Campus facility intelligent monitoring management method and system using internet of things and edge computing

Abstract

The present application relates to the technical field of smart campus, in particular to a kind of campus facility intelligent monitoring management method and system using Internet of Things and edge computing.The present application calculates and obtains the maximum similarity between each campus fault device and other campus fault devices by fault type information respectively, then the two campus fault devices corresponding to the maximum similarity are divided together respectively, thereby obtaining multiple campus device sets, which helps to classify and integrate maintenance tasks, then obtains the fault damage information of all campus devices in the campus device set, and calculates the damage value through a specific formula combined with the basic information of the fault facility, the facility damage degree table obtained by the damage value can intuitively see the relative severity of the fault facilities in each campus device set, and distinguish the urgency, which helps to reasonably allocate maintenance resources, and preferentially solve the fault facilities in the campus device set that have a great impact on normal teaching, life and other aspects in the campus, to ensure the basic operation order of the campus.

Description

Technical Field

[0001] This invention relates to the field of smart campus technology, and in particular to a method and system for intelligent monitoring and management of campus facilities utilizing the Internet of Things and edge computing. Background Technology

[0002] A smart campus refers to an intelligent and personalized educational environment built upon emerging technologies such as the Internet of Things, big data, and artificial intelligence. It utilizes various application systems and sensors to collect diverse data within the campus, enabling comprehensive perception, intelligent analysis, and efficient management of campus resources and information. Achieving digital and intelligent campus management reduces manual operation and management costs, improves the scientific accuracy of management decisions, enhances the overall management level of the school, thereby improving the campus life experience, creating a positive learning and working environment, and providing convenient and efficient living services and safety guarantees for teachers and students.

[0003] Campus facilities will inevitably suffer damage during long-term use. When multiple campus facilities malfunction simultaneously, maintenance personnel often arrange repairs based solely on their subjective judgment or simply according to the order in which repairs are reported. This leads to arbitrariness in the repair order, which prevents important facilities from being repaired in a timely manner, thus seriously disrupting the normal teaching and living order on campus. Furthermore, the chaotic repair order causes maintenance personnel to frequently switch between different types and urgency tasks, which not only increases the time and manpower costs of repairs but also reduces repair efficiency. Summary of the Invention

[0004] The main objective of this invention is to provide a method and system for intelligent monitoring and management of campus facilities using the Internet of Things and edge computing, aiming to solve the technical problems in the prior art.

[0005] This invention proposes a method for intelligent monitoring and management of campus facilities using the Internet of Things and edge computing, comprising: Obtain basic information on faulty facilities and abnormal feature information for each campus device, wherein the abnormal feature information includes abnormal data information and abnormal image information; Based on each of the abnormal data and each of the abnormal image information, obtain the corresponding campus equipment fault type information; Based on the multiple fault type information, the maximum fault similarity value between each campus device and other campus devices is obtained, and the corresponding campus devices are divided according to each maximum fault similarity value to obtain multiple campus device sets; Obtain fault and damage information of all campus equipment in each campus equipment cluster, and obtain corresponding comprehensive damage information based on multiple fault and damage information. Based on each of the comprehensive damage information, obtain the corresponding damage degree information, and calculate the corresponding damage value based on each of the damage degree information and the basic information of the faulty facility; The damage values ​​are sorted in order of magnitude to obtain a table of facility damage levels; The maintenance sequence information is obtained based on the facility damage severity table, and maintenance recommendations for each campus device are generated based on the maintenance sequence information.

[0006] Preferably, the step of obtaining the fault type information of the corresponding campus equipment based on each of the abnormal data information and each of the abnormal image information includes: Obtain the corresponding facility failure characteristic information based on each of the abnormal data information; Obtain corresponding parameter change information based on each of the facility fault characteristic information, and obtain corresponding performance degradation information based on each of the parameter change information; Obtain corresponding appearance damage information based on each of the abnormal image information, and obtain corresponding damage area contour information based on each of the appearance damage information; Based on the outline information of each damaged area, obtain the corresponding damage range information, and based on the damage range information, obtain the corresponding damage location information; Based on each performance degradation information and the corresponding damage location information, obtain the fault type information of the corresponding campus equipment.

[0007] Preferably, the step of obtaining the maximum fault similarity value between each campus device and other campus devices based on multiple fault type information includes: Fault repair information is obtained based on each fault type information, and skill requirement information is obtained based on each fault repair information; Based on each skill requirement information, obtain the corresponding skill type information, and based on each skill type information, obtain the corresponding skill difficulty quantification value; Based on the multiple skill difficulty quantification values, obtain the skill requirement set corresponding to each fault type information, and obtain the corresponding skill requirement vector based on each skill requirement set; The similarity value is calculated sequentially based on the skill requirement vectors corresponding to any two of the campus devices, wherein the calculation formula is: Among them, S (i，j) This represents the similarity value between the i-th campus device and the j-th campus device. Let i represent the skill demand vector corresponding to any i-th campus device. This represents the skill requirement vector corresponding to the j-th campus equipment (excluding the i-th campus equipment). The multiple similarity values ​​are sorted, and the maximum fault similarity value between each campus device and other campus devices is obtained.

[0008] Preferably, the step of obtaining corresponding comprehensive damage information based on multiple fault damage information includes: Based on each of the aforementioned fault / damage information, obtain the corresponding first damage details information and second damage details information; First fault performance information is obtained based on the first damage details information, and initial first fault performance information and current first fault performance information are obtained based on the first fault performance information. The evolution information of the first fault is obtained based on the initial performance information of the first fault and the current performance information of the first fault. The second fault performance information is obtained based on the second damage details information, and the initial performance information and current performance information of the second fault are obtained based on the second fault performance information. The evolution information of the second fault is obtained based on the initial performance information of the second fault and the current performance information of the second fault. Based on the first fault performance information and the second fault performance information, obtain common fault information; Based on the first fault evolution information and the second fault evolution information, fault difference information is obtained, and based on the fault commonality information and fault difference information, comprehensive damage information is obtained.

[0009] Preferably, the step of calculating the corresponding damage value based on each of the damage severity information and the basic information of the faulty facility includes: Based on each of the damage severity information, obtain the corresponding fault cause information and fault accident association information, and obtain the safety hazard index based on each of the fault cause information and the corresponding fault accident association information; Obtain the first weighting coefficient for each of the aforementioned safety hazard indices; Based on each of the damage severity information, obtain the corresponding environmental correlation information and facility damage scale information, and obtain the environmental damage index based on each of the environmental correlation information and the corresponding facility damage scale information; Obtain the second weighting coefficient for each of the environmental damage indices; Based on the basic information of each faulty facility, obtain the corresponding facility usage frequency information and facility application scope information, and obtain the teaching interference index based on the facility usage frequency information and corresponding application scope information. Obtain the third weighting coefficient for each of the teaching interference indices; The damage value is calculated based on each safety hazard index and its corresponding first weighting coefficient, environmental damage index, second weighting coefficient, teaching interference index, and third weighting coefficient. The formula for calculating the damage value is as follows: ; Where D represents the damage value, s represents the safety hazard index, w1 represents the first weighting coefficient, e represents the environmental damage index, w2 represents the second weighting coefficient, t represents the teaching interference index, and w3 represents the third weighting coefficient.

[0010] Preferably, the step of generating maintenance recommendations for each campus device based on the maintenance sequence information includes: Repair plan information is obtained based on the repair sequence information, and repair step information is obtained based on the repair plan information; Based on the aforementioned maintenance procedure information, obtain the total skill requirements and estimated tool information; Available resource information is obtained based on the resource management information, wherein the available resource information includes available personnel information and available equipment information; Skill characteristic information is obtained based on the available personnel information, and designated maintenance personnel information is obtained based on the total skill requirement information and skill characteristic information; Based on the expected equipment information and available equipment information, obtain the specified equipment information and the equipment purchase information.

[0011] This application also provides a campus facility intelligent monitoring and management system utilizing the Internet of Things and edge computing, including: The first acquisition module is used to acquire basic information on faulty facilities and abnormal feature information of each campus device, wherein the abnormal feature information includes abnormal data information and abnormal image information; The second acquisition module is used to acquire the fault type information of the corresponding campus equipment based on each of the abnormal data information and each of the abnormal image information; The segmentation module is used to obtain the maximum fault similarity value between each campus device and other campus devices based on multiple fault type information, and to segment the corresponding campus devices based on each maximum fault similarity value to obtain multiple campus device sets; The third acquisition module is used to acquire fault and damage information of all campus equipment in each campus equipment set, and to acquire corresponding comprehensive damage information based on multiple fault and damage information. The calculation module is used to obtain the corresponding damage degree information based on each of the comprehensive damage information, and to calculate the corresponding damage value based on each of the damage degree information and the basic information of the faulty facility; The fourth acquisition module is used to sort the multiple damage values ​​in order of magnitude to obtain a table of facility damage levels; The fifth acquisition module is used to acquire maintenance sequence information based on the facility damage severity table, and generate maintenance recommendations for each campus device based on the maintenance sequence information.

[0012] Preferably, the partitioning module includes: The first acquisition unit is used to acquire fault repair information based on each fault type information, and to acquire skill requirement information based on each fault repair information; The second acquisition unit is used to acquire corresponding skill type information based on each skill requirement information, and to acquire corresponding skill difficulty quantification value based on each skill type information; The third acquisition unit is used to acquire the skill requirement set corresponding to each fault type information based on the multiple skill difficulty quantification values, and to acquire the corresponding skill requirement vector based on each skill requirement set. The calculation unit is used to calculate similarity values ​​sequentially based on the skill requirement vectors corresponding to any two of the campus devices, wherein the calculation formula is: Among them, S (i，j) This represents the similarity value between the i-th campus device and the j-th campus device. Let i represent the skill demand vector corresponding to any i-th campus device. This represents the skill requirement vector corresponding to the j-th campus equipment (excluding the i-th campus equipment). The fourth acquisition unit is used to sort the multiple similar values ​​and acquire the maximum fault similarity value between each campus device and other campus devices.

[0013] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method for improving the security of the encryption chip described above.

[0014] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for improving the security of the encryption chip described above.

[0015] The beneficial effects of this invention are as follows: This invention acquires abnormal feature information of each campus device, and uses an edge server deployed near the campus facilities to perform preliminary processing on the transmitted abnormal feature information, further acquiring fault type information. Then, based on the fault type information, it calculates the similarity value between each faulty campus device and other faulty campus devices, extracts the maximum similarity value between each faulty campus device and other faulty campus devices, and groups the two faulty campus devices corresponding to the maximum similarity value together, thus obtaining multiple campus device sets. This helps to classify and integrate maintenance tasks, discover common maintenance needs, and facilitate the unified allocation of maintenance tools, parts, and other resources, improving efficiency. The system optimizes resource utilization, obtains fault and damage information for all campus equipment in the campus equipment center, and calculates the damage value using a specific formula based on the basic information of the faulty facilities. The damage values ​​are then sorted in order to form a damage severity table. This table clearly shows the relative severity of each faulty facility, distinguishing between priorities. Determining maintenance priorities based on damage values ​​helps to rationally allocate maintenance resources, prioritizing faulty facilities in the campus equipment center that significantly impact normal teaching and living conditions, ensuring the basic operational order of the campus, and preventing situations where staff arbitrarily arrange maintenance sequences, potentially leading to resource waste or prolonged unusable critical facilities. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of a method flow according to an embodiment of this application.

[0017] Figure 2 This is a schematic diagram of the system structure according to an embodiment of this application.

[0018] Figure 3 This is a schematic diagram of the internal structure of a computer device according to an embodiment of this application.

[0019] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0021] like Figures 1-3 As shown, this application provides a method for intelligent monitoring and management of campus facilities using the Internet of Things and edge computing, including: S1. Obtain basic information on faulty facilities and abnormal feature information for each campus device, wherein the abnormal feature information includes abnormal data information and abnormal image information; S2. Obtain the fault type information of the corresponding campus equipment based on each of the abnormal data information and each of the abnormal image information; S3. Obtain the maximum fault similarity value between each campus device and other campus devices based on the multiple fault type information, and divide the corresponding campus devices according to each maximum fault similarity value to obtain multiple campus device sets; S4. Obtain fault and damage information of all campus equipment in each campus equipment group, and obtain corresponding comprehensive damage information based on multiple fault and damage information; S5. Obtain the corresponding damage level information based on each of the damage comprehensive information, and calculate the corresponding damage value based on each of the damage level information and the basic information of the faulty facility; S6. Sort the multiple damage values ​​in order of magnitude to obtain a table of facility damage levels; S7. Obtain maintenance sequence information based on the facility damage severity table, and generate maintenance recommendations for each campus device based on the maintenance sequence information.

[0022] As described in steps S1-S7 above, campus facilities are prone to damage during long-term use. When multiple campus facilities malfunction simultaneously, maintenance personnel often arrange repairs based solely on their subjective judgment or simply according to the order in which repair requests are made. This leads to arbitrariness in the repair order, which prevents important facilities from being repaired in a timely manner, thus seriously disrupting the normal teaching and living order on campus. Furthermore, the chaotic repair order causes maintenance personnel to frequently switch between different types and urgency levels of tasks, which not only increases the time and manpower costs of repairs but also reduces repair efficiency. This invention acquires abnormal feature information for each campus device. This abnormal feature information refers to a set of relevant information reflecting the abnormal state of campus equipment when a malfunction occurs. Abnormal feature information includes abnormal data information and abnormal image information. Abnormal data information refers to data on equipment malfunctions obtained through various monitoring methods, while abnormal image information refers to visual images of equipment malfunctions acquired through image acquisition devices. By leveraging IoT technology, different types of abnormal information are comprehensively acquired. Simultaneously, edge servers deployed near campus facilities perform preliminary processing on the transmitted abnormal feature information and further acquire fault type information. Fault type information describes the category of the malfunction occurring in the campus equipment. Information processing is performed through edge servers, rather than transmitting all data to the cloud or central server for analysis. This reduces network bandwidth consumption, improves response speed, and because the abnormal feature information covers both data and images, it avoids inaccurate or missed anomaly judgments due to relying on only a single type of information. This allows for a more comprehensive understanding of the actual operating status of campus facilities. The similarity value between each faulty campus device and other faulty campus devices is calculated based on fault type information. The similarity value is a quantitative indicator of the similarity in maintenance skill requirements among different faulty campus facilities. Next, the maximum similarity value between each faulty campus device and other faulty campus devices is extracted. Then, the two campus devices corresponding to the maximum similarity value are grouped together, resulting in multiple campus device sets. Each campus device set is a collection of campus faulty devices with high similarity (measured by the maximum similarity value). Faulty devices in this set are highly similar in terms of maintenance skill requirements, meaning that repairing these devices may require similar technologies, tools, and knowledge. This helps to classify and integrate maintenance tasks, identify common maintenance needs, and for facilities with common maintenance needs, maintenance tools, spare parts, and other resources can be allocated uniformly to avoid resource dispersion or waste and improve resource utilization efficiency. Finally, fault and damage information for all campus devices in the campus device sets is obtained. This fault and damage information refers to specific information about which component, module, or system within the facility has what kind of problem.Multiple fault and damage information entries are then aggregated into comprehensive damage information. This comprehensive damage information is a complete set of information on the damage to centralized campus facilities, including detailed information on all aspects of the damage. Damage severity information is then obtained from this comprehensive damage information, which measures the severity of damage or functional loss caused by the facility malfunction. Next, the basic information of the malfunctioning facility and the damage severity information are integrated. The basic information of the malfunctioning facility refers to the basic data and related attribute information of the malfunctioning campus equipment itself. A damage value is calculated using a specific formula. This damage value measures the degree of impact of each centralized campus equipment malfunction on normal teaching and living conditions on campus. The equipment is then sorted according to the magnitude of the damage value, ultimately forming a facility damage severity table. This facility damage severity table, after systematic organization and quantitative sorting, is used to visually display the impact of centralized campus equipment malfunctions on the campus. The table, which shows the relative severity of each faulty facility, clearly distinguishes between urgent and less urgent issues. By ranking the damage levels, maintenance priorities are determined, facilitating the rational allocation of maintenance resources. Prioritizing faulty facilities with significant impacts on normal campus operations, such as teaching and daily life, ensures the basic operational order of the campus. Furthermore, it provides managers with a quantifiable and intuitive reference for developing maintenance plans, avoiding arbitrary scheduling that could lead to resource waste or prolonged unavailability of critical facilities. Finally, the table provides maintenance sequence information, which determines the order in which multiple faulty facilities on campus are repaired. This information allows for coordinated planning of maintenance work across multiple facilities, ensuring a smooth and orderly process. This comprehensive and scientific scheduling of maintenance efforts improves the overall efficiency and effectiveness of maintenance work.

[0023] In one embodiment, step S2, which involves obtaining the fault type information of the corresponding campus equipment based on each of the abnormal data information and each of the abnormal image information, includes: S21. Obtain the corresponding facility fault characteristic information based on each of the abnormal data information; S22. Obtain corresponding parameter change information based on each of the facility fault characteristic information, and obtain corresponding performance degradation information based on each of the parameter change information; S23. Obtain corresponding appearance damage information based on each of the abnormal image information, and obtain corresponding damage area contour information based on each of the appearance damage information; S24. Obtain the corresponding damage range information based on the outline information of each damaged area, and obtain the corresponding damage location information based on the damage range information; S25. Obtain the fault type information of the corresponding campus equipment based on each of the performance degradation information and the corresponding damage location information.

[0024] As described in steps S21-S25 above, this invention analyzes abnormal data from campus malfunctioning facilities to extract facility malfunction characteristic information. This facility malfunction characteristic information refers to the specific manifestations of abnormal states that occur during the operation of the malfunctioning facility. After obtaining the facility malfunction characteristic information, parameter change information is further obtained based on it. This parameter change information refers to the changes in the values ​​of various measurable physical quantities and performance indicators relative to normal operating conditions when campus equipment malfunctions. Parameter change information can provide more precise clues for determining the specific cause of the malfunction. Different causes of malfunctions often... This leads to different parameter change patterns. Performance degradation information can be obtained through parameter change information. Performance degradation information refers to the decline in various performance indicators of a facility compared to its normal state after a malfunction or a period of use. In addition, abnormal image information is a direct source for discovering external problems of facilities. By observing and analyzing these images, information on external damage can be obtained. External damage information refers to information related to damage to the visible external parts of campus equipment after being affected by various factors. External damage information mainly describes the physical damage to the surface or outer shell of the equipment and is one of the important bases for judging the degree of damage and inferring the cause of damage. After determining the external damage information, the outline of the damaged area is further delineated to obtain the damage area outline information. This damage area outline information refers to a precise description of the edge shape, size, and extent of the damaged portion of the campus equipment. It helps to accurately describe the scope and shape of the damage, providing more accurate location information for subsequent maintenance work. Next, based on the damage area outline information, the extent of the damage is further quantified, and the corresponding damage location information is obtained based on the damage extent information of the faulty facility. The damage extent information refers to a comprehensive description of the area involved by the damaged part of the campus equipment within the overall equipment structure or functional unit. Combining the damage extent information with the overall structure of the facility allows for the determination of the damage location. The system retrieves damage location information, which refers to the specific location of the damaged facility on campus and its position within the facility's structure. Edge computing devices can update performance degradation and damage location information in real time and store this information locally. Finally, the performance degradation and damage location information are combined to obtain fault type information for the campus equipment. Fault type information refers to the classification and specific description of the faults occurring in the campus equipment. It is based on the manifestation of equipment performance degradation and the location of the damage, categorizing equipment faults into different types so that maintenance personnel can quickly understand the general direction of the fault and the scope of potential problems, thereby enabling more efficient fault diagnosis and repair.

[0025] In one embodiment, step S3, which involves obtaining the maximum fault similarity value between each campus device and other campus devices based on multiple fault type information, includes: S31. Obtain fault repair information based on each fault type information, and obtain skill requirement information based on each fault repair information; S32. Obtain the corresponding skill type information based on each skill requirement information, and obtain the corresponding skill difficulty quantification value based on each skill type information; S33. Obtain the skill requirement set corresponding to each fault type information based on the multiple skill difficulty quantification values, and obtain the corresponding skill requirement vector based on each skill requirement set; S34. Calculate the similarity value sequentially based on the skill requirement vectors corresponding to any two of the campus devices, where the calculation formula is: Among them, S (i，j) This represents the similarity value between the i-th campus device and the j-th campus device. Let i represent the skill demand vector corresponding to any i-th campus device. This represents the skill requirement vector corresponding to the j-th campus equipment (excluding the i-th campus equipment). S35. Sort the multiple similar values ​​and obtain the maximum fault similarity value between each campus device and other campus devices.

[0026] As described in steps S31-S35 above, this invention obtains corresponding fault repair information based on the fault type information of each campus facility. This fault repair information refers to detailed information for restoring the normal operation of the faulty equipment for a specific fault type of each campus facility. It is customized maintenance guidance based on the fault type, providing maintenance personnel with a comprehensive maintenance operation guide. Next, corresponding skill requirement information is obtained based on each piece of fault repair information. This skill requirement information refers to the essential skills required to complete the maintenance task of the faulty facility. Then, skill type information is obtained based on this skill information. This skill type information refers to the category information of the skills required to complete the maintenance task. It is a classification description of maintenance skills, used to clarify the knowledge and skill areas involved in the maintenance process. Finally, a corresponding skill difficulty quantification value is obtained based on the skill type information. This skill difficulty quantification value is a measure of the complexity of the maintenance skill corresponding to each skill type in the skill type information. The skill difficulty quantification value transforms the abstract skill difficulty into a concrete number through a certain standard. To more intuitively compare the difficulty levels of different skill types, the system first obtains a skill requirement set based on the quantified difficulty values. This skill requirement set is a comprehensive set containing multiple skill types and their corresponding quantified difficulty values. Finally, a skill requirement vector is obtained from this set. This vector represents the skill requirement set using specific rules, transforming the information about skill types and their difficulty values ​​from a set into ordered, computable vectors in a vector space. This facilitates mathematical analysis. For example, if the skill requirement set is A={1,2,3,0,5}, and we consider 5 skill types, the numerical value in the first skill requirement set represents the difficulty level of the skill at that position. The vector is constructed according to the order of skill types; if a corresponding skill type element exists, it takes a value at the corresponding position (the corresponding value in the set); otherwise, it takes a value of 0. Therefore, the first skill requirement vector obtained from the first skill requirement set is... = (1,2,3,0,5), and then calculate the similarity value according to the skill requirement vector corresponding to any two campus equipments in turn using a specific formula. The similarity value can comprehensively reflect the degree of overlap of skill requirements. For example, if there are many identical maintenance skills in the maintenance information of two faulty facilities, then their similarity value will be high. Finally, sort the multiple similarity values ​​and obtain the maximum fault similarity value between each campus equipment and other campus equipment. Then, group the two campus faulty equipment corresponding to the maximum similarity value together. The faulty equipment grouped together are highly similar in terms of maintenance skill requirements, which means that maintaining these equipment may require similar technologies, tools and knowledge, which facilitates unified maintenance management, resource allocation and maintenance planning.

[0027] In one embodiment, step S4, which involves obtaining corresponding comprehensive damage information based on multiple fault damage information, includes: S41. Obtain corresponding first damage details information and second damage details information based on each of the fault / damage information; S42. Obtain first fault performance information based on the first damage details information, and obtain first fault initial performance information and first fault current performance information based on the first fault performance information; S43. Obtain first fault evolution information based on the first fault initial performance information and the first fault current performance information; S44. Obtain second fault performance information based on the second damage details information, and obtain second fault initial performance information and second fault current performance information based on the second fault performance information; S45. Obtain second fault evolution information based on the second fault initial performance information and the second fault current performance information; S46. Obtain common fault information based on the first fault performance information and the second fault performance information; S47. Obtain fault difference information based on the first fault evolution information and the second fault evolution information, and obtain comprehensive damage information based on the fault commonality information and the fault difference information.

[0028] As described in steps S41-S47 above, in this invention, by using the fault damage information corresponding to two faulty devices in each campus equipment cluster, first damage details information and second damage details information corresponding to the two fault damage information are obtained respectively. The first damage details information refers to a comprehensive and detailed description of the damage situation of one faulty device in each campus equipment cluster, and the second damage details information refers to a comprehensive and detailed description of the damage situation of another faulty device in the same campus equipment cluster. Then, based on the first damage details information, first fault manifestation information is obtained. The first fault manifestation information refers to the intuitive phenomena exhibited by the facility when the faulty device in each campus equipment cluster fails. This is achieved by focusing on the intuitive phenomena exhibited by the facility when the failure occurs. It will be found that some facility failures exhibit repetitive and similar characteristics. Therefore, common fault information can be obtained by using the first and second fault performance information. Common fault information refers to the same or similar characteristics exhibited by different faulty facilities in each campus equipment cluster. This common fault information helps determine similar maintenance measures to be taken in different maintenance instances. Next, the first fault performance information can be used to obtain the initial first fault performance information and the current first fault performance information. The initial first fault performance information refers to the information related to the initial abnormal state exhibited when one device in each campus equipment cluster begins to fail. The current first fault performance information refers to the information related to the development of the fault in one device in each campus equipment cluster to the point where... Information on all anomalies exhibited at any given time is compiled to outline the changes in the fault from its initial state to the present, forming the first fault evolution information. This first fault evolution information describes the development and change of a fault in one device within each campus equipment cluster from its initial manifestation to its current state. This first fault evolution information clarifies and quantifies the previously vague fault development process. Similarly, the second fault manifestation information is used to obtain the initial and current manifestation information of the second fault. Based on this, the second fault evolution information is then derived. This second fault evolution information describes the development and change of a fault in another device within the same campus equipment cluster from its initial manifestation to its current state. The information description obtains fault difference information through the first fault evolution information and the second fault evolution information. The fault difference information refers to the relevant information on the differences between different faults found when each campus equipment is compared in a centralized manner, and the differences between them in various dimensions. These differences can help distinguish the unique nature of different faults, so as to more accurately understand the specific situation and possible causes of each fault. By integrating the common fault information and the difference information into the comprehensive damage information, a comprehensive profile of equipment faults is constructed. It includes both common problems that exist in general and the unique differences of each fault, making the understanding of equipment faults more complete and accurate. The comprehensive damage information can provide comprehensive data support for subsequent fault maintenance tasks.

[0029] In one embodiment, step S5, which calculates the corresponding damage value based on each of the damage severity information and the basic information of the faulty facility, includes: S51. Obtain the corresponding fault cause information and fault accident association information based on each of the damage degree information, and obtain the safety hazard index based on each of the fault cause information and the corresponding fault accident association information. S52. Obtain the first weighting coefficient for each of the safety hazard indices; S53. Obtain corresponding environmental association information and facility damage scale information based on each of the damage degree information, and obtain an environmental damage index based on each of the environmental association information and the corresponding facility damage scale information; S54. Obtain the second weighting coefficient for each of the environmental damage indices; S55. Obtain the corresponding facility usage frequency information and facility application scope information based on the basic information of each faulty facility, and obtain the teaching interference index based on the facility usage frequency information and corresponding application scope information. S56. Obtain the third weighting coefficient for each of the teaching interference indices; S57. Calculate the damage value based on each safety hazard index and its corresponding first weighting coefficient, environmental damage index, second weighting coefficient, teaching interference index, and third weighting coefficient. The formula for calculating the damage value is as follows: ; Where D represents the damage value, s represents the safety hazard index, w1 represents the first weighting coefficient, e represents the environmental damage index, w2 represents the second weighting coefficient, t represents the teaching interference index, and w3 represents the third weighting coefficient.

[0030] As described in steps S51-S57 above, in this invention, by analyzing the damage extent information, fault cause information and fault accident correlation information can be obtained. Fault cause information refers to the reason for the fault, and fault accident correlation information refers to the correlation information between possible accidents and fault causes. Based on the fault cause information and fault accident correlation information, a safety hazard index is determined. The safety hazard index is a quantitative assessment of the safety risks that a fault may cause. By analyzing the correlation between faults and safety accidents, potential safety risks can be predicted in advance, which helps to take preventative measures before an accident occurs. The damage extent information can also be used to obtain environmental correlation information and facility damage scale information. Environmental correlation information refers to the relationship between the faulty facility and its surroundings. Information related to the interrelationship between the natural and artificial environments, and facility damage scale information refers to the description of the physical damage to the facility itself, including information such as the scope of damage and the number of damaged components. An environmental damage index is obtained based on environmental correlation information and facility damage scale information. The environmental damage index is a quantitative assessment of the damage caused to the surrounding environment by a faulty facility. Considering both facility damage scale and environmental correlation information makes the assessment of facility failure more comprehensive, focusing not only on the damage to the facility itself but also on its potential impact on the surrounding ecological environment, which is conducive to the sustainable development of the campus. Then, a teaching interference index is obtained based on facility usage frequency information and application scope information. Facility usage frequency information refers to the impact of a facility's use within a certain time range... Data on the frequency or duration of use of campus facilities reflects their activity level in daily campus activities and is an important indicator of their contribution to campus functionality. Facility application scope information describes the area, population, or activity range served by the malfunctioning facility, reflecting its functional coverage within the campus and helping to understand the breadth of its potential impact. The teaching interference index is a quantitative assessment of the degree of disruption to campus teaching activities caused by the malfunctioning facility. This index highlights the impact of facility malfunctions on core campus activities—teaching—allowing maintenance arrangements to prioritize ensuring the normal operation of teaching activities. The first weighting coefficient corresponding to the safety hazard index and the environmental damage index are obtained separately. The second weighting coefficient corresponds to the number of faulty facilities, and the third weighting coefficient corresponds to the teaching interference index. These weighting coefficients can be adjusted according to the campus's management goals and actual conditions. Finally, the damage value is calculated. The damage value comprehensively considers the impact of faulty facilities on safety, environment, and teaching, enabling a comprehensive and objective measurement of the overall impact of each campus equipment cluster's faulty facilities on the campus. The damage value provides a quantitative basis for maintenance decisions, avoiding the problem of determining maintenance priorities solely based on subjective judgment. Through the damage value, different campus equipment clusters can be compared and ranked, which helps to formulate reasonable maintenance plans, prioritize the faulty facilities in campus equipment clusters with high damage values, and minimize the serious impact of facility failures on the normal operation of the campus.This will help improve the scientific nature and efficiency of maintenance management.

[0031] In one embodiment, step S7, which generates maintenance recommendations for each campus device based on the maintenance sequence information, includes: S71. Obtain maintenance plan information based on the maintenance sequence information, and obtain maintenance step information based on the maintenance plan information; S72. Obtain total skill requirements and estimated tool information based on the maintenance step information; S73. Obtain available resource information based on the resource management information, wherein the available resource information includes available personnel information and available equipment information; S74. Obtain skill characteristic information based on the available personnel information, and obtain designated maintenance personnel information based on the total skill demand information and skill characteristic information; S75. Obtain designated equipment information and purchase equipment information based on the expected equipment information and available equipment information.

[0032] As described in steps S71-S75 above, this invention sequentially obtains corresponding maintenance plan information through maintenance sequence information. The maintenance plan information refers to a systematic maintenance strategy formulated for campus equipment malfunctions, aiming to construct a complete, orderly, and logically coherent maintenance action framework to ensure that campus facility maintenance work can be carried out in an orderly manner. The maintenance step information obtained based on the maintenance plan information details the specific operational procedures for repairing each facility. The maintenance step information, based on the maintenance plan information, refines the maintenance process into specific, sequentially executed operational steps, detailing every stage of the maintenance work from start to finish, including... By analyzing the sequence of operations, the specific tasks of each step, and the corresponding technical requirements, we can summarize the total skill requirements and estimated tool information needed to complete all maintenance tasks. The total skill requirements refer to the summary of all skill types and corresponding skill level requirements needed to complete the entire equipment maintenance work. The estimated tool information refers to the types, models, and quantities of tools and equipment estimated in advance based on the maintenance steps. Furthermore, we obtain available personnel and tool information through resource management information. Available personnel information refers to the number of personnel on campus who can participate in facility maintenance and their professional skills. Information such as work schedules and available tools information refers to the types, quantities, and storage locations of existing maintenance tools, equipment, spare parts, and other materials. Then, based on available personnel information, the skill characteristics of each maintenance worker are further analyzed. Skill characteristics information describes the features, strengths, and proficiency levels of various skills possessed by each worker. This information is used to assess each worker's ability level in different skill areas. Finally, the total skill requirements information is matched with these skill characteristics to determine the assigned maintenance personnel—that is, which maintenance personnel are assigned to which maintenance tasks. This precise matching is based on the skills of the maintenance personnel and the needs of the maintenance tasks. Matching tools allows for full utilization of each repair technician's professional expertise, improving repair efficiency and quality. Finally, comparing the anticipated tool information with the available tool information determines which tools and parts are readily available in inventory (specified tool information) and identifies any missing items (purchase tool information). This clarifies whether existing tools and parts meet the repair needs, facilitating advance procurement and preventing delays due to tool or part shortages. It also ensures the rational use of existing inventory resources, reducing waste. Conducting repair work based on this information improves efficiency and quality, ensuring the work proceeds smoothly according to plan.

[0033] This application also provides a campus facility intelligent monitoring and management system utilizing the Internet of Things and edge computing, including: The first acquisition module is used to acquire basic information on faulty facilities and abnormal feature information of each campus device, wherein the abnormal feature information includes abnormal data information and abnormal image information; The second acquisition module is used to acquire the fault type information of the corresponding campus equipment based on each of the abnormal data information and each of the abnormal image information; The segmentation module is used to obtain the maximum fault similarity value between each campus device and other campus devices based on multiple fault type information, and to segment the corresponding campus devices based on each maximum fault similarity value to obtain multiple campus device sets; The third acquisition module is used to acquire fault and damage information of all campus equipment in each campus equipment set, and to acquire corresponding comprehensive damage information based on multiple fault and damage information. The calculation module is used to obtain the corresponding damage degree information based on each of the comprehensive damage information, and to calculate the corresponding damage value based on each of the damage degree information and the basic information of the faulty facility; The fourth acquisition module is used to sort the multiple damage values ​​in order of magnitude to obtain a table of facility damage levels; The fifth acquisition module is used to acquire maintenance sequence information based on the facility damage severity table, and generate maintenance recommendations for each campus device based on the maintenance sequence information.

[0034] In one embodiment, the partitioning module includes: The first acquisition unit is used to acquire fault repair information based on each fault type information, and to acquire skill requirement information based on each fault repair information; The second acquisition unit is used to acquire corresponding skill type information based on each skill requirement information, and to acquire corresponding skill difficulty quantification value based on each skill type information; The third acquisition unit is used to acquire the skill requirement set corresponding to each fault type information based on the multiple skill difficulty quantification values, and to acquire the corresponding skill requirement vector based on each skill requirement set. The calculation unit is used to calculate similarity values ​​sequentially based on the skill requirement vectors corresponding to any two of the campus devices, wherein the calculation formula is: Among them, S (i，j) This represents the similarity value between the i-th campus device and the j-th campus device. Let i represent the skill demand vector corresponding to any i-th campus device. This represents the skill requirement vector corresponding to the j-th campus equipment (excluding the i-th campus equipment). The fourth acquisition unit is used to sort the multiple similar values ​​and acquire the maximum fault similarity value between each campus device and other campus devices.

[0035] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method for improving the security of the encryption chip described above.

[0036] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for improving the security of the encryption chip described above.

[0037] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0038] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0039] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for intelligent monitoring and management of campus facilities utilizing the Internet of Things and edge computing, characterized in that, include: Obtain basic information on faulty facilities and abnormal feature information for each campus device, wherein the abnormal feature information includes abnormal data information and abnormal image information; Based on each of the abnormal data and each of the abnormal image information, obtain the corresponding campus equipment fault type information; Based on the multiple fault type information, the maximum fault similarity value between each campus device and other campus devices is obtained, and the corresponding campus devices are divided according to each maximum fault similarity value to obtain multiple campus device sets; Obtain fault and damage information of all campus equipment in each campus equipment cluster, and obtain corresponding comprehensive damage information based on multiple fault and damage information. Based on each of the comprehensive damage information, obtain the corresponding damage degree information, and calculate the corresponding damage value based on each of the damage degree information and the basic information of the faulty facility; The damage values ​​are sorted in order of magnitude to obtain a table of facility damage levels; The maintenance sequence information is obtained based on the facility damage severity table, and maintenance recommendations for each campus device are generated based on the maintenance sequence information.

2. The intelligent monitoring and management method for campus facilities utilizing the Internet of Things and edge computing as described in claim 1, characterized in that, The step of obtaining the fault type information of the corresponding campus equipment based on each of the abnormal data information and each of the abnormal image information includes: Obtain the corresponding facility failure characteristic information based on each of the abnormal data information; Obtain corresponding parameter change information based on each of the facility fault characteristic information, and obtain corresponding performance degradation information based on each of the parameter change information; Obtain corresponding appearance damage information based on each of the abnormal image information, and obtain corresponding damage area contour information based on each of the appearance damage information; Based on the outline information of each damaged area, obtain the corresponding damage range information, and based on the damage range information, obtain the corresponding damage location information; Based on each performance degradation information and the corresponding damage location information, obtain the fault type information of the corresponding campus equipment.

3. The intelligent monitoring and management method for campus facilities utilizing the Internet of Things and edge computing as described in claim 1, characterized in that, The step of obtaining the maximum fault similarity value between each campus device and other campus devices based on multiple fault type information includes: Fault repair information is obtained based on each fault type information, and skill requirement information is obtained based on each fault repair information; Based on each skill requirement information, obtain the corresponding skill type information, and based on each skill type information, obtain the corresponding skill difficulty quantification value; Based on the multiple skill difficulty quantification values, obtain the skill requirement set corresponding to each fault type information, and obtain the corresponding skill requirement vector based on each skill requirement set; The similarity value is calculated sequentially based on the skill requirement vectors corresponding to any two of the campus devices, wherein the calculation formula is: Among them, S (i，j) This represents the similarity value between the i-th campus device and the j-th campus device. Let i represent the skill demand vector corresponding to any i-th campus device. This represents the skill requirement vector corresponding to the j-th campus equipment (excluding the i-th campus equipment). The multiple similarity values ​​are sorted, and the maximum fault similarity value between each campus device and other campus devices is obtained.

4. The intelligent monitoring and management method for campus facilities utilizing the Internet of Things and edge computing as described in claim 1, characterized in that, The step of obtaining corresponding comprehensive damage information based on multiple fault damage information includes: Based on each of the aforementioned fault / damage information, obtain the corresponding first damage details information and second damage details information; First fault performance information is obtained based on the first damage details information, and initial first fault performance information and current first fault performance information are obtained based on the first fault performance information. The evolution information of the first fault is obtained based on the initial performance information of the first fault and the current performance information of the first fault. The second fault performance information is obtained based on the second damage details information, and the initial performance information and current performance information of the second fault are obtained based on the second fault performance information. The evolution information of the second fault is obtained based on the initial performance information of the second fault and the current performance information of the second fault. Based on the first fault performance information and the second fault performance information, obtain common fault information; Based on the first fault evolution information and the second fault evolution information, fault difference information is obtained, and based on the fault commonality information and fault difference information, comprehensive damage information is obtained.

5. The intelligent monitoring and management method for campus facilities utilizing the Internet of Things and edge computing as described in claim 1, characterized in that, The step of calculating the corresponding damage value based on each of the damage severity information and the basic information of the faulty facility includes: Based on each of the damage severity information, obtain the corresponding fault cause information and fault accident association information, and obtain the safety hazard index based on each of the fault cause information and the corresponding fault accident association information; Obtain the first weighting coefficient for each of the aforementioned safety hazard indices; Based on each of the damage severity information, obtain the corresponding environmental correlation information and facility damage scale information, and obtain the environmental damage index based on each of the environmental correlation information and the corresponding facility damage scale information; Obtain the second weighting coefficient for each of the environmental damage indices; Based on the basic information of each faulty facility, obtain the corresponding facility usage frequency information and facility application scope information, and obtain the teaching interference index based on the facility usage frequency information and corresponding application scope information. Obtain the third weighting coefficient for each of the teaching interference indices; The damage value is calculated based on each safety hazard index and its corresponding first weighting coefficient, environmental damage index, second weighting coefficient, teaching interference index, and third weighting coefficient. The formula for calculating the damage value is as follows: ； Where D represents the damage value, s represents the safety hazard index, w1 represents the first weighting coefficient, e represents the environmental damage index, w2 represents the second weighting coefficient, t represents the teaching interference index, and w3 represents the third weighting coefficient.

6. The intelligent monitoring and management method for campus facilities utilizing the Internet of Things and edge computing as described in claim 1, characterized in that, The step of generating maintenance recommendations for each campus device based on the maintenance sequence information includes: Repair plan information is obtained based on the repair sequence information, and repair step information is obtained based on the repair plan information; Based on the aforementioned maintenance procedure information, obtain the total skill requirements and estimated tool information; Available resource information is obtained based on the resource management information, wherein the available resource information includes available personnel information and available equipment information; Skill characteristic information is obtained based on the available personnel information, and designated maintenance personnel information is obtained based on the total skill requirement information and skill characteristic information; Based on the expected equipment information and available equipment information, obtain the specified equipment information and the equipment purchase information.

7. A campus facility intelligent monitoring and management system utilizing the Internet of Things and edge computing, characterized in that, include: The first acquisition module is used to acquire basic information on faulty facilities and abnormal feature information of each campus device, wherein the abnormal feature information includes abnormal data information and abnormal image information; The second acquisition module is used to acquire the fault type information of the corresponding campus equipment based on each of the abnormal data information and each of the abnormal image information; The segmentation module is used to obtain the maximum fault similarity value between each campus device and other campus devices based on multiple fault type information, and to segment the corresponding campus devices based on each maximum fault similarity value to obtain multiple campus device sets; The third acquisition module is used to acquire fault and damage information of all campus equipment in each campus equipment set, and to acquire corresponding comprehensive damage information based on multiple fault and damage information. The calculation module is used to obtain the corresponding damage degree information based on each of the comprehensive damage information, and to calculate the corresponding damage value based on each of the damage degree information and the basic information of the faulty facility; The fourth acquisition module is used to sort the multiple damage values ​​in order of magnitude to obtain a table of facility damage levels; The fifth acquisition module is used to acquire maintenance sequence information based on the facility damage severity table, and generate maintenance recommendations for each campus device based on the maintenance sequence information.

8. A campus facility intelligent monitoring and management system utilizing the Internet of Things and edge computing as described in claim 7, characterized in that, The partitioning module includes: The first acquisition unit is used to acquire fault repair information based on each fault type information, and to acquire skill requirement information based on each fault repair information; The second acquisition unit is used to acquire corresponding skill type information based on each skill requirement information, and to acquire corresponding skill difficulty quantification value based on each skill type information; The third acquisition unit is used to acquire the skill requirement set corresponding to each fault type information based on the multiple skill difficulty quantification values, and to acquire the corresponding skill requirement vector based on each skill requirement set. The calculation unit is used to calculate similarity values ​​sequentially based on the skill requirement vectors corresponding to any two of the campus devices, wherein the calculation formula is: Among them, S (i，j) This represents the similarity value between the i-th campus device and the j-th campus device. Let i represent the skill demand vector corresponding to any i-th campus device. This represents the skill requirement vector corresponding to the j-th campus equipment (excluding the i-th campus equipment). The fourth acquisition unit is used to sort the multiple similar values ​​and acquire the maximum fault similarity value between each campus device and other campus devices.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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