67results about How to "Reasonable decision" patented technology

The embodiment of the invention provides a medical image aided decision-making method and system, electronic equipment and a storage medium, and belongs to the field of medical treatment. The method comprises the following steps: collecting multi-modal medical data, wherein the multi-modal medical data comprises a medical image and corresponding text data; constructing a multi-modal large language model, and performing pre-training to obtain a target multi-modal large language model; extracting image and text feature vectors corresponding to the medical image and the text data by using the target multi-modal large language model, and fusing the image and text feature vectors to obtain a fused feature vector; and according to different clinical tasks, adopting models corresponding to the clinical tasks, and combining with the fusion feature vector to obtain an auxiliary decision scheme. By fusing detailed clinical information in medical images and text data, the multi-modal large language model can more comprehensively understand the health condition of a patient, so that more accurate and comprehensive diagnosis suggestions are provided, the model configuration can be flexibly adjusted for different clinical tasks, and the diagnosis efficiency is improved. And a targeted auxiliary decision-making scheme is generated in combination with the fusion feature vector.

The invention provides a micro robot motion control method and system, and the method comprises the steps: carrying out the synchronization and calibration processing of environment state information, and generating a comprehensive perception image; analyzing the comprehensive perception image to obtain a classification result, and predicting the environment state change influencing the motion track of the micro robot in a period of time in the future based on the classification result; generating a candidate path by using a fast random tree, and optimizing the candidate path in combination with an AI search algorithm to obtain a target path; constructing a three-dimensional dynamic probability graph, continuously updating the three-dimensional dynamic probability graph by using a reinforcement learning algorithm, and generating an instant decision map; and estimating the position of the micro-robot by using a Kalman filter to obtain the position state of the micro-robot, adjusting the speed and steering angle of a driving motor of the micro-robot, and generating trajectory tracking of the micro-robot. According to the technical scheme provided by the invention, the flexibility and intelligence of the micro robot are improved.

The invention relates to the technical field of police service management, discloses a police service management system and method based on an image recognition technology, and aims to improve the police service management efficiency and accuracy. The system comprises a data receiving module, a data preprocessing module, a target detection module, a behavior analysis module and an information integration module. A monitoring video stream is received, data is preprocessed to form an image data set, and then a specific target is detected by using a deep convolutional neural network and a track sequence is output. And carrying out behavior analysis on the target track by adopting a long-short-term memory network, and identifying an abnormal mode. And finally, integrating the detection result and the behavior analysis data, and generating a police service management report. According to the invention, intelligent processing of monitoring videos is realized, police service departments are assisted to respond to abnormal conditions in time, and urban public security is maintained. Through the deep learning technology, the accuracy of target detection and behavior recognition is improved, and powerful support is provided for modern police service management.

The invention discloses an intelligent wind field forecasting method and an intelligent wind field forecasting system based on combined application of ST-LSTM (Long Short Term Memory) and Swin-Transform. The method belongs to the technical field of data processing, and comprises the following steps: constructing a time sequence prediction model based on ST-LSTM; inputting the historical data of the wind field into a time sequence prediction model based on ST-LSTM to obtain future wind field prediction data; the method comprises the following steps: constructing a wind field space downscaling model based on a Swin Transform architecture; and inputting the wind field forecast data into the wind field space downscaling model based on the Swin Transform architecture to obtain wind field refined forecast data. According to the method, the STM-LSTM is adopted as the extension of the LSTM, the long-term and short-term dependency relationship in time and space can be better captured when time sequence data is processed, and the Swindow-Transform can extract features in a global range through a self-attention mechanism of the Swindow-Transform, effectively capture the mutual influence between different positions in a wind field and further improve the accuracy of forecasting.

The invention discloses a dynamic obstacle avoidance control system based on machine vision, and relates to the technical field of machine vision and automatic control, and the main scheme is that an information collection module obtains image information, machine basic operation and task target data in real time; the image processing module calculates obstacle influence factor parameters and ranges, task complexity indexes and operation obstruction indexes according to the images and the operation data, and the data adjusting module judges whether machine transportation is obstructed or not according to a comparison result of the operation obstruction indexes and a threshold value and sets an obstacle avoidance route. The control decision module calculates predicted delay time according to the operation obstruction index and the like, further obtains a self-adaptive task speed value and steering, judges whether normal work is affected by comparing the predicted delay time with a threshold set, adjusts the normal work into a self-adaptive value when the normal work is affected, and controls the task speed value and the steering direction. According to the system, the autonomous obstacle avoidance capability and the task execution efficiency of a machine in a complex environment are improved.

The invention provides a hotspot work order identification method which can be applied to the technical field of intelligent customer service, the technical field of artificial intelligence and the technical field of big data, and the hotspot work order identification method comprises the steps that basic attribute information of at least one to-be-identified work order is determined, and the basic attribute information at least comprises a work order category; based on the work order category, primary work orders related to the work order to be identified are screened from historical work orders; calculating the similarity between the primary selection work orders, so as to screen out optimal work orders related to the work order to be identified from the primary selection work orders; clustering processing is carried out on a work order set composed of the work orders to be identified and the optimized work orders; and in response to a clustering processing result, under the condition that the number of the work orders in the target class cluster to which the to-be-identified work order belongs meets a preset numerical value condition, determining that the to-be-identified work order is a hotspot work order. The invention further provides a hotspot work order recognition device and equipment, a storage medium and a program product.

The invention relates to the technical field of energy storage risk prediction, and particularly discloses an electrochemical energy storage risk prediction method, which comprises the following steps of: determining a risk characteristic value of an electrochemical energy storage system, obtaining an abnormal deviation value of an operation environment of the electrochemical energy storage system, and obtaining a risk judgment threshold value of the electrochemical energy storage system; according to the method, the real-time operation state of the electrochemical energy storage system is monitored, whether the electrochemical energy storage system has risks or not is judged by combining a risk judgment threshold value of the electrochemical energy storage system, and the risk level of the electrochemical energy storage system with the risks is determined. According to the method, the problems that the existing risk management system is not easy to carry out and is lack of comprehensive analysis are solved, rapid response and implementation of effective rectification measures are facilitated, the accident occurrence probability is remarkably reduced, the efficiency and pertinence of maintenance work are improved, a perfect risk judgment threshold system is established, potential risks can be systematically identified and evaluated, and the overall risk management capability is improved.

The invention relates to the technical field of logistics, in particular to a digital twin network traffic prediction method, device and equipment and a storage medium, and the method comprises the steps: obtaining historical traffic data of a digital twin network of a logistics circulation center, and obtaining traffic training data from the historical traffic data; constructing an initialization model based on the TimeGPT time sequence model, and performing parameter configuration on the initialization model to obtain a basic prediction model; performing training processing on the basic prediction model based on the traffic training data to obtain a traffic prediction model; acquiring updated traffic data from the digital twin network of the logistics circulation center according to a preset period, and performing updating training on the traffic prediction model according to the updated traffic data to obtain an updated traffic prediction model; acquiring real-time traffic data of the digital twin network of the logistics circulation center, and performing traffic prediction based on the updated traffic prediction model and the real-time traffic data to obtain a predicted traffic result; according to the method, the problem of overfitting of the model can be effectively prevented.

The invention belongs to the technical field of automatic testing, and particularly relates to a test task scheduling method and system of a control system based on a graph model and deep reinforcement learning. The method comprises the following steps: analyzing a test task demand, and then constructing a Single-rate DAG task scheduling model; according to a Single-rate DAG task scheduling model, generating a directed graph as the input of a GNN; establishing a heat map about the edge based on the directed graph and the GNN; generating a task scheduling sequence according to the heat map, and planning a scheduling scheme according to the task scheduling sequence; calculating a loss function, and updating the graph neural network according to the loss function; repeatedly establishing the heat map to calculate the loss function until a high-quality solution conforming to an expected target can be planned or a difference value between a mean value and a minimum value of the planned test scheme makespan is smaller than a certain threshold value, and ending iteration; and step 7, outputting an optimized solution. The method is used for solving the problem that a planning result of a high-correlation test task scheduling process is often high in randomness and is difficult to meet actual requirements.

The invention discloses a real-time optimal power flow method based on a generative pre-training model (GPT) and deep reinforcement learning, relates to the technical field of intelligent regulation and control of a power grid, and utilizes technical breakthrough in the field of large language models to directly model non-quantifiable natural language stipulations as targets and constraints in an OPF problem. The method comprises the steps of establishing comprehensive performance indexes, setting contexts and problems, converting GPT agent feedback into reward signals, carrying out iterative sampling, outputting GPT agent results in a JSON format and importing the GPT agent results into a DRL agent, carrying out offline training and online application and the like. Three deep neural networks are used in the DRL process, a cost function is optimized through a primitive-dual method, a series of constraint conditions including language specification constraints are set, and the method can be applied online to solve the OPF problem in real time.

The invention provides an intelligent game role dynamic extension and cooperative combat method for a virtual battlefield, and relates to the technical field of intelligent games, the method adopts an UpDeT module to dynamically increase or reduce the number or type of agents according to the real-time battlefield condition and strategy requirements of the virtual battlefield; state features and action features corresponding to different agent types of different scenes are converted to a unified feature space through a feature mapping function; based on a self-attention mechanism of Transform, carrying out fusion processing on the feature representation of each agent to obtain a fusion feature; generating a cooperative combat decision according to the fusion feature and the communication information; therefore, the problems of fixed roles, poor expansibility and difficulty in strategy migration in the prior art are solved, the generalization ability of the intelligent game strategy is improved, and the intelligent game level is improved.

The invention discloses a multi-virtual power plant intelligent scheduling decision-making method and system considering uncertain risks, and the method comprises the steps: constructing an objective function, and comprehensively optimizing the response degree, the total response cost and the uncertain risks of provincial scheduling demands; setting constraint conditions in the virtual power plant intelligent scheduling decision model, wherein the constraint conditions comprise setting of upper and lower limit constraints of response power, setting of response rate constraints, setting of response time constraints and setting of load state constraints of an energy storage system; data collection and preprocessing: the virtual power plant collects diversified data through a user layer measurement unit, and a decision-making layer preprocesses the data; and scheduling and adjusting in real time. The model proposed by the invention not only ensures that each virtual power plant can maximize own profit when participating in market bidding through an upper layer optimization strategy quotation coefficient, but also gives consideration to the stability, safety and economic benefits of the whole power grid, and the design ingeniously balances the local benefit of a single VPP and the global optimality of the power grid, and improves the market bidding efficiency. And the decision-making process is more scientific and reasonable.

The invention belongs to the technical field of highlight injection mold temperature controller data regulation and control, and particularly discloses and provides a highlight injection mold temperature controller intelligent control regulation and control system which comprises the steps that whether an injection mold insert is replaced or not is recognized, flaw detection is conducted on a pre-material, and operation data of a highlight injection mold temperature controller are preset; the defect of the pre-material can be prevented from being amplified in the injection molding process; by identifying the defect parameters corresponding to the operation data, the specific operation parameters causing the defects can be accurately identified, so that the preset operation data of the next operation of the highlight injection mold temperature controller can be corrected in a targeted manner, the generation of the defects can be reduced, the consistency of the quality of products produced by injection molding each time is ensured, and the production efficiency is improved. The difference between products is reduced; the regulation and control trend of the preset operation data is determined by identifying the operation parameter deviation trend of the highlight injection molding target, and a scientific basis can be provided for a decision maker, so that the decision maker can make a more accurate and reasonable decision.

The invention provides a power load prediction method based on an enhanced channel perception attention frequency mode estimation network. Firstly, power load data is acquired; performing data preprocessing based on the obtained data; based on the preprocessed data, establishing and solving a channel perception attention network, and performing effective modeling on a dependency relationship of variables to obtain feature data of the relationship between the variables after analysis; establishing and solving a frequency mode estimation network based on characteristic data obtained by analyzing the relationship between variables, and extracting a time frequency mode necessary for representing the rhythmicity of the power load data to obtain a prediction result; and based on the prediction result, performing model training and optimization by taking reduction of a loss function as a target to obtain a final power load prediction result. By capturing the complex time dynamics of the time sequence data and the dependency relationship between the variables, the power load prediction accuracy is improved, the knowledge system of power load prediction is enriched, and the method has guiding significance for the development of an energy management system.

The invention discloses a vehicle-mounted target tracker based on computer vision, and relates to the technical field of computer vision, and the vehicle-mounted target tracker comprises a sensor data collection module which is used for collecting real-time data from various sensors; the data fusion module is used for integrating the data from the plurality of sensors of the sensor data collection module and generating comprehensive information about the environment and the target; the data processing module is used for processing the complex data, extracting features and carrying out target identification and classification; according to the invention, by fusing the environment information and the target information, a more comprehensive and accurate context environment is provided for the system, the current scene can be understood more accurately, the authenticity of the target object can be judged more accurately, so that a more reasonable decision can be made, and the accuracy of the system is improved. And through combined use of an advanced target tracking algorithm, the precision, robustness and real-time performance of target tracking can be significantly improved.

The present invention discloses an automatic driving collision detection method based on a directional bounding box, which relates to the technical field of automatic driving. Firstly, relatively reliable obstacle bounding box (OBB) data is obtained in real time from a vehicle perception system; secondly, a planned candidate trajectory is obtained from a vehicle trajectory planning module and discretized, and a vehicle OBB bounding box is created for each discrete point; thirdly, a distance between the center of the vehicle and the center of the obstacle is preliminarily calculated to clarify whether the two are absolutely safe (no collision will occur); finally, a separation axis test is performed on obstacles and vehicles with possible collision to determine whether a collision occurs, and the collision risk is calculated based on the shortest distance between the two, so as to make the next decision for the unmanned driving system. The present invention improves the adaptability, safety and accuracy of the automatic driving vehicle and the surrounding environment by analyzing the collision between dynamic obstacles and static obstacles.

The present invention discloses an intelligent beam width optimization method based on reinforcement learning. The method suppresses the beam drift effect in millimeter wave communication by dynamically adjusting the beam width. The algorithm models the beam width dynamic optimization problem as a Markov decision process, and selects the optimal beam width for data transmission at each decision moment. Its state design can characterize the severity of the beam drift effect of the current system from multiple angles, and each action corresponds to a different beam width, thereby optimizing and adjusting the width of the data transmission beam by sensing the speed of environmental changes. In each state, the optimal beam width for data transmission is selected according to the AC algorithm, and the policy network and value network are continuously updated during training to improve the rationality and reliability of model selection, while introducing a dynamic network update mechanism to reduce the computational burden of network updates. The throughput of the millimeter wave communication system under the beam drift effect is improved, thereby continuously ensuring the quality of the communication link.

The invention relates to the technical field of resource transmission configuration, and discloses a transmission configuration method and system for medical air transportation, and the method comprises the steps: obtaining user data and resource data through a scene modeling module, constructing a task scene model, and providing a basis for subsequent decision making; the post-sending decision-making module generates a post-sending decision-making strategy based on a result of the task scene model to obtain a post-sending scheme, and selects the post-sending scheme according to an actual situation, so that the decision-making accuracy and the resource utilization rate are improved; the simulation deduction module performs simulation deduction according to the task scene model and the selected post-delivery scheme, simulates the medical air delivery post-delivery process, and visually detects deduction data in the simulation deduction process; and after simulation deduction is finished, the decision evaluation module evaluates deduction data obtained by visual detection to optimize a post-delivery decision strategy, so that the delivery configuration system can quickly and effectively respond to a medical air delivery post-delivery scene of batch sick and wounded in wartime, and the emergency response capability of the delivery configuration system is improved.

This invention relates to the field of road construction safety monitoring technology, specifically to an intelligent identification and early warning method for road construction areas from a low-altitude perspective. This method aims to address the technical problem that existing technologies cannot accurately identify risks in construction areas when visual perception is degraded, such as in the presence of blurred images. The method includes: collecting multimodal data of the construction area using multiple sensors mounted on a drone; real-time evaluation of visible light image clarity, triggering a multimodal risk inference mode when the clarity falls below a preset threshold; extracting residual visual and non-visual features, inputting them into a risk inference model that integrates a construction safety knowledge graph and a probabilistic graphical network, and inferring scene completion results and their posterior probabilities; calculating the current comprehensive risk probability, generating early warning information and pushing it to the management terminal when the probability exceeds a threshold. This invention improves the environmental adaptability and robustness of road construction inspection and early warning systems, enabling all-weather, all-scenario construction safety management.

The invention provides an anti-intrusion alarm method and alarm system for a mine transportation track, and relates to the technical field of intrusion monitoring, and the method comprises the steps of first laying, first collection, intrusion judgment, alarm and the like. The system comprises a first layout module, a first acquisition module, an intrusion judgment module and an alarm module, the transportation track is monitored through different sensors, track intrusion events can be found and prevented in time, and then the safety of mine transportation is improved.

The invention discloses a monitoring method, system and device based on a Spark stream-oriented computing framework and a medium. The method comprises the steps that the name of a Spark stream processing program is configured; the ID of the Spark stream processing program is inquired; acquiring statistical data of the Spark Web UI, and acquiring an HTML (Hypertext Markup Language) file for displaying statistical information of the stream processing program; the method comprises the following steps of: extracting a Javascript statistical code snippet in an HTML (Hypertext Markup Language) file, and extracting a Javascript code snippet containing time sequence statistical data; the Javascript code snippets are executed, and data are extracted; monitoring data is assembled into a Prometheus format, and the extracted data is assembled into a Prometheus-compatible monitoring data format in combination with an ID, a name and a monitoring index class of a Spark stream processing program; and the monitoring data is stored in a Promeheus, and the assembled monitoring data is pushed to a Promeheus monitoring system. The performance indexes of the system can be fed back in real time through real-time monitoring, so that a worker can quickly recognize and solve the performance bottleneck and accurately position the problem, resource utilization is optimized, the processing efficiency is improved, and it is ensured that the Spark streaming computing framework can continuously maintain the optimal performance state.

The invention discloses an HEVC (High Efficiency Video Coding) intra-frame CU (Coding Unit) division method and device based on deep learning and a coding method. The method comprises the following steps: firstly, acquiring video or image information of a coding unit to be divided; carrying out standardization processing on the video or image information to obtain a CTU image to be coded; then, the CTU image is input into a CPAC-I model, a CU division decision is obtained, and the CPAC-I model is a neural network model based on combination of an attention mechanism of SKnet and a convolution technology; and finally, according to the CU division decision, dividing the HEVC frame so as to complete the HEVC intra-frame CU division based on deep learning. The method can effectively improve the overall performance of video coding and decoding, and can flexibly adapt to different types of video contents and coding scenes.

The present invention relates to the field of intelligent transportation Internet of Things application technology, and specifically discloses a driving behavior coding and scenario modeling system for unmanned vehicles. By comprehensively collecting various parameters of the environment in which the unmanned vehicle operates and accurately evaluating the risk impact of environmental factors on the operation of the unmanned vehicle, a suitable unmanned vehicle behavior reference set is intelligently matched. On this basis, the system further collects performance parameters of the perception layer to which the unmanned vehicle belongs, the vehicle's operating parameters, and the real-time perception parameters of the perception layer. By comprehensively considering the perception layer performance, the vehicle's operating status, and the behavior reference set, the effective behavior factors of the unmanned vehicle can be deeply analyzed and extracted. Based on the comparison between the effective behavior factors of the unmanned vehicle and the effective behavior threshold, the perception parameters of the perception layer of the unmanned vehicle are finely adjusted and encoded, thereby realizing accurate modeling of the unmanned vehicle operation scenario.

The application discloses a kind of brain-like reinforcement learning method and system based on layered experience playback, it is related to reinforcement learning and brain-like computing technical field.The method comprises the following steps: S1, collecting observation data and pre-processing;S2, initialize experience buffer pool and actor network, critic network and corresponding target network, parameter initialization;S3, initialize exploration noise and select action from actor network according to current state and execute, store the obtained experience sample to experience buffer pool;S4, obtain new sample from experience buffer pool, carry out short-term memory pool update;S5, use attention discrimination module to determine whether part of experience in short-term memory experience pool is transferred to long-term memory experience pool;S6, the parameters of actor network, critic network and corresponding target network are updated.The application uses the above method, improves the experience utilization rate of intelligent agent, improves the performance of reinforcement learning, and has wide application potential in many fields.

The invention relates to the technical field of parking space lock unlocking, in particular to a parking space lock unlocking method and system based on vehicle recognition. The method comprises the following steps: a position range unit captures image data of a vehicle close to an empty parking space and analyzes whether the vehicle enters a preset range of the empty parking space; according to the parking unlocking module, the comprehensive score is calculated according to the vehicle position score, the vehicle speed score and the vehicle direction score, whether the vehicle is parked towards the empty parking space or not is judged according to the comprehensive score, and multiple factors of the position, the speed and the direction of the vehicle are considered through the comprehensive score; the parking behavior of the vehicle can be accurately analyzed, the misjudgment condition of the parking space lock caused by the wandering of the vehicle in the empty parking space is reduced, the condition that the vehicle cannot be parked in the empty parking space due to the misjudgment is avoided, the experience effect of a vehicle owner is improved, meanwhile, when the vehicle approaches the empty parking space, the empty parking space lock unlocks the parking space in advance according to the comprehensive score, and the user experience is improved. The waiting time after the vehicle arrives at the parking space is reduced, and the parking efficiency is improved.

The present invention relates to the field of crowd intelligence sensing technology, and in particular to a crowd intelligence sensing participant selection method and system based on cooperation willingness prediction. It includes obtaining a participant data set, obtaining an estimated cooperation cost through platform tasks; constructing a multi-armed bandit problem model, virtually employing the participant data set, and calculating the participant pair exploration rate; setting a threshold for the participant pair exploration rate, processing the data set using a link prediction model to obtain a predicted cooperation cost, and obtaining a converged cooperation cost based on the employed worker pair data and the estimated cooperation cost or predicted cooperation cost; obtaining a participant set using a minimum cost priority participant selection strategy; and updating the estimated cooperation cost and the employed worker pair data. It solves the problem of how the platform selects participants to minimize the cost of task completion when the relationship between the participants is not completely known or there is no historical data of a large number of participant pairs cooperating.

The anti-collision structure of the stacking door relates to the technical field of stacking door collision prevention and comprises a door frame and a door curtain, sliding grooves are formed in the two sides of the door frame, first supporting plates are connected to the two sliding grooves in a sliding mode, a first motor is installed on the side, away from the door curtain, of each first supporting plate, and a second motor is installed on the side, away from the door curtain, of each first supporting plate. The output end of the first motor is connected with a first lead screw, the first lead screw is rotationally connected with the first supporting plate, the first lead screw is in threaded connection with a first sliding block, the side, away from the door curtain, of the first sliding block is connected with a damping spring, and the end, away from the first sliding block, of the damping spring is fixedly connected with a baffle. By installing the baffle with the adjustable position, a protection layer can be formed at the front end and the two sides of the stacking door, the collision face is reduced, and therefore the collision risk is effectively reduced, the baffle plays a buffering role when collided through the arranged damping spring, the impact force is reduced, and the damage risk is reduced.

The application discloses a kind of based on artificial intelligence's consumer goods harmful substance intelligent screening method, it is related to artificial intelligence technical field, it is difficult to effectively identify and analyze the technical problem that a large number of natural language, commercial name or commonly known as etc. non-standardized description exists in ingredient list, it is also difficult to accurately convert these fuzzy text information into the standardized terminology that can be accurately matched by regulation database;Including the following steps: the fuzzy ingredient list containing natural language or commercial name description is preprocessed by multi-task text regularization model;Then, utilize embedding semantic similarity model and the multi-evidence fusion presumption unit of improved attention mechanism, fuzzy component is intelligently analyzed and matched, and standard substance identification and confidence are output;Finally, through multi-agent risk decision framework combines real-time regulation database, dynamic compliance screening and risk assessment are carried out;The application improves the intelligent level of consumer goods safety compliance management.

The invention provides a block chain technology applicability evaluation method for a large-scale manufacturing industry, and relates to the technical field of block chains. According to the method, the expected income of the enterprise when the block chain is not adopted or the block chain is adopted is calculated on the basis of the game theory from the possible recall event as a starting point, some references are provided for the enterprise, and the enterprise can make a more reasonable decision better for the problem of whether to adopt the block chain or not. In the data processing link, processing such as least squares and normalization needs to be carried out on input data to obtain some important parameters; suggestions are given from the perspective of the game theory, calculation can be carried out on the basis of estimating behaviors of other enterprises, and the result can better reflect reality.

The invention discloses a new energy station digital attack and defense environment simulation method and device, relates to the salinity gradient energy station technology field, introduces a digital simulation method to realize comprehensive monitoring and analysis of ocean dynamics and climate conditions, and provides abundant data support for managers. The data not only help to identify the change trend of the power generation efficiency, but also provides a scientific basis for the decision-making process. A manager can make a more reasonable decision based on a data analysis result, and an alarm mechanism can respond to the conditions of environment change and equipment performance reduction in time. Through monitoring and identification of potential risks, the system can rapidly take measures when the power generation efficiency is reduced, equipment faults or extreme climate changes occur, and managers can reasonably allocate equipment and personnel through analysis of the power generation efficiency and requirements of different areas, so that unnecessary operation cost is avoided. And effective resource configuration improves the working efficiency.