A security operation and maintenance system for an intelligent chassis

Abstract

The present invention relates to the technical field of power facility monitoring, and discloses a safety operation and maintenance system for an intelligent chassis, including a switch disposed in a distribution box. The switch includes: a data acquisition module for acquiring real-time data transmitted in real time by a monitoring unit arranged in the distribution box; a data storage and troubleshooting module for entering the transmitted real-time data and extracting detected abnormal quantities; an abnormal quantity processing module that, based on the extracted abnormal quantities, generates a temporary abnormal processing window Cn, and extracts historical power supply data of the distribution box to which the abnormal quantities belong under the abnormal processing window Cn for comparison with the abnormal quantities to give a result; and a data processing module that generates an execution command based on the obtained result. The invention not only realizes real-time monitoring and intelligent management of the distribution box, but also significantly improves the efficiency and accuracy of anti-stealing electricity work, reduces the cost and risk of manual troubleshooting, and provides a strong guarantee for the safe and stable operation of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power facility monitoring, and particularly relates to a security operation and maintenance system for an intelligent chassis. Background Art

[0002] With the gradual upgrade of the intelligent construction of expressways, the Internet of Things perception system has gradually become the main data source of the entire intelligent system. In the Internet of Things perception system, the chassis is the basic node of the system network and power. Traditional chassis have disadvantages such as poor reliability, single function, poor security, and insufficient intelligence, and have gradually become the weak link in the construction of expressway electromechanical equipment. In view of the deficiencies of the chassis and fully considering the construction requirements of the new generation of Internet of Things perception system, CCCC Group has formed an intelligent chassis system with the "terminal-cloud" architecture as the core, constructed an intelligent node of the new generation of Internet of Things perception system, and realized functions such as equipment operation status monitoring, chassis intelligent control, and information transmission encryption.

[0003] Referring to Chinese Patent, Publication No.: CN106654901A, a remotely monitored outdoor distribution box is disclosed.

[0004] And Chinese Patent, Publication No.: CN106358023A, discloses a method for remotely monitoring an outdoor distribution box.

[0005] In the prior art including the above two patents, since the entire power supply system takes the power supply station as the origin and the transformer as the node, the entire power grid is laid out in a tree-like shape to form a network, that is, the power supply network. The smart grid is to collect the operation status of each electronic component in the distribution box through various detection elements arranged in the distribution box and then feedback it to the terminal, so as to judge the operation status of the current distribution box.

[0006] And the distribution boxes are all arranged outdoors and are far from areas with large and concentrated population flows. Therefore, most of them are in sparsely populated areas, so there will be illegal acts such as maliciously opening the distribution box by people to steal electricity. Although it is possible to judge the electricity theft behavior by monitoring abnormal electricity consumption (such as Chinese Patent, Publication No.: CN105319421A, a theft-proof electricity system for a community), manual investigation by staff is still required. Summary of the Invention

[0007] The purpose of the present invention is to provide a security operation and maintenance system for an intelligent chassis to solve the above problems.

[0008] To achieve the above purpose, the present invention provides the following technical solution: A security operation and maintenance system for an intelligent chassis, including a switch disposed in a distribution box, and the switch includes:

[0009] A data acquisition module, which is used to acquire the real-time data transmitted in real time by the monitoring units deployed in the distribution box;

[0010] A data storage and troubleshooting module, which enters the transmitted real-time data and extracts the detected abnormal quantities;

[0011] An abnormal quantity processing module, which generates a temporary abnormal processing window Cn based on the extracted abnormal quantity, and extracts the historical power supply data of the distribution box to which the abnormal quantity belongs under the abnormal processing window Cn for comparison with the abnormal quantity to give a result;

[0012] A data processing module, which generates an execution command based on the obtained result;

[0013] A data feedback module, which actively obtains the location of the distribution box to which the above abnormal quantity belongs, and searches for the nearest station based on this location to send the execution command.

[0014] Preferably, the monitoring unit includes an intelligent report, a camera, and a voltage sensor;

[0015] The data acquisition module includes:

[0016] A timing unit, which acquires the data of the monitoring unit within a predetermined window based on a predetermined time interval;

[0017] A data calibration unit, which sets the acquisition frequency of the predetermined window, and compares the number of data items of the intelligent report, the camera, and the voltage sensor included. If the number of acquired data items of the three is the same, the real-time data is uploaded. If they are different, the acquisition of the data of the next predetermined window starts immediately after the end of the current predetermined window time, and the predetermined time interval also follows;

[0018] A data sorting unit, which receives the uploaded real-time data. The real-time data includes the data collected by the intelligent report, denoted as the Xn data set; the data collected by the camera, denoted as the Fn data set; and the data collected by the voltage sensor, denoted as the Sn data set;

[0019] A fault reporting unit, if the data calibration unit re-executes the acquisition continuously for no less than three times the predetermined window time during the acquisition process, it is determined that the monitoring unit of the current distribution box has a fault, generates a fault feedback, and uploads it to the data feedback module.

[0020] Preferably, the data storage and troubleshooting module includes:

[0021] A storage unit, which is used to receive the real-time data and store it according to the time unit of the belonging predetermined window for identification;

[0022] A data parsing unit generates a temporary cache space for the current real-time data and decompresses the real-time data to obtain an Xn data set, an Fn data set, and an Sn data set respectively;

[0023] A data preprocessing unit generates corresponding data processing channels based on the obtained Fn data set and Sn data set;

[0024] A data comparison unit matches the Sn data set in the real-time data with the same time digit in the historical data for three days or more with the Sn data set obtained by the data preprocessing unit one by one according to time, and extracts the data in the abnormal Sn data set and the data in the Fn data set corresponding to the time digit of this data;

[0025] An abnormal quantity extraction and integration module integrates the data in the obtained abnormal Sn data set and the data in the Fn data set corresponding to the time digit of this data to obtain an abnormal quantity data set.

[0026] Preferably, the time digit is the data in the same time period.

[0027] Preferably, in the data comparison unit, the data in the abnormal Sn data set and the data in the Fn data set corresponding to the time digit at the same time are extracted correspondingly, including:

[0028] S01. Generate a time mark based on the time corresponding to the first data in the data in the abnormal Sn data set;

[0029] S02. Trace back based on the time mark to obtain multiple frames of pictures in the Fn data set within multiple predetermined windows until a target picture sufficient to be identified as a human feature is extracted;

[0030] S03. Create a grid for the target picture and determine that the feature sufficient to be identified as a human is located in the coordinate cluster of the grid;

[0031] S04. Extract the pixel areas in each square of the coordinate cluster, and select the pixel areas with high brightness and low degree of tubercle adhesion for analysis;

[0032] S05. Convert each piece of pixel in the pixel area into a grayscale pixel to obtain a grayscale feature image;

[0033] S06. Use an edge detection algorithm to identify the edges of the features in the grayscale feature image, thereby simplifying the image and making the feature contour more obvious;

[0034] S07. Perform binarization on the grayscale feature image processed in step S06 using the local threshold method to obtain the processed feature image;

[0035] S08. Process all the target pictures of the features sufficient to be recognized as a person according to steps S04 to S05, and extract and combine multiple said feature images according to obvious contours to obtain at least two or more identical or similar complete portrait pictures.

[0036] Preferably, in step S07, binarization of the processed grayscale feature image based on the local threshold method includes:

[0037] S71. The formula of the local threshold method is: T(x, y) = mean(I) + k × stddev(I), where:

[0038] I represents the grayscale value of a pixel block in the grid coordinates (x, y);

[0039] mean(I) is the average value of the difference between the grayscale value at the edge of the pixel block and the grayscale value at the center of the pixel block;

[0040] stddev(I) is the standard deviation of the pixel grayscale values in the local area of the pixel block;

[0041] k is a constant, taking 4;

[0042] S72. The binarization formula is:

[0043] ;

[0044] where the I bin（x，y） represents the pixel value of the binarized pixel block at the position (x, y).

[0045] Preferably, the abnormal quantity processing module includes:

[0046] An abnormal quantity processing window generation unit that generates a corresponding number of abnormal quantity processing windows Cn based on the obtained number of abnormal quantities, where: multiple said abnormal quantity processing windows Cn are sorted in the order of the abnormal quantity acquisition time;

[0047] A historical data acquisition unit that acquires the cooperative difference of multiple annual historical Sn data sets with the same number of time digits. If it exceeds the threshold, it is determined that there is an abnormal quantity in the Sn data set, and then the complete portrait pictures in the corresponding processed Fn data set are extracted, where: the threshold is the average value of the power consumption voltages in multiple annual historical Sn data sets with the same number of time digits.

[0048] A data comparison unit compares the complete portrait picture with the stored portrait picture of the target person. If they match, the abnormal quantity fault code is eliminated. If they do not match, the result is output;

[0049] Among them, the stored portrait picture of the target person is the portrait picture of a leaving or in-service staff member.

[0050] Preferably, the data feedback module uses Beidou navigation to perform the positioning of the distribution box to which the abnormal quantity belongs and the positioning of the stations adjacent to the distribution box to which the abnormal quantity belongs.

[0051] In the above technical solution, an intelligent chassis safety operation and maintenance system provided by the present invention has the following beneficial effects:

[0052] By integrating multiple modules such as data collection, storage, troubleshooting, processing, and feedback, the real-time data collection and abnormal detection of the monitoring unit in the distribution box are realized. Compared with the traditional manual troubleshooting method, the present invention can automatically detect abnormal electricity consumption, quickly locate the abnormal distribution box, and send an execution command to the nearest station, effectively improving the efficiency and accuracy of the anti-electricity-theft work, and reducing the cost and risk of manual troubleshooting.

[0053] Through the comprehensive monitoring of the intelligent report form, camera, and voltage sensor, and the precise comparison of the data calibration unit, the present invention can ensure the accuracy and integrity of the real-time data, and improve the accuracy of abnormal detection.

[0054] The data storage and troubleshooting module can accurately extract the abnormal quantity through the comparison of historical data and real-time data, and conduct comprehensive analysis in combination with camera data (Fn data set) and voltage sensor data (Sn data set), further improving the accuracy and reliability of abnormal detection.

[0055] By extracting and grid-processing multiple frames of pictures collected by the camera second by second, the present invention can accurately identify the characteristic image of the target person, providing strong evidence support for the processing of the abnormal quantity.

[0056] The abnormal quantity processing module can accurately judge whether the abnormal quantity is caused by human factors through the comparison of historical data and the matching of the portrait pictures of personnel, further improving the intelligent level of the anti-electricity-theft work. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0058] Figure 1 It is the module architecture diagram of Embodiment 1 provided by the embodiments of the present invention;

[0059] Figure 2 It is the unit architecture diagram of the data acquisition module in Embodiment 1 provided by the embodiments of the present invention;

[0060] Figure 3 It is the unit architecture diagram of the data storage and troubleshooting module in Embodiment 1 provided by the embodiments of the present invention;

[0061] Figure 4 It is Embodiment 1 provided by the embodiments of the present invention Figure 3 The flowchart of the data comparison unit in;

[0062] Figure 5 It is the unit architecture diagram of the abnormal quantity processing module in Embodiment 1 provided by the embodiments of the present invention. Specific embodiments

[0063] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of the present invention.

[0064] It should be noted that the terms "primary", "secondary", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0065] Embodiment 1:

[0066] As Figure 1 shown, this embodiment provides a security operation and maintenance system for an intelligent chassis, including a switch disposed in a distribution box, and the switch includes:

[0067] A data acquisition module, which is used to acquire real-time data transmitted in real time by monitoring units arranged in the distribution box;

[0068] Data storage and troubleshooting module, which enters the transmitted real-time data and extracts the detected abnormal quantities;

[0069] Abnormal quantity processing module, which generates a temporary abnormal processing window Cn based on the extracted abnormal quantity, and extracts the historical power supply data of the distribution box to which the abnormal quantity belongs under this abnormal processing window Cn for comparison with the abnormal quantity to give a result;

[0070] Data processing module, which generates an execution command based on the obtained result;

[0071] Data feedback module, which actively obtains the location of the distribution box to which the above abnormal quantity belongs, and searches for the nearest station based on this location to send the execution command.

[0072] In the above technology, the data acquisition module is responsible for collecting in real time the data transmitted by monitoring units such as intelligent reports, cameras, and voltage sensors installed in the distribution box to ensure the timeliness and integrity of the data. Then the data storage and troubleshooting module is used to receive and securely store all real-time data from the data acquisition module, and at the same time use advanced algorithms to deeply troubleshoot the stored data to identify potential abnormal quantities and provide key information for subsequent processing.

[0073] In a further embodiment, the abnormal quantity processing module, for the identified abnormal quantity, can automatically generate a temporary abnormal processing window Cn, and under this window, compare and analyze the historical power supply data of the distribution box to which the abnormal quantity belongs with the real-time abnormal quantity, so as to accurately judge the nature of the abnormality and provide a scientific basis for subsequent processing.

[0074] Secondly, based on the analysis result of the abnormal quantity processing module, if the result shows that the obtained complete portrait picture is not a staff member, it is determined that the power consumption abnormality is caused by a dangerous human behavior. Then this module generates a corresponding execution command, and the so-called execution command is to dispatch personnel for troubleshooting.

[0075] Specifically, as shown in Figure 2 the monitoring unit includes an intelligent report, a camera, and a voltage sensor;

[0076] Furthermore, the data acquisition module includes:

[0077] A timing unit, which collects the data of the monitoring unit within a predetermined window based on a predetermined time interval;

[0078] A data calibration unit, which sets the predetermined acquisition frequency of the predetermined window, and compares the number of data items of the intelligent report, camera, and voltage sensor collected. If the number of data items collected by the three is the same, the real-time data is uploaded. If they are inconsistent, the data collection of the next predetermined window starts immediately after the end of the current predetermined window time, and the predetermined time interval also follows.

[0079] A data sorting unit that receives uploaded real-time data. The real-time data includes data collected by an intelligent report, denoted as the Xn data set; data collected by a camera, denoted as the Fn data set; and data collected by a voltage sensor, denoted as the Sn data set.

[0080] A fault reporting unit. If the re-execution of data collection continuously occurs for no less than three predetermined window times during the data collection process of the data calibration unit, it is determined that the monitoring unit of the current distribution box has a fault, and a fault feedback is generated and uploaded to the data feedback module.

[0081] In the above technology, the intelligent report collects power consumption, which is used for later calculation of user electricity bills and simultaneous monitoring of the operation faults of the intelligent report.

[0082] The design focus of the present invention lies in the data acquisition of the camera and the voltage sensor. The data of the monitoring unit within a predetermined window is automatically collected at a predetermined time interval. For example, at an interval of 20 minutes, the data of one window is collected. If the window time is 10 minutes, the collection frequency is to collect one every 3 minutes within the window, forming a data set about the window, and this data set includes the Xn data set, the Fn data set, and the Sn data set.

[0083] Furthermore, the data collected by the intelligent report, the camera, and the voltage sensor are compared and calibrated in terms of the number of items to ensure the accuracy and consistency of the data. If the number of data items is inconsistent, the data collection of the next window is immediately started after the end of the current predetermined window, and the predetermined time interval is appropriately adjusted until the number of data items is consistent. If the number of items is inconsistent, for example, the camera collects 5 times within one window while the voltage sensor collects 3 times, it indicates that there is a fault in the circuit board of the intelligent report, the voltage sensor, or the camera. In short, it can be said that there is a problem with the timing module. All in all, one or more of the intelligent report, the camera, and the voltage sensor have faults that need to be manually checked. If they are consistent, it indicates that the intelligent report, the camera, and the voltage sensor are operating normally and there are no faults.

[0084] And when the first inconsistency occurs, after the system executes the data collection of this window cycle, it re-executes the data collection of one window again. If the number of data items collected in the second collection is still inconsistent, it is executed a third time. If the result is still the same, it indicates that one or more of the intelligent report, the camera, and the voltage sensor have faults that need to be manually checked.

[0085] Specifically, combined with Figure 3 it can be seen that the data storage and troubleshooting module includes:

[0086] A storage unit that is used to receive real-time data and store it according to the time unit and the belonging predetermined window for identification.

[0087] The data parsing unit generates a temporary buffer space for the current real-time data and decompresses the real-time data to obtain the Xn data set, the Fn data set, and the Sn data set respectively;

[0088] A data preprocessing unit generates corresponding data processing channels based on the obtained Fn data set and Sn data set;

[0089] The data comparison unit matches the Sn data set in the real-time data of the same time digit for three days or more with the Sn data set obtained by the data preprocessing unit one by one according to time, extracts the data in the abnormal Sn data set and the Fn data in the Fn data set with the time digit corresponding to the data;

[0090] The abnormal quantity extraction and integration module integrates the Sn data in the Sn data set with abnormal quantities and the Fn data in the Fn data set with the time digits corresponding to the data to obtain the abnormal quantity data set.

[0091] The time bits in the above embodiment are data of the same time period.

[0092] In the above technology, the storage unit is responsible for receiving and securely storing all real-time data, categorizing and managing it by predefined window and time unit for subsequent query and analysis. Leveraging high-reliability storage devices and large-capacity storage, the storage unit enables long-term preservation and rapid access to historical data. During execution, the data parsing unit then generates a temporary cache for the current real-time data and decompresses it to generate intelligent report data (Xn data set), camera data (Fn data set), and voltage sensor data (Sn data set). Through data parsing, the system enables rapid processing and categorized storage of different data types.

[0093] Furthermore, the obtained data is processed based on the Fn and Sn data sets, generating corresponding data processing channels to facilitate subsequent data comparison and analysis. Through data preprocessing, the system can preprocess and format different types of data, improving the accuracy of data comparison and analysis.

[0094] Furthermore, the Sn data sets in the real-time data with the same number of historical time digits over three days or more are matched one by one with the Sn data sets in the current real-time data. When the data in the extracted abnormal Sn data set and the Fn data in the Fn data set corresponding to the time digits are obtained. The system integrates and processes the extracted abnormal data to obtain an abnormal quantity data set, providing key information for subsequent processing. Through data comparison and analysis, the system can automatically identify and classify abnormal data, providing a scientific basis for subsequent processing.

[0095] It realizes in-depth comparison and analysis of historical data and real-time data, can accurately identify potential abnormal quantities, and provides a scientific basis for subsequent processing. At the same time, through the data comparison and analysis mechanism, it realizes the automatic identification and processing of abnormal data, improving the reliability and stability of the power system.

[0096] Specifically, as shown in Figure 4 the data corresponding to the abnormal Sn data set and the Fn data set of the time digits at the same time in the data comparison unit are extracted correspondingly, including:

[0097] S01. Generate a time stamp based on the time corresponding to the first data in the data of the abnormal Sn data set;

[0098] S02. Trace back based on the time stamp to obtain multiple frames of pictures in the Fn data set within multiple predetermined windows until a target picture sufficient to be recognized as a human feature is extracted;

[0099] S03. Create a grid for the target picture and determine that the features sufficient to be recognized as a human are located in the coordinate cluster of the grid;

[0100] S04. Extract the pixel areas in each square of the coordinate cluster, and select the pixel areas with high brightness and low degree of tubercle adhesion for analysis;

[0101] S05. Convert each block of pixels in the pixel area into grayscale pixels to obtain a grayscale feature image;

[0102] S06. Use an edge detection algorithm to identify the edges of the features in the grayscale feature image, thereby simplifying the image and making the feature contours more obvious;

[0103] S07. Adopt a local threshold method to binarize the grayscale feature image processed in step S06 to obtain a processed feature image;

[0104] S08. Process all target pictures of features sufficient to be recognized as a person according to steps S04 to S05, and extract multiple feature image combinations based on obvious contours to obtain at least two or more identical or similar complete portrait pictures.

[0105] Further, in step S07, binarize the processed grayscale feature image based on the local threshold method, including:

[0106] S71. The formula of the local threshold method is: T(x, y) = mean(I) + k × stddev(I), where:

[0107] I represents the grayscale value of a pixel block in the grid coordinates (x, y);

[0108] mean(I) is the average value of the difference between the grayscale value at the edge of the pixel block and the grayscale value at the center of the pixel block;

[0109] stddev(I) is the standard deviation of the pixel grayscale values in the local area of the pixel block;

[0110] k is a constant, taking 4;

[0111] S72. The binarization formula is:

[0112] ;

[0113] where, I bin（x，y） represents the pixel value of the binarized pixel block at the position (x, y).

[0114] Above, by tracing back in time for the determined abnormal amount of Sn data, multiple Fn data are obtained, and then the pictures in the Fn data are acquired, and then the processing of multiple frames of images is performed, namely gridification, feature extraction, and feature marginalization processing, so as to obtain a clear feature sufficient to be recognized. Since the obtained features may not be complete portraits, combination is required. Through the combination method, multiple pictures that can be recognized as complete portraits by the system are obtained, and there can be multiple such complete portrait pictures.

[0115] In the above technology, further processing is also performed through the local threshold method and binarization, making the contours of the grayscale image more obvious. Thus, the feature images of the target person can be accurately recognized, providing strong evidence support for the processing of the abnormal amount.

[0116] Specifically, as Figure 5 shown, the abnormal amount processing module includes:

[0117] An abnormal quantity processing window generation unit generates corresponding numbers of abnormal quantity processing windows Cn based on the number of acquired abnormal quantities, where: the multiple abnormal quantity processing windows Cn are sorted in the order of abnormal quantity acquisition time;

[0118] A historical data acquisition unit acquires the cooperative difference of multiple annual historical Sn data sets with the same number of time digits. If it exceeds the threshold, it is determined that the abnormal quantity in the Sn data set is determined, and then the complete portrait pictures in the corresponding processed Fn data set are extracted, where: the threshold is the average value of the power consumption voltages in the Sn data sets of multiple annual histories with the same number of time digits.

[0119] A data comparison unit compares the complete portrait pictures with the stored portrait pictures of the target personnel. If they match, the abnormal quantity fault code is eliminated. If they do not match, the result is output;

[0120] Among them, the stored portrait pictures of the target personnel are the portrait pictures of former or current employees.

[0121] Above, during the execution process, a secondary calibration will be performed with the abnormal quantity. Because the Sn data of the abnormal quantity given by the data storage and troubleshooting module is based on the fact that there is an Sn data in the Sn data set collected on the same day that is greater than the average power consumption voltage data of the previous three days. And the historical data acquisition unit is used to sort the Sn data of multiple consecutive abnormal quantities acquired on the same day in chronological order, and then perform a comparison of historical data in chronological order. And acquire the Sn data sets of multiple annual histories with the same number of time digits. For example, if the currently collected abnormal quantity data is at 15:00 on December 4, 2023, then acquire the power consumption voltages at the same time period of 15:00 on December 4, 2022, 15:00 on December 4, 2021, and 15:00 on December 4, 2020, that is, the Sn data.

[0122] If the current Sn data is greater than the threshold, that is, it is determined that the abnormal quantity Sn data is real, then the data comparison unit is started to compare the acquired multiple complete portrait pictures with the portrait pictures of former or current employees. If the comparison is not, it means that the abnormal quantity is artificially generated. If so, it is determined that the abnormal quantity is normal, then the distribution box with the abnormal quantity is marked and observed for a continuous week to see if there is still an abnormal quantity in the future. If there is still an abnormal quantity, arrange for manual troubleshooting. If there is no longer an abnormal quantity, the mark is eliminated.

[0123] If the current Sn data is less than or equal to the threshold value, it is considered a normal quantity. However, the distribution box that has shown an abnormal quantity will be marked and observed for a continuous week to check whether there is still an abnormal quantity in the future. If there is still an abnormal quantity, arrange for manual investigation. If there is no longer an abnormal quantity, remove the identification. The purpose is to avoid losses caused by incorrect system judgments, that is, to increase data maintenance for cases where an abnormal quantity is misjudged as normal, conduct continuous monitoring, and thus eliminate the problem of incorrect judgments caused by system failures, and increase the stability and reliability during the actual operation of the system.

[0124] Specifically, the data feedback module uses Beidou navigation to perform the positioning of the distribution box to which the abnormal quantity belongs and the positioning of the stations adjacent to the distribution box to which the abnormal quantity belongs. Precise positioning and station search using Beidou navigation ensure that the execution command can be quickly and accurately delivered to the nearest station, improving the speed and efficiency of emergency response.

[0125] In the first embodiment, by integrating multiple modules such as data collection, storage, investigation, processing, and feedback, real-time data collection and abnormal detection of the monitoring unit in the distribution box are achieved. Compared with the traditional manual investigation method, the present invention can automatically detect abnormal electricity consumption, quickly locate the abnormal distribution box, and send an execution command to the nearest station, effectively improving the efficiency and accuracy of anti-electricity-theft work, reducing the cost and risk of manual investigation. And through the comprehensive monitoring of intelligent reports, cameras, and voltage sensors, as well as the precise comparison of the data calibration unit, the present invention can ensure the accuracy and integrity of real-time data, improving the accuracy of abnormal detection. Secondly, by comparing historical data with real-time data, abnormal quantities can be accurately extracted, and comprehensive analysis can be carried out in combination with camera data (Fn data set) and voltage sensor data (Sn data set), further improving the accuracy and reliability of abnormal detection. Moreover, by extracting and grid-processing multiple frames of pictures collected by the camera second by second, the present invention can accurately identify the characteristic images of the target person, providing strong evidence support for the processing of abnormal quantities. And it can accurately judge whether the abnormal quantity is caused by human factors, further improving the intelligent level of anti-electricity-theft work.

[0126] Embodiment Two:

[0127] The embodiment of the present invention provides a non-transitory computer-readable storage medium, in which at least one instruction or at least one program segment is stored, and at least one instruction or at least one program segment is loaded and executed by a processor to implement the steps:

[0128] Collect the real-time data transmitted in real time by the monitoring unit arranged in the distribution box;

[0129] Enter the transmitted real-time data and extract the detected abnormal quantities;

[0130] Based on the extracted abnormal quantity, a temporary abnormal handling window Cn is generated, and historical power supply data of the distribution box to which the abnormal quantity belongs is extracted under the abnormal handling window Cn and compared with the abnormal quantity to give a result;

[0131] An execution command is generated based on the obtained result;

[0132] Actively obtain the location of the distribution box to which the above abnormal quantity belongs, and search for the nearest station based on this location to send the execution command.

[0133] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to the memory, storage, database or other media used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0134] Those skilled in the art can clearly understand that for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0135] Embodiment 3:

[0136] The embodiment of the present invention provides an electronic device, including a processor and a memory. At least one instruction or at least one program segment is stored in the memory, and the at least one instruction or the at least one program segment is loaded and executed by the processor to implement the steps:

[0137] Collect the real-time data transmitted in real time by the monitoring unit installed in the distribution box for monitoring and control;

[0138] Enter the transmitted real-time data and extract the detected abnormal quantities;

[0139] Based on the extracted abnormal quantities, generate a temporary abnormal handling window Cn, and extract the historical power supply data of the distribution box to which the abnormal quantities belong under this abnormal handling window Cn for comparison with the abnormal quantities to give a result;

[0140] Generate an execution command based on the obtained result;

[0141] Actively obtain the location of the distribution box to which the above abnormal quantities belong, and search for the nearest station based on this location to send the execution command.

[0142] The above is only a preferred embodiment of the present invention, and it does not impose any form of limitation on the present invention. Although the present invention has been disclosed above with a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art, without departing from the scope of the technical solution of the present invention, makes some changes or modifications using the technical content disclosed above, which are regarded as equivalent embodiments of equivalent changes. However, as long as it does not depart from the content of the technical solution of the present invention, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A security operation and maintenance system for an intelligent chassis, characterized in that, including a switch disposed in a distribution box, the switch comprising: a data acquisition module for acquiring real-time data transmitted in real time by a monitoring unit arranged in the distribution box; a data storage and troubleshooting module for entering the transmitted real-time data and extracting detected abnormal quantities; an abnormal quantity processing module for generating a temporary abnormal processing window Cn based on the extracted abnormal quantity, and extracting historical power supply data of the distribution box to which the abnormal quantity belongs under the abnormal processing window Cn for comparison with the abnormal quantity to give a result; a data processing module for generating an execution command based on the obtained result; a data feedback module for actively obtaining the location of the distribution box to which the above abnormal quantity belongs, and searching for the nearest station based on the location to send the execution command; the data storage and troubleshooting module includes: a storage unit for receiving the real-time data and storing it according to the time unit in a predetermined window to which it belongs; a data parsing unit for generating a temporary cache space for the current real-time data and decompressing the real-time data to obtain an Xn data set, an Fn data set, and an Sn data set respectively; a data preprocessing unit for generating corresponding data processing channels based on the obtained Fn data set and Sn data set; a data comparison unit for matching the Sn data set in the real-time data with the same historical time digits for three days or more with the Sn data set obtained by the data preprocessing unit one by one according to time, and extracting the data in the abnormal Sn data set and the data in the Fn data set corresponding to the time digits of the data; an abnormal quantity extraction and integration module for integrating the data in the obtained abnormal Sn data set and the data in the Fn data set corresponding to the time digits of the data to obtain an abnormal quantity data set; in the data comparison unit, the data in the abnormal Sn data set and the data in the Fn data set corresponding to the time digits are extracted correspondingly under the same time, including: S01. generating a time label based on the time corresponding to the first data in the data in the abnormal Sn data set; S02. tracing back based on the time label to obtain multiple frames of pictures in the Fn data set within a plurality of the predetermined windows until a target picture sufficient to be identified as a human feature is extracted; S03. creating a grid for the target picture and determining that the feature sufficient to be identified as a human is located in a coordinate cluster of the grid; S04. extracting pixel regions in each square of the coordinate cluster, and selecting pixel regions with high brightness and low degree of tubercle adhesion for analysis; S05. converting each block of pixels in the pixel region into grayscale pixels to obtain a grayscale feature image; S06. using an edge detection algorithm to identify the edges of features in the grayscale feature image, thereby simplifying the image and making the feature contours more obvious; S07. performing binarization on the grayscale feature image processed in step S06 by adopting a local threshold method to obtain a processed feature image; S08. Process all the target pictures of the features sufficient to be recognized as a person according to steps S04 to S05, and extract multiple combinations of the feature images according to the obvious contours to obtain at least two or more identical or similar complete portrait pictures; In step S07, binarize the processed grayscale feature image based on the local threshold method, including: S71. The formula of the local threshold method is: T(x, y) = mean(I) + k × stddev(I), where: I represents the grayscale value of a pixel block in the grid coordinates (x, y); mean(I) is the average value of the difference between the grayscale value at the edge of the pixel block and the grayscale value at the center of the pixel block; stddev(I) is the standard deviation of the pixel grayscale values in the local area of the pixel block; k is a constant, taking 4; S72. The binarization formula is: ； wherein, the I bin（x，y） represents the pixel value of the binarized pixel block at the position (x, y).

2. The safety operation and maintenance system of an intelligent chassis according to claim 1, wherein The monitoring unit includes an intelligent report, a camera, and a voltage sensor; The data acquisition module includes: A timing unit that acquires the data of the monitoring unit within a predetermined window based on a predetermined time interval; A data calibration unit that sets the acquisition frequency of the predetermined window, and compares the number of data items of the intelligent report, the camera, and the voltage sensor collected. If the number of collected data items of the three is the same, the real-time data is uploaded. If they are inconsistent, the acquisition of the data of the next predetermined window starts immediately after the end of the current predetermined window time, and the predetermined time interval also follows; A data sorting unit that receives the uploaded real-time data. The real-time data includes the data collected by the intelligent report, denoted as the Xn data set; the data collected by the camera, denoted as the Fn data set, and the data collected by the voltage sensor, denoted as the Sn data set; A fault reporting unit. If the data calibration unit re-executes the acquisition of the predetermined window time continuously not less than three times during the acquisition process, it is determined that the monitoring unit of the current distribution box has a fault, and a fault feedback is generated and uploaded to the data feedback module.

3. The security operation and maintenance system of an intelligent chassis according to claim 1, characterized in that, The number of digits of the time is the data of the same time period.

4. The security operation and maintenance system of an intelligent chassis according to claim 1, characterized in that, The abnormal quantity processing module includes: An abnormal quantity processing window generation unit that generates corresponding numbers of abnormal quantity processing windows Cn based on the number of the obtained abnormal quantities, where: the multiple abnormal quantity processing windows Cn are sorted in the order of the abnormal quantity acquisition time; A historical data acquisition unit that obtains the difference of the Sn data sets of multiple annual histories with the same number of digits of time. If it exceeds the threshold, it is determined that the abnormal quantity in the Sn data set is determined, and then the complete portrait pictures in the corresponding processed Fn data set are extracted, where: the threshold is the average value of the power consumption voltages in the Sn data sets of multiple annual histories with the same number of digits of time; A data comparison unit that compares the complete portrait pictures with the stored portrait pictures of the target personnel. If they match, the abnormal quantity fault code is eliminated. If they do not match, the result is output; Among them, the stored portrait pictures of the target personnel are the portrait pictures of the separated or on-the-job staff.

5. The security operation and maintenance system of an intelligent chassis according to claim 1, characterized in that, The data feedback module uses Beidou navigation to perform the positioning of the distribution box to which the abnormal quantity belongs and the positioning of the stations adjacent to the distribution box to which the abnormal quantity belongs.

6. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the safety operation and maintenance system of the intelligent chassis according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the safety operation and maintenance system of the intelligent chassis according to any one of claims 1 to 5.

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