2318 results about "Data interface" patented technology

The invention discloses a data acquisition method and system and a storage medium, and relates to the technical field of data acquisition and processing.According to the technical scheme, multiple sensor devices and data interfaces are integrated, multi-source data are acquired in real time, format standardization processing, time synchronization correction and spatial information alignment are carried out through a multi-mode fusion module, and the data acquisition efficiency is improved. Calculating to obtain a data consistency factor Tyhz and evaluating the data consistency factor Tyhz; when the data consistency factor Tyhz does not reach the standard, a data optimization module performs noise filtering and abnormal value elimination on a multi-source data set, and an intelligent analysis module calculates a risk prediction parameter Fcyz by using a convolutional neural network; the early warning evaluation module compares the Fcyz with a risk evaluation threshold Fth, calculates a risk early warning index Gyzs, compares the risk early warning index Gyzs with a risk early warning threshold E, and dynamically generates an early warning execution scheme, so that information pushing and emergency resource scheduling are realized, the problems of low multi-source data fusion efficiency and insufficient early warning precision are solved, and the safety of the system is improved. And the risk identification and emergency response capabilities of the urban emergency management system are effectively improved.

The invention relates to the technical field of intelligent bid evaluation, and provides an intelligent multi-dimensional bid evaluation analysis and decision-making method based on big data, which comprises the following steps: acquiring original bid evaluation data from a multi-source heterogeneous data interface, and fusing through semantic role labeling and a timestamp alignment algorithm to generate a time-space association data set. And performing multi-level cleaning to generate a high-confidence bid evaluation data set. And extracting a multi-dimensional index based on the domain knowledge graph, generating a dynamic feature tensor, and dynamically allocating a weight by adopting a coupling attenuation weight model. And constructing a bidder association network, calculating a node influence score, detecting a potential bidding behavior and generating a risk correction coefficient. And injecting the real-time data stream into the dynamic feature tensor, updating the index weight, and generating a three-dimensional scoring vector through a multi-target aggregation decision algorithm. And performing Pareto optimization by using the asymmetric game equilibrium model, and outputting an optimal bid-winning party sequence and a risk early warning report. The bid evaluation efficiency and fairness can be improved, and the bid invitation risk is reduced.

The invention relates to the technical field of intelligent control and unmanned aerial vehicle application, in particular to a bird repelling and unmanned aerial vehicle dispatching integrated platform based on big data analysis. Comprising a multi-source data acquisition module; a dynamic scheduling decision module; and a federal learning optimization module. According to the invention, the millimeter-wave radar, the infrared camera, the voiceprint recognition unit and the meteorological data interface are integrated through the multi-source data acquisition module, so that the defects of one-sided data and extensive evaluation of a traditional scheme are overcome; a bird threat level, unmanned aerial vehicle endurance time, an electric quantity attenuation model and an airspace control rule are fused through a dynamic scheduling decision module, dynamic balance of priority response of a high-risk area and unmanned aerial vehicle endurance and energy consumption is realized, and the problems of response lag and insufficient endurance caused by unreasonable task allocation in a traditional scheme are avoided; the federal learning optimization module adapts to a specific geographical environment through transfer learning, and the problem of strategy stiffness of a traditional scheme in a complex environment is solved.

The invention relates to a building module data interface intelligent monitoring system based on the Internet of Things and a deployment method, and belongs to the technical field of building information monitoring. The system is composed of a distributed sensing unit, an edge computing gateway, a cloud platform and a visual terminal, a self-adaptive filtering algorithm and an abnormal mode recognition model are built in the edge computing gateway, the cloud platform adopts a time sequence database to construct a multi-dimensional data warehouse, and structural health degree evaluation is carried out by fusing an LSTM neural network and a random forest algorithm. The deployment method comprises the steps of optimizing a sensor distribution strategy based on a BIM model, establishing a wireless Mesh ad hoc network communication architecture, and configuring a grading early warning mechanism and a fault tracing function. The innovation point is that a dynamic threshold adjustment algorithm and an interface performance degradation prediction model are provided, and real-time monitoring of the connection state of the building module and life prediction are realized. The system has the advantages of flexible deployment, high detection precision and low maintenance cost, and the intelligent level of building structure safety monitoring is effectively improved.

The invention relates to the technical field of marine disaster monitoring and early warning, in particular to an AI-based marine disaster prevention monitoring and early warning system. Comprising a multi-source data acquisition module for acquiring multi-dimensional data through a sensor network, remote sensing data access and a meteorological data interface; the data preprocessing and fusion module is used for carrying out cleaning, space-time calibration and feature fusion on the collected data; the artificial intelligence analysis module is used for realizing accurate analysis and prediction of different levels of storm surge by using a multi-scale prediction model cluster unit; the risk assessment and early warning generation module is used for calculating risk indexes according to a related index system and dividing early warning grades; and the interaction and release module is used for realizing visual display and multi-channel release of the early warning information. According to the invention, advantages of multi-source data and artificial intelligence technology are integrated, and real-time and accurate monitoring and early warning can be carried out on storm surge and coast submerging conditions.

The invention relates to the technical field of intelligent control, in particular to an intelligent power optimization scheduling method in a new energy grid-connected scene, which comprises the following steps of: acquiring a current load, a rated load, an adjustment rate and a temperature rise value through a data interface, and normalizing to generate a characteristic parameter matrix; the product of the adjusting rate and the temperature rise value is used for calculating the correlation degree with the aging factor to obtain an elastic weight, extracting the voltage stability and the weight, inputting the voltage stability and the weight into a Dijkstra algorithm to screen a path meeting the safety margin, monitoring the slope of an inertia change curve, adjusting a compensation coefficient through fuzzy PID control, and feeding back the compensation coefficient to correlation degree calculation. According to the method, multi-source parameters are fused through a rule tree structure, the load regulation rate and the aging factor weight are dynamically distributed, bus voltage stability and elastic weight path optimization are combined, a synchronous inertia margin safety correction system is constructed, the system response precision, the state cooperation efficiency and robustness are improved, local optimum is avoided, and the system reliability is improved. And the standby capacity configuration and the frequency advance correction capability are optimized.

The invention relates to an aviation equipment manufacturing digital main line engine system supporting multi-source heterogeneous data access, and belongs to the technical field of aviation manufacturing data management. The system comprises a multi-source heterogeneous data access adaptation module, a data fusion processing module and a full-process data link construction module. The multi-source heterogeneous data access adaptation module passes through a data interface protocol and format conversion component; the data standardization and fusion processing module completes association fusion through an aviation heterogeneous data collaborative computing framework by means of an aviation manufacturing field data element standard library, a unified heterogeneous data model is generated, and the framework comprises a data fragmentation layer, a parallel node layer and a result aggregation layer; and the full-process data link construction module constructs a data link covering the full life cycle of the product based on a time sequence association algorithm and a product unique identifier mapping mechanism. The system can effectively solve the integration and management problems of multi-source heterogeneous data in aviation equipment manufacturing, and improves the data processing efficiency and the whole-process data tracing capability.

The invention discloses an advertisement putting strategy optimization method and system based on preference data collaboration, and relates to the technical field of advertisement putting, and the method comprises the steps: collecting the internal user data of a local advertisement putting platform, obtaining the external user data of a third-party platform through a safety data interface, carrying out the integration and preprocessing of the data, and obtaining an advertisement putting strategy; extracting explicit preference data and implicit preference data; a user-advertisement scoring matrix is constructed based on the preference data, and user preference vectors are generated after decomposition; acquiring copywriting text, image and category information of an advertisement to be put, constructing an advertisement feature expression model and generating an advertisement feature vector; calculating a matching degree score based on the user preference vector and the advertisement characteristic vector, and dynamically optimizing an advertisement putting strategy according to the score; according to the method, through comprehensive utilization of explicit and implicit preferences of the user, more accurate user interest modeling and advertisement matching are realized, and the advertisement putting effect and putting efficiency are effectively improved.

The present disclosure describes methods, systems, apparatus, and media for object identification and classification, utilizing multi-feature and multi-modal data. This includes shape, material, brand, price, odor, taste, tactility, and sound. The system integrates a server space for data processing, a querying device for iterative searches, and a data interface module for refining results. It features AI-driven image optimization, feature extraction, and pattern recognition, employing novel techniques for fusing multi-feature and multi-modal embeddings utilizing multi-head attention. Additionally, a linker module powered by two active learning with feedback loops AI models consolidates scattered data into a unified object information database. The system also employs novel AI algorithms for isolating the object of interest through a saliency map and semantic analysis, as well as for enhancing raw images with a GAN-autoencoder.

The invention provides a digital employee robot performance intelligent evaluation method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the feature extraction of user interaction contents in all scenes; performing fluctuation detection, identifying a time period of abnormal fluctuation, and extracting user request content and robot response content in the time period; restoring the current session path based on a preset business process template, performing path consistency comparison in combination with a response rule of the robot under the path, and generating an abnormal node set if the path is interrupted or the response is offset; extracting a user request format, a robot response behavior and an external data calling state corresponding to each abnormal node, performing abnormal source analysis, calculating user input abnormity, and identifying an attribution probability of abnormity and data interface abnormity; according to the invention, autonomy and accuracy of robot performance intelligent evaluation are improved.

The invention discloses a multi-agent learning event flow scheduling method and device and a storage medium. The method comprises the steps that structured data and unstructured texts of an event text set are received through a preset data interface; extracting text semantic features of the event text set by adopting a preset text understanding model, and generating event text features from the extracted text semantic features through a structured data processing model; and inputting the event text features into an agent combination for data processing, and scheduling process nodes according to an output result of the agent combination. According to the method, a multi-modal data fusion mechanism and a self-adaptive process scheduling system are constructed, so that comprehensive extraction of event text features and efficient collaboration among intelligent agents are realized, the automation level and the analysis depth of interaction abnormal behavior event processing are remarkably improved, and more accurate and more efficient technical support is provided for event processing.

The invention discloses an active real-time interaction system based on LLM. The active real-time interaction system comprises a self-cognition module, a real-time perception module, an active planning module, a situation decision module, a dynamic execution module, an emotion engine module, an adaptive interaction module and a safety management and control module. The system adopts a multi-dimensional vector to represent role attributes, and constructs a hierarchical memory storage structure to record interaction experience; generating an environment state vector through a multi-modal data processing technology; executing the target task decomposition algorithm to generate a multi-path execution plan; making a real-time decision based on the multi-dimensional decision factor; mapping the abstract decision into an instruction sequence and monitoring an execution process; simulating a system emotional state and influencing decision expression; dynamically adjusting an interaction strategy according to the user characteristics; evaluating decision rationality and starting a corresponding intervention mechanism. All the modules form a closed-loop workflow through a standardized data interface, the problems that a traditional AI system is passive in response, lacks self-cognition, is limited in environmental perception, is single in planning capability, lacks flexibility in decision making and the like are solved, and active service, self-evolution and safety controllability of the system are achieved.

The invention relates to the technical field of commodity trade management, and discloses a commodity trade business management system based on a block chain. A block chain data acquisition module of the system obtains commodity basic information, trade enterprise qualification information and logistics node information in real time through a preset data interface, and synchronizes the information to a block chain distributed account book; the trade risk quantitative analysis module generates a trade link real-time risk coefficient set based on account book real-time data in combination with a trade compliance rule base, historical order fulfillment data and current logistics state data; a risk control strategy collaborative analysis module generates a plurality of candidate risk control strategy schemes when the risk coefficient exceeds a preset threshold value, calculates a collaborative execution cost factor when the enterprise qualification state is changed in combination with logistics node information, and obtains an interference influence value set of each scheme on the trade process; and a risk control strategy execution feedback module updates a block chain smart contract execution instruction according to the optimal risk control strategy scheme, and facilitates efficient management and control of commodity trade business.

The invention relates to the technical field of electric power science and technology project dynamic supervision, and discloses an electric power science and technology project dynamic supervision system based on a computer network. The power science and technology project dynamic supervision system based on the computer network comprises a data acquisition module, a data transmission module, a data storage and management module, a data processing and analysis module, a monitoring and alarm module, a user interface module, a safety and authority management module, a system integration module, a log and audit module and a maintenance and upgrade module. According to the electric power science and technology project dynamic supervision system based on the computer network, operation data of electric power equipment are collected in real time through the sensor network and the data interface, and the real-time performance and accuracy of the data are ensured in combination with efficient data transmission and storage technologies. The system supports intelligent data analysis and fault prediction, monitors the equipment state in real time and gives an alarm, so that the fault response speed and the management efficiency are remarkably improved, and the equipment damage and the power failure loss are reduced.

Embodiments described herein provide A method of training a neural network based model for predicting time series data. The method may include receiving, via a data interface, multi-variate time-series data; generating a plurality of tokens based on flattening the multi-variate time-series data; generating a first intermediate representation via a first cross-attention layer of the neural network based model with a plurality of dispatcher tokens as the query, and the plurality of tokens as the key and value; generating a second intermediate representation via a second cross-attention layer of the neural network based model with the plurality of tokens as the query, and the first intermediate representation as the key and value; generating a predicted time-series value based on the second intermediate representation; computing a loss based on a comparison of the predicted time-series value and a ground-truth value; and training the neural network based model based on the loss.

The invention belongs to the field of parameter verification, and provides a dynamically configured software interface parameter verification method and system. The dynamically configured software interface parameter verification method comprises the following steps: dynamically configuring a matched interface information table, an interface verification field table and an interface verification field rule table according to a business scene of an interface; receiving an interface calling request, obtaining interface message information, and converting the interface message information into a structured data model of a corresponding attribute according to the interface information table; checking field information in the interface checking field table is extracted from the converted structured data model; performing basic verification on the extracted verification field information according to the interface verification field rule table; when the basic verification succeeds, whether expansion verification is carried out or not is judged, and an expansion verification result is returned; and when the basic verification fails, stopping subsequent processing of the interface and returning a verification result. The method supports a complex nested structure and supports parameter verification of a multi-level relational data interface.

The invention provides an intelligent identification and early warning method for scientific and technological consultation project risk factors, and the method comprises the steps: obtaining a calculation cluster use record, training time period reservation data and department task distribution data in a scientific research consultation project, and carrying out the map node mapping of cluster conflict and training reservation data, constructing a resource competition distribution characteristic spectrum containing department cooperation contradictions; when the interference degree exceeds a threshold value, task priorities and resource requirements of all departments are obtained, a task overlapping dependency subgraph is constructed, and task allocation imbalance indexes are quantitatively analyzed; extracting distributed data interface configuration information, detecting inconsistency of a protocol and task distribution data in combination with a task distribution imbalance index, and identifying an influence path of data synchronization delay; and analyzing a dependency path between the data synchronization delay influence path and resource competition risks in the distribution characteristics of computing resource competition, and constructing a risk map containing department cooperation contradictions and linkage risk diffusion to obtain risk factor distribution.

The invention, which relates to the technical field of mineral product screening, discloses an intelligent construction management platform for city updating, comprising the following steps: establishing a unified data standard and a data interface protocol to ensure that data of different systems, departments and enterprises in the platform can be seamlessly jointed; according to the invention, through unifying the data standard and the interface protocol, data sharing of different systems is realized, and project management efficiency and decision accuracy are improved. The real-time data acquisition and intelligent analysis technology enhances the safety monitoring and intelligent early warning functions, and ensures that potential safety hazards in the construction process are recognized and processed in time. Meanwhile, the flexibility and the emergency response capability of the project are improved by a dynamic adjustment and intelligent early warning mechanism, resources and construction plans can be automatically adjusted in the progress of the project, emergency problems can be dealt with, progress delay and resource waste are reduced, and smooth progress of the project is guaranteed.

The invention discloses a data management system and method based on NLP, and relates to the technical field of natural language processing and data management, and the method comprises the following steps: obtaining unstructured electronic medical record data through a data interface, and carrying out data preprocessing; according to the method, disease keywords are extracted, data are analyzed according to treatment time, a rule base is constructed for keyword matching, a decision tree model is used for optimizing a classification result, and classification quality is evaluated; the method comprises the following steps: extracting a relationship between related entities from unstructured medical record data through an NLP technology, constructing a knowledge graph, calculating a similarity and a causal relationship between the entities by analyzing structures of nodes and edges of the graph, and performing optimization modeling on the knowledge graph in combination with a graph neural network; according to the method, the electronic medical records are sorted, time features are extracted, the timeliness of diagnosis information is evaluated and marked, meanwhile, the reliability of the medical record data in the knowledge graph is evaluated, and unstructured electronic medical record data management is achieved.

The invention discloses a thermal insulation color steel sandwich composite board product quality detection system based on digital twinning, and particularly relates to the field of quality control. The system comprises a composite board full-dimension three-dimensional digital model construction module, a production digital twinborn body creation module, a total-factor real-time data acquisition module, a data preprocessing module, a twinborn data driving and model calibration module, a quality intelligent analysis decision module and an optimization execution module. The model construction module constructs a reference model and embeds a process standard and a quality threshold value; the twin creation module builds a virtual production line, standardizes a data interface and assigns an ID to a plate; the data acquisition module acquires real-time data; the preprocessing module removes abnormal values, standardizes data and associates the data; the calibration module updates the twin body, calculates the deviation and calibrates the model; the analysis module realizes compliance judgment, defect analysis and quality prediction; and the optimization module generates and executes an instruction, so that the problems of low efficiency, data splitting and no closed loop of traditional detection are solved, and the quality control precision and efficiency are improved.

The invention relates to the technical field of quality detection of building materials, in particular to a quality detection method and system for mirror-surface bare concrete. The quality detection method for the mirror-surface bare concrete comprises the following steps: S100, starting three-dimensional detection; s200, carrying out spatial dimension multi-index cooperative detection; s300, time dimension dynamic attenuation analysis is carried out; s400, carrying out environmental adaptability calibration; s500, carrying out digital dimension topological modeling; and S600, carrying out comprehensive quality evaluation. The invention further discloses a system for detecting the quality of the mirror-surface bare concrete. The system comprises a three-dimensional holographic detection module; a dynamic tracking and environment calibration module; a digital modeling engine; a comprehensive evaluation platform; and a data interface module. According to the method, through four-dimensional fusion of space-time-number-environment, multi-dimensional features such as flatness, glossiness, color uniformity and surface and near-surface bubbles are synchronously captured, and quality misjudgment caused by index omission is avoided.

The invention provides a sphygmomanometer automatic calibration inspection method and system. The method comprises the steps that a data interface of a sphygmomanometer is connected with external inspection equipment, and a cuff interface of the sphygmomanometer is connected with a programmable standard pressure source and a pressure sensor in the inspection equipment; the sphygmomanometer enters a calibration mode after being connected with the inspection equipment; obtaining the atmospheric pressure of the inspection equipment in the current detection environment, and determining whether the external air pressure fluctuation can influence the output of the inspection equipment or not based on the atmospheric pressure in the current detection environment; under the condition that the external air pressure fluctuation does not influence the output of the inspection equipment, applying pressure according to a preset pressure point, and respectively obtaining a high-precision reference pressure value in the inspection equipment and a measurement pressure value of the sphygmomanometer; dynamically calibrating and compensating the sphygmomanometer based on the error between the high-precision reference pressure value and the measured pressure value; wherein each sphygmomanometer corresponds to a unique identifier and is used for data binding and tracing management.

The invention discloses a gas pipeline operation risk dynamic evaluation system based on big data analysis, and relates to the technical field of gas pipeline safety monitoring, and the system comprises a multi-source data processing module, a feature extraction module, a local risk modeling module, a risk evolution prediction module and a dynamic early warning module. The multi-source data processing module is used for acquiring original multi-source time sequence data from a pressure sensor, a flow sensor, a temperature sensor, a geological monitoring device and a third-party construction disturbance data interface of a gas pipeline network, and performing time alignment, abnormity elimination, normalization and characteristic standardization processing on the data; a uniform input feature vector is formed; and the feature extraction module is used for decomposing the normalized time series data into a low-frequency trend component and a high-frequency residual signal by using discrete wavelet decomposition. The gas pipeline risk assessment system solves the problems of low gas pipeline risk assessment precision, insufficient risk propagation modeling and lack of dynamic early warning and emergency strategies in the prior art.

The invention relates to the field of data fusion evaluation, and provides a dynamic quantitative evaluation method for an enterprise development process, which comprises the following steps of: S1, acquiring operation data and compliance data of an enterprise through a multi-source heterogeneous data interface, and executing entity association and conflict resolution operation to generate a structured basic data set; s2, extracting dynamic evaluation features of the structured basic data set through a data analysis engine, wherein the data analysis engine comprises an industry adaptive rule base and / or a deep feature mining network; s3, calculating a multi-dimensional score value of the enterprise development situation based on the dynamic evaluation features; and S4, according to a historical evaluation record, mapping the multi-dimensional score value to a preset grade interval and presenting the multi-dimensional score value. And furthermore, objective and real-time evaluation is carried out on the growth performance and the anti-risk capability of the non-listed companies.

The invention discloses an all-media operation intelligent analysis platform based on big data, and relates to the technical field of computers, the platform comprises a data acquisition module, the data acquisition module applies a distributed crawler technology, compiles customized scripts, and combines social media official API calling and data interface docking or pushing of a cooperation platform to obtain a data acquisition module; all-media data including texts, images, audios and videos are collected from web pages, social media and news clients in real time; through a distributed crawler technology, social media official API calling and cooperation platform data interface docking, all-media data including texts, images, audios and videos are collected in real time, and cross-modal data are converted into feature vectors in a unified format for fusion by using a dynamic weight fusion algorithm. The problem that in a traditional analysis method, single-modal data information is limited is solved, and a cross-modal dynamic fusion formula is adopted.

The invention discloses a multi-protocol hardware communication adaptation module based on an energy storage system and a configuration method, the module comprises a protocol template library, an intelligent mapping engine and a model generator, and the method comprises the following steps: storing structured protocol point location information through the protocol template library; an intelligent mapping engine is used for shielding the isomerism of Modbus, MQTT, IEC104 and other underlying protocols, and a unified data interface is provided; intelligent matching among different protocol terms is realized by adopting a natural language processing technology through a semantic level mapping unit; and finally, generating a target data model by a model generator through one key. The method effectively solves the problems that when the energy storage system integrates multi-manufacturer equipment, protocol configuration depends on manpower, efficiency is low, errors are prone to occurring, and expansibility is poor.

The invention discloses a strong-isolation private domain agent data processing method and server, and relates to the field of data security, and the method comprises the steps: receiving a data processing request sent by a user through a user terminal, and obtaining a department identifier; according to the department identification, the data processing request is routed to a corresponding internal data interface module, the internal data interface module is used for analyzing the data processing request, obtaining a processing task and calling an intelligent agent engine, and the intelligent agent engine is used for obtaining a temporary access credential according to the department identification and sending the temporary access credential to the corresponding internal data interface module; accessing an internal data storage area of the department based on the temporary access credential to obtain to-be-processed data; and the agent engine is used for executing the processing task, analyzing and processing the to-be-processed data to obtain a processing result, and transmitting the processing result to the user terminal through the output interface module. According to the application, the risk of data leakage and the risk of unauthorized access and information leakage among different departments can be effectively reduced.

Embodiments described herein provide A method of fine-tuning a neural network based model. In some embodiments, a system receives, via a data interface, a training dataset including a plurality of input samples. The system generates, via a pre-trained neural network based model, a first response based on a first input sample of the plurality of input samples, and a second response based on the first input sample. The system generates, via a trained reward model, a first reward score based on the first input sample and the first response, and a second reward score based on the first input sample and the second response. The system computes a loss function based on the first prompt, the first response, the second response, the first reward score, and the second reward score. The system updates parameters of the neural network based model based on the loss function.

The invention relates to the field of cross-border trade, in particular to a cross-border trade full-chain risk identification and management model and method. The model comprises a data access module, a score calculation module, an auditing and judging module and a result output module, multi-dimensional data such as enterprise basic information, commodity declaration information, path transportation information and historical trade behaviors are integrated by means of a unified data interface structure, and an expert weight evaluation (AHP) method and a machine learning algorithm are combined to obtain a comprehensive evaluation result. And performing multi-dimensional risk scoring on the enterprises and the declaration receipts, and automatically judging whether the enterprises and the declaration receipts have exceptions or supervision requirements or not. The system introduces a scoring code system, a price list auditing state code and an AHP weight code to construct a standardized evaluation logic, so that the risk control efficiency and the judgment transparency are improved. The model has good expandability and application universality, is suitable for various business scenes such as cross-border supervision, credit assessment, customs affair auditing and the like, can realize pre-warning and process controllability of risks, and remarkably improves the compliance level and management intelligence degree of cross-border trade.

The invention discloses a multi-device cooperative control system and method for a construction site intelligent capsule bin, and relates to the technical field of industrial control, and the system comprises a data interface layer, a model construction module, a situation prediction module, a cooperative control unit, a self-adaptive cooperative optimization module, and a safety helmet positioning module. The method comprises a model construction step, a data acquisition step, a simulation prediction step, a situation judgment step, a cooperative control step and a self-adaptive cooperative optimization step. According to the method, unified data fusion and cooperative control are carried out on various devices, dynamic adjustment and strategy optimization of the devices can be carried out in time according to the actual situation of the site, the resource scheduling efficiency is improved, and the utilization efficiency of data is improved.