A method and device for locating and analyzing faults of locks based on digital intelligence

Abstract

The present invention discloses a method and device for fault location analysis based on digital intelligent locks, which relates to the technical field of fault location analysis, and includes the following steps: obtaining lock status data and heartbeat packet data; preliminarily screening the heartbeat packet data according to the lock status data to obtain preliminarily screened heartbeat packet data; obtaining feature data of the preliminarily screened heartbeat packet data, and merging the preliminarily screened heartbeat packet data based on each feature in the feature data to generate a plurality of repeated processed data sets; comparing the coefficients of variation of a plurality of processed data sets with the same preliminarily screened heartbeat packet data, and retaining the preliminarily screened heartbeat packet data with the smallest coefficient of variation to obtain a plurality of non-repeated processed data sets; using the non-repeated processed data sets as a heartbeat packet instead of the heartbeat reported data to perform fault location. The present application solves the problem of server fault location delay and inability to locate faults due to limited lock data processing capabilities.

Description

Technical Field

[0001] The present invention relates to the technical field of digital lock fault location, and more specifically, to a digital lock fault location analysis method and device. Background Art

[0002] With the rapid development of smart grid and Internet of Things technologies, the application of digital locks in power systems is becoming more and more extensive. Digital locks not only have the security functions of traditional mechanical locks, but also integrate intelligent features such as remote control, status monitoring and data reporting. In power systems, digital locks are mainly used for access control of key equipment and facilities to ensure the safety of power infrastructure. However, with the expansion of application scale and increase in complexity, digital locks may experience various faults in actual operation, such as communication interruption, equipment abnormality, etc., which pose a potential threat to the safe operation of power systems.

[0003] In order to ensure the reliability and safety of the system, it is particularly important to locate and handle the faults of digital locks in a timely and accurate manner. Traditional fault location methods usually rely on the heartbeat reporting mechanism, which monitors the operating status of the equipment by sending status information at regular intervals to ensure the safe operation of the power system.

[0004] For example, the power operation and maintenance process management system announced in the invention patent with announcement number: CN112001577B includes the following modules: operation and maintenance management module, dispatching management module, service support management module, configuration maintenance module, service delivery management module and information statistics and comprehensive display module. The power operation and maintenance process management system of the present invention fully considers the existing operation and maintenance mode and the future operation and maintenance mode, adopts advanced technical architecture concepts, and realizes safe operation management that integrates monitoring, management and control; the software and hardware configuration is efficient, and the operation and maintenance management is orderly, realizing efficient power intelligent operation and maintenance management in a networked environment.

[0005] For example, the power operation and maintenance method, device, equipment and medium disclosed in the invention patent with announcement number CN117314683B include: obtaining status data of power equipment; judging whether the power equipment is abnormal based on the status data and obtaining a judgment result; obtaining a first maintenance time and a maintenance cycle, wherein the first maintenance time is the most recent maintenance time of the power equipment and the maintenance cycle is the time interval between two adjacent maintenances; modifying the first maintenance time and the maintenance cycle based on the judgment result; determining an operation and maintenance strategy based on the judgment result, the modified first maintenance time and the modified maintenance cycle. This application has the effect of improving the efficiency of power operation and maintenance.

[0006] The above disclosed technical solutions have at least the following technical problems:

[0007] Due to the limited data processing capabilities of the lock, in the fault location method of heartbeat reporting, the data volume of the heartbeat packet is large or the number of devices monitored by the system is large. The server may not be able to analyze and respond based on the heartbeat report in a timely manner due to insufficient processing power, resulting in fault location delays and inability to locate the fault on the server.

[0008] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0009] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and device based on digital lock fault location analysis, which solves the problems of server fault location delay and inability to locate faults caused by limited lock data processing capabilities through digital lock fault location analysis.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] A method and device for fault location analysis based on digital intelligent locks, including the following steps: obtaining lock status data and heartbeat packet data of intelligent locks in an electric power system; performing abnormal analysis on data changes according to the lock status data, and preliminarily screening the heartbeat packet data, removing redundant data, and obtaining preliminarily screened heartbeat packet data; obtaining feature data of the preliminarily screened heartbeat packet data, and merging the preliminarily screened heartbeat packet data based on each feature in the feature data, generating a plurality of repeated processed data sets; comparing the coefficients of variation of the plurality of processed data sets with the same preliminarily screened heartbeat packet data, and retaining the preliminarily screened heartbeat packet data with the smallest coefficient of variation, and obtaining a plurality of non-repeated processed data sets; using the non-repeated processed data sets as a heartbeat packet instead of the heartbeat reported data for fault location. In a preferred embodiment, the heartbeat packet data includes a device identifier, a timestamp, environmental parameters, and location data; the lock status data includes a power supply status, a communication status, and a device working status.

[0012] In a preferred embodiment, the data changes are analyzed for abnormalities based on the lock status data, specifically: the battery power represents the power status, and the change rate analysis is used to calculate the change rate of the battery power, and screening is performed based on the change rate of the battery power; the signal strength represents the communication status, and the moving average method is used to calculate the average signal strength within a certain period of time, and screening is performed based on the deviation; when there is no abnormality in the device working status of the smart lock, the heartbeat packet data is eliminated; the heartbeat packet data of the smart lock corresponding to the lock status data is screened according to the power status, communication status, and device working status, respectively, to obtain the initial screening heartbeat packet data.

[0013] In a preferred embodiment, the characteristic data of the initial screening heartbeat packet data includes time characteristic data, difference coding characteristic data and distance characteristic data.

[0014] In a preferred embodiment, the specific method for merging the initial screening heartbeat packet data based on time feature data is as follows: define the time window length of multiple processing data sets merged by time feature data; collect all heartbeat packet data within the time window, calculate the average value, sum, maximum value, and minimum value of all heartbeat packet data within the time window to construct multiple merged processing data sets with duplication; the specific method for merging the initial screening heartbeat packet data based on difference coding feature data is as follows: calculate the difference between each heartbeat packet data and the heartbeat packet data at the previous preset difference moment, and establish a coded difference set based on the time series relationship for the difference; calculate the mean, square, and value of the coded difference set; Difference, maximum value, minimum value, and based on the cluster analysis method, multiple processing data sets with duplications after merging are constructed; the specific method of the cluster analysis method is as follows: the set length of the coded difference set is used as the number of cluster centers; each coded difference set is assigned to the nearest cluster center; the center of each cluster and the heartbeat packet data in the cluster are updated, and each cluster is used as one of the multiple processing data sets; the specific method of merging the initial screening heartbeat packet data based on distance feature data is as follows: obtain the heartbeat packet data of all smart locks, calculate the Euclidean distance and Manhattan distance between the heartbeat packet data respectively, and merge the heartbeat packet data whose Euclidean distance and Manhattan distance are both higher than the preset reference threshold into a batch processing set.

[0015] In a preferred embodiment, the specific method for obtaining the multiple non-repetitive processed data sets is as follows: compare the coefficients of variation of multiple repeatable processed data sets with the same heartbeat packet data, retain the one with the smallest coefficient of variation, and obtain multiple non-repetitive processed data sets; the method for calculating the coefficient of variation is as follows: calculate the standard deviation and mean of the heartbeat packet data in the multiple processed data sets; and calculate the coefficient of variation based on the standard deviation and the mean.

[0016] In a preferred embodiment, the non-repetitive multiple processed data sets are used as a heartbeat packet for heartbeat reporting, specifically: each non-repetitive multiple processed data set is formatted into a standard heartbeat packet format, the data structure of the heartbeat packet includes a device identifier, timestamp, environmental parameters, and location data; the merged heartbeat packet is sent to a server for processing, and the server performs fault detection and positioning based on the merged heartbeat packet data.

[0017] The technical effects and advantages of the digital lock fault location analysis method and device of the present invention are as follows:

[0018] 1. This embodiment of the present invention significantly improves the fault location efficiency of the system in the face of limited data processing capabilities by performing detailed screening and merging of the heartbeat packet data of the smart lock; specifically, through abnormal analysis and initial data screening, redundant data is effectively reduced and data quality is improved; using merging methods such as time window method, difference coding and distance measurement, the amount of data is further compressed, and the data transmission and processing process is optimized. This method not only improves the processing efficiency and accuracy of the data, but also reduces the burden on the server and reduces the processing delay caused by too much data. This comprehensive optimization strategy enables the system to respond and locate faults more quickly, thereby improving the operational reliability of the power system and the real-time performance of fault detection.

[0019] 2. The present invention calculates and compares the coefficient of variation of multiple processed data sets with the same initial screening heartbeat packet data, retains the data with the smallest coefficient of variation, and thus obtains several non-repetitive multiple processed data sets, which significantly improves the efficiency and accuracy of data processing. The method for calculating the coefficient of variation can effectively evaluate the stability and consistency of the data by analyzing the standard deviation and mean of the data set, and further screen out the most representative data set. This method reduces data redundancy and unnecessary fluctuations, and improves the quality and reliability of the data. By reporting non-repetitive multiple processed data sets as a heartbeat packet, the amount of data is reduced, the processing burden of the server is reduced, and the data transmission efficiency is improved. When processing the merged heartbeat packet, the server can detect and locate faults faster and more accurately, and optimize resource utilization and system responsiveness. This method effectively solves the problem of server processing delay caused by excessive data volume, and improves the real-time, accuracy and overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The figure is a structural schematic diagram of a digital lock fault location analysis method according to the present invention.

[0021] Figure 2 This is a structural schematic diagram of a digital lock fault location and analysis device according to the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Embodiment 1, Figure 1A structural schematic diagram of a digital lock fault location analysis method is provided in the present invention, which includes the following steps:

[0024] S1, obtaining lock status data and heartbeat packet data of smart locks in the power system.

[0025] The heartbeat packet data includes a device identifier, a timestamp, environmental parameters, and location data;

[0026] The lock status data includes power status, communication status, and device working status.

[0027] It should be noted that in the power system, heartbeat packet data is a small data packet sent regularly by smart lock devices to report the real-time status of smart locks to the server, ensuring that the system can continuously monitor the operation of smart locks and detect faults in a timely manner; due to the large amount of heartbeat packet data and the large number of devices, it is easy to cause insufficient server processing capacity, resulting in fault location delays. Heartbeat packet data helps the server monitor the online status and health of the device.

[0028] S2, performing abnormal analysis on the changes in the lock status data, and preliminarily screening the heartbeat packet data, removing redundant data, and obtaining preliminarily screened heartbeat packet data.

[0029] In this embodiment, the change of the lock status data is analyzed for abnormality, specifically:

[0030] The battery power level is used to represent the power state, and the rate of change of the battery power level is calculated using the rate of change analysis, and the selection is performed based on the rate of change of the battery power level;

[0031] When the change rate is less than the power status threshold, the heartbeat packet data is removed;

[0032] The signal strength represents the communication status, and the moving average method is used to calculate the average signal strength within a certain period of time, and then filter according to the deviation;

[0033] When the average signal strength within a certain period of time is greater than the communication status threshold, the heartbeat packet data is removed;

[0034] When the working status of the smart lock device is normal, the heartbeat packet data is removed;

[0035] The heartbeat packet data of the smart lock corresponding to the lock status data is screened according to the power status, communication status, and device working status to obtain the initial screening heartbeat packet data.

[0036] The use change rate analysis is used to calculate the change rate of the battery power, specifically:

[0037]

[0038] In the formula, is the rate of change of the battery power of the i-th smart lock, is the battery power at time t, The battery power at the last moment; is the time interval.

[0039] The signal strength represents the communication status, and the moving average method is used to calculate the average signal strength within a certain period of time, specifically:

[0040]

[0041] In the formula, is the average signal strength of the ith device at time z, is the size of the pan window, is the signal strength at time zu.

[0042] It should be noted that the size of the moving average window, i.e., the number of data points used to calculate the average value, determines how many data points will be included in the calculation; it should be noted that the specific analysis of the lock status data only selects a specific form of expression, and does not only require the data provided in the embodiment for abnormal analysis.

[0043] Perform abnormal analysis on the changes in lock status data and preliminarily screen the heartbeat packet data. The preliminarily screened heartbeat packet data has the following advantages in solving the problem of server fault location delay and inability to locate faults due to limited lock data processing capabilities: Reduce the impact of data fluctuations: The moving average method reduces the sharp fluctuations of instantaneous data by smoothing the data over a period of time; this can eliminate occasional noise and abnormal data points, ensuring that the system only processes changes with practical significance; improve data quality, reduce processing pressure on the server, and thus improve the accuracy and speed of fault location;

[0044] Reduce the burden of data processing: By calculating the moving average, a large amount of raw data is compressed into a smaller amount of average data; this not only reduces the amount of data that needs to be uploaded and analyzed, but also reduces the computing and storage burden on the server; it reduces the load on the server during high data traffic and avoids processing delays caused by too much data;

[0045] Highlighting long-term trends: Moving averages help smooth short-term fluctuations and highlight long-term trends and patterns; this helps identify the long-term operating status of equipment and potential failure trends; by capturing long-term trends, the system can more accurately detect abnormal patterns and warn of possible failures in advance;

[0046] Improve data screening efficiency: Using the moving average method to screen data can effectively extract meaningful information from large amounts of data and filter out unnecessary redundant data; improving data screening efficiency allows the server to process and respond to fault location requests more quickly.

[0047] S3, obtaining feature data of the initial screening heartbeat packet data, and merging the initial screening heartbeat packet data based on each feature in the feature data to generate a plurality of duplicate processed data sets;

[0048] The characteristic data of the initial screening heartbeat packet data includes time characteristic data, difference coding characteristic data and distance characteristic data;

[0049] The data of the initial screening heartbeat packet is merged based on each feature in the feature data, specifically:

[0050] Through the time window method, the process of merging multiple heartbeat packets into one data packet includes collecting all heartbeat packet data within the set time window, and using merging methods such as average, sum, maximum or minimum to reduce the amount of data; the core of this method is to compress multiple data points in the window into a representative value, thereby reducing the frequency and amount of data transmission and reducing the processing burden of the server; for the case of limited lock data processing capabilities, this merging method can significantly reduce the amount of data uploaded each time, effectively alleviating the server processing delay problem caused by excessive data. By reducing the amount of data, the server can locate and respond to faults more quickly, improve the real-time performance and accuracy of the system, and thus solve the problem of fault location delay and inability to locate faults caused by insufficient data processing capabilities.

[0051] The specific method of merging the initial screening heartbeat packet data based on time feature data is as follows:

[0052] Define the time window length of multiple processing data sets for merging time feature data;

[0053] Collect all heartbeat packet data within the time window, calculate the average, sum, maximum, and minimum values ​​of all heartbeat packet data within the time window, and construct multiple merged processing data sets with duplication.

[0054] The specific combination formula of the average values ​​is:

[0055]

[0056] In the formula, is the average value of the data in the heartbeat packet, F is the total amount of data in the heartbeat packet, i is the number of the data in the heartbeat packet, It is the data value in the heartbeat packet.

[0057] Merging the initial screening heartbeat packet data based on time feature data has the following advantages compared to merging other features:

[0058] Data compression: By combining data within a set time window, the amount of data that needs to be uploaded is significantly reduced; this not only reduces network bandwidth consumption, but also reduces server processing pressure;

[0059] More efficient data processing: The merged data packets represent the overall situation within a period of time, so that the server only needs to process fewer merged data packets, thereby improving processing efficiency and response speed.

[0060] Reduce noise: The time window method can effectively filter out instantaneous noise and coefficient of variation by merging data for a certain period of time; this makes the data more stable and helps to accurately reflect the true status of the equipment;

[0061] Improved data quality: Smoothed data reduces the impact of short-term fluctuations, helps better identify long-term trends and abnormal patterns, and improves the accuracy of fault detection;

[0062] Flexibility: The length of the time window can be adjusted according to actual needs, and can flexibly adapt to different data traffic and processing requirements; for example, a shorter window can be set to capture rapid changes, or a longer window can be set to obtain more stable data;

[0063] Adaptability: Selecting appropriate window length and merging method according to different application scenarios and data characteristics can better balance data accuracy and processing efficiency;

[0064] Easy to analyze: The merged data package usually contains aggregate information such as average and total for a period of time, which is easier to analyze and interpret than processing a large amount of raw data;

[0065] Data compression: Compressing the data in a time window into a single value simplifies the data structure, making subsequent analysis and processing more efficient and intuitive;

[0066] Reduce fluctuations: The time window method can smooth short-term fluctuations and reduce system instability caused by data fluctuations; this improves system stability and reliability, especially when dealing with high data traffic;

[0067] Reduced failure risk: By reducing the data processing burden, the risk of system failure caused by excessive data is reduced, and the overall performance of the system is improved;

[0068] The time window method merging provides an efficient data compression and processing method. By reducing the amount of data, smoothing data fluctuations, improving adaptability, simplifying the analysis process and improving system stability, it can effectively solve the problems of server fault location delay and inability to locate faults caused by limited lock data processing capabilities. This method not only improves the efficiency and accuracy of data processing, but also optimizes the system's resource utilization and stability.

[0069] By combining multiple heartbeat packets into one data packet through difference coding and statistical aggregation, the problems of server fault location delay and inability to locate faults caused by limited lock data processing capability are effectively solved. Difference coding calculates the difference between consecutive heartbeat packets to compress the data volume, reduce the amount of data transmitted, and thus reduce the processing burden of the server. Statistical aggregation extracts statistical features such as mean, variance, maximum and minimum values ​​within a set time window, and condenses the data into several key indicators, further reducing the computing pressure on the server. Combining these two methods can significantly reduce the amount of data that needs to be processed, improve data transmission and processing efficiency, shorten the response time of fault location, thereby improving the real-time performance and accuracy of the system and solving the delay problem caused by insufficient data processing capability.

[0070] The specific method of merging the initial screening heartbeat packet data based on the difference encoding feature data is as follows:

[0071] Calculate the difference between each heartbeat packet data and the heartbeat packet data at the previous preset difference moment, and establish a coded difference set based on the time sequence relationship of the difference;

[0072] Calculate the mean, variance, maximum and minimum values ​​of the coded difference set, and construct multiple merged and repeated processed data sets based on cluster analysis;

[0073] The specific method of the cluster analysis method is as follows:

[0074] The set length of the coded difference set is taken as the number of cluster centers;

[0075] Assign each set of coded differences to the nearest cluster center;

[0076] Update the center of each cluster and the heartbeat packet data within the cluster, and treat each cluster as a multiple processing data set;

[0077] The specific calculation formula of the cluster center is as follows:

[0078]

[0079] In the formula, is the cluster center, is the bth heartbeat data of the heartbeat packet data. It is the heartbeat packet data of the i-th lock.

[0080] Merging the initial screening heartbeat packet data based on the difference coding feature data has the following advantages compared to merging other features:

[0081] Higher data compression efficiency: Difference encoding can usually represent data changes more compactly by storing the differences between data points instead of the original data; compared with multiple data points stored in time window merging or multiple cluster center values ​​in cluster merging, difference encoding usually provides a smaller data volume;

[0082] Less data redundancy: Differential encoding focuses on data changes, avoiding repeated storage of multiple similar data points, thereby reducing data redundancy;

[0083] Improve processing and transmission efficiency: Difference coding only needs to store data change information, which reduces the amount of data that needs to be transmitted. It is more efficient than multiple statistical features contained in time window merging or multiple cluster data in cluster merging;

[0084] Fast decoding: The data decoding process is relatively simple, and the original data is directly restored from the difference calculation, which makes the processing speed faster;

[0085] Stronger adaptability to real-time data: Difference coding can quickly adapt to rapid changes in data because it directly captures changes between data points; compared to time window merging, which may require processing statistical features of data over a long period of time, difference coding can reflect data changes more quickly;

[0086] Real-time update: Difference coding is suitable for processing real-time data streams and can calculate and update difference information in real time, while other methods such as position merging or cluster merging may require more complex processing steps.

[0087] Smoothing coefficient of variation: Difference encoding helps reduce the impact of a single outlier data point on the overall data analysis by processing difference data rather than absolute values; this is different from the situation in which the statistical features in time window merging may be affected by the coefficient of variation or the cluster centers in cluster merging may be offset by outliers.

[0088] Noise resistance: In difference coding, the impact of data noise is usually smaller because it focuses on data changes rather than absolute values; this makes data analysis more robust;

[0089] Simplification of data analysis and calculation: The calculation process of difference coding is relatively simple, involving only the difference calculation between data points, while time window merging and cluster merging may require more complex statistical operations and aggregation processes;

[0090] Easy to implement: Difference coding is simple to implement and maintain, and easy to deploy in practical applications. Compared with the complex data processing and algorithm implementation required in time window and clustering methods, difference coding is more operational;

[0091] Compared with time window merging, cluster merging and position merging methods, difference coding has the advantages of higher data compression efficiency, improved processing and transmission efficiency, stronger real-time data adaptability, reduced impact of data anomalies and simplified data analysis and calculation. These characteristics enable difference coding to more effectively solve the problems of server fault location delay and limited data processing capacity when processing lock data, especially in scenarios with large data volume and high real-time requirements.

[0092] The specific method of merging the initial screening heartbeat packet data based on the distance feature data is as follows:

[0093] Obtain the heartbeat packet data of all smart locks, calculate the Euclidean distance and Manhattan distance between the heartbeat packet data respectively, and merge the heartbeat packet data whose Euclidean distance and Manhattan distance are both higher than the preset reference threshold into a batch processing set;

[0094] The specific calculation formula of the Euclidean distance is as follows:

[0095]

[0096] In the formula, is the Euclidean distance between locks i and j, The timestamp of the heartbeat packet data of lock i, is the timestamp of the heartbeat packet data of lock j, is the ambient temperature of the heartbeat packet data of lock i, , is the ambient temperature of the heartbeat packet data of lock j, The location data of the heartbeat packet data of lock i, The location data of the heartbeat packet data of lock j;

[0097] The specific calculation formula of the Manhattan distance is as follows:

[0098]

[0099] is the Manhattan distance between locks i and j.

[0100] Merging the initial screening heartbeat packet data based on distance feature data has the following advantages compared to merging other features:

[0101] High data similarity: The distance measurement method can effectively merge similar data packets by calculating the similarity of each parameter in the heartbeat packet (such as Euclidean distance and Manhattan distance), ensuring that the information contained in the merged data packet is more consistent and representative; this method avoids inaccurate merging caused by inconsistent data features;

[0102] Strong dynamic adaptability: Cluster analysis can automatically adapt to the dynamic changes and distribution of data; compared with fixed time windows or fixed coding methods, distance-based clustering methods can be adjusted according to the actual distribution of data, thereby better handling the heterogeneity and volatility of data;

[0103] Data redundancy reduction: By merging similar heartbeat packets through distance measurement, redundant data can be effectively reduced. Compared with the difference encoding method, the distance measurement not only considers the difference of data, but also considers the global similarity of data, thereby reducing redundancy more comprehensively.

[0104] Improved fault detection accuracy: Distance measurement can more accurately identify similar patterns of abnormal equipment status, thereby improving the accuracy of fault detection; compared with the merging method based on statistical features, the distance measurement method can better capture the abnormal patterns hidden in the data;

[0105] Improved processing efficiency: Cluster analysis can effectively merge a large number of heartbeat packets into a few smaller packets, reducing the burden of data transmission and processing; compared with the time window method and statistical aggregation method, distance-based merging can maintain higher efficiency when processing large-scale data;

[0106] High flexibility: The distance-based merging method can flexibly process feature data of different types and dimensions; compared with the single feature merging method (such as only based on environmental parameters), the distance measurement method can comprehensively consider multi-dimensional information such as timestamps, environmental parameters, and location data;

[0107] In summary, the merging method based on distance feature data can provide higher data processing accuracy and efficiency by comprehensively considering the similarity and dynamic changes of data, while effectively reducing data redundancy and improving the overall performance of the system.

[0108] This embodiment significantly improves the system's fault location efficiency in the face of limited data processing capabilities by performing detailed screening and merging of the smart lock heartbeat packet data; specifically, through abnormal analysis and initial data screening, redundant data is effectively reduced and data quality is improved; using merging methods such as time window method, difference coding and distance measurement, the data volume is further compressed, and the data transmission and processing process is optimized. These methods not only improve the processing efficiency and accuracy of the data, but also reduce the burden on the server and reduce processing delays caused by excessive data. This comprehensive optimization strategy enables the system to respond and locate faults more quickly, thereby improving the operational reliability of the power system and the real-time nature of fault detection.

[0109] In Example 2, S4, the coefficient of variation is calculated and compared for multiple processed data sets with the same initial screening heartbeat packet data, and the initial screening heartbeat packet data with the smallest coefficient of variation is retained to obtain multiple non-repetitive processed data sets.

[0110] It should be noted that the same heartbeat packet data is used to construct multiple repeatable processing data sets;

[0111] The coefficients of variation of multiple repeatable processed data sets with the same heartbeat packet data are compared, and the one with the smallest coefficient of variation is retained to obtain multiple non-repetitive processed data sets.

[0112] In this embodiment, the calculation method of the coefficient of variation is as follows:

[0113] Calculate the standard deviation and mean of the heartbeat packet data in multiple processing data sets;

[0114] Based on the standard deviation and the mean, the coefficient of variation was calculated;

[0115] The specific calculation formula of the coefficient of variation is as follows:

[0116]

[0117] In the formula, For variation, The p-th heartbeat packet data in multiple processing data sets, The total number of data in multiple processing data sets.

[0118] S5: Report the non-repetitive processed data sets as a heartbeat packet and perform fault location.

[0119] The heartbeat reporting of the non-repetitive multiple processed data sets as one heartbeat packet is specifically as follows:

[0120] Each non-repetitive multiple processed data set is formatted into a standard heartbeat packet format. The data structure of the heartbeat packet includes a device identifier, timestamp, environmental parameters, and location data. The merged heartbeat packet is sent to the server for processing. The server performs fault detection and positioning based on the merged heartbeat packet data.

[0121] It should be noted that the integration of multiple processing data sets: In the previous steps, a large amount of initial screening heartbeat packet data is compressed into several non-repetitive processing data sets through different data merging methods (such as time window method, difference encoding, distance measurement, etc.). Each of these multiple processing data sets represents the merging result of a certain time period or data change, removing redundant and invalid data;

[0122] Generate heartbeat packets: Format each non-repetitive multiple processed data set into a standard heartbeat packet format. The data structure of the heartbeat packet includes device identifiers, timestamps, environmental parameters, location data, etc. By repackaging these merged data into the heartbeat packet format, it is ensured that it can be correctly identified and processed by the server;

[0123] Reporting heartbeat packets: Send the generated heartbeat packets to the server for processing. Since these heartbeat packets are merged and compressed, the amount of data sent is significantly reduced compared to the number of original data packets; this helps to reduce the processing burden of the server and improve data transmission efficiency;

[0124] Server-side processing: After receiving multiple processing data sets, the server performs further data analysis and processing; specifically, it includes: data decompression and decoding: decompressing and decoding the data in multiple processing data sets to restore the original heartbeat packet information;

[0125] Data verification: Verify the merged data to ensure data integrity and accuracy.

[0126] Fault detection and location: Fault detection and location are performed based on the merged heartbeat packet data. This step may include: Data analysis: Analyze the equipment status data to check whether there are abnormal patterns or signs of faults; Fault diagnosis: Determine the specific location and cause of the fault by comparing the abnormal information in the data with historical fault records; Response measures: Take appropriate maintenance or scheduling measures based on the fault location results to solve the equipment fault problem;

[0127] Reduce the data processing burden: Reporting multiple processed data sets as one heartbeat packet significantly reduces the number and data volume of heartbeat packets, and reduces the burden on the server during data reception and processing.

[0128] Improve fault location efficiency: By merging and compressing data, the server can process and analyze data faster, improve the real-time and accuracy of fault detection, and reduce processing delays caused by excessive data;

[0129] Optimize resource usage: Reduce data transmission volume and processing burden, optimize the use of network bandwidth and server resources, and improve the performance and stability of the overall system;

[0130] Enhance system responsiveness: By reducing the amount of data and improving data processing efficiency, the system can respond to fault events more quickly and locate and repair them in a timely manner, thereby improving the reliability of equipment and systems.

[0131] This embodiment calculates and compares the coefficient of variation of multiple processed data sets with the same initial screening heartbeat packet data, retains the data with the smallest coefficient of variation, and thus obtains several non-repetitive processed data sets. This process significantly improves the efficiency and accuracy of data processing. The method for calculating the coefficient of variation can effectively evaluate the stability and consistency of the data by analyzing the standard deviation and mean of the data set, and further screen out the most representative data set. This method reduces data redundancy and unnecessary fluctuations, and improves the quality and reliability of the data. By reporting multiple non-repetitive processed data sets as a heartbeat packet, the amount of data is reduced, the processing burden of the server is reduced, and the data transmission efficiency is improved. When processing the merged heartbeat packet, the server can detect and locate faults faster and more accurately, and optimize resource usage and system responsiveness. This method effectively solves the problem of server processing delay caused by excessive data volume, and improves the real-time, accuracy and overall performance of the system.

[0132] Embodiment 3, Figure 2 The present invention provides a structural schematic diagram of a digital lock fault location and analysis device, including a data acquisition module, a preliminary screening module, a merging module, a merging and rejection module, and a heartbeat reporting module;

[0133] A data acquisition module, used to acquire lock status data and heartbeat packet data of smart locks in the power system;

[0134] The initial screening module is used to perform abnormal analysis on data changes based on the lock status data, and to perform initial screening on the heartbeat packet data, remove redundant data, and obtain initial screening heartbeat packet data;

[0135] A merging module is used to obtain feature data of the initial screening heartbeat packet data, and merge the initial screening heartbeat packet data based on each feature in the feature data to generate a plurality of duplicate processed data sets;

[0136] The merging and elimination module is used to compare the coefficients of variation of multiple processed data sets with the same initial screening heartbeat packet data, and retain the initial screening heartbeat packet data with the smallest coefficient of variation, thereby obtaining multiple non-repetitive processed data sets;

[0137] The heartbeat reporting module is used to replace the heartbeat reporting data with non-repetitive multiple processing data sets as a heartbeat packet to locate the fault.

[0138] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0139] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0140] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0141] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0142] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0143] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A digital lock fault location analysis method, characterized in that: The method comprises the following steps: obtaining lock status data and heartbeat packet data of a smart lock in a power system; According to the lock status data, perform abnormal analysis on the data changes, and preliminarily screen the heartbeat packet data to remove redundant data and obtain the preliminarily screened heartbeat packet data; The abnormal analysis of data changes is performed based on the lock status data, specifically: The battery power level is used to represent the power state, and the rate of change of the battery power level is calculated using the rate of change analysis, and the selection is performed based on the rate of change of the battery power level; The signal strength represents the communication status, and the moving average method is used to calculate the average signal strength within a certain period of time, and then filter according to the deviation; When the working status of the smart lock device is normal, the heartbeat packet data is removed; The heartbeat packet data of the smart lock corresponding to the lock status data is screened according to the power status, communication status, and device working status to obtain the initial screening heartbeat packet data; Acquire feature data of the initial screening heartbeat packet data, and merge the initial screening heartbeat packet data based on each feature in the feature data to generate multiple processing data sets with duplication; The preliminary screening heartbeat packet data are merged based on each feature in the feature data, including merging the preliminary screening heartbeat packet data based on time feature data, merging the preliminary screening heartbeat packet data based on difference coding feature data, and merging the preliminary screening heartbeat packet data based on distance feature data; The specific method for merging the initial screening heartbeat packet data based on time feature data is as follows: Define the time window length of multiple processing data sets for merging time feature data; Collect all heartbeat packet data within the time window, calculate the average, sum, maximum, and minimum values ​​of all heartbeat packet data within the time window, and construct multiple merged processing data sets with duplication; The specific method of merging the initial screening heartbeat packet data based on the difference encoding feature data is as follows: Calculate the difference between each heartbeat packet data and the heartbeat packet data at the previous preset difference moment, and establish a coded difference set based on the time sequence relationship of the difference; Calculate the mean, variance, maximum and minimum values ​​of the coded difference set, and construct multiple merged and repeated processed data sets based on cluster analysis; The specific method of merging the initial screening heartbeat packet data based on the distance feature data is as follows: Obtain the heartbeat packet data of all smart locks, calculate the Euclidean distance and Manhattan distance between the heartbeat packet data respectively, and merge the heartbeat packet data whose Euclidean distance and Manhattan distance are both higher than the preset reference threshold into a batch processing set; The coefficients of variation of multiple processed data sets with the same initial screening heartbeat packet data are compared, and the initial screening heartbeat packet data with the smallest coefficient of variation is retained, thereby obtaining multiple non-repetitive processed data sets; The specific method for obtaining the plurality of non-repetitive processed data sets is as follows: Compare the coefficients of variation of multiple repeatable processed data sets with the same heartbeat packet data, retain the one with the smallest coefficient of variation, and obtain multiple non-repetitive processed data sets; The coefficient of variation is calculated as follows: Calculate the standard deviation and mean of the heartbeat packet data in multiple processing data sets; Based on the standard deviation and the mean, the coefficient of variation was calculated; Multiple non-repetitive processed data sets are used as a heartbeat packet to replace the data reported by the heartbeat for fault location.

2. The method for locating and analyzing faults of digital locks according to claim 1 is characterized in that: The heartbeat packet data includes a device identifier, a timestamp, environmental parameters, and location data; the lock status data includes a power status, a communication status, and a device working status.

3. The digital lock fault location analysis method according to claim 2 is characterized in that: The characteristic data of the initial screening heartbeat packet data includes time characteristic data, difference coding characteristic data and distance characteristic data.

4. The digital lock fault location analysis method according to claim 3 is characterized in that: The non-repetitive processing data sets are used as a heartbeat packet to replace the heartbeat reported data, specifically: Format each non-repetitive multiple processed data set into a standard heartbeat packet format, where the data structure of the heartbeat packet includes a device identifier, a timestamp, environmental parameters, and location data; The merged heartbeat packet is sent to the server for processing, and the server performs fault detection and location based on the merged heartbeat packet data.

5. The method for locating and analyzing faults of digital locks according to claim 4 is characterized in that: The specific calculation formula of the Euclidean distance is as follows: In the formula, is the Euclidean distance between locks i and j, The timestamp of the heartbeat packet data of lock i, is the timestamp of the heartbeat packet data of lock j, is the ambient temperature of the heartbeat packet data of lock i, is the ambient temperature of the heartbeat packet data of lock j, The location data of the heartbeat packet data of lock i, The location data of the heartbeat packet data of lock j; The specific calculation formula of the Manhattan distance is as follows: is the Manhattan distance between locks i and j.

6. The digital lock fault location analysis method according to claim 5 is characterized in that: The specific calculation formula of the coefficient of variation is as follows: In the formula, is the coefficient of variation, The p-th heartbeat packet data in multiple processing data sets, The total number of data in multiple processing data sets.

7. A device using the digital lock fault location analysis method according to any one of claims 1 to 6, comprising a data acquisition module, a preliminary screening module, a merging module, a merging and rejection module, and a heartbeat reporting module; A data acquisition module, used to acquire lock status data and heartbeat packet data of smart locks in the power system; The initial screening module is used to perform abnormal analysis on data changes based on the lock status data, and to perform initial screening on the heartbeat packet data, remove redundant data, and obtain initial screening heartbeat packet data; A merging module is used to obtain feature data of the initial screening heartbeat packet data, and merge the initial screening heartbeat packet data based on each feature in the feature data to generate a plurality of duplicate processed data sets; The merging and elimination module is used to compare the coefficients of variation of multiple processed data sets with the same initial screening heartbeat packet data, and retain the initial screening heartbeat packet data with the smallest coefficient of variation, thereby obtaining multiple non-repetitive processed data sets; The heartbeat reporting module is used to replace the heartbeat reporting data with non-repetitive multiple processing data sets as a heartbeat packet to locate the fault.

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