 students' achievements are selected for analysis. e scores of 180 students are divided into five grades (that is, excellent, good, intermediate, pass, and fail); the traditional division: those with 90 points or more are excellent, and those with 80 points or more and less than 90 points are considered as excellent, scores greater than or equal to 70 points and less than 80 points are considered medium, scores greater than or equal to 60 points and less than 70 points are passed, and scores less than 60 points are failed. e results are shown in Figure 7.
Divide the score into five grades (i.e., excellent, good, medium, pass, and fail), and divide it according to the basic k-means algorithm. If the initial cluster center is 75, 60, 65, 75, and 60, the results of the division are shown in Figure 8. e results are divided into five grades (i.e., excellent, good, medium, pass, and fail). e results of the k-means algorithm based on fuzzy genetic algorithm are shown in Figure 9.
From the comparison of Figures 7 and 8, it can be seen that only one person is excellent in the traditional division method, while the number of excellent people obtained according to the basic k-means algorithm is four. is division is for students with 89.87 points, which is more reasonable. In addition, by comparing Figure 8 with Figure 9, it can be seen that the result of dividing according to the basic k-means algorithm is quite dependent on the initial clustering center, and dividing according to different initial clustering centers will result in different clustering results. If the initial clustering center is not well selected, the clustering result is easy to fall into the local optimum. If you want to get a better clustering result, you should select multiple groups of initial cluster centers for multiple clustering divisions and then compare them to find a better division as the final result. But this method is too dependent on the operator's mastery of the data. Figure 9 is the clustering results obtained by taking 10 groups of initial cluster centers as 10 subgroups, and then       Figure 9 can be seen. e error is obviously smaller than the other two; that is to say, the clustering result based on fuzzy genetic algorithm is more scientific, fair, and reasonable. Ullah et al. [30] pointed out that Terahertz-based 6G networks promise the best speed and reliability, but they will face new man-in-the-middle attacks. In such critical and high-sensitive environments, the security of data and privacy of information is still a big challenge. Without privacypreserving considerations, the configuration state may be attacked or modified, thus causing security problems and damage to data. In their article, motivated by the need to secure 6G IoT networks, an ant colony optimization (ACO) approach is presented by adopting multiple objectives, as well as using transaction deletion to secure confidential and sensitive information. We will work on the security of our method in the next step.

Conclusion
As an important part of data mining, cluster analysis has been widely used in various fields. Although various clustering algorithms have been proposed, different algorithms have their own characteristics. erefore, in practical applications, the best clustering method should be selected or designed according to specific analysis of specific problems. Aiming at the deficiencies of the k-means clustering algorithm, this paper proposes a new idea, combining fuzzy genetic algorithm with an improved k-means algorithm, which, to a certain extent, avoids the sensitivity of the k-means algorithm to the initial clustering center. It is easy to fall into the defect of local optimal solution. e student information system contains a lot of useful information to be explored. Today, when the country is vigorously advocated by science and education, this information is useful for schools to better formulate learning.

Data Availability
e data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest
e authors declare that they have no known conflicts of interest or personal relationships that could have appeared to influence the work reported in this paper.