Intelligent risk early warning method for virtual power plant and related equipment

Abstract

The invention provides an intelligent risk early warning method for a virtual power plant and related equipment, and is applied to the technical field of data processing. The method comprises the steps of obtaining to-be-evaluated virtual power plant information; processing the to-be-assessed virtual power plant information to generate a target virtual power plant operation risk influence factor set; processing the target virtual power plant operation risk influence factor set to generate target virtual power plant risk identification information; obtaining an initial virtual power plant risk early warning model and a training sample set; processing the training sample set based on a preset processing rule to generate a training set and a verification set; processing the initial virtual power plant risk early warning model based on the training set and the verification set to generate a target virtual power plant risk early warning model; processing the positive ideal solution and the negative ideal solution based on a target virtual power plant risk early warning model to generate a virtual power plant risk early warning value; and processing the virtual power plant risk early warning value to generate a virtual power plant risk level.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an intelligent risk early warning method for a virtual power plant and related equipment. Background Art

[0002] With the continuous development of virtual power plant technology, multi-energy complementary virtual power plants with electric vehicles and distributed photovoltaics as the main body are gaining more and more attention. Virtual power plants are an important direction for the deep integration of energy technology and information technology. In the process of virtual power plant operation, it can improve the adaptability of my country's power market to a high proportion of new energy and realize the construction of an integrated intelligent energy system of power source, grid, load and storage. At the same time, virtual power plants are an emerging energy technology.

[0003] Since virtual power plants aggregate a large number of power batteries (electric vehicles) and energy storage batteries, the safety of lithium batteries during the charging and discharging process is still a focus of the industry. The publication number is CN117196173B, and the patent name is a distributed scheduling method for virtual power plants considering operation risks and network transmission. The steps disclosed are: 1) Establishing the objective function of the distributed scheduling model of virtual power plants considering operation risks and network transmission; 2) Establishing the constraints of the distributed scheduling model of virtual power plants considering operation risks and network transmission; 3) Solving the distributed scheduling model of virtual power plants considering operation risks and network transmission to obtain the distributed scheduling decision of virtual power plants; 4) Based on step (3), calculating the risk loss cost of virtual power plants; 5) Establishing a virtual power plant profit distribution model based on generalized Nash bargaining to realize the profit distribution of distributed scheduling of virtual power plants. The existing virtual power plant risk control technology focuses on managing and controlling risks from the level of power dispatch optimization, and lacks scientific grading assessment and effective early warning functions for risk levels. In addition, the existing technology does not take into account the impact of the current development of digital technology and systems on the risk early warning process of virtual power plants.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of this application is to provide an intelligent risk warning method and related equipment for a virtual power plant, to overcome the problems existing in the prior art to at least a certain extent, to conduct an overall graded assessment of multiple risk factors in the operation of a virtual power plant, to build a multi-level risk warning capability for a virtual power plant, to promptly discover potential risk points in a virtual power plant, to intervene and resolve them after a low-level risk warning, to promptly curb the spread of risks in a virtual power plant and their expansion to high-level risks, to avoid major economic losses for virtual power plant enterprises, and to enhance the ability of technical managers to effectively measure the operational risks of virtual power plants.

[0006] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present invention.

[0007] According to one aspect of the present application, a smart risk warning method for a virtual power plant is provided, comprising: obtaining information of a virtual power plant to be evaluated; processing the information of the virtual power plant to be evaluated to generate a set of operating risk influencing factors of a target virtual power plant; processing the set of operating risk influencing factors of the target virtual power plant to generate risk identification information of a target virtual power plant, wherein the risk identification information of the target virtual power plant includes a positive ideal solution and a negative ideal solution; obtaining an initial virtual power plant risk warning model that matches the risk identification information of the target virtual power plant and a training sample set that matches the initial virtual power plant risk warning model; processing the training sample set based on preset processing rules to generate a training set and a verification set; processing the initial virtual power plant risk warning model based on the training set and the verification set to generate a target virtual power plant risk warning model; processing the positive ideal solution and the negative ideal solution based on the target virtual power plant risk warning model to generate a virtual power plant risk warning value; processing the virtual power plant risk warning value to generate a virtual power plant risk level.

[0008] In one embodiment of the present application, the virtual power plant information to be evaluated is processed to generate a set of target virtual power plant operation risk influencing factors, including: processing the virtual power plant information to be evaluated to generate physical attribute information of the virtual power plant information to be evaluated; performing feature extraction processing on the physical attribute information of the virtual power plant information to be evaluated to generate a number of feature sample types; performing feature screening processing on the several feature sample types to generate real-time feature sample data; and generating a set of target virtual power plant operation risk influencing factors based on the several real-time feature sample data.

[0009] In one embodiment of the present application, the target virtual power plant operation risk impact factor set is processed to generate the target virtual power plant risk identification information, including: obtaining the target virtual power plant operation rated value matrix, the weight vector of the target virtual power plant operation risk impact factor and the initial evaluation matrix; processing the initial evaluation matrix and the target virtual power plant operation rated value matrix to generate the deviation matrix of the target virtual power plant operation risk factor; processing the deviation matrix of the target virtual power plant operation risk factor and the weight vector of the target virtual power plant operation risk factor to generate the weighted matrix of the target virtual power plant operation risk factor; processing the weighted matrix of the target virtual power plant operation risk factor to generate a positive ideal solution and a negative ideal solution; the method includes a calculation formula for calculating the positive ideal solution, and the calculation formula is:

[0010]

[0011] The method includes a calculation formula for calculating a negative ideal solution, the calculation formula being:

[0012]

[0013]

[0014] z ij =x ij -a j , i = 1, 2, ..., m; j = 1, 2, ..., n; where, is a positive ideal solution, is a negative ideal solution, z ij is the deviation matrix of the target virtual power plant operation risk factor, ω j is the weight vector of the target virtual power plant operation risk factor, a j Run the rating matrix for the target virtual power plant.

[0015] In one embodiment of the present application, a training data set is processed based on preset processing rules to generate a training set and a validation set, including: obtaining other virtual power plant information that matches the target virtual power plant risk identification information; performing feature extraction on other virtual power plant information to generate a feature data set; obtaining any number of data features in the feature data set; generating adjacent features based on the distance between the any number of data features and other numbers of data features of the same category, wherein the adjacent features include a preset number of the any number of data features; determining a sampling ratio based on the number of each data feature in the training set; determining a sampling ratio based on the sampling ratio; sampling the adjacent features based on the sampling ratio to generate a preset number of sampling features; generating multiple data groups based on any data feature and each sampling feature, wherein each data group includes a preset number of data samples, wherein each data group includes a preset number of data samples, wherein at least one data sample includes identification information; processing multiple data groups to generate a training set and a validation set, wherein both the training set and the validation set include a number of data features.

[0016] In one embodiment of the present application, the positive ideal solution and the negative ideal solution are processed based on the target virtual power plant risk warning model to generate a virtual power plant risk warning value, including:

[0017] Processing the positive ideal solution based on the target virtual power plant risk warning model to generate a first Euclidean distance;

[0018] Processing the negative ideal solution based on the target virtual power plant risk warning model to generate a second Euclidean distance;

[0019] The target virtual power plant risk warning model includes a calculation formula for calculating the first Euclidean distance, which is:

[0020]

[0021] in, is the risk factor x of the virtual power plant at time t ij To the positive ideal solution The Euclidean distance of

[0022] The target virtual power plant risk warning model includes a calculation formula for calculating the second Euclidean distance, which is:

[0023]

[0024] in, is the risk factor x of the virtual power plant at time t ij To negative ideal solution The Euclidean distance of .

[0025] In one embodiment of the present application, the positive ideal solution and the negative ideal solution are processed based on the target virtual power plant risk warning model to generate a virtual power plant risk warning value, including: the target virtual power plant risk warning model includes a calculation formula for calculating the virtual power plant risk warning value, and the calculation formula is:

[0026] in, is the risk warning value of the virtual power plant.

[0027] In one embodiment of the present application, after the risk warning value of the virtual power plant is processed to generate the risk level of the virtual power plant, it includes: obtaining a preset virtual power plant risk warning table; processing the risk level of the virtual power plant based on the preset virtual power plant risk warning table to generate a target warning time and a warning level matching the target warning time; processing the target warning time and the warning level matching the target warning time to generate a risk association factor, wherein the risk association factor is generated based on the virtual power plant operation risk influencing factor corresponding to the same warning level and adjacent warning times; processing the risk management factor to generate risk warning information.

[0028] Another aspect of the present application is an intelligent risk warning device for a virtual power plant, characterized in that it includes: an acquisition module for acquiring virtual power plant information to be evaluated; acquiring an initial virtual power plant risk warning model that matches the target virtual power plant risk identification information and a training sample set that matches the initial virtual power plant risk warning model; a processing module for processing the virtual power plant information to be evaluated to generate a set of target virtual power plant operation risk influencing factors; processing the target virtual power plant operation risk influencing factor set to generate target virtual power plant risk identification information, wherein the target virtual power plant risk identification information includes a positive ideal solution and a negative ideal solution; processing the training sample set based on preset processing rules to generate a training set and a verification set; processing the initial virtual power plant risk warning model based on the training set and the verification set to generate a target virtual power plant risk warning model; processing the positive ideal solution and the negative ideal solution based on the target virtual power plant risk warning model to generate a virtual power plant risk warning value; processing the virtual power plant risk warning value to generate a virtual power plant risk level.

[0029] According to another aspect of the present application, an electronic device is provided, characterized in that it comprises: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to execute the above-mentioned intelligent risk warning method for the virtual power plant by executing the executable instructions.

[0030] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a second processor, the intelligent risk warning method for the virtual power plant described above is implemented.

[0031] According to another aspect of the present application, a computer program product is provided, including a computer program, characterized in that when the computer program is executed by a third processor, the above-mentioned intelligent risk warning method for the virtual power plant is implemented.

[0032] The present application provides an intelligent risk warning method and related equipment for a virtual power plant, in which a server conducts an overall graded assessment of multiple risk factors in the operation of a virtual power plant, builds a multi-level risk warning capability for a virtual power plant, promptly discovers potential risk points in the virtual power plant, intervenes and resolves them after a low-level risk warning, and promptly curbs the spread of risks in the virtual power plant and their expansion to high-level risks, thereby avoiding major economic losses for virtual power plant enterprises and enhancing the ability of technical managers to effectively measure the operational risks of virtual power plants.

[0033] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flow chart showing an intelligent risk early warning method for a virtual power plant provided by an embodiment of the present application;

[0035] Figure 2 A schematic diagram of the structure of an intelligent risk warning device for a virtual power plant provided in one embodiment of the present application is shown;

[0036] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present application is shown;

[0037] Figure 4 A schematic diagram of a storage medium provided by an embodiment of the present application is shown;

[0038] Figure 5 An interface design diagram of an intelligent risk early warning system for a virtual power plant provided by an embodiment of the present application is shown;

[0039] Figure 6 An interface design diagram of another intelligent risk warning system for a virtual power plant provided in one embodiment of the present application is shown. DETAILED DESCRIPTION

[0040] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0041] Combine the following Figure 1 To describe the intelligent risk warning method of a virtual power plant according to an exemplary embodiment of the present application. It should be noted that the following application scenarios are only shown to facilitate understanding of the spirit and principles of the present application, and the implementation of the present application is not limited in this regard. On the contrary, the implementation of the present application can be applied to any applicable scenario.

[0042] In one implementation, the present application also proposes an intelligent risk warning method and related equipment for a virtual power plant. Figure 1 The following schematically shows a flow chart of an intelligent risk warning method for a virtual power plant according to an embodiment of the present application. Figure 1 As shown, the method is applied to a server, comprising:

[0043] S101, obtaining information of a virtual power plant to be evaluated.

[0044] In one implementation, a virtual power plant (VPP) is a new type of power coordination management system implemented through information and communication technology. It aggregates distributed power sources, energy storage equipment, controllable loads and other distributed resources through software and intelligent technology to achieve optimized control and scheduling of these resources. A virtual power plant is not a physical power plant, but a smart grid technology that can participate in the operation and scheduling of the power grid, provide electricity and ancillary services, and is flexible and economical.

[0045] The key technologies of virtual power plants include Internet of Things communication technology, smart metering technology, energy Internet technology, load forecasting technology and optimization and control technology. Internet of Things communication technology receives status information and sends control signals through edge gateways, smart metering technology provides the basis for real-time monitoring and control, and energy Internet technology realizes the two-way flow and sharing of energy. Load forecasting technology predicts the temporal and spatial distribution of power loads, and optimization and control technology uses data-driven methods to optimize resource allocation. By obtaining the relevant actual operating data of the virtual power plant information to be evaluated, the target virtual power plant can be risk assessed, where the relevant actual operating data of the virtual power plant information to be evaluated includes but is not limited to real-time voltage, real-time current, temperature, humidity, load power, and load quantity.

[0046] S102: Process the virtual power plant information to be evaluated to generate a set of operating risk influencing factors of a target virtual power plant.

[0047] In one implementation, the virtual power plant information to be evaluated is processed to generate physical attribute information of the virtual power plant information to be evaluated, feature extraction is performed on the physical attribute information of the virtual power plant information to be evaluated to generate a number of feature sample types, feature screening is performed on the number of feature sample types to generate real-time feature sample data, and a set K of target virtual power plant operation risk influencing factors is generated based on the number of real-time feature sample data, where K = {k1, k2, …, k n There are n influencing factors in}, and the weight vector of the influencing factor is calculated by the eigenvector method integrating information entropy. The weight vector result is recorded as ω={ω1,ω2,…,ω n}, where ω j ≥0,j=1,2,…,n, vector satisfies The numerical constraints required.

[0048] Specifically, in the process of processing the virtual power plant information to be evaluated, first, physical attribute information is extracted from the original data of the virtual power plant, including voltage, current, temperature, equipment status, etc. These data can be obtained in real time through smart metering devices and sensors, and preliminary processing such as data cleaning and standardization is performed.

[0049] After obtaining the physical property information, the next step is to perform feature extraction. Feature extraction is a key step in understanding and analyzing the status of the virtual power plant. It can be performed from multiple dimensions such as "real-time voltage, real-time current, air temperature, humidity, load power, and load quantity". Among them, electrical signal data can be collected by sensors and used to analyze the working status and load conditions of virtual power plants. Feature extraction may include the effective value, root mean square value, waveform parameters, etc. of voltage and current. These features help monitor the operating status of equipment and prevent potential failures. Temperature data is particularly important for virtual power plants because temperature-sensitive loads account for a large proportion of electricity demand. Feature extraction may involve time series analysis of temperature, such as using time series generative adversarial networks (TimeGAN) to expand small sample data and then predicting through convolution-long short-term memory neural networks (CNN-LSTM). Humidity measurement can reflect environmental conditions and is particularly important for maintaining power equipment. The extraction of humidity features may include statistical analysis of relative humidity and correlation analysis with equipment failure rate. Load power is an important indicator for measuring the output capacity of virtual power plants. Feature extraction may include power distribution characteristics, peak power, power change rate, etc. These features help optimize the allocation and scheduling of power resources. The number of loads reflects the number of devices or users connected to the virtual power plant. Feature extraction may involve time series analysis of the number of loads, as well as trends and patterns of changes in the number of loads.

[0050] By extracting and processing the features of these physical property information, key information that helps describe and understand the state of the virtual power plant can be generated, thereby providing support for the optimized operation and risk management of the virtual power plant. For example, through feature extraction, a risk evaluation index library for virtual power plants can be constructed, and then the cloud model evaluation method based on Bayesian feedback correction can be used to obtain the evaluation scores of indicators at all levels and evaluate the risk level of the virtual power plant. In addition, the results of feature extraction can also be used for non-invasive load identification, extracting features through the current waveform diagram of the visual image to improve the accuracy of load identification.

[0051] After feature extraction, several feature sample types are generated. These sample types can be based on different working conditions and operating conditions to ensure that the performance and status of the virtual power plant can be fully evaluated. Feature sample types are processed for feature screening to generate real-time feature sample data. Feature screening is a key step that helps reduce the complexity of the model, improve the performance of the model, and speed up training. Feature screening can be performed using statistical, model-based, and embedded methods.

[0052] Finally, a set of influencing factors of the target virtual power plant operation risk is generated based on real-time characteristic sample data. This can be achieved by building a risk assessment index library and evaluation model, such as using ordered weighted average (OWA) and improved analytic hierarchy process (AHP) to weight the indicators, and using the cloud model evaluation method based on Bayesian feedback correction to obtain the evaluation scores of indicators at all levels, so as to comprehensively evaluate the operation risk of the virtual power plant and provide support and guidance for the construction and operation of the virtual power plant.

[0053] S103: Process the target virtual power plant operation risk influencing factor set to generate target virtual power plant risk identification information.

[0054] In one implementation, the target virtual power plant operation rating matrix (A = {a1, a2, ..., a n}), the weight vector of the target virtual power plant operation risk influencing factors ω={ω1,ω2,…,ω n}, initial evaluation matrix. Specifically, the target virtual power plant operation risk is related to six factors, namely real-time voltage, real-time current, temperature, humidity, load power, and load quantity. The weight vectors of the target virtual power plant operation risk influencing factors are [0.2, 0.2, 0.2, 0.1, 0.2, 0.1], and the target virtual power plant operation rated value matrix is ​​[380, 200, 26, 30, 5000, 1200]. In addition, the warning period is from 9:00 am to 12:00 pm, and the risk warning level is updated every hour.

[0055] Based on the intelligent data acquisition equipment of the virtual power plant, the data of the operation of the virtual power plant is recorded, that is, the initial evaluation matrix, as shown below:

[0056]

[0057] The initial evaluation matrix and the target virtual power plant operation rated value matrix are processed to generate the deviation matrix of the target virtual power plant operation risk factor. The deviation matrix is ​​calculated as follows: the value of the initial matrix at each moment (each row) is subtracted from the corresponding value of the target virtual power plant operation rated value matrix [380, 200, 26, 30, 5000, 1200] to obtain the following matrix.

[0058] Warning moment Real-time voltage Real-time current Temperature humidity Load power Load quantity 9:00 -3 9 0 0 -3500 -700 10:00 1 12 2 5 -3000 -500 11:00 -1 9 6 8 0 -200 12:00 -2 50 9 10 3000 300

[0059] The deviation matrix and the weight vector of the target virtual power plant operation risk factor are processed to generate a weighted matrix of the target virtual power plant operation risk factor. Specifically, each row value in the deviation matrix is ​​multiplied by the value of the weighting matrix [0.2, 0.2, 0.2, 0.1, 0.2, 0.1], and the calculated results are as follows.

[0060]

[0061] The target virtual power plant risk identification information includes a positive ideal solution and a negative ideal solution. The weighted matrix of the target virtual power plant operation risk factor is processed to generate a positive ideal solution and a negative ideal solution. The present application includes a calculation formula for calculating the positive ideal solution, which is:

[0062]

[0063] The method includes a calculation formula for calculating a negative ideal solution, the calculation formula being:

[0064]

[0065] z ij =x ij -a j ,i=1,2,…,m;j=1,2,…,n;

[0066] in, is a positive ideal solution, is a negative ideal solution, z ij is the deviation matrix of the target virtual power plant operation risk factor, ω j is the weight vector of the target virtual power plant operation risk factor, a j Run the rating matrix for the target virtual power plant.

[0067] For example, the larger the value of all risk elements, the greater the risk. The positive ideal solution is the maximum value of each column in the weighted matrix of the target virtual power plant operation risk factor; the negative ideal solution is the minimum value of each column in the weighted matrix of the target virtual power plant operation risk factor. That is, the positive ideal solution is: [0.2, 10, 1.8, 1, 600, 30], and the negative ideal solution is: [-0.6, 1.8, 0, 0, -700, -70].

[0068] S104, obtaining an initial virtual power plant risk warning model that matches the target virtual power plant risk identification information and a training sample set that matches the initial virtual power plant risk warning model.

[0069] In one implementation, in order to construct an initial virtual power plant risk warning model, the applicant focuses on several key aspects: operational risk, economic risk, safety risk and management risk. These risk factors can be quantified and evaluated through a series of indicators to achieve classification and early warning of virtual power plant operation risks.

[0070] It involves equipment availability, failure rate, power outage events, etc. For example, equipment availability factor, equipment failure shutdown rate and power outage time caused by relay protection are all important third-level indicators, which can reflect the operating efficiency and reliability of the virtual power plant. It includes the operation and maintenance cost of renewable energy power generation system, the purchase and sale price of energy storage system, the response rate of controllable load, etc. These indicators help to evaluate the economic feasibility and market competitiveness of virtual power plants. Data security, information security index, compliance with business information system data standards, etc. are all key indicators that can reveal the potential risks of virtual power plants in information security. It involves technological innovation risks, investment / merger and acquisition risks, marketing management risks, etc. These indicators help to evaluate the internal management efficiency and external market adaptability of virtual power plants.

[0071] After building the initial virtual power plant risk warning model, the applicant needs to develop a matching training sample set. This sample set should contain historical data covering all the above risk factors and corresponding indicators. By analyzing this data, the model can be trained to identify patterns of different risk levels and predict possible risks in the future.

[0072] In order to improve the ability of technical managers to effectively measure the operational risks of virtual power plants, a cloud model evaluation method based on Bayesian feedback correction can be used. This method can weight indicators through ordered weighted average (OWA) and improved analytic hierarchy process (AHP), and obtain evaluation scores for indicators at all levels, thereby evaluating the risk level of virtual power plants. By comprehensively considering multiple risk factors and using appropriate evaluation tools, a multi-level risk warning capability can be effectively constructed to provide support for the stable operation of virtual power plants.

[0073] S105, processing the training sample set based on preset processing rules to generate a training set and a validation set.

[0074] In one implementation, other virtual power plant information matching the risk identification information of the target virtual power plant is obtained, and features of other virtual power plant information are extracted to generate a feature data set. Specifically, relevant operating data is collected from different virtual power plants, including but not limited to physical attribute information such as real-time voltage, real-time current, temperature, humidity, load power and load quantity, which are the basis for constructing a feature data set. The collected data is processed to extract key information that can describe and understand the state of the virtual power plant. For example, voltage stability, current fluctuation and other features can be extracted from real-time voltage and current data; the impact of environmental conditions on equipment operation can be extracted from temperature and humidity data; and demand response capabilities and system load characteristics can be extracted from load power and quantity. Based on feature extraction, the most representative features for virtual power plant risk assessment are screened out through statistical analysis or machine learning methods. This step is to reduce the complexity of the data set and improve the generalization ability and prediction accuracy of the model.

[0075] Based on the distance between any number of data features and other number of data features of the same category, adjacent features are generated, wherein the adjacent features include a preset number of any number of data features; in the field of data analysis and machine learning, generating adjacent features based on the distance between data features is a feature engineering method that aims to discover the relationship and pattern between features. Specifically, a certain number of data features are selected from the feature data set, which can reflect the use of electronic certificates, and the distance between any data feature and other data features of the same category is calculated using a distance metric (such as Euclidean distance, Manhattan distance or cosine similarity). According to the calculated distance, other data features closest to any data feature are determined, and these features are considered to be adjacent. A preset number is set, that is, the number of data features that are expected to be included in the adjacent feature set. For each data feature, the preset number of data features closest to it is selected to form an adjacent feature set, and the adjacent feature set is analyzed to identify the potential relationships and patterns between the features, which may be crucial to understanding the use of electronic certificates, and the importance of each data feature in the adjacent feature set is evaluated to determine which features are most critical to predicting the target variable. That is, which places may users go to in which time periods, and whether there is a sequence of appearance between different places, etc.

[0076] If there are too many features in the adjacent feature set, it may be necessary to apply dimensionality reduction techniques (such as principal component analysis PCA) to reduce the number of features while retaining important information. The generated adjacent feature set is used to train the machine learning model to predict or classify the use of electronic certificates, evaluate the performance of the model, such as accuracy, recall rate, and F1 score, and optimize the model as needed. It may be necessary to adjust the preset number and distance measurement method multiple times to find the optimal feature set. This method can better understand the complexity of the use of electronic certificates and improve the predictive ability of the early warning model. This is very important for the security management and risk assessment of electronic certificates. At the same time, this also helps to improve the model's ability to identify abnormal behaviors and ensure the security and reliability of the electronic certificate system.

[0077] Based on the number of data features in the training set, a sampling ratio is determined; based on the sampling ratio, a sampling ratio is determined; based on the sampling ratio, adjacent features are sampled to generate a preset number of sampling features; based on any data feature and each sampling feature, multiple data groups are generated, wherein each data group contains a preset number of data samples, wherein each data group contains a preset number of data samples, wherein at least one data sample includes identification information.

[0078] In data analysis and machine learning, the sampling ratio and sampling rate are determined based on the number of data features in the training set. By counting the number of each data feature in the training set, the distribution of different features can be understood, and the sampling rate can be determined according to the number of data features and the size of the data set. The sampling rate can help applicants decide how many samples to extract from each feature, and calculate the specific sampling rate based on the sampling rate. The sampling rate is the ratio of the actual number of samples to the total number. The adjacent features are sampled using the sampling rate to generate a preset number of sampling features. This process ensures the representativeness and diversity of the samples. Through the sampling process, a set of sampling features is generated, which will be used for subsequent data analysis and model training. Multiple data sets are constructed based on any data feature and each sampling feature. Each data set contains a preset number of data samples. Ensure that the samples in each data set are diverse and contain at least one sample with identification information, which helps the model learn the feature performance in different situations. Use these data sets for model training to evaluate the contribution of different features to the prediction target. Use the sampled feature set to train the machine learning model, and use the validation set to evaluate the performance of the model. According to the results of model training and validation, the sampling rate and rate may need to be adjusted to optimize the prediction ability of the model. With this approach, large-scale data sets can be processed efficiently while maintaining data quality and model generalization capabilities, helping to identify potential security risks and abnormal behaviors.

[0079] S106: Process the initial virtual power plant risk warning model based on the training set and the validation set to generate a target virtual power plant risk warning model.

[0080] In one implementation, the screened features are integrated into a feature data set, which will be used to train and verify the virtual power plant risk warning model. The data set should contain labels, that is, the risk level or category corresponding to each data, to facilitate model learning. The initial virtual power plant risk warning model is trained using the constructed feature data set, and the performance of the model is evaluated through cross-validation and other methods. The model should be able to conduct an overall graded assessment of multiple risk factors in the operation of the virtual power plant and build a multi-level warning capability.

[0081] Use the validation set to evaluate the trained model and check the model's performance indicators, such as accuracy, recall, F1 score, ROC-AUC, etc. In order to ensure the stability and generalization ability of the model, use the cross-validation method to further evaluate the model, and optimize the model based on the evaluation results. This may include adjusting the model structure, reselecting features, using feature selection algorithms, adjusting hyperparameters, etc.

[0082] Use an independent validation set or model evaluation techniques (such as confusion matrix analysis) to verify the model's predictive ability to ensure that the model accurately identifies risks. Based on the model's performance on the validation set, iteratively update the model until it reaches the expected performance standard. Deploy the trained and verified final model to the production environment for real-time monitoring and early warning of virtual power plant operational risks. Through the above steps, an effective virtual power plant risk early warning model can be constructed, and a matching training sample set can be generated to provide support for the stable operation and risk management of virtual power plants.

[0083] S107, processing the positive ideal solution and the negative ideal solution based on the target virtual power plant risk warning model to generate a virtual power plant risk warning value.

[0084] In one implementation, the positive ideal solution is processed based on the target virtual power plant risk warning model to generate a first Euclidean distance; the negative ideal solution is processed based on the target virtual power plant risk warning model to generate a second Euclidean distance; the target virtual power plant risk warning model includes a calculation formula for calculating the first Euclidean distance, and the calculation formula is:

[0085]

[0086] in, is the risk factor x of the virtual power plant at time t ij To the positive ideal solution The Euclidean distance of

[0087] The target virtual power plant risk warning model includes a calculation formula for calculating the second Euclidean distance, which is:

[0088]

[0089] in, is the risk factor x of the virtual power plant at time t ij To negative ideal solution The Euclidean distance of .

[0090] In another implementation, the target virtual power plant risk warning model includes a calculation formula for calculating the risk warning value of the virtual power plant, and the calculation formula is:

[0091]

[0092] in, is the risk warning value of the virtual power plant.

[0093] According to the positive and negative ideal points of each risk factor, combined with the calculation formulas of the first Euclidean distance and the second Euclidean distance, the Euclidean distances of the corresponding data at the above four moments are calculated, as shown below:

[0094]

[0095] Combined with the above calculation formula for calculating the risk warning value of the virtual power plant, the risk numerical result is calculated as follows:

[0096] Warning moment Risk Value 9:00 0 10:00 0.078171 11:00 0.538211 12:00 0.99954

[0097] S108: Process the risk warning value of the virtual power plant to generate a risk level of the virtual power plant.

[0098] In one implementation, the risk warning levels are graded based on the calculation results. i The larger the value, the higher the risk warning level. i The smaller the value, the lower the risk warning level. If the value is 0, it means there is no risk. The specific risk levels are as follows:

[0099] 1) C i If the value is in the range of [0.8,1], it is classified as a level 1 risk warning;

[0100] 2) C i If the value is in the range of [0.6, 0.8), it is classified as a level 2 risk warning;

[0101] 3) C i If the value is in the range of [0.4, 0.6), it is classified as a level 3 risk warning;

[0102] 4) C i If the value is in the range of [0.2, 0.4), it is classified as a level 4 risk warning;

[0103] 5) C i When the value is in the range of (0, 0.2), it is classified as level 5 risk warning.

[0104] According to the above-mentioned virtual power plant risk warning value results, combined with the classification standards set by the present invention, the warning level is divided as follows:

[0105]

[0106]

[0107] Optionally, in another embodiment of the method of the present application, after the risk warning value of the virtual power plant is processed to generate the risk level of the virtual power plant, the following steps are included:

[0108] Obtain the preset virtual power plant risk warning table;

[0109] Processing the risk level of the virtual power plant based on the preset virtual power plant risk warning table, generating a target warning time and a warning level matching the target warning time;

[0110] The target warning moment and the warning level matching the target warning moment are processed to generate a risk association factor, wherein the risk association factor is generated based on the virtual power plant operation risk impact factor corresponding to the same warning level and adjacent warning moments;

[0111] Process risk management factors and generate risk warning information.

[0112] In one implementation, the applicant presets a corresponding virtual power plant risk warning table according to actual needs. This table usually contains the definition criteria of different risk levels and corresponding warning indicators. The risk level of the current virtual power plant is judged by using the real-time data and historical data of the virtual power plant in combination with the risk warning table. This can be achieved by analyzing key indicators such as equipment failure rate, load response rate, etc. The target warning moment is determined, that is, the specific time point when the risk reaches a certain level, and the warning level matching the moment is generated together, which can be obtained through real-time data analysis of the monitoring system.

[0113] Conduct in-depth analysis of the target warning moments and warning levels to identify key factors that affect risk level changes. These factors may include equipment operating status, environmental factors, market changes, etc. In particular, it is necessary to analyze the correlation between adjacent warning moments determined by the warning levels corresponding to the same warning level and adjacent warning moments, as well as other warning moments with the same warning level as the current warning level. For example, if a risk with a warning level of 2 occurs at 9 a.m. and a risk of level 2 occurs again at 6 p.m., it is necessary to analyze whether the risk factors at 6 p.m. are the same as those at 9 a.m., so as to better solve and troubleshoot for subsequent work.

[0114] Based on the above analysis results, risk warning information is generated. This information should clearly indicate the risk level, warning time, influencing factors and their degree of influence, and provide decision support for risk management. The generation of risk warning information should take into account the management of risk factors, and adjust the operating environment that may cause potential losses by reducing the level of risk factors, changing their distribution or the sensitivity of enterprises to risk factors. In addition, risk classification control measures are also an important part of the risk warning information generation process. According to the severity of the risk, corresponding control measures can be formulated, such as strengthening monitoring, formulating rules and regulations, setting alarms and warning signals, etc., to ensure that risks are effectively controlled, so as to accurately predict and evaluate the operational risks of virtual power plants and provide a scientific basis for risk warning.

[0115] In another implementation, if the risk warning value of the virtual power plant is greater than or equal to 0.8, and the deviation values ​​of the real-time voltage and real-time current in the operating indicators reach the corresponding threshold, then it can be judged that the virtual power plant has reached the first-level risk level standard, which means there is a major explosion risk. Accordingly, the virtual power plant management personnel need to perform corresponding response actions according to the risk prompts, including cutting off the load and conducting manual inspections.

[0116] During the implementation process, if the risk warning value of the virtual power plant is in the range of [0.6, 0.8), and the deviation values ​​of real-time voltage, real-time current, etc. in the operating indicators reach the corresponding threshold, then it can be judged that the virtual power plant has reached the second-level risk level standard, which is a serious deviation from the normal safety threshold. Accordingly, the virtual power plant management personnel need to perform corresponding response actions according to the risk prompts, including reducing the load and conducting manual inspections.

[0117] During the implementation process, if the risk warning value of the virtual power plant is in the range of [0.4, 0.6), and the deviation values ​​of real-time voltage, real-time current, etc. in the operating indicators reach the corresponding threshold, then it can be judged that the virtual power plant has reached the third-level risk level standard, which is a moderate deviation from the normal safety threshold. Accordingly, the virtual power plant management personnel need to perform corresponding response actions and conduct manual inspections based on the risk prompts.

[0118] During the implementation process, if the risk warning value of the virtual power plant is in the range of [0.2, 0.4), and the deviation values ​​of real-time voltage, real-time current, etc. in the operating indicators reach the corresponding threshold, then it can be judged that the virtual power plant has reached the fourth-level risk level standard, which is a slight deviation from the normal safety threshold. Accordingly, the virtual power plant management personnel need to execute corresponding response actions according to the risk prompts and perform remote load adjustments.

[0119] During the implementation process, if the risk warning value of the virtual power plant is in the range of [0,0.2), and the deviation values ​​of real-time voltage, real-time current, etc. in the operating indicators reach the corresponding threshold, then it can be judged that the virtual power plant has reached the fifth-level risk level standard, which is a small fluctuation in the operation of the virtual power plant. Accordingly, the virtual power plant managers need to execute corresponding response actions according to the risk warnings and strengthen remote monitoring.

[0120] During the implementation process, the risk warning situation of the virtual power plant will be fed back to the intelligent risk warning system of the virtual power plant. The present invention also designs the intelligent risk warning system of the virtual power plant. Figure 5 and Figure 6 The interface design diagram of the intelligent risk early warning system of the virtual power plant explains the intelligent risk early warning system of the virtual power plant. Figure 5The interface design composition of the intelligent risk warning system of the virtual power plant is shown, which belongs to the interface composition under normal conditions. It includes real-time input voltage, real-time input current, current internal temperature, rated input voltage, rated input current, rated operating temperature, voltage deviation degree, current deviation degree, internal temperature deviation degree, etc. At the same time, it also includes a status indication output box indicating whether there is a risk in the current power plant operation. The current status indicates that there is no risk in the operation of the virtual power plant for the time being. Figure 6 It shows the interface design composition of the intelligent risk warning system of the virtual power plant in the presence of risk warning. Among them, it includes real-time input voltage, real-time input current, current internal temperature, rated input voltage, rated input current, rated operating temperature, voltage deviation degree, current deviation degree, internal temperature deviation degree, etc. At the same time, it also includes indicators indicating the current power plant operation risk level. In this embodiment, the voltage and current deviation rates meet the requirements of the second-level risk warning indicators. Therefore, the current warning information will be displayed in the current status bar: second-level warning. In addition, in the interface of the intelligent risk warning system of the virtual power plant, a triangular exclamation mark risk logo will pop up to remind the virtual power plant managers to pay attention.

[0121] In this application, a server obtains information of a virtual power plant to be evaluated; processes the information of the virtual power plant to be evaluated to generate physical property information of the virtual power plant to be evaluated; performs feature extraction processing on the physical property information of the virtual power plant to be evaluated to generate a number of feature sample types; performs feature screening processing on a number of feature sample types to generate real-time feature sample data; generates a set of target virtual power plant operation risk influencing factors based on a number of real-time feature sample data; obtains a target virtual power plant operation rated value matrix, a weight vector of a target virtual power plant operation risk influencing factor, and an initial evaluation matrix; processes the initial evaluation matrix and the target virtual power plant operation rated value matrix to generate a deviation matrix of the target virtual power plant operation risk factor; processes the deviation matrix of the target virtual power plant operation risk factor and the weight vector of the target virtual power plant operation risk factor to generate a weighted matrix of the target virtual power plant operation risk factor; processes the weighted matrix of the target virtual power plant operation risk factor to generate a positive ideal solution and a negative ideal solution.

[0122] Acquire an initial virtual power plant risk warning model that matches the target virtual power plant risk identification information and a training sample set that matches the initial virtual power plant risk warning model; acquire other virtual power plant information that matches the target virtual power plant risk identification information; perform feature extraction on other virtual power plant information to generate a feature data set; acquire any number of data features in the feature data set; generate adjacent features based on the distance between any number of data features and other numbers of data features of the same category, wherein the adjacent features include a preset number of any number of data features; determine a sampling ratio based on the number of each data feature in the training set; determine a sampling ratio based on the sampling ratio; sample adjacent features based on the sampling ratio to generate a preset number of sampling features; generate multiple data groups based on any data feature and each sampling feature, wherein each data group includes a preset number of data samples, wherein each data group includes a preset number of data samples, wherein at least one data sample includes identification information; process the multiple data groups to generate a training set and a validation set, wherein the training set and the validation set both include a number of data features; process the initial virtual power plant risk warning model based on the training set and the validation set to generate a target virtual power plant risk warning model.

[0123] Based on the target virtual power plant risk warning model, the positive ideal solution is processed to generate the first Euclidean distance; based on the target virtual power plant risk warning model, the negative ideal solution is processed to generate the second Euclidean distance; the virtual power plant risk warning value is processed to generate the virtual power plant risk level; the preset virtual power plant risk warning table is obtained; based on the preset virtual power plant risk warning table, the virtual power plant risk level is processed to generate the target warning time and the warning level matching the target warning time; the target warning time and the warning level matching the target warning time are processed to generate the risk association factor, where the risk association factor is generated based on the virtual power plant operation risk impact factor corresponding to the same warning level and adjacent warning moments; the risk management factor is processed to generate risk warning information. An overall hierarchical assessment is conducted on multiple risk factors of virtual power plant operation, and a multi-level warning capability for virtual power plant risk is constructed to timely discover potential risk points of virtual power plants, intervene and solve them after low-level risk warnings, and timely curb the spread of virtual power plant risks and their expansion to high-level risks, avoid major economic losses to virtual power plant enterprises, and enhance the effective measurement capabilities of technical managers for virtual power plant operation risks.

[0124] In one embodiment, if Figure 2 As shown, the present application also provides an intelligent risk early warning device for a virtual power plant, including:

[0125] The acquisition module 201 is used to acquire information of the virtual power plant to be evaluated; acquire an initial virtual power plant risk warning model that matches the risk identification information of the target virtual power plant and a training sample set that matches the initial virtual power plant risk warning model;

[0126] Processing module 202 is used to process the virtual power plant information to be evaluated and generate a set of target virtual power plant operation risk influencing factors; process the target virtual power plant operation risk influencing factors set to generate target virtual power plant risk identification information, wherein the target virtual power plant risk identification information includes positive ideal solutions and negative ideal solutions; process the training sample set based on preset processing rules to generate a training set and a verification set; process the initial virtual power plant risk warning model based on the training set and the verification set to generate a target virtual power plant risk warning model; process the positive ideal solution and the negative ideal solution based on the target virtual power plant risk warning model to generate a virtual power plant risk warning value; process the virtual power plant risk warning value to generate a virtual power plant risk level.

[0127] The present application embodiment provides an electronic device, such as Figure 3 As shown, the electronic device 3 includes a first processor 300, a memory 301, a bus 302 and a communication interface 303. The first processor 300, the communication interface 303 and the memory 301 are connected via the bus 302; the memory 301 stores a computer program that can be run on the first processor 300, and when the first processor 300 runs the computer program, it executes the intelligent risk warning method for the virtual power plant provided in any of the aforementioned embodiments of the present application.

[0128] The memory 301 may include a high-speed random access memory (RAM), or may include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 303 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0129] The bus 302 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store a program, and the first processor 300 executes the program after receiving an execution instruction. The intelligent risk warning method for a virtual power plant disclosed in any implementation of the embodiment of the present application may be applied to the first processor 300, or implemented by the first processor 300.

[0130] The first processor 300 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the first processor 300. The above-mentioned first processor 300 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor to be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 301, and the first processor 300 reads the information in the memory 301 and completes the steps of the above method in combination with its hardware.

[0131] The electronic device provided in the above-mentioned embodiments of the present application and the intelligent risk warning method for a virtual power plant provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0132] The present application embodiment provides a computer-readable storage medium, such as Figure 4 As shown, the computer-readable storage medium 401 stores a computer program, and when the computer program is read and executed by the second processor 402, the intelligent risk warning method for the virtual power plant as described above is implemented.

[0133] The technical solution of the embodiment of the present application, 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, which is stored in a storage medium and includes several instructions for enabling an electronic device (which may be an air conditioner, a refrigeration device, a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the method described in the embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0134] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the intelligent risk warning method for a virtual power plant provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0135] An embodiment of the present application provides a computer program product, including a computer program, wherein the computer program is executed by a third processor to implement the method described above.

[0136] The computer program product provided by the above-mentioned embodiments of the present application and the intelligent risk warning method for a virtual power plant provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0137] It should be noted that, in this application, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements includes not only those elements, but also other elements not explicitly listed, or still includes elements inherent to such process, method, article or equipment. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or equipment including the elements.

[0138] Each embodiment in this application is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the intelligent risk warning method, electronic device, electronic device, and readable storage medium embodiment for evaluating virtual power plants, since they are basically similar to the above-mentioned embodiment of the intelligent risk warning method for virtual power plants, the description is relatively simple, and the relevant parts can be referred to the partial description of the embodiment of the intelligent risk warning method for virtual power plants.

[0139] Although the present application is disclosed as above, the present application is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application. Therefore, the protection scope of the present application shall be subject to the scope defined by the claims.

Claims

1. An intelligent risk early warning method for a virtual power plant, characterized in that: include: Obtain information on virtual power plants to be evaluated; Processing the virtual power plant information to be evaluated to generate a set of operating risk influencing factors of the target virtual power plant; Processing the target virtual power plant operation risk influencing factor set to generate target virtual power plant risk identification information, wherein the target virtual power plant risk identification information includes a positive ideal solution and a negative ideal solution; Acquire an initial virtual power plant risk warning model that matches the target virtual power plant risk identification information and a training sample set that matches the initial virtual power plant risk warning model; Process the training sample set based on preset processing rules to generate a training set and a validation set; Processing the initial virtual power plant risk warning model based on the training set and the validation set to generate a target virtual power plant risk warning model; Processing the positive ideal solution and the negative ideal solution based on a target virtual power plant risk warning model to generate a virtual power plant risk warning value; The virtual power plant risk warning value is processed to generate a virtual power plant risk level.

2. The method according to claim 1, characterized in that The virtual power plant information to be evaluated is processed to generate a set of operating risk influencing factors of the target virtual power plant, including: Processing the virtual power plant information to be evaluated to generate physical attribute information of the virtual power plant information to be evaluated; Performing feature extraction processing on the physical attribute information of the virtual power plant information to be evaluated to generate a number of feature sample types; Performing feature screening processing on the several feature sample types to generate real-time feature sample data; A set of target virtual power plant operation risk influencing factors is generated based on several real-time characteristic sample data.

3. The method according to claim 1, characterized in that The target virtual power plant operation risk influencing factor set is processed to generate target virtual power plant risk identification information, including: Obtaining a target virtual power plant operation rating matrix, a target virtual power plant operation risk influencing factor weight vector and an initial evaluation matrix; Processing the initial evaluation matrix and the target virtual power plant operation rated value matrix to generate a deviation matrix of the target virtual power plant operation risk factor; Processing the deviation matrix of the target virtual power plant operation risk factor and the weight vector of the target virtual power plant operation risk factor to generate a weighted matrix of the target virtual power plant operation risk factor; Processing the weighted matrix of the target virtual power plant operation risk factor to generate a positive ideal solution and a negative ideal solution; The method includes a calculation formula for calculating a positive ideal solution, the calculation formula being: The method includes a calculation formula for calculating a negative ideal solution, the calculation formula being: z ij =x ij -a j ,i=1,2,…,m;j=1,2,…,n; in, is a positive ideal solution, is a negative ideal solution, z ij is the deviation matrix of the target virtual power plant operation risk factor, ω j is the weight vector of the target virtual power plant operation risk factor, a j Run the rating matrix for the target virtual power plant.

4. The method according to claim 1, characterized in that The training data set is processed based on the preset processing rules to generate the training set and the validation set, including: Acquire other virtual power plant information matching the target virtual power plant risk identification information; Extract features of other virtual power plant information to generate feature data sets; Obtaining any number of data features in the feature data set; Generate adjacent features based on the distance between the any number of data features and other numbers of data features of the same category, wherein the adjacent features include a preset number of the any number of data features; Determining a sampling ratio based on the number of each data feature in the training set; Based on the sampling ratio, determining a sampling ratio; Sampling the adjacent features based on the sampling ratio to generate a preset number of sampling features; Based on any data feature and each sampling feature, generating a plurality of data groups, wherein each data group comprises a preset number of data samples, wherein each data group comprises a preset number of data samples, wherein at least one data sample comprises identification information; The plurality of data sets are processed to generate a training set and a validation set, wherein the training set and the validation set both include a number of data features.

5. The method according to claim 1, characterized in that The positive ideal solution and the negative ideal solution are processed based on the target virtual power plant risk warning model to generate a virtual power plant risk warning value, including: Processing the positive ideal solution based on the target virtual power plant risk warning model to generate a first Euclidean distance; Processing the negative ideal solution based on the target virtual power plant risk warning model to generate a second Euclidean distance; The target virtual power plant risk warning model includes a calculation formula for calculating the first Euclidean distance, which is: in, is the risk factor x of the virtual power plant at time t ij To the positive ideal solution The Euclidean distance of The target virtual power plant risk warning model includes a calculation formula for calculating the second Euclidean distance, which is: in, is the risk factor x of the virtual power plant at time t ij To negative ideal solution The Euclidean distance of .

6. The method according to claim 4, characterized in that The positive ideal solution and the negative ideal solution are processed based on the target virtual power plant risk warning model to generate a virtual power plant risk warning value, including: The target virtual power plant risk warning model includes a calculation formula for calculating the risk warning value of the virtual power plant, and the calculation formula is: in, is the risk warning value of the virtual power plant.

7. The method according to claim 1, characterized in that After the risk warning value of the virtual power plant is processed to generate the risk level of the virtual power plant, it includes: Obtain the preset virtual power plant risk warning table; Processing the risk level of the virtual power plant based on the preset virtual power plant risk warning table to generate a target warning time and a warning level matching the target warning time; Processing the target warning moment and the warning level matching the target warning moment to generate a risk association factor, wherein the risk association factor is generated based on the virtual power plant operation risk impact factor corresponding to the same warning level and adjacent warning moments; Process risk management factors and generate risk warning information.

8. An intelligent risk early warning device for a virtual power plant, characterized in that: The device comprises: An acquisition module is used to acquire information of a virtual power plant to be evaluated; acquire an initial virtual power plant risk warning model that matches the risk identification information of a target virtual power plant and a training sample set that matches the initial virtual power plant risk warning model; A processing module is used to process the virtual power plant information to be evaluated and generate a set of target virtual power plant operation risk influencing factors; process the target virtual power plant operation risk influencing factors set and generate target virtual power plant risk identification information, wherein the target virtual power plant risk identification information includes positive ideal solutions and negative ideal solutions; process the training sample set based on preset processing rules to generate a training set and a verification set; process the initial virtual power plant risk warning model based on the training set and the verification set to generate a target virtual power plant risk warning model; process the positive ideal solution and the negative ideal solution based on the target virtual power plant risk warning model to generate a virtual power plant risk warning value; process the virtual power plant risk warning value to generate a virtual power plant risk level.

9. An electronic device, characterized in that: include: a first processor; and a memory for storing executable instructions of the first processor; Wherein, the first processor is configured to execute the intelligent risk warning method for a virtual power plant as described in any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the second processor, the intelligent risk warning method for a virtual power plant described in any one of claims 1 to 7 is implemented.

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