Power distribution network automation management and control method and system based on intelligent fusion terminal

Abstract

The application relates to the technical field of distribution network data transmission, and particularly discloses a distribution network automation management and control method and system based on an intelligent fusion terminal, which comprises the following steps: obtaining heterogeneous data at a distribution network node in real time based on the intelligent fusion terminal, and identifying operation situation data containing a time label; generating a control instruction based on the operation situation data, and transmitting the control instruction to a corresponding energy storage node of the intelligent fusion terminal; obtaining operation situation data of each distribution network node, obtaining a monitoring data body, and recursively updating a transmission mapping relationship between the intelligent fusion terminal, the distribution network node and the energy storage node according to the monitoring data body; the intelligent fusion terminal with a mobile function is used to obtain and identify data at the distribution network node, then an adjustment instruction is generated, the identification result is regularly counted, the dynamic mapping relationship between the intelligent fusion terminal and the distribution network node is determined, the transmission process is updated in real time, the flexibility is extremely high, and the transmission resource utilization rate is extremely high.

Description

Technical Field

[0001] This invention relates to the field of distribution network data transmission technology, specifically a distribution network automation management and control method and system based on intelligent converged terminals. Background Technology

[0002] With the deepening of the construction of new power systems, the widespread access of distributed energy (such as photovoltaic power generation, wind power generation, etc.), flexible loads and energy storage devices in the distribution network has led to the increasing complexity of the distribution network structure and a significant increase in the uncertainty and dynamism of its operation. In order to improve the autonomous capabilities, interactive performance and self-healing characteristics of the distribution network, edge computing and cloud-edge collaboration technologies have been gradually introduced into distribution network management.

[0003] At the data processing level, terminals deployed at distribution network nodes (such as distribution automation terminals (DTUs) and feeder terminals (FTUs) collect various heterogeneous data such as switch status, voltage, and current. By identifying this heterogeneous data, the operating status of each distribution network node in the power system can be determined. However, in the existing heterogeneous data acquisition and transmission process based on edge computing architecture, the edge terminals are mostly fixed, and the data transmission process of each edge terminal is also fixed. Since the power system has uncertainties, the balance of transmission resources is very poor in this fixed architecture. How to provide a more flexible edge terminal data transmission architecture to improve the balance of transmission resources is the technical problem that this invention aims to solve. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for automated management and control of distribution networks based on intelligent converged terminals, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for automated management and control of distribution networks based on intelligent converged terminals, the method comprising:

[0007] Based on the real-time acquisition of electrical quantities, status quantities, and environmental quantities at distribution network nodes by intelligent fusion terminals, electrical quantities, status quantities, and environmental quantities are constructed and identified to obtain operational status data containing time tags; the operational status data is used to characterize the status of distribution network nodes, and its value space is a preset value.

[0008] Control commands are generated based on operational status data and transmitted to the energy storage node corresponding to the intelligent fusion terminal. The transmission mapping relationship between the intelligent fusion terminal, the distribution network node, and the energy storage node is dynamic, with the initial relationship being a preset value. The intelligent fusion terminal is a mobile terminal.

[0009] For any intelligent converged terminal, the monitoring matrix of the intelligent converged terminal at each time moment is obtained based on the operation status data of each distribution network node obtained by statistical analysis of the node matrix composed of all distribution network nodes; wherein, the row and column positions in the node matrix correspond one-to-one with the distribution network nodes.

[0010] The monitoring matrices corresponding to all smart converged terminals are periodically merged to obtain a monitoring data body. The transmission mapping relationship between the smart converged terminals, distribution network nodes, and energy storage nodes is recursively updated based on the monitoring data body.

[0011] As a further aspect of the present invention: the step of acquiring electrical quantities, status quantities, and environmental quantities at distribution network nodes in real time based on intelligent fusion terminals, constructing identification of electrical quantities, status quantities, and environmental quantities, and obtaining operational status data containing time tags includes:

[0012] The system acquires electrical quantities, status quantities, and environmental quantities at distribution network nodes in real time using intelligent fusion terminals. The electrical quantities include load power, voltage amplitude, current amplitude, and power factor. The status quantities include frequency fluctuations and switching status. The environmental quantities include ambient temperature and ambient humidity.

[0013] For each parameter, the acquired data is processed for time synchronization and data cleaning to eliminate sampling errors and abnormal noise;

[0014] The processed data at each time step is normalized to obtain standard data;

[0015] The fusion algorithm based on DS evidence theory identifies the standard data of each parameter at each time point, and obtains operational status data with time labels.

[0016] As a further aspect of the present invention: the step of generating control commands based on operational status data and transmitting them to the energy storage node corresponding to the intelligent fusion terminal includes:

[0017] Real-time reading of operational status data for each distribution network node to locate abnormal distribution network nodes;

[0018] The abnormal distribution network nodes are identified statistically, and their historical data is queried to determine demand information; the demand information is represented by the demand within the predicted time period.

[0019] Based on the demand information, control commands are determined and sent to the energy storage nodes; simultaneously, shutdown commands and warning messages are generated for abnormal distribution network nodes.

[0020] As a further aspect of the present invention: the step of obtaining the monitoring matrix of the intelligent fusion terminal at each moment based on the operational status data of each distribution network node obtained by statistical analysis of the node matrix composed of all distribution network nodes includes:

[0021] Obtain the coordinates of the distribution network nodes and create a node matrix based on the coordinates; wherein, each row and column position in the node matrix corresponds one-to-one with the distribution network nodes, and the row order and column order are determined by the spatial order of the distribution network nodes;

[0022] For any intelligent fusion terminal at any given time, the operational status data of each distribution network node corresponding to the terminal is statistically analyzed based on the node matrix.

[0023] For empty data in the node matrix, preset default data is inserted to obtain the monitoring matrix of the intelligent fusion terminal at that moment.

[0024] As a further aspect of the present invention: the step of periodically merging the monitoring matrices corresponding to all intelligent fusion terminals to obtain a monitoring data body, and recursively updating the transmission mapping relationship between the intelligent fusion terminals, distribution network nodes, and energy storage nodes based on the monitoring data body includes:

[0025] A merge instruction is generated every preset time period;

[0026] The data range is determined based on the generation time of the current merge instruction and the generation time of the previous merge instruction;

[0027] Read the monitoring matrix corresponding to all intelligent fusion terminals within the data range, and merge the monitoring matrices at the same time.

[0028] The monitoring data volume is obtained by arranging the merged monitoring matrix in chronological order.

[0029] The transmission mapping relationship between the intelligent fusion terminal, distribution network node, and energy storage node is recursively updated based on the monitoring data.

[0030] As a further aspect of the present invention: the step of recursively updating the transmission mapping relationship between the intelligent fusion terminal, the distribution network node, and the energy storage node based on the monitoring data includes:

[0031] For any location in the monitoring data volume, read the data at various times at that location and fit the data change function;

[0032] Feature identification is performed on the data change function to extract risk values;

[0033] The risk value at each location is calculated to obtain the compression matrix;

[0034] The total number of intelligent fusion terminals is queried, and the total number is used as the maximum number of clusters. A clustering process with the number of clusters continuously increasing is performed in the monitoring data body until a preset condition is met; the minimum number of clusters is a preset value.

[0035] For each type of location, query its location center as the target location for the intelligent fusion terminal;

[0036] Query the smart fusion terminal closest to the target location, generate a motion command pointing to the target location, and send it to the smart fusion terminal;

[0037] When each smart converged terminal reaches its corresponding target location, update the transmission mapping relationship between the smart converged terminal and the distribution network node;

[0038] Query all energy storage nodes corresponding to the distribution network nodes, and update the transmission mapping relationship between the smart converged terminal and the energy storage nodes.

[0039] As a further aspect of the present invention: the preset condition is: for each type of location, the sum of the risk values ​​of each location is less than a preset risk value.

[0040] Before generating motion commands pointing to the target location, a distance threshold is added. When the distance between the smart fusion terminal closest to a target location and the target location is less than the distance threshold, the original location is taken as the target location.

[0041] When the number of categories decreases, inspection instructions are sent to intelligent fusion terminals that have not received motion instructions to acquire data and verify the operation of other intelligent fusion terminals.

[0042] The process of updating the transmission mapping relationship is as follows: retain the original transmission channels for all locations, and disconnect the original transmission channels when all locations build connection channels based on the new transmission mapping relationship.

[0043] The present invention also provides a power distribution network automation management and control system based on an intelligent converged terminal, the system comprising:

[0044] The operation status identification module is used to acquire electrical quantities, status quantities and environmental quantities at the distribution network nodes in real time based on the intelligent fusion terminal, construct electrical quantities, status quantities and environmental quantities for identification, and obtain operation status data containing time tags; the operation status data is used to characterize the status of the distribution network nodes, and its value space is a preset value.

[0045] The instruction transmission module is used to generate control instructions based on the operational status data and transmit them to the energy storage node corresponding to the smart fusion terminal; wherein, the transmission mapping relationship between the smart fusion terminal and the distribution network node and the energy storage node is a dynamic relationship, and the initial relationship is a preset value, and the smart fusion terminal is a mobile terminal;

[0046] The data integration module is used to obtain the monitoring matrix of any intelligent fusion terminal at any time based on the operation status data of each distribution network node obtained by statistical analysis of the node matrix composed of all distribution network nodes; wherein, the row and column positions in the node matrix correspond one-to-one with the distribution network nodes.

[0047] The transmission parameter update module is used to periodically merge the monitoring matrices corresponding to all smart fusion terminals to obtain monitoring data volumes, and recursively update the transmission mapping relationship between smart fusion terminals, distribution network nodes, and energy storage nodes based on the monitoring data volumes.

[0048] As a further aspect of the present invention: the operating status identification module includes:

[0049] The data acquisition unit is used to acquire electrical quantities, status quantities, and environmental quantities at the distribution network nodes in real time based on the intelligent fusion terminal; the electrical quantities include load power, voltage amplitude, current amplitude, and power factor; the status quantities include frequency fluctuations and switching status; and the environmental quantities include ambient temperature and ambient humidity.

[0050] The data integration unit is used to perform time synchronization processing and data cleaning on the acquired data for each parameter, eliminating sampling errors and abnormal noise;

[0051] The standard processing unit is used to normalize the processed data at each time step to obtain standard data.

[0052] The identification execution unit is used to identify the standard data of each parameter at each time step using a fusion algorithm based on DS evidence theory, thereby obtaining operational status data with time labels.

[0053] As a further aspect of the present invention: the instruction transmission module includes:

[0054] The anomaly location unit is used to read the operational status data of each distribution network node in real time and locate the abnormal distribution network node.

[0055] The demand information acquisition unit is used to statistically analyze the located abnormal distribution network nodes, query the historical data of the distribution network nodes, and determine the demand information; the demand information is represented by the demand within the predicted time period.

[0056] The transmission execution unit is used to determine control commands based on demand information and send them to the energy storage nodes; it also synchronously generates shutdown commands and warning messages for abnormal distribution network nodes.

[0057] Compared with the prior art, the beneficial effects of the present invention are: the present invention acquires and identifies data at the distribution network node through a smart fusion terminal with mobile function, thereby generating adjustment instructions, periodically statistically analyzing the identification results, determining the dynamic mapping relationship between the smart fusion terminal and the distribution network node, and updating the transmission process in real time, which is highly flexible and has a very high utilization rate of transmission resources. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention.

[0059] Figure 1 This is a flowchart of a distribution network automation management and control method based on intelligent converged terminals.

[0060] Figure 2 This is a block diagram of the composition structure of a distribution network automation management and control system based on intelligent converged terminals. Detailed Implementation

[0061] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0062] Figure 1 The flowchart below illustrates a distribution network automation management and control method based on an intelligent converged terminal. In this embodiment of the invention, a distribution network automation management and control method based on an intelligent converged terminal includes:

[0063] Step S100: Based on the intelligent fusion terminal, the electrical quantities, status quantities and environmental quantities at the distribution network node are acquired in real time, and the electrical quantities, status quantities and environmental quantities are constructed for identification to obtain the operation status data containing time tags; the operation status data is used to characterize the status of the distribution network node, and its value space is a preset value.

[0064] Distribution network nodes are information acquisition devices specifically installed within the network architecture. Intelligent fusion terminals, on the other hand, are information integration devices deployed in various areas, essentially acting as edge devices. These terminals, deployed at distribution network nodes, collect multi-source heterogeneous data on electrical quantities, status quantities, and environmental quantities. These intelligent fusion terminals can be configured to integrate multiple sensor interfaces. For example, they can connect to voltage and current transformers via analog input ports to collect electrical quantity data, connect to switch position sensors via digital input ports to obtain status quantity data, and collect environmental quantity data such as temperature and humidity via environmental sensor interfaces. The intelligent fusion terminals then identify this data to obtain a comprehensive status reflecting each distribution network node. This status can be represented using tags, known as operational status data. Its values ​​are very limited; the simplest example is divided into abnormal and normal. However, in reality, it is certainly more complex, representing operational status in different dimensions, i.e., potentially including multiple tags. Even so, its values ​​are still extremely limited.

[0065] Step S200: Generate control commands based on operational status data and transmit them to the energy storage node corresponding to the smart fusion terminal; wherein, the transmission mapping relationship between the smart fusion terminal, the distribution network node, and the energy storage node is a dynamic relationship, the initial relationship is a preset value, and the smart fusion terminal is a mobile terminal;

[0066] Operational status data represents the state of distribution network nodes. Analyzing this data allows for the determination of control commands. These commands are not complex; a common approach is to generate a shutdown command for certain abnormal distribution network points, then query the energy storage nodes to supply power to the shut-down points. Of course, if the operational status data itself is more complex, more control commands can be generated based on the complex data, specifically determining the output of the energy storage nodes. In fact, in the distribution network management and control architecture, the only thing that can be controlled is the energy storage nodes. There are many specific control processes in existing technologies, which will not be elaborated here.

[0067] Step S300: For any intelligent converged terminal, based on the operational status data of each distribution network node obtained by statistical analysis of the node matrix composed of all distribution network nodes, the monitoring matrix of the intelligent converged terminal at each time moment is obtained; wherein, the row and column positions in the node matrix correspond one-to-one with the distribution network nodes.

[0068] In the technical solution of this invention, the distribution network nodes are fixed. All distribution network nodes are counted to generate a matrix called the node matrix. The row and column positions in the node matrix correspond one-to-one with the distribution network nodes. For any intelligent fusion terminal, it has already obtained the operational status data of all distribution network nodes in a certain area, that is, it has obtained the status of all distribution network nodes. At this time, it is inserted into the node matrix (which is equivalent to a template), and a small part of the data is filled in to obtain the monitoring matrix of the intelligent fusion terminal. For the row and column positions that do not correspond to the intelligent fusion terminal, default empty data can be inserted into them.

[0069] In addition, it should be noted that regarding the time information mentioned above, time tags are used throughout the entire process. The intelligent fusion terminal acquires data in real time, and the acquired data contains time tags. By identifying these data, the operational status data at that moment is obtained. Then, the monitoring matrix obtained by statistics is also the monitoring matrix at that moment.

[0070] Step S400: Periodically merge the monitoring matrices corresponding to all smart converged terminals to obtain monitoring data volume, and recursively update the transmission mapping relationship between smart converged terminals, distribution network nodes and energy storage nodes according to the monitoring data volume;

[0071] A merging command is generated periodically. When the merging command is generated, all monitoring matrices from the previous period are merged. This process is actually divided into two steps: first, a horizontal merging is performed at each time point, merging the monitoring matrices of all smart fusion terminals at the same time point. The merging process involves replacing empty data and concentrating them into a large matrix. Then, the large matrices from different time points are arranged according to the time sequence to obtain the monitoring data volume. The monitoring data volume reflects the working status of the distribution network over a period of time. Thus, the transmission mapping relationship between the smart fusion terminal and the distribution network nodes and energy storage nodes is updated. This is the core of the technical solution of this invention. The smart fusion terminal, as an edge terminal, is a mobile edge terminal with mobility capabilities, which can update the connection channel at any time to obtain a better transmission architecture.

[0072] Regarding step S100, the step of acquiring electrical quantities, status quantities, and environmental quantities at the distribution network nodes in real time based on the intelligent fusion terminal, constructing identification of electrical quantities, status quantities, and environmental quantities, and obtaining operational status data containing time tags includes:

[0073] The system acquires electrical quantities, status quantities, and environmental quantities at distribution network nodes in real time using intelligent fusion terminals. The electrical quantities include load power, voltage amplitude, current amplitude, and power factor. The status quantities include frequency fluctuations and switching status. The environmental quantities include ambient temperature and ambient humidity.

[0074] For each parameter, the acquired data is processed for time synchronization and data cleaning to eliminate sampling errors and abnormal noise;

[0075] The processed data at each time step is normalized to obtain standard data;

[0076] The fusion algorithm based on DS evidence theory identifies the standard data of each parameter at each time point, and obtains operational status data with time labels.

[0077] A fusion algorithm based on DS evidence theory is employed to fuse multi-source heterogeneous data, generating comprehensive situational data describing the local power grid's operational status. The implementation of this fusion algorithm includes: after receiving data from different sensors or devices, the terminal first preprocesses each data source, such as data cleaning and normalization. Then, according to a preset confidence level allocation rule, each data source is treated as an evidence source, and its support for different power grid operational states (e.g., normal, abnormal, fault precursor) is calculated. These support levels can be set based on expert experience or historical data. Finally, using the combination rules of DS evidence theory, the evidence from different data sources is fused to obtain a comprehensive judgment on the local power grid's operational status. For example, when voltage data indicates low voltage, current data indicates overload, and switch status indicates abnormality, DS evidence theory can integrate this information to give a higher confidence level that the local power grid is in a "fault precursor" or "abnormal operation" state; the resulting comprehensive judgment is the operational situational data mentioned above.

[0078] Specifically, the time synchronization processing, data cleaning, and elimination of sampling errors and abnormal noise mentioned above are all routine processing procedures. In addition, the data at each time point after processing is normalized to obtain standard data. This process is used to eliminate the dimensions of the data so that it can be used for subsequent operations.

[0079] Regarding step S200, the step of generating control commands based on operational status data and transmitting them to the energy storage node corresponding to the intelligent fusion terminal includes:

[0080] Real-time reading of operational status data for each distribution network node to locate abnormal distribution network nodes;

[0081] The abnormal distribution network nodes are identified statistically, and their historical data is queried to determine demand information; the demand information is represented by the demand within the predicted time period.

[0082] Based on the demand information, control commands are determined and sent to the energy storage nodes; simultaneously, shutdown commands and warning messages are generated for abnormal distribution network nodes.

[0083] In one example of the technical solution of this invention, the transmission process of control commands is described. The operational status data of each distribution network node is read in real time and compared with preset abnormal conditions to locate abnormal distribution network nodes. The located abnormal distribution network nodes are statistically analyzed, and their historical data is queried to determine demand information. This demand information is represented by the demand within a predicted time period, generally including peak demand and average demand. This is actually only one indicator used to determine control commands; other indicators are also feasible, and even a function fitted to the demand over time can be used as a feature to match control commands. The matching process is not complex; staff pre-determine the control commands corresponding to different features and store them in a preset database. When matching is needed, the database is directly traversed for matching. Control commands are determined based on the demand information and sent to the energy storage nodes. Simultaneously, shutdown commands and warning messages pointing to abnormal distribution network nodes are generated to prompt staff to check the abnormal distribution network nodes.

[0084] Regarding step S300, the step of obtaining the monitoring matrix of the intelligent fusion terminal at each moment based on the operational status data of each distribution network node obtained by statistical analysis of the node matrix composed of all distribution network nodes includes:

[0085] Obtain the coordinates of the distribution network nodes and create a node matrix based on the coordinates; wherein, each row and column position in the node matrix corresponds one-to-one with the distribution network nodes, and the row order and column order are determined by the spatial order of the distribution network nodes;

[0086] For any intelligent fusion terminal at any given time, the operational status data of each distribution network node corresponding to the terminal is statistically analyzed based on the node matrix.

[0087] For empty data in the node matrix, preset default data is inserted to obtain the monitoring matrix of the intelligent fusion terminal at that moment.

[0088] The above describes the generation process of the monitoring matrix, which is essentially a data statistics process. However, the above process optimizes the correspondence, obtaining the coordinates of the distribution network nodes and creating a node matrix based on these coordinates. This process creates the node matrix based on the positional relationship of the distribution network nodes. At this point, each row and column position in the node matrix corresponds one-to-one with a distribution network node, and the row and column order are determined by the spatial order of the distribution network nodes. This means that adjacent rows and columns in the node matrix correspond to adjacent distribution network nodes in space. In this case, the row and column positions have practical significance, and the resulting merged monitoring data also has actual spatial information, which can be directly interfaced with the visualization module, making it easy for viewers to intuitively understand the working status of the entire distribution network system. Specifically, the data filling process of the monitoring matrix involves statistically analyzing the operating status data of each distribution network node corresponding to any intelligent fusion terminal at any given time based on the node matrix. For empty data in the node matrix, preset default data is inserted to obtain the monitoring matrix of the intelligent fusion terminal at that time.

[0089] Regarding step S400, the step of periodically merging the monitoring matrices corresponding to all intelligent fusion terminals to obtain a monitoring data body, and recursively updating the transmission mapping relationship between the intelligent fusion terminals, distribution network nodes, and energy storage nodes based on the monitoring data body includes:

[0090] A merge instruction is generated every preset time period;

[0091] The data range is determined based on the generation time of the current merge instruction and the generation time of the previous merge instruction;

[0092] Read the monitoring matrix corresponding to all intelligent fusion terminals within the data range, and merge the monitoring matrices at the same time.

[0093] The monitoring data volume is obtained by arranging the merged monitoring matrix in chronological order.

[0094] The transmission mapping relationship between the intelligent fusion terminal, distribution network node, and energy storage node is recursively updated based on the monitoring data.

[0095] The above describes the generation and application process of the monitoring data body. Every preset time period, a merging instruction is generated. The data interval is determined based on the generation time of the current merging instruction and the generation time of the previous merging instruction. The data interval is actually a time region. The monitoring matrices corresponding to all smart fusion terminals within the data interval are read, and the monitoring matrices at the same time are merged. Then, the merged monitoring matrices are arranged in chronological order to obtain the monitoring data body. After the monitoring data body is obtained, the transmission mapping relationship between the smart fusion terminal and the distribution network node and energy storage node is recursively updated based on the monitoring data body.

[0096] Furthermore, the step of recursively updating the transmission mapping relationship between the intelligent fusion terminal, distribution network node, and energy storage node based on the monitoring data includes:

[0097] For any location in the monitoring data volume, read the data at various times at that location and fit the data change function;

[0098] Feature identification is performed on the data change function to extract risk values;

[0099] The risk value at each location is calculated to obtain the compression matrix;

[0100] The total number of intelligent fusion terminals is queried, and the total number is used as the maximum number of clusters. A clustering process with the number of clusters continuously increasing is performed in the monitoring data body until a preset condition is met; the minimum number of clusters is a preset value.

[0101] For each type of location, query its location center as the target location for the intelligent fusion terminal;

[0102] Query the smart fusion terminal closest to the target location, generate a motion command pointing to the target location, and send it to the smart fusion terminal;

[0103] When each smart converged terminal reaches its corresponding target location, update the transmission mapping relationship between the smart converged terminal and the distribution network node;

[0104] Query all energy storage nodes corresponding to the distribution network nodes, and update the transmission mapping relationship between the smart converged terminal and the energy storage nodes.

[0105] The monitoring data volume is actually a three-dimensional array, with one dimension representing time. During analysis, dimensionality reduction is required first. This involves reducing the time dimension, extracting data from each location over a period of time, determining the data characteristics at that location, and then retaining the original location, resulting in a two-dimensional matrix representing the data situation over a period of time. Specifically, regarding how to determine the data characteristics at a location, the approach is to read data from any location in the monitoring data volume at various times, fit a data change function, perform feature identification on the data change function, extract risk values, and statistically analyze the risk values ​​at each location to obtain a compression matrix. Regarding the risk value, it can actually be obtained using the fluctuation identification results. This involves first determining the derivative function, then determining the absolute value function of the derivative function, calculating the mean of the absolute value function over the interval, comparing this mean with the average mean under normal operating conditions, determining a ratio as the difference relative to the normal operating conditions, and thus using this as the risk value.

[0106] The resulting compressed matrix is ​​a two-dimensional matrix. The total number of intelligent fusion terminals is queried, and this total is used as the maximum number of clusters. A clustering process with progressively increasing cluster numbers, such as K-means clustering, is then performed on the monitoring data until a preset condition is met. At this point, the total number is taken as the maximum K value. After the clustering process is complete, for each cluster, its location center is queried and used as the target location for the intelligent fusion terminal. The intelligent fusion terminal closest to the target location is then queried, and a motion command pointing to the target location is generated and sent to the intelligent fusion terminal. When each intelligent fusion terminal reaches its corresponding target location, the transmission mapping relationship between the intelligent fusion terminal and the distribution network node is updated. The update process involves pairing the intelligent fusion terminal with a new type of distribution network node and establishing a connection channel. Based on this, all energy storage nodes corresponding to the distribution network node are queried, and the transmission mapping relationship between the intelligent fusion terminal and the energy storage node is updated.

[0107] In the above content, the preset condition is: for each type of location, the sum of the risk values ​​of each location is less than the preset risk value; the number of clusters in the clustering process is constantly increasing. In extreme cases, if the risk values ​​of all distribution network nodes are very small, then clustering is performed according to the minimum value. At this time, data acquisition and processing of all distribution network nodes are performed through the minimum number of intelligent fusion terminals.

[0108] Furthermore, before generating motion commands pointing to the target location, a distance threshold is added. When the distance between the nearest intelligent fusion terminal and the target location is less than the distance threshold, the original location is taken as the target location. This means that if the position of an intelligent fusion terminal does not change much, there is no need to update it. Based on this, when the number of categories decreases, it indicates that the overall risk value is smaller, and so many intelligent fusion terminals are not needed. At this time, inspection commands are sent to intelligent fusion terminals that have not received motion commands to acquire data and verify the operation of other intelligent fusion terminals. The verification process is to compare the acquired data to determine whether there are any acquisition errors. This architecture introduces a self-checking function, which is highly flexible and provides a dynamic edge terminal. Correspondingly, if the number of categories increases, for some target locations, the nearest intelligent fusion terminal is the intelligent fusion terminal to be inspected. At this time, the intelligent fusion terminal no longer performs inspections, but executes a fixed management process.

[0109] In addition, the process of updating the transmission mapping relationship is as follows: retain the original transmission channels of all locations. When all locations build connection channels based on the new transmission mapping relationship, disconnect the original transmission channels. This process is used to prevent data loss during the update process. However, it will also generate some duplicate data, which can be deleted. Of course, the duplicate data acquisition itself is also a kind of verification. If the data acquired by the new transmission channel is different from that acquired by the original transmission channel, it still cannot be disconnected. Instead, a warning message needs to be generated and a check needs to be performed. Only when the data is the same will the original transmission channel be disconnected.

[0110] Figure 2 The diagram shows the structural composition of a distribution network automation management and control system based on an intelligent converged terminal. In this embodiment of the invention, a distribution network automation management and control system based on an intelligent converged terminal, system 10, includes:

[0111] The operation status identification module 11 is used to acquire electrical quantities, status quantities and environmental quantities at the distribution network node in real time based on the intelligent fusion terminal, construct electrical quantities, status quantities and environmental quantities for identification, and obtain operation status data containing time tags; the operation status data is used to characterize the status of the distribution network node, and its value space is a preset value.

[0112] The instruction transmission module 12 is used to generate control instructions based on the operating status data and transmit them to the energy storage node corresponding to the smart fusion terminal; wherein, the transmission mapping relationship between the smart fusion terminal and the distribution network node and the energy storage node is a dynamic relationship, and the initial relationship is a preset value, and the smart fusion terminal is a mobile terminal;

[0113] The data integration module 13 is used to obtain the monitoring matrix of any intelligent fusion terminal at each time moment based on the operation status data of each distribution network node obtained by statistical analysis of the node matrix composed of all distribution network nodes; wherein, the row and column positions in the node matrix correspond one-to-one with the distribution network nodes.

[0114] The transmission parameter update module 14 is used to periodically merge the monitoring matrices corresponding to all smart fusion terminals to obtain monitoring data bodies, and recursively update the transmission mapping relationship between smart fusion terminals, distribution network nodes, and energy storage nodes based on the monitoring data bodies.

[0115] Furthermore, the operating status identification module 11 includes:

[0116] The data acquisition unit is used to acquire electrical quantities, status quantities, and environmental quantities at the distribution network nodes in real time based on the intelligent fusion terminal; the electrical quantities include load power, voltage amplitude, current amplitude, and power factor; the status quantities include frequency fluctuations and switching status; and the environmental quantities include ambient temperature and ambient humidity.

[0117] The data integration unit is used to perform time synchronization processing and data cleaning on the acquired data for each parameter, eliminating sampling errors and abnormal noise;

[0118] The standard processing unit is used to normalize the processed data at each time step to obtain standard data.

[0119] The identification execution unit is used to identify the standard data of each parameter at each time step using a fusion algorithm based on DS evidence theory, thereby obtaining operational status data with time labels.

[0120] Specifically, the instruction transmission module 12 includes:

[0121] The anomaly location unit is used to read the operational status data of each distribution network node in real time and locate the abnormal distribution network node.

[0122] The demand information acquisition unit is used to statistically analyze the located abnormal distribution network nodes, query the historical data of the distribution network nodes, and determine the demand information; the demand information is represented by the demand within the predicted time period.

[0123] The transmission execution unit is used to determine control commands based on demand information and send them to the energy storage nodes; it also synchronously generates shutdown commands and warning messages for abnormal distribution network nodes.

[0124] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for automated control of distribution networks based on intelligent converged terminals, characterized in that, The method includes: Based on the real-time acquisition of electrical quantities, status quantities, and environmental quantities at distribution network nodes by intelligent fusion terminals, electrical quantities, status quantities, and environmental quantities are constructed and identified to obtain operational status data containing time tags; the operational status data is used to characterize the status of distribution network nodes, and its value space is a preset value. Control commands are generated based on operational status data and transmitted to the energy storage node corresponding to the intelligent fusion terminal. The transmission mapping relationship between the intelligent fusion terminal, the distribution network node, and the energy storage node is dynamic, with the initial relationship being a preset value. The intelligent fusion terminal is a mobile terminal. For any intelligent converged terminal, the monitoring matrix of the intelligent converged terminal at each time moment is obtained based on the operation status data of each distribution network node obtained by statistical analysis of the node matrix composed of all distribution network nodes; wherein, the row and column positions in the node matrix correspond one-to-one with the distribution network nodes. Periodically merge the monitoring matrices corresponding to all smart converged terminals to obtain monitoring data volume, and recursively update the transmission mapping relationship between smart converged terminals, distribution network nodes and energy storage nodes based on the monitoring data volume; The step of periodically merging the monitoring matrices corresponding to all intelligent fusion terminals to obtain a monitoring data body, and recursively updating the transmission mapping relationship between the intelligent fusion terminals, distribution network nodes, and energy storage nodes based on the monitoring data body includes: A merge instruction is generated every preset time period; The data range is determined based on the generation time of the current merge instruction and the generation time of the previous merge instruction; Read the monitoring matrix corresponding to all intelligent fusion terminals within the data range, and merge the monitoring matrices at the same time. The monitoring data volume is obtained by arranging the merged monitoring matrix in chronological order. The transmission mapping relationship between the intelligent fusion terminal, distribution network node, and energy storage node is recursively updated based on the monitoring data. The step of recursively updating the transmission mapping relationship between the intelligent fusion terminal, distribution network node, and energy storage node based on the monitoring data includes: For any location in the monitoring data volume, read the data at various times at that location and fit the data change function; Feature identification is performed on the data change function to extract risk values; The risk value at each location is calculated to obtain the compression matrix; The total number of intelligent fusion terminals is queried, and the total number is used as the maximum number of clusters. A clustering process with the number of clusters continuously increasing is performed in the monitoring data body until a preset condition is met; the minimum number of clusters is a preset value. For each type of location, query its location center as the target location for the intelligent fusion terminal; Query the smart fusion terminal closest to the target location, generate a motion command pointing to the target location, and send it to the smart fusion terminal; When each smart converged terminal reaches its corresponding target location, update the transmission mapping relationship between the smart converged terminal and the distribution network node; Query all energy storage nodes corresponding to the distribution network nodes, and update the transmission mapping relationship between the smart converged terminal and the energy storage nodes; The preset condition is: for each type of location, the sum of the risk values ​​for each location is less than the preset risk value. Before generating motion commands pointing to the target location, a distance threshold is added. When the distance between the smart fusion terminal closest to a target location and the target location is less than the distance threshold, the original location is taken as the target location. When the number of categories decreases, inspection instructions are sent to intelligent fusion terminals that have not received motion instructions to acquire data and verify the operation of other intelligent fusion terminals. The process of updating the transmission mapping relationship is as follows: retain the original transmission channels for all locations, and disconnect the original transmission channels when all locations build connection channels based on the new transmission mapping relationship.

2. The method for automated control of distribution networks based on intelligent converged terminals according to claim 1, characterized in that, The steps of acquiring electrical, status, and environmental quantities at distribution network nodes in real time based on intelligent fusion terminals, constructing identification of electrical, status, and environmental quantities, and obtaining operational status data containing time tags include: The system acquires electrical quantities, status quantities, and environmental quantities at distribution network nodes in real time using intelligent fusion terminals. The electrical quantities include load power, voltage amplitude, current amplitude, and power factor. The status quantities include frequency fluctuations and switching status. The environmental quantities include ambient temperature and ambient humidity. For each parameter, the acquired data is processed for time synchronization and data cleaning to eliminate sampling errors and abnormal noise; The processed data at each time step is normalized to obtain standard data; The fusion algorithm based on DS evidence theory identifies the standard data of each parameter at each time point, and obtains operational status data with time labels.

3. The method for automated control of distribution networks based on intelligent converged terminals according to claim 1, characterized in that, The step of generating control commands based on operational status data and transmitting them to the energy storage node corresponding to the intelligent fusion terminal includes: Real-time reading of operational status data for each distribution network node to locate abnormal distribution network nodes; The abnormal distribution network nodes are identified statistically, and their historical data is queried to determine demand information; the demand information is represented by the demand within the predicted time period. Based on the demand information, control commands are determined and sent to the energy storage nodes; simultaneously, shutdown commands and warning messages are generated for abnormal distribution network nodes.

4. The method for automated control of distribution networks based on intelligent converged terminals according to claim 1, characterized in that, The step of obtaining the monitoring matrix of any intelligent fusion terminal at each time moment based on the operational status data of each distribution network node obtained by statistical analysis of the node matrix composed of all distribution network nodes includes: Obtain the coordinates of the distribution network nodes and create a node matrix based on the coordinates; wherein, each row and column position in the node matrix corresponds one-to-one with the distribution network nodes, and the row order and column order are determined by the spatial order of the distribution network nodes; For any intelligent fusion terminal at any given time, the operational status data of each distribution network node corresponding to the terminal is statistically analyzed based on the node matrix. For empty data in the node matrix, preset default data is inserted to obtain the monitoring matrix of the intelligent fusion terminal at that moment.

5. A power distribution network automation management and control system based on an intelligent converged terminal, characterized in that, The system is used to implement the distribution network automation management and control method based on intelligent fusion terminals as described in any one of claims 1 to 4, and the system includes: The operation status identification module is used to acquire electrical quantities, status quantities and environmental quantities at the distribution network nodes in real time based on the intelligent fusion terminal, construct electrical quantities, status quantities and environmental quantities for identification, and obtain operation status data containing time tags; the operation status data is used to characterize the status of the distribution network nodes, and its value space is a preset value. The instruction transmission module is used to generate control instructions based on the operational status data and transmit them to the energy storage node corresponding to the smart fusion terminal; wherein, the transmission mapping relationship between the smart fusion terminal and the distribution network node and the energy storage node is a dynamic relationship, and the initial relationship is a preset value, and the smart fusion terminal is a mobile terminal; The data integration module is used to obtain the monitoring matrix of any intelligent fusion terminal at any time based on the operation status data of each distribution network node obtained by statistical analysis of the node matrix composed of all distribution network nodes; wherein, the row and column positions in the node matrix correspond one-to-one with the distribution network nodes. The transmission parameter update module is used to periodically merge the monitoring matrices corresponding to all smart fusion terminals to obtain monitoring data volumes, and recursively update the transmission mapping relationship between smart fusion terminals, distribution network nodes, and energy storage nodes based on the monitoring data volumes.

6. The distribution network automation management and control system based on intelligent converged terminals according to claim 5, characterized in that, The operation status identification module includes: The data acquisition unit is used to acquire electrical quantities, status quantities, and environmental quantities at the distribution network nodes in real time based on the intelligent fusion terminal; the electrical quantities include load power, voltage amplitude, current amplitude, and power factor; the status quantities include frequency fluctuations and switching status; and the environmental quantities include ambient temperature and ambient humidity. The data integration unit is used to perform time synchronization processing and data cleaning on the acquired data for each parameter, eliminating sampling errors and abnormal noise; The standard processing unit is used to normalize the processed data at each time step to obtain standard data. The identification execution unit is used to identify the standard data of each parameter at each time step using a fusion algorithm based on DS evidence theory, thereby obtaining operational status data with time labels.

7. The power distribution network automation management and control system based on intelligent converged terminals according to claim 5, characterized in that, The instruction transmission module includes: The anomaly location unit is used to read the operational status data of each distribution network node in real time and locate the abnormal distribution network node. The demand information acquisition unit is used to statistically analyze the located abnormal distribution network nodes, query the historical data of the distribution network nodes, and determine the demand information; the demand information is represented by the demand within the predicted time period. The transmission execution unit is used to determine control commands based on demand information and send them to the energy storage nodes; it also synchronously generates shutdown commands and warning messages for abnormal distribution network nodes.

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