112 results about "Data mining algorithm" patented technology

The invention provides a reimbursement data management system and method based on AI artificial intelligence, and the system comprises a multi-modal data collection and fusion module which is used for providing an access reimbursement application entrance; the auditing and compliance judgment module is used for invoice authenticity identification, reason rationality identification, reimbursement item classification and automatic auditing; the approval process optimization module is used for learning and optimizing a reimbursement approval process and automatically simplifying or increasing approval levels according to reimbursement application features; the analysis and prediction module is used for performing multi-dimensional analysis on the reimbursement data based on a big data processing framework and a data mining algorithm and generating a visual report and result; and the data storage and block chain module is used for constructing a distributed and non-tampering data storage account book by adopting a block chain technology, and executing a reimbursement process automation rule by utilizing a block chain intelligent contract. The efficiency, quality and safety of reimbursement data management are comprehensively improved, and enterprise financial management is promoted to develop towards the intelligent and refined direction.

The invention provides an LED product surface defect detection method, which comprises the following steps: carrying out defect feature extraction on a preprocessed LED surface image by adopting a multi-scale morphological algorithm, and respectively carrying out morphological opening operation and closing operation on surface recesses and fine scratches by setting structural elements with different sizes and shapes to obtain candidate regions of the recesses and the scratches; according to the geometric dimensions, morphological characteristics and spatial distribution detail attributes of the recesses and scratches, constructing defect feature vectors, and performing classification and severity evaluation on defect candidate areas through a support vector machine algorithm to obtain the types, positions and quantitative description parameters of defects; and updating the light source parameter database and the defect feature database according to a quality detection result of the LED product, analyzing an association rule between defects and light source parameters through a data mining algorithm, and optimizing a light source parameter configuration strategy and a defect detection model.

The invention provides an intelligent learning behavior monitoring and abnormity early warning method and device, and relates to the technical field of education analysis, and the device architecture comprises a multi-modal data collection module, a behavior analysis module, a dynamic abnormity grading early warning module and a processing measure module. The multi-modal data acquisition unit integrates learning platform operation records, facial expression data and physiological data as well as environment and situation information collected by the situation awareness data acquisition unit, so that omnibearing and multi-dimensional data acquisition is realized; the long-time monitoring and personalized model construction unit uses a plurality of data mining algorithms and LSTM in deep learning to combine with an attention mechanism, accurately mines student behavior rules according to long-term data and constructs a personalized model, and can flexibly and accurately judge abnormal behaviors according to student individual conditions and learning situations. Graded early warning is provided for related personnel in time, deterioration of learning problems is effectively prevented, and timeliness and effectiveness of education intervention are improved.

The invention discloses a cooking robot menu management method and system based on big data and a medium, and relates to the technical field of cooking robot menu management. The cooking robot menu management method comprises the following steps: a data acquisition step: collecting various menu data through a web crawler technology; a data preprocessing step: cleaning the collected menu data; a data analysis and classification step: analyzing the preprocessed menu data by using a data mining algorithm; a menu recommendation and customization step: recommending a proper menu to the user by using a collaborative filtering algorithm or a content-based recommendation algorithm according to historical ordering records, health requirements and personal taste preference information of the user; and an intelligent cooking control step: the cooking robot receives the customized cooking instruction. According to the invention, the modules cooperatively and efficiently operate, links of data acquisition, processing, analysis, recommendation, cooking control and the like are closely linked, the system response is rapid, and the user operation experience and cooking efficiency are greatly improved.

The embodiment of the invention belongs to the field of data processing, and relates to a business risk assessment method and device, computer equipment and a storage medium, and the method comprises the following steps: constructing a risk knowledge graph according to business data; acquiring a risk conduction path in the risk knowledge graph based on a graph search algorithm; acquiring associated risk event information in a database according to the risk conduction path, and analyzing the associated risk event information according to a data mining algorithm to obtain risk event influence information; quantifying the risk event influence information, and training a risk event influence evaluation model according to quantified risk event influence index data; performing risk simulation evolution according to the risk conduction path and the risk event influence evaluation model to obtain a risk event influence result; and performing risk analysis according to the risk event influence result to obtain a comprehensive risk value. According to the invention, accurate risk assessment can be carried out on business of an enterprise.

The invention provides a cross-border e-commerce big data analysis system and method, and the method comprises the steps: obtaining multi-dimensional original business data of a cross-border e-commerce platform, carrying out the real-time collection through a distributed data collection system, and obtaining a structured business data set; data collected in real time is cleaned, converted and structured, a unified data model is constructed, and integration of heterogeneous data sources is achieved; storing the processed structured data in a time sequence database to provide data support for real-time analysis; performing real-time aggregation and statistical analysis on the newly added data to generate business index and key performance index data; a Lambda framework is adopted, batch processing and stream processing are combined, the historical data and the real-time data are subjected to full-amount analysis, and an analysis result is obtained; based on an analysis result, a data mining algorithm is adopted to identify a service mode and predict a future trend; a multi-dimensional data cube is constructed, multi-dimensional and multi-granularity data exploration and interactive analysis are supported, and management decision making is assisted.

The invention provides a multi-platform collaborative data acquisition and analysis method, and relates to the technical field of data processing and analysis. The method comprises the following steps: receiving and converting data from different platforms into a unified intermediate format through a data adaptation intermediate layer, and processing a data structure and an interface of a specific platform by using a plurality of adapters; the data abstraction layer defines a standardized interface, so that multiple platforms request data through a unified interface, and a mapping rule converts the request into calling of the adaptation layer; the data integration engine preprocesses the data to ensure data consistency and accuracy; the data warehouse adopts a distributed architecture to ensure the service continuity through load balancing, data partitioning, a caching mechanism and a redundant storage and fault transfer mechanism; and the data processing and analysis module converts the data into decision suggestions by using a visual tool and a data mining algorithm. According to the method, manual intervention is reduced, the data processing speed and accuracy are improved, the data mode and trend are deeply mined, and service decision is promoted.

The invention relates to a risk analysis method, and particularly provides a dynamic risk analysis method based on a Bayesian network. Converting the risk influence factors into a first group of network nodes based on a historical risk knowledge model, and constructing an initial topological structure according to a knowledge model logic relationship; secondly, acquiring multi-source real-time monitoring data, learning correlation among variables through a data mining algorithm, and generating a second group of nodes and a data-driven topological structure; then, connecting the historical information Bayesian network with the data-driven Bayesian network through a shared risk node, and constructing a comprehensive Bayesian network; meanwhile, a dynamic probability updating mechanism is established, and comprehensive network probability parameters are dynamically adjusted by adopting a weighted fusion algorithm in combination with a historical prior probability and a real-time posterior probability; and finally, performing risk reasoning based on the dynamically updated integrated network, and outputting risk quantitative indexes.

The invention relates to the technical field of intelligent water affairs, in particular to a water supply and drainage pipe network inspection order dispatching method and system. The method comprises the steps of obtaining historical inspection data of a water supply and drainage pipe network; identifying dynamic inspection behavior information from the historical inspection data by adopting a data mining algorithm; constructing an inspection task evaluation index system based on the identified dynamic inspection behavior information; acquiring water supply and drainage pipe network monitoring data and inspector load data in real time; extracting required indexes from the monitoring data of the water supply and drainage pipe network and the load data of the inspector based on an inspection task evaluation index system; dynamically adjusting the priority of each inspection task and an order dispatching algorithm based on the extracted indexes; and order dispatching of the inspection tasks is carried out based on the priority of each inspection task and the order dispatching algorithm, so that the overall effect and the flexible adjustment capability of the inspection order dispatching of the water supply and drainage pipe network are improved.

The invention discloses an AI-based BOM material information automatic coding method, which comprises the following steps of: unifying data structures of BOMs of products at different stages, storing the data structures as a single BOM data set, screening single data with a single bit number, comparing part of data with a standard database, cleaning and then comparing when the data are not consistent with the standard database; a knowledge graph, vector conversion, cosine distance and the like can be used for further processing so as to convert single bill of material data into standard data; for data which cannot be matched, multiple strategies can be adopted to select matching entries; the invention further relates to format recognition of the bill of materials and selection of processing tools so as to ensure data integrity. When data of a single bill of material is denoised, key parameters are extracted in combination with multiple data sources, association rules are mined through a data mining algorithm, and denoising operation is executed. The method can improve the accuracy and standardization degree of material data, greatly improves the coding efficiency, and achieves the intelligentization and refinement of material management.

The invention discloses an electric vehicle charging compatible fault diagnosis method based on a knowledge graph, and the method comprises the following steps: constructing the knowledge graph, carrying out the fault diagnosis reasoning, and updating the knowledge graph, and the construction of the knowledge graph comprises the following steps: collecting multi-source data in the charging process of an electric vehicle, knowledge extraction is performed by applying a natural language processing technology and a data mining algorithm, knowledge from different sources is fused, a knowledge graph is constructed in a graph form, nodes represent entities, edges represent relationships between the entities, and attributes are attached to the nodes or the edges. According to the method, the knowledge graph is constructed, the electric vehicle charging multi-source data is comprehensively collected, knowledge is extracted and fused by using natural language processing and a data mining algorithm, and during fault diagnosis reasoning, input data is preprocessed based on combination of rules and machine learning, for example, abnormal value detection by using Z-score, missing value filling by using linear interpolation and the like.

The invention discloses a wolfberry quality stability evaluation method and system based on a neural network, and the method comprises the steps: collecting wolfberry samples from different producing areas, obtaining the growth environment parameters and harvesting and processing information of the wolfberry samples, and building a correlation database of the producing area environment and the wolfberry quality, analyzing the influence rule of the producing area factors on the quality stability of the Chinese wolfberry through a data mining algorithm to obtain a quality stability static prediction model of the Chinese wolfberry in different producing areas; a long-short-term memory neural network is adopted, a multi-level feature extraction structure is designed, network parameters are optimized through an adaptive learning algorithm, generalization and robustness constraints are introduced at the same time, a quality stability dynamic prediction model is obtained, and the prediction capacity and the anti-interference capacity of the model for new samples are enhanced. According to the method, the problems of unstable quality caused by large climate and soil difference in the wolfberry planting area and high subjectivity of wolfberry quality evaluation are solved, and accurate prediction of the wolfberry quality stability is realized.

The invention discloses an intelligent leg training rehabilitation method which comprises the following steps: constructing a leg training rehabilitation system based on a deep learning model and a data mining analysis algorithm; according to the output of the leg training rehabilitation system, the rehabilitation training healing degree of the patient is analyzed, and the leg rehabilitation training content of the patient is customized; monitoring the expression and progress of the patient in the rehabilitation training process through the leg training rehabilitation system, and adjusting the leg rehabilitation training content; based on the monitoring result of the leg training rehabilitation system, an expression analysis module is introduced, the expression state of the patient is monitored, and human intervention is carried out; evaluating and optimizing the training content of the leg training rehabilitation system; in order to avoid excessive dependence on deep learning and a data mining algorithm, an expression analysis module is introduced, real-time expression state analysis is provided, a training rehabilitation strategy is adjusted in real time according to an analysis result, leg rehabilitation support better conforming to the expression state of a patient is provided for the patient, and the rehabilitation speed of the legs of the patient is increased.

The invention discloses an advertisement putting method and system based on big data analysis, and belongs to the technical field of advertisement putting, and the method comprises the steps: obtaining multi-source data of a user, carrying out the preprocessing of the multi-source data of the user through a big data processing technology, guaranteeing the quality and consistency of the data, integrating the data of different sources, and carrying out the processing of the data. Establishing a unified user data warehouse; a machine learning and / or data mining algorithm is used for deeply analyzing the user behavior data, user preference characteristics are mined, and the user preference characteristics comprise interest preference, consumption habits and advertisement acceptability; according to the preference characteristics of the user, constructing a precise portrait of the user; obtaining an advertisement putting target and platform characteristic data; and according to the accurate portrait of the user and an advertisement putting target, combining platform characteristic data, and applying collaborative filtering and a content recommendation algorithm to recommend personalized advertisement content to the user. According to the invention, the advertising effect of the advertisement on different platforms is ensured.

The embodiment of the invention discloses a data analysis method and device, electronic equipment and a storage medium, which are used for analyzing and aligning different field names in a plurality of historical data tables by using at least one of a large language model and a data mining algorithm, so that the data analysis efficiency is improved. The method comprises the steps that historical data is obtained, the historical data comprises a plurality of historical data tables, historical natural language query statements and historical structured query language SQL query statements corresponding to the historical natural language query statements, and each historical data table comprises at least one field name; the historical data are input into at least one of a large language model and a data mining algorithm, a target field alignment table is generated, and the target field alignment table comprises the mapping relation of different field names with semantic equivalence in multiple historical data tables; and according to the target field alignment table, updating the structured description information of the plurality of historical data tables, and generating updated structured description information of the plurality of historical data tables.

The embodiment of the invention provides a wafer defect detection method and device based on a data mining algorithm. The wafer defect detection method based on the data mining algorithm comprises the following steps: acquiring image data of a wafer; preprocessing the image data to obtain determined preprocessing data; feature extraction is carried out through clustering analysis based on the preprocessed data, and defect features are determined; and judging the defect features based on an integrated learning classification model, and determining defect information. A data preprocessing process is adopted, the data quality and the accuracy of subsequent analysis are improved, feature vectors are constructed in combination with multi-dimensional features such as gray scale, texture and edge subsequently, the similarity of image blocks is calculated through Euclidean distance or Manhattan distance, the feature vectors are clustered by using a K-means algorithm, and the similarity of the image blocks is calculated. The features capable of effectively representing the small defects of the wafer are automatically extracted, complicated feature extraction rules do not need to be manually designed, and the efficiency and adaptability of feature extraction are improved.

In view of the problems existing in the sensor data processing of the above-mentioned existing industrial fresh air systems, the present invention discloses a multi-source sensor data analysis method and a sensor device for an IoT fresh air system. Indoor and outdoor air quality parameter information is obtained and determined through fresh air sensors, and the indoor and outdoor air quality parameter information is integrated and processed based on the fresh air system. After edge computing and analysis, correlation analysis is performed through a cloud computing platform and dynamic adjustment is performed in response to the fresh air system. The present invention uses machine learning to perform in-depth analysis on the collected data, and uses data mining algorithms to identify patterns and trends, so as to intelligently control and optimize the industrial fresh air system. It also has the function of synchronous edge computing of multi-source sensor data based on the Internet of Things nodes, which is closer to the data source of the user terminal, thereby improving the processing effect, high efficiency, security and low latency of multi-source fresh air related data.

The invention discloses an equipment data analysis early warning method and system based on the Internet of Things, and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring an Internet of Things device, and monitoring a multi-dimensional operation data stream of a target electric meter device in real time; analyzing the data stream to obtain a communication link, and transmitting the data stream to a data processing center based on the communication link; processing the data stream according to a standardized program of a data processing center to obtain a standard multi-dimensional data stream; constructing an electricity meter anomaly analysis adaptive network by using a data mining algorithm, and analyzing a standard data stream to obtain an abnormal operation parameter; a hierarchical early warning mechanism is constructed, early warning is triggered based on abnormal parameters, a strategy is determined, and early warning is performed on the electric meter equipment, so that the technical problem that multi-dimensional data streams and abnormal states of the electric meter equipment cannot be monitored timely and accurately due to insufficient transmission and abnormal detection mechanisms of data of the electric meter equipment is solved.

The invention relates to a multidimensional data analysis method for an entrepreneurship incubation platform, which improves the incubation efficiency and success rate of entrepreneurship enterprises through systematic data processing and intelligent analysis. Comprising a data acquisition module for acquiring multi-source data in multiple modes; the multi-dimensional data modeling module is used for constructing a data cube by utilizing an OLAP (On-Line Analytical Processing) technology to realize multi-angle observation; the data analysis module is used for carrying out deep portraying on the enterprise by using data mining algorithms such as clustering and causal analysis, and predicting a future trend by using a time sequence and a regression model; the machine learning module is used for constructing an intelligent recommendation system and providing customized incubation strategies for enterprises; the visual display module is used for displaying the analysis result in the forms of charts and instrument panels and supporting interactive operation; and the dynamic feedback and optimization module is used for updating an analysis result in real time and sending early warning to a manager through an anomaly detection mechanism. According to the invention, through comprehensive and dynamic data analysis, scientific decision is supported, and rapid growth and development of entrepreneurship enterprises are promoted.

The invention relates to the technical field of data credibility evaluation, and discloses a whole-process flow mining data credibility evaluation system, which comprises a processor used for controlling the operation of the system; the data source credibility evaluation module is used for objectively evaluating the credibility of the data source; the data quality evaluation module is used for evaluating the consistency, integrity, accuracy and the like of the data by using objective data quality evaluation indexes; the data cleaning and preprocessing module is used for cleaning and preprocessing the mining data according to the evaluation result; and the data mining algorithm module is used for mining the processed data by using a data mining algorithm. Through objective data quality evaluation indexes, the consistency, integrity, accuracy and the like of the data are evaluated, and the influence of subjective judgment is reduced. And different judgments on the quality and credibility of the data due to subjective judgments of the user are avoided, so that the certainty of a system evaluation result is improved.

The invention discloses a bulk commodity supply chain management system, and relates to bulk commodity supply chain management, and the system comprises a digital supply chain module which carries out supply chain business process examination and approval management and online business management and control; the coaling business management module is used for generating a supply plan according to user requirements and the station coal inventory information, controlling vehicles to enter and unload coal, collecting coal unloading data and updating the station coal inventory information; the station cargo control module controls the vehicles to enter and exit the station according to the supply plan and collects cargo loading and unloading metering data of the vehicles; the field short-time transfer business module is used for obtaining coal transfer starting quantity and stopping quantity data through interaction with belt scale Internet of Things equipment at a loading port and an unloading port, and forming coal transfer data in combination with the collected vehicle loading and unloading cargo metering data; processing the coal circulation data by using a flow calculation framework, and predicting the time and quantity of coal circulation through a data mining algorithm; aiming at low management efficiency of bulk commodity supply chains in the prior art, the efficiency is improved.

The invention discloses a method, a system, equipment and a medium for enhancing message processing stability of the Internet of Things, and the method comprises the steps: a data source sends data to a monitoring module, and the monitoring module recognizes and filters the data; sending the abnormal data to a compensation module for processing; abnormal data can also be sent to the alarm module for strategy alarm; the normally circulated data is directly sent to the service module to be processed; if the business system encounters an error in the processing process, the error data and related abnormal information are transferred to the data mining module; the data mining module distributes the data to mining sub-modules of all professions according to data characteristics and error types; according to the method, advanced data mining algorithms such as TF-IDF are introduced and used for mining error keywords from error data and generating rules capable of predicting future errors. Compared with traditional statistical analysis or simple rule matching, the targeted data mining method has higher accuracy and intelligent level.

The invention relates to the technical field of building material science, in particular to a synthetic gradation evaluation method and system.The method comprises the steps that aggregate with different particle sizes is separated by adopting a method of reducing the size of screen holes step by step, and screening parameters are adjusted in real time through an optimization algorithm; calculating the ratio of the aggregates with various particle sizes, and performing particle size distribution analysis on the uniformly mixed aggregates to obtain high-precision particle size distribution data; drawing a gradation curve according to the high-precision particle size distribution data, and marking particle size section information influencing the material performance; using an image processing algorithm to extract compactness characteristics of the sample image, and using a convolutional neural network model to perform compactness evaluation to obtain a compactness evaluation result; integrating particle size distribution and compactness evaluation results, and analyzing the relationship between the particle size distribution and the compactness by using a data mining algorithm and a regression analysis model; and establishing an optimization model according to an analysis result between the particle size and the compactness, setting a standardized evaluation index, and carrying out comprehensive evaluation on gradation. By means of the method, the defects of an existing method in the aspects of particle size distribution accuracy, grading curve reliability and compactness evaluation scientificity are effectively overcome, and the precision and credibility of synthesis grading evaluation are remarkably improved.

The invention relates to the technical field of customer and merchant credit evaluation, in particular to a dynamic performance behavior-based customer and merchant credit evaluation method and system and a medium. The method comprises the steps of obtaining historical transaction data of a customer and a merchant; identifying dynamic performance behavior information from historical transaction data of the customer and the merchant by adopting a data mining algorithm; based on the identified dynamic performance behavior information, constructing a dynamic index system of customer and merchant credit evaluation; weighting each index in the dynamic index system to obtain the weight of each index; obtaining latest dynamic performance behavior information of the customer and the merchant; the method comprises the following steps of: extracting required indexes from latest dynamic performance behavior information of a customer service provider based on a dynamic index system of customer service provider credit evaluation, and performing standardization processing on the indexes to obtain standardized values of the indexes; and carrying out weighted calculation on the standardized value of each index and the weight of the corresponding index to obtain the credit score of the customer service provider, so that the accuracy and the real-time performance of the customer service provider credit evaluation can be improved.

The invention relates to a risk assessment method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: acquiring a risk data set of the power industry in a preset area range; the risk data set at least comprises power accident data and operator data; performing feature extraction processing on the risk data set by using a data mining algorithm to obtain risk features; inputting the risk features into a pre-trained risk prediction model to obtain a risk prediction result; the risk detection result is used for evaluating the risk of the power industry in the preset area range. The method can improve the risk assessment accuracy of the power industry.

According to the equipment inspection data analysis method based on the Internet of Things provided by the invention, the data interface and the conversion standard are set, and the intelligent middleware, the general data acquisition terminal and the protocol conversion module are used for adapting to access of different equipment to the inspection system, so that uniform format transmission of data is ensured. And a visual low-code function customization module is created, a local cache and synchronization mechanism is constructed, and offline work and data synchronization are realized. A state evaluation model is established in combination with a sensor data calibration algorithm, and the operation state and potential faults of the equipment are accurately judged. And an intelligent decision support module is constructed, a fault prediction model is established by using a data mining algorithm, and personalized decision suggestions are generated. And an intelligent inspection path guiding module is set, and an inspection path is optimized according to decision suggestions and fault prediction. According to the scheme, the compatibility of equipment access is improved, the offline working capability is ensured, the equipment state is accurately evaluated, personalized suggestions are provided, the inspection path is optimized, and the inspection efficiency and accuracy are improved.

The invention discloses a safety intelligent diagnosis system for offshore facilities. The safety intelligent diagnosis system comprises a safety overview function module, a safety monthly report function module, an early warning center function module, an element setting function module and a weight setting function module. The invention further discloses an intelligent diagnosis method for safety of the offshore facilities, management elements, human elements, environmental elements and other elements are organized by applying intelligent technical means such as a big data mining algorithm, the Internet of Things and AI, and the method has three functions of overall analysis, monitoring and early warning and auxiliary decision making. Informatization and intelligentization of more than 220 safety management functions such as basic information, major hazard sources, dual prevention mechanisms, intelligent diagnosis, personnel positioning, operation process monitoring and the like are achieved, and the problems that traditional facility safety management and control means cannot be seen, management is incomplete and control is not good are successfully solved.

This invention belongs to the field of data processing technology and discloses a unified encapsulation method and system for data mining algorithms. The method includes encapsulating data mining algorithms into algorithm components that match a common interface template, building an evaluation model based on the algorithm components, constructing a pipeline using the algorithm components through a graphical interface to obtain the data mining model, automatically adjusting the data mining model through machine learning and optimization algorithms, and finally encapsulating and deploying the data mining model to a target area. This invention enables users to call different algorithms through a unified interface. Calling the algorithms as a service makes the algorithm components more adaptable, facilitates the maintenance and expansion of data mining software, lowers the barrier to entry for data mining, improves the interactive operation and user experience of machine learning, enhances the flexibility and maintainability of data mining, reduces reliance on business and domain knowledge, saves time and resources, and improves model performance.

One or more embodiments of the invention provide a financial data analysis method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring business data and financial data; according to the business data and the financial data, a business and financial relationship network is constructed through a data mining algorithm, and the business and financial relationship network reflects association relationships between businesses and between the businesses and finance; according to the financial data, the business data and the business and financial relationship network, quantifying the influence of the business on the finance through a causal inference algorithm, and constructing a business and financial dynamic mapping model; and performing data analysis on the business data and the financial data according to the business dynamic mapping model, and displaying a data analysis result. According to the technical scheme, the analysis depth of enterprise financial data can be effectively improved, the enterprise operation effect is improved, and powerful technical support is provided for scientific decision-making of enterprise operation and maintenance.

The application provides a charging pile remote regulation and control method and system based on a cloud platform, and belongs to the technical field of new energy infrastructure and smart grid technology.The method comprises the following steps: collecting historical charging data of charging pile users and historical operation data of power grids in the regions where the users are located, generating user charging original data sets and power grid original operation data sets; and using a data mining algorithm to construct a user behavior and power grid state double-dimensional model and generate double-dimensional model data; through the construction of the user behavior and power grid state double-dimensional model, the user charging habits and the real-time operation characteristics of the power grid can be accurately captured, detailed and comprehensive data support is provided for the regulation and control of the charging pile, the problem of inaccurate regulation and control caused by single data dimension in the traditional mode is effectively avoided, and the scientificity and rationality of the charging regulation and control decision are improved.