1335 results about "Management efficiency" patented technology

The invention discloses an intelligent interaction system based on a large language model and a knowledge graph, and relates to the technical field of artificial intelligence and intelligent interaction. According to the system, data processed by a data acquisition and preprocessing module is stored in a local knowledge warehouse in a three-layer nested structure; the multi-modal semantic understanding module constructs a knowledge system in which a triple knowledge graph and a vector database are complementary by optimizing a BERT model and performing dual-channel analysis; the intelligent interaction module realizes natural language interaction and business task automatic triggering based on an RAG technology and a dialogue memory mechanism; the information change identification and analysis module generates a personnel change risk report and performs early warning; and the summary report automatic generation module outputs a structured report. According to the method, the problems of data splitting, complex interaction and slow response in traditional enterprise management are solved, the functions of system intelligent question answering, personnel change risk analysis, structured report automatic generation and the like are realized, and the management efficiency and the information consistency are improved.

The invention provides a logistics supply chain management method and system based on a block chain technology, and relates to the technical field of data processing, and the method comprises the steps that a storage center scans a traceability code and verifies goods information when goods are warehoused, and after verification is passed, the goods are encrypted and stored in a block chain to obtain warehouse-in data; a dynamic inventory database is established according to the goods category and the storage location number through the warehousing data, and an inventory state early warning index is generated. According to the logistics supply chain management method and system based on the block chain technology provided by the invention, dynamic and accurate management of inventory, real-time optimization of logistics paths and credible traceability of full-chain information are realized through distributed storage and non-tampering characteristics of the block chain in combination with an intelligent contract and the Internet of Things technology, the supply chain collaboration efficiency is improved, and the logistics supply chain management efficiency is improved. And the operation risk is reduced.

The invention discloses a medical insurance intelligent auditing and path recommending method and system based on a large language model, and particularly relates to the technical field of medical insurance auditing, and the method comprises the steps: S1, analyzing massive medical insurance policy documents through a first LLM, and generating a comprehensive confidence coefficient in combination with triple confidence coefficients of semantics, context support degree and timeliness, after grading processing, constructing a dynamically updated medical insurance rule knowledge base; s2, converting free text medical advices of doctors into structured data by means of a second LLM, quantifying the integrity of the medical advices through field weights, and dynamically asking and complementing information in combination with constraint urgency; s3, associating the diagnosis and treatment scheme with the medical insurance information of the patient, matching rules in real time, and generating a color identifier for early warning violation; s4, recommending compliance alternative schemes in combination with the medical knowledge graph, and sorting according to multi-dimensional scores such as medical insurance economy and the like; and S5, automatically extracting multi-system data, and generating a standardized medical insurance declaration report. The medical insurance management efficiency is improved, the burden of medical care and medical insurance departments is relieved, and reasonable use of medical insurance funds is guaranteed.

The invention relates to the technical field of digital twinning, in particular to a smart park digital twinning construction method and system based on big data, and the method comprises the steps: constructing a multi-source heterogeneous data fusion collection system, deploying an Internet of Things sensor in a smart park to collect data, and carrying out the data preprocessing; constructing a distributed big data storage architecture to store the collected data, and extracting data depth features in combination with machine learning and deep learning algorithms; constructing a park high-precision digital twin three-dimensional model, and accurately associating the extracted data depth features with a model entity; a real-time data monitoring mechanism is set, a message queue and edge calculation are adopted for data transmission and processing, a multi-terminal human-computer interaction interface regular evaluation model is developed, and an optimization algorithm is used for iteratively upgrading the digital twinborn body. Through full-process data driving and model iteration, digital mapping of the smart park is realized, and the management efficiency and decision scientificity of the smart park are improved.

The invention discloses a power monitoring system mimicry defense architecture based on dynamic heterogeneous redundancy, which relates to the field of smart grid network security protection and comprises a physical layer, a twinborn layer and a simulation layer. All the layers communicate through encryption channels, a collaborative architecture of a physical layer, a twinborn layer, a simulation layer and an application layer is constructed, accurate mapping of a virtual environment and a physical power monitoring system is achieved by means of the digital twinborn technology, and a virtual carrier fitting the reality is provided for anomaly detection and attack simulation. The adaptability of the defense architecture to the power business logic is improved through analysis and simulation support on the power dedicated protocol; each layer ensures that the normal operation of the power monitoring system is not interfered through encryption communication and reasonable resource control; meanwhile, the digital twinborn body is used for replacing the traditional physical test environment, so that the construction, operation and maintenance cost of a defense system is reduced, the management efficiency is improved, and an adaptive service, efficient and reliable basic framework is laid for the safety protection of the power monitoring system.

The invention relates to an enterprise data operation multi-source data virtualization access and index automatic management and control method and system, belongs to the technical field of computers, and aims to solve the problems of index definition fragmentation, frequent logic conflicts, dependency static management and verification rule rigid coupling in the prior art. Comprising the following steps: constructing a unified virtualized data access layer to integrate heterogeneous data sources; the indexes are disassembled into semantic atoms capable of being managed in a versioning mode and registered to an atom knowledge base; dynamically constructing an index dependency relation directed acyclic graph based on semantic atom combination; dynamically binding the data quality rules with semantic atoms; and analyzing an index request through a virtualized query engine, reversely traversing atlas aggregation dependency and rules, generating and executing local query, and automatically checking result data quality at the same time. According to the method, unified index definition, dependence on automatic conduction, quality embedded verification and multi-source transparent access are realized, and the data consistency, the treatment efficiency and the system flexibility are remarkably improved.

The invention relates to the technical field of online education, and discloses an online course learning management method based on a knowledge graph. The method comprises the following steps: acquiring multi-modal learning behavior data of a learner, and extracting a deep learning state vector reflecting knowledge understanding depth, learning input degree and cognitive confusion through semantic fusion; and dynamically calculating and updating the logical relationship strength among the knowledge points in the course knowledge graph by using the vector, so that the knowledge structure can adaptively evolve along with the actual cognitive state of the learning group. And generating a real-time personalized learning path based on the updated knowledge graph and the current state vector of the learner. Meanwhile, according to cognitive confusion features in the state vector, intervention measures such as pushing of remedial resources, adjusting of content sequence or starting of self-adaptive testing are triggered in real time. According to the method, the dynamic optimization of the knowledge graph and the accurate and immediate response of learning intervention are realized, and the adaptability and management efficiency of online learning are improved.

The invention discloses a construction site-oriented multi-mode inspection data intelligent analysis and closed-loop management system, and belongs to the technical field of building construction management. The system comprises a multi-modal data acquisition module, a fusion and feature extraction module, an intelligent analysis module, a decision distribution module and a closed loop verification module. The system performs space-time alignment on the collected visual, sensing and positioning heterogeneous data, and generates unified representation by using cross-modal attention fusion; modal contradictions are eliminated through joint reasoning, risks are evaluated, and a disposal task is automatically generated; after the task is completed, a data rechecking process is automatically started, the improvement degree is calculated by comparing the data before and after rectification so as to verify the effect, and automatic rollback is performed if the result is unqualified. The problem of heterogeneous data integration is effectively solved, a whole-process closed loop from hidden danger recognition to automatic verification is realized, and the risk response speed and management efficiency of a construction site are remarkably improved.

The invention provides a digital intelligent base service achievement management method and system, and relates to the technical field of digital intelligent base service management.The method comprises the steps that firstly, a basic feature set containing attributes, sources and process features of service achievements in a digital intelligent base is collected, then associated features of the basic feature set are mined, and a service achievement associated network is formed; then, a multi-level management index containing a transverse attribute dimension and a longitudinal flow node dimension is constructed based on the service achievement association network, and a management strategy covering storage, updating and sharing features is generated in combination with index features and service achievement real-time state features; and finally, performing dynamic management and control on the business achievements in the digital intelligent base according to the management strategy, thereby deeply mining the relationship between the business achievements, realizing multi-dimensional fine management, dynamically adapting to business changes, and improving the management efficiency.

The invention discloses an industrial control safety target range construction method and system based on a digital twinborn technology. The method comprises the following steps: constructing a digital twinborn simulation environment for an actual industrial scene, wherein the digital twinborn simulation environment comprises physical system modeling, communication network modeling and co-simulation modeling; defining an interactive interface between the simulation environment and an external tool, including selecting a communication mode according to a data exchange demand, defining a logic communication channel based on the selected communication mode and standardizing a data exchange format; converting an attack instruction initiated by an external tool into a simulation operation instruction in the simulation environment through the interactive interface, and feeding back a physical state change caused by the simulation operation instruction to the external tool; and an automatic target range resetting mechanism is established, and the state of the simulation environment is backed up and recovered through a snapshot mechanism. According to the invention, the management efficiency of the industrial control safety target range is improved, and the test reliability is improved.

The invention discloses a business risk management method and device, a computer program product and electronic equipment. The method relates to the field of artificial intelligence and big data, and comprises the following steps: obtaining business data, and encoding the business data to obtain a business vector; the business vector is input into a target model to obtain a risk prediction level, the target model comprises an input layer, a hidden layer, an attention mechanism layer and an output layer, the input layer is used for extracting a state vector from the business vector, the hidden layer is used for extracting hidden features from the state vector, the attention mechanism layer is used for calculating an attention weight, and the output layer is used for outputting the attention weight; the target feature is determined through the hidden feature and the attention weight, the attention weight is determined by the similarity between the business risk index and the hidden feature and the position code of the hidden feature, and the output layer outputs a risk prediction level based on the target feature; and determining a target risk management strategy through the risk prediction level. Through the method and the device, the problem of low business risk management efficiency in related technologies is solved.

The invention relates to the technical field of power distribution lines, in particular to a power distribution line abnormal line loss analysis and treatment method and system, and the method comprises the following steps: synchronously extracting real-time monitoring data and historical data based on a power distribution line network, carrying out the data alignment and data cleaning, screening features associated with current leakage and voltage drop, and carrying out the analysis and treatment of the abnormal line loss of the power distribution line. Comprising load change rate and temperature fluctuation. According to the invention, through application of real-time and historical data analysis, the detection and management efficiency of the abnormal line loss of the distribution line is improved, the accuracy of data processing is improved through synchronous extraction and data alignment, early recognition of abnormal modes is allowed, the possibility of energy loss and unplanned power failure is reduced, and through adoption of Fourier transform and wavelet transform, the detection efficiency of the abnormal line loss of the distribution line is improved. According to the method, abnormal signals are accurately recognized, the analysis capability of the signals is enhanced, the accuracy of abnormal detection is improved, fault positioning and maintenance plan making are effectively optimized through combination of the GIS system, and the pertinence and efficiency of maintenance work are remarkably improved.

The invention provides a fabricated building full life cycle intelligent management system based on BIM and Internet of Things, and relates to the technical field of intelligent management, and the system comprises a stage data collection module which is used for collecting building data of different life cycle stages; and the life cycle influence module is used for constructing an Internet of Things-BIM collaborative model, generating exclusive digital information in combination with building data, and analyzing the influence of different life cycle stages on building functions to obtain life cycle influence. And the danger coefficient determination module is used for determining danger coefficients of different life cycle stages based on the life cycle influence and the exclusive digital information, and constructing and determining danger thresholds of the different life cycle stages. And the risk traceability module is used for judging whether the danger coefficient exceeds a danger threshold value or not, and if yes, associating the exclusive digital information to generate a risk traceability scheme. According to the invention, the management efficiency can be improved, the cooperation capability is enhanced, the risk assessment accuracy is improved, and sustainable development is promoted.

The invention relates to an electronic component information management method and system based on multivariate heterogeneous data fusion. The method comprises the following steps: S1, data acquisition and preprocessing; step S2, multi-modal data fusion is carried out; s3, intelligent analysis and decision making; and S4, data interaction and servitization. Multi-source heterogeneous data is collected through an industrial Internet of Things sensor, cross-modal data fusion is realized in combination with a federated learning framework and a graph neural network, intelligent decision suggestions are generated by using a deep reinforcement learning model and a knowledge graph inference engine, the problems of insufficient data fusion capability and lack of real-time performance of a traditional method are solved, and the method is suitable for large-scale popularization and application. The method has the advantages that deep fusion of multi-modal data is realized, real-time intelligent decision is supported, and the full-life-cycle management efficiency is improved.

The invention provides an electric power training knowledge graph construction method and device based on multi-source heterogeneous data, equipment and a storage medium, and relates to the technical field of knowledge graphs. The method comprises the following steps: acquiring and preprocessing multi-source heterogeneous post training data to obtain structured post description data and course description data; respectively extracting post feature vectors and course feature vectors; calculating the semantic similarity between the post feature vector and the course feature vector; comparing the semantic similarity with a preset matching threshold value, screening out candidate courses matched with the target post, and generating a triple; constructing and updating a post training knowledge graph based on the triple; and when a new post or course is added, generating a new triple to update the knowledge graph. Through unified processing of multi-source heterogeneous data, post-course semantic matching and dynamic triple updating, precision, structuring and real-time updating of post training content are realized, and training pertinence and management efficiency are improved.

The invention discloses an engineering construction whole process supervision method and system based on multi-technology fusion, and relates to the technical field of engineering supervision, and the supervision steps are as follows: S1, collecting construction site data through a mobile terminal multi-source channel, carrying out the space-time alignment and association fusion of data preprocessing, and carrying out the panoramic visual monitoring of a construction site; abnormal events and potential risks are identified through a preset rule and a machine learning algorithm; and S2, analyzing the engineering data by using a natural language processing technology. According to the invention, on the aspect of data and risk management and control, panoramic visual monitoring is realized through multi-source data fusion, and risks are accurately identified through an intelligent algorithm; the NLP technology analyzes the engineering data, automatically generates the check list, ensures compliance, realizes conversion from passive supervision to active prevention and from artificial experience to data driving, has remarkable effects in the aspects of improving management efficiency, reducing risks, saving cost and enhancing transparency, and is suitable for various large complex engineering scenes.

The invention provides a geomembrane on-line leakage detection system and detection method, and relates to the technical field of geomembrane detection, the system comprises a signal monitoring module used for arranging a sensing detection network under a geomembrane in an array, carrying out on-line monitoring, and determining a real-time leakage signal of the geomembrane; the sensing and positioning module is used for executing compressed sensing and positioning based on sparse reconstruction according to a plurality of leakage points corresponding to the plurality of leakage signals, and identifying a source sensing matrix; and the leakage early warning module is used for carrying out leakage point early warning based on the source sensing matrix and carrying out leaked area early warning based on the plurality of leakage signals. The technical problems that in the prior art, geomembrane leakage is difficult to accurately recognize and position, response is not timely, and the anti-seepage safety and the management efficiency are reduced are solved. The technical effects of accurately positioning and early warning the leakage points in real time, effectively improving the sensitivity and response speed of geomembrane leakage detection, and improving the anti-seepage safety and management efficiency are achieved.

The invention relates to the technical field of smart agriculture and Internet of Things, and specifically provides a breeding environment monitoring and early warning method and system, and the method comprises the following steps: S1, data collection and preprocessing; s2, machine learning modeling and risk prediction; and S3, performing real-time early warning and system deployment. Compared with the prior art, the method can greatly reduce the breeding risk, improves the management efficiency, and has remarkable market competitiveness and commercial value.

The invention provides an intelligent research and development system, device and equipment based on Ai and a storage medium, relates to the field of research and development systems, and aims to convert project management from experience driving to data and AI driving so as to greatly improve the accuracy of task allocation, risk identification and progress prediction. The system can automatically monitor, analyze and actively intervene the research and development process, manual intervention is reduced, and the management efficiency is improved; the burden of compiling the report by a project manager is greatly reduced, and the timeliness, the accuracy and the objectivity of the report are ensured; a complete research and development traceability chain enables the project process to be extremely transparent, and audit, problem positioning and knowledge inheritance are facilitated; scattered research and development knowledge is structurally precipitated in a knowledge graph to become digital assets which can be inquired and reused by subsequent projects.

The invention relates to the technical field of talent cultivation management systems, in particular to an enterprise talent cultivation and management platform based on big data, which constructs a'data perception-portrait construction-intelligent decision-feedback closed loop 'dynamic ecological system, realizes continuous perception and dynamic evaluation of employee ability, and improves the enterprise talent cultivation and management efficiency. The problem of evaluation lag of a traditional culture management platform is solved; the multi-source data acquisition and multi-source data fusion module is matched with the dynamic ability portrait construction and updating module in real time, and evaluation, cultivation and selection of talents become extremely objective and accurate through data-driven ability portraits; prediction and early warning capabilities are provided, so that human resource management is changed from passive response to active planning and intervention; according to the method, the isolated relationship between enterprise talent management and talent cultivation is broken through, various HR data islands are broken through, the situation that cultivation resources are input to employees with high demission risks is avoided, human resource transactional work is greatly reduced, and management efficiency and decision scientificity are improved.

The invention relates to the technical field of lathe numerical control, in particular to a lathe digital intelligent management method and system based on industrial visual analysis. The method comprises the following steps that multidirectional lathe workpiece monitoring is carried out based on a heterogeneous visual sensing array, and a theoretical lathe machining finished product is obtained; real-time lathe tool and workpiece interactive monitoring is carried out, and time sequence tool cutting state data are constructed; according to the time sequence cutter cutting state data, carrying out finished product comprehensive form deviation evaluation on the theoretical lathe machining finished product so as to obtain a machining form deviation evaluation result; and performing cutter cutting performance attenuation analysis according to the machining form deviation evaluation result to generate a dynamic machining parameter set of the lathe. The lathe management efficiency and the machining precision are improved through dynamic and real-time lathe machining parameter adjustment.

The invention relates to the technical field of traffic engineering management, and particularly discloses a traffic engineering quality safety dynamic supervision and management system and method, and the method comprises the steps: constructing a multi-dimensional sensing network which comprises an entity, an environment and a behavior sensing layer, and collecting data; a three-dimensional model is built based on the BIM technology, and standardized data and model millisecond mapping are fused and visualized; an LSTM and random forest hybrid algorithm is adopted to establish a three-level early warning model, and risks are identified and pushed in a graded manner; matching the disposal flow according to the early warning level, tracking the progress by the mobile terminal, and checking the closed loop in a grading manner; and key node data are stored through a multi-level block structure, an alliance chain permission, a consensus mechanism and an intelligent contract, so that reverse tracing is realized. According to the invention, real-time acquisition, dynamic modeling, intelligent early warning, co-processing and credible tracing of quality safety data in the traffic engineering construction process are realized, the quality safety risk is reduced, and the management efficiency and supervision transparency are improved.

The invention relates to a human resource information management method and device based on LLM + RPA, and the method comprises the following steps: recognizing the abnormality of employee basic information in a human resource system through RPA, marking and generating abnormal marking information; and generating a correction notification template, and forming a notification delivery record by combining batch filling and distribution of employee contact information. And based on the delivery record, intelligently sorting the overdue uncorrected items according to the abnormal level, the overdue day number and the influence range by utilizing LLM, generating an urging sequence, sequentially triggering secondary reminding by RPA, and obtaining an execution result. And finally, the RPA associates and verifies the secondary reminding result and the corrected information, converts the secondary reminding result and the corrected information into a system compatible format, updates the secondary reminding result and the corrected information to a human resource system, and generates an employee information management table, thereby solving the technical problems that a traditional human resource management system still depends on a large amount of manual intervention in the aspect of dynamic update of employee information, so that the management efficiency is low and errors are easy to occur.

The method comprises the following steps: S1, establishing a binding relationship between an identity code ID and a UWB tag physical address, and deploying a UWB positioning base station to construct a spatial topological graph; s2, arranging timing and circle counting devices on the starting line and the detection line; s3, a competition starting instruction is sent through the wireless command sender, and the timing and circle counting process is started through the timing and circle counting device; s4, performing joint verification on the identity code ID and the real-time coordinate by adopting an anti-cheating discrimination algorithm based on a space-time heterogeneous arbitration network; s5, generating a spatio-temporal data packet according to the accumulated number of turns of the athletes passing verification and the competition time, generating a hash value according to the spatio-temporal data packet, and uploading the hash value to the block chain evidence storage network; and S6, generating an anti-cheating analysis report based on the space trajectory and a final school report after the competition. According to the invention, automatic timing and circle counting management of middle and long distance race events is realized, the data accuracy and real-time performance are improved, cheating is effectively prevented and controlled, and the fairness and management efficiency of the race are guaranteed.

The invention is suitable for the technical field of water conservancy informatization engineering project management, and provides an intelligent water conservancy EPC project benefit dynamic evaluation model and an Exce l application method, and the model comprises an EPC project total value dynamic value model, an EPC subpackage value model, a leading unit project surplus value model, a leading unit project profit model and a stage profit early warning model. Rich index type and coefficient type dimensionless influence factors are adopted, an EPC project comprehensive management and control model penetrating through all stages and all subentry projects of the whole period of EPC project construction is constructed, and dynamic evaluation, decision making and management and control bases are provided for the whole process of bidding, subpackaging and changing to settlement auditing for EPC project construction; the model can be embedded into an existing project budget Exce l report system in the water conservancy industry, dynamic data linkage analysis is supported, and the management efficiency and decision scientificity of an EPC project are remarkably improved.

The invention provides a multi-source sensing integrated intelligent feeding scheduling and dieting control method and system for a cowshed, and relates to the technical field of intelligent agricultural control, and the method comprises the steps: collecting cattle physiology, cowshed environment, feed state and equipment operation parameters in real time through a distributed sensing network; a machine learning model fusing a time sequence convolutional network and a self-attention mechanism is utilized to predict an individualized feeding demand and a dieting threshold value, and a feeding-dieting risk map for quantifying health and production risks is generated; simulating an extreme scene based on a digital twinborn model to perform dual consistency check, and generating a robust detailed scheduling scheme; converting the scheme into a control instruction to drive execution equipment, and iteratively optimizing the model and the rule; the system comprises a multi-source sensing data acquisition module, an instruction execution module, a feedback optimization module and the like. According to the method, the accurate and self-adaptive feeding management efficiency of individual cattle in a group is improved, the feed utilization efficiency is effectively improved, and the metabolic disease risk is reduced.

The invention discloses an interface calling control method for a cake electronic coupon API (Application Program Interface) docking system. The method relates to the technical field of interface call control, and comprises the following steps: evaluating high concurrency stability of an interface; high-concurrency stability optimization and circulation are realized; evaluating management and control compliance and problem tracing efficiency; and controlling compliance optimization and iterative closed loop. According to the method, interface calling characteristic parameters and compliance-traceability parameters are collected in real time, a double-layer evaluation optimization system from interface stability to management and control compliance is constructed, bimodal regulation and control of a current-limiting threshold value and cache frequency are intelligently triggered based on stability evaluation, the verification request amount and a positioning mode are dynamically adjusted based on compliance efficiency evaluation, and the reliability of the system is improved. A complete closed loop of monitoring-evaluation-optimization-iteration is formed, the interface calling stable operation capability and compliance control efficiency of the cake electronic coupon API system in a high-concurrency scene are improved, and the problem of low interface calling control stability in the high-concurrency scene in the prior art is solved.

The invention relates to the technical field of fault diagnosis models, and discloses an adaptive AI fault diagnosis model fusing reinforcement learning and an industrial equipment predictive maintenance system. Comprising an industrial equipment data acquisition and preprocessing module, a reinforcement learning-fused adaptive fault diagnosis model module, an equipment health degree evaluation and residual life prediction module, a predictive maintenance decision and execution module and a system management and integration module, and the output end of the industrial equipment data acquisition and preprocessing module is electrically connected with the input end of a self-adaptive fault diagnosis model module fusing reinforcement learning. According to the system, data during equipment operation are effectively collected, the equipment health degree evaluation module is matched to convert the feature deviation degree into 0-100 visual scores, the health trend visualization unit displays changes through a broken line diagram and a thermodynamic diagram, fault risk points are marked, historical backtracking and multi-equipment comparison are supported, non-professional personnel can also rapidly judge the equipment state, and the equipment health degree evaluation module is matched to convert the feature deviation degree into 0-100 visual scores. And the equipment management efficiency is improved by 30%.

The invention relates to the field of tool management, and provides a coal-fired power plant tool intelligent management method and system. The method comprises the following steps: carrying out digital identity construction on a purchasing tool to obtain a tool digital identity file comprising a unique identification code of the tool; constructing a tool circulation record based on the tool digital identity file, and obtaining a block chain traceability record; monitoring an inventory tool state through the block chain traceability record to obtain a tool behavior baseline model; based on the tool behavior baseline model, attenuation prediction and maintenance scheduling are carried out on an inventory tool, and a tool health attenuation trend prediction result and an optimal maintenance scheduling scheme are obtained; and performing decision analysis based on the optimal maintenance scheduling scheme to obtain a tool management decision instruction set. According to the method and the system, non-tampering recording of tool full-life-cycle data, accurate health state prediction and scientific decision support are realized, and the management efficiency and the data credibility are improved.

The invention relates to the technical field of data processing, and discloses a low-code business data storage method and system based on an EAV model, and the method comprises the steps: carrying out the data model transformation of a common data type for the EAV model of each entity in a database, and carrying out the data model transformation when a user carries out the interaction of a target entity, according to the method, the corresponding data model can be called to realize rapid data query, the problem of low query efficiency caused by data format conversion is avoided, meanwhile, load limitation brought to system memory and CPU resources by performing data model transformation on each entity in each operation unit time period is considered as a constraint condition, and maximization of the query efficiency is taken as a target. The most reasonable data model transformation and updating of each entity are executed for each operation unit time period, the data model management efficiency of the database is improved, the data query efficiency is improved while excessive resources of the system are not occupied, and a dynamic database model capable of realizing low-code development flexibility and expandable service attributes is provided.