Power grid data processing method and device, computer equipment and program product
Through the gradient enhancement tree algorithm, the grid data processing accuracy and encryption processing are improved, which solves the problems of low efficiency and insufficient security of grid data processing, and realizes an efficient and secure data processing process.
Patent Information
- Application Number
- CN202411520935.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-29
- Publication Date
- 2024-12-27
AI Technical Summary
Existing power grid data processing methods may cause lag and delays when processing large-scale data or complex operations, affecting the user experience and work efficiency.
A variety of gradient enhancement tree algorithms are used to train the model parameters of the data processing system, integrate model parameters to improve data processing accuracy, and improve the accuracy of the power grid data through the trained model, and encrypt the processed data. Elliptic curve encryption algorithm for data protection.
Improve data processing efficiency, reduce system delay, ensure the security of power grid data, and prevent data leakage and tampering.
Smart Images
Figure CN119203241A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of computer science and technology, and in particular to a power grid data processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art
[0002] Computer science is a discipline that studies computers and related principles, theories and technologies. It covers computer hardware, software, algorithms, data structures, artificial intelligence, computing theory, computer networks, database systems, operating systems and other aspects. Computer science aims to study the design, development, application and impact of computers to solve various problems, and is committed to creating new computer technologies and methods.
[0003] This field involves the construction, design, and optimization of computer systems, as well as the study of how computers are used in science, engineering, business, and social behavior. However, existing power grid data processing methods may experience lags and delays when processing large-scale data or complex operations, affecting user experience and work efficiency. Summary of the invention
[0004] Based on this, it is necessary to provide a power grid data processing method, device, computer equipment, computer-readable storage medium and computer program product that can improve data processing efficiency in response to the above technical problems.
[0005] In a first aspect, the present application provides a power grid data processing method, comprising:
[0006] Based on multiple gradient boosting tree algorithms, the model parameters of the precision improvement model in the data processing system are trained, the model parameters trained by each gradient boosting algorithm are integrated to obtain the final model parameters of the precision improvement model, and the precision improvement model corresponding to the final model parameters is used as the trained precision improvement model;
[0007] The precision improvement model trained in the data processing system is used to improve the precision of the power grid data to obtain processed power grid data;
[0008] A data access request is obtained, the processed power grid data is encrypted, and based on the data access request, the encrypted power grid data is returned.
[0009] In one embodiment, the generation process of the data processing system includes:
[0010] Identifying at least one type of business function related to power grid data processing;
[0011] Encapsulate the microservice program corresponding to the business function type into a container, wherein the business function type includes user management, file management and authority control;
[0012] Based on the container, a data processing system is generated, and the container is managed through a container orchestration platform; wherein the management operation includes creating, starting, stopping and destroying the container.
[0013] In one embodiment, encrypting the processed power grid data includes:
[0014] Determine an elliptic curve;
[0015] Generate a pair of public key and private key according to the elliptic curve and the first random number;
[0016] The processed power grid data is encrypted according to the public key and the second random number.
[0017] In one embodiment, the method further comprises:
[0018] During the abnormality analysis, the failure risk of the data processing system is predicted, and an early warning is issued when a failure risk is predicted.
[0019] In one embodiment, the method further comprises:
[0020] Preprocessing the electricity consumption records of users in the data processing system, wherein the preprocessing includes cleaning and deduplication;
[0021] Through the large model, the corresponding electricity consumption recommendation plan is generated according to the pre-processed electricity consumption records.
[0022] In a second aspect, the present application further provides a power grid data processing device, including:
[0023] A training module is used to train model parameters of a precision improvement model in a data processing system based on multiple gradient boosting tree algorithms, integrate the model parameters trained by each gradient boosting algorithm to obtain final model parameters of the precision improvement model, and use the precision improvement model corresponding to the final model parameters as the trained precision improvement model;
[0024] A processing module, used to perform precision improvement processing on the power grid data through the precision improvement model trained in the data processing system to obtain processed power grid data;
[0025] The access module is used to obtain a data access request, encrypt the processed power grid data, and return the encrypted power grid data based on the data access request.
[0026] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:
[0027] Based on multiple gradient boosting tree algorithms, the model parameters of the precision improvement model in the data processing system are trained, the model parameters trained by each gradient boosting algorithm are integrated to obtain the final model parameters of the precision improvement model, and the precision improvement model corresponding to the final model parameters is used as the trained precision improvement model;
[0028] The precision improvement model trained in the data processing system is used to improve the precision of the power grid data to obtain processed power grid data;
[0029] A data access request is obtained, the processed power grid data is encrypted, and based on the data access request, the encrypted power grid data is returned.
[0030] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:
[0031] Based on multiple gradient boosting tree algorithms, the model parameters of the precision improvement model in the data processing system are trained, the model parameters trained by each gradient boosting algorithm are integrated to obtain the final model parameters of the precision improvement model, and the precision improvement model corresponding to the final model parameters is used as the trained precision improvement model;
[0032] The precision improvement model trained in the data processing system is used to improve the precision of the power grid data to obtain processed power grid data;
[0033] A data access request is obtained, the processed power grid data is encrypted, and based on the data access request, the encrypted power grid data is returned.
[0034] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:
[0035] Based on multiple gradient boosting tree algorithms, the model parameters of the precision improvement model in the data processing system are trained, the model parameters trained by each gradient boosting algorithm are integrated to obtain the final model parameters of the precision improvement model, and the precision improvement model corresponding to the final model parameters is used as the trained precision improvement model;
[0036] The precision improvement model trained in the data processing system is used to improve the precision of the power grid data to obtain processed power grid data;
[0037] A data access request is obtained, the processed power grid data is encrypted, and based on the data access request, the encrypted power grid data is returned.
[0038] The above-mentioned power grid data processing method, device, computer equipment, computer-readable storage medium and computer program product first train the model parameters of the precision improvement model in the data processing system based on multiple gradient boosting tree algorithms, integrate the model parameters obtained by training each gradient boosting algorithm to obtain the final model parameters of the precision improvement model, and use the precision improvement model corresponding to the final model parameters as the trained precision improvement model. Through the trained precision improvement model in the data processing system, the power grid data is processed for precision improvement to obtain the processed power grid data; obtain a data access request, encrypt the processed power grid data, and return the encrypted power grid data based on the data access request. In this way, by using the trained data processing model to process the power grid data, the processing efficiency of the system is improved, and at the same time, the power grid data in the processing process is encrypted, which effectively prevents the leakage and tampering of the power grid data. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments of the present application or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.
[0040] FIG1 is a schematic flow chart of a method for processing power grid data in one embodiment;
[0041] FIG2 is a structural block diagram of a power grid data processing device in one embodiment;
[0042] FIG. 3 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION
[0043] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0044] In one embodiment, as shown in FIG1 , a method for processing power grid data is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps 102 to 106:
[0045] Step 102, based on multiple gradient boosting tree algorithms, the model parameters of the precision improvement model in the data processing system are trained, the model parameters trained by each gradient boosting algorithm are integrated to obtain the final model parameters of the precision improvement model, and the precision improvement model corresponding to the final model parameters is used as the trained precision improvement model.
[0046] Among them, the gradient boosting algorithm is an integrated learning algorithm that iteratively trains the decision tree model and then weightedly fuses multiple decision tree models to build a more powerful classification or regression model.
[0047] In some embodiments, the terminal introduces a cache mechanism. When access to power grid data is required, the data is first read from the cache. If the data is not in the cache, it is read from the database storing the power grid data and cached in the memory.
[0048] Furthermore, the terminal will train the model parameters of the precision improvement model in the data processing system based on multiple gradient boosting tree algorithms, integrate the model parameters trained by each gradient boosting algorithm, obtain the final model parameters of the precision improvement model, and use the precision improvement model corresponding to the final model parameters as the trained precision improvement model. The specific formula is shown in (1).
[0049] Assume there are k trees and the predicted output of each tree is , then the final model It can be expressed as:
[0050]
[0051] Step 104, performing precision improvement processing on the power grid data through the precision improvement model trained in the data processing system to obtain processed power grid data.
[0052] Specifically, the terminal will process the power grid data through the precision improvement model trained in the data processing system to improve its precision and finally obtain the processed power grid data.
[0053] Step 106, obtaining a data access request, encrypting the processed power grid data, and returning the encrypted power grid data based on the data access request.
[0054] In some embodiments, when receiving a data access request, the terminal encrypts the processed power grid data and returns the encrypted power grid data according to the data access request, wherein the encryption method is an ECC (elliptic curve encryption algorithm) encryption algorithm.
[0055] In the above-mentioned power grid data processing method, based on multiple gradient boosting tree algorithms, the model parameters of the precision improvement model in the data processing system are trained, the model parameters obtained by each gradient boosting algorithm are integrated to obtain the final model parameters of the precision improvement model, and the precision improvement model corresponding to the final model parameters is used as the trained precision improvement model. The precision improvement model trained in the data processing system is used to improve the precision of the power grid data to obtain the processed power grid data; a data access request is obtained, the processed power grid data is encrypted, and the encrypted power grid data is returned based on the data access request. In this way, by using the trained data processing model to process the power grid data, the processing efficiency of the system is improved, and at the same time, the power grid data in the processing process is encrypted, which effectively prevents the leakage and tampering of the power grid data.
[0056] In an exemplary embodiment, the generation process of a data processing system includes: determining at least one business function type related to power grid data processing; encapsulating a microservice program corresponding to the business function type into a container, where the business function type includes user management, file management, and permission control; generating a data processing system based on the container, and managing the container through a container orchestration platform; wherein management operations include creating, starting, stopping, and destroying containers.
[0057] In actual implementation, the terminal will determine the business function types related to power grid data processing, including user management, file management, and permission control, and establish a microservice module for each business function type. Each microservice module is packaged into an independent container using containerization technology. The container contains the application, runtime environment, and dependencies to ensure that each container can run consistently in any environment. The containerization technology can be Kubernetes or other technologies that can package microservices into containers, which is not limited in this embodiment.
[0058] Furthermore, the terminal will generate a data processing system based on the container, and the container orchestration platform can create, start, stop and destroy the container.
[0059] In the above embodiment, the business function type is split into multiple independent microservices, and each microservice is responsible for completing a clearly defined business function type, which can improve the flexibility of the system and make the modification, upgrade or replacement of specific business functions easier and safer. The use of a container orchestration platform to manage the life cycle of these microservices can more conveniently achieve rapid deployment and horizontal expansion of the system to meet growing business needs. Containerization technology is used to package each microservice into an independent container, including an application, runtime environment and dependencies, which can ensure consistent operation in any environment and decouple from the underlying environment, thereby improving the stability and consistency of the system.
[0060] In an exemplary embodiment, encrypting processed power grid data includes: determining an elliptic curve; generating a pair of public key and private key according to the elliptic curve and a first random number; and encrypting the processed power grid data according to the public key and a second random number.
[0061] In actual implementation, the terminal will select the ECC encryption algorithm to encrypt the processed power grid data.
[0062] Specifically, we select an elliptic curve E and a base point G, usually expressed as , where a, b are curve parameters, q is the prime field;
[0063] Randomly select a private key d, which is the first random number (within the preset range), and calculate the public key , that is, perform d addition operations on the base point G to obtain the public key Q.
[0064] Encryption process: Assume the message to be encrypted is M, select the second random number k;
[0065] calculate Get point C1 and calculate Get point C2;
[0066] Encrypt the message , where H is a hash function.
[0067] Signature process: Hash calculation of message M ;
[0068] Randomly select the third random number k and calculate the point The x-coordinate of , where r is the hash value of the x-coordinate of point R, n is a large prime number in the curve parameters, and the signature is represented by (r, s).
[0069] Furthermore, the terminal library uses the corresponding private key to decrypt the encrypted power grid data. The decryption process is to receive the ciphertext C and decrypt it to obtain .
[0070] In the above embodiment, the ECC encryption algorithm uses points on the elliptic curve for calculation. Under the same security strength, its calculation speed is usually better than the traditional RSA algorithm. For real-time data transmission and large-scale encryption operations, the ECC algorithm can provide better performance.
[0071] In some exemplary embodiments, the power grid data processing method further includes: obtaining performance indicators of the data processing system, the performance indicators including system load, response time and throughput; performing anomaly analysis based on the performance indicators, and displaying the operating status of the data processing system according to the anomaly analysis results.
[0072] In actual implementation, the terminal will determine the key performance indicators that need to be monitored in real time, including system load, response time and throughput, collect real-time data of the system, and send the power grid data to the processing engine through streaming processing tools; the streaming processing engine will perform real-time processing and anomaly analysis on the data, and design a visual dashboard to intuitively display the system operation status and performance.
[0073] In the above embodiment, the operating status and performance of the data processing system are monitored in real time through a visual dashboard, so that the status of the data processing system can be obtained more quickly and intuitively, so as to perform the next step of processing according to the status of the data processing system.
[0074] In an exemplary embodiment, during the abnormality analysis, the failure risk of the data processing system is predicted, and when the failure risk is predicted, an early warning is issued.
[0075] In actual implementation, when the terminal monitors the status of the data processing system in real time, it will analyze the power grid data in real time and make predictions. When a failure risk is predicted, it will issue an early warning or indicator statistics.
[0076] In the above embodiment, by predicting in advance the risk of failures that may occur during the power grid data processing process, measures can be taken in advance to prevent the occurrence of failures, thereby improving the stability of the system.
[0077] In some exemplary embodiments, the power grid data processing method further includes: preprocessing the electricity consumption records of users in the data processing system, the preprocessing including cleaning and deduplication; and generating corresponding electricity consumption recommendation plans based on the preprocessed electricity consumption records through a large model.
[0078] In some embodiments, the terminal collects the user's electricity usage records, cleans and removes duplicates from the electricity usage records, and obtains preprocessed electricity usage records. Relevant features including user preferences are extracted from the preprocessed electricity usage records. The recommendation model is trained using a collaborative filtering algorithm, and the model parameters are continuously optimized to improve the accuracy of the recommendation. Based on the relevant features and the latest recommendation model, personalized recommendation results are generated to provide users with personalized electricity usage suggestions.
[0079] In the above embodiment, a series of processes are performed on the user's electricity consumption records, and personalized recommendations are provided to the user according to the processing results, thereby effectively improving the practicality of the system.
[0080] To explain the power grid data processing method in the present application in detail, an embodiment is used for explanation below. By way of example, the present application is explained with a specific power grid data processing method.
[0081] First, the terminal will determine the business function types related to power grid data processing, which include user management, file management, and permission control. A microservice module will be established for each business function type, and containerization technology will be used to package each microservice module into an independent container. The container contains the application, runtime environment, and dependencies to ensure that each container can run consistently in any environment.
[0082] Furthermore, the terminal will generate a data processing system based on the container, and the container orchestration platform can create, start, stop and destroy the container.
[0083] The terminal will introduce a cache mechanism. When accessing power grid data, it will first read the data from the cache. If the data is not in the cache, it will read it from the database storing the power grid data and cache the power grid data in the memory.
[0084] Furthermore, the terminal will train the model parameters of the precision improvement model in the data processing system based on multiple gradient boosting tree algorithms, integrate the model parameters trained by each gradient boosting algorithm, obtain the final model parameters of the precision improvement model, and use the precision improvement model corresponding to the final model parameters as the trained precision improvement model. The specific formula is shown in (1). The terminal will process the power grid data through the precision improvement model trained in the data processing system to improve its precision, and finally obtain the processed power grid data.
[0085] When receiving a data access request, the terminal will encrypt the processed power grid data and return the encrypted power grid data according to the data access request. The encryption method is the ECC (elliptic curve encryption algorithm) encryption algorithm.
[0086] Specifically, we select an elliptic curve E and a base point G, usually expressed as , where a, b are curve parameters, q is the prime field;
[0087] Randomly select a private key d, which is the first random number (within the preset range), and calculate the public key , that is, perform d addition operations on the base point G to obtain the public key Q.
[0088] Encryption process: Assume the message to be encrypted is M, select the second random number k;
[0089] calculate Get point C1 and calculate Get point C2;
[0090] Encrypt the message , where H is a hash function.
[0091] Signature process: Hash calculation of message M ;
[0092] Randomly select the third random number k and calculate the point The x-coordinate of , where r is the hash value of the x-coordinate of point R, n is a large prime number in the curve parameters, and the signature is represented by (r, s).
[0093] Furthermore, the terminal library uses the corresponding private key to decrypt the encrypted power grid data. The decryption process is to receive the ciphertext C and decrypt it to obtain .
[0094] Determine the key performance indicators that need to be monitored in real time, including system load, response time, and throughput, collect real-time data from the system, and send the power grid data to the processing engine through streaming processing tools; use the streaming processing engine to process data in real time and analyze exceptions, and design a visual dashboard to intuitively display the system's operating status and performance. When monitoring the status of the data processing system in real time, the power grid data will be analyzed in real time and predicted. If a failure risk is predicted, early warnings or indicator statistics will be issued.
[0095] The terminal collects the user's electricity usage records, cleans and removes duplicates from them, and obtains preprocessed electricity usage records. Relevant features including user preferences are extracted from the preprocessed electricity usage records. The recommendation model is trained using collaborative filtering algorithms, and model parameters are continuously optimized to improve recommendation accuracy. Based on relevant features and the latest recommendation model, personalized recommendation results are generated to provide users with personalized electricity usage recommendations.
[0096] This application splits the entire system into multiple independent microservices, each of which is responsible for a clearly defined business function. It uses containerization technology Kubernetes to achieve rapid deployment and horizontal expansion of microservices, ensure the stability and flexibility of the system, and introduces caching mechanism and machine learning algorithm (gradient boosting tree algorithm) to optimize the data processing process and improve the system's response speed and processing efficiency.
[0097] This application uses the ECC encryption algorithm to achieve secure encryption of data, and combines blockchain technology to ensure the integrity and immutability of data, so that users' privacy data and transaction information are effectively protected.
[0098] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.
[0099] Based on the same inventive concept, the embodiment of the present application also provides a power grid data processing device for implementing the power grid data processing method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more power grid data processing device embodiments provided below can refer to the limitations of the power grid data processing method above, and will not be repeated here.
[0100] In an exemplary embodiment, as shown in FIG2 , a power grid data processing device is provided, including: a training module 201, a processing module 202 and an access module 203, wherein:
[0101] The training module is used to train the model parameters of the precision improvement model in the data processing system based on multiple gradient boosting tree algorithms, integrate the model parameters trained by each gradient boosting algorithm to obtain the final model parameters of the precision improvement model, and use the precision improvement model corresponding to the final model parameters as the trained precision improvement model.
[0102] The processing module is used to perform precision improvement processing on the power grid data through the precision improvement model trained in the data processing system to obtain processed power grid data.
[0103] The access module is used to obtain a data access request, encrypt the processed power grid data, and return the encrypted power grid data based on the data access request.
[0104] In some embodiments, the device also includes a generation module for determining at least one business function type related to power grid data processing; encapsulating the microservice program corresponding to the business function type into a container, the business function type includes user management, file management and permission control; based on the container, a data processing system is generated, and the container is managed through a container orchestration platform; wherein the management operations include creating, starting, stopping and destroying containers.
[0105] In some embodiments, the device also includes an encryption module for determining an elliptic curve; generating a pair of public keys and private keys based on the elliptic curve and the first random number; and encrypting the processed power grid data based on the public key and the second random number.
[0106] In some embodiments, the device also includes an analysis module, which is also used to obtain performance indicators of the data processing system, including system load, response time and throughput; based on the performance indicators, an abnormality analysis is performed, and according to the abnormality analysis results, the operating status of the data processing system is displayed.
[0107] In some embodiments, the device also includes an early warning module for predicting the failure risk of the data processing system during the abnormality analysis process, and issuing an early warning when the failure risk is predicted.
[0108] In some embodiments, the device also includes a recommendation module for preprocessing the electricity usage records of users in the data processing system, the preprocessing including cleaning and deduplication; through the large model, a corresponding electricity usage recommendation plan is generated according to the preprocessed electricity usage records.
[0109] Each module in the above-mentioned power grid data processing device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module above.
[0110] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG3. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store power grid data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a power grid data processing method is implemented.
[0111] The display unit of the computer device is used to form a visually visible picture, and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball, or touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.
[0112] Those skilled in the art will understand that the structure shown in FIG. 3 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.
[0113] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:
[0114] Based on multiple gradient boosting tree algorithms, the model parameters of the precision improvement model in the data processing system are trained, the model parameters trained by each gradient boosting algorithm are integrated to obtain the final model parameters of the precision improvement model, and the precision improvement model corresponding to the final model parameters is used as the trained precision improvement model;
[0115] The precision improvement model trained in the data processing system is used to improve the precision of the power grid data to obtain processed power grid data;
[0116] A data access request is obtained, the processed power grid data is encrypted, and based on the data access request, the encrypted power grid data is returned.
[0117] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:
[0118] Based on multiple gradient boosting tree algorithms, the model parameters of the precision improvement model in the data processing system are trained, the model parameters trained by each gradient boosting algorithm are integrated to obtain the final model parameters of the precision improvement model, and the precision improvement model corresponding to the final model parameters is used as the trained precision improvement model;
[0119] The precision improvement model trained in the data processing system is used to improve the precision of the power grid data to obtain processed power grid data;
[0120] A data access request is obtained, the processed power grid data is encrypted, and based on the data access request, the encrypted power grid data is returned.
[0121] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:
[0122] Based on multiple gradient boosting tree algorithms, the model parameters of the precision improvement model in the data processing system are trained, the model parameters trained by each gradient boosting algorithm are integrated to obtain the final model parameters of the precision improvement model, and the precision improvement model corresponding to the final model parameters is used as the trained precision improvement model;
[0123] The precision improvement model trained in the data processing system is used to improve the precision of the power grid data to obtain processed power grid data;
[0124] A data access request is obtained, the processed power grid data is encrypted, and based on the data access request, the encrypted power grid data is returned.
[0125] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.
[0126] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.
[0127] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.
[0128] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.
Claims
1. A method for processing power grid data, characterized in that: The method comprises: Based on multiple gradient boosting tree algorithms, the model parameters of the precision improvement model in the data processing system are trained, the model parameters trained by each gradient boosting algorithm are integrated to obtain the final model parameters of the precision improvement model, and the precision improvement model corresponding to the final model parameters is used as the trained precision improvement model; The precision improvement model trained in the data processing system is used to improve the precision of the power grid data to obtain processed power grid data; A data access request is obtained, the processed power grid data is encrypted, and based on the data access request, the encrypted power grid data is returned.
2. The method according to claim 1, characterized in that The generation process of the data processing system includes: Identifying at least one type of business function related to power grid data processing; Encapsulate the microservice program corresponding to the business function type into a container, wherein the business function type includes user management, file management and authority control; Based on the container, a data processing system is generated, and the container is managed through a container orchestration platform; wherein the management operation includes creating, starting, stopping and destroying the container.
3. The method according to claim 1, characterized in that The encrypting the processed power grid data includes: Determine an elliptic curve; Generate a pair of public key and private key according to the elliptic curve and the first random number; The processed power grid data is encrypted according to the public key and the second random number.
4. The method according to claim 1, characterized in that: The method further comprises: Acquiring performance indicators of the data processing system, wherein the performance indicators include system load, response time and throughput; Based on the performance indicators, an abnormality analysis is performed, and according to the abnormality analysis results, the operating status of the data processing system is displayed.
5. The method according to claim 4, characterized in that The method further comprises: During the abnormality analysis, the failure risk of the data processing system is predicted, and an early warning is issued when a failure risk is predicted.
6. The method according to claim 1, characterized in that The method further comprises: Preprocessing the electricity consumption records of users in the data processing system, wherein the preprocessing includes cleaning and deduplication; Through the large model, the corresponding electricity consumption recommendation plan is generated according to the pre-processed electricity consumption records.
7. A power grid data processing device, characterized in that: The device comprises: A training module is used to train model parameters of a precision improvement model in a data processing system based on multiple gradient boosting tree algorithms, integrate the model parameters trained by each gradient boosting algorithm to obtain final model parameters of the precision improvement model, and use the precision improvement model corresponding to the final model parameters as the trained precision improvement model; A processing module, used to perform precision improvement processing on the power grid data through the precision improvement model trained in the data processing system to obtain processed power grid data; The access module is used to obtain a data access request, encrypt the processed power grid data, and return the encrypted power grid data based on the data access request.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.