A method for identifying the operating state of a distribution network gateway based on cloud-edge coordination technology

Abstract

The present invention discloses a method for identifying the operating state of a distribution gateway based on cloud-edge coordination technology, which relates to the field of distribution technology. The method includes: the cloud server obtains the distribution data uploaded by each edge distribution gateway to obtain the overall distribution data, conducts comprehensive analysis, screens out several potential influencing factors that have an impact on the distribution data, analyzes the overall influencing factor indicators of the distribution gateway nodes, obtains the historical distribution data of each distribution gateway node, analyzes the distribution characteristics of each distribution gateway node, predicts the distribution change trend of each distribution gateway node based on the distribution characteristics of each distribution gateway node and the overall influencing factor indicators of the distribution gateway node, and adjusts the distribution overall planning scheme of each distribution gateway node based on the distribution change trend of each distribution gateway node. The advantages of the present invention are: improving the pertinence and effect of the strategy, optimizing resource allocation, and the operating efficiency of the distribution gateway.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution, and in particular to a method for identifying the operating state of a distribution network gateway based on cloud-edge coordination technology. Background Art

[0002] The distribution network refers to the power network that receives electric energy from the transmission network or regional power plants and distributes it locally through distribution facilities or step by step according to voltage levels to various users. It is composed of overhead lines, cables, poles, distribution transformers, disconnect switches, reactive power compensators, and some auxiliary facilities, etc., and plays an important role in distributing electric energy in the power network. With the development of digital technology, the construction of the power grid is gradually moving towards digitalization. The construction process of the digital power grid is the process of digitalization, intelligentization, and Internetization of the traditional power grid. For the digital transformation of the traditional power grid, a corresponding digital twin power grid is constructed by following network security standards and a unified power grid data model. With an advanced digital technology platform, a powerful "computing power" is formed with "computing power + data + model + algorithm". Relying on the Internet of Things and the Internet, the perception, analysis, decision-making, business, and other links of all parties related to the power grid are connected, enabling the power grid company to have super perception ability, wise decision-making ability, and fast execution ability, expanding the boundary of the digital power grid from the traditional power grid to all aspects of society, transforming the management, operation, and service modes of the traditional power grid, and driving the extensive allocation of energy flow, capital flow, logistics, business flow, and talent flow in related industries.

[0003] The existing distribution network gateway monitoring technology for digital power grids lacks the coordination and overall planning technology between the edge distribution network gateway and the cloud server. For the identification of the operating state of the distribution network gateway, it is only limited to the state of a single distribution network gateway itself, without considering the overall coordinated planning between distribution network gateways, resulting in the lack of systematic overall management in the operation of the distribution system. Summary of the Invention

[0004] To solve the above technical problems, a method for identifying the operating state of a distribution network gateway based on cloud-edge coordination technology is provided. This technical solution solves the problems of the existing distribution network gateway monitoring technology for digital power grids, which lacks the coordination and overall planning technology between the edge distribution network gateway and the cloud server. For the identification of the operating state of the distribution network gateway, it is only limited to the state of a single distribution network gateway itself, without considering the overall coordinated planning between distribution network gateways, resulting in the lack of systematic overall management in the operation of the distribution system.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for identifying the operating state of a distribution network gateway based on cloud-edge coordination technology, including:

[0007] The cloud server obtains the distribution data uploaded by each edge distribution network gateway to obtain the overall distribution data;

[0008] Based on the overall power distribution data, comprehensive analysis is carried out to screen out several relevant factors that affect the power distribution data;

[0009] Determine that each power distribution network joint point meets several relevant factors that affect the power distribution data, and standardize the data of several relevant factors to obtain the influence characteristics of relevant factors;

[0010] According to the power distribution data of each power distribution network joint point and the influence characteristics of relevant factors of each power distribution network joint point, combine them into the influence relevant factor matrix B of each power distribution network joint point; , where is the k-th relevant factor data that affects the power distribution data of the -th power distribution network joint point is the total number of power distribution data of each power distribution network joint point, and m is the total number of influence characteristics of relevant factors that affect the power distribution data of each power distribution network joint point;

[0011] Based on each influence characteristic of relevant factors in the influence relevant factor matrix of each power distribution network joint point, determine the influence proportion of the relevant factors of each power distribution network joint point on the power distribution data of the power distribution network joint point;

[0012] According to the influence proportion of the relevant factors of each power distribution network joint point on the power distribution data of the power distribution network joint point, calculate the entropy value of the influence characteristics of relevant factors;

[0013] Based on the entropy value of the influence characteristics of relevant factors, use the entropy weight method to calculate the influence weight of the relevant factors of each power distribution network joint point on the power distribution data of the power distribution network joint point;

[0014] Based on the influence weight of the relevant factors of each power distribution network joint point on the power distribution data of the power distribution network joint point and the power distribution data of the power distribution network joint point, calculate the overall influence factor index of the power distribution network joint point;

[0015] Based on the power distribution of different power distribution network joint points, obtain the historical power distribution data of each power distribution network joint point;

[0016] According to the historical power distribution data of each different power distribution network joint point, analyze the power distribution characteristics of each power distribution network joint point;

[0017] Based on the power distribution characteristics of each power distribution network joint point and the overall influence factor index of the power distribution network joint point, predict the power distribution change trend of each power distribution network joint point;

[0018] Based on the power distribution change trend of each power distribution network joint point, adjust the power distribution overall plan of each power distribution network joint point.

[0019] Preferably, the entropy weight method is specifically as follows:

[0020]

[0021] In the formula, is the influence proportion of the k-th relevant factor on the distribution data affecting the j-th joint node of the distribution network, is the entropy value of the influence characteristics of the k-th relevant factor, is the influence weight of the relevant factor on the distribution data of the joint node of the distribution network, is the logarithmic function, and m is the total number of influence characteristic of relevant factors affecting the distribution data of each joint node of the distribution network;

[0022] Among them, the specific calculation of the overall influence factor index of the joint node of the distribution network is:

[0023]

[0024] In the formula, is the overall influence factor index of the joint node of the distribution network, is the distribution data of the j-th joint node of the distribution network.

[0025] Preferably, based on the overall distribution data, comprehensive analysis is carried out, and several relevant factors affecting the distribution data are screened out, specifically including:

[0026] Based on the overall distribution data, an associated binary scatter plot between the distribution data and the overall distribution data is established;

[0027] Based on the distribution data in the associated binary scatter plot and each factor data in the overall distribution data, using the linear equation formula, the linear regression coefficient between the distribution data and each factor data is calculated;

[0028] According to the linear regression coefficients between the distribution data and each factor data, several relevant factor data positively correlated with the distribution data are screened out to determine the relevant factors affecting the distribution data.

[0029] Preferably, the linear equation formula is:

[0030]

[0031] In the formula, is the linear regression coefficient between the distribution data and each factor data, is the distribution data corresponding to the -th scatter point in the associated scatter plot, is the relevant factor data corresponding to the -th scatter point in the associated scatter plot, is the total number of scatter points in the associated scatter plot.

[0032] Preferably, according to the historical distribution data of each different joint node of the distribution network, the analysis of the distribution characteristics of each joint node of the distribution network specifically includes:

[0033] Determine the overall volume of historical power distribution data of each distribution network joint point and the power distribution policy characteristics of each distribution network joint point;

[0034] Based on the historical power distribution data of each distribution network joint point, divide the power distribution data of each distribution network joint point according to the monthly unit time to obtain the quarterly power distribution data array;

[0035] Based on the quarterly power distribution data array and the overall volume of historical power distribution data of each distribution network joint point, screen out the quarters greater than the overall volume and determine the characteristics of each distribution network joint point;

[0036] According to the power distribution type characteristics and corresponding power distribution attribute characteristics in the quarterly power distribution data array, determine several preference type characteristics;

[0037] Based on the characteristics, preference type characteristics and power distribution policy characteristics of the distribution network joint point, combine them into the power distribution characteristic array of each distribution network joint point.

[0038] Preferably, based on the power distribution characteristics of each distribution network joint point and the overall influencing factor index of the distribution network joint point, predict the power distribution change trend of each distribution network joint point, specifically including:

[0039] Based on the power distribution characteristic array of each distribution network joint point and the overall influencing factor index of the distribution network joint point, establish the power distribution influencing factor array;

[0040] Establish a power distribution prediction and analysis model;

[0041] Based on each power distribution network joint point overall influencing factor index in the power distribution influencing factor array as the power distribution network joint point constraint condition, analyze and predict the power distribution change trend of each power distribution network joint point under the constraint condition.

[0042] Preferably, the expression of the power distribution prediction and analysis model is:

[0043]

[0044] In the formula, F is the power distribution change trend predicted for each distribution network joint point, is the power distribution characteristics of v distribution network joint points, is the total number of distribution network joint points, 、 、 、 are all coefficients of the model.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] The present invention proposes a recognition scheme for the operation status of a distribution gateway based on cloud-edge coordination technology. The cloud server collects the operation status data of each edge distribution gateway as a whole, aggregates it into overall distribution data, identifies relevant factors that have a significant impact on power distribution through comprehensive analysis of the overall distribution data, and combines the historical data of each distribution gateway node to deeply analyze the characteristics and potential influencing factors of the distribution gateway node, establish a power distribution prediction and analysis model, accurately predict the power distribution change trend of each distribution gateway node, and adjust the overall power distribution plan of the distribution gateway node accordingly, so as to improve the pertinence and effectiveness of the power distribution strategy, optimize resource allocation, and improve the operation efficiency of the distribution gateway. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flowchart of a method for identifying the operation status of a distribution gateway based on cloud-edge coordination technology proposed by the present invention;

[0048] Figure 2 It is a flowchart of a method for screening several relevant factors that affect power distribution data in the present invention;

[0049] Figure 3 It is a flowchart of a method for analyzing the power distribution characteristics of each distribution gateway node in the present invention;

[0050] Figure 4 It is a flowchart of a method for predicting the power distribution change trend of each distribution gateway node in the present invention;

[0051] Figure 5 It is a schematic diagram of the architecture of an electronic device in this solution;

[0052] Figure 6 It is a schematic diagram of the structure of a computer-readable storage medium in this solution. DETAILED DESCRIPTION OF THE INVENTION

[0053] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0054] Referring to Figure 1 As shown, a method for identifying the operation status of a distribution gateway based on cloud-edge coordination technology includes:

[0055] The cloud server obtains the power distribution data uploaded by each edge distribution gateway to obtain overall power distribution data;

[0056] Based on the overall power distribution data, comprehensive analysis is performed to screen out several relevant factors that affect the power distribution data;

[0057] Determine several relevant factors that the joints of each distribution network conform to affecting the distribution data, and standardize the data of several relevant factors to obtain the influencing characteristics of relevant factors;

[0058] According to the distribution data of each joint of the distribution network and the influencing characteristics of relevant factors of each joint of the distribution network, combine them into the relevant factor influence matrix B of each joint of the distribution network; , where is the kth relevant factor data affecting the distribution data of the th joint of the distribution network is the total number of distribution data of each joint of the distribution network, and m is the total number of influencing characteristics of relevant factors affecting the distribution data of each joint of the distribution network;

[0059] Based on each influencing characteristic of relevant factors in the relevant factor influence matrix of each joint of the distribution network, determine the influence proportion of the relevant factors of each joint of the distribution network on the distribution data of the joint of the distribution network;

[0060] According to the influence proportion of the relevant factors of each joint of the distribution network on the distribution data of the joint of the distribution network, calculate the entropy value of the influencing characteristics of relevant factors;

[0061] Based on the entropy value of the influencing characteristics of relevant factors, use the entropy weight method to calculate the influence weight of the relevant factors of each joint of the distribution network on the distribution data of the joint of the distribution network;

[0062] Based on the influence weight of the relevant factors of each joint of the distribution network on the distribution data of the joint of the distribution network and the distribution data of the joint of the distribution network, calculate the overall influencing factor index of the joint of the distribution network;

[0063] Based on the distribution of different joints of the distribution network, obtain the historical distribution data of each joint of the distribution network;

[0064] According to the historical distribution data of each different joint of the distribution network, analyze the distribution characteristics of each joint of the distribution network;

[0065] Based on the distribution characteristics of each joint of the distribution network and the overall influencing factor index of the joint of the distribution network, predict the distribution change trend of each joint of the distribution network;

[0066] Based on the distribution change trend of each joint of the distribution network, adjust the distribution overall planning scheme of each joint of the distribution network.

[0067] The entropy weight method is specifically as follows:

[0068]

[0069] In the formula, is the influence proportion of the kth relevant factor affecting the distribution data of the jth joint of the distribution network, The entropy value of the k-th relevant factor influencing the feature, is the influence weight of the relevant factor on the distribution power data of the distribution network joint node, is the logarithmic function, and m is the total number of relevant factor influencing features that affect the distribution power data of each distribution network joint node;

[0070] Among them, the specific calculation of the overall influencing factor index of the distribution network joint node is as follows:

[0071]

[0072] In the formula, is the overall influencing factor index of the distribution network joint node, is the distribution power data of the j-th distribution network joint node.

[0073] This solution analyzes the overall influencing factor index of each distribution network joint node on power distribution by identifying relevant factors that affect power distribution, evaluates the power distribution change trend under the overall influencing factor index of the distribution network joint node, and determines the final overall power distribution plan to improve the response ability and marketing effect.

[0074] It can be understood that the overall influencing factor index refers to the factors that have an associated impact on the power distribution factors within the power distribution area corresponding to a distribution network joint node. For example, if the supply price per unit in the power distribution area corresponding to a distribution network joint node increases, it will affect the power distribution volume of this distribution network joint node. Secondly, if the entertainment factor is strong in the power distribution area corresponding to a distribution network joint node, it means that the night power distribution demand of this distribution network joint node increases. By analyzing the overall influencing factor index of the distribution network joint node, the status of each distribution network joint node can be accurately determined, and the overall power distribution plan can be planned specifically, which can improve the pertinence and effect of the power distribution strategy, optimize the resource allocation, and improve the operation efficiency of the distribution network.

[0075] Refer to Figure 2 As shown, based on the overall power distribution data, through comprehensive analysis, several relevant factors that affect the power distribution data are screened out, specifically including:

[0076] Based on the overall power distribution data, establish a correlation binary scatter plot between the power distribution data and the overall power distribution data;

[0077] Based on the power distribution data in the correlation binary scatter plot and each factor data in the overall power distribution data, use the linear equation formula to calculate the linear regression coefficient between the power distribution data and each factor data;

[0078] According to the linear regression coefficient between the power distribution data and each factor data, screen out several relevant factor data that are positively correlated with the power distribution data to determine the relevant factors that affect the power distribution data.

[0079] The linear equation formula is:

[0080]

[0081] Wherein, is the linear regression coefficient between the power distribution data and each factor data, is the power distribution data corresponding to the th scatter point in the correlation scatter plot, is the relevant factor data corresponding to the th scatter point in the correlation scatter plot, is the total number of scatter points in the correlation scatter plot.

[0082] Referring to Figure 3 as shown, according to the historical power distribution data of each different power distribution network node, analyzing the power distribution characteristics of each power distribution network node specifically includes:

[0083] Determine the overall volume of the historical power distribution data of each power distribution network node and the power distribution policy characteristics of each power distribution network node;

[0084] Based on the historical power distribution data of each power distribution network node, divide the power distribution data of each power distribution network node by month as the unit time to obtain the quarterly power distribution data array;

[0085] Based on the quarterly power distribution data array and the overall volume of the historical power distribution data of each power distribution network node, screen out the quarters greater than the overall volume to determine the characteristics of each power distribution network node;

[0086] According to the power distribution type characteristics and the corresponding power distribution attribute characteristics in the quarterly power distribution data array, determine several preference type characteristics;

[0087] Based on the characteristics, preference type characteristics and power distribution policy characteristics of the power distribution network node, combine them into the power distribution characteristic array of each power distribution network node.

[0088] It should be noted that the power distribution change trend refers to the predictive analysis of multiple aspects such as future power distribution volume, electricity price fluctuation, policy impact, economic factors, and social and environmental factors. These trends reflect the possible directions of power distribution at different power distribution network nodes under specific conditions, providing insights and a basis for strategic planning for formulating power distribution strategies.

[0089] Referring to Figure 4 as shown, based on the power distribution characteristics of each power distribution network node and the overall influencing factor index of the power distribution network node, predicting the power distribution change trend of each power distribution network node specifically includes:

[0090] Based on the power distribution characteristic array of each power distribution network node and the overall influencing factor index of the power distribution network node, establish a power distribution influencing factor array;

[0091] Establish a power distribution prediction analysis model;

[0092] Based on each overall influencing factor index of the distribution network joint point in the distribution influencing factor array as the distribution network joint point constraint condition, analyze and predict the distribution change trend of each distribution network joint point under the constraint condition.

[0093] The expression of the distribution prediction analysis model is:

[0094]

[0095] In the formula, F is the distribution change trend of each predicted distribution network joint point, is the distribution characteristics of v distribution network joint points, is the total number of distribution network joint points, , , , are all coefficients of the model.

[0096] Furthermore, the method according to the embodiment of the present application can also be implemented by means of Figure 5 the architecture of the electronic device shown. As Figure 5 shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to the network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, can store a method for identifying the operating state of a distribution network gateway based on cloud-edge coordination technology provided by the present application. The electronic device 500 may further include a user interface 508. Of course, Figure 5 the architecture shown is only exemplary. When implementing different devices, one or more components shown in the Figure 5 electronic device may be omitted according to actual needs.

[0097] Figure 6 is a schematic diagram of the structure of a computer-readable storage medium provided by an embodiment of the present application. As Figure 6 shown, it is a computer-readable storage medium 600 according to an embodiment of the present application. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are run by a processor, a method for identifying the operating state of a distribution network gateway based on cloud-edge coordination technology according to the embodiment of the present application described with reference to the above drawings can be executed. The storage medium 600 includes but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory, etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0098] In summary, the advantages of the present invention are as follows: By collecting the operation data of each distribution network joint point through the cloud server, comprehensively analyzing the operation status of each distribution network joint point based on cloud-edge coordination, and then carrying out the overall planning of the distribution network joint point, the resource allocation can be effectively optimized and the operation efficiency of the distribution network joint can be improved.

[0099] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and all these changes and improvements fall within the scope of the present invention claimed. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for identifying the operating state of a distribution network gateway based on cloud-edge coordination technology, characterized in that, Including: The cloud server obtains the power distribution data uploaded by each edge power distribution gateway to obtain the overall power distribution data; Based on the overall power distribution data, perform comprehensive analysis to screen out several relevant factors that affect the power distribution data; Determine that each power distribution network node meets several relevant factors that affect the power distribution data, and standardize the data of several relevant factors to obtain the influence characteristics of relevant factors; According to the power distribution data of each distribution network joint point and the influence characteristic of relevant factors of each distribution network joint point, they are combined into the relevant factor matrix B of each distribution network joint point; , where is the kth relevant factor data affecting the power distribution data of the th distribution network joint point is the total number of power distribution data of each distribution network joint point, and m is the total number of influence characteristics of relevant factors affecting the power distribution data of each distribution network joint point; Based on each influence characteristic of relevant factors in the relevant factor influence matrix of each power distribution network node, determine the influence proportion of the relevant factors of each power distribution network node on the power distribution data of the power distribution network node; According to the influence proportion of the relevant factors of each power distribution network node on the power distribution data of the power distribution network node, calculate the entropy value of the influence characteristics of relevant factors; Based on the entropy value of the influence characteristics of relevant factors, use the entropy weight method to calculate the influence weight of the relevant factors of each power distribution network node on the power distribution data of the power distribution network node; Based on the influence weight of the relevant factors of each power distribution network node on the power distribution data of the power distribution network node and the power distribution data of the power distribution network node, calculate the overall influence factor index of the power distribution network node; Based on the power distribution of different power distribution network nodes, obtain the historical power distribution data of each power distribution network node; According to the historical power distribution data of each different power distribution network node, analyze the power distribution characteristics of each power distribution network node; Based on the power distribution characteristics of each power distribution network node and the overall influence factor index of the power distribution network node, predict the power distribution change trend of each power distribution network node; Based on the power distribution change trend of each power distribution network node, adjust the power distribution overall plan of each power distribution network node.

2. The method for identifying the operating state of a distribution network gateway based on cloud-edge coordination technology according to claim 1, wherein The entropy weight method is specifically as follows: ； In the formula, is the influence proportion of the k-th relevant factor on the distribution data affecting the j-th joint node of the distribution network, is the entropy value of the influence characteristics of the k-th relevant factor, is the influence weight of the relevant factor on the distribution data of the joint node of the distribution network, is the logarithmic function, and m is the total number of influence characteristics of the relevant factors affecting the distribution data of each joint node of the distribution network; Among them, calculating the overall influence factor index of the power distribution network node is specifically as follows: ； Wherein, is the overall influencing factor index of the distribution network joint point, is the power distribution data of the j-th distribution network joint point.

3. The method for identifying the operating state of a distribution network gateway based on cloud-edge coordination technology according to claim 2, wherein Based on the overall power distribution data, performing comprehensive analysis to screen out several relevant factors that affect the power distribution data specifically includes: Based on the overall power distribution data, establish an associated binary scatter plot between the power distribution data and the overall power distribution data; Based on the power distribution data in the associated binary scatter plot and each factor data in the overall power distribution data, use the linear equation formula to calculate the linear regression coefficient between the power distribution data and each factor data; According to the linear regression coefficients between the power distribution data and each factor data, screen out several relevant factor data that are positively correlated with the power distribution data, and determine the relevant factors that affect the power distribution data.

4. A method for identifying the operating state of a distribution network gateway based on cloud-edge coordination technology according to claim 3, characterized in that The linear equation formula is: ； Wherein, is the linear regression coefficient between the power distribution data and each factor data, is the power distribution data corresponding to the th scatter point in the correlation scatter plot, is the relevant factor data corresponding to the th scatter point in the correlation scatter plot, is the total number of scatter points in the correlation scatter plot.

5. A method for identifying the operating state of a distribution network gateway based on cloud-edge coordination technology according to claim 4, characterized in that, According to the historical power distribution data of each different power distribution network node, analyzing the power distribution characteristics of each power distribution network node specifically includes: Determine the overall volume of the historical power distribution data of each power distribution network node and the power distribution policy characteristics of each power distribution network node; Based on the historical power distribution data of each power distribution network node, divide the power distribution data of each power distribution network node by month unit time to obtain an array of quarterly power distribution data; Based on the array of quarterly power distribution data and the overall volume of the historical power distribution data of each power distribution network node, screen out the quarters greater than the overall volume to determine the characteristics of each power distribution network node; According to the power distribution type characteristics and corresponding power distribution attribute characteristics in the array of quarterly power distribution data, determine several preference type characteristics; Based on the feature, preference type feature, and power distribution policy feature of the distribution network joint point, they are combined into a power distribution feature array for each distribution network joint point.

6. The method for identifying the operating state of a distribution network gateway based on cloud-edge coordination technology according to claim 5, wherein Based on the power distribution features of each distribution network joint point and the overall influencing factor indicators of the distribution network joint point, predicting the power distribution change trend of each distribution network joint point specifically includes: Based on the power distribution feature array of each distribution network joint point and the overall influencing factor indicators of the distribution network joint point, establish a power distribution influencing factor array; Establish a power distribution prediction analysis model; Based on each overall influencing factor indicator of the distribution network joint point in the power distribution influencing factor array as the distribution network joint point constraint condition, analyze and predict the power distribution change trend of each distribution network joint point under the constraint condition.

7. An electronic device, characterized in that, Including: At least one processor; And a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for identifying the operating state of a distribution network gateway based on cloud-edge coordination technology as described in any one of claims 1-6.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements a method for identifying the operating state of a distribution network gateway based on cloud-edge coordination technology as described in any one of claims 1-6.

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