Distributed power failure rapid diagnosis system for smart power grid

Abstract

The invention provides a distributed power fault rapid diagnosis system for an intelligent power grid. The system comprises n power monitoring nodes, a diagnosis node distribution module and m diagnosis nodes, the n power monitoring nodes are all arranged at specified positions of the smart power grid; the first electric power monitoring node obtains first identification information of the first electric power monitoring node, obtains electric power operation data in a preset time period, and sends the first identification information and the electric power operation data to the diagnosis node distribution module; the first power monitoring node is any one of the n power monitoring nodes; the diagnosis node distribution module determines a first diagnosis node according to the first identification information and the power operation data, and sends the power operation data to the first diagnosis node; and the first diagnosis node performs power fault diagnosis according to the power operation data to obtain a target power fault diagnosis result. According to the embodiment of the invention, the power fault diagnosis efficiency of the smart grid can be improved.

Description

Technical Field

[0001] This application relates to the technical field of smart grid or artificial intelligence, and particularly relates to a distributed power fault rapid diagnosis system for a smart grid. Background Art

[0002] With the rapid development of power grid technology, smart grids have also entered people's lives. A smart grid can be simply understood as the intellectualization of the power grid. Specifically, a smart grid can be based on an integrated, high-speed two-way communication network, and it is a technical application in the power grid through advanced equipment technology, advanced sensing and measurement technology, advanced control methods, and advanced decision support system technology.

[0003] Currently, the power fault diagnosis efficiency of smart grids is relatively low. Therefore, the problem of how to improve the power fault diagnosis efficiency of smart grids needs to be solved urgently. Summary of the Invention

[0004] An embodiment of this application provides a distributed power fault rapid diagnosis system for a smart grid, which can improve the power fault diagnosis efficiency of the smart grid.

[0005] In a first aspect, an embodiment of this application provides a distributed power fault rapid diagnosis system for a smart grid. The system includes: n power monitoring nodes, a diagnosis node allocation module, and m diagnosis nodes; the n power monitoring nodes are all set at designated positions of the smart grid; both n and m are integers greater than 1. Among them,

[0006] A first power monitoring node is configured to obtain first identification information of the first power monitoring node, obtain power operation data for a preset time period, and send the first identification information and the power operation data to the diagnosis node allocation module; the first power monitoring node is any one of the n power monitoring nodes;

[0007] The diagnosis node allocation module is configured to determine a first diagnosis node according to the first identification information and the power operation data, and send the power operation data to the first diagnosis node; the first diagnosis node is at least one of the m diagnosis nodes;

[0008] The first diagnosis node is configured to perform power fault diagnosis according to the power operation data to obtain a target power fault diagnosis result.

[0009] Implementing the embodiments of this application has the following beneficial effects:

[0010] It can be seen that the distributed power fault rapid diagnosis system for smart grid described in the embodiments of the present application includes: n power monitoring nodes, a diagnostic node allocation module, and m diagnostic nodes; the n power monitoring nodes are all set at designated positions of the smart grid; both n and m are integers greater than 1; the first power monitoring node obtains the first identification information of the first power monitoring node, obtains the power operation data in a preset time period, and sends the first identification information and the power operation data to the diagnostic node allocation module; the first power monitoring node is any one of the n power monitoring nodes; the diagnostic node allocation module determines the first diagnostic node according to the first identification information and the power operation data, and sends the power operation data to the first diagnostic node; the first diagnostic node is at least one of the m diagnostic nodes; the first diagnostic node performs power fault diagnosis according to the power operation data to obtain the target power fault diagnosis result. On the one hand, as an aggregation node, the diagnostic node allocation module can receive the power operation data of one or more power monitoring nodes and perform intelligent allocation according to the situation of these power operation data. On the other hand, by allocating corresponding diagnostic nodes based on the source situation of the power operation data and the stability of the power operation, it can further ensure the accuracy and efficiency of subsequent power fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0012] Figure 1 is a schematic structural diagram of a distributed power fault rapid diagnosis system for smart grid provided by an embodiment of the present application;

[0013] Figure 2 is a schematic structural diagram of a smart grid provided by an embodiment of the present application;

[0014] Figure 3 is a schematic flowchart of a distributed power fault rapid diagnosis method for smart grid provided by an embodiment of the present application;

[0015] Figure 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present application;

[0016] Figure 5 is a block diagram of the functional units of a distributed power fault rapid diagnosis device for smart grid provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The terms "first", "second", etc. in the description, claims and the above-mentioned drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may also include steps or units not listed in a possible example, or may also include other steps or units inherent to these processes, methods, products or devices in a possible example.

[0018] Referring to "embodiment" herein means that the specific features, structures or characteristics described in connection with the embodiment may be included in at least one embodiment of this application. The phrase appearing at various positions in the description does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0019] In order to enable those skilled in the art of this technology to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts belong to the scope of protection of this application.

[0020] The power monitoring nodes, diagnostic node allocation modules, diagnostic devices, and diagnostic nodes involved in the embodiments of this application may include any electronic device with communication functions. The electronic device may include but is not limited to: smart phones, tablet computers, intelligent robots, vehicle-mounted devices, intelligent transformers, intelligent gateways, intelligent switches, intelligent power devices, intelligent distribution boxes, intelligent power generation devices, intelligent charging piles, wearable devices, computing devices or other processing devices connected to a wireless modem, as well as various forms of user equipment (UE), mobile stations (MS), terminal devices, etc., which are not limited herein. The electronic device may also be a server (such as an edge server or a cloud server) or a computer cluster, for example, a distributed computer cluster.

[0021] Please refer to Figure 1 , Figure 1It is a schematic architecture diagram of a distributed power fault rapid diagnosis system for a smart grid provided by an embodiment of the present application. The distributed power fault rapid diagnosis system for a smart grid includes: n power monitoring nodes, a diagnosis node allocation module, and m diagnosis nodes; the n power monitoring nodes are all set at designated positions of the smart grid; both n and m are integers greater than 1; wherein,

[0022] The first power monitoring node is configured to obtain first identification information of the first power monitoring node, obtain power operation data for a preset time period, and send the first identification information and the power operation data to the diagnosis node allocation module; the first power monitoring node is any one of the n power monitoring nodes;

[0023] The diagnosis node allocation module is configured to determine a first diagnosis node according to the first identification information and the power operation data, and send the power operation data to the first diagnosis node; the first diagnosis node is at least one of the m diagnosis nodes;

[0024] The first diagnosis node is configured to perform power fault diagnosis according to the power operation data to obtain a target power fault diagnosis result.

[0025] In an embodiment of the present application, each of the n power monitoring nodes is responsible for monitoring the power operation conditions of a region. The diagnosis node allocation module is configured to allocate diagnosis nodes according to the conditions of the power operation data and its corresponding power monitoring node. The diagnosis node performs power fault diagnosis on the power operation data. Specifically, it can diagnose whether there is a power fault, the power fault level, and possible power fault causes or power fault locations, etc., which are not limited herein.

[0026] In a specific implementation, as Figure 1 shown, the distributed power fault rapid diagnosis system may include: n power monitoring nodes, a diagnosis node allocation module, and m diagnosis nodes. Among them, the n power monitoring nodes are all set at designated positions of the smart grid. Both n and m are integers greater than 1. The n power monitoring nodes, the diagnosis node allocation module, and the m diagnosis nodes are communicatively connected. Taking the first power monitoring node as an example, the first power monitoring node is any one of the n power monitoring nodes. The first identification information of the first power monitoring node may include at least one of the following: device number, physical address, IP address, device location, etc., which are not limited herein. The first identification information of the first power monitoring node is used to uniquely identify the first power monitoring node.

[0027] Among them, the power operation data is used to characterize the power operation condition at a specified location in the smart grid, and the specified location can be set in advance or be the system default. In the embodiments of the present application, the power operation data may include at least one of the following: voltage, current, power, temperature, frequency, electric energy, etc., which are not limited herein. The power operation data may include one or more types of power operation data. When there are multiple types of power operation data, these power operation data can be integrated into one type of power operation data. For example, the weight value of each type of power operation data can be determined, and these power operation data are weighted to obtain the final one type of power operation data.

[0028] Among them, the preset time period can be set in advance or be the system default. The time length of the preset time period is deeply related to the actual power consumption situation. If the power consumption is large, the time length is short; if the power consumption is small, the time length is long.

[0029] In a specific implementation, the first power monitoring node obtains the first identification information of the first power monitoring node, obtains the power operation data for the preset time period, and sends the first identification information and the power operation data to the diagnosis node allocation module. The diagnosis node allocation module, as an aggregation node, can receive the power operation data of one or more power monitoring nodes and perform intelligent allocation according to the situation of these power operation data to ensure the accuracy and efficiency of subsequent power fault diagnosis.

[0030] Next, the diagnosis node allocation module can determine the first diagnosis node according to the first identification information and the power operation data, and send the power operation data to the first diagnosis node; the first diagnosis node is at least one of the m diagnosis nodes. Since the first identification information reflects the source situation of the power operation data, and the power operation data reflects the power operation stability to a certain extent. Furthermore, the corresponding diagnosis node can be allocated based on the source situation of the power operation data and the power operation stability, which can further ensure the accuracy and efficiency of subsequent power fault diagnosis.

[0031] Finally, the first diagnosis node can perform power fault diagnosis according to the power operation data to obtain the target power fault diagnosis result, thus ensuring the accuracy and rapid fault diagnosis of subsequent power fault diagnosis.

[0032] Among them, the number of diagnosis node allocation modules can be one or more. The number of diagnosis nodes included in the first diagnosis node can be one or more.

[0033] For example, Figure 2As shown in the figure, the smart grid may include multiple distributed power fault rapid diagnosis systems. Specifically, the number of distributed power fault rapid diagnosis systems is based on the scale of the smart grid. Each distributed power fault rapid diagnosis system is responsible for diagnosing power faults in at least one area of the smart grid.

[0034] Optionally, in terms of determining the first diagnostic node according to the first identification information and the power operation data, the diagnostic node allocation module is specifically configured to:

[0035] Determine k diagnostic node identifiers corresponding to the first identification information according to the mapping relationship between the preset identification information and the diagnostic node identifiers, where k is a positive integer;

[0036] Determine the k diagnostic nodes corresponding to the k diagnostic node identifiers;

[0037] Determine the first data volume of the power operation data;

[0038] Determine the first stability evaluation value of the power operation data;

[0039] Screen the k diagnostic nodes according to the first data volume and the first stability evaluation value to obtain the first diagnostic node.

[0040] In the embodiments of the present application, since the diagnostic capabilities and diagnostic advantages of different diagnostic nodes are different, corresponding diagnostic nodes can be adapted to different power monitoring nodes based on experience. That is, the mapping relationship between the preset identification information and the diagnostic node identifiers can be stored in advance, and then according to the mapping relationship between the preset identification information and the diagnostic node identifiers, k diagnostic node identifiers corresponding to the first identification information are determined, where k is a positive integer less than m. The k diagnostic nodes corresponding to the k diagnostic node identifiers are determined. Furthermore, a tacit relationship between the diagnostic nodes and the power monitoring nodes can be established to ensure the accuracy and speed of subsequent power fault diagnosis.

[0041] Then, the first data volume of the power operation data can also be determined. The first data volume can characterize the data complexity or the size of the data volume. Of course, to a certain extent, the first data volume also reflects the demand for computing power resources. The first stability evaluation value of the power operation data can also be determined. The first stability evaluation value characterizes the power operation stability of the area responsible for the first power monitoring node, that is, it characterizes the area characteristics of the area responsible for the first power monitoring node.

[0042] Finally, the k diagnostic nodes can be screened according to the first data volume and the first stability evaluation value to obtain the first diagnostic node, which can further screen out the diagnostic nodes that deeply meet the demand for computing power resources and the area characteristics of the area responsible for the first power monitoring node.

[0043] Optionally, in the aspect of performing power fault diagnosis based on the power operation data to obtain a target power fault diagnosis result, the first diagnosis node is used for:

[0044] Obtain the first attribute information of the power operation data;

[0045] Determine a first power fault diagnosis model corresponding to the first attribute information and a first model parameter corresponding to the first attribute information;

[0046] Determine a first adjustment parameter corresponding to the first stability evaluation value;

[0047] Adjust the first model parameter according to the first adjustment parameter to obtain a second model parameter;

[0048] Perform power fault diagnosis on the power operation data according to the first power fault diagnosis model and the second model parameter to obtain the target power fault diagnosis result.

[0049] Among them, the first attribute information of the power operation data may include at least one of the following: the source of the power operation data, the type of power data, etc., which is not limited here. The first attribute information of the power operation data characterizes the data characteristics of the power operation data itself.

[0050] In specific implementation, a mapping relationship between the preset attribute information of the power operation data and the power fault diagnosis model can be stored in advance. Furthermore, based on this mapping relationship, the first power fault diagnosis model corresponding to the first attribute information can be determined. In this way, a power fault diagnosis model corresponding to the data characteristics of the power operation data itself can be determined.

[0051] Among them, the power fault diagnosis model can be set in advance or be the system default, which is not limited here. For example, the power fault diagnosis model may include at least one of the following: a neural network model, a deep learning model, a large model (such as ChatGPT), etc., which is not limited here.

[0052] Correspondingly, a mapping relationship between the preset attribute information of the power operation data and the model parameters of the first power fault diagnosis model can also be stored in advance. Furthermore, based on this mapping relationship, the first model parameter corresponding to the first attribute information can be determined. In this way, the first model parameter of the first power fault diagnosis model corresponding to the data characteristics of the power operation data itself can be determined, which helps to ensure the model performance.

[0053] Next, the mapping relationship between the preset stability evaluation value and the adjustment parameter can also be pre-stored. The value range of the adjustment parameter can be preset in advance or be the system default. For example, the value range of the adjustment parameter can be -0.1 to 0.1. Furthermore, based on the first adjustment parameter corresponding to the first stability evaluation value, some or all of the model parameters of the first model parameter can be adjusted according to the first adjustment parameter to obtain the second model parameter. For example, the second model parameter = (1 + the first adjustment parameter) * the first model parameter. Then, based on the first power fault diagnosis model and the second model parameter, power fault diagnosis is performed on the power operation data to obtain the target power fault diagnosis result. In this way, the model parameters can be dynamically adjusted based on the regional characteristics (the power operation stability of the region) of the area responsible for by the first power monitoring node, which helps to reduce the volatility of the fault diagnosis result. Thus, while ensuring the fault diagnosis speed, the accuracy of power fault diagnosis can also be guaranteed.

[0054] Optionally, the power operation data includes p pieces of power operation data, and each piece of power operation data corresponds to a sampling moment. In terms of determining the first stability evaluation value of the power operation data, the diagnostic node allocation module is specifically configured to:

[0055] Perform fitting based on the p pieces of power operation data and the sampling moment corresponding to each piece of power operation data to obtain the first fitting straight line and the first fitting curve segment corresponding to the preset time period;

[0056] Obtain the absolute value of the slope of the first fitting straight line to obtain the first absolute value;

[0057] Determine the maximum value and the minimum value in the first fitting curve segment to obtain a plurality of maximum values and a plurality of minimum values;

[0058] Perform standard deviation calculation based on the plurality of maximum values and the plurality of minimum values to obtain the first standard deviation;

[0059] Determine the time interval between adjacent maximum values and minimum values based on the plurality of maximum values and the plurality of minimum values to obtain a plurality of time intervals;

[0060] Perform standard deviation calculation based on the plurality of time intervals to obtain the second standard deviation;

[0061] Determine the first stability evaluation value based on the first absolute value, the first standard deviation, and the second standard deviation.

[0062] In specific implementation, the power operation data can include p pieces of power operation data, each piece of power operation data corresponds to a sampling moment, and p is an integer greater than 1.

[0063] In the embodiments of the present application, fitting can be performed based on p power operation data and a sampling moment corresponding to each power operation data to obtain a first fitting straight line and a first fitting curve segment corresponding to a preset time period. That is, the p power operation data and each power operation data can be regarded as p coordinate points. Mapping the p coordinate points to a coordinate system, the horizontal axis of the coordinate system is time, and the vertical axis is the power operation data. Based on the p coordinate points, a fitting operation can be performed to obtain a first fitting straight line and a first fitting curve segment corresponding to a preset time period.

[0064] In a specific implementation, the absolute value of the slope of the first fitting straight line can be obtained to get a first absolute value, which to a certain extent reflects the stability of the power operation in the horizontal dimension. Correspondingly, the maximum value and the minimum value in the first fitting curve segment can also be determined to obtain a plurality of maximum values and a plurality of minimum values, and then a standard deviation operation is performed according to the plurality of maximum values and the plurality of minimum values to obtain a first standard deviation, which to a certain extent reflects the stability of the power operation in the vertical dimension.

[0065] Correspondingly, according to the plurality of maximum values and the plurality of minimum values, the time interval between adjacent maximum values and minimum values is determined to obtain a plurality of time intervals. Each time interval can represent the half cycle of the power operation situation. That is, a standard deviation operation can be performed according to the plurality of time intervals to obtain a second standard deviation, which to a certain extent reflects the periodic stability of the power operation situation.

[0066] Finally, a first stability evaluation value can be determined according to the first absolute value, the first standard deviation, and the second standard deviation. That is, based on the stability of the power operation in the horizontal dimension, the stability of the power operation in the vertical dimension, and the periodic stability of the power operation situation, the regional characteristics of the area responsible for the first power monitoring node can be deeply evaluated, that is, the power operation stability situation of the area, which helps to screen out diagnostic nodes that deeply meet the demand situation of computing power resources and the regional characteristics of the area responsible for the first power monitoring node.

[0067] Optionally, in terms of determining the first stability evaluation value according to the first absolute value, the first standard deviation, and the second standard deviation, the diagnostic node allocation module is specifically used for:

[0068] Determine a first reference stability evaluation value corresponding to the first absolute value;

[0069] Determine a second reference stability evaluation value corresponding to the first standard deviation;

[0070] Determine the first weight pair corresponding to the second standard deviation, where the first weight pair includes the first weight corresponding to the first reference stability evaluation value and the second weight corresponding to the second reference stability evaluation value; the sum of the first weight and the second weight is 1;

[0071] Perform a weighted operation based on the first weight pair, the first reference stability evaluation value, and the second reference stability evaluation value to obtain the first stability evaluation value.

[0072] In a specific implementation, a mapping relationship between a preset absolute value and a stability evaluation value can be pre-stored in advance. Based on this mapping relationship, the first reference stability evaluation value corresponding to the first absolute value can be determined. Correspondingly, a mapping relationship between a preset standard deviation and a stability evaluation value can also be pre-stored in advance. Based on this mapping relationship, the second reference stability evaluation value corresponding to the first standard deviation can be determined.

[0073] Furthermore, a mapping relationship between a preset standard deviation (a standard deviation related to periodicity) and a weight pair can be pre-stored in advance. The weight pair can include two weights, and the two weights correspond to the first reference stability evaluation value and the second reference stability evaluation value respectively, and the sum of the two weights is 1. Furthermore, based on this mapping relationship, the first weight pair corresponding to the second standard deviation can be determined. The first weight pair can include the first weight corresponding to the first reference stability evaluation value and the second weight corresponding to the second reference stability evaluation value; the sum of the first weight and the second weight is 1. In this way, the weight pair can be deeply adapted to the periodic stability of the power operation situation.

[0074] Finally, a weighted operation can be performed based on the first weight pair, the first reference stability evaluation value, and the second reference stability evaluation value to obtain the first stability evaluation value, as follows: First weight * First reference stability evaluation value + Second weight * Second reference stability evaluation value = First stability evaluation value. In this way, the regional characteristics of the area responsible for the first power monitoring node can be deeply evaluated based on the stability of the power operation situation in the horizontal dimension, the stability of the power operation situation in the vertical dimension, and the periodic stability of the power operation situation, that is, the power operation stability situation of this area, which helps to screen out diagnostic nodes that deeply meet the demand situation of computing power resources and the regional characteristics of the area responsible for the first power monitoring node.

[0075] Optionally, in the aspect of screening the k diagnostic nodes according to the first data volume and the first stability evaluation value to obtain the first diagnostic node, the diagnostic node allocation module is specifically used for:

[0076] Determine the degree of fit between each diagnostic node among the k diagnostic nodes and the first stability evaluation value to obtain k degrees of fit;

[0077] Select the fitness values among the k fitness values that are greater than the first threshold to obtain a fitness values, where a is a positive integer less than or equal to k;

[0078] Determine the diagnostic nodes corresponding to the a fitness values to obtain a diagnostic nodes;

[0079] Determine the remaining available computing resource amounts corresponding to the a diagnostic nodes to obtain a remaining available computing resource amounts;

[0080] Determine the reference computing resource amount corresponding to the first data amount;

[0081] Determine the first diagnostic node according to the a remaining available computing resource amounts and the reference computing resource amount.

[0082] In a specific implementation, the fitness between each diagnostic node among the k diagnostic nodes and the first stability evaluation value can be determined to obtain k fitness values. Specifically, the reference stability evaluation value corresponding to each diagnostic node can be obtained. That is, the mapping relationship between the preset diagnostic node and the stability evaluation value can be stored in advance. Based on this mapping relationship, the reference stability evaluation value of each diagnostic node can be determined. Then, the difference between the reference stability evaluation value and the first stability evaluation value is determined. The larger the difference, the greater the fitness, and vice versa. That is, the mapping relationship between the preset difference and the fitness can be stored in advance. Based on this mapping relationship, the fitness corresponding to the difference is determined. The fitness reflects the data processing ability of the diagnostic node to a certain extent. In this way, appropriate diagnostic nodes can be selected based on the data processing of each diagnostic node.

[0083] Among them, the first threshold can be set in advance or be the system default, and the first threshold can be related to the first attribute information.

[0084] Furthermore, the fitness values among the k fitness values that are greater than the first threshold can be selected to obtain a fitness values, where a is a positive integer less than or equal to k. Then, the diagnostic nodes corresponding to the a fitness values are determined to obtain a diagnostic nodes. That is, appropriate diagnostic nodes are initially selected based on the data processing of each diagnostic node.

[0085] Further, it is also possible to determine the remaining available computing resource amounts corresponding to a diagnostic nodes, obtaining a remaining available computing resource amounts, and the remaining available computing resource amounts reflect the computing power resource situation of the diagnostic nodes. In a specific implementation, a mapping relationship between a preset data amount and a computing resource amount can be pre-stored. Furthermore, based on this mapping relationship, a reference computing resource amount corresponding to the first data amount can be determined, and the reference computing resource amount reflects the computing power resource requirement situation corresponding to the first data amount. Then, the first diagnostic node can be determined according to the a remaining available computing resource amounts and the reference computing resource amount. That is, the best diagnostic node can be further selected based on the computing power resource situation of the diagnostic nodes and the computing power resource requirement situation corresponding to the first data amount, that is, a diagnostic node that deeply meets the requirement situation of the computing power resources and the regional characteristics of the area responsible for the first power monitoring node is selected. Thus, it helps to ensure the speed and accuracy of power fault diagnosis.

[0086] Optionally, in terms of determining the first diagnostic node according to the a remaining available computing resource amounts and the reference computing resource amount, the diagnostic node allocation module is specifically configured to:

[0087] When the reference computing resource amount is less than or equal to the maximum remaining available computing resource amount among the a remaining available computing resource amounts, the diagnostic node corresponding to the first remaining available computing resource amount is used as the first diagnostic node, and the first remaining available computing resource amount is any one of the a remaining available computing resource amounts that is greater than or equal to the reference computing resource amount;

[0088] Or,

[0089] When the reference computing resource amount is greater than the maximum remaining available computing resource amount among the a remaining available computing resource amounts, b remaining available computing resource amounts are determined, and the sum of the b remaining available computing resource amounts is greater than or equal to the reference computing resource amount; b is an integer greater than or equal to 2;

[0090] The diagnostic nodes corresponding to the b remaining available computing resource amounts are obtained, getting b diagnostic nodes, and the first diagnostic node is determined according to the b diagnostic nodes.

[0091] In a specific implementation, when the reference computing resource amount is less than or equal to the maximum remaining available computing resource amount among a remaining available computing resource amounts, the diagnostic node corresponding to the first remaining available computing resource amount can be used as the first diagnostic node. The first remaining available computing resource amount is any one of the a remaining available computing resource amounts that is greater than or equal to the reference computing resource amount. That is, one diagnostic node can meet the requirements of ensuring the power failure diagnosis speed and the power failure diagnosis accuracy. Then, only one diagnostic node can be used for power failure diagnosis to ensure the power failure diagnosis speed and the power failure diagnosis accuracy.

[0092] Correspondingly, when the reference computing resource amount is greater than the maximum remaining available computing resource amount among the a remaining available computing resource amounts, it indicates that one diagnostic node cannot quickly complete the power failure diagnosis. Then, b remaining available computing resource amounts can be determined, and the sum of the b remaining available computing resource amounts is greater than or equal to the reference computing resource amount; b is an integer greater than or equal to 2. Then, the diagnostic nodes corresponding to the b remaining available computing resource amounts are obtained to get b diagnostic nodes, and the first diagnostic node is determined according to the b diagnostic nodes. That is, the first diagnostic node includes b diagnostic nodes, and the best diagnostic node can be further selected based on the computing power resource situation of the diagnostic nodes and the computing power resource requirements corresponding to the first data volume, that is, the diagnostic node whose depth meets the requirements of the computing power resource situation and the regional characteristics of the area responsible for the first power monitoring node. Thus, it helps to ensure the power failure diagnosis speed and the power failure diagnosis accuracy.

[0093] Optionally, in terms of sending the power operation data to the first diagnostic node, the diagnostic node allocation module is specifically used for:

[0094] When the reference computing resource amount is less than or equal to the maximum remaining available computing resource amount among the a remaining available computing resource amounts, directly send the power operation data to the first diagnostic node;

[0095] When the reference computing resource amount is greater than the maximum remaining available computing resource amount among the a remaining available computing resource amounts, obtain the remaining available computing resource amount of each of the b diagnostic nodes to get b remaining available computing resource amounts;

[0096] Determine b - 1 sampling parameters according to the b remaining available computing resource amounts;

[0097] Sample the power operation data according to the b - 1 sampling parameters to obtain b - 1 sampled power operation data and remaining power operation data;

[0098] Send the b - 1 sampled power operation data and the remaining power operation data to the b diagnostic nodes respectively.

[0099] In a specific implementation, when the reference computing resource amount is less than or equal to the maximum remaining available computing resource amount among a remaining available computing resource amounts, that is, the number of the first diagnostic nodes is 1, the power operation data can be directly sent to the first diagnostic node, that is, the power fault diagnosis is completed through one diagnostic node.

[0100] Wherein, the sampling parameters may include sampling priorities, and / or sampling intervals (i.e., sampling time intervals).

[0101] Correspondingly, when the reference computing resource amount is greater than the maximum remaining available computing resource amount among a remaining available computing resource amounts, it indicates that one diagnostic node cannot complete the fast power fault diagnosis. Then, the remaining available computing resource amounts of each of the b diagnostic nodes can be obtained to get b remaining available computing resource amounts, and then b - 1 sampling parameters are determined according to the b remaining available computing resource amounts. The power operation data is sampled according to the b - 1 sampling parameters to obtain b - 1 sampled power operation data and remaining power operation data. Specifically, the power operation data can be sampled first to obtain a sampling result and the remaining power operation data. Then, at the next sampling time, the remaining power operation data can be directly sampled, and so on. Since each sampling will obtain a sampling result and the remaining power operation data, that is, only b - 1 samplings are required to obtain b - 1 sampled power operation data and remaining power operation data. Finally, the b - 1 sampled power operation data and the remaining power operation data can be respectively sent to the b diagnostic nodes, and the b diagnostic nodes can use the corresponding power operation data for power fault diagnosis to obtain b power fault diagnosis results, and then the b power fault diagnosis results are integrated to obtain the target power fault diagnosis result.

[0102] In a specific implementation, the b diagnostic nodes can synchronously execute the power fault diagnosis, and then the b power fault diagnosis results are aggregated to the target diagnostic node, and the b power fault diagnosis results are integrated through the target diagnostic node to obtain the target power fault diagnosis result. The target diagnostic node is one of the b diagnostic nodes.

[0103] In specific implementation, the fitness degrees of b diagnostic nodes can be obtained to get b fitness degrees, the mean value of the b fitness degrees can be determined, the difference between each fitness degree among the b fitness degrees and the mean value can be determined to get b differences. A mapping relationship between preset differences and fine-tuning parameters can be stored in advance, and the value range of the fine-tuning parameters can be set in advance or be the system default. For example, the value range of the fine-tuning parameters is -0.02 to 0.02. Based on the mapping relationship, the fine-tuning parameter corresponding to each of the b differences can be determined to get b fine-tuning parameters. That is, the second model parameters can be fine-tuned by using the b fine-tuning parameters to get b third model parameters. The third model parameter = (1 + fine-tuning parameter) * the second model parameter. Then, according to the first power fault diagnosis model and the b third model parameters, power fault diagnosis is performed on the power operation data corresponding to each diagnostic node to get b power fault diagnosis results. The b power fault diagnosis results are integrated to get the target power fault diagnosis result. In this way, the self-characteristics of each diagnostic node can be deeply considered to further optimize the model parameters, which helps to ensure the adaptability between the diagnostic nodes and the power operation data. Furthermore, the accuracy of power fault diagnosis can be guaranteed.

[0104] Optionally, in terms of determining b - 1 sampling parameters according to the b remaining available computing resource amounts, the diagnostic node allocation module is specifically configured to:

[0105] Determine the sum of the b remaining available computing resource amounts to get the total remaining available computing resource amount;

[0106] Determine the ratio between the b remaining available computing resource amounts and the total remaining available computing resource amount to get b ratio values;

[0107] Determine b priorities according to the b remaining available computing resource amounts. The larger the remaining available computing resource amount, the higher the priority;

[0108] Determine the b - 1 sampling parameters according to the b priorities and the b ratio values.

[0109] In specific implementation, the sum of the b remaining available computing resource amounts can be determined to get the total remaining available computing resource amount, then the ratio between the b remaining available computing resource amounts and the total remaining available computing resource amount can be determined to get b ratio values. b priorities are determined according to the b remaining available computing resource amounts. The larger the remaining available computing resource amount, the higher the priority. Of course, the larger the ratio value, the larger the amount of data obtained from the power operation data. Then, the b - 1 sampling parameters are determined according to the b priorities and the b ratio values. In this way, it can be ensured that the diagnostic nodes with sufficient computing resources sample first and obtain the power operation data corresponding to their computing resources.

[0110] Optionally, in determining the b-1 sampling parameters according to the b priorities and the b proportional values, the diagnostic node allocation module is specifically configured to:

[0111] Determine b-1 sampling priorities according to the b priorities;

[0112] Determine b-1 sampling intervals according to the b-1 sampling priorities, where each sampling interval corresponds to the corresponding sampling priority and the remaining available computing resource amount corresponding to the sampling priority;

[0113] Determine the b-1 sampling parameters according to the b-1 sampling priorities and the b-1 sampling intervals.

[0114] In specific implementation, since power operation data is sampled first to obtain a sampling result and the remaining power operation data, then in the next sampling, the remaining power operation data can be directly sampled, and so on. Since each sampling will obtain a sampling result and the remaining power operation data, that is, only b-1 samplings are required to obtain b-1 sampled power operation data and the remaining power operation data, then b-1 sampling priorities can be determined according to the b priorities. The higher the priority, the higher the sampling priority. Then, b-1 sampling intervals are determined according to the b-1 sampling priorities. Specifically, the b-1 sampling parameters can be determined according to the b-1 sampling priorities and the b-1 sampling intervals, that is, each sampling parameter corresponds to a sampling priority and a sampling interval.

[0115] In specific implementation, each sampling interval corresponds to the corresponding sampling priority and the remaining available computing resource amount corresponding to the sampling priority, so as to ensure that the diagnostic nodes with sufficient computing resources are sampled first and obtain the power operation data corresponding to their computing resources.

[0116] It can be seen that the distributed power fault rapid diagnosis system for smart grid described in the embodiments of the present application includes: n power monitoring nodes, a diagnostic node allocation module, and m diagnostic nodes; the n power monitoring nodes are all set at designated positions of the smart grid; both n and m are integers greater than 1; the first power monitoring node obtains the first identification information of the first power monitoring node, obtains the power operation data in a preset time period, and sends the first identification information and the power operation data to the diagnostic node allocation module; the first power monitoring node is any one of the n power monitoring nodes; the diagnostic node allocation module determines the first diagnostic node according to the first identification information and the power operation data, and sends the power operation data to the first diagnostic node; the first diagnostic node is at least one of the m diagnostic nodes; the first diagnostic node performs power fault diagnosis according to the power operation data to obtain the target power fault diagnosis result. On the one hand, as an aggregation node, the diagnostic node allocation module can receive the power operation data of one or more power monitoring nodes and perform intelligent allocation according to the situation of these power operation data. On the other hand, allocating corresponding diagnostic nodes based on the source situation of the power operation data and the stability of the power operation can further ensure the accuracy and fault diagnosis efficiency of subsequent power fault diagnosis.

[0117] Please refer to Figure 3 , Figure 3 is a schematic flowchart of a distributed power fault rapid diagnosis method for smart grid provided by an embodiment of the present application, which is applied to a distributed power fault rapid diagnosis system for smart grid as shown in Figure 1 The system includes: n power monitoring nodes, a diagnostic node allocation module, and m diagnostic nodes; the n power monitoring nodes are all set at designated positions of the smart grid; both n and m are integers greater than 1; the distributed power fault rapid diagnosis method for smart grid of the present application includes:

[0118] 301. The first power monitoring node obtains the first identification information of the first power monitoring node, obtains the power operation data in a preset time period, and sends the first identification information and the power operation data to the diagnostic node allocation module; the first power monitoring node is any one of the n power monitoring nodes.

[0119] 302. The diagnostic node allocation module determines the first diagnostic node according to the first identification information and the power operation data, and sends the power operation data to the first diagnostic node; the first diagnostic node is at least one of the m diagnostic nodes.

[0120] 303. The first diagnostic node performs power fault diagnosis according to the power operation data to obtain the target power fault diagnosis result.

[0121] Among them, the specific descriptions of the above steps 301 - 303 can refer to the corresponding parts of the distributed power fault rapid diagnosis system for the smart grid described above, which will not be elaborated here. Figure 1 It can be seen that the distributed power fault rapid diagnosis method for the smart grid described in the embodiments of this application is applied to the distributed power fault rapid diagnosis system for the smart grid. The system includes: n power monitoring nodes, a diagnostic node allocation module, and m diagnostic nodes; the n power monitoring nodes are all set at designated positions of the smart grid; both n and m are integers greater than 1; the first power monitoring node obtains the first identification information of the first power monitoring node, obtains the power operation data for a preset time period, and sends the first identification information and the power operation data to the diagnostic node allocation module; the first power monitoring node is any one of the n power monitoring nodes; the diagnostic node allocation module determines the first diagnostic node according to the first identification information and the power operation data, and sends the power operation data to the first diagnostic node; the first diagnostic node is at least one of the m diagnostic nodes; the first diagnostic node performs power fault diagnosis according to the power operation data to obtain the target power fault diagnosis result. On the one hand, the diagnostic node allocation module, as an aggregation node, can receive the power operation data of one or more power monitoring nodes and perform intelligent allocation according to the situation of these power operation data. On the other hand, based on the source situation of the power operation data and the stability of the power operation, the corresponding diagnostic nodes are allocated, which can further ensure the accuracy and fault diagnosis efficiency of subsequent power fault diagnosis.

[0122] Please refer to

[0123] Please refer to Figure 4 , Figure 4 is a schematic structural diagram of an electronic device provided by an embodiment of this application. The electronic device includes a processor, a memory, a communication interface, and one or more programs. Among them, the above one or more programs are stored in the above memory and are configured to be executed by the above processor. In the embodiments of this application, the electronic device is applied to the distributed power fault rapid diagnosis system for the smart grid. The system includes: n power monitoring nodes, a diagnostic node allocation module, and m diagnostic nodes; the n power monitoring nodes are all set at designated positions of the smart grid; both n and m are integers greater than 1; the above programs include instructions for performing the following steps:

[0124] The first power monitoring node obtains the first identification information of the first power monitoring node, obtains the power operation data for a preset time period, and sends the first identification information and the power operation data to the diagnostic node allocation module; the first power monitoring node is any one of the n power monitoring nodes.

[0125] The diagnosis node allocation module determines a first diagnosis node according to the first identification information and the power operation data, and sends the power operation data to the first diagnosis node; the first diagnosis node is at least one of the m diagnosis nodes.

[0126] The first diagnosis node performs power fault diagnosis according to the power operation data to obtain a target power fault diagnosis result.

[0127] Among them, this electronic device can be used to implement all the functions of the above Figure 1 described distributed power fault rapid diagnosis system for smart grid, which will not be elaborated here.

[0128] It can be seen that the electronic device described in the embodiment of the present application is applied to a distributed power fault rapid diagnosis system for smart grid. The system includes: n power monitoring nodes, a diagnosis node allocation module, and m diagnosis nodes; the n power monitoring nodes are all set at designated positions of the smart grid; both n and m are integers greater than 1; the first power monitoring node obtains the first identification information of the first power monitoring node, obtains the power operation data in a preset time period, and sends the first identification information and the power operation data to the diagnosis node allocation module; the first power monitoring node is any one of the n power monitoring nodes; the diagnosis node allocation module determines a first diagnosis node according to the first identification information and the power operation data, and sends the power operation data to the first diagnosis node; the first diagnosis node is at least one of the m diagnosis nodes; the first diagnosis node performs power fault diagnosis according to the power operation data to obtain a target power fault diagnosis result. On the one hand, as an aggregation node, the diagnosis node allocation module can receive the power operation data of one or more power monitoring nodes and perform intelligent allocation according to the situation of these power operation data. On the other hand, allocating corresponding diagnosis nodes based on the source situation of the power operation data and the stability of the power operation can further ensure the accuracy and efficiency of subsequent power fault diagnosis.

[0129] Figure 5 It is a functional unit composition block diagram of a distributed power fault rapid diagnosis device 500 for smart grid involved in the embodiment of the present application. The distributed power fault rapid diagnosis device 500 for smart grid is applied to a distributed power fault rapid diagnosis system for smart grid. The system includes: n power monitoring nodes, a diagnosis node allocation module, and m diagnosis nodes; the n power monitoring nodes are all set at designated positions of the smart grid; both n and m are integers greater than 1; the distributed power fault rapid diagnosis device 500 for smart grid includes: a monitoring unit 501, an allocation unit 502, and a diagnosis unit 503. Among them,

[0130] The monitoring unit 501 is configured to obtain the first identification information of the first power monitoring node through the first power monitoring node, obtain the power operation data for a preset time period, and send the first identification information and the power operation data to the diagnosis node allocation module; the first power monitoring node is any one of the n power monitoring nodes.

[0131] The allocation unit 502 is configured to determine a first diagnosis node according to the first identification information and the power operation data through the diagnosis node allocation module, and send the power operation data to the first diagnosis node; the first diagnosis node is at least one of the m diagnosis nodes.

[0132] The diagnosis unit 503 is configured to perform power fault diagnosis according to the power operation data through the first diagnosis node to obtain a target power fault diagnosis result.

[0133] It can be seen that the distributed power fault rapid diagnosis device for the smart grid described in the embodiments of the present application is applied to a distributed power fault rapid diagnosis system for the smart grid. The system includes: n power monitoring nodes, a diagnosis node allocation module, and m diagnosis nodes; the n power monitoring nodes are all set at designated positions of the smart grid; n and m are both integers greater than 1; the first power monitoring node obtains the first identification information of the first power monitoring node, obtains the power operation data for a preset time period, and sends the first identification information and the power operation data to the diagnosis node allocation module; the first power monitoring node is any one of the n power monitoring nodes; the diagnosis node allocation module determines a first diagnosis node according to the first identification information and the power operation data, and sends the power operation data to the first diagnosis node; the first diagnosis node is at least one of the m diagnosis nodes; the first diagnosis node performs power fault diagnosis according to the power operation data to obtain a target power fault diagnosis result. On the one hand, as an aggregation node, the diagnosis node allocation module can receive the power operation data of one or more power monitoring nodes and perform intelligent allocation according to the situation of these power operation data. On the other hand, by allocating corresponding diagnosis nodes based on the source situation of the power operation data and the stability of the power operation, it can further ensure the accuracy and fault diagnosis efficiency of subsequent power fault diagnosis.

[0134] It can be understood that the functions of the respective program modules of the distributed power fault rapid diagnosis device for the smart grid in this embodiment can be specifically implemented according to the methods in the method embodiments described above. The specific implementation process can refer to the relevant descriptions in the method embodiments above and will not be elaborated here.

[0135] An embodiment of the present application also provides a computer storage medium. The computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute some or all of the steps of any one of the methods described in the foregoing method embodiments. The above computer includes an electronic device.

[0136] An embodiment of the present application also provides a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute some or all of the steps of any one of the methods described in the foregoing method embodiments. The computer program product can be a software installation package, and the above computer includes an electronic device.

[0137] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0138] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0139] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the above division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0140] The units described as separate components above may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0141] In addition, in each embodiment of the present application, each functional unit can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0142] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned memory includes: USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs and other media that can store program codes.

[0143] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. This program can be stored in a computer-readable memory, and the memory can include: flash drives, read-only memories (abbreviation: ROM), random access memories (abbreviation: RAM), magnetic disks, or optical discs, etc.

[0144] The above has introduced the embodiments of the present application in detail. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A distributed power fault rapid diagnosis system for smart grid, characterized in that, The system includes: n power monitoring nodes, a diagnostic node allocation module, and m diagnostic nodes; the n power monitoring nodes are all set at designated positions of the smart grid; both n and m are integers greater than 1; wherein, The first power monitoring node is configured to obtain the first identification information of the first power monitoring node, obtain power operation data for a preset time period, and send the first identification information and the power operation data to the diagnostic node allocation module; the first power monitoring node is any one of the n power monitoring nodes; The diagnostic node allocation module is configured to determine a first diagnostic node according to the first identification information and the power operation data, and send the power operation data to the first diagnostic node; the first diagnostic node is at least one of the m diagnostic nodes; The first diagnostic node is configured to perform a power fault diagnosis according to the power operation data to obtain a target power fault diagnosis result.

2. The system according to claim 1, wherein In terms of determining the first diagnostic node according to the first identification information and the power operation data, the diagnostic node allocation module specifically is configured to: Determine k diagnostic node identifiers corresponding to the first identification information according to a preset mapping relationship between the identification information and the diagnostic node identifiers, where k is a positive integer; Determine the k diagnostic nodes corresponding to the k diagnostic node identifiers; Determine the first data volume of the power operation data; Determine the first stability evaluation value of the power operation data; Screen the k diagnostic nodes according to the first data volume and the first stability evaluation value to obtain the first diagnostic node.

3. The system according to claim 2, wherein In terms of performing a power fault diagnosis according to the power operation data to obtain a target power fault diagnosis result, the first diagnostic node is configured to: Obtain the first attribute information of the power operation data; Determine a first power fault diagnosis model corresponding to the first attribute information and a first model parameter corresponding to the first attribute information; Determine a first adjustment parameter corresponding to the first stability evaluation value; Adjust the first model parameter according to the first adjustment parameter to obtain a second model parameter; Perform a power fault diagnosis on the power operation data according to the first power fault diagnosis model and the second model parameter to obtain the target power fault diagnosis result.

4. The system according to claim 2 or 3, characterized in that, The power operation data includes p pieces of power operation data, and each piece of power operation data corresponds to a sampling moment; in terms of determining the first stability evaluation value of the power operation data, the diagnostic node allocation module specifically is configured to: Perform fitting according to the p pieces of power operation data and the sampling moment corresponding to each piece of power operation data to obtain a first fitting straight line and a first fitting curve segment corresponding to the preset time period; Obtain the absolute value of the slope of the first fitting straight line to obtain a first absolute value; Determine the maximum value and the minimum value in the first fitting curve segment to obtain a plurality of maximum values and a plurality of minimum values; Perform a standard deviation operation according to the plurality of maximum values and the plurality of minimum values to obtain a first standard deviation; Determine the time intervals between adjacent maxima and minima based on the multiple maxima and the multiple minima to obtain a plurality of time intervals; Perform a standard deviation operation based on the plurality of time intervals to obtain a second standard deviation; Determine the first stability evaluation value based on the first absolute value, the first standard deviation, and the second standard deviation.

5. The system according to claim 4, characterized in that, In terms of determining the first stability evaluation value based on the first absolute value, the first standard deviation, and the second standard deviation, the diagnostic node allocation module is specifically configured to: Determine a first reference stability evaluation value corresponding to the first absolute value; Determine a second reference stability evaluation value corresponding to the first standard deviation; Determine a first weight pair corresponding to the second standard deviation, where the first weight pair includes a first weight corresponding to the first reference stability evaluation value and a second weight corresponding to the second reference stability evaluation value; The sum of the first weight and the second weight is 1; Perform a weighted operation based on the first weight pair, the first reference stability evaluation value, and the second reference stability evaluation value to obtain the first stability evaluation value.

6. The system according to claim 2 or 3, wherein In terms of screening the k diagnostic nodes based on the first data volume and the first stability evaluation value to obtain the first diagnostic node, the diagnostic node allocation module is specifically configured to: Determine the fitness between each diagnostic node among the k diagnostic nodes and the first stability evaluation value to obtain k fitness values; Select the fitness values greater than a first threshold among the k fitness values to obtain a fitness values, where a is a positive integer less than or equal to k; Determine the diagnostic nodes corresponding to the a fitness values to obtain a diagnostic nodes; Determine the remaining available computing resource amounts corresponding to the a diagnostic nodes to obtain a remaining available computing resource amounts; Determine a reference computing resource amount corresponding to the first data volume; Determine the first diagnostic node based on the a remaining available computing resource amounts and the reference computing resource amount.

7. The system according to claim 6, wherein In terms of determining the first diagnostic node based on the a remaining available computing resource amounts and the reference computing resource amount, the diagnostic node allocation module is specifically configured to: When the reference computing resource amount is less than or equal to the maximum remaining available computing resource amount among the a remaining available computing resource amounts, use the diagnostic node corresponding to the first remaining available computing resource amount as the first diagnostic node, where the first remaining available computing resource amount is any one of the a remaining available computing resource amounts that is greater than or equal to the reference computing resource amount; Or, When the reference computing resource amount is greater than the maximum remaining available computing resource amount among the a remaining available computing resource amounts, determine b remaining available computing resource amounts, and the sum of the b remaining available computing resource amounts is greater than or equal to the reference computing resource amount; b is an integer greater than or equal to 2; Obtain the diagnostic nodes corresponding to the b remaining available computing resource amounts to obtain b diagnostic nodes, and determine the first diagnostic node based on the b diagnostic nodes.

8. The system according to claim 7, characterized in that, In terms of sending the power operation data to the first diagnostic node, the diagnostic node allocation module is specifically configured to: When the reference computing resource amount is less than or equal to the maximum remaining available computing resource amount among the a remaining available computing resource amounts, directly send the power operation data to the first diagnosis node; When the reference computing resource amount is greater than the maximum remaining available computing resource amount among the a remaining available computing resource amounts, obtain the remaining available computing resource amount of each of the b diagnosis nodes to obtain b remaining available computing resource amounts; Determine b - 1 sampling parameters according to the b remaining available computing resource amounts; Sample the power operation data according to the b - 1 sampling parameters to obtain b - 1 sampled power operation data and remaining power operation data; Send the b - 1 sampled power operation data and the remaining power operation data to the b diagnosis nodes respectively.

9. The system according to claim 8, wherein In terms of determining the b - 1 sampling parameters according to the b remaining available computing resource amounts, the diagnosis node allocation module is specifically configured to: Determine the sum of the b remaining available computing resource amounts to obtain the total remaining available computing resource amount; Determine the ratio between the b remaining available computing resource amounts and the total remaining available computing resource amount to obtain b ratio values; Determine b priorities according to the b remaining available computing resource amounts, where the larger the remaining available computing resource amount, the higher the priority; Determine the b - 1 sampling parameters according to the b priorities and the b ratio values.

10. The system according to claim 9, wherein In terms of determining the b - 1 sampling parameters according to the b priorities and the b ratio values, the diagnosis node allocation module is specifically configured to: Determine b - 1 sampling priorities according to the b priorities; Determine b - 1 sampling intervals according to the b - 1 sampling priorities, where each sampling interval corresponds to the corresponding sampling priority and the remaining available computing resource amount corresponding to the sampling priority; Determine the b - 1 sampling parameters according to the b - 1 sampling priorities and the b - 1 sampling intervals.

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