Power equipment configuration method and device, equipment, storage medium and product

Abstract

The invention discloses a power equipment configuration method and device, equipment, a storage medium and a product. The method comprises the following steps: in response to detection of access of power equipment, acquiring equipment attribute data of the power equipment; determining target configuration data of the power equipment according to the equipment attribute data and a trained target equipment configuration model; sending the target configuration data to the power equipment, so that the power equipment is configured according to the target configuration data; wherein the equipment attribute data comprises at least one of physical attribute data, communication attribute data and functional attribute data, the problem of low configuration efficiency of the access equipment is solved, the flexibility and accuracy of equipment configuration are improved, and the stability of a power system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid configuration, and in particular to a configuration method, device, equipment, storage medium and product for power equipment. Background Art

[0002] As an important part of the national infrastructure, the power system is responsible for providing electricity to thousands of households. With the access of new energy, the expansion of the power grid and the diversification of user needs, the power system is facing an increasingly complex operating environment and challenges.

[0003] In order to improve the stability and reliability of the power system and reduce operation and maintenance costs, the power system needs to achieve efficient interconnection and intelligent management between equipment.

[0004] At present, due to the wide variety of access equipment in the power system and different configuration requirements and parameters, the traditional configuration method requires manual intervention, resulting in low configuration efficiency and poor flexibility, making it difficult to meet the needs of large-scale equipment interconnection. At the same time, the accuracy of configuration parameters cannot be guaranteed, and abnormal operation of access equipment is prone to occur, affecting the stability of the power system. Summary of the invention

[0005] The embodiments of the present invention provide a configuration method, apparatus, device, storage medium and product for power equipment to solve the problem of low configuration efficiency of access equipment, improve the flexibility and accuracy of equipment configuration, and ensure the stability of the power system.

[0006] According to an embodiment of the present invention, a method for configuring an electric device is provided, the method comprising:

[0007] In response to detecting access to an electric device, acquiring device attribute data of the electric device;

[0008] Determining target configuration data of the power equipment according to the equipment attribute data and the trained target equipment configuration model;

[0009] sending the target configuration data to the electric device so that the electric device is configured according to the target configuration data;

[0010] The device attribute data includes at least one of physical attribute data, communication attribute data and functional attribute data.

[0011] According to another embodiment of the present invention, a configuration device for electric power equipment is provided, the device comprising:

[0012] A device attribute data acquisition module, configured to acquire device attribute data of the power device in response to detecting access to the power device;

[0013] A target configuration data determination module, used to determine the target configuration data of the power device according to the device attribute data and the trained target device configuration model;

[0014] a target configuration data sending module, used to send the target configuration data to the electric device, so that the electric device is configured according to the target configuration data;

[0015] The device attribute data includes at least one of physical attribute data, communication attribute data and functional attribute data.

[0016] According to another embodiment of the present invention, there is provided an electronic device, the electronic device comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for configuring the power equipment described in any embodiment of the present invention.

[0020] According to another embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the configuration method of the power equipment described in any embodiment of the present invention when executed.

[0021] According to another embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method for configuring the power equipment described in any embodiment of the present invention is implemented.

[0022] The technical solution of the embodiment of the present invention obtains device attribute data of the power equipment in response to detecting the access of the power equipment, determines the target configuration data of the power equipment according to the device attribute data and the trained target device configuration model, and sends the target configuration data to the power equipment so that the power equipment is configured according to the target configuration data, thereby solving the problem of low configuration efficiency of the access equipment, improving the flexibility and accuracy of the equipment configuration, and ensuring the stability of the power system.

[0023] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A flow chart of a method for configuring an electric power device provided by an embodiment of the present invention;

[0026] Figure 2 A flowchart of another method for configuring electric power equipment provided by one embodiment of the present invention;

[0027] Figure 3 A flowchart of another method for configuring electric power equipment provided by one embodiment of the present invention;

[0028] Figure 4 A schematic diagram of the structure of a configuration device for electric power equipment provided by one embodiment of the present invention;

[0029] Figure 5 The present invention is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", "initial", "target", "reference", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] Figure 1This is a flowchart of a method for configuring an electric device provided by an embodiment of the present invention. This embodiment is applicable to the case of configuring an electric device connected to an electric power system. The method can be executed by a configuration device of the electric device. The configuration device of the electric device can be implemented in the form of hardware and / or software. The configuration device of the electric device can be configured in a terminal device. Figure 1 As shown, the method includes:

[0033] S110 . In response to detecting access to the power device, acquiring device attribute data of the power device.

[0034] Specifically, the power equipment refers to a firmware device that establishes an interconnection with the power system. Exemplarily, the types of power equipment include, but are not limited to, smart meters, relay protection devices, power sensors, and remote terminal units. Among them, smart meters are used to measure the power consumption of the power system and can transmit data through a communication interface; relay protection devices are used to detect fault events that occur during the operation of the power system, such as short circuits, overloads, etc., and automatically cut off the fault circuit to ensure the safety of the power system; exemplarily, the power sensor can be a current sensor or a voltage sensor, etc., but is not limited to the example case; the remote terminal unit has multiple input channels, which can be connected to various sensors for collecting and transmitting physical quantities of the power system. The physical quantities can be power parameters such as voltage, current, power and category, or environmental parameters such as temperature and humidity.

[0035] There is no limitation on the equipment type of the power equipment here, and the specific settings can be customized according to actual needs.

[0036] In this embodiment, the device attribute data includes at least one of physical attribute data, communication attribute data and functional attribute data. Specifically, the physical attribute data represents the data content related to the physical attribute parameters of the power equipment. For example, the physical attribute data includes but is not limited to the device type, hardware specifications, device size, device weight, device material and device appearance. Specifically, the communication attribute data represents the data content related to the communication attribute parameters of the power equipment. For example, the communication attribute data includes but is not limited to the communication interface type, communication protocol, network bandwidth, network delay, data transmission rate and signal strength. Specifically, the functional attribute data represents the data content related to the functional attribute parameters of the power equipment. For example, when the power equipment is a smart meter, the functional attribute data includes but is not limited to power demand, interactive interface, electric energy metering, data storage function and display function. When the power equipment is a relay protection device, the functional attribute data includes but is not limited to power demand, interactive interface, fault detection function, fault alarm function and action parameter function.

[0037] The specific attribute parameters in the physical attribute data, communication attribute data and functional attribute data are exemplified here, but are not limited thereto, and can be customized according to actual needs or the type of the power equipment.

[0038] S120: Determine target configuration data of the power equipment according to the equipment attribute data and the trained target equipment configuration model.

[0039] Specifically, the target device configuration model represents a machine learning model for predicting configuration parameters of the power device. Exemplarily, the target device configuration model is a decision tree model, a Bayesian model, a support vector machine or a neural network model, etc., but is not limited to the example case.

[0040] In an optional embodiment, the target configuration data of the power equipment is determined according to the equipment attribute data and the trained target equipment configuration model, including: inputting the equipment attribute data into the target equipment configuration model to obtain the output target configuration data of the power equipment.

[0041] Specifically, the target configuration data includes parameter values ​​corresponding to at least one configuration parameter. For example, when the power device is a smart meter, the target configuration data includes parameter values ​​corresponding to configuration parameters such as metering parameters, communication parameters, and alarm parameters; when the power device is a relay protection device, the target configuration data includes parameter values ​​corresponding to configuration parameters such as action current parameters, action time parameters, and sensitivity coefficients.

[0042] There is no limitation on the configuration parameters corresponding to the target configuration data, and they can be customized according to actual needs or the type of power equipment.

[0043] Based on the above embodiment, optionally, the method also includes: obtaining a training device data set; wherein the training device data set includes device attribute data corresponding to at least one training device; inputting the training device data set into the initial device configuration model to obtain an output predicted device configuration data set; wherein the predicted device configuration data set includes predicted configuration data corresponding to each training device; determining a loss function value of the initial device configuration model based on the predicted device configuration data set, and adjusting model parameters of the initial device configuration model based on the loss function value; until the training end condition is met, a trained target device configuration model is obtained.

[0044] Specifically, the training end condition includes the convergence of the loss function value and / or the number of training times reaching the number threshold. Exemplarily, the loss function corresponding to the loss function value includes but is not limited to the square loss function, logarithmic loss function, exponential loss function, mean square error loss function, logistic regression loss function, Huber loss function, cross entropy loss function and Kullback-Leibler divergence loss function, etc.

[0045] Take the initial device configuration model as a support vector machine as an example. During the training process of the support vector machine, the support vector machine aims to find a hyperplane (decision boundary) that can be used to distinguish data points of different configuration data. Exemplarily, the hyperplane of the support vector machine is represented as wx+b=0, where w represents the normal vector and b represents the bias term.

[0046] In order to achieve optimal segmentation, the objective function of the support vector machine can be expressed as:

[0047]

[0048] Among them, ||w|| 2 represents the width of the hyperplane; ξ i represents the slack variable and C represents the penalty parameter, which is used to balance the penalty for maximizing the interval and misclassification.

[0049] The constraints of the objective function are identified as:

[0050] y i (w·x i +b)≥1-ξ i , i=1,2,...,n

[0051] Among them, y i represents the predicted configuration data of the i-th training device, x i Represents the device attribute data of the i-th training device.

[0052] For the nonlinearly separable device attribute data in this embodiment, the device attribute data is mapped to a high-dimensional feature space so that it can be linearly separable in the high-dimensional space. Using Lagrangian duality, w and b are solved by solving the Lagrangian dual problem. Accordingly, the objective function of the support vector machine is expressed as:

[0053]

[0054] Among them, α i represents the Lagrange multiplier, and the constraints are as follows:

[0055]

[0056] By solving the optimization result of the objective function of the support vector machine, the Lagrange multiplier α is obtained. i , only if α is satisfied i >0 are called support vectors, which determine the position of the hyperplane. i And support vectors, w and b can be calculated to construct the final hyperplane wx+b=0.

[0057] For example, the normal vector w=(w1, w2, ... w m ), where w m represents the weight corresponding to the mth attribute parameter in the device attribute data. In the t+1th iteration of the support vector machine, the weight w of the mth attribute parameter m (t+1) satisfies the following formula:

[0058]

[0059] Among them, η represents the learning rate, Represents the loss function value Relative to the weight w m The gradient of (t).

[0060] For example, the loss function value Satisfies the following formula:

[0061]

[0062] Specifically, the dynamic feature weighting mechanism of the support vector machine automatically adjusts the contribution of each attribute parameter to the final decision by assigning different feature weights to each attribute parameter, thereby more accurately capturing important information in the device attribute data. For high-dimensional data with incompletely correlated features, dynamic feature weighting helps reduce unnecessary noise and improves the generalization ability and accuracy of the target device configuration model.

[0063] S130. Send the target configuration data to the electric device, so that the electric device is configured according to the target configuration data.

[0064] The technical solution of this embodiment obtains device attribute data of the power equipment in response to detecting the access of the power equipment, determines the target configuration data of the power equipment based on the device attribute data and the trained target device configuration model, and sends the target configuration data to the power equipment so that the power equipment is configured according to the target configuration data, thereby solving the problem of low configuration efficiency of the access equipment, improving the flexibility and accuracy of the equipment configuration, and ensuring the stability of the power system.

[0065] Figure 2This is a flow chart of another method for configuring a power device provided by an embodiment of the present invention. This embodiment further refines the method for configuring a power device in the above embodiment. Figure 2 As shown, the method includes:

[0066] S210 . In response to detecting access to the power device, acquiring device attribute data of the power device.

[0067] S210 in this embodiment corresponds to or is similar to S110 in the above embodiment, and will not be described in detail in this embodiment.

[0068] S220: Determine target configuration data of the power equipment according to the equipment attribute data and the trained target equipment configuration model.

[0069] In another optional embodiment, target configuration data of the power equipment is determined based on the equipment attribute data and a trained target equipment configuration model, including: inputting the equipment attribute data into the target equipment configuration model to obtain output reference configuration data; determining parameter weights corresponding to at least one configuration parameter in the reference configuration data based on parameter characteristic data; and determining the target configuration data of the power equipment based on at least one parameter weight and the reference configuration data.

[0070] In this embodiment, the parameter characteristic data includes at least one parameter characteristic of a device network characteristic, a performance requirement characteristic, and a historical configuration characteristic.

[0071] Specifically, according to the parameter characteristic data, the parameter weights corresponding to at least one configuration parameter in the reference configuration data are determined, including: for each configuration parameter in the reference configuration data, obtaining the configuration weights corresponding to the configuration parameters of at least one parameter characteristic in the parameter characteristic data, and taking the weighted sum of at least one configuration weight as the parameter weight of the configuration parameter.

[0072] For example, the parameter weight w of the i-th configuration parameter is i Satisfies the following formula:

[0073] w i =α·w n，i +β·w d，i +γ·w h，i

[0074] Among them, α, β and γ represent the adjustment coefficients, w n，i represents the configuration weight corresponding to the device network characteristics and the i-th configuration parameter, w d，i represents the configuration weight corresponding to the performance requirement characteristic and the i-th configuration parameter, w h，i Represents the configuration weight corresponding to the historical configuration feature and the i-th configuration parameter.

[0075] Specifically, “determining target configuration data of the power equipment according to at least one parameter weight and reference configuration data” here is the same as or similar to S260 in this embodiment, and will not be repeated here.

[0076] S230: Send the target configuration data to the electric power device, so that the electric power device is configured according to the target configuration data.

[0077] S230 in this embodiment is similar to that in the above embodiment. Figure 1 The S130 shown corresponds to the same or similar steps, and will not be described in detail in this embodiment.

[0078] S240. Obtain current equipment performance metrics of the power equipment.

[0079] Specifically, the current equipment performance metric represents the equipment operation status of the power equipment under the target configuration data, and the current equipment performance metric is a statistical value of the index value corresponding to at least one equipment performance index. Exemplarily, the equipment performance index is response time, accuracy, response speed, trouble-free time and communication performance index, but is not limited to the example case.

[0080] S250: When the current device performance metric meets the performance update condition, determine, according to the parameter characteristic data, a parameter weight corresponding to at least one configuration parameter in the target configuration data.

[0081] In an optional embodiment, the performance update condition includes that the current device performance metric is less than the performance metric threshold, and / or the device performance increment is less than the performance increment threshold. The device performance increment represents the metric increment of the current device performance metric compared to the previous device performance metric of the power device in the previous configuration update phase.

[0082] Specifically, when the configuration of the power equipment is updated less than 1 time, the performance update conditions include that the current device performance metric is less than the performance metric threshold; when the configuration of the power equipment is updated more than 1 time, the performance update conditions include that the current device performance metric is less than the performance metric threshold, and / or the device performance increment of the current device performance metric compared to the previous device performance metric of the power equipment in the last configuration update stage is less than the performance increment threshold.

[0083] In a specific embodiment, the configuration update phase includes an instance in which the reference configuration data is updated to obtain the target configuration data in the above embodiment.

[0084] For example, the device performance increment ΔP total Satisfies the following formula:

[0085] ΔP total =∑P t -∑Pt-1

[0086] Among them, ∑P t represents the current device performance metric, ∑P t-1 Represents the last device performance metric.

[0087] In this embodiment, the parameter characteristic data includes at least one parameter characteristic of a device network characteristic, a performance requirement characteristic, and a historical configuration characteristic.

[0088] In an optional embodiment, the parameter weights corresponding to at least one configuration parameter in the target configuration data are determined based on the parameter characteristic data, including: for each configuration parameter in the target configuration data, obtaining the configuration weights corresponding to the configuration parameters of at least one parameter characteristic in the parameter characteristic data, and taking the weighted sum of at least one configuration weight as the parameter weight of the configuration parameter.

[0089] In another optional embodiment, parameter weights corresponding to at least one configuration parameter in the target configuration data are determined based on parameter characteristic data, including: for each configuration parameter in the target configuration data, determining an equipment performance increment of the power equipment based on a current equipment performance metric; taking a weighted sum of at least one parameter characteristic and the configuration weights corresponding to the configuration parameter as a reference weight of the configuration parameter; and determining the parameter weight of the configuration parameter based on the reference weight and the equipment performance increment.

[0090] Specifically, the parameter weight represents the weighted sum of the reference weight and the device performance increment. For example, the parameter weight w of the i-th configuration parameter is i ' satisfies the following formula:

[0091] w i ′=αw i +β·ΔP total

[0092] Among them, w i represents the reference weight of the i-th configuration parameter, and α and β represent the adjustment coefficients.

[0093] The advantage of setting the equipment performance increment to make weighted adjustment to the parameter weights is that it makes the parameter weights adaptive and more suitable for the current equipment performance status of the power equipment, thereby enhancing the adaptability of the updated configuration data to the current equipment performance status of the power equipment, and further ensuring the accuracy of the configuration data.

[0094] S260: Determine update configuration data of the power equipment according to at least one parameter weight and the target configuration data.

[0095] In an optional embodiment, based on at least one parameter weight and target configuration data, update configuration data of the power equipment is determined, including: for each configuration parameter in the target configuration data, obtaining the parameter value of the configuration parameter in the target configuration data, and taking the product of the parameter value of the configuration parameter and the parameter weight as the parameter value of the configuration parameter in the update configuration data.

[0096] Exemplarily, the parameter value p of the i-th configuration parameter in the configuration data is updated i ' satisfies the following formula:

[0097] p i ′=w i ·p i

[0098] Among them, w i represents the parameter weight of the i-th configuration parameter, p i Indicates updating the parameter value of the i-th configuration parameter in the configuration data.

[0099] In another optional embodiment, based on at least one parameter weight and target configuration data, updated configuration data of the power equipment is determined, including: correcting the target configuration data based on at least one parameter weight to obtain corrected configuration data; constructing a target optimization function based on the corrected configuration data; wherein the target optimization function represents the statistical value of the penalty item corresponding to at least one configuration parameter, and the penalty item represents the sum of the penalty functions corresponding to the configuration parameter and at least one parameter feature; and minimizing the target optimization function to obtain the updated configuration data of the power equipment.

[0100] Specifically, for each configuration parameter in the target configuration data, the parameter value of the configuration parameter in the target configuration data is obtained, and the product of the parameter value of the configuration parameter and the parameter weight is used as the parameter value of the configuration parameter in the modified configuration data.

[0101] Exemplarily, the modified configuration data C is expressed as C=[p′1, p′2, ..., p′ n ], when the statistical value is the sum value, the objective optimization function P(C) satisfies the following formula:

[0102]

[0103] Among them, p′ n represents the parameter value of the nth configuration parameter, Penalty(i) represents the penalty term corresponding to the i-th configuration parameter in the modified configuration data, α i Represents the weight coefficient of the penalty term for modifying the i-th configuration parameter in the configuration data.

[0104] Exemplarily, the penalty term Penalty(i) of the i-th configuration parameter satisfies the following formula:

[0105] Penalty(i)=f network (p′ i )+f device (p′ i )+f history (p′ i )

[0106] Among them, f network (p′ i ), f device (p′ i ) and f history (p′ i ) represents the penalty function corresponding to the i-th configuration parameter and the device network characteristics, performance requirement characteristics and historical configuration characteristics respectively.

[0107] In an optional embodiment, the penalty function f corresponding to the device network characteristics network (p′ i ) satisfies the following formula:

[0108]

[0109] In an optional embodiment, the penalty function f corresponding to the performance requirement characteristic device (p i ′) satisfies the following formula:

[0110]

[0111] Among them, p′ i Indicates the parameter value of the i-th configuration parameter in the modified configuration data, S network Indicates the configuration parameter set in the modified configuration data that matches the device network characteristics. represents the maximum parameter value of the i-th configuration parameter, S device Indicates the configuration parameter set in the modified configuration data that matches the performance requirement characteristics. represents the maximum parameter value of the i-th configuration parameter, and λ1 and λ2 represent the adjustment coefficients.

[0112] Exemplary configuration parameter set S that matches the device network characteristics network Including but not limited to configuration parameters such as network bandwidth, network delay, data transmission rate and signal strength, and configuration parameter set S that matches the performance requirement characteristics device Including but not limited to configuration parameters such as resource demand, power consumption, data storage capacity and data collection frequency.

[0113] Specifically, the penalty function of the historical configuration feature is represented by the configuration data of the power equipment in the historical configuration stage, and can be measured, for example, by the failure rate of the power equipment or the degradation of the equipment performance.

[0114] In an optional embodiment, the penalty function f corresponding to the historical configuration feature history (p′ i ) satisfies the following formula:

[0115]

[0116] in, represents the statistical value of the historical value data of the i-th configuration parameter, and λ3 represents the adjustment coefficient.

[0117] In an optional embodiment, the historical value data is composed of at least one historical value of the configuration parameter that was successfully configured in the historical configuration phase. Specifically, the penalty function corresponding to the historical configuration feature represents the historical failure rate of the configuration parameter.

[0118] Exemplarily, the minimization algorithm includes but is not limited to a genetic algorithm, a particle swarm optimization algorithm, an ant colony algorithm, and the like.

[0119] The benefit of setting up a minimization solution is that it further improves the accuracy of the configuration data and ensures the operating performance of the power equipment, thereby reducing the probability of failure of the power equipment after the updated configuration and ensuring the stability of the power system.

[0120] S270: Send the updated configuration data as target configuration data to the power device, so that the power device performs updated configuration according to the target configuration data.

[0121] Based on the above embodiments, optionally, the method also includes: when the target configuration data is updated configuration data and the current device performance metric does not meet the performance update condition, fine-tuning the target device configuration model according to the updated configuration data to obtain an optimized target device configuration model; obtaining the model performance metric corresponding to the optimized target device configuration model; when the model performance metric meets the model performance condition, using the optimized target device configuration model as the trained target device configuration model; when the model performance metric does not meet the model performance condition, using the target device configuration model before optimization as the trained target device configuration model.

[0122] Exemplarily, the fine-tuning method may be an online gradient descent algorithm or an online update algorithm of a random forest. For example, the model parameter θ in the optimized target device configuration model k+1 Satisfies the following formula:

[0123]

[0124] Among them, θ k represents the model parameters in the target device configuration model before optimization, η represents the learning rate, represents the gradient of the loss function with respect to the model parameters θ.

[0125] Specifically, the model performance metric represents the statistical value of the index value corresponding to at least one model performance indicator. Exemplarily, the at least one model performance indicator includes but is not limited to accuracy, precision, F1 value, mean square error and AUC (Area Under the Curve), etc.

[0126] The advantage of this setting is that it realizes closed-loop management of the configuration process of power equipment, further improves the configuration accuracy of power equipment, and ensures the stability of the power system.

[0127] After the power equipment is connected to the power system, the actual operating environment of the power equipment may change. The single and fixed configuration data cannot adapt to the impact of the actual operating environment on the power equipment.

[0128] The technical solution of this embodiment obtains the current equipment performance measurement of the power equipment, and when the current equipment performance measurement meets the performance update condition, determines the parameter weight corresponding to at least one configuration parameter in the target configuration data according to the parameter characteristic data, determines the update configuration data of the power equipment according to the at least one parameter weight and the target configuration data, and sends the updated configuration data as the target configuration data to the power equipment, so that the power equipment updates the configuration according to the target configuration data, solves the problem of fixed configuration data of the power equipment, reduces the need for manual maintenance, enables the power equipment to better adapt to changes in the actual operating environment, improves the overall equipment performance of the power equipment during operation, and also helps to increase the service life of the power equipment and reduce the equipment cost of the power system.

[0129] Figure 3 This is a flow chart of another method for configuring a power device provided by an embodiment of the present invention. This embodiment further refines the training method for the target device configuration model in the above embodiment. Figure 3 As shown, the method includes:

[0130] S310: Obtain a training device data set.

[0131] In an optional embodiment, an initial device data set is obtained; wherein the initial device data set contains device attribute data corresponding to at least one training device; data preprocessing is performed on the initial device data set to obtain a training device data set; wherein the data preprocessing includes at least one of outlier processing, duplicate value processing, normalization processing, encoding processing and parameter screening.

[0132] In a specific embodiment, the outlier processing is outlier deletion or outlier correction.

[0133] Specifically, a parameter data set corresponding to each attribute parameter in the initial device data set is obtained; wherein the parameter data set contains parameter values ​​of at least one training device corresponding to the attribute parameters; for each parameter data set, the parameter data set is clustered to obtain at least two cluster sets and cluster center data corresponding to at least two cluster sets; for each cluster set, the distance between each training device in the cluster set and the cluster center data is determined based on the cluster center data of the cluster set and the parameter value corresponding to each training device in the cluster set; and the training device whose distance is greater than the distance threshold corresponding to the attribute parameter is regarded as an abnormal device.

[0134] Exemplarily, clustering algorithms used in clustering processing include but are not limited to K-means clustering algorithm, K-Medoids algorithm, hierarchical clustering algorithm and density clustering algorithm, and distance algorithms include but are not limited to Euclidean distance and Manhattan distance.

[0135] Specifically, an abnormal device indicates that the device attribute data of the training device contains an abnormal value whose distance is greater than a distance threshold corresponding to the attribute parameter. In one embodiment, when the abnormal value processing is abnormal value deletion, the device attribute data of the abnormal device is deleted from the initial device data set; when the abnormal value processing is abnormal value correction, the abnormal value of the abnormal device in the parameter data set and the parameter values ​​of other devices in the parameter data set are obtained, and the abnormal value is corrected according to the average value corresponding to the parameter values ​​of other devices.

[0136] Specifically, the other devices may be all training devices whose distance in the parameter data set is less than or equal to the distance threshold corresponding to the attribute parameter, or may be at least one training device whose distance in the parameter data set is less than or equal to the distance threshold corresponding to the attribute parameter and whose parameter value is close to the outlier.

[0137] For example, assuming that the attribute parameter is network delay, the parameter values ​​corresponding to the five training devices (training devices AE) of the parameter data set corresponding to network delay are 20, 22, 25, 15 and 200 respectively, and training device E is an abnormal device, and the abnormal value is 200. If the other devices are three training devices with a short distance including the abnormal value, the average value of the parameter values ​​of training devices AC is used as the correction value of the abnormal value, that is, the correction value = (20 + 22 + 25) / 3 = 22.33.

[0138] In a specific embodiment, when the data preprocessing includes repeated value processing, a hash algorithm, such as a SHA-256 algorithm or an MD5 algorithm, is used to convert the device attribute data X of each training device in the initial device data set into a single hash. A They are mapped to hash values ​​H(X A ). A hash value is a fixed-length number or character code. The hash value compresses device attribute data into a unique identifier for quick search.

[0139] Specifically, create a hash table to store the hash value of each training device. The hash table is a key-value pair data structure, where the key is the hash value and the value is the device attribute data of the training device. For each training device, query the hash table to see if there is a hash value that is the same as the hash value of the training device. If so, it means that the device attribute data of the training device is duplicated, and only one hash value is retained in the hash table, and other identical hash values ​​are deleted from the hash table.

[0140] In a specific embodiment, when data preprocessing includes normalization processing, a parameter data set corresponding to each attribute parameter in the initial device data set is obtained; wherein the parameter data set contains parameter values ​​corresponding to the attribute parameters of at least one training device; and normalization processing is performed on each parameter data set.

[0141] Exemplarily, the normalization process satisfies the following formula:

[0142]

[0143] Among them, x norm represents the normalized parameter value, x represents the parameter value before normalization, and x min Indicates the smallest parameter value in the parameter data set, x max Indicates the maximum parameter value in the parameter data set, x target，max Indicates the maximum value of the target mapping range, x target，min Indicates the minimum value of the target mapping range.

[0144] In a specific embodiment, when data preprocessing includes encoding processing, exemplarily, the encoding algorithm used in the encoding processing includes but is not limited to an adaptive feature encoding algorithm, one-hot encoding or label encoding, etc.

[0145] For example, when the encoding algorithm is an adaptive feature encoding algorithm, the encoding value represents the number of encoding bits of the attribute parameter, and the encoding value b corresponding to the i-th training device is i Satisfies the following formula:

[0146]

[0147] Where N is the amount of data for the training device in the initial device training set, n i Represents the number of occurrences of the parameter value of the attribute parameter in the device attribute data of the i-th training device in the initial device training set.

[0148] In a specific embodiment, when data preprocessing includes parameter screening, for each attribute parameter in the initial device data set, the correlation coefficient between the attribute parameter and the standard configuration data is determined based on the parameter data set and standard configuration data corresponding to the attribute parameter; the attribute parameters with correlation coefficients greater than the correlation coefficient threshold are used as the attribute parameters in the training device data set.

[0149] Exemplarily, the correlation coefficient algorithm includes but is not limited to the mutual information algorithm, Pearson correlation coefficient, Spearman rank correlation coefficient or Kendall rank correlation coefficient, etc.

[0150] For example, when the correlation coefficient algorithm is the mutual information algorithm, the correlation coefficient satisfies the following formula:

[0151]

[0152] Where X represents the parameter data set, Y represents the standard configuration data, p(x, y) represents the probability of the co-occurrence of the parameter value of the attribute parameter and the parameter value of the configuration parameter, p(x) represents the marginal probability of the parameter value of the attribute parameter, and p(y) represents the marginal probability of the parameter value of the configuration parameter.

[0153] The benefit of setting output preprocessing is that it ensures the accuracy and stability of the target device configuration model and improves the training speed and generalization ability of the target device configuration model.

[0154] S320, dividing the training device data set to obtain at least two training device subsets, and training the initial device configuration model according to the at least two training device subsets respectively to obtain at least two trained reference device configuration models.

[0155] Specifically, the data amounts corresponding to different training device subsets may be the same or different.

[0156] In an optional embodiment, when the initial device configuration model is a support vector machine and the kernel function is a radial basis kernel function (RBF), the initial device configuration model is trained according to at least two training device subsets to obtain at least two trained reference device configuration models, including: for each training device subset, determining a width parameter of the radial basis kernel function of the initial device configuration model according to the training device subset, and training the initial device configuration model according to the training device subset and the width parameter to obtain a trained reference device configuration model.

[0157] Specifically, the width parameter σ of the radial basis kernel function satisfies the following formula:

[0158]

[0159] Where n represents the number of device attribute data in the training device subset, x i represents the device attribute data of the i-th training device in the training device subset, x j Represents the device attribute data of the jth training device in the training device subset.

[0160] S330: Obtain loss function values ​​corresponding to at least two reference device configuration models respectively, and screen the at least two reference device configuration models according to the at least two loss function values ​​to obtain a target device configuration model.

[0161] For example, the target device configuration model K selected Satisfies the following formula:

[0162]

[0163] in, represents at least two reference device configuration models, Represents the loss function value of the reference device configuration model.

[0164] S340: In response to detecting access to the power device, acquiring device attribute data of the power device.

[0165] S350: Determine target configuration data of the power equipment according to the equipment attribute data and the trained target equipment configuration model.

[0166] S360: Send the target configuration data to the power device, so that the power device is configured according to the target configuration data.

[0167] S340-S360 in this embodiment are similar to those in the above embodiment. Figure 1 The S110-S130 shown in the figure correspond to the same or similar ones, or to the above-mentioned embodiments. Figure 2 S210 - S230 shown correspond to the same or similar ones, and are not described in detail in this embodiment.

[0168] The technical solution of this embodiment obtains a training device data set, divides the training device data set to obtain at least two training device subsets, and trains the initial device configuration model according to the at least two training device subsets to obtain at least two trained reference device configuration models, obtains loss function values ​​corresponding to the at least two reference device configuration models, and screens the at least two reference device configuration models according to the at least two loss function values ​​to obtain a target device configuration model, which solves the problem of fixed model architecture of the device configuration model, and enables the target device configuration model to better adapt to the distribution characteristics of device attribute data, especially when the device attribute data has a highly nonlinear relationship, thereby ensuring the generalization ability of the target device configuration model, further improving the configuration accuracy of the power equipment and ensuring the stability of the power system.

[0169] The following is an embodiment of a configuration device for electric power equipment provided in an embodiment of the present invention. The device and the configuration method for electric power equipment in the above-mentioned embodiment belong to the same inventive concept. For details not fully described in the embodiment of the configuration device for electric power equipment, reference can be made to the contents of the configuration method for electric power equipment in the above-mentioned embodiment.

[0170] Figure 4 This is a schematic diagram of the structure of a configuration device for power equipment provided by an embodiment of the present invention. Figure 4 As shown, the apparatus includes: a device attribute data acquisition module 410 , a target configuration data determination module 420 and a target configuration data sending module 430 .

[0171] The device attribute data acquisition module 410 is used to acquire device attribute data of the power device in response to detecting access of the power device;

[0172] The target configuration data determination module 420 is used to determine the target configuration data of the power equipment according to the equipment attribute data and the trained target equipment configuration model;

[0173] A target configuration data sending module 430 is used to send the target configuration data to the power device so that the power device is configured according to the target configuration data;

[0174] Among them, the device attribute data includes at least one of physical attribute data, communication attribute data and functional attribute data.

[0175] The technical solution of this embodiment obtains device attribute data of the power equipment in response to detecting the access of the power equipment, determines the target configuration data of the power equipment based on the device attribute data and the trained target device configuration model, and sends the target configuration data to the power equipment so that the power equipment is configured according to the target configuration data, thereby solving the problem of low configuration efficiency of the access equipment, improving the flexibility and accuracy of the equipment configuration, and ensuring the stability of the power system.

[0176] In an optional embodiment, the device further comprises:

[0177] A current device performance metric acquisition module, used to acquire the current device performance metric of the power device after sending the target configuration data to the power device so that the power device is configured according to the target configuration data;

[0178] A parameter weight determination module, used to determine the parameter weight corresponding to at least one configuration parameter in the target configuration data according to the parameter characteristic data when the current device performance metric meets the performance update condition;

[0179] An update configuration data determination module, configured to determine update configuration data of the power device according to at least one parameter weight and target configuration data;

[0180] An update configuration data sending module, used to send the update configuration data as target configuration data to the power equipment, so that the power equipment performs update configuration according to the target configuration data;

[0181] The parameter characteristic data includes at least one parameter characteristic of device network characteristics, performance requirement characteristics and historical configuration characteristics.

[0182] In an optional embodiment, the updating configuration data determination module is specifically used to:

[0183] Modifying the target configuration data according to at least one parameter weight to obtain modified configuration data;

[0184] Constructing a target optimization function according to the modified configuration data; wherein the target optimization function represents the statistical value of the penalty item corresponding to at least one configuration parameter, and the penalty item represents the summation result of the penalty function corresponding to the configuration parameter and at least one parameter feature;

[0185] The target optimization function is minimized and solved to obtain the updated configuration data of the power equipment.

[0186] In an optional embodiment, the penalty function f corresponding to the device network characteristics network (p′ i ) satisfies the following formula:

[0187]

[0188] The penalty function f corresponding to the performance requirement characteristics device (p′ i ) satisfies the following formula:

[0189]

[0190] The penalty function f corresponding to the historical configuration characteristics history (p i ′) satisfies the following formula:

[0191]

[0192] Among them, p′ i Indicates the parameter value of the i-th configuration parameter in the modified configuration data, S network Indicates the configuration parameter set in the modified configuration data that matches the device network characteristics. represents the maximum parameter value of the i-th configuration parameter, S device Indicates the configuration parameter set in the modified configuration data that matches the performance requirement characteristics. Indicates the maximum parameter value of the i-th configuration parameter, represents the statistical value of the historical value data of the i-th configuration parameter, and λ1, λ2, and λ3 represent the adjustment coefficients.

[0193] In an optional embodiment, the parameter weight determination module is specifically used to:

[0194] For each configuration parameter in the target configuration data, determining the device performance increment of the power device according to the current device performance metric;

[0195] Taking the weighted sum of the configuration weights corresponding to the at least one parameter feature and the configuration parameter as the reference weight of the configuration parameter;

[0196] Determine parameter weights of configuration parameters based on reference weights and device performance increments;

[0197] The device performance increment represents a metric increment of the current device performance metric compared to the previous device performance metric of the power device in the previous configuration update phase.

[0198] In an optional embodiment, the device further comprises:

[0199] A training device data set acquisition module is used to acquire a training device data set; wherein the training device data set includes device attribute data corresponding to at least two training devices respectively;

[0200] A reference device configuration model training module, used to divide the training device data set to obtain at least two training device subsets, and train the initial device configuration model according to the at least two training device subsets to obtain at least two trained reference device configuration models;

[0201] The target device configuration model determination module is used to obtain the loss function values ​​corresponding to at least two reference device configuration models respectively, and screen the at least two reference device configuration models according to the at least two loss function values ​​to obtain the target device configuration model.

[0202] In an optional embodiment, the training device data set acquisition module is specifically used to:

[0203] Acquire an initial device data set; wherein the initial device data set includes device attribute data corresponding to at least one training device;

[0204] The initial device data set is preprocessed to obtain a training device data set; wherein the data preprocessing includes at least one of outlier processing, duplicate value processing, normalization processing, encoding processing and parameter screening.

[0205] The configuration device of the electric power equipment provided in the embodiment of the present invention can execute the configuration method of the electric power equipment provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0206] Figure 5 A schematic diagram of the structure of an electronic device provided for one embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0207] like Figure 5As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor 11, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the ROM 12 or the computer program loaded from the storage unit 18 to the RAM 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0208] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information or data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0209] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSP), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as the configuration method of the power equipment provided in the above embodiments.

[0210] In some embodiments, the configuration method of the power device provided in the above embodiments may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps in the configuration method of the power device described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the configuration method of the power device in any other appropriate manner (e.g., by means of firmware).

[0211] Various embodiments of the systems and techniques described above herein may be implemented in the following systems or combinations thereof: digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard parts (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0212] The computer program for implementing the configuration method of the power equipment of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or the block diagram are implemented. The computer program can be executed completely on the machine, partially on the machine, partially on the machine as an independent software package and partially on a remote machine, or completely on a remote machine or server.

[0213] In the context of the present application, a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device or equipment. A computer readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium may be a machine readable storage medium. Examples of machine readable storage media may include electrical connections based on at least one line, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM), flash memories, optical fibers, portable compact disc read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0214] To provide interaction with a user, the systems and techniques described herein may be implemented on a terminal device having: a display device (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the terminal device. Other types of devices may also provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0215] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a Local Area Network (LAN), a Wide Area Network (WAN), a blockchain network, and the Internet.

[0216] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and virtual private servers (VPS) services.

[0217] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0218] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for configuring an electric power device, characterized in that: include: In response to detecting access to an electric device, acquiring device attribute data of the electric device; Determining target configuration data of the power equipment according to the equipment attribute data and the trained target equipment configuration model; sending the target configuration data to the electric device so that the electric device is configured according to the target configuration data; The device attribute data includes at least one of physical attribute data, communication attribute data and functional attribute data.

2. The method according to claim 1, characterized in that After sending the target configuration data to the electric device so that the electric device is configured according to the target configuration data, the method further includes: Obtaining current device performance metrics of the electrical device; In a case where the current device performance metric satisfies the performance update condition, determining, according to the parameter characteristic data, a parameter weight corresponding to at least one configuration parameter in the target configuration data; determining updated configuration data of the electrical device according to at least one parameter weight and the target configuration data; Sending the updated configuration data as target configuration data to the electric device, so that the electric device performs updated configuration according to the target configuration data; The parameter characteristic data includes at least one parameter characteristic of device network characteristics, performance requirement characteristics and historical configuration characteristics.

3. The method according to claim 2, characterized in that The step of determining the updated configuration data of the electric power device according to at least one parameter weight and the target configuration data comprises: Modifying the target configuration data according to at least one parameter weight to obtain modified configuration data; Constructing a target optimization function according to the modified configuration data; wherein the target optimization function represents a statistical value of a penalty term corresponding to at least one configuration parameter, and the penalty term represents a summation result of penalty functions corresponding to the configuration parameter and at least one parameter feature; The objective optimization function is minimized to obtain updated configuration data of the power equipment.

4. The method according to claim 3, characterized in that The penalty function f corresponding to the device network characteristics network (p′ i ) satisfies the following formula: The penalty function f corresponding to the performance requirement characteristics device (p′ i ) satisfies the following formula: The penalty function f corresponding to the historical configuration feature history (p′ i ) satisfies the following formula: Among them, p′ i represents the parameter value of the i-th configuration parameter in the modified configuration data, S network represents a configuration parameter set in the modified configuration data that matches the network characteristics of the device, represents the maximum parameter value of the i-th configuration parameter, S device represents a configuration parameter set in the modified configuration data that matches the performance requirement characteristics, Indicates the maximum parameter value of the i-th configuration parameter, represents the statistical value of the historical value data of the i-th configuration parameter, and λ1, λ2, and λ3 represent the adjustment coefficients.

5. The method according to claim 2, characterized in that: The determining, according to the parameter characteristic data, a parameter weight corresponding to at least one configuration parameter in the target configuration data, comprises: For each configuration parameter in the target configuration data, determining a device performance increment of the power device according to the current device performance metric; taking a weighted sum of the configuration weights corresponding to the at least one parameter feature and the configuration parameter as a reference weight of the configuration parameter; Determining a parameter weight of the configuration parameter according to the reference weight and the device performance increment; The device performance increment represents a metric increment of the current device performance metric compared to the previous device performance metric of the power device in the previous configuration update phase.

6. The method according to any one of claims 1 to 5, characterized in that: The method comprises: Acquire a training device data set; wherein the training device data set includes device attribute data corresponding to at least two training devices respectively; Dividing the training device data set to obtain at least two training device subsets, and training the initial device configuration model according to the at least two training device subsets respectively to obtain at least two trained reference device configuration models; Loss function values ​​corresponding to at least two reference device configuration models are obtained, and the at least two reference device configuration models are screened according to the at least two loss function values ​​to obtain a target device configuration model.

7. The method according to claim 6, characterized in that The step of obtaining a training device data set includes: Acquire an initial device data set; wherein the initial device data set includes device attribute data corresponding to at least one training device; The initial device data set is subjected to data preprocessing to obtain a training device data set; wherein the data preprocessing includes at least one of outlier processing, duplicate value processing, normalization processing, encoding processing and parameter screening.

8. A configuration device for electric power equipment, characterized in that: include: A device attribute data acquisition module, configured to acquire device attribute data of the power device in response to detecting access to the power device; A target configuration data determination module, used to determine the target configuration data of the power device according to the device attribute data and the trained target device configuration model; a target configuration data sending module, used to send the target configuration data to the electric device, so that the electric device is configured according to the target configuration data; The device attribute data includes at least one of physical attribute data, communication attribute data and functional attribute data.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the configuration method of the power equipment according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the configuration method of the power equipment according to any one of claims 1 to 7 when executed.

11. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the configuration method of the power equipment according to any one of claims 1 to 7.

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