CN117437082A - Power grid operation optimization method based on cloud edge fusion intelligent scheduling operation platform - Google Patents
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Abstract
The application relates to a power grid operation optimization method based on a cloud edge fusion intelligent scheduling operation platform. The method comprises the following steps: acquiring initial data of a power grid production environment of a target power grid; the method comprises the steps that initial data of a power grid production environment are obtained by monitoring a target power grid through a power grid edge cluster; inputting initial data of the power grid production environment into a power grid operation data optimization model to obtain power grid production environment optimization data; inputting the initial data of the power grid production environment and the optimization data of the power grid production environment into a power grid data operation state analysis model to obtain power grid data operation analysis information; inputting the power grid data operation analysis information into a power grid data safety state analysis model to obtain power grid data safety analysis information; and generating grid operation optimization information according to the grid data operation analysis information and the grid data safety analysis information. By adopting the method, the optimizing effect on the operation of the power grid can be improved.
Description
Technical Field
The application relates to the technical field of computers, in particular to a power grid operation optimization method, a device, computer equipment, a storage medium and a computer program product based on a cloud edge fusion intelligent scheduling operation platform.
Background
With the development of computer technology, smart grids have emerged, which are based on integrated, high-speed two-way communication networks, through the application of advanced sensing and measurement technologies, advanced device technologies, advanced control methods, and advanced decision support system technologies. However, with the popularization of smart grids, more and more grid devices are connected to the industrial internet with an operation platform as a control center, and on the basis of huge grid devices, how to realize efficient use of grid resources is a current popular research direction.
In the traditional technology, different power grid automation systems are adopted for optimizing resource parameters aiming at different power grid parts; because of the data isolation characteristic of different power grid parts among different power grid automation systems, resource parameter optimization is only limited to each power grid part of the whole power grid, and whole-grid overall planning cannot be performed, so that the optimization effect on power grid operation is poor.
Disclosure of Invention
Based on the foregoing, it is necessary to provide a method, an apparatus, a computer device, a computer readable storage medium and a computer program product for optimizing power grid operation based on a cloud edge fusion intelligent scheduling operation platform, which can improve the effect of optimizing power grid operation.
In a first aspect, the application provides a power grid operation optimization method based on cloud edge fusion intelligent scheduling operation platform. The method comprises the following steps: acquiring initial data of a power grid production environment of a target power grid; the initial data of the power grid production environment is obtained by monitoring the target power grid through a power grid edge cluster; inputting the initial data of the power grid production environment into a power grid operation data optimization model to obtain power grid production environment optimization data; inputting the initial data of the power grid production environment and the optimization data of the power grid production environment into a power grid data operation state analysis model to obtain power grid data operation analysis information; inputting the power grid data operation analysis information into a power grid data safety state analysis model to obtain power grid data safety analysis information; generating grid operation optimization information according to the grid data operation analysis information and the grid data safety analysis information; the grid operation optimization information is used for optimizing the grid operation of the target grid.
In a second aspect, the application further provides a power grid operation optimization device based on the cloud edge fusion intelligent scheduling operation platform. The device comprises: the production data acquisition module is used for acquiring the initial data of the power grid production environment of the target power grid; the initial data of the power grid production environment is obtained by monitoring the target power grid through a power grid edge cluster; the production data optimization module is used for inputting the initial data of the power grid production environment into a power grid operation data optimization model to obtain power grid production environment optimization data; the production data analysis module is used for inputting the initial data of the power grid production environment and the optimization data of the power grid production environment into a power grid data operation state analysis model to obtain power grid data operation analysis information; the production data analysis module is also used for inputting the power grid data operation analysis information into a power grid data safety state analysis model to obtain power grid data safety analysis information; the power grid resource optimization module is used for generating power grid operation optimization information according to the power grid data operation analysis information and the power grid data safety analysis information; the grid operation optimization information is used for optimizing the grid operation of the target grid.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of: acquiring initial data of a power grid production environment of a target power grid; the initial data of the power grid production environment is obtained by monitoring the target power grid through a power grid edge cluster; inputting the initial data of the power grid production environment into a power grid operation data optimization model to obtain power grid production environment optimization data; inputting the initial data of the power grid production environment and the optimization data of the power grid production environment into a power grid data operation state analysis model to obtain power grid data operation analysis information; inputting the power grid data operation analysis information into a power grid data safety state analysis model to obtain power grid data safety analysis information; generating grid operation optimization information according to the grid data operation analysis information and the grid data safety analysis information; the grid operation optimization information is used for optimizing the grid operation of the target grid.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of: acquiring initial data of a power grid production environment of a target power grid; the initial data of the power grid production environment is obtained by monitoring the target power grid through a power grid edge cluster; inputting the initial data of the power grid production environment into a power grid operation data optimization model to obtain power grid production environment optimization data; inputting the initial data of the power grid production environment and the optimization data of the power grid production environment into a power grid data operation state analysis model to obtain power grid data operation analysis information; inputting the power grid data operation analysis information into a power grid data safety state analysis model to obtain power grid data safety analysis information; generating grid operation optimization information according to the grid data operation analysis information and the grid data safety analysis information; the grid operation optimization information is used for optimizing the grid operation of the target grid.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of: acquiring initial data of a power grid production environment of a target power grid; the initial data of the power grid production environment is obtained by monitoring the target power grid through a power grid edge cluster; inputting the initial data of the power grid production environment into a power grid operation data optimization model to obtain power grid production environment optimization data; inputting the initial data of the power grid production environment and the optimization data of the power grid production environment into a power grid data operation state analysis model to obtain power grid data operation analysis information; inputting the power grid data operation analysis information into a power grid data safety state analysis model to obtain power grid data safety analysis information; generating grid operation optimization information according to the grid data operation analysis information and the grid data safety analysis information; the grid operation optimization information is used for optimizing the grid operation of the target grid.
According to the grid operation optimization method, the device, the computer equipment, the storage medium and the computer program product based on the cloud edge fusion intelligent scheduling operation platform, initial data of the grid production environment of the target grid are obtained; the method comprises the steps that initial data of a power grid production environment are obtained by monitoring a target power grid through a power grid edge cluster; inputting initial data of the power grid production environment into a power grid operation data optimization model to obtain power grid production environment optimization data; inputting the initial data of the power grid production environment and the optimization data of the power grid production environment into a power grid data operation state analysis model to obtain power grid data operation analysis information; inputting the power grid data operation analysis information into a power grid data safety state analysis model to obtain power grid data safety analysis information; generating grid operation optimization information according to the grid data operation analysis information and the grid data safety analysis information; the grid operation optimization information is used for optimizing the grid operation of the target grid.
Optimizing the initial data of the power grid production environment of the target power grid to obtain power grid production environment optimization data, analyzing the power grid production environment optimization data to obtain power grid data operation analysis information and power grid data safety analysis information respectively, and finally generating power grid operation optimization information for optimizing power grid operation on the basis of guaranteeing operation efficiency and safety protection. The production environment data of all parts of the whole power grid can be comprehensively and optimally managed, meanwhile, the self-adaptive cloud edge fusion intelligent scheduling operation platform meets the high real-time optimization requirement of the power grid operation of all parts, and the optimization effect on the power grid operation is improved.
Drawings
FIG. 1 is an application environment diagram of a power grid operation optimization method based on a cloud edge fusion intelligent scheduling operation platform in one embodiment;
FIG. 2 is a schematic flow diagram of a power grid operation optimization method based on a cloud edge fusion intelligent scheduling operation platform in an embodiment;
FIG. 3 is a flow chart of a method for obtaining grid production environment optimization data in one embodiment;
FIG. 4 is a flowchart of a method for obtaining an optimization model of power grid operation data in one embodiment;
FIG. 5 is a flowchart of a method for obtaining an optimization model of power grid operation data according to another embodiment;
FIG. 6 is a flow chart of a method for obtaining analysis information of grid data operation in one embodiment;
FIG. 7 is a flow chart of a method for obtaining information of security analysis of power grid data according to an embodiment;
FIG. 8 is a schematic diagram of an architecture of a grid operation optimization method based on a cloud edge fusion intelligent scheduling operation platform in an embodiment;
FIG. 9 is a block diagram of a power grid operation optimization device based on a cloud edge fusion intelligent scheduling operation platform in one embodiment;
fig. 10 is an internal structural view of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The power grid operation optimization method based on the cloud edge fusion intelligent scheduling operation platform can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The server 104 obtains initial data of the power grid production environment of the target power grid through the terminal 102; the method comprises the steps that initial data of a power grid production environment are obtained by monitoring a target power grid through a power grid edge cluster; inputting initial data of the power grid production environment into a power grid operation data optimization model to obtain power grid production environment optimization data; inputting the initial data of the power grid production environment and the optimization data of the power grid production environment into a power grid data operation state analysis model to obtain power grid data operation analysis information; inputting the power grid data operation analysis information into a power grid data safety state analysis model to obtain power grid data safety analysis information; generating grid operation optimization information according to the grid data operation analysis information and the grid data safety analysis information; the grid operation optimization information is used for optimizing the grid operation of the target grid. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In one embodiment, as shown in fig. 2, a power grid operation optimization method based on cloud edge fusion intelligent scheduling operation platform is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
step 202, obtaining initial data of a grid production environment of a target grid.
The target power grid may be a power grid that needs to be optimized for grid operation.
The initial data of the power grid production environment can be data generated by running the power grid in the production application environment.
Specifically, the server 104 obtains initial data of the grid production environment of the target grid from the terminal 102, where the initial data of the grid production environment is obtained by monitoring the target grid through a grid edge cluster, and the grid edge cluster may be a collection of computing nodes located at an edge of the computing architecture or near a grid data source. These computing nodes are typically located at distributed locations in the grid equipment, such as power stations, substations, transmission lines, etc., rather than being concentrated on a cloud-edge fusion intelligent dispatch operation platform. Further, the obtained initial data of the power grid production environment is stored in a storage unit, and when the server needs to process the initial data of the power grid production environment, volatile storage resources are called from the storage unit for calculation by a central processing unit. The initial data of the power grid production environment can be single data input to the central processing unit, or a plurality of data can be simultaneously input to the central processing unit.
And 204, inputting the initial data of the power grid production environment into a power grid operation data optimization model to obtain power grid production environment optimization data.
The power grid operation data optimization model can be a data optimization model formed by combining a plurality of global optimization core algorithms as combination factors. The global optimization core algorithm may search for a global optimal solution in a complex parameter space, rather than just a local optimal solution.
The grid production environment optimization data may be data obtained by optimizing initial data of the grid production environment.
Specifically, the grid production environment initial data is input into a grid operation data optimization model, and at least one target global optimization core algorithm is selected from a plurality of global optimization core algorithms through analysis of the grid production environment initial data, wherein the global optimization core algorithms include, but are not limited to, genetic algorithm (Genetic Algorithms), particle swarm optimization (Particle Swarm Optimization, PSO), simulated annealing algorithm (Simulated Annealing), ant colony algorithm (Ant Colony Optimization, ACO), differential evolution (Differential Evolution, DE), bayesian optimization (Bayesian Optimization), multi-arm slot machine algorithm (Multi-armed Bandit Algorithms) and the like.
If the number of the target global optimization core algorithms is one, the initial data of the power grid production environment are input into the target global optimization core algorithms, and the initial data of the power grid production environment are optimized by using the target global optimization core algorithms, so that the optimized data of the power grid production environment are obtained.
If the number of the target global optimization core algorithms is plural, two cases are provided. First: and under the condition that the initial data of the power grid production environment is static, according to the initial data of the power grid production environment, carrying out random permutation and combination on a plurality of target global optimization core algorithms, and determining an environment data optimization sequence for optimizing the initial data of the power grid production environment, wherein the environment data optimization sequence is static (namely, the environment data optimization sequence is not changed in a period of time). For example: the environmental data optimization sequences are arranged in a mode of 1, a genetic algorithm, 2, particle swarm optimization, 3, a simulated annealing algorithm, 4 and an ant colony algorithm, or different arrangements are adopted by the same target global optimization core algorithm.
Second,: and under the condition that the initial data of the power grid production environment is dynamic, according to the initial data of the power grid production environment, carrying out random permutation and combination on a plurality of target global optimization core algorithms, and determining an environment data optimization sequence for optimizing the initial data of the power grid production environment, wherein the environment data optimization sequence is dynamic (namely, the environment data optimization sequence changes along with the dynamic change of the initial data of the power grid production environment). And optimizing the initial data of the power grid production environment in sequence according to the global optimization core algorithm and the sequence of each target in the environment data optimization sequence to obtain the power grid production environment optimization data. Since the initial data of the power grid production environment exists dynamically or statically, the optimization data of the power grid production environment correspondingly exists dynamically or statically.
And 206, inputting the initial data of the power grid production environment and the optimization data of the power grid production environment into a power grid data operation state analysis model to obtain power grid data operation analysis information.
The power grid data operation state analysis model can be a data analysis model formed by combining a plurality of original strategies as combination factors. The original policies include, but are not limited to, a data access control original policy, a security setting original policy, a rights management original policy, a password policy original policy, and the like.
The power grid data operation analysis information can be analysis data obtained by combining initial data of the power grid production environment and optimization data of the power grid production environment to analyze the power grid operation state.
Specifically, according to the initial data of the power grid production environment and the optimization data of the power grid production environment, at least one original strategy is selected from a plurality of original strategies to serve as a target original strategy; and if the number of the target original strategies is one, the data analysis sequence generated according to the target original strategies is the target original strategies.
If the number of target original policies is plural, two cases are provided. First: under the condition that the initial data of the power grid production environment and the optimized data of the power grid production environment are static, according to the initial data of the power grid production environment and the optimized data of the power grid production environment, a plurality of target original strategies are arranged and combined randomly, and a data analysis sequence for analyzing the initial data of the power grid production environment and the optimized data of the power grid production environment is determined, wherein the data analysis sequence is static (namely, the data analysis sequence is not changed in a period of time).
Second,: and under the condition that one or both of the grid production environment initial data and the grid production environment optimization data are dynamic, according to the grid production environment initial data and the grid production environment optimization data, carrying out random permutation and combination on a plurality of target original strategies, and determining a data analysis sequence for analyzing the grid production environment initial data and the grid production environment optimization data, wherein the data analysis sequence is dynamic (namely, the data analysis sequence changes along with the dynamic change of the grid production environment initial data).
And according to each target original strategy and sequence in the data analysis sequence, sequentially utilizing the initial data of the power grid production environment and the optimized data of the power grid production environment to perform operation state analysis, and obtaining operation analysis information of the power grid data. If the initial data of the power grid production environment and the optimization data of the power grid production environment are static, the operation analysis information of the power grid data is static; if one or both of the grid production environment initial data and the grid production environment optimization data are dynamic, the grid data operation analysis information is dynamic.
And step 208, inputting the power grid data operation analysis information into a power grid data safety state analysis model to obtain power grid data safety analysis information.
The power grid data safety state analysis model can be a safety analysis model formed by combining a plurality of safety condition information serving as combination factors.
The power grid data safety analysis information may be analysis data obtained by analyzing a power grid safety state by using power grid data operation analysis information.
Specifically, traversing data information of power grid data operation analysis information, extracting real-time data and monitoring information of a target power grid, and constructing a self-adaptive security analysis layer of a power grid data security state analysis model according to the real-time data and the monitoring information of the target power grid; similarly, traversing data information of the power grid data operation analysis information, extracting access control rules, authority allocation and the like of a target power grid, and constructing a security policy control layer of a power grid data security state analysis model according to the access control rules, the authority allocation and the like of the target power grid; and similarly, traversing the data information of the power grid data operation analysis information, extracting the operation architecture data of the target power grid, and constructing a safety architecture dynamic layer of the power grid data safety state analysis model according to the operation architecture data of the target power grid.
And selecting at least one calculation layer from the self-adaptive security analysis layer, the security policy control layer and the security architecture dynamic layer as a target security analysis layer according to the power grid data operation analysis information, and then inputting the power grid data operation analysis information into the target security analysis layer to obtain the power grid data security analysis information. If the power grid data operation analysis information is static, the power grid data safety analysis information is also static; if the power grid data operation analysis information is dynamic, the power grid data safety analysis information is also dynamic.
Step 210, generating grid operation optimization information according to the grid data operation analysis information and the grid data safety analysis information.
The grid operation optimization information may be data for optimizing the grid operation of the target grid.
Specifically, because the power grid data operation analysis information and the power grid data safety analysis information have two modes of static and dynamic, time is introduced as a parameter, after the power grid data operation analysis information and the power grid data safety analysis information are fused, the cloud edge fusion intelligent scheduling operation platform is used for calculating power grid operation optimization information aiming at the operation condition of the target power grid, and the power grid operation optimization information can be static or dynamic.
In the grid operation optimization method based on the cloud edge fusion intelligent scheduling operation platform, the initial data of the grid production environment of the target grid is obtained; the method comprises the steps that initial data of a power grid production environment are obtained by monitoring a target power grid through a power grid edge cluster; inputting initial data of the power grid production environment into a power grid operation data optimization model to obtain power grid production environment optimization data; inputting the initial data of the power grid production environment and the optimization data of the power grid production environment into a power grid data operation state analysis model to obtain power grid data operation analysis information; inputting the power grid data operation analysis information into a power grid data safety state analysis model to obtain power grid data safety analysis information; generating grid operation optimization information according to the grid data operation analysis information and the grid data safety analysis information; the grid operation optimization information is used for optimizing the grid operation of the target grid.
Optimizing the initial data of the power grid production environment of the target power grid to obtain power grid production environment optimization data, analyzing the power grid production environment optimization data to obtain power grid data operation analysis information and power grid data safety analysis information respectively, and finally generating power grid operation optimization information for optimizing power grid operation on the basis of guaranteeing operation efficiency and safety protection. The production environment data of all parts of the whole power grid can be comprehensively and optimally managed, meanwhile, the self-adaptive cloud edge fusion intelligent scheduling operation platform meets the high real-time optimization requirement of the power grid operation of all parts, and the optimization effect on the power grid operation is improved.
In one embodiment, as shown in fig. 3, inputting the initial data of the grid production environment into the grid operation data optimization model to obtain the grid production environment optimization data, including:
and step 302, selecting a target global optimization core algorithm from the power grid operation data optimization model according to the initial data of the power grid production environment.
The target global optimization core algorithm may be an algorithm for optimizing initial data of the grid production environment.
Specifically, the initial data of the power grid production environment is input into a power grid operation data optimization model, and at least one target global optimization core algorithm is selected from a plurality of global optimization core algorithms through analysis of the initial data of the power grid production environment.
And step 304, optimizing the initial data of the power grid production environment by using a target global optimization core algorithm to obtain the optimized data of the power grid production environment.
Specifically, if the number of the target global optimization core algorithms is one, the initial data of the power grid production environment is input into the target global optimization core algorithms, and the initial data of the power grid production environment is optimized by using the target global optimization core algorithms to obtain optimized data of the power grid production environment.
If the number of the target global optimization core algorithms is plural, two cases are provided. First: and under the condition that the initial data of the power grid production environment is static, according to the initial data of the power grid production environment, carrying out random permutation and combination on a plurality of target global optimization core algorithms, and determining an environment data optimization sequence for optimizing the initial data of the power grid production environment, wherein the environment data optimization sequence is static (namely, the environment data optimization sequence is not changed in a period of time). For example: the environmental data optimization sequences are arranged in a mode of 1, a genetic algorithm, 2, particle swarm optimization, 3, a simulated annealing algorithm, 4 and an ant colony algorithm, or different arrangements are adopted by the same target global optimization core algorithm.
Second,: and under the condition that the initial data of the power grid production environment is dynamic, according to the initial data of the power grid production environment, carrying out random permutation and combination on a plurality of target global optimization core algorithms, and determining an environment data optimization sequence for optimizing the initial data of the power grid production environment, wherein the environment data optimization sequence is dynamic (namely, the environment data optimization sequence changes along with the dynamic change of the initial data of the power grid production environment). And optimizing the initial data of the power grid production environment in sequence according to the global optimization core algorithm and the sequence of each target in the environment data optimization sequence to obtain the power grid production environment optimization data.
In this embodiment, the initial data of the power grid production environment is optimized by using the matched global optimization core algorithm to obtain the power grid production environment optimization data, so that the global information of the whole target power grid can be analyzed and optimized, and the cloud edge fusion intelligent scheduling operation platform is ensured to use the power grid production environment optimization data to improve the operation performance, efficiency and reliability of the target power grid.
In one embodiment, as shown in fig. 4, before the step of selecting the target global optimization core algorithm from the grid operation data optimization model according to the grid production environment initial data, the method further includes:
step 402, obtaining an initial power grid operation data optimization model and at least one global optimization core algorithm.
The initial grid operation data optimization model may be an untrained grid operation data optimization model.
Specifically, the server 104 acquires the initial power grid operation data optimization model and at least one global optimization core algorithm from the terminal 102, further stores the acquired initial power grid operation data optimization model and each global optimization core algorithm in a storage unit, and when the server needs to call the initial power grid operation data optimization model and each global optimization core algorithm, the server calls volatile storage resources from the storage unit for the central processing unit to calculate. The initial power grid operation data optimization model and each global optimization core algorithm can be single data input to the central processing unit, or multiple data can be simultaneously input to the central processing unit.
And step 404, training an initial power grid operation data optimization model according to the power grid production environment initial data and each global optimization core algorithm to obtain a power grid operation data optimization model.
Specifically, initial data of the power grid production environment and each global optimization core algorithm are input into an initial power grid operation data optimization model, and model parameters of the initial power grid operation data optimization model are adjusted according to output results of the initial power grid operation data optimization model until the initial power grid operation data optimization model meets requirements of the power grid operation data optimization model, so that the power grid operation data optimization model is obtained.
In the embodiment, the initial power grid operation data optimization model is trained through the initial data of the power grid production environment and each global optimization core algorithm, so that the optimization performance of the power grid operation data optimization model can meet the power grid operation actual condition of the current condition, and the optimization capacity of the power grid operation data optimization model in the cloud edge fusion intelligent scheduling operation platform is improved.
In one embodiment, as shown in fig. 5, before the step of selecting the target global optimization core algorithm from the grid operation data optimization model according to the grid production environment initial data, the method further includes:
Step 502, a trained grid operation data optimization model is obtained.
The trained power grid operation data optimization model may be a trained power grid operation data optimization model.
Specifically, the server 104 obtains the trained power grid operation data optimization model from the terminal 102, further stores the obtained trained power grid operation data optimization model in a storage unit, and when the server needs to call the trained power grid operation data optimization model, the server calls the trained power grid operation data optimization model to volatile storage resources from the storage unit for the central processing unit to calculate.
And step 504, adjusting model parameters of the trained power grid operation data optimization model according to the initial data of the power grid production environment to obtain the power grid operation data optimization model.
Specifically, initial data of the power grid production environment is input into a trained power grid operation data optimization model, and model parameters of the trained power grid operation data optimization model are adjusted according to the output result of the trained power grid operation data optimization model until the trained power grid operation data optimization model meets the requirements of the power grid operation data optimization model, so that the power grid operation data optimization model is obtained.
In the embodiment, the model parameters of the trained power grid operation data optimization model are adjusted through the initial data of the power grid production environment, so that the optimization performance of the power grid operation data optimization model can be enabled to consider the long-term operation condition of the power grid, and the optimization capacity of the power grid operation data optimization model in the cloud edge fusion intelligent scheduling operation platform is improved.
In one embodiment, as shown in fig. 6, inputting initial data of a power grid production environment and optimization data of the power grid production environment into a power grid data operation state analysis model to obtain operation analysis information of the power grid data, including:
step 602, determining a data analysis sequence according to the initial data of the power grid production environment and the optimization data of the power grid production environment.
The data analysis sequence may be an analysis sequence in a power grid data operation state analysis model.
Specifically, according to the initial data of the power grid production environment and the optimization data of the power grid production environment, at least one original strategy is selected from a plurality of original strategies to serve as a target original strategy; and if the number of the target original strategies is one, the data analysis sequence generated according to the target original strategies is the target original strategies.
If the number of target original policies is plural, two cases are provided. First: under the condition that the initial data of the power grid production environment and the optimized data of the power grid production environment are static, according to the initial data of the power grid production environment and the optimized data of the power grid production environment, a plurality of target original strategies are arranged and combined randomly, and a data analysis sequence for analyzing the initial data of the power grid production environment and the optimized data of the power grid production environment is determined, wherein the data analysis sequence is static (namely, the data analysis sequence is not changed in a period of time).
Second,: and under the condition that one or both of the grid production environment initial data and the grid production environment optimization data are dynamic, according to the grid production environment initial data and the grid production environment optimization data, carrying out random permutation and combination on a plurality of target original strategies, and determining a data analysis sequence for analyzing the grid production environment initial data and the grid production environment optimization data, wherein the data analysis sequence is dynamic (namely, the data analysis sequence changes along with the dynamic change of the grid production environment initial data).
And step 604, performing operation state analysis according to the initial data of the power grid production environment and the optimization data of the power grid production environment according to the data analysis sequence to obtain operation analysis information of the power grid data.
The power grid data operation analysis information may be analysis data obtained by analyzing an operation state of a target power grid.
Specifically, according to each target original strategy and sequence in the data analysis sequence, the operation state analysis is sequentially carried out by utilizing the initial data of the power grid production environment and the optimization data of the power grid production environment, so as to obtain the operation analysis information of the power grid data.
In the embodiment, the data analysis sequence is generated according to the initial data of the power grid production environment and the optimization data of the power grid production environment, and the running state of the target power grid is analyzed by utilizing the data analysis sequence, so that the running state of the target power grid can be comprehensively analyzed, and the optimization efficiency of the cloud edge fusion intelligent scheduling running platform on the running of the target power grid is improved.
In one embodiment, as shown in fig. 7, the inputting the operation analysis information of the grid data into the analysis model of the safety state of the grid data to obtain the analysis information of the safety state of the grid data includes:
step 702, constructing an adaptive security analysis layer of a power grid data security state analysis model according to power grid data operation analysis information.
The adaptive security analysis layer may be an algorithm that analyzes the ability to adjust and optimize security measures to the actual threat situation.
Specifically, traversing data information of the power grid data operation analysis information, extracting real-time data and monitoring information of a target power grid, and constructing an adaptive security analysis layer of a power grid data security state analysis model according to the real-time data and the monitoring information of the target power grid.
And step 704, constructing a safety strategy control layer of the power grid data safety state analysis model according to the power grid data operation analysis information.
The security policy control layer may be an algorithm that analyzes the security of the grid system and the grid data, among other things.
Specifically, traversing the data information of the power grid data operation analysis information, extracting access control rules, authority allocation and the like of a target power grid, and constructing a security policy control layer of a power grid data security state analysis model according to the access control rules, the authority allocation and the like of the target power grid.
Step 706, according to the power grid data operation analysis information, a security architecture dynamic layer of a power grid data security state analysis model is constructed.
The security architecture dynamic layer may be an algorithm for analyzing security of the security architecture of the power grid system.
Specifically, traversing the data information of the power grid data operation analysis information, extracting the operation architecture data of the target power grid, and constructing a safety architecture dynamic layer of the power grid data safety state analysis model according to the operation architecture data of the target power grid.
Step 708, inputting the operation analysis information of the power grid data into the self-adaptive security analysis layer, and/or the security policy control layer, and/or the security architecture dynamic layer, to obtain the security analysis information of the power grid data.
The power grid data safety analysis information may be analysis data obtained by analyzing a safety state of a target power grid.
Specifically, according to the power grid data operation analysis information, at least one calculation layer is selected from the self-adaptive security analysis layer, the security policy control layer and the security architecture dynamic layer to serve as a target security analysis layer, and then the power grid data operation analysis information is input into the target security analysis layer to obtain the power grid data security analysis information. Fig. 8 is a schematic diagram of an architecture of a power grid operation optimization method based on a cloud-edge fusion intelligent scheduling operation platform.
In the embodiment, the analysis information is operated on the power grid data by selecting the corresponding analysis layer, so that safety analysis can be performed aiming at the specific condition of the target power grid, unnecessary programs are avoided from being operated, and the optimization efficiency of the cloud-edge fusion intelligent scheduling operation platform on the safety aspect of power grid operation is improved.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a power grid operation optimization device based on the cloud edge fusion intelligent scheduling operation platform, which is used for realizing the power grid operation optimization method based on the cloud edge fusion intelligent scheduling operation platform. The implementation scheme of the device for solving the problems is similar to the implementation scheme recorded in the method, so the specific limitation in the embodiment of the power grid operation optimization device based on the cloud edge fusion intelligent scheduling operation platform can be seen from the above description of the limitation of the power grid operation optimization method based on the cloud edge fusion intelligent scheduling operation platform, and the description is omitted here.
In one embodiment, as shown in fig. 9, a power grid operation optimization device based on cloud edge fusion intelligent scheduling operation platform is provided, including: a production data acquisition module 902, a production data optimization module 904, a production data analysis module 906, and a grid resource optimization module 908, wherein:
a production data acquisition module 902, configured to acquire grid production environment initial data of a target grid; the method comprises the steps that initial data of a power grid production environment are obtained by monitoring a target power grid through a power grid edge cluster;
the production data optimization module 904 is configured to input initial data of a power grid production environment into the power grid operation data optimization model to obtain power grid production environment optimization data;
the production data analysis module 906 is configured to input initial data of a power grid production environment and optimization data of the power grid production environment into a power grid data operation state analysis model to obtain power grid data operation analysis information;
the production data analysis module 906 is further configured to input the grid data operation analysis information into a grid data security state analysis model to obtain grid data security analysis information;
the grid resource optimization module 908 is configured to generate grid operation optimization information according to the grid data operation analysis information and the grid data security analysis information; the grid operation optimization information is used for optimizing the grid operation of the target grid.
In one embodiment, the production data optimization module 904 is further configured to select a target global optimization core algorithm from the grid operation data optimization model according to the grid production environment initial data; and optimizing the initial data of the power grid production environment by using a target global optimization core algorithm to obtain the power grid production environment optimization data.
In one embodiment, the production data optimization module 904 is further configured to obtain an initial grid operation data optimization model and at least one global optimization core algorithm; and training an initial power grid operation data optimization model according to the initial data of the power grid production environment and each global optimization core algorithm to obtain a power grid operation data optimization model.
In one embodiment, the production data optimization module 904 is further configured to obtain a trained grid operation data optimization model; the trained power grid operation data optimization model is obtained through training of power grid production environment sample data; and adjusting model parameters of the trained power grid operation data optimization model according to the initial data of the power grid production environment to obtain the power grid operation data optimization model.
In one embodiment, the production data analysis module 906 is further configured to determine a data analysis sequence according to the grid production environment initial data and the grid production environment optimization data; and according to the data analysis sequence, performing operation state analysis according to the initial data of the power grid production environment and the optimization data of the power grid production environment to obtain operation analysis information of the power grid data.
In one embodiment, the production data analysis module 906 is further configured to operate analysis information according to the grid data, and construct an adaptive security analysis layer of the grid data security state analysis model; according to the operation analysis information of the power grid data, a safety strategy control layer of a power grid data safety state analysis model is constructed; according to the operation analysis information of the power grid data, a safety architecture dynamic layer of a power grid data safety state analysis model is constructed; and inputting the power grid data operation analysis information into the self-adaptive security analysis layer, and/or the security policy control layer, and/or the security architecture dynamic layer to obtain the power grid data security analysis information.
All or part of each module in the power grid operation optimization device based on the cloud edge fusion intelligent scheduling operation platform can be realized through software, hardware and a combination of the software and the hardware. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 10. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing server data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize a power grid operation optimization method based on cloud edge fusion intelligent scheduling operation platform.
It will be appreciated by those skilled in the art that the structure shown in fig. 10 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, storing a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
In one embodiment, a computer program product or computer program is provided that includes computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-described method embodiments.
It should be noted that, user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. The utility model provides a grid operation optimization method based on cloud edge fusion intelligent scheduling operation platform, which is characterized by comprising the following steps:
acquiring initial data of a power grid production environment of a target power grid; the initial data of the power grid production environment is obtained by monitoring the target power grid through a power grid edge cluster;
inputting the initial data of the power grid production environment into a power grid operation data optimization model to obtain power grid production environment optimization data;
Inputting the initial data of the power grid production environment and the optimization data of the power grid production environment into a power grid data operation state analysis model to obtain power grid data operation analysis information;
inputting the power grid data operation analysis information into a power grid data safety state analysis model to obtain power grid data safety analysis information;
generating grid operation optimization information according to the grid data operation analysis information and the grid data safety analysis information; the grid operation optimization information is used for optimizing the grid operation of the target grid.
2. The method according to claim 1, wherein the inputting the grid production environment initial data into a grid operation data optimization model to obtain grid production environment optimization data comprises:
selecting a target global optimization core algorithm from the power grid operation data optimization model according to the power grid production environment initial data;
and optimizing the initial data of the power grid production environment by using the target global optimization core algorithm to obtain the power grid production environment optimization data.
3. The method of claim 2, wherein prior to the step of selecting a target global optimization core algorithm from the grid operational data optimization model based on the grid production environment initial data, the method further comprises:
Acquiring an initial power grid operation data optimization model and at least one global optimization core algorithm;
and training the initial power grid operation data optimization model according to the power grid production environment initial data and each global optimization core algorithm to obtain the power grid operation data optimization model.
4. The method of claim 2, wherein prior to the step of selecting a target global optimization core algorithm from the grid operational data optimization model based on the grid production environment initial data, the method further comprises:
acquiring a trained power grid operation data optimization model; the trained power grid operation data optimization model is obtained through power grid production environment sample data training;
and adjusting model parameters of the trained power grid operation data optimization model according to the power grid production environment initial data to obtain the power grid operation data optimization model.
5. The method according to claim 1, wherein the inputting the grid production environment initial data and the grid production environment optimization data into a grid data operation state analysis model to obtain grid data operation analysis information includes:
Determining a data analysis sequence according to the initial data of the power grid production environment and the power grid production environment optimization data;
and according to the data analysis sequence, performing operation state analysis according to the initial data of the power grid production environment and the power grid production environment optimization data to obtain power grid data operation analysis information.
6. The method according to claim 1, wherein the inputting the grid data operation analysis information into a grid data safety state analysis model to obtain grid data safety analysis information includes:
constructing a self-adaptive safety analysis layer of the power grid data safety state analysis model according to the power grid data operation analysis information;
according to the power grid data operation analysis information, a safety strategy control layer of the power grid data safety state analysis model is constructed;
constructing a safety architecture dynamic layer of the power grid data safety state analysis model according to the power grid data operation analysis information;
and inputting the power grid data operation analysis information to the self-adaptive security analysis layer, and/or the security policy control layer and/or the security architecture dynamic layer to obtain the power grid data security analysis information.
7. Electric wire netting operation optimizing device based on cloud limit fuses intelligent scheduling operation platform, its characterized in that, the device includes:
the production data acquisition module is used for acquiring the initial data of the power grid production environment of the target power grid; the initial data of the power grid production environment is obtained by monitoring the target power grid through a power grid edge cluster;
the production data optimization module is used for inputting the initial data of the power grid production environment into a power grid operation data optimization model to obtain power grid production environment optimization data;
the production data analysis module is used for inputting the initial data of the power grid production environment and the optimization data of the power grid production environment into a power grid data operation state analysis model to obtain power grid data operation analysis information;
the production data analysis module is also used for inputting the power grid data operation analysis information into a power grid data safety state analysis model to obtain power grid data safety analysis information;
the power grid resource optimization module is used for generating power grid operation optimization information according to the power grid data operation analysis information and the power grid data safety analysis information; the grid operation optimization information is used for optimizing the grid operation of the target grid.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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