Distribution network adaptive wiring method, device and computer equipment

Abstract

The present application relates to a distribution network adaptive wiring method, device and computer equipment, and relates to the field of power grid control technology. The method includes: obtaining load data of multiple nodes in the distribution network between a preset time and a current time; classifying load scenarios according to the load data to obtain the load scenario category corresponding to each node; determining a wiring adjustment strategy according to the load scenario category; generating a wiring adjustment instruction according to the wiring adjustment strategy, and sending the wiring adjustment instruction to a wiring device to instruct the wiring device to connect each node according to the wiring adjustment instruction. The use of this method can improve the power supply efficiency of the distribution network.

Description

Technical Field

[0001] The present application relates to the field of power grid control technology, and in particular to a distribution network adaptive wiring method, device and computer equipment. Background Art

[0002] The distribution network is the core link in the supply of electric energy to users and is also an important infrastructure for realizing electric energy conversion and utilization. Reasonable and accurate distribution network wiring methods are of great significance to improving the reliability and stability of power supply and reducing the energy consumption and operating costs of the power system.

[0003] Traditional distribution network wiring methods often use fixed wiring methods and are performed manually offline. However, with the increasing complexity of the distribution network structure, the traditional method cannot adapt to changes in the load in the distribution network and has the problem of low power supply efficiency. Summary of the invention

[0004] Based on this, it is necessary to provide a distribution network adaptive wiring method, device, computer equipment, computer readable storage medium and computer program product that can improve the power supply efficiency of the distribution network in response to the above technical problems.

[0005] In a first aspect, the present application provides a distribution network adaptive wiring method, comprising:

[0006] Obtaining the load data of multiple nodes in the distribution network between a preset time and the current time;

[0007] Classify the load scenarios according to the load data to obtain the load scenario categories corresponding to each node;

[0008] Determine wiring adjustment strategy based on load scenario category;

[0009] A wiring adjustment instruction is generated according to the wiring adjustment strategy, and the wiring adjustment instruction is sent to the wiring device to instruct the wiring device to connect each node according to the wiring adjustment instruction.

[0010] In one embodiment, load scenario classification is performed according to the load data to obtain the load scenario category corresponding to each node, including:

[0011] For each node, a preset sliding window is used to slide and select the load data of the current node, and the average load value of the corresponding sliding window is calculated according to the load data in each sliding window;

[0012] Based on the changing trend of the average load value of each sliding window, the load scenario category corresponding to the current node is determined.

[0013] In one embodiment, based on the change trend of the average load value of each sliding window, the load scenario category corresponding to the current node is determined, including:

[0014] When the change trend of the average load value of each sliding window is continuously increasing, determining that the load scenario category corresponding to the current node is the first density scenario category;

[0015] When the change trend of the average load value of each sliding window is continuously decreasing or changing within a preset load range, it is determined that the load scenario category corresponding to the current node is the second density scenario category.

[0016] In one embodiment, determining a wiring adjustment strategy according to a load scenario category includes:

[0017] Obtain the current network structure of the distribution network and the target wiring strategy corresponding to the load scenario category;

[0018] The wiring strategy of the current network structure is adjusted based on the target wiring strategy to obtain a wiring adjustment strategy.

[0019] In one embodiment, the step of obtaining the target wiring strategy includes:

[0020] When the load scenario category is the first density scenario category, determining the target wiring strategy is to add a circuit;

[0021] When the load scenario category is the second density scenario category, the target wiring strategy is determined to be loop merging.

[0022] In one embodiment, load scenario classification is performed according to the load data to obtain the load scenario category corresponding to each node, including:

[0023] For each node, obtain the load information of the line connected to the current node;

[0024] Calculate the load density of the current node based on the load information and the load data of the current node;

[0025] The load scenario is classified according to the load density to obtain the load scenario category corresponding to the current node.

[0026] In a second aspect, the present application also provides a power distribution network adaptive wiring device, comprising:

[0027] An acquisition module is used to acquire load data of multiple nodes in the distribution network between a preset time and a current time;

[0028] A classification module is used to classify load scenarios according to load data to obtain the load scenario category corresponding to each node;

[0029] A determination module, used to determine a wiring adjustment strategy according to a load scenario category;

[0030] The wiring module is used to generate a wiring adjustment instruction according to the wiring adjustment strategy, and send the wiring adjustment instruction to the wiring device to instruct the wiring device to connect each node according to the wiring adjustment instruction.

[0031] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0032] Obtaining the load data of multiple nodes in the distribution network between a preset time and the current time;

[0033] Classify the load scenarios according to the load data to obtain the load scenario categories corresponding to each node;

[0034] Determine wiring adjustment strategy based on load scenario category;

[0035] A wiring adjustment instruction is generated according to the wiring adjustment strategy, and the wiring adjustment instruction is sent to the wiring device to instruct the wiring device to connect each node according to the wiring adjustment instruction.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0037] Obtaining the load data of multiple nodes in the distribution network between a preset time and the current time;

[0038] Classify the load scenarios according to the load data to obtain the load scenario categories corresponding to each node;

[0039] Determine wiring adjustment strategy based on load scenario category;

[0040] A wiring adjustment instruction is generated according to the wiring adjustment strategy, and the wiring adjustment instruction is sent to the wiring device to instruct the wiring device to connect each node according to the wiring adjustment instruction.

[0041] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0042] Obtaining the load data of multiple nodes in the distribution network between a preset time and the current time;

[0043] Classify the load scenarios according to the load data to obtain the load scenario categories corresponding to each node;

[0044] Determine wiring adjustment strategy based on load scenario category;

[0045] A wiring adjustment instruction is generated according to the wiring adjustment strategy, and the wiring adjustment instruction is sent to the wiring device to instruct the wiring device to connect each node according to the wiring adjustment instruction.

[0046] The above-mentioned distribution network adaptive wiring method, device, computer equipment, computer-readable storage medium and computer program product classify load scenarios through the load data of each of the multiple nodes in the distribution network between the preset time and the current time. The load scenario classification can be performed in combination with the current load data and the historical load data, which is beneficial to improving the accuracy of the load scenario category to which each node of the distribution network belongs. Since different load scenario categories have different load conditions, the wiring adjustment strategy can be adaptively and flexibly adjusted according to different load scenario categories. Therefore, wiring the lines in the corresponding nodes according to the wiring adjustment strategy determined based on the load scenario category is beneficial to ensure that the distribution network meets the load requirements under different load scenarios, thereby improving the power supply efficiency of the distribution network. In addition, by generating a wiring adjustment instruction and instructing the wiring device to perform automatic wiring, automatic wiring and adaptive wiring adjustment of each node of the distribution network are realized, further improving the power supply efficiency of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0048] FIG1 is a diagram showing an application environment of a method for adaptive wiring of a power distribution network according to an embodiment;

[0049] FIG2 is a schematic diagram of a flow chart of a method for adaptive wiring of a power distribution network in one embodiment;

[0050] FIG3 is a schematic diagram of the overall process of a method for adaptive wiring of a power distribution network in one embodiment;

[0051] FIG4 is a schematic diagram of the overall flow of a method for adaptive wiring of a power distribution network in another embodiment;

[0052] FIG5 is a structural block diagram of a power distribution network adaptive wiring device in one embodiment;

[0053] FIG. 6 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0055] The adaptive wiring method for the distribution network provided in the embodiment of the present application can be applied in the application environment shown in FIG1. ​​Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The terminal 102 obtains the load data of each of the multiple nodes in the distribution network between the preset time and the current time; classifies the load scenario according to the load data, and obtains the load scenario category corresponding to each node; determines the wiring adjustment strategy according to the load scenario category; generates a wiring adjustment instruction according to the wiring adjustment strategy, and sends the wiring adjustment instruction to the wiring device to instruct the wiring device to connect each node according to the wiring adjustment instruction. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices, and the Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head mounted device may be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0056] In an exemplary embodiment, as shown in FIG2 , a distribution network adaptive wiring method is provided, which is described by taking the method applied to the terminal 102 in FIG1 as an example, and includes the following steps 202 to 208. Among them:

[0057] Step 202, obtaining load data of each of a plurality of nodes in the distribution network between a preset time and a current time.

[0058] The nodes in the distribution network refer to the electrical connection points between the electrical devices in the distribution network. For example, the nodes include substation nodes, load nodes, switchgear nodes and other equipment nodes.

[0059] Load data refers to the data related to the load of the equipment in the node. Load data can reflect the power demand of each node in the distribution network at a certain moment or over a period of time. For example, load data includes information such as the voltage of the node and the current of the node.

[0060] In some embodiments, the terminal collects the load data of each node in real time through devices such as smart meters or sensors deployed in the distribution network. The current moment refers to the moment when the adaptive wiring method is performed. The preset moment refers to a historical moment with a preset time from the current moment. The adaptive wiring method of the distribution network can be performed based on the load data between the preset moment and the current moment to ensure the real-time and accuracy of the adaptive wiring method.

[0061] Step 204 , classify the load scenarios according to the load data to obtain the load scenario categories corresponding to each node.

[0062] Among them, load scenario classification refers to a method of classifying each node in the distribution network according to load data. The load scenario category refers to the scenario category to which the load condition of the node belongs. In some embodiments, the load scenario category can be determined according to the load level, for example, including a high load scenario category and a low load scenario category. For another example, the load scenario category can also include an industrial area category, a commercial area category, and a residential area category, wherein the load of the industrial area category is higher than that of the commercial area category, and the load of the commercial area category is higher than that of the residential area category.

[0063] Since the demand for electricity may be different in different scenarios, the traditional method of using fixed wiring cannot flexibly adapt to load changes, and may easily lead to a mismatch between power supply capacity and load demand. Therefore, the embodiment of the present application proposes to classify load scenarios according to load data to obtain the load scenario category corresponding to each node, and then perform adaptive wiring based on the load scenario category, which is beneficial to improving the balance of load demand under different load scenarios.

[0064] Step 206, determining a connection adjustment strategy according to the load scenario category.

[0065] The wiring adjustment strategy refers to a strategy for adjusting the wiring method. Different load scenario categories correspond to different wiring adjustment strategies, which is conducive to determining a wiring method that meets the load conditions of the node. For example, the load scenario category can correspond to the wiring adjustment strategy one by one, or the wiring adjustment strategy can be determined based on the load scenario category and the current wiring condition of the node.

[0066] Step 208: Generate a wiring adjustment instruction according to the wiring adjustment strategy, and send the wiring adjustment instruction to the wiring device to instruct the wiring device to connect each node according to the wiring adjustment instruction.

[0067] The wiring device refers to a device for automatic or semi-automatic wiring. The wiring device can receive instructions sent by the terminal and automatically connect according to the instructions. For example, the terminal converts the wiring adjustment strategy into a corresponding wiring adjustment instruction according to the communication protocol with the wiring device, and sends the wiring adjustment instruction to the wiring device. After receiving the wiring adjustment instruction, the wiring device connects each node according to the wiring adjustment instruction, thereby realizing adaptive wiring of the distribution network.

[0068] In the above-mentioned adaptive wiring method for distribution network, load scenario classification is performed by using the load data of each of the multiple nodes in the distribution network between the preset time and the current time. The load scenario classification can be performed in combination with the current load data and the historical load data, which is beneficial to improving the accuracy of the load scenario category to which each node of the distribution network belongs. Since different load scenario categories have different load conditions, the wiring adjustment strategy can be adaptively and flexibly adjusted according to different load scenario categories. Therefore, wiring the lines in the corresponding nodes according to the wiring adjustment strategy determined based on the load scenario category is beneficial to ensure that the distribution network meets the load requirements under different load scenarios, thereby improving the power supply efficiency of the distribution network. In addition, by generating a wiring adjustment instruction and instructing the wiring device to perform automatic wiring, automatic wiring and adaptive wiring adjustment of each node of the distribution network are realized, further improving the power supply efficiency of the distribution network.

[0069] In an exemplary embodiment, load scenario classification is performed based on load data to obtain load scenario categories corresponding to each node, including: for each node, a preset sliding window is used to perform sliding selection on the load data of the current node, and based on the load data in each sliding window, the average load value of the corresponding sliding window is calculated; based on the change trend of the average load value of each sliding window, the load scenario category corresponding to the current node is determined.

[0070] The current node refers to any node in the distribution network. The preset sliding window refers to a window of preset size. When the preset sliding window is used to slide and select the load data of the current node, each slide can select the load data of the preset data from the load data of the current node. Each slide can be in a fixed step size or an unfixed step size, which is not specifically limited in the embodiments of the present application.

[0071] For each sliding window, the terminal averages the load data corresponding to the sliding window to obtain the average load value corresponding to the sliding window. After multiple sliding selections, the average load values ​​corresponding to multiple sliding windows can be determined.

[0072] The changing trend of the average load value of each sliding window includes increasing continuously, decreasing continuously, changing within a preset load range, etc. Among them, the average load values ​​corresponding to each sliding window are sorted according to the sliding order. If each average load value is increasing continuously, the terminal determines that the changing trend of the average load value of each sliding window is increasing continuously. If each average load value is decreasing continuously, the terminal determines that the changing trend of the average load value of each sliding window is decreasing continuously. If each average load value is in a preset load range, the terminal determines that the changing trend of the average load value of each sliding window is changing within a preset load range.

[0073] Different load scenario categories correspond to different change trends. In some embodiments, the terminal includes a preset mapping relationship, which is used to characterize the correspondence between the change trend and the load scenario category. The terminal searches for the mapping relationship and uses the load scenario category corresponding to the change trend as the load scenario category corresponding to the current node.

[0074] In this embodiment, a method of sliding selection of load data using a preset sliding window is adopted to obtain load data in multiple sliding windows, and the load scenario category is determined based on the change trend of the average load value of each sliding window. The load scenario category obtained in this way can be used to characterize the load change of the corresponding node, which is conducive to improving the accuracy of the load scenario category.

[0075] In an exemplary embodiment, based on the changing trend of the average load value of each sliding window, the load scenario category corresponding to the current node is determined, including: when the changing trend of the average load value of each sliding window is continuously increasing, the load scenario category corresponding to the current node is determined to be the first density scenario category; when the changing trend of the average load value of each sliding window is continuously decreasing or changing within a preset load range, the load scenario category corresponding to the current node is determined to be the second density scenario category.

[0076] Among them, when the changing trend of the average load value of each sliding window is continuously increasing, it indicates that the load of the current node continues to increase, and the connection of the current node may not be able to meet the load demand. The terminal determines that the load scenario category corresponding to the current node is the first density scenario category.

[0077] When the changing trend of the average load value of each sliding window is continuously decreasing or changing within a preset load range, it indicates that the wiring condition of the current node can meet the load demand, and the terminal determines that the load scenario category corresponding to the current node is the second density scenario category.

[0078] In some embodiments, the terminal may send the load scenario category corresponding to each node to the display device to instruct the display device to display the load scenario category corresponding to each node.

[0079] In this embodiment, the load scenario category is determined by the change trend of the average load value, which is conducive to obtaining an accurate load scenario category.

[0080] In an exemplary embodiment, a wiring adjustment strategy is determined according to a load scenario category, including: obtaining a current network structure of the distribution network and a target wiring strategy corresponding to the load scenario category; adjusting the wiring strategy of the current network structure based on the target wiring strategy to obtain a wiring adjustment strategy.

[0081] The current network structure refers to the network structure of the distribution network at the current moment, and includes the connection relationship between each node and the nodes.

[0082] The terminal pre-stores a mapping relationship between load scenario categories and wiring strategies, and queries the mapping relationship to obtain a target wiring strategy corresponding to the load scenario category.

[0083] The wiring adjustment strategy is adjusted based on the wiring strategy of the current network structure. For example, if the target wiring strategy is to add a loop, and the wiring strategy of the current network structure is to connect node A and node B, then the wiring adjustment strategy can be to add a new loop between node A and node B. For example, if the target wiring strategy is to merge loops, and the wiring strategy of the current network structure is to connect node A and node B, then the wiring adjustment strategy can be to merge node A and node B into a new node.

[0084] In this embodiment, the wiring strategy of the current network structure is adjusted through the target wiring strategy corresponding to the load scenario category to obtain a wiring adjustment strategy. Wiring adjustment is performed based on the wiring adjustment strategy, which can ensure that the wiring adjustment strategy is in line with the load conditions of the node, which is beneficial to improving the power supply efficiency of the distribution network.

[0085] In an exemplary embodiment, the step of obtaining the target wiring strategy includes: when the load scenario category is a first density scenario category, determining the target wiring strategy to be adding circuits; when the load scenario category is a second density scenario category, determining the target wiring strategy to be merging circuits.

[0086] Among them, since the average load value of the node is constantly increasing when the load scenario category is the first density scenario category, the current node wiring method may not be able to meet the load requirements. Therefore, the terminal can determine the target wiring strategy as adding a circuit to increase the power supply capacity, improve the power supply reliability, and improve the voltage instructions.

[0087] When the load scenario category is the second density scenario category, the average load value of the node is constantly decreasing or changing within the preset load range, and the current node wiring method can meet the load requirements. Therefore, the terminal can determine the target wiring strategy as a merged circuit to improve power supply reliability and reduce energy loss.

[0088] In this embodiment, by determining the target wiring strategy under the first density scenario category as adding circuits, it is beneficial to increase power supply capacity, improve power supply reliability, and improve voltage instructions; determining the target wiring strategy under the second density scenario category as merging circuits is beneficial to improve power supply reliability and reduce energy loss.

[0089] In an exemplary embodiment, load scenario classification is performed based on load data to obtain a load scenario category corresponding to each node, including: for each node, obtaining load information of the line connected to the current node; calculating the load density of the current node based on the load information and the load data of the current node; and classifying the load scenario based on the load density to obtain a load scenario category corresponding to the current node.

[0090] The nodes of the distribution network are connected through lines. The line connected to the current node can be directly connected to the current node or indirectly connected to the current node.

[0091] Load information refers to a series of data or parameters describing the power load status of the line, including line voltage and line current. Load data may include the node area of ​​the current node.

[0092] Load density is a parameter that describes the distribution of power load at the current node. The calculation method of load density can be: load density = Σ (load power) / node area. Among them, load power = line voltage × line current.

[0093] The terminal sums the load powers of the lines connected to the current node and divides the sum by the node area to obtain the load density. The terminal can classify the load scenarios according to the load density to obtain the load scenario category corresponding to the current node.

[0094] In this embodiment, the load density is calculated by using the load information of the lines connected to the current node and the load data of the current node, so that the load scenarios are classified according to the load density. The load scenario categories obtained in this way can reflect the changing trend of the load density. Adaptive adjustment of the wiring based on the load scenario categories is beneficial to improving the power supply efficiency of the distribution network.

[0095] To explain the adaptive wiring method and effect of the distribution network in this solution in detail, a most detailed embodiment is described below:

[0096] FIG3 is a schematic diagram of the overall process of a distribution network adaptive wiring method in an embodiment. The distribution network adaptive wiring method mainly includes the following steps: S31 data collection and analysis, S32 situation classification and judgment, S33 load strategy adjustment, S34 adaptive wiring implementation and S35 real-time monitoring and optimization.

[0097] S31. Data collection and analysis: Through the equipped smart meters and sensors and other equipment, the load data of each node in the distribution network and the load information of the line are collected in real time, and a real-time monitoring and data analysis system is established to process and analyze the collected data to obtain the load density of each node;

[0098] S32, scenario classification and determination: Based on the load data, load scenario classification is performed to obtain the load scenario category corresponding to each node, such as industrial area category, commercial area category and residential area category, to provide a reference basis for wiring adjustment;

[0099] S33, load strategy adjustment: Combine the load scenario category and the current network structure of the distribution network to formulate wiring adjustment strategies for different load scenario categories. When the load scenario category is the first density scenario category, the target wiring strategy is determined to be adding circuits to increase the power supply capacity; when the load scenario category is the second density scenario category, the target wiring strategy is determined to be merging circuits to reduce the length of the power supply line and reduce energy consumption;

[0100] S34, adaptive wiring implementation: generating a wiring adjustment instruction according to the wiring adjustment strategy, and sending the wiring adjustment instruction to the wiring device to instruct the wiring device to connect each node according to the wiring adjustment instruction, so as to realize automatic or semi-automatic wiring adjustment, and be able to intelligently select a suitable loop connection mode to adjust the wiring of the distribution network;

[0101] S35. Real-time monitoring and optimization: Equipped with an intelligent monitoring system to monitor the load data of each node in real time, perform feedback control and optimization adjustments based on real-time data and algorithm models, and continuously improve wiring solutions to meet load requirements in different scenarios.

[0102] The embodiments of the present application realize adaptive wiring of the distribution network through intelligent data analysis, situation classification and load adjustment strategy determination, flexibly adjust the wiring method of the distribution network according to real-time load density requirements, improve power supply efficiency, optimize load balancing, and provide users with stable and reliable power supply.

[0103] Specifically, the specific calculation process of steps S31-S35 is:

[0104] S31. Data collection and analysis:

[0105] The following data are preset: load data of node A: the actual load value (Load) is 1000kW; load data of node B: the actual load value (Load) is 1500kW; load data of node C: the actual load value (Load) is 800kW;

[0106] S32. Situation classification and judgment:

[0107] The following historical load data between the preset time and the current time (in units of each hour): the load at 2023-12-01 00:00 is 1200kW, the load at 2023-12-01 01:00 is 1300kW, the load at 2023-12-01 02:00 is 1100kW, etc.

[0108] Through these historical load data, different forecasting models can be used to predict future load conditions, including moving average method, exponential smoothing method, and regression analysis. For example, using the moving average method to predict future load, a 3-hour sliding window is preset to calculate the average load value. For example, the average load value corresponding to 2023-12-01 02:00 is 1200kW, and the average load value corresponding to 2023-12-01 03:00 is 1266.7kW, etc.

[0109] Based on these average load values, the future load conditions are classified and determined. If the average load value continues to increase, the load scenario category corresponding to the current node is determined to be the first density scenario category; if the average load value continues to decrease or changes within the preset load range, the load scenario category corresponding to the current node is determined to be the second density scenario category.

[0110] S33, Load strategy adjustment:

[0111] When the load scenario category is the first density scenario category, the target wiring strategy is determined to be adding circuits; when the load scenario category is the second density scenario category, the target wiring strategy is determined to be merging circuits.

[0112] S34, Adaptive wiring implementation:

[0113] In this step, a wiring adjustment strategy is determined according to the load scenario category, and the wiring adjustment strategy includes adding a circuit, merging circuits, etc. A wiring adjustment instruction is generated according to the wiring adjustment strategy, thereby instructing the wiring device to connect each node according to the wiring adjustment instruction.

[0114] S35, Real-time monitoring and optimization:

[0115] Real-time monitoring, feedback control and optimization adjustment will be carried out, with preset data: load density of node A: 0.8kW / m^2, load density of node B: 1.2kW / m^2, load density of node C: 0.5kW / m^2.

[0116] According to these data and formulas, specific calculations are performed. First, the load power is calculated: the line current of node A is preset to be 50A, the line voltage is 220V, the line current of node B is 60A, the line voltage is 220, and the line current of node C is 40A, and the line voltage is 220V; load power = line current × line voltage. The load power of node A = 50A × 220V = 11000W = 11kW, the load power of node B = 60A × 220V = 13200W = 13.2kW, and the load power of node C = 40A × 220V = 8800W = 8.8kW.

[0117] Next, calculate the load density: load density = Σ(load power) / node area. Assume that the node area of ​​node A is 500m^2, the node area of ​​node B is 800m^2, and the node area of ​​node C is 600m^2; then the load density of node A = (11kW) / 500m^2 = 0.022kW / m^2; the load density of node B = (13.2kW) / 800m^2 = 0.0165kW / m^2; the load density of node C = (8.8kW) / 600m^2 = 0.0147kW / m^2. Based on the above calculations, the load density of each node is obtained;

[0118] In S34, adaptive wiring is an important measure. By using the load density threshold to select the loop connection method, the connection status of different loops in the system is adjusted to reduce the load density of nodes exceeding the threshold and achieve load balancing.

[0119] Embodiment 1. Please refer to Figure 4. The embodiment of the present application works as follows in the scenario of dynamic load scheduling to achieve load balancing and energy consumption optimization: preset data: load density threshold: 0.8kW / m^2, load of node G: 800kW, node area of ​​node G: 1000m^2, load of node H: 700kW, node area of ​​node H: 1200m^2, load of node I: 600kW, node area of ​​node I: 900m^2.

[0120] Implementation steps:

[0121] S41, real-time load collection: use smart meters and sensor devices to collect real-time loads of nodes G, H and I to obtain accurate load data;

[0122] S42, load density calculation: calculate the load density of node G: load density of node G = load power of node G / node area of ​​node G = 800kW / 1000m^2 = 0.8kW / m^2; calculate the load density of node H: load density of node H = load power of node H / node area of ​​node H = 700kW / 1200m^2 ≈ 0.583kW / m^2; calculate the load density of node I: load density of node I = load power of node I / node area of ​​node I = 600kW / 900m^2 ≈ 0.667kW / m^2;

[0123] S43, determine the load density: compare the load density of nodes G, H and I with the load density threshold to determine whether load adjustment is required; according to the preset data, only the load density of node G is equal to the threshold of 0.8kW / m^2, and load adjustment is required;

[0124] S44, load adjustment strategy selection: for the first density scenario category, a dynamic load scheduling method is used to achieve load balancing and energy consumption optimization;

[0125] S45. Implementation of dynamic load dispatch: Based on real-time load data and wiring adjustment strategy, dynamic load dispatch is performed through the wiring device to divert part of the load from node G to node H and node I to achieve load balancing and reduce energy consumption.

[0126] Specific calculation process:

[0127] Calculation of load density of node G: load density of node G = load power of node G / node area of ​​node G = 800kW / 1000m^2 = 0.8kW / m^2; calculation of load density of node H: load density of node H = load power of node H / node area of ​​node H = 700kW / 1200m^2≈0.583kW / m^2; calculation of load density of node I: load density of node I = load power of node I / node area of ​​node I = 600kW / 900m^2≈0.667kW / m^2.

[0128] Through the above embodiments, it is described in detail that according to the load density threshold and the load adjustment strategy, in the steps of real-time load collection, load density calculation, load density judgment, load adjustment strategy selection and dynamic load scheduling implementation, the problem of dynamic load scheduling to achieve load balancing and energy consumption optimization is solved by using an adaptive wiring method. This embodiment clearly demonstrates the process of acquiring and processing load data, the method of calculating load density, the selection of load judgment and adjustment strategy, and the specific steps of dynamic load scheduling implementation to ensure the stability and reliability of power supply and optimize energy consumption.

[0129] Embodiment 2, please refer to Figure 4, the embodiment of the present application works as follows in the scenario of adding a circuit to increase the power supply capacity: preset data: load density threshold: 1.0kW / m^2, load density of node A: 1.2kW / m^2, load density of node B: 0.8kW / m^2.

[0130] Calculation and implementation steps:

[0131] In S41, smart meters and sensor devices are used to collect load data of nodes A and B in real time to ensure accurate measurement; based on the collected load data, the load density of node A is calculated to be 1.2kW / m^2 in S42, and in S43, it is determined that the load density exceeds the load density threshold of 1.0kW / m^2; in S44, according to the load adjustment strategy, in the first density scenario category, the method of adding a circuit is selected to increase the power supply capacity and ensure reliable power supply; in S45, adaptive wiring is implemented, and in the first density scenario category, a new circuit is automatically added through the intelligent wiring device, and the load is evenly distributed to the original circuit and the new circuit to increase the power supply capacity.

[0132] Specific calculation process: Calculation of load density of node A: Load density of node A = load power of node A / node area of ​​node A; according to preset data, the load power of node A is 1.2kW, and the node area of ​​node A is 1m^2, so the load density of node A is 1.2kW / m^2.

[0133] Embodiment 3, please refer to FIG4 , the embodiment of the present application works as follows in the scenario of merging loops to reduce energy consumption and maintenance costs:

[0134] Preset data: load density threshold: 0.5kW / m^2, load density of node C: 0.6kW / m^2, load density of node D: 0.4kW / m^2.

[0135] Calculation and implementation steps:

[0136] In S41, smart meters and sensor devices are used to collect load data of nodes C and D in real time to ensure accurate measurement; based on the collected load data, the load density of node D is calculated to be 0.4kW / m^2 in S42, and in S43, it is determined that the load density is lower than the load density threshold of 0.5kW / m^2; in S44, according to the load adjustment strategy, under the second density scenario category, the method of merging loops is selected to reduce energy consumption and maintenance costs and improve energy efficiency; in S45, adaptive wiring is implemented, and under the second density scenario category, the loop of node D is merged with other loops through the intelligent wiring device to reduce the length of the power supply line, thereby reducing energy consumption and maintenance costs.

[0137] Specific calculation process: Calculation of load density of node D: Load density of node D = load power of node D / node area of ​​node D; according to preset data, the load power of node D is 0.4kW, and the node area of ​​node D is 1m^2, so the load density of node D is 0.4kW / m^2.

[0138] Embodiment 4, please refer to FIG. 4 , the embodiment of the present application works as follows in the scenario of dynamic load scheduling to achieve load balancing:

[0139] Preset data: load density threshold: 0.8kW / m^2, load density of node E: 1.0kW / m^2, load density of node F: 0.7kW / m^2.

[0140] Calculation and implementation steps:

[0141] In S41, smart meters and sensor devices are used to collect load data of nodes E and F in real time to ensure accurate measurement; based on the collected load data, the load density of node E is calculated to be 1.0kW / m^2 in S42, and in S43, it is determined that the load density exceeds the load density threshold of 0.8kW / m^2; in S44, according to the load adjustment strategy, under the first density scenario category, dynamic load scheduling is selected to distribute the load evenly to different circuits to reduce the risk of overload; in S45, adaptive wiring is implemented, and under the first density scenario category, dynamic load scheduling is performed through smart wiring devices according to real-time load data and control strategies, and part of the load is diverted to node F to achieve load balancing and reduce the load density of node E.

[0142] Specific calculation process:

[0143] Calculation of the load density of node E: Load density of node E = load power of node E / node area of ​​node E; according to the preset data, the load power of node E is 1.0kW, and the node area of ​​node E is 1m^2, so the load density of node E is 1.0kW / m^2.

[0144] The above-mentioned adaptive wiring method for distribution network classifies load scenarios by using the load data of each of the multiple nodes in the distribution network between the preset time and the current time. The load scenario classification can be performed in combination with the current load data and the historical load data, which is beneficial to improving the accuracy of the load scenario category to which each node of the distribution network belongs. Since different load scenario categories have different load conditions, the wiring adjustment strategy can be adaptively and flexibly adjusted according to different load scenario categories. Therefore, wiring the lines in the corresponding nodes according to the wiring adjustment strategy determined based on the load scenario category is beneficial to ensure that the distribution network meets the load requirements under different load scenarios, thereby improving the power supply efficiency of the distribution network. In addition, by generating a wiring adjustment instruction and instructing the wiring device to perform automatic wiring, automatic wiring and adaptive wiring adjustment of each node of the distribution network are realized, further improving the power supply efficiency of the distribution network. At the same time, it can accurately evaluate load density, effectively classify and determine load density, predict load conditions in advance, provide guidance for wiring adjustments, match power supply capacity with load demand, flexibly adapt to load changes, select appropriate loop connection methods, realize automatic or semi-automatic wiring adjustments, monitor load conditions and line status in real time, feedback control and optimization adjustments, improve wiring plans, meet load demands in different scenarios, improve the stability and reliability of power supply, improve power supply efficiency, reduce energy consumption and maintenance costs, flexibly adjust wiring plans, enhance the adaptability of distribution networks, optimize load balancing, and provide stable and reliable power supply.

[0145] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0146] Based on the same inventive concept, the embodiment of the present application also provides a distribution network adaptive wiring device for implementing the above-mentioned distribution network adaptive wiring method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more distribution network adaptive wiring device embodiments provided below can refer to the limitations of the distribution network adaptive wiring method above, and will not be repeated here.

[0147] In an exemplary embodiment, as shown in FIG5 , a power distribution network adaptive wiring device 100 is provided, comprising: an acquisition module 120, a classification module 140, a determination module 160 and a wiring module 180, wherein:

[0148] The acquisition module 120 is used to acquire the load data of each of the multiple nodes in the distribution network between a preset time and a current time.

[0149] The classification module 140 is used to classify the load scenarios according to the load data to obtain the load scenario categories corresponding to each node.

[0150] The determination module 160 is used to determine a connection adjustment strategy according to the load scenario category.

[0151] The wiring module 180 is used to generate a wiring adjustment instruction according to the wiring adjustment strategy, and send the wiring adjustment instruction to the wiring device to instruct the wiring device to connect each node according to the wiring adjustment instruction.

[0152] The above-mentioned distribution network adaptive wiring device classifies load scenarios through the load data of each of the multiple nodes in the distribution network between the preset time and the current time. The load scenario classification can be performed in combination with the current load data and the historical load data, which is beneficial to improving the accuracy of the load scenario category to which each node of the distribution network belongs. Since different load scenario categories have different load conditions, the wiring adjustment strategy can be adaptively and flexibly adjusted according to different load scenario categories. Therefore, wiring the lines in the corresponding nodes according to the wiring adjustment strategy determined based on the load scenario category is beneficial to ensure that the distribution network meets the load requirements under different load scenarios, thereby improving the power supply efficiency of the distribution network. In addition, by generating a wiring adjustment instruction and instructing the wiring device to perform automatic wiring, automatic wiring and adaptive wiring adjustment of each node of the distribution network are realized, further improving the power supply efficiency of the distribution network.

[0153] In one embodiment, load scenario classification is performed based on load data to obtain load scenario categories corresponding to each node. The classification module 140 is also used to: for each node, use a preset sliding window to slide and select the load data of the current node, and calculate the average load value of the corresponding sliding window based on the load data in each sliding window; based on the change trend of the average load value of each sliding window, determine the load scenario category corresponding to the current node.

[0154] In one embodiment, based on the changing trend of the average load value of each sliding window, the load scenario category corresponding to the current node is determined, and the classification module 140 is also used to: when the changing trend of the average load value of each sliding window is continuously increasing, determine that the load scenario category corresponding to the current node is the first density scenario category; when the changing trend of the average load value of each sliding window is continuously decreasing or changing within a preset load range, determine that the load scenario category corresponding to the current node is the second density scenario category.

[0155] In one embodiment, the wiring adjustment strategy is determined according to the load scenario category, and the determination module 160 is also used to: obtain the current network structure of the distribution network and the target wiring strategy corresponding to the load scenario category; adjust the wiring strategy of the current network structure based on the target wiring strategy to obtain the wiring adjustment strategy.

[0156] In one embodiment, in the step of acquiring the target wiring strategy, the determination module 160 is also used to: when the load scenario category is the first density scenario category, determine the target wiring strategy as adding loops; when the load scenario category is the second density scenario category, determine the target wiring strategy as merging loops.

[0157] In one embodiment, load scenario classification is performed based on load data to obtain the load scenario category corresponding to each node. The classification module 140 is also used to: for each node, obtain the load information of the line connected to the current node; calculate the load density of the current node based on the load information and the load data of the current node; and classify the load scenario based on the load density to obtain the load scenario category corresponding to the current node.

[0158] Each module in the above-mentioned distribution network adaptive wiring device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0159] In an exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG6. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a method for adaptive wiring of a distribution network is implemented. The display unit of the computer device is used to form a visually visible picture, which may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0160] Those skilled in the art will understand that the structure shown in FIG. 6 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0161] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0162] Obtain the load data of each of the multiple nodes in the distribution network between the preset time and the current time; classify the load scenarios according to the load data to obtain the load scenario category corresponding to each node; determine the wiring adjustment strategy according to the load scenario category; generate a wiring adjustment instruction according to the wiring adjustment strategy, and send the wiring adjustment instruction to the wiring device to instruct the wiring device to connect each node according to the wiring adjustment instruction.

[0163] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0164] For each node, a preset sliding window is used to slide and select the load data of the current node. According to the load data in each sliding window, the average load value of the corresponding sliding window is calculated; based on the changing trend of the average load value of each sliding window, the load scenario category corresponding to the current node is determined.

[0165] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0166] When the changing trend of the average load value of each sliding window is continuously increasing, the load scenario category corresponding to the current node is determined to be the first density scenario category; when the changing trend of the average load value of each sliding window is continuously decreasing or changing within a preset load range, the load scenario category corresponding to the current node is determined to be the second density scenario category.

[0167] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0168] The current network structure of the distribution network and the target wiring strategy corresponding to the load scenario category are obtained; the wiring strategy of the current network structure is adjusted based on the target wiring strategy to obtain a wiring adjustment strategy.

[0169] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0170] When the load scenario category is the first density scenario category, the target wiring strategy is determined to be adding circuits; when the load scenario category is the second density scenario category, the target wiring strategy is determined to be merging circuits.

[0171] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0172] For each node, the load information of the lines connected to the current node is obtained; the load density of the current node is calculated based on the load information and the load data of the current node; the load scenario is classified according to the load density to obtain the load scenario category corresponding to the current node.

[0173] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0174] Obtain the load data of each of the multiple nodes in the distribution network between the preset time and the current time; classify the load scenarios according to the load data to obtain the load scenario category corresponding to each node; determine the wiring adjustment strategy according to the load scenario category; generate a wiring adjustment instruction according to the wiring adjustment strategy, and send the wiring adjustment instruction to the wiring device to instruct the wiring device to connect each node according to the wiring adjustment instruction.

[0175] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0176] For each node, a preset sliding window is used to slide and select the load data of the current node. According to the load data in each sliding window, the average load value of the corresponding sliding window is calculated; based on the changing trend of the average load value of each sliding window, the load scenario category corresponding to the current node is determined.

[0177] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0178] When the changing trend of the average load value of each sliding window is continuously increasing, the load scenario category corresponding to the current node is determined to be the first density scenario category; when the changing trend of the average load value of each sliding window is continuously decreasing or changing within a preset load range, the load scenario category corresponding to the current node is determined to be the second density scenario category.

[0179] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0180] The current network structure of the distribution network and the target wiring strategy corresponding to the load scenario category are obtained; the wiring strategy of the current network structure is adjusted based on the target wiring strategy to obtain a wiring adjustment strategy.

[0181] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0182] When the load scenario category is the first density scenario category, the target wiring strategy is determined to be adding circuits; when the load scenario category is the second density scenario category, the target wiring strategy is determined to be merging circuits.

[0183] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0184] For each node, the load information of the lines connected to the current node is obtained; the load density of the current node is calculated based on the load information and the load data of the current node; the load scenario is classified according to the load density to obtain the load scenario category corresponding to the current node.

[0185] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0186] Obtain the load data of each of the multiple nodes in the distribution network between the preset time and the current time; classify the load scenarios according to the load data to obtain the load scenario category corresponding to each node; determine the wiring adjustment strategy according to the load scenario category; generate a wiring adjustment instruction according to the wiring adjustment strategy, and send the wiring adjustment instruction to the wiring device to instruct the wiring device to connect each node according to the wiring adjustment instruction.

[0187] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0188] For each node, a preset sliding window is used to slide and select the load data of the current node. According to the load data in each sliding window, the average load value of the corresponding sliding window is calculated; based on the changing trend of the average load value of each sliding window, the load scenario category corresponding to the current node is determined.

[0189] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0190] When the changing trend of the average load value of each sliding window is continuously increasing, the load scenario category corresponding to the current node is determined to be the first density scenario category; when the changing trend of the average load value of each sliding window is continuously decreasing or changing within a preset load range, the load scenario category corresponding to the current node is determined to be the second density scenario category.

[0191] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0192] The current network structure of the distribution network and the target wiring strategy corresponding to the load scenario category are obtained; the wiring strategy of the current network structure is adjusted based on the target wiring strategy to obtain a wiring adjustment strategy.

[0193] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0194] When the load scenario category is the first density scenario category, the target wiring strategy is determined to be adding circuits; when the load scenario category is the second density scenario category, the target wiring strategy is determined to be merging circuits.

[0195] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0196] For each node, the load information of the lines connected to the current node is obtained; the load density of the current node is calculated based on the load information and the load data of the current node; the load scenario is classified according to the load density to obtain the load scenario category corresponding to the current node.

[0197] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0198] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0199] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0200] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A distribution network adaptive wiring method, characterized in that: The method comprises: Obtaining load data of multiple nodes in the distribution network between a preset time and a current time; the preset time is a historical time with a preset time distance from the current time; Classify the load scenarios according to the load data to obtain the load scenario categories corresponding to each node; determining a wiring adjustment strategy according to the load scenario category; Generate a wiring adjustment instruction according to the wiring adjustment strategy, and send the wiring adjustment instruction to the wiring device to instruct the wiring device to connect each node according to the wiring adjustment instruction; The load scenario classification is performed according to the load data to obtain the load scenario category corresponding to each node, including: For each node, a preset sliding window is used to slide and select the load data of the current node, and the average load value of the corresponding sliding window is calculated according to the load data in each sliding window; the preset sliding window refers to a window with a preset size; Based on the change trend of the average load value of each sliding window, determine the load scenario category corresponding to the current node; the change trend of the average load value of each sliding window includes continuous increase, continuous decrease, and change within a preset load range; the load scenario category is used to characterize the load change of the corresponding node; Determining the load scenario category corresponding to the current node based on the change trend of the average load value of each sliding window includes: When the change trend of the average load value of each sliding window is continuously increasing, determining that the load scenario category corresponding to the current node is a first density scenario category; When the change trend of the average load value of each sliding window is continuously decreasing or changing within a preset load range, determining that the load scenario category corresponding to the current node is a second density scenario category; The determining of the connection adjustment strategy according to the load scenario category includes: Obtaining a current network structure of the distribution network and a target wiring strategy corresponding to the load scenario category; Adjusting the wiring strategy of the current network structure based on the target wiring strategy to obtain a wiring adjustment strategy; The step of acquiring the target wiring strategy includes: When the load scenario category is a first density scenario category, determining the target wiring strategy to be adding a circuit; When the load scenario category is the second density scenario category, the target wiring strategy is determined to be loop merging.

2. The method according to claim 1, characterized in that The load scenario classification is performed according to the load data to obtain the load scenario category corresponding to each node, including: For each node, obtain the load information of the line connected to the current node; Calculating the load density of the current node according to the load information and the load data of the current node; The load scenario is classified according to the load density to obtain the load scenario category corresponding to the current node.

3. A distribution network adaptive wiring device, characterized in that: The device comprises: An acquisition module, used to acquire load data of each of a plurality of nodes in the distribution network between a preset time and a current time; the preset time is a historical time with a preset time distance from the current time; A classification module, used to classify load scenarios according to the load data to obtain load scenario categories corresponding to each node; A determination module, configured to determine a wiring adjustment strategy according to the load scenario category; A wiring module, used to generate a wiring adjustment instruction according to the wiring adjustment strategy, and send the wiring adjustment instruction to the wiring device to instruct the wiring device to connect each node according to the wiring adjustment instruction; The classification module is also used for sliding selection of the load data of the current node using a preset sliding window for each node, and calculating the average load value of the corresponding sliding window according to the load data in each sliding window; the preset sliding window refers to a window with a preset size; based on the change trend of the average load value of each sliding window, the load scenario category corresponding to the current node is determined; the change trend of the average load value of each sliding window includes continuous increase, continuous decrease, and change within a preset load range; the load scenario category is used to characterize the load change of the corresponding node; The classification module is further configured to determine that the load scenario category corresponding to the current node is a first density scenario category when the change trend of the average load value of each sliding window is continuously increasing; and to determine that the load scenario category corresponding to the current node is a second density scenario category when the change trend of the average load value of each sliding window is continuously decreasing or changing within a preset load range; The determination module is further used to obtain the current network structure of the distribution network and the target wiring strategy corresponding to the load scenario category; adjust the wiring strategy of the current network structure based on the target wiring strategy to obtain a wiring adjustment strategy; The determination module is further configured to determine, when the load scenario category is a first density scenario category, that the target wiring strategy is to add a circuit; and, when the load scenario category is a second density scenario category, determine that the target wiring strategy is to merge circuits.

4. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 2 are implemented.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 2 are implemented.

6. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 2 are implemented.

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