Power distribution network switch cabinet control method and system based on intelligent monitoring

Abstract

The invention relates to the technical field of power systems, in particular to a power distribution network switch cabinet control method and system based on intelligent monitoring. Importing the real-time environment characteristic data into an index knowledge graph for pairing analysis and evaluation; if the power distribution control performance of the target switch cabinet at the preset time node is in a normal state, the target switch cabinet is not processed; if the power distribution control performance of the target switch cabinet at the preset time node is in an early warning state, backtracking early warning working parameters of the target switch cabinet at the preset time node in the index knowledge graph, and performing drift analysis on the early warning working parameters; and if the drift analysis result of the early warning working parameters is the working parameters needing to be regulated and controlled, regulating and controlling the working parameters needing to be regulated and controlled. According to the invention, intelligent regulation and control of the switch cabinet are realized, so that the stability and reliability of a power distribution system are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a distribution network switch cabinet control method and system based on intelligent monitoring. Background Art

[0002] In the current power system field, the safe, stable and reliable operation of the distribution network is of vital importance. As a key equipment in the distribution network, the switchgear bears the important responsibilities of controlling, protecting and distributing electric energy. However, with the increasing complexity of the power system and the continuous growth of the load, the traditional switchgear control method has gradually revealed its limitations. On the one hand, the traditional control method mainly relies on manual experience and regular inspections, which makes it difficult to achieve real-time and comprehensive monitoring of the switchgear status, and it is impossible to timely and accurately warn and handle potential faults and abnormal conditions. On the other hand, as the operating time of the switchgear increases, its internal components, insulation materials, etc. will age and deteriorate, and changes in the operating environment, load fluctuations and other factors will also affect the performance of the switchgear, which may cause various abnormalities or even failures in the switchgear, and the traditional control method is difficult to comprehensively analyze and effectively respond to these complex factors.

[0003] In addition, when dealing with abnormal events in switch cabinets, existing technologies often lack sufficient mining and utilization of historical data, making it difficult to establish an effective association between abnormal events and equipment status, resulting in a lack of accuracy and foresight in the judgment and handling of abnormal events. At the same time, there is also a lack of in-depth research and application on the relationship between environmental feature data and equipment operating parameters when abnormal events occur, resulting in the inability to fully utilize these data to improve the intelligence level of control methods. Summary of the invention

[0004] The present invention overcomes the shortcoming of low intelligent control degree of the existing switch cabinet, and provides a distribution network switch cabinet control method and system based on intelligent monitoring.

[0005] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: The present invention discloses a distribution network switch cabinet control method based on intelligent monitoring, comprising the following steps: S102: Acquire various abnormal working environment events that have occurred during the operation of the target switch cabinet, perform correlation evaluation on the various abnormal working environment events that have occurred during the operation of the target switch cabinet, and acquire the associated working parameters of the target switch cabinet when the various abnormal working environment events occur; S104: Acquire historical environmental feature data of the target switch cabinet when various abnormal working environment events occur, and construct an index knowledge graph based on the historical environmental feature data of the target switch cabinet when various abnormal working environment events occur and the associated working parameters; S106: During the actual operation of the target switch cabinet, real-time environmental feature data of the target switch cabinet is collected at a preset time node, and the real-time environmental feature data is imported into the index knowledge graph for pairing analysis and evaluation, so as to obtain the power distribution control performance status of the target switch cabinet at the preset time node; S108: If the power distribution control performance of the target switch cabinet at the preset time node is in a normal state, the target switch cabinet is not processed, and the power distribution control performance is continuously analyzed and evaluated at the next preset time node; S110: If the power distribution control performance of the target switch cabinet at the preset time node is in a warning state, then the warning working parameters of the target switch cabinet at the preset time node are traced back in the index knowledge graph, and drift analysis is performed on the warning working parameters to obtain the drift analysis results; S112: If the drift analysis result of the warning working parameter is that the working parameter does not need to be adjusted, the warning working parameter is continuously monitored; if the drift analysis result of the warning working parameter is that the working parameter needs to be adjusted, the working parameter that needs to be adjusted is adjusted.

[0006] More specifically, various abnormal working environment events that have occurred during the operation of the target switch cabinet are obtained, and correlation evaluation is performed on the various abnormal working environment events that have occurred during the operation of the target switch cabinet, and the associated working parameters of the target switch cabinet when various abnormal working environment events occur are obtained, specifically: S202: Obtaining a work log of the target switch cabinet, and obtaining various abnormal working environment events that occurred during the operation of the target switch cabinet according to the work log; S204: randomly extracting a certain type of abnormal working environment event that has not been extracted before, obtaining various historical working parameters of the target switch cabinet when the abnormal working environment event occurs according to the work record book, and respectively calculating the parameter difference between each historical working parameter and the corresponding preset working parameter to obtain the offset amplitude of each historical working parameter in the target switch cabinet when the abnormal working environment event occurs; S206: Marking the historical working parameters corresponding to the offset amplitude being greater than the preset threshold as the associated working parameters of the target switch cabinet when the abnormal working environment event occurs; S208: Return to execute steps S204 to 206 to continue to perform correlation evaluation on abnormal working environment events that occurred during the operation of the target switch cabinet until all abnormal working environment events are extracted to obtain the associated working parameters of the target switch cabinet when various abnormal working environment events occur.

[0007] More specifically, the historical environmental feature data of the target switch cabinet when various abnormal working environment events occur is obtained, and an index knowledge graph is constructed based on the historical environmental feature data of the target switch cabinet when various abnormal working environment events occur and the associated working parameters, specifically: Perform feature extraction and processing on various abnormal working environment events to obtain historical environmental feature data of the target switch cabinet when various abnormal working environment events occur; and obtaining the associated working parameters of the target switch cabinet when various abnormal working environment events occur; Construct a knowledge graph, initialize the various abnormal working environment events as child nodes, and divide a plurality of index nodes in the knowledge graph according to the child nodes; and respectively create a first storage space and a second storage space in each index node; The historical environmental characteristic data of the target switch cabinet when various abnormal working environment events occur are stored in the first storage space of the corresponding index node; and the associated working parameters of the target switch cabinet when various abnormal working environment events occur are stored in the second storage space of the corresponding index node; After storing the historical environmental feature data and related working parameters of the target switch cabinet when various abnormal working environment events occur, an indexed knowledge graph is constructed.

[0008] More specifically, the real-time environmental feature data is imported into the index knowledge graph for pairing analysis and evaluation, and the distribution control performance status of the target switch cabinet at a preset time node is obtained by evaluation, specifically: Importing the real-time environmental feature data into the index knowledge graph, and calculating the Pearson correlation coefficient between the real-time environmental feature data and the historical environmental feature data in each first storage space; If the Pearson correlation coefficients between the real-time environmental characteristic data and the historical environmental characteristic data in each first storage space are not greater than the preset coefficient value, the power distribution control performance of the target switch cabinet at the preset time node is marked as normal; If the Pearson correlation coefficient between the real-time environmental characteristic data and the historical environmental characteristic data in one or more first storage spaces is greater than a preset coefficient value, the power distribution control performance of the target switch cabinet at the preset time node is marked as a warning state.

[0009] More specifically, if the power distribution control performance of the target switch cabinet at the preset time node is in a warning state, then tracing back the warning working parameters of the target switch cabinet at the preset time node in the index knowledge graph also includes the following steps: At the same time, if the Pearson correlation coefficient between the real-time environmental feature data and the historical environmental feature data in one or more first storage spaces is greater than a preset coefficient value, then the first storage space corresponding to the Pearson correlation coefficient being greater than the preset coefficient value is marked; The index node to which the marked first storage space belongs is retroactively marked, the associated working parameters of the second storage space in the marked index node are extracted, and the extracted associated working parameters are calibrated as the early warning working parameters of the target switch cabinet at the preset time node.

[0010] More specifically, a drift analysis is performed on the warning working parameters to obtain a drift analysis result, specifically: Obtaining the actual parameter value of the warning working parameter of the target switch cabinet at the preset time node, calculating the difference between the actual parameter value of the warning working parameter of the target switch cabinet at the preset time node and the preset value, and obtaining the drift value of the warning working parameter of the target switch cabinet at the preset time node; Determine whether the drift value of the warning working parameter of the target switch cabinet at the preset time node is greater than the preset drift value; If the drift value of the warning working parameter of the target switch cabinet at the preset time node is greater than the preset drift value, the warning working parameter is calibrated as the working parameter to be regulated; If the drift value of the early warning working parameter of the target switch cabinet at the preset time node is not greater than the preset drift value, the early warning working parameter is calibrated as a working parameter that does not need to be adjusted, and the early warning working parameter is continuously monitored.

[0011] More specifically, if the drift analysis result of the warning working parameter is that the working parameter needs to be adjusted, the working parameter that needs to be adjusted is adjusted, specifically: Formulate control measures corresponding to the drift of various working parameters of the target switchgear during operation within different amplitude ranges; Constructing a database, and importing the control measures corresponding to the drift of various working parameters of the target switch cabinet in different amplitude ranges during operation into the database to obtain a control scheme database; and regularly updating the control scheme database; If the drift analysis result of the warning working parameter is that the working parameter needs to be adjusted, then obtaining the drift value of the working parameter that needs to be adjusted; Importing the drift value of the working parameter to be regulated into the regulation scheme database for pairing, and obtaining corresponding regulation measures; The acquired control measures are transmitted to the controller of the target switch cabinet, so as to perform control processing on the target switch cabinet according to the acquired control measures.

[0012] The present invention also discloses a distribution network switch cabinet control system based on intelligent monitoring, the distribution network switch cabinet control system includes a memory and a processor, the memory stores a distribution network switch cabinet control method program, when the distribution network switch cabinet control method program is executed by the processor, any one of the distribution network switch cabinet control method steps is implemented.

[0013] The present invention solves the technical defects existing in the background technology, and has the following beneficial effects: real-time environmental feature data is imported into the index knowledge graph for pairing analysis and evaluation; if the power distribution control performance of the target switch cabinet at the preset time node is in a normal state, the target switch cabinet is not processed; if the power distribution control performance of the target switch cabinet at the preset time node is in a warning state, the warning working parameters of the target switch cabinet at the preset time node are traced back in the index knowledge graph, and the drift analysis of the warning working parameters is performed; if the drift analysis result of the warning working parameter is that the working parameter does not need to be adjusted, the warning working parameter is continuously monitored; if the drift analysis result of the warning working parameter is that the working parameter needs to be adjusted, the working parameter that needs to be adjusted is adjusted. The present invention can comprehensively evaluate the operating status of the switch cabinet and discover potential problems in time by performing real-time monitoring and intelligent analysis on the target switch cabinet. By evaluating the correlation of abnormal events and constructing an index knowledge graph, it can accurately locate and process the warning working parameters, realize the refined management and active prevention and control of the switch cabinet, and thus ensure the stability and reliability of the power distribution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0015] Figure 1 This is the overall method flow chart of the distribution network switch cabinet control method; Figure 2 This is a partial method flow chart of the distribution network switch cabinet control method; Figure 3 This is the system block diagram of the distribution network switch cabinet control system. DETAILED DESCRIPTION

[0016] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0018] like Figure 1 As shown, the present invention discloses a distribution network switch cabinet control method based on intelligent monitoring, comprising the following steps: S102: Acquire various abnormal working environment events that have occurred during the operation of the target switch cabinet, perform correlation evaluation on the various abnormal working environment events that have occurred during the operation of the target switch cabinet, and acquire the associated working parameters of the target switch cabinet when the various abnormal working environment events occur; S104: Acquire historical environmental feature data of the target switch cabinet when various abnormal working environment events occur, and construct an index knowledge graph based on the historical environmental feature data of the target switch cabinet when various abnormal working environment events occur and the associated working parameters; S106: During the actual operation of the target switch cabinet, real-time environmental feature data of the target switch cabinet is collected at a preset time node, and the real-time environmental feature data is imported into the index knowledge graph for pairing analysis and evaluation, so as to obtain the power distribution control performance status of the target switch cabinet at the preset time node; S108: If the power distribution control performance of the target switch cabinet at the preset time node is in a normal state, the target switch cabinet is not processed, and the power distribution control performance is continuously analyzed and evaluated at the next preset time node; S110: If the power distribution control performance of the target switch cabinet at the preset time node is in a warning state, then the warning working parameters of the target switch cabinet at the preset time node are traced back in the index knowledge graph, and drift analysis is performed on the warning working parameters to obtain the drift analysis results; S112: If the drift analysis result of the warning working parameter is that the working parameter does not need to be adjusted, the warning working parameter is continuously monitored; if the drift analysis result of the warning working parameter is that the working parameter needs to be adjusted, the working parameter that needs to be adjusted is adjusted.

[0019] The present invention can comprehensively evaluate the operating status of the target switch cabinet and promptly discover potential problems by performing real-time monitoring and intelligent analysis of the target switch cabinet. By evaluating the correlation of abnormal events and building an index knowledge graph, it can accurately locate and process early warning working parameters, realize refined management and active prevention and control of the switch cabinet, and thus ensure the stability and reliability of the power distribution system.

[0020] More specifically, various abnormal working environment events that occurred during the operation of the target switch cabinet are obtained, and correlation evaluation is performed on various abnormal working environment events that occurred during the operation of the target switch cabinet, and the associated working parameters of the target switch cabinet when various abnormal working environment events occur are obtained, such as Figure 2 As shown, specifically: S202: Obtaining a work log of the target switch cabinet, and obtaining various abnormal working environment events that occurred during the operation of the target switch cabinet according to the work log; The abnormal working environment events include, but are not limited to, abnormal temperature, abnormal humidity, abnormal electromagnetic interference, abnormal dust concentration, and abnormal air pressure; S204: randomly extracting a certain type of abnormal working environment event that has not been extracted before, obtaining various historical working parameters of the target switch cabinet when the abnormal working environment event occurs according to the work record book, and respectively calculating the parameter difference between each historical working parameter and the corresponding preset working parameter to obtain the offset amplitude of each historical working parameter in the target switch cabinet when the abnormal working environment event occurs; The historical operating parameters include but are not limited to input voltage, output voltage, incoming current, outgoing current, power factor, frequency parameters and ground resistance value; S206: Marking the historical working parameters corresponding to the offset amplitude being greater than the preset threshold as the associated working parameters of the target switch cabinet when the abnormal working environment event occurs; S208: Return to execute steps S204 to 206 to continue to perform correlation evaluation on abnormal working environment events that occurred during the operation of the target switch cabinet until all abnormal working environment events are extracted to obtain the associated working parameters of the target switch cabinet when various abnormal working environment events occur.

[0021] In general, by obtaining the work record book, various abnormal working environment events that have occurred are determined from it, and then for each abnormal event that has not been extracted, the corresponding historical working parameters are obtained according to the work record book, and the difference with the preset working parameters is calculated to obtain the offset amplitude, and the historical working parameters with large offset amplitudes are calibrated as associated working parameters. Repeat this process until all abnormal events are processed, so as to obtain comprehensive associated working parameters. Through this step, various abnormal working environment events that occur in the operation of the target switch cabinet and their association with the working parameters can be systematically and comprehensively sorted out, and the working parameters closely related to the abnormal events can be accurately found, which helps to deeply understand the essential connection between the operating status of the switch cabinet and the abnormal events, and provides an accurate and critical data basis for the subsequent construction of knowledge graphs, evaluation of distribution control performance, and taking targeted measures, thereby improving the effectiveness of analysis and processing of abnormal situations in the switch cabinet.

[0022] More specifically, the historical environmental feature data of the target switch cabinet when various abnormal working environment events occur is obtained, and an index knowledge graph is constructed based on the historical environmental feature data of the target switch cabinet when various abnormal working environment events occur and the associated working parameters, specifically: Perform feature extraction and processing on various abnormal working environment events to obtain historical environmental feature data of the target switch cabinet when various abnormal working environment events occur; Among them, first of all, all data information related to various abnormal events in the working environment is comprehensively collected, including but not limited to data on temperature, humidity, electromagnetics, etc. and specific descriptions of the occurrence of abnormal events. Then, these data are carefully classified and sorted to distinguish different types of abnormal events and their related characteristics. Next, data mining and pattern recognition technology are used to deeply analyze the data characteristics of each abnormal event, and extract key features that can represent the abnormal event, such as specific temperature ranges, humidity change trends, etc. At the same time, combined with the operation records and related parameters of the target switch cabinet, the historical environmental feature data closely related to the abnormal events are further screened and determined. Finally, the extracted features and screened data are integrated and summarized to form a historical environmental feature data set for each abnormal event in the working environment, so as to be used for subsequent construction of index knowledge graphs and in-depth analysis and evaluation; and obtaining the associated working parameters of the target switch cabinet when various abnormal working environment events occur; Construct a knowledge graph, initialize the various abnormal working environment events as child nodes, and divide a plurality of index nodes in the knowledge graph according to the child nodes; and respectively create a first storage space and a second storage space in each index node; The historical environmental characteristic data of the target switch cabinet when various abnormal working environment events occur are stored in the first storage space of the corresponding index node; and the associated working parameters of the target switch cabinet when various abnormal working environment events occur are stored in the second storage space of the corresponding index node; After storing the historical environmental feature data and related working parameters of the target switch cabinet when various abnormal working environment events occur, an indexed knowledge graph is constructed.

[0023] Specifically, firstly, feature extraction is performed on various abnormal events in the working environment to obtain the corresponding historical environmental feature data of the target switch cabinet, and the associated working parameters are obtained at the same time. Then, a knowledge graph is constructed, various abnormal events are taken as child nodes, and index nodes are cut out. In each index node, a first storage space for storing historical environmental feature data and a second storage space for storing associated working parameters are created, and the corresponding data are stored in them respectively. After the data storage is completed, an index knowledge graph is constructed. The index knowledge graph constructed in this way can clearly associate and integrate abnormal events in the working environment with the corresponding historical environmental feature data and associated working parameters to form a systematic and hierarchical knowledge structure. This makes it possible to quickly and accurately find relevant data based on specific abnormal events in the subsequent analysis and evaluation of the switch cabinet, which helps to deeply understand the relationship between abnormal events and the status of the switch cabinet, and provides a strong data support and analysis basis for accurately evaluating the performance of distribution control, early warning and taking countermeasures, and improving the intelligence level and efficiency of the entire switch cabinet monitoring and control.

[0024] More specifically, the real-time environmental feature data is imported into the index knowledge graph for pairing analysis and evaluation, and the distribution control performance status of the target switch cabinet at a preset time node is obtained by evaluation, specifically: Importing the real-time environmental feature data into the index knowledge graph, and calculating the Pearson correlation coefficient between the real-time environmental feature data and the historical environmental feature data in each first storage space; If the Pearson correlation coefficients between the real-time environmental characteristic data and the historical environmental characteristic data in each first storage space are not greater than the preset coefficient value, the power distribution control performance of the target switch cabinet at the preset time node is marked as normal; If the Pearson correlation coefficient between the real-time environmental characteristic data and the historical environmental characteristic data in one or more first storage spaces is greater than a preset coefficient value, the power distribution control performance of the target switch cabinet at the preset time node is marked as a warning state.

[0025] This step can accurately determine whether the current power distribution control performance status of the target switch cabinet is normal or in a warning state based on the comparison between the real-time environmental characteristic data and the historical data. This helps to discover potential problems in a timely manner so that appropriate measures can be taken to intervene and adjust, ensure the stable operation of the switch cabinet and the reliability of power distribution, and improve the accuracy of monitoring and evaluation of the switch cabinet operation status.

[0026] More specifically, if the power distribution control performance of the target switch cabinet at the preset time node is in a warning state, then tracing back the warning working parameters of the target switch cabinet at the preset time node in the index knowledge graph also includes the following steps: At the same time, if the Pearson correlation coefficient between the real-time environmental feature data and the historical environmental feature data in one or more first storage spaces is greater than a preset coefficient value, then the first storage space corresponding to the Pearson correlation coefficient being greater than the preset coefficient value is marked; The index node to which the marked first storage space belongs is retroactively marked, the associated working parameters of the second storage space in the marked index node are extracted, and the extracted associated working parameters are calibrated as the early warning working parameters of the target switch cabinet at the preset time node.

[0027] When it is determined that the power distribution control performance of the target switch cabinet at the preset time node is in the early warning state, first further determine which historical environmental feature data in the first storage space has a larger Pearson correlation coefficient. Then mark the first storage space corresponding to these correlation coefficients greater than the preset coefficient value. Next, since the index node in the index knowledge graph has a corresponding relationship with the first storage space and the second storage space, the index node to which the marked first storage space belongs is back-trace marked. Finally, the associated working parameters are extracted from the second storage space in these marked index nodes, and these associated working parameters are determined as the early warning working parameters of the target switch cabinet at the preset time node. Through this step, after determining the early warning state, the index node corresponding to the specific historical environmental feature data related to the early warning state can be accurately found, and then the working parameters associated with it can be extracted, which makes the analysis of the early warning situation more accurate and targeted, and can clarify the specific working parameters that cause the early warning, so as to conduct targeted diagnosis of the corresponding working parameters, greatly shorten the diagnosis time, so as to carry out targeted regulation and processing of the switch cabinet in a shorter time, effectively improve the efficiency and accuracy of handling abnormal situations of the switch cabinet, and ensure the stability and safety of the power distribution control of the switch cabinet.

[0028] More specifically, a drift analysis is performed on the warning working parameters to obtain a drift analysis result, specifically: Obtaining the actual parameter value of the warning working parameter of the target switch cabinet at the preset time node, calculating the difference between the actual parameter value of the warning working parameter of the target switch cabinet at the preset time node and the preset value, and obtaining the drift value of the warning working parameter of the target switch cabinet at the preset time node; Determine whether the drift value of the warning working parameter of the target switch cabinet at the preset time node is greater than the preset drift value; If the drift value of the warning working parameter of the target switch cabinet at the preset time node is greater than the preset drift value, the warning working parameter is calibrated as the working parameter to be regulated; If the drift value of the early warning working parameter of the target switch cabinet at the preset time node is not greater than the preset drift value, the early warning working parameter is calibrated as a working parameter that does not need to be adjusted, and the early warning working parameter is continuously monitored.

[0029] Specifically, firstly, the actual parameter value of the warning working parameter of the target switch cabinet at the preset time node is obtained, and then the difference between the actual parameter value and the preset value is calculated to obtain the drift value of the warning working parameter. Then, a judgment is made. If the drift value is greater than the preset drift value set in advance, it means that the warning working parameter needs to be regulated, so it is calibrated as the working parameter that needs to be regulated; if the drift value is not greater than the preset drift value, the warning working parameter is calibrated as the working parameter that does not need to be regulated, and it is continuously monitored to observe its subsequent changes. It can accurately analyze the degree of deviation between the actual state of the warning working parameter and the normal state, and accurately determine whether the warning working parameter needs to be regulated or only needs to be continuously monitored by calculating the drift value and comparing it with the preset drift value. It helps to take corresponding measures in a timely and accurate manner. The working parameters that need to be regulated can be processed in time to avoid the deterioration of potential problems. For those that do not need to be regulated, continuous monitoring can also ensure real-time grasp of their status, thereby improving the overall management and control level of the switch cabinet working parameters and ensuring the stable and safe operation of the switch cabinet.

[0030] More specifically, if the drift analysis result of the warning working parameter is that the working parameter needs to be adjusted, the working parameter that needs to be adjusted is adjusted, specifically: Formulate control measures corresponding to the drift of various working parameters of the target switchgear during operation within different amplitude ranges; Constructing a database, and importing the control measures corresponding to the drift of various working parameters of the target switch cabinet in different amplitude ranges during operation into the database to obtain a control scheme database; and regularly updating the control scheme database; If the drift analysis result of the warning working parameter is that the working parameter needs to be adjusted, then obtaining the drift value of the working parameter that needs to be adjusted; Importing the drift value of the working parameter to be regulated into the regulation scheme database for pairing, and obtaining corresponding regulation measures; The acquired control measures are transmitted to the controller of the target switch cabinet, so as to perform control processing on the target switch cabinet according to the acquired control measures.

[0031] Specifically, relevant technical personnel first formulate control measures corresponding to the drift of various working parameters in different amplitude ranges when the target switch cabinet is in operation (for example, by increasing or decreasing the output voltage value to adapt to load changes or correct voltage drift; when the power factor is too low, add capacitor groups to improve the power factor), and then build a database, import these formulated measures into it, form a control plan database, and regularly update this database to ensure its timeliness and adaptability.

[0032] When it is determined that the drift analysis result of a certain warning working parameter is a working parameter that needs to be regulated, the drift value of the working parameter that needs to be regulated is obtained (i.e., the difference between the actual parameter value and the preset value of the warning working parameter of the target switch cabinet at the preset time node mentioned above), and then the drift value is paired with the control scheme database to find the corresponding control measures, and finally the found control measures are transmitted to the controller of the target switch cabinet, and the controller performs actual control processing on the switch cabinet according to these measures. It can be seen that through this step, it is possible to quickly and accurately find appropriate countermeasures from the pre-built and updated control scheme database for the working parameters that need to be regulated due to drift, and apply them to the target switch cabinet in time for regulation, thereby effectively avoiding problems that may be caused by abnormal drift of working parameters, ensuring the stable and reliable operation of the switch cabinet, improving the management accuracy of the working parameters of the switch cabinet and the ability to respond to emergencies, and ensuring that the switch cabinet can maintain a good operating state and performance under various circumstances.

[0033] In the actual application process, after the real-time environmental characteristic data of the target switch cabinet is collected at the preset time node, the step of denoising the real-time environmental characteristic data may also be included: Constructing a voxel space, dividing the voxel space into a plurality of voxel arrays of equal size, and acquiring real-time environmental feature data of the target switch cabinet collected at a preset time node; Importing each collected real-time environment feature data into a blank voxel array separately, and after the importing is completed, introducing a fuzzy algorithm, and calculating the fuzziness between the real-time environment feature data in each voxel array based on the fuzzy algorithm; Compare the ambiguity between the real-time environmental feature data in each voxel array with a preset ambiguity threshold; fusing the voxel arrays attached to the real-time environmental feature data whose ambiguity is greater than a preset ambiguity threshold to obtain a plurality of voxel blocks; Acquire the real-time environment feature data attached to each voxel block, calculate the variance of the real-time environment feature data attached to each voxel block, and determine the voxel center of each voxel block according to the calculated variance; Calculate the Euclidean distance between the real-time environmental feature data attached to each voxel block and the voxel center, sum the Euclidean distances between the real-time environmental feature data attached to each voxel block and the voxel center, and then take the average value to obtain the density coefficient of each voxel block; If the density coefficient of a certain voxel block is greater than a preset density coefficient value, the voxel block is calibrated as a high-density voxel block; if the density coefficient of a certain voxel block is not greater than the preset density coefficient value, the voxel block is calibrated as a low-density voxel block; The real-time environment feature data attached to the low-density voxel block is calibrated as noise data, and the real-time environment feature data calibrated as noise data is completely deleted to obtain the denoised real-time environment feature data.

[0034] In general, first, construct the voxel space and divide it into voxel arrays of equal size, and then import the real-time environmental feature data into these arrays. Next, use the fuzzy algorithm to calculate the fuzziness between the data, and compare it with the preset threshold, and merge the data with high fuzziness into voxel blocks. Then, calculate the variance of the data in each voxel block, determine the voxel center, and calculate the Euclidean distance between the data and the voxel center. The sum is averaged to obtain the density coefficient. According to the comparison of the density coefficient with the preset value, the voxel block is calibrated as high density or low density. Finally, the data in the low-density voxel block is calibrated as noise data and deleted, so as to obtain the denoised real-time environmental feature data.

[0035] The reason why the data in the low-density voxel blocks are marked as noise data is that these data points are sparsely distributed in the voxel space, which means that they have low similarity with other data points and do not form obvious clusters or patterns. In the fuzzy algorithm, these data points have low fuzziness with other data points, indicating that they are quite different in features from other data points. Since noise data refers to data points that are inconsistent with or not clearly associated with normal data patterns, the data points in the low-density voxel blocks are likely to be caused by measurement errors, transmission interference, or other abnormal factors, rather than being part of the normal operation of the switchgear. Therefore, marking these data points as noise data and deleting them can reduce the adverse effects on the analysis of the operating status of the switchgear and improve the accuracy and reliability of data analysis.

[0036] In summary, through denoising processing, noise and outliers in real-time environmental feature data can be effectively removed, the accuracy and reliability of the data can be improved, and it is helpful to more accurately analyze and evaluate the operating status of the switchgear, so as to take more effective regulation and optimization measures to ensure the stable operation of the switchgear and the reliability of power distribution.

[0037] In addition, the method may further comprise the following steps: The dynamic working data of the target switch cabinet in the preset time period before the fault state occurs are obtained through the big data network; and the dynamic working data of the target switch cabinet in the preset time period before the fault state occurs are discretized to obtain the working data of each timestamp of the target switch cabinet in the preset time period before the fault state occurs; A hidden Markov chain is introduced, and the working data of each timestamp of the target switch cabinet in a preset time period before the fault state occurs is used as the observation state. Based on the hidden Markov chain, the state transition probability value of each observation state transferring to another observation state is calculated; If the state transition probability value is greater than the preset probability value, the observation state of the corresponding timestamp is updated to another observation state; if the state transition probability value is not greater than the preset probability value, the observation state of the corresponding timestamp is maintained unchanged; After the observation state of each timestamp is updated, the observation state feature matrix is ​​constructed according to the observation state of each timestamp; Building a hidden Markov model based on a deep learning network, embedding the observed state feature matrix into the hidden Markov model for back propagation learning until the model prediction accuracy meets the preset requirements; During the actual operation of the target switch cabinet, the working data of the target switch cabinet is collected at several time nodes to obtain the actual dynamic working data of the target switch cabinet; Importing the actual dynamic working data of the target switch cabinet into the hidden Markov model for prediction, and obtaining the observed state of the target switch cabinet at several future time stamps; The observed status of the target switch cabinet at several future time stamps is analyzed. If the observed status of one or more future time stamps is a fault state, fault warning information is generated, the target switch cabinet is controlled to stop working, and the fault warning information is sent to a preset terminal for display.

[0038] It should be noted that the dynamic working data of the target switch cabinet in the preset time period before the fault state occurs is obtained through the big data network, and these data are discretized for subsequent analysis. Next, the hidden Markov chain is introduced to calculate the state transition probability value between the working data (observation state) of each timestamp. According to these probability values, the observation state is updated. If the probability value is greater than the preset value, the state is updated; otherwise, the state remains unchanged. After the update is completed, the observation state feature matrix is ​​constructed, and the hidden Markov model is constructed based on the deep learning network. The model is optimized through back-propagation learning until the prediction accuracy meets the preset requirements.

[0039] In the actual operation process, the working data of the target switch cabinet is collected, and the trained hidden Markov model is used for prediction to obtain the observation state of the future timestamp. If the prediction result shows that the observation state of a future timestamp is a fault state, fault warning information is generated and sent to the preset terminal for display. By combining big data, hidden Markov chain and deep learning technology, the fault state of the target switch cabinet can be effectively predicted, and a warning can be issued before the fault occurs, which helps to take measures in advance to prevent the occurrence of faults, improve the reliability and safety of the switch cabinet, and reduce potential losses and risks.

[0040] like Figure 3As shown, the present invention also discloses a distribution network switch cabinet control system 6 based on intelligent monitoring, wherein the distribution network switch cabinet control system includes a memory 41 and a processor 52, wherein the memory 41 stores a distribution network switch cabinet control method program, and when the distribution network switch cabinet control method program is executed by the processor 52, any one of the steps of the distribution network switch cabinet control method is implemented.

[0041] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0042] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0043] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0044] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0045] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0046] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A distribution network switch cabinet control method based on intelligent monitoring, characterized in that: The following steps are involved: S102: Acquire various abnormal working environment events that have occurred during the operation of the target switch cabinet, perform correlation evaluation on the various abnormal working environment events that have occurred during the operation of the target switch cabinet, and acquire the associated working parameters of the target switch cabinet when the various abnormal working environment events occur; S104: Acquire historical environmental feature data of the target switch cabinet when various abnormal working environment events occur, and construct an index knowledge graph based on the historical environmental feature data of the target switch cabinet when various abnormal working environment events occur and the associated working parameters; S106: During the actual operation of the target switch cabinet, real-time environmental feature data of the target switch cabinet is collected at a preset time node, and the real-time environmental feature data is imported into the index knowledge graph for pairing analysis and evaluation, so as to obtain the power distribution control performance status of the target switch cabinet at the preset time node; S108: If the power distribution control performance of the target switch cabinet at the preset time node is in a normal state, the target switch cabinet is not processed, and the power distribution control performance is continuously analyzed and evaluated at the next preset time node; S110: If the power distribution control performance of the target switch cabinet at the preset time node is in a warning state, then the warning working parameters of the target switch cabinet at the preset time node are traced back in the index knowledge graph, and drift analysis is performed on the warning working parameters to obtain the drift analysis results; S112: If the drift analysis result of the early warning working parameter is that the working parameter does not need to be adjusted, the early warning working parameter is continuously monitored; If the drift analysis result of the early warning working parameter is that the working parameter needs to be adjusted, the working parameter that needs to be adjusted is adjusted.

2. A distribution network switch cabinet control method based on intelligent monitoring according to claim 1, characterized in that: Obtain various abnormal working environment events that have occurred during the operation of the target switch cabinet, perform correlation evaluation on various abnormal working environment events that have occurred during the operation of the target switch cabinet, and obtain the associated working parameters of the target switch cabinet when various abnormal working environment events occur, specifically: S202: Obtaining a work log of the target switch cabinet, and obtaining various abnormal working environment events that occurred during the operation of the target switch cabinet according to the work log; S204: randomly extracting a certain type of abnormal working environment event that has not been extracted before, obtaining various historical working parameters of the target switch cabinet when the abnormal working environment event occurs according to the work record book, and respectively calculating the parameter difference between each historical working parameter and the corresponding preset working parameter to obtain the offset amplitude of each historical working parameter in the target switch cabinet when the abnormal working environment event occurs; S206: Marking the historical working parameters corresponding to the offset amplitude being greater than the preset threshold as the associated working parameters of the target switch cabinet when the abnormal working environment event occurs; S208: Return to execute steps S204 to 206 to continue to perform correlation evaluation on abnormal working environment events that occurred during the operation of the target switch cabinet until all abnormal working environment events are extracted to obtain the associated working parameters of the target switch cabinet when various abnormal working environment events occur.

3. A distribution network switch cabinet control method based on intelligent monitoring according to claim 2, characterized in that: Obtain the historical environmental feature data of the target switch cabinet when various abnormal working environment events occur, and construct an index knowledge graph based on the historical environmental feature data of the target switch cabinet when various abnormal working environment events occur and the associated working parameters, specifically: Perform feature extraction and processing on various abnormal working environment events to obtain historical environmental feature data of the target switch cabinet when various abnormal working environment events occur; and obtaining the associated working parameters of the target switch cabinet when various abnormal working environment events occur; Construct a knowledge graph, initialize the various abnormal working environment events as child nodes, and divide a plurality of index nodes in the knowledge graph according to the child nodes; and respectively create a first storage space and a second storage space in each index node; The historical environmental characteristic data of the target switch cabinet when various abnormal working environment events occur are stored in the first storage space of the corresponding index node; and the associated working parameters of the target switch cabinet when various abnormal working environment events occur are stored in the second storage space of the corresponding index node; After storing the historical environmental feature data and related working parameters of the target switch cabinet when various abnormal working environment events occur, an indexed knowledge graph is constructed.

4. A distribution network switch cabinet control method based on intelligent monitoring according to claim 3, characterized in that: The real-time environmental feature data is imported into the index knowledge graph for pairing analysis and evaluation, and the distribution control performance status of the target switch cabinet at a preset time node is obtained by evaluation, specifically: Importing the real-time environmental feature data into the index knowledge graph, and calculating the Pearson correlation coefficient between the real-time environmental feature data and the historical environmental feature data in each first storage space; If the Pearson correlation coefficients between the real-time environmental characteristic data and the historical environmental characteristic data in each first storage space are not greater than the preset coefficient value, the power distribution control performance of the target switch cabinet at the preset time node is marked as normal; If the Pearson correlation coefficient between the real-time environmental characteristic data and the historical environmental characteristic data in one or more first storage spaces is greater than a preset coefficient value, the power distribution control performance of the target switch cabinet at the preset time node is marked as a warning state.

5. A distribution network switch cabinet control method based on intelligent monitoring according to claim 4, characterized in that: If the power distribution control performance of the target switch cabinet at the preset time node is in the warning state, the warning working parameters of the target switch cabinet at the preset time node are traced back in the index knowledge graph, and The following steps are involved: At the same time, if the Pearson correlation coefficient between the real-time environmental feature data and the historical environmental feature data in one or more first storage spaces is greater than a preset coefficient value, then the first storage space corresponding to the Pearson correlation coefficient being greater than the preset coefficient value is marked; The index node to which the marked first storage space belongs is retroactively marked, the associated working parameters of the second storage space in the marked index node are extracted, and the extracted associated working parameters are calibrated as the early warning working parameters of the target switch cabinet at the preset time node.

6. A distribution network switch cabinet control method based on intelligent monitoring according to claim 1, characterized in that: Perform drift analysis on the warning working parameters to obtain drift analysis results, specifically: Obtaining the actual parameter value of the warning working parameter of the target switch cabinet at the preset time node, calculating the difference between the actual parameter value of the warning working parameter of the target switch cabinet at the preset time node and the preset value, and obtaining the drift value of the warning working parameter of the target switch cabinet at the preset time node; Determine whether the drift value of the warning working parameter of the target switch cabinet at the preset time node is greater than the preset drift value; If the drift value of the warning working parameter of the target switch cabinet at the preset time node is greater than the preset drift value, the warning working parameter is calibrated as the working parameter to be regulated; If the drift value of the early warning working parameter of the target switch cabinet at the preset time node is not greater than the preset drift value, the early warning working parameter is calibrated as a working parameter that does not need to be adjusted, and the early warning working parameter is continuously monitored.

7. A distribution network switch cabinet control method based on intelligent monitoring according to claim 6, characterized in that: If the drift analysis result of the warning working parameter is a working parameter that needs to be adjusted, the working parameter that needs to be adjusted is adjusted, specifically: Formulate control measures corresponding to the drift of various working parameters of the target switchgear during operation within different amplitude ranges; Constructing a database, and importing the control measures corresponding to the drift of various working parameters of the target switch cabinet in different amplitude ranges during operation into the database to obtain a control scheme database; and regularly updating the control scheme database; If the drift analysis result of the warning working parameter is that the working parameter needs to be adjusted, then obtaining the drift value of the working parameter that needs to be adjusted; Importing the drift value of the working parameter to be regulated into the regulation scheme database for pairing, and obtaining corresponding regulation measures; The acquired control measures are transmitted to the controller of the target switch cabinet, so as to perform control processing on the target switch cabinet according to the acquired control measures.

8. A distribution network switch cabinet control system based on intelligent monitoring, characterized in that: The distribution network switch cabinet control system includes a memory and a processor, wherein the memory stores a distribution network switch cabinet control method program. When the distribution network switch cabinet control method program is executed by the processor, the distribution network switch cabinet control method steps as described in any one of claims 1 to 7 are implemented.

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