A method for safety monitoring of power equipment in smart communities based on 5G communication

Abstract

The present invention discloses a method for safety monitoring of power equipment in a smart community based on 5G communication, which relates to the technical field of safety monitoring of power equipment, including collecting real-time operation data and historical fault data of power equipment and transmitting them to a central monitoring system in real time through a 5G communication network; performing anomaly detection on real-time data and identifying abnormal patterns; predicting abnormal risks of power equipment through historical fault data and abnormal detection results; formulating measures to deal with abnormal risks and automatically issuing control instructions through a 5G communication network to adjust the operating parameters of power equipment; storing and backing up data and performing access control. The present invention realizes intelligent monitoring and automatic control of power equipment by collecting and transmitting power equipment data in real time, combining advanced data analysis technology for anomaly detection and risk prediction, and can effectively improve the safety monitoring capability of power equipment, reduce safety risks, and improve the stability and reliability of community power supply.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment safety monitoring, and in particular to a smart community power equipment safety monitoring method based on 5G communication. Background Art

[0002] With the rapid development of smart communities, the safety monitoring of power equipment has become an important task to ensure the safety of community residents and improve the operating efficiency of the power system. Traditional power equipment monitoring methods mainly rely on regular manual inspections and simple sensor monitoring. These methods have many shortcomings, including limited monitoring range, poor data real-time performance and weak fault prediction capabilities. With the advancement of communication technology, especially the rise of 5G communication technology, new solutions have been provided for the safety monitoring of power equipment. 5G technology has the characteristics of high speed, low latency, and a large number of connections, making real-time remote monitoring and big data processing possible, thus bringing new opportunities for the safety monitoring of power equipment in smart communities. Despite this, the existing power equipment safety monitoring methods still have many shortcomings. First of all, Traditional monitoring systems mostly rely on wired communication networks. Due to the limitations of network layout and transmission distance, it is difficult to achieve effective monitoring of remote or difficult-to-access power equipment. Secondly, existing monitoring methods are also limited in data processing and analysis capabilities. They are unable to cope with large-scale, high-frequency data collection and processing, and it is difficult to meet the dual requirements of real-time and accuracy. In addition, the intelligence level of anomaly detection and risk prediction is not high, and most of them rely on preset rules and experience judgments. They lack sufficient flexibility and adaptability and cannot effectively cope with the complex and changeable operating environment of power equipment. Therefore, the existing technology has deficiencies in the comprehensive utilization of real-time data and historical data, intelligent anomaly detection and risk assessment, and data security management, and urgently needs to be improved and optimized. Summary of the invention

[0003] In view of the problems existing in the above-mentioned existing method for safety monitoring of power equipment in smart communities based on 5G communication, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that the existing technology has deficiencies in the comprehensive utilization of real-time data and historical data, intelligent anomaly detection and risk assessment, and data security management, and urgently needs to be improved and optimized.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a smart community power equipment safety monitoring method based on 5G communication, which includes collecting real-time operation data and historical fault data of power equipment and transmitting them to a central monitoring system in real time through a 5G communication network; performing anomaly detection on real-time data and identifying abnormal patterns; predicting abnormal risks of power equipment through historical fault data and anomaly detection results; formulating abnormal risk response measures and automatically issuing control instructions through a 5G communication network to adjust the operating parameters of the power equipment; storing and backing up the data and performing access control.

[0006] As a preferred solution of the smart community power equipment safety monitoring method based on 5G communication described in the present invention, the real-time operation data of the power equipment is collected by installing intelligent sensors with 5G communication modules on the power equipment, including temperature sensors, current sensors and voltage sensors, to continuously monitor and collect current and voltage data, temperature data, power data and environmental data, collect historical fault records of the power equipment, preliminarily format the collected raw data and compress the data using a data compression algorithm.

[0007] As a preferred solution of the smart community power equipment safety monitoring method based on 5G communication described in the present invention, the real-time transmission to the central monitoring system through the 5G communication network is to use the network slicing technology of the 5G network to provide dedicated network resources for different types of data transmission:

[0008]

[0009] Where W is the network resource allocation efficiency, n is the number of data types, m is the number of network slices, and λ i is the importance weight of the i-th category data, D i is the amount of data of the i-th category, is an exponential decay function, S i is the amount of resources allocated to the network slice for the i-th type of data, α i is a parameter related to the security requirements of the i-th type of data, β j is the resource utilization of the jth network slice, L j is the technical coefficient of the jth network slice;

[0010] Design corresponding network slices according to the transmission requirements of different data types, allocate corresponding network resources to each network slice in the 5G network, and assign data transmission tasks to corresponding network slices according to the data type, including transmitting real-time monitoring data through low-latency network slices and transmitting historical data through high-bandwidth network slices.

[0011] As a preferred solution of the smart community power equipment safety monitoring method based on 5G communication described in the present invention, the abnormal detection of real-time data and the identification of abnormal patterns are to decompress the data back to the original format and pre-process it after receiving the operation data of the power equipment, and then analyze the data to identify the abnormal pattern:

[0012]

[0013]

[0014] Among them, R is the real-time anomaly detection result, E is the real-time operation data collected from the power equipment, and E ‘ is the preprocessed data set, A(E ‘ ) is the anomaly detection algorithm, S is the sensitivity parameter of the anomaly detection algorithm, and d l YesE ‘ The data points in μ l and They are d l The expected value and variance of E ‘ The total number of data points in the data set, l is the index, and the collected real-time operation data of the power equipment is input into the analysis formula to analyze the operation status of the power equipment in real time and identify abnormal patterns.

[0015] As a preferred solution of the smart community power equipment safety monitoring method based on 5G communication described in the present invention, wherein: the prediction of abnormal risk of power equipment through historical fault data and abnormal detection results is to predict the future abnormal risk of power equipment through historical fault data and abnormal detection results:

[0016]

[0017] Where P is the predicted anomaly risk score, which indicates the probability of failure of power equipment, γ and δ are weight coefficients, T is the time range considered, ε is the time decay factor, φ(H(t), t) is a time function based on the historical fault data H, taking into account the time t of the fault occurrence, R is the real-time anomaly detection result, M is the total number of historical fault events, and w q is the weight of the qth historical fault event, t q is the time when the qth historical fault event occurred, is the standard deviation of time decay, k is a constant, δ g,q is an indicator function, which takes the value 1 when the fault q belongs to type g, otherwise it takes the value 0. g is a predefined, fixed score for the g-th fault type.

[0018] As a preferred solution of the intelligent community power equipment safety monitoring method based on 5G communication according to the present invention, wherein: formulating abnormal risk response measures is to calculate the future abnormal risk score of the power equipment and compare it with the set risk threshold B to judge the future abnormal risk of the power equipment;

[0019] If P < B, it indicates that the power equipment has no future abnormal risk, then regularly conduct inspections and maintenance of the power equipment, including cleaning the surface of the power equipment, checking wiring and connection components, updating firmware and software, real-time monitoring of the operating parameters of the power equipment, and regularly predicting the future abnormal risk of the power equipment;

[0020] If P ≥ B, it indicates that the power equipment has future abnormal risk, then immediately use diagnostic tools and technologies to determine the cause and nature of the abnormality, and take corresponding emergency measures according to the severity of the abnormality, including immediately cutting off the power supply, isolating the affected area and starting the standby system, formulating a detailed recovery plan, including repairing or replacing damaged components, recalibrating equipment parameters, conducting system tests and validations, and after the abnormal event is resolved, conduct post-event analysis, summarize experience and lessons, and update risk assessment and response measures.

[0021] As a preferred solution of the intelligent community power equipment safety monitoring method based on 5G communication according to the present invention, wherein: automatically sending control instructions through the 5G communication network to adjust the operating parameters of the power equipment is to adjust the operating parameters of the power equipment according to the real-time anomaly detection results, abnormal risk scores and the current operating state of the power equipment:

[0022]

[0023] Among them, O ′ is the set of adjusted operating parameters, O is the set of current operating parameters of the power equipment, R is the real-time anomaly detection result, P max is the maximum possible value of the risk score, P is the predicted abnormal risk score, τ R and τ P are weight coefficients, is a vector, and the power equipment immediately executes the corresponding adjustment operation after receiving the control instruction.

[0024] As a preferred solution of the intelligent community power equipment safety monitoring method based on 5G communication according to the present invention, wherein: storing and backing up data and implementing access control is to adopt a distributed storage system, regularly and automatically back up the real-time operating data and historical fault data of the power equipment, implement strict access control policies, only allow authorized personnel to access the data of the power equipment, and record all access and operation behaviors of the data, including access time, operator identity and operation type.

[0025] A computer device comprises: a memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, it implements the steps of a smart community power equipment safety monitoring method based on 5G communication.

[0026] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a method for safety monitoring of power equipment in a smart community based on 5G communication are implemented.

[0027] The beneficial effects of the present invention are as follows: the present invention realizes intelligent monitoring and automatic control of power equipment by real-time collection and transmission of power equipment data, combined with advanced data analysis technology for anomaly detection and risk prediction, which can effectively improve the safety monitoring capability of power equipment, reduce safety risks, and improve the stability and reliability of community power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0029] Figure 1 This is a flow chart of the smart community power equipment safety monitoring method based on 5G communication.

[0030] Figure 2 Schematic diagram of power equipment data detection in a smart community power equipment safety monitoring method based on 5G communication. DETAILED DESCRIPTION

[0031] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0032] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0033] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0034] Example 1

[0035] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and this embodiment provides a smart community power equipment safety monitoring method based on 5G communication. The smart community power equipment safety monitoring method based on 5G communication includes the following steps:

[0036] S1. Collect real-time operation data and historical fault data of power equipment and transmit them to the central monitoring system in real time through the 5G communication network;

[0037] Specifically, the real-time operation data of power equipment is collected by installing smart sensors with 5G communication modules on the power equipment, including temperature sensors, current sensors and voltage sensors, to continuously monitor and collect current and voltage data, temperature data, power data and environmental data, collect historical fault records of power equipment, preliminarily format the collected raw data and compress the data using a data compression algorithm.

[0038] In this embodiment, the data compression algorithm is a ZIP decompression algorithm. An intelligent sensor with a 5G communication module is installed to continuously monitor and collect key parameters such as current, voltage, temperature, power, and environmental data. Combined with historical fault records, it provides a rich source of information for subsequent data analysis and anomaly detection, ensuring the comprehensiveness and accuracy of the analysis results, improving the meticulousness and accuracy of monitoring, and greatly improving the accuracy and timeliness of fault prediction through a comprehensive data collection basis, thereby more effectively preventing and reducing failures and accidents of power equipment.

[0039] Furthermore, real-time transmission to the central monitoring system via the 5G communication network is to utilize the network slicing technology of the 5G network to provide dedicated network resources for different types of data transmission:

[0040]

[0041] Where W is the network resource allocation efficiency, n is the number of data types, which indicates the total number of different types of data that the system needs to process and transmit, m is the number of network slices, which indicates the total number of network resource slices allocated for different data types or service quality requirements, and λ i is the importance weight of the i-th type of data, reflecting the priority or importance of this type of data in the entire communication system. This weight is determined according to the application scenario and business requirements of the data. i is the amount of data of the i-th category, which can be the size of the data packet, the transmission frequency, or the total amount of data transmission in a specific time. It is estimated by monitoring network traffic and data usage. is an exponential decay function used to simulate the increase in resource usage as the amount of resources S allocated to the data type increases. i As the number of resources increases, the marginal effect of additional resources on improving transmission security gradually decreases. i It is the amount of resources allocated to the network slice of the i-th type of data, including a comprehensive reflection of indicators such as bandwidth, latency, and reliability. This amount is determined based on the configuration and management strategy of the network slicing technology. i is a parameter related to the security requirements of the i-th type of data, indicating the degree of resources required to ensure the security of data transmission of this type, β j is the resource utilization rate of the jth network slice, indicating the extent to which the slice resources are used. This ratio can be calculated by real-time monitoring of network resource usage. j is the technical coefficient of the jth network slice, which reflects the maturity, stability and efficiency of the network slicing technology. This coefficient can be evaluated based on technical literature, experimental data or expert experience;

[0042] Design corresponding network slices according to the transmission requirements of different data types, allocate corresponding network resources to each network slice in the 5G network, and assign data transmission tasks to corresponding network slices according to the data type, including transmitting real-time monitoring data through low-latency network slices and transmitting historical data through high-bandwidth network slices.

[0043] The network slicing technology of 5G network provides dedicated network resources for different types of data transmission, realizing efficient and secure data transmission. It not only solves the demand for real-time transmission of large amounts of data, but also ensures the priority and security of key data transmission through optimized allocation of network resources. The application of network slicing technology effectively improves the efficiency and reliability of data transmission, while reducing the delay and packet loss rate in the data transmission process, providing strong data support for real-time monitoring and abnormal response of power equipment.

[0044] S2, perform anomaly detection on real-time data and identify abnormal patterns;

[0045] Specifically, the real-time data is detected for anomalies and the abnormal patterns are identified by decompressing the data back to the original format and preprocessing it after receiving the operation data of the power equipment, and then analyzing the data to identify the abnormal patterns:

[0046]

[0047] Among them, R is the real-time anomaly detection result, E is the real-time operation data collected from the power equipment, and E ‘ is the preprocessed data set, A(E ‘ ) is the anomaly detection algorithm, S is the sensitivity parameter of the anomaly detection algorithm, which is used to adjust the sensitivity of anomaly detection, and dl YesE ‘ The data points in μ l and They are d l The expected value and variance of E are used to describe the statistical characteristics of normal operation data. ‘ The total number of data points in the data set, l is the index, and the cumulative result of operating all elements from the 1st to the nth. The collected real-time operation data of the power equipment is input into the analysis formula to analyze the operating status of the power equipment in real time and identify abnormal patterns.

[0048] In this embodiment, data decompression is performed using a ZIP decompression algorithm, and data preprocessing is performed using a median filter, a Gaussian filter, or other suitable filtering methods in noise filtering to remove noise, retain the real signal of the data, and perform missing value processing. For missing data, an interpolation method (such as linear interpolation or using the average value of adjacent points) is used to fill in the missing data to ensure the integrity of the data set. The data is normalized / standardized. After receiving the operating data of the power equipment, advanced data analysis technology is used to detect anomalies in real-time data and identify abnormal patterns, so that the abnormal state of the power equipment can be discovered and identified in time, providing a basis for taking corresponding countermeasures. Through accurate anomaly detection and pattern recognition, the safe operation level of the power system is greatly improved, power outages and accidents caused by equipment failures are reduced, and the power supply safety and stability of community residents are guaranteed.

[0049] S3. Predict abnormal risks of power equipment through historical fault data and abnormal detection results;

[0050] Specifically, predicting the abnormal risk of power equipment through historical fault data and abnormal detection results is to predict the future abnormal risk of power equipment through historical fault data and abnormal detection results:

[0051]

[0052] Among them, P is the predicted abnormal risk score, which represents the probability of failure of power equipment. γ and δ are weight coefficients used to adjust the contribution of historical fault data analysis and real-time anomaly detection results to the final risk score. T is the considered time range, which represents the length of time extending from the current moment to the past, and is determined according to the specific application scenario and the availability of historical data. ε is the time decay factor, which is used to reduce the impact of past events on the current risk assessment. φ(H(t), t) is a time function based on the historical fault data HH, taking into account the time t of the fault occurrence. R is the real-time anomaly detection result. M is the total number of historical fault events. w q is the weight of the qth historical fault event. This weight can be assigned based on the severity of the fault or the impact on the system.q is the time when the q-th historical fault event occurred. is the standard deviation of time decay, which is used to control the impact of time distance on the weight. k is a constant used to adjust the overall scale of the impact of fault types. is a function that returns a score according to the fault type, δ g,q is an indicator function that takes the value of 1 when fault q belongs to type g, and 0 otherwise. Y g is predefined and is a fixed score for the g-th fault type, determined based on the severity of the impact of the fault type on the system operation.

[0053] Predicting the future abnormal risks of power equipment through a mathematical model can comprehensively consider historical fault information and current abnormal manifestations, thereby improving the accuracy of risk assessment. By predicting future risks, early identification of potential problems in power equipment can be achieved, providing a scientific basis for subsequent risk response measures. This prediction method can effectively reduce the safety risks caused by faults in power equipment and improve the stability and reliability of the power system.

[0054] S4. Develop abnormal risk response measures and automatically issue control instructions through the 5G communication network to adjust the operating parameters of power equipment;

[0055] Specifically, developing abnormal risk response measures is to calculate the future abnormal risk score of power equipment and compare it with the set risk threshold B to judge the future abnormal risk of power equipment;

[0056] If P < B, it indicates that there is no future abnormal risk for the power equipment. Then, regular inspections and maintenance of the power equipment are carried out, including cleaning the surface of the power equipment, checking wiring and connection components, updating firmware and software, real-time monitoring of the operating parameters of the power equipment, and regularly predicting the future abnormal risks of the power equipment;

[0057] If P ≥ B, it indicates that there is a future abnormal risk for the power equipment. Then, immediately use diagnostic tools and technologies to determine the cause and nature of the abnormality, and take corresponding emergency measures according to the severity of the abnormality, including immediately cutting off the power supply, isolating the affected area, and starting the standby system. Develop a detailed recovery plan, including repairing or replacing damaged components, recalibrating equipment parameters, conducting system testing and verification. After the abnormal event is resolved, conduct post-event analysis, summarize experience and lessons, and update risk assessment and response measures.

[0058] By comparing with the risk threshold to decide whether emergency measures or routine maintenance and inspection are needed, it can ensure that the operation and maintenance decisions of power equipment are more data-driven and accurate, reduce unnecessary maintenance costs, and take quick and effective emergency measures when really needed, thereby improving the operating efficiency of power equipment, extending equipment life, and being able to take quick action when abnormal risks occur to minimize damage, effectively preventing the occurrence or expansion of faults, and ensuring the stability of community power supply.

[0059] Furthermore, control instructions are automatically issued through the 5G communication network to adjust the operating parameters of the power equipment according to the real-time anomaly detection results, anomaly risk scores and the current operating status of the power equipment:

[0060]

[0061] Among them, O ′ is the adjusted operating parameter set, O is the current operating parameter set of the power equipment, R is the real-time anomaly detection result, P max is the maximum possible value of the risk score, P is the predicted abnormal risk score, τ R and τ P is the weight coefficient, which is used to balance the impact of anomaly detection results and risk scores on parameter adjustment. It is a vector that specifies the adjustment direction and relative importance of each operating parameter. Its specific value is determined based on the operating characteristics and safety requirements of the equipment. The power equipment will immediately perform the corresponding adjustment operation after receiving the control instruction.

[0062] By utilizing the high-speed and low-latency characteristics of the 5G network, remote real-time control of power equipment is achieved. By timely adjusting the operating parameters, it is possible to effectively respond to sudden abnormal situations, reduce the possibility of failures, and ensure the stable operation of power equipment. This automated control mechanism not only improves the response speed of power equipment, but also reduces the risk of errors in human operations, enhances the adaptability and resilience of the power system, and ensures the continuity and safety of power supply.

[0063] S5, backing up the data and performing access control;

[0064] Data storage and back-up and access control are carried out by adopting a distributed storage system, regularly and automatically backing up the real-time operation data and historical fault data of power equipment, implementing strict access control policies, allowing only authorized personnel to access the data of power equipment, and recording all access and operation behaviors to the data, including access time, operator identity and operation type.

[0065] A distributed storage system is used to perform regular and automatic backup of the real-time operation data and historical fault data of power equipment, and a strict access control strategy is implemented to ensure the security and integrity of the data, providing reliable data support for subsequent data analysis and fault diagnosis. By recording all access and operation behaviors to the data, data leakage and abuse can be effectively prevented, and the privacy and security of power equipment data can be protected.

[0066] Example 2

[0067] This is the second embodiment of the present invention, and the difference between this embodiment and the previous embodiment is that:

[0068] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0069] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0070] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0071] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A method for safety monitoring of power equipment in a smart community based on 5G communication, characterized in that: Including, Collecting the real-time operation data and historical fault data of power equipment and transmitting them to the central monitoring system in real time through the 5G communication network; Performing anomaly detection on the real-time data to identify anomaly patterns; Predicting the anomaly risk of power equipment based on the historical fault data and the results of anomaly detection; Formulating countermeasures for anomaly risk and automatically issuing control instructions through the 5G communication network to adjust the operation parameters of power equipment; Storing and backing up the data and performing access control; The real-time transmission to the central monitoring system through the 5G communication network utilizes the network slicing technology of the 5G network to provide dedicated network resources for different types of data transmission: Where W is the network resource allocation efficiency, n is the number of data types, m is the number of network slices, and λ i is the importance weight of the i-th category data, D i is the amount of data of the i-th category, is an exponential decay function, S i is the amount of resources allocated to the network slice for the i-th type of data, α i is a parameter related to the security requirements of the i-th type of data, β j is the resource utilization of the jth network slice, L j is the technical coefficient of the jth network slice; Designing corresponding network slices according to the transmission requirements of different data types, allocating corresponding network resources to each network slice in the 5G network, and allocating data transmission tasks to the corresponding network slices according to the data type, including transmitting real-time monitoring data through a low-latency network slice and transmitting historical data through a high-bandwidth network slice; The performing anomaly detection on the real-time data to identify anomaly patterns is to, after receiving the operation data of power equipment, decompress the data back to the original format, perform preprocessing, and then analyze the data to identify anomaly patterns: Among them, R is the real-time anomaly detection result, E is the real-time operation data collected from the power equipment, and E ‘ is the preprocessed data set, A(E ‘ ) is the anomaly detection algorithm, S is the sensitivity parameter of the anomaly detection algorithm, and d l YesE ‘ The data points in μ l and They are d l The expected value and variance of E ‘ The total number of data points in the data set, l is the index, and the collected real-time operation data of the power equipment is input into the analysis formula to analyze the operation status of the power equipment in real time and identify abnormal patterns; The predicting the anomaly risk of power equipment based on the historical fault data and the results of anomaly detection is to predict the future anomaly risk of power equipment based on the historical fault data and the results of anomaly detection: Where P is the predicted anomaly risk score, which indicates the probability of failure of power equipment, γ and δ are weight coefficients, T is the time range considered, ε is the time decay factor, φ(H(t), t) is a time function based on the historical fault data H, taking into account the time t of the fault occurrence, R is the real-time anomaly detection result, M is the total number of historical fault events, and w q is the weight of the qth historical fault event, t q is the time when the qth historical fault event occurred, is the standard deviation of time decay, k is a constant, δ g,q is an indicator function, which takes the value 1 when the fault q belongs to type g, otherwise it takes the value 0. g is a predefined, fixed score for the g-th fault type; The automatically issuing control instructions through the 5G communication network to adjust the operation parameters of power equipment is to adjust the operation parameters of power equipment according to the real-time anomaly detection results, anomaly risk scores, and the current operation status of power equipment: Among them, O ′ is the adjusted operating parameter set, O is the current operating parameter set of the power equipment, R is the real-time anomaly detection result, P max is the maximum possible value of the risk score, P is the predicted abnormal risk score, τ R and τ P is the weight coefficient, It is a vector. After receiving the control instruction, the power equipment will immediately perform the corresponding adjustment operation.

2. The method for safety monitoring of power equipment in a smart community based on 5G communication according to claim 1, characterized in that: The collecting the real-time operation data of power equipment is to install intelligent sensors with 5G communication modules on power equipment, including temperature sensors, current sensors, and voltage sensors, continuously monitor and collect current and voltage data, temperature data, power data, and environmental data, collect the historical fault records of power equipment, perform preliminary formatting on the collected original data, and compress the data using a data compression algorithm.

3. The method for safety monitoring of power equipment in a smart community based on 5G communication as claimed in claim 2, characterized in that: The formulating countermeasures for anomaly risk is to calculate the future anomaly risk score of power equipment and compare it with the set risk threshold B to judge the future anomaly risk of power equipment; If P < B, it indicates that there is no future anomaly risk for the power equipment, then regular inspections and maintenance of the power equipment are carried out, including cleaning the surface of the power equipment, checking wiring and connection components, updating firmware and software, real-time monitoring the operation parameters of the power equipment, and regularly predicting the future anomaly risk of the power equipment; If P ≥ B, it indicates that there is a future anomaly risk for the power equipment, then immediately use diagnostic tools and techniques to determine the cause and nature of the anomaly, take corresponding emergency measures according to the severity of the anomaly, including immediately cutting off the power supply, isolating the affected area, and starting the standby system, formulating a detailed recovery plan, including repairing or replacing damaged components, recalibrating equipment parameters, performing system testing and verification, and conducting post-event analysis after the anomaly event is resolved to summarize experience and lessons, and update the risk assessment and countermeasures.

4. The method for safety monitoring of power equipment in a smart community based on 5G communication as claimed in claim 3 is characterized in that: The data storage and back-up and access control described above are to use a distributed storage system to automatically and regularly back up the real-time operation data and historical fault data of the power equipment, implement a strict access control strategy, only allow authorized personnel to access the data of the power equipment, and record all access and operation behaviors to the data, including access time, operator identity and operation type.

5. A computer device comprising: Memory and processor; The memory stores a computer program, wherein the processor implements the steps of the method according to any one of claims 1 to 4 when executing the computer program.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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