36 results about "Response strategy" patented technology

The invention discloses an intelligent identification and grading early warning method based on mine production abnormity, and a system and equipment thereof. The method comprises the following steps: obtaining space-time evolution data, earth surface deformation data and environment monitoring data of rock mass stress in a target area; performing abnormal mode detection on the data by using a space-time convolutional network to obtain accident risk probability distribution data; using an ultra wide band positioning system to track the movement track of the person entering the target area in real time to obtain the movement track data of the person; and constructing a three-dimensional early warning index system in combination with the accident risk probability distribution data in the target area and a dynamic early warning mechanism, performing dynamic early warning grading on different positions in the target area by using the three-dimensional early warning index system, and generating a grading response strategy matched with the personnel activity trajectory data. According to the invention, the problems of incapability of tracking a personnel movement track in real time and low timeliness of mine production abnormity early warning are solved.

The invention belongs to the technical field of intelligent manufacturing and production scheduling, discloses a flexible job shop dynamic scheduling method and system considering machine aging, and designs a hierarchical environmental response strategy which can firstly evaluate the severity of environmental change. When the change is not violent, only a lightweight local optimization strategy is adopted for fine adjustment; and when the change is relatively violent, a global reconstruction strategy combining knowledge migration, reinitialization and directional repair is started. Therefore, the algorithm can intelligently allocate computing resources according to the intensity of environment change, blind global search is avoided, and the response speed and the operation efficiency of the algorithm are greatly improved. The design effectively balances the exploration and utilization capabilities of the algorithm, and maintains the diversity of the population while ensuring rapid convergence, thereby obtaining a group of Pareto optimal solution sets with good convergence and wider distribution.

The invention discloses a self-adaptive adjustment method and system based on an energy management system, and relates to the technical field of energy management, and the method comprises the following steps: collecting multi-dimensional heterogeneous data including weather conditions, economic situations, social activities, user behavior patterns and energy price fluctuations; according to the method, the multi-dimensional heterogeneous data is fused, the space-time attention mechanism and the graph neural network are utilized to construct the high-precision prediction model, ultra-short-term to medium-and-long-term energy demand dynamic prediction is realized, the problem of insufficient multi-factor fusion is solved, the hierarchical response strategy is triggered based on the prediction result, and the prediction efficiency is improved. Distributed energy storage, a flexible load and a production end are linked to perform dynamic power adjustment, strategy autonomous optimization is realized through reinforcement learning, the adaptive adjustment ability to a complex scene is improved, an adaptive adjustment closed loop is formed through real-time error calculation and model parameter correction, and adjustment parameters and model weights are corrected in real time, so that the real-time performance of the system is improved. And the stability and accuracy of long-term operation of the system are ensured.

The invention relates to the technical field of data processing, and particularly provides a disaster response decision-making method and device based on multiple modes, a medium and equipment, and the method can comprise the steps: obtaining structural data corresponding to original data of a disaster site in real time; wherein the original data comprises video data, image data, voice data or disaster monitoring data; the structured data comprises disaster event types, spatial positions, confidence coefficients and timestamps; taking the structured data as a retrieval element, positioning a semantic position of the disaster site from a pre-constructed disaster chain knowledge base, and reasoning a chain evolution path of the disaster site; obtaining a response measure set corresponding to the semantic position; and acquiring a disaster response strategy according to the original data, the structured data, the chained evolution path and the response measure set. According to the embodiment of the invention, the decision-making precision of disaster treatment can be improved.

The invention discloses a power transmission line fault prediction and strategy generation method and system in severe convective weather, and the method comprises the steps: obtaining the grid weather forecast data of a target region, extracting severe convective weather parameters, calculating the meteorological threat index of a power transmission line based on the data, dividing the power transmission line into a plurality of line units, and carrying out the fault prediction of the power transmission line. Extracting anti-disaster toughness attributes of the line units, fusing meteorological threat indexes of meteorological grid points of the positions where the line units are located, predicting failure rates of the line units through a pre-trained machine learning model, determining risk levels of the line units according to the failure rates, and combining a preset risk level-disposal rule mapping library to determine the risk levels of the line units. And generating and pushing a corresponding coping strategy for each line unit. According to the method, the fault rate of the individual line unit is accurately and quantitatively predicted by using the pre-trained machine learning model, the early warning accuracy, the strategy performability and the operation and maintenance resource use efficiency are greatly improved, and an effective technical support is provided for ensuring safe and stable operation of a power grid in extreme weather.

The present application belongs to the technical field of intelligent regulation, and discloses an intelligent control cabinet regulation method and system based on environmental regulation, comprising: obtaining environmental parameter regions and corresponding three-dimensional environmental data; obtaining environmental regulation sensitivity and environmental stability through parameter coupling analysis and air flow state distribution analysis; evaluating air flow interaction based on environmental stability to obtain air flow balance strength and regulation prediction index; constructing an adaptive control framework to form a dynamic fusion control strategy; constructing an anomaly detection model to analyze environmental parameter abnormal fluctuations and generate an abnormal response strategy; combining the dynamic fusion control strategy and the abnormal response strategy to perform parameter optimization and energy efficiency adjustment, and generating an optimal environmental regulation scheme; finally, executing the scheme and performing real-time monitoring and feedback adjustment; the present application realizes precise environmental regulation of an intelligent control cabinet.

This invention relates to the field of highway inspection technology, specifically to an adaptive low-altitude highway inspection method and system for extreme environments. The method includes: collecting heterogeneous data from UAV airborne sensors, roadside weather stations, and geographic information systems; constructing an environmental feature vector including a sensor reliability index; retrieving an extreme scenario database based on this vector; running a hybrid planning model to generate a flexible task package including a main execution strategy and a scenario-response strategy; scheduling a UAV swarm to execute the flexible task package; calculating an environmental comprehensive score and dynamic link weights to dynamically reconstruct the network topology of the UAV swarm; adaptively enhancing the collected data to remove interference; and using a nonlinear confidence model to fuse the judgment results of multiple algorithms to generate a highway inspection judgment report. This invention ensures flight safety and communication stability in extreme environments through environment-driven flexible task planning and dynamic topology reconstruction.

The application provides a robot control method, computer equipment and a computer readable storage medium. The method comprises: acquiring multi-modal data of a user, the multi-modal data comprising posture data, sound data and force control data; performing feature extraction and fusion processing on the multi-modal data to obtain multi-modal fusion features; determining context information of human-computer interaction according to environmental information of an area where a robot is located and historical interaction data of the robot; generating a response strategy according to the multi-modal fusion features and the context information, and controlling movement of the robot according to the response strategy, so as to improve naturalness and accuracy of human-computer interaction.

The invention discloses a multi-scene-oriented volatile organic compound real-time monitoring and early warning method, and relates to the technical field of intelligent sensor monitoring. The method comprises the following steps: dividing microenvironment types in a target scene, defining the health tolerance of a benchmarking object, selecting whether to send a marking signal or not, synchronously constructing a microenvironment health tolerance model, and outputting microenvironment health tolerance indexes corresponding to different microenvironment types; the method is technically characterized in that a dual-condition trigger logic of a mark signal and an anomaly residual error is utilized, so that a hierarchical response strategy is associated with spatial semantics and reflects pollution causes; and on the other hand, fine control of grading and enhanced response measures is also realized, balance between excessive intervention and insufficient response is sought, and the extensive problem existing in a switching mode in the traditional early warning is solved, so that the intervention measures are accurately matched with the risk grades, and the effects of efficiency and user experience are considered to a certain extent.

The present invention relates to an excess power consumption prediction system that collects power consumption, power generation, and weather information of a factory, predicts a power excess state in which the consumption exceeds the generation, analysis causes using operation information and correction information, and generates a response strategy.

The invention discloses a recognition and driving system for preventing biological invasion and a control method, and the system comprises a dynamic risk field modeling module which is used for carrying out the real-time scanning of the spatial information of a monitoring site; the YOLO-GAN collaborative recognition module is used for recognizing and positioning a target, extracting target features and generating a dynamic track; the environment coupling control engine is used for collecting environment parameters of the monitoring site in real time; the driving device is used for driving according to the control instruction; and an edge intelligent decision-making unit. The laser radar is arranged for space partitioning and real-time scanning and monitoring, suspicious organisms can be recognized more accurately through YOLO-GAN collaborative biological recognition, meteorological self-adaption can be achieved through the environment coupling electromagnetic field, normal operation can be achieved even in an extreme environment, a multi-stage response strategy is formed in cooperation with the upper driving device, and the driving device can be used for driving a driving device to drive the driving device to drive the driving device to drive the driving device to drive the driving device to drive the driving device to drive the driving device to drive the driving device. And the transformer substation protection system which is low in cost, low in complexity and eco-friendly is formed.

The invention relates to the technical field of geophysical detection and intelligent signal processing, in particular to a geomagnetic anomaly feature recognition method and system based on big data. The method comprises the following steps: carrying out integrated deep preprocessing consisting of high-precision time synchronization, double-loop drift correction, environment association annotation and noise fingerprinting on multi-source heterogeneous geomagnetic observation data; the point type record is converted into a gridding multi-scale behavior vector fusing a background and an abnormal residual error through partition focusing, sequence sparse warping and local disturbance layer construction; performing node manifold embedding and abnormal mode propagation based on a space-time graph structure, and performing coding to form a low-dimensional abnormal mode vector; abnormal types are distinguished by utilizing clustering classification, an abnormal trend is predicted through machine learning in combination with a historical mode, and a response strategy is automatically generated. According to the method, full-link intelligent processing from data acquisition, feature extraction, pattern recognition to prediction response is realized, and the accuracy and early discovery capability of geomagnetic anomaly recognition are remarkably improved.

The application provides an artificial intelligence early warning and management method for smart ocean, and is applied to the field of data processing.The present application is aimed at the problems that the smart ocean data is large in scale, multi-source and complex, the threat identification of the prior art is single, the early warning model lacks self-adaptive adjustment and is prone to false alarm and missed alarm, and it is difficult to guarantee data security, and is based on multi-source data of smart ocean and preset data sets containing data types, security levels and the like, and after preprocessing, a threat identification and early warning model is trained by using a deep learning framework.The model detects four types of features such as sensitive information in real-time data, extracts parameters, matches threat features to determine the risk level, and establishes the mapping relationship between data types and threats and abnormal patterns.The false alarm is removed through abnormal threshold evaluation to generate initial early warning parameters, the model is iteratively optimized in combination with machine learning, the risk data is finally sorted, the response strategy is constructed, the safety management scheme is formulated, and the result is generated, so that the intelligent upgrading from passive response to active defense is realized.

The invention discloses an intelligent identification and linkage defense method, system and device for a water area invasion target of a hydraulic power plant and a medium, and belongs to the technical field of water area invasion identification of the hydraulic power plant. Establishing a mapping relation between a radar detection equipment coordinate system and a video monitoring equipment coordinate system; generating a control instruction based on the mapping relation, driving a holder mechanism of the video monitoring equipment to be adjusted to a monitoring orientation corresponding to the target, and obtaining video image data and thermal imaging data of the target; extracting appearance characteristic parameters and thermal characteristic parameters of the target; calculating a threat metric value of the target relative to a preset protection area of the hydraulic power plant; and dividing the targets into different threat levels according to the threat metric values, and generating corresponding linkage response strategies. According to the invention, the combination of long-distance early warning and accurate identification is realized through the coordinate mapping of the radar and the video, and accurate threat assessment is realized through multi-dimensional feature fusion.

The invention belongs to the technical field of low-altitude economy, and provides a low-altitude takeoff and landing field two-way safety verification and early warning method based on data feature fusion, which comprises the following steps: acquiring and preprocessing multi-source heterogeneous environmental parameters and takeoff and landing guarantee data to form a consistent data set, and aiming at four core scenes of microclimate, road surface, airspace and equipment environment, establishing a data set; the method comprises the following steps: respectively constructing cross verification logics of environmental parameters to take-off and landing guarantee data, further performing anti-verification on a corresponding environment by using the take-off and landing guarantee data when data distortion is found through verification, realizing bidirectional verification of the environment and the data, and finally performing credible judgment based on a consistency index. And the airspace management system is linked to execute a five-level grading response strategy. According to the method, the data credibility, the hidden danger recognition accuracy and the operation response efficiency can be remarkably improved, and reliable technical support is provided for regularized and safe operation of low-altitude take-off and landing occasions.

The invention relates to the field of information processing, and provides an identifier query method and device, equipment, a medium and a program product. The identifier query method comprises the steps of obtaining an identifier query request, wherein the identifier query request comprises a target service identifier; determining environment characteristics when the identification query request occurs and attribute information of an access subject sending the identification query request; determining a target analysis view matched with the identification query request in a plurality of pre-stored analysis views according to the environment characteristics and the attribute information, wherein the target analysis view represents a response strategy corresponding to the identification query request; obtaining service data corresponding to the target service identifier according to the target analysis view and the attribute information, and obtaining an identifier query result corresponding to the identifier query request according to the service data; and sending the identification query result to the equipment of the access main body. According to the method and the device, the problems of unnecessary sensitive information exposure, high data security risk and low identification query efficiency existing in an identification query mechanism in the prior art can be solved.

The invention discloses an automatic arrangement and response method and device, a medium and equipment. The method comprises the following steps: collecting safety events of various oil fields through an automatic tool, adding the safety events into a safety event library, and formulating a response strategy; the coordination event corresponding team, the evidence obtaining team and the IT team cooperate, a workflow is created, and an automatic arrangement and response process is formulated; performing predefined response operation based on the automatic response tool; monitoring an execution result after the response action, adjusting a strategy and optimizing a process in real time, and identifying and tracking corresponding system vulnerabilities after correlating threat intelligence and a security event; and executing a vulnerability repair process, and generating a compliance report. According to the invention, when threat information appears and needs to make a response, people do not need to make a response manually, so that the response efficiency is greatly improved, and the response time is shortened. According to the invention, the investigation and evidence obtaining process is improved, and the accuracy and efficiency of threat information and security event processing are improved.

The invention discloses a multi-energy fusion power generation scheduling method and system based on power gap prediction. The method comprises the steps that a power gap curve in a future preset time period is calculated based on collected environmental meteorological data and historical load data; extracting a power gap amplitude and a corresponding gap duration based on the power gap curve; the gap duration is compared with a preset time threshold value, a comparison result is obtained, and the time threshold value is used for defining the time limit of the short-time power gap; and according to a comparison result, determining a target response main body matched with the gap duration, and generating a dynamic operation strategy based on the target response main body. Compared with the prior art, the power gap prediction precision, the matching degree of the response strategy and the gap duration and the equipment operation economy of the multi-energy system can be remarkably improved, and the technical defects that in a traditional scheme, power gap prediction deviation is large, the response strategy and the gap duration are not matched, and equipment is frequently started and stopped, and losses are high are overcome.

The invention discloses a mine disaster zoning dynamic regulation and control method, which relates to the field of mine safety, and comprises the following steps: establishing a multi-disaster distributed sensing network, collecting gas concentration, surrounding rock stress, seepage pressure and temperature and humidity parameters, realizing high-frequency synchronous sensing of multiple physical quantities, and forming dynamic multi-source data. According to the method, multi-source data such as gas, stress, water damage and temperature and humidity are fused, a disaster coupling probability model is constructed, an intelligent partition engine and an early warning transition mechanism are introduced, and dynamic fine-grained division and response strategy adjustment of mine risks are achieved. Key control parameters are adjusted in a linkage mode by responding to a regulation and control matrix and a feedback loop, the risk suppression effect is continuously quantified, and the adaptability and robustness of the system are enhanced. The traditional static monitoring limitation is effectively broken through, the multi-disaster linkage risk is reduced, and the emergency response capability and the safety level of mine disasters are improved.

The invention provides a satellite multi-objective optimization planning method and system based on deep reinforcement learning, and relates to the technical field of deep learning, and the method comprises the steps: determining a visible satellite set corresponding to a to-be-detected region based on satellite orbit data and to-be-detected region information; the satellite orbit data comprises a satellite geocentric coordinate, and the information of the to-be-measured area comprises central point position information of the to-be-measured area; the time sequence combination of the to-be-detected area information and the visible satellite set is used as an action space, the to-be-detected area information, historical coupling parameters and the visible satellite set are used as a state space, and an initialized preset DQN network is trained; minimizing a mean square error between a current Q value and a target Q value through a preset reward function to adjust parameters of the DQN network, and obtaining an expected DQN network; determining a fire disaster response strategy based on the expected DQN network; the fire disaster response strategy comprises a target satellite and a reward function corresponding to the maximum Q value at each time point.

The application relates to the field of supply chain intelligent management, and provides a supply chain management method for coping with geopolitical risks and a corresponding product. The method comprises the following steps: acquiring multi-source heterogeneous geopolitical risk related data; performing semantic analysis and vectorization processing on the geopolitical risk related data by using a cross-modal natural language processing model to generate a geopolitical risk feature vector; dynamically calculating a geopolitical risk level of a target region based on the geopolitical risk feature vector; triggering a supply chain reconstruction response mechanism of a corresponding level according to a numerical interval of the risk level by a multi-level response strategy generation engine; simulating an execution process of the supply chain reconstruction response mechanism in a virtual supply chain network in a digital twin environment, verifying material flow continuity and node fault tolerance, and optimizing a backup path topology structure according to a simulation result; and outputting the verified reconstruction strategy to a cross-border supply chain management system to realize automatic adjustment of a supplier layout, capacity allocation and a transportation route.

This invention belongs to the field of intelligent manufacturing and production scheduling technology, and discloses a dynamic scheduling method and system for flexible workshops that considers machine aging. The invention designs a hierarchical environmental response strategy, which first assesses the severity of environmental changes. When the changes are not drastic, only a lightweight local optimization strategy is used for fine-tuning; when the changes are more drastic, a global reconstruction strategy combining knowledge transfer, re-initialization, and targeted repair is activated. This allows the algorithm to intelligently allocate computing resources according to the severity of environmental changes, avoiding blind global searches and greatly improving the algorithm's response speed and operating efficiency. This design effectively balances the algorithm's exploration and utilization capabilities, ensuring rapid convergence while maintaining population diversity, thereby obtaining a set of Pareto optimal solutions with good convergence and wider distribution.

The present application relates to the technical field of power system operation control technology, and more particularly to a micro-balance area responsibility transfer and incentive adjustment method, system and medium, the method comprising: obtaining historical output, weather and real-time clearing data of each market subject in the micro-balance area, predicting actual output and determining preliminary imbalance responsibility and system total imbalance power; determining the final target responsibility according to the direction of the system total imbalance power, and transferring the new energy up-regulation responsibility to the thermal power market subject when there is a power shortage; constructing a master-slave game model based on the final target responsibility, determining the incentive price and response power, solving to obtain the optimal response strategy, publishing the incentive price and performing deviation settlement. Through the present application, the core technical problem of the existing technology that the new energy up-regulation responsibility cannot be physically executed and lacks an effective incentive mechanism is effectively solved.

The present invention relates to the field of intelligent construction, and in particular to a digital monitoring system and method for intelligent construction. The system comprises: a data acquisition module which acquires environmental data, an equipment state and a personnel state during a construction process, and generates a current dataset and a historical dataset; a data analysis module which reads the current dataset and the historical dataset generated by the data acquisition module for analysis processing, identifies trends and abnormalities in the current dataset and the historical dataset, and generates a real-time monitoring report; a prediction and evaluation module which analyzes the real-time monitoring report generated by the data analysis module to obtain a project progress prediction result, a project cost prediction result and a safety risk evaluation result; and a response strategy generation module which correspondingly generating a response strategy on the basis of the project progress prediction result, the project cost prediction result and the personnel safety risk evaluation result obtained by the prediction and evaluation module. The present invention involves collecting data, analyzing data, predicting results, and formulating targeted response strategies, to provide decision support for managers and assist managers in making decisions more quickly and accurately, thereby improving management efficiency.

This invention relates to the field of mine safety monitoring and geological disaster early warning, specifically disclosing an intelligent hierarchical early warning method for steep slopes in sand and gravel mines based on multi-source data fusion. The method includes: S1, collecting multi-source data, including multivariate data; S2, customized preprocessing: InSAR data is de-obfuscated using U-Net and phase unwrapping, GNSS data is denoised using Kalman filtering, and deep data is filtered for anomalies using isolated forest filtering; S3, unifying the spatiotemporal reference of the data through three-level registration, and dynamically allocating fusion weights using reinforcement learning; S4, establishing a four-dimensional index of deformation, mechanics, environment, and geology, and generating five-level dynamic early warning thresholds using XGBoost; S5, predicting short-term deformation using Transformer-LSTM, coupling with ABAQUS to assess medium- and long-term risks and trigger hierarchical early warnings; S6, outputting customized response strategies and visualizing them on a digital twin platform. This invention solves the problems of traditional data silos and inaccurate early warnings, adapts to complex working conditions, and improves the efficiency of mine safety management.

This application discloses an emergency control method and system for hydropower stations based on earthquake early warning, relating to the field of hydropower engineering safety and intelligent control technology. The method includes: acquiring multi-source correlated data such as earthquake early warning data, hydropower station operation data, and historical archive data; predicting disaster effect parameters caused by future earthquakes using a disaster effect prediction model; combining the disaster effect parameters with the current state of the hydropower station and generating an initial response strategy using a deep reinforcement learning algorithm; then calculating revision coefficients using equipment feedback data to dynamically correct the initial strategy and form a final response strategy; controlling the hydropower station equipment according to the final strategy and recording data to optimize the model. This method can predict disaster effects in advance and combines multi-objective optimization with real-time equipment status for closed-loop control, effectively solving the problems of delayed emergency response, rigid strategies, and execution failure under extreme conditions in traditional emergency response systems, significantly improving the safety and reliability of hydropower station earthquake emergency response.

This invention relates to a trust space governance method for inland waterway shipping data based on a zero-trust architecture. The method includes modeling shipping data features and calculating data quality scores, constructing trust triples to calculate expected probabilities, performing consensus fusion on multi-source data, calculating the expected fusion probability and trust index of various types of shipping data, constructing a risk assessment model to output risk assessment values, building a group risk consensus mechanism based on an improved PBFT consensus algorithm to output a group risk consistency index, generating optimal security strategies based on reinforcement learning algorithms, and designing dynamic access control rules, risk handling priority ranking rules, and dynamic response strategies. This method solves problems such as difficulty in data trust assessment, inconsistent group risk perception, and insufficient dynamic security protection capabilities in inland waterway shipping systems, achieving comprehensive security assurance for the shipping system.

The invention discloses a public opinion crisis-oriented multi-modal emotion grading early warning response method and system, and relates to the technical field of multi-modal data processing, and the method comprises the steps: collecting multi-modal public opinion data through a multi-modal data collection module, and carrying out the emotion analysis to generate a multi-layer emotion grading result; performing weighted calculation to obtain a comprehensive emotion value so as to calculate a crisis index; mapping the crisis index into an early warning level, and matching a strategy knowledge base to obtain a recommended response strategy; and finally, synchronizing to a collaborative response platform and generating a public opinion crisis collaborative response strategy. The technical problems that the traditional public opinion management technology is difficult to accurately perceive the multi-modal public opinion emotion and scientifically quantify the crisis level, and the response strategy is low in efficiency after early warning are solved, and the technical effects that the multi-modal public opinion emotion is accurately perceived, the crisis level is scientifically quantified, the response strategy can be intelligently matched, and efficient cooperative execution is achieved are achieved.

This application provides a risk early warning method and system for mine ecological restoration areas based on environmental perception. It collects multi-source environmental datasets through a constructed four-level environmental perception system (space-air-ground-depth). Spatial spectral features and temporal dependence features are extracted from the multi-source environmental datasets, and cross-modal feature fusion is performed to obtain a fused environmental feature tensor. The environmental feature tensor is decomposed and dimensionality reduced, and multi-task temporal prediction is performed based on the dimensionality-reduced feature sequence to obtain trend prediction values ​​for four risk indices in the mine ecological restoration area: geological hazards, vegetation degradation, soil and water pollution, and degradation of restoration effectiveness. Based on the trend prediction values ​​of each risk index, the risk early warning level is determined and corresponding response strategies are triggered. Based on the above scheme, multi-dimensional environmental perception and cross-modal feature fusion can achieve multi-task risk trend prediction to complete accurate early warning level determination and differentiated response.

The present application relates to the technical field of power system operation control technology, and more particularly to a micro-balance area responsibility transfer and incentive adjustment method, system and medium, the method comprising: obtaining historical output data, historical clearing plans and meteorological data of each market subject in the micro-balance area, predicting the output value of each market subject in the future dispatch period and determining the preliminary imbalance responsibility and the system total imbalance power; determining the final target responsibility according to the direction of the system total imbalance power, and transferring the new energy up-regulation responsibility to the thermal power market subject when there is a power shortage; constructing a master-slave game model based on the final target responsibility, determining the incentive price and the response power, solving the optimal response strategy, publishing the incentive price and performing deviation settlement. Through the present application, the core technical problem of the existing technology that the new energy up-regulation responsibility cannot be physically executed and lacks an effective incentive mechanism is effectively solved.