434results about How to "Facilitate decision-making" patented technology

The invention provides a three-dimensional geological modeling parameter optimization method and system based on deep learning, and relates to the field of geological prospecting and modeling.The method comprises the steps that drilling data and remote sensing data of a geological prospecting area are obtained, and multi-source geological data of three-dimensional geological modeling are formed; generating geological profile map data of the geological features according to the drilling data, generating landform profile map data corresponding to the topographic features according to the remote sensing data, and extracting boundary information of the geological exploration area; carrying out gridding processing on a three-dimensional space, dividing a geologic body of the geological exploration area into a plurality of small grid units, and defining geological modeling parameters; the preliminary three-dimensional geological grid model is constructed according to the geological profile map data, the landform contour map data and the boundary information, the size and the resolution of the grid are dynamically adjusted according to the geological change area, the three-dimensional geological model is obtained through optimization and adjustment, the modeling precision is improved, manual intervention is reduced, and computing resources are saved.

The invention provides an intelligent power distribution cabinet safety early warning method, system and device and a storage medium, and relates to the field of electric power. The method comprises the steps of obtaining multi-source heterogeneous operation parameters of the intelligent power distribution cabinet, and performing feature extraction on the multi-source heterogeneous operation parameters to obtain operation feature parameters; performing parameter fusion on the operation characteristic parameters to obtain fused characteristic parameters, and inputting the fused characteristic parameters into a preset fault early warning model to obtain fault early warning data; and inputting the fault early warning data into a preset rule matching engine for rule matching to obtain a matching result, and generating an early warning signal according to the matching result. According to the method, the accuracy and timeliness of safety early warning of the intelligent power distribution cabinet are improved.

The invention provides an intelligent garden environment monitoring data analysis method and system. Sensors are deployed to collect temperature, humidity and other data in real time to form a data set. Analyzing an environment parameter change mode of each region by adopting a time sequence clustering algorithm, and determining characteristics and trends; and carrying out dimension reduction on the data by using a multi-dimensional scale transformation algorithm to a three-dimensional space for visualization, and generating an environment state map. And evaluating the plant fitness in combination with a plant physiological ecology knowledge base, and generating a scoring matrix to guide the preliminary layout scheme. And dynamically adjusting the layout by applying reinforcement learning, selecting an optimal scheme, and predicting a future environmental evolution path based on the optimal scheme and historical data. An automatic control system framework is constructed, a self-adaptive control algorithm is designed, intelligent regulation and control of plant layout are realized, garden management efficiency is improved, and ecological protection and sustainable development are promoted. Therefore, full-chain intelligent management from environment monitoring to plant layout optimization is realized, and the intelligent level of garden environment monitoring and management is remarkably improved.

The invention relates to the technical field of natural disaster early warning, in particular to a natural disaster intelligent early warning system based on image processing. Comprising a multi-modal sensor data acquisition module, an image and data preprocessing module, a deep learning and confrontation generative network identification module, a virtual reality disaster situation simulation module, a disaster prediction and trend analysis module, an alarm and notification module, a decision support and emergency response module, and a data storage, management and feedback optimization module. The multi-modal sensor data acquisition module is used for acquiring and fusing various sensor data; the image and data preprocessing module is used for processing multi-source data and aligning and fusing multi-modal data. Through fusion of a multi-modal sensor and a deep learning algorithm, the system improves the recognition capability in a complex environment, can maintain high-precision disaster monitoring under severe conditions, reduces false alarm and missing alarm, and remarkably improves the reliability of an early warning system.

The invention belongs to the technical field of artificial intelligence and big data analysis, and particularly relates to a scenic spot passenger flow trend prediction analysis method based on an AI big language model. According to the method, big data and artificial intelligence technologies are utilized, LBS data, scenic spot data, holiday and festival information, third-party maps, weather data and other multi-source data are comprehensively applied, a comprehensive multi-dimensional knowledge base is constructed, and meanwhile, the knowledge search and generation capability is improved by utilizing an RAG technology; through model pre-training and fine tuning, the universal model capability is developed, and a multi-agent system is further constructed; based on the natural language processing capability of the AI large language model, the LLM can intelligently analyze the text input of the scenic region administrator, drive the cooperative work among multiple agents, and achieve the intelligent prediction and analysis of the scenic region passenger flow trend. The method can more accurately predict the passenger flow trend of the scenic area.

The invention relates to the technical field of geological disaster evaluation, in particular to a mine geological disaster risk comprehensive evaluation method considering spatial distribution characteristics. The method comprises the following steps: acquiring mine multi-source spatial data; performing mine geological disaster risk distribution analysis on the mine multi-source spatial data to generate a mine geological risk distribution map; performing spatial region potential disaster probability analysis on the mine geological risk distribution map to generate mine geological distribution disaster probability data; and performing high-complexity spatial region screening on the mine geological risk distribution map through the mine geological distribution disaster probability data to obtain high-complexity spatial region data. According to the method, multi-source data are integrated, spatial distribution characteristics are emphasized, active and inactive areas are distinguished, ecological and water body changes are analyzed, and the accuracy and comprehensiveness of mine geological disaster risk comprehensive evaluation are improved.

The invention discloses a coal mine geological disaster and environment early warning method based on multi-source monitoring data fusion. The method comprises the following steps: determining a mine water disaster early warning evaluation index, a mine pressure disaster early warning evaluation index and a geological environment evolution early warning index; a corresponding first early warning threshold value is set; data are acquired and preprocessed, and a mine water disaster early warning model, a mine pressure disaster early warning model and a geological environment evolution early warning model are established; collecting new data to carry out iterative optimization on the model, and correcting an early warning threshold value; and integrating the iteratively optimized models into an early warning index system library, collecting and fusing various monitoring system and production dynamic data of the mine, and inputting the data into the early warning index system library for coal mine geological disaster and environment early warning analysis. The method solves the problems that in the prior art, multi-source monitoring data cannot be fully fused, an early warning model lacks self-learning and optimization capabilities, and an early warning threshold cannot be dynamically adjusted along with the change of a mining environment.

The invention relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses an information processing method based on sentiment analysis, and the method comprises the steps: collecting data and information which comprise text data, user behavior data and event information; performing natural language processing and sentiment analysis on the text data, and identifying sentiment features of the text data; analyzing the user behavior data to determine a behavior pattern; analyzing the event information to determine an event change trend; generating an emotional state index based on the emotional characteristics, the behavior pattern and the event change trend; monitoring the online platform to obtain user emotion feedback; comprehensively analyzing the emotion state index and the user emotion feedback to generate an emotion analysis result; and generating target output content based on the emotion analysis result. Through combination of emotion analysis, behavior data and event change trends, the emotion state of the user can be captured in real time, decision suggestions are dynamically adjusted, and market or environment changes are effectively coped with.

The invention relates to the technical field of data transmission and updating, in particular to a data transmission and updating system and method of a cloud mobile phone. According to the technical scheme, the system comprises an intelligent network monitoring module, a dynamic bandwidth allocation module, a server load balancing module, an intelligent data synchronization module and a cache management module, and the intelligent network monitoring module monitors network conditions, including network bandwidth, network congestion degree and network stability, between a user terminal and a cloud server in real time. Through the synergistic effect of the intelligent network monitoring module, the dynamic bandwidth allocation module, the server load balancing module, the intelligent data synchronization module, the cache management module and the like, the network condition is accurately monitored, the bandwidth is reasonably allocated, the server load is optimized, the data synchronization and completeness are ensured, and the cache efficiency is improved; stable and reliable network connection, preferential key data transmission and rapid data synchronization and loading are brought to users, and the use experience of the cloud mobile phone and the overall performance of the system are greatly improved.

The invention relates to the technical field of photovoltaic system monitoring, in particular to a photovoltaic power station fault supervision system based on a neural network model, and the system comprises a data collection module which is used for collecting string current data and voltage data; the IV analysis module is used for obtaining a fault diagnosis result; the digital power station modeling module is used for identifying and numbering the coordinates of the photovoltaic string; an inspection route planning module; the unmanned aerial vehicle inspection module is used for performing inspection control on the unmanned aerial vehicle to obtain inspection data; an inspection data uploading module; the CV analysis module is used for performing defect identification on the inspection data to obtain a defect identification result; the fusion inspection analysis module is used for outputting a fusion diagnosis result and also used for selecting a final fusion diagnosis result; the visual presentation module is used for carrying out visual presentation; the report module is used for generating a report; and the navigation defect elimination module is used for carrying out navigation defect elimination. According to the invention, the automation level and inspection efficiency of fault diagnosis are improved.

The invention discloses a visual network freight platform transportation transaction management system. The system comprises an order management module, a vehicle scheduling module, a transportation monitoring module, a transaction settlement module and a visual display module. Each module has innovative functions, such as order intelligent classification, vehicle optimization scheduling algorithm to minimize comprehensive cost, real-time monitoring and accurate settlement. The corresponding management method adopts advanced technologies to ensure efficient operation of each link, including block chain recording, intelligent contract settlement and the like. The whole system realizes efficient management, real-time monitoring and convenient visualization of transportation transaction of a freight platform, and improves the efficiency and quality of logistics transportation; according to the visual network freight platform management system, efficient and accurate order management is realized, vehicle scheduling is optimized, the cost is reduced, comprehensive and real-time transportation monitoring is guaranteed to be safe, safe and accurate transaction settlement is realized, convenient and visual display is convenient to make decisions, and the logistics transportation efficiency, quality and user experience are improved.

The invention provides a construction method, a control method and a system of a flight control network, and the method comprises the steps: employing a depth deterministic strategy gradient algorithm, building an attitude control network of an unmanned aerial vehicle in a flight process based on the state information and a target flight value of the unmanned aerial vehicle in the flight process, employing a depth reinforcement learning algorithm, and carrying out the attitude control of the unmanned aerial vehicle in the flight process. Based on the state information and the information of the environment where the unmanned aerial vehicle is located, a path planning network and an intelligent avoidance network of the unmanned aerial vehicle in the flight process are built respectively; based on the attitude control network, the path planning network and the intelligent avoidance network, constructing a hierarchical deep reinforcement learning network of the unmanned aerial vehicle in the flight process; and training the hierarchical deep reinforcement learning network by adopting historical flight data of the unmanned aerial vehicle in different flight environments and a preset flight reward mechanism to obtain a flight control network of the unmanned aerial vehicle. According to the invention, the adaptability and flexibility of the unmanned aerial vehicle in the flight process can be improved.

The invention relates to a network data leakage monitoring system and method, and the system comprises a data collection module, a data preprocessing module, an anomaly detection module, a risk assessment module and an alarm module. The data acquisition module is used for acquiring network data from a network data source in real time; the data preprocessing module is used for cleaning and standardizing the acquired network data to obtain preprocessed network data; the anomaly detection module identifies an anomaly mode and calculates a confidence score according to the preprocessed network data by using a pre-trained neural network model; the risk assessment module assesses the potential data leakage risk in real time based on the type of the abnormal mode and the confidence score, and generates a comprehensive risk assessment result; and the alarm module generates and sends alarm information when the comprehensive risk assessment result exceeds a preset threshold value. The system can accurately detect and evaluate the network data leakage risk in real time, and provides effective security alarm.

The invention discloses an intelligent online reservation management system, and relates to the technical field of reservation management, the system comprises a login module, an outpatient service information display module, an online reservation module, a data analysis module and a customer service module, the online reservation module comprises an intelligent triage unit, a number source distribution unit and a reservation priority unit, the intelligent triage unit recommends corresponding departments for patients, the number source allocation unit is used for allocating number source numbers for the patients, the corresponding departments are recommended for the patients through the intelligent triage unit, the number sources are dynamically allocated through the number source allocation unit according to the capacities of the departments, the reserved numbers and the illness state importance degrees, reasonable allocation of resources is ensured, and the number source allocation efficiency is improved. The appointment priority unit determines a doctor-seeing sequence according to factors such as illness state importance and fulfillment rate of the patients, preferentially processes emergency and important patients, and promotes the patients to fulfill the appointment on time by rating the patients, so that the effect of online appointment experience of the patients is improved.

The invention provides a robot system based on self-adaptive cruise and remote operation and a control method thereof, and belongs to the field of robot inspection, the system comprises a six-axis mechanical arm, an intelligent inspection robot, an automatic inspection task module and a remote teleoperation control unit; the intelligent inspection robot carries a six-axis mechanical arm and an automatic inspection task module, the automatic inspection task module comprises a preset inspection task, the remote teleoperation control unit sends a control instruction of an operator to the intelligent inspection robot through wireless communication, and the control instruction comprises inspection path planning and operation tasks. The intelligent inspection robot performs an inspection task according to a preset inspection task of the automatic inspection task module or a control instruction sent by the remote operation control unit, and the six-axis mechanical arm is used for executing a target operation task. Through integration of advanced sensor technology, image processing technology, artificial intelligence algorithm and wireless communication technology, efficient, accurate and safe inspection of a complex environment is realized.

The invention relates to the technical field of power industry, and discloses a distribution network three-dimensional visualization dynamic operation and maintenance system development method and system, and the method comprises the steps: scanning power distribution equipment and environment, and obtaining three-dimensional point cloud data; preprocessing and feature extraction are carried out on the point cloud data, and a three-dimensional model of the power distribution network equipment is constructed. And importing the three-dimensional model and the real-time monitoring data of the power distribution network into a visual platform, and evaluating the running state of the power distribution network. Different early warning mechanisms are set, and abnormity can be found in time. According to the invention, the fault detection and response speed is improved. And the accuracy of state evaluation and fault positioning is improved. In combination with machine learning and multi-level state evaluation, the intelligent operation and maintenance of the power distribution network are realized, risks can be automatically identified, early warning can be generated, manual operation errors are reduced, and the stability and safety of the system are improved. Through the three-dimensional visualization platform, operation and maintenance personnel can understand the state and the fault condition of the power distribution network more visually, the decision process is optimized, and the working efficiency is improved.

The invention discloses an ecological carbon sink measurement and estimation data fusion platform for the thermal power industry, and belongs to the technical field of data fusion. The method is realized through five steps of a data acquisition module, a carbon sink measurement and calculation module, a prediction model construction module, a data fusion module and a decision support module. According to the invention, through butt joint with a thermal power enterprise energy management system, production data including generating capacity and coal consumption are acquired in real time, and the timeliness of the data is greatly improved. Ecological environment data and meteorological data around a thermal power enterprise are comprehensively collected by using a satellite remote sensing technology and a ground sensor, and the comprehensiveness and accuracy of the data are ensured. According to the method, a dynamic adjustment mechanism is adopted, and the pre-estimation model is continuously optimized according to real-time data feedback, so that the model can adapt to the change of the environment. And time sequence analysis and regression analysis are combined to form an integrated model, so that the accuracy and reliability of a pre-estimation result are greatly improved.

The invention relates to a data calibration method suitable for an atmospheric pollutant sensor deployed in an embedded terminal. The method comprises the following steps: acquiring original response data of different sensors with different concentrations in different atmospheric environments after temperature compensation, and corresponding temperature, humidity, air pressure data and sampling time; collecting real concentration values of detection objects corresponding to different sensors at corresponding moments given by a national control station or a provincial control station; preprocessing the obtained data to obtain a final data set; a random forest algorithm is used as a teacher model, a gradient elevator algorithm is used as a student model, and hyper-parameters of the gradient elevator algorithm and the random forest algorithm are optimized by using a hedera helix algorithm; substituting a hyper-parameter optimal solution found by optimization into a corresponding algorithm, and retraining the teacher model and the student model by using hyper-parameter combinations; and deploying, updating and displaying the model in the embedded equipment. And the model size is reduced to realize the deployment of the embedded end on the premise of ensuring the accuracy.

The invention discloses a mine high and steep slope landslide machine learning prediction method based on an unmanned aerial vehicle technology and multi-source data fusion. The method comprises the steps that a high-resolution digital surface model is shot by an unmanned aerial vehicle; removing non-topographic features from the digital surface model data in ArcGIS software, then converting the digital surface model data into digital elevation model data, obtaining a surface refining model of the surface mine in the digital elevation model, taking the surface refining model as a constraint to trim the surface of the three-dimensional geologic model, and fusing factors of ArcGIS software and three-dimensional geologic model analysis by using Python to obtain a three-dimensional geologic model surface refining model; complete slope data are constructed; inputting the fused complete side slope data into a machine learning algorithm training model, and generating a side slope landslide susceptibility prediction model; the optimized model is used for carrying out landslide susceptibility prediction on new slope data. According to the method, various factors influencing the slope stability can be comprehensively analyzed, high efficiency can be kept during large-scale data processing, and the slope stability in mine engineering can be evaluated more accurately.

The invention relates to a platform management method and system based on low code development, and the method comprises the steps: obtaining a plurality of heterogeneous data sources, extracting first data from the plurality of heterogeneous data sources, and carrying out the preprocessing of the first data, and obtaining standard data used for constructing a knowledge graph; and acquiring a plurality of different entities and entity relationships among the different entities from the standard data, and constructing the knowledge graph according to the plurality of different entities and the entity relationships among the different entities. And mapping entity nodes in the knowledge graph and entity relationships among different entity nodes to a low-dimensional space through a graph embedding model to perform vector representation so as to extract global knowledge including the entity relationships among different entity nodes and a network topology structure. And acquiring a predefined business process and a plurality of corresponding decision points when the business process is executed, and evaluating an execution result of the business process by executing global knowledge and a current business process variable to obtain a corresponding guidance decision.

The invention relates to a multi-unmanned battle vessel cooperative control optimization method based on machine learning and an improved genetic algorithm, and the method comprises the following steps: obtaining unmanned vessel state data and environment data, carrying out the preprocessing, encrypting task operation parameters, and transmitting the encrypted task operation parameters to an unmanned vessel; determining task properties and requirements, and monitoring a state in real time to determine a matching coefficient; constructing a memory function and proficiency, taking the memory function and the proficiency as input feature vectors together with other data, performing decision making on task allocation by using a reinforcement learning algorithm, and performing optimization by using a multi-objective optimization algorithm to obtain an optimized task allocation result; performing feedback reinforcement learning by using an optimization result, and optimizing a task allocation decision; and initializing an unmanned ship cluster, and optimizing the hybrid formation by using an improved genetic algorithm. Compared with the prior art, the method can dynamically adapt to complex and changeable task requirements, improves the accuracy and reliability of task allocation, and remarkably improves the performance and efficiency of the whole formation through real-time formation structure optimization and learning process iteration.

The invention discloses a GNN and GRU-based bridge structure time-varying fire response prediction method and system, and the method comprises the steps: building a to-be-detected bridge structure FEM model, randomly generating a fire curve, and generating a data set through the combination of the FEM model and the fire curve; establishing a graph structure according to the FEM model, and embedding static and dynamic characteristics for temperature, stress and displacement prediction in nodes of the graph structure; a message transmission layer is constructed, the GNN is responsible for space information transmission, and the GRU is responsible for time information transmission; simulating a heat conduction formula by using a message passing formula combining the two; receiving node input vectors including temperature, stress and displacement prediction characteristics from the graph structure; determining a loss function and separately training GNN + GRU networks for temperature, stress and displacement prediction; and when the to-be-detected bridge structure encounters a fire, acquiring a fire response condition of the to-be-detected bridge structure through the trained model. The problem that a traditional GNN model can only predict non-time-varying boundary fire response is solved.

The invention provides a financial bill risk value calculation method and device, equipment and a storage medium, relates to the technical field of financial bill risk assessment, and can comprehensively and deeply mine potential risks in financial bills by integrating various anomaly detection models and classification models. The anomaly detection model can accurately identify the anomaly in the data, and provides a high-quality risk data sample for the classification model, thereby ensuring the accuracy and reliability of risk probability calculation. The risk probability is predicted by using the pre-trained risk classification model, so that the calculation efficiency is greatly improved. Compared with a traditional risk assessment method, the method does not need to manually carry out tedious data analysis and judgment, and the risk identification period is remarkably shortened. And the risk probability is converted into the corresponding risk value, so that the risk assessment result is more visual and easy to understand. Risk management and decision making of related financial departments are facilitated, and comprehensive control of financial risk conditions of enterprises is facilitated.

The invention provides a pressure pipeline inspection and detection digital system based on the Internet of Things. Comprises: an axonometric mapping module for drawing a three-dimensional model by using an axonometric mapping instrument to obtain an initial axonometric map; comprising an automatic drawing mode and a manual drawing mode. And the positioning module is used for collecting real-time geographic position information of the pipeline and marking and recording the real-time geographic position information in the initial axonometric map to obtain a complete axonometric map. And the data processing module is used for acquiring, storing and analyzing actual detection data of the pipeline. And the information association module is used for associating the macroscopic inspection information with the safety accessory information of the pipeline on the complete axonometric map. The detection report module is used for generating a detection record and a detection report; an interaction terminal, a tablet computer and a two-dimensional code printer. According to the invention, data can be automatically collected, processed and analyzed, personal errors are reduced, and the working efficiency of inspection work is improved; and digital management of a pressure pipeline inspection and detection process is realized.

The invention relates to a construction method of an anti-typhoon sea wave sea-crossing steel trestle and platform adjacent to a residential building. The construction method comprises the steps that firstly, deep fusion application of BIM and VR is conducted; step 2, surveying and setting out; step 3, embedding steel bars and pouring strip-shaped concrete; fourthly, the steel pipe piles are transported and lengthened; fifthly, steel pipe piles are driven; sixthly, parallel connection and diagonal bridging construction is conducted; seventhly, bearing beams are installed; eighthly, longitudinal beams are installed; ninthly, the distribution beams are installed; tenthly, bridge deck slabs and guardrails are installed; and 11, an intelligent monitoring and early warning system.

The invention discloses an intelligent financial consultation system and method based on a large language model, and the system comprises the steps: receiving a multi-mode financial consultation request of a customer, analyzing the input of the customer through the natural language processing and computer vision technology, extracting key information and intention, and dynamically constructing and updating a customer portrait. And specific requirements of clients can be accurately identified. And after the identified demands are stored in a problem database, the system performs real-time analysis on historical data, market dynamics and customer behaviors by using a machine learning algorithm, thereby automatically identifying and predicting potential demands and market trends of customers. Afterwards, on the basis of the prediction results, the system uses a large language model to generate personalized and multi-dimensional financial suggestions, including different risk levels and profit expectations, so as to help customers to make better decisions.

The invention discloses an intelligent agriculture platform construction method based on big data analysis and a visual model, and the method comprises the steps: arranging a sensor network, and monitoring the key environment parameters of a farmland in real time; using the unmanned aerial vehicle to shoot crop images; according to the shot crop image, identifying a part with pest and disease damage characteristics in the crop image; deducing the age of the crop leaf according to the shot crop image; a cloud platform is built, farmland related data is displayed in an integrated mode through the cloud platform, and decision suggestions are provided based on big data analysis according to all data input into the cloud platform. The system integrates multiple functions such as farmland data display, unmanned aerial vehicle fixed-point automatic cruise, disease and pest recognition and early warning, agricultural question and answer assistant and the like, and provides one-stop intelligent agricultural management service for farmers. The platform provides scientific guidance suggestions for agricultural production based on big data analysis, and assists in improving yield and quality.

The invention relates to a power grid fluctuation optimization method and system based on air conditioner electric quantity prediction, and the method comprises the following steps: S1, collecting historical power consumption data, including historical weather data and user power consumption behavior data, and measuring and calculating the data of the use condition of an air conditioner in a user region; s2, selecting key weather characteristics influencing the use of the air conditioner, and constructing an air conditioner electric quantity prediction model based on the calculated historical air conditioner electric quantity to predict the future electric quantity demand of the air conditioner; s3, on the basis of the predicted future electric quantity demand of the air conditioner, power grid load optimization is carried out on the basis of a DQN algorithm model; and S4, performing energy scheduling optimization by adopting a heuristic algorithm, maximizing the use of renewable energy sources and the utilization rate of the energy storage system, and providing personalized incentive measures and adjusting peak power load by utilizing user behavior analysis. Electric power fluctuation caused by air conditioner use is effectively optimized, and the stability and efficiency of a power grid are improved.

The invention relates to the technical field of environment monitoring, in particular to a pollution source online monitoring and analysis system and method, and the system comprises a map building unit which is used for carrying out the three-dimensional map modeling of a target monitoring area; the man-machine interaction unit is used for issuing a monitoring control command; the acquisition monitoring unit is used for calculating data sampling density and acquiring environment data; the threshold value early warning unit is used for comparing data to judge whether the pollution source is a suspected pollution source or not; the pollution confirmation unit is used for setting an identification framework for the received environmental data of the suspected pollution source so as to judge whether the suspected pollution source is a real pollution source or not; and the diffusion analysis unit is used for matching the environmental data of the real pollution source with the pollutant diffusion model to obtain the diffusion range of the real pollution source. According to the invention, through environment data acquisition of the unmanned ground vehicle, multiple environment data of a water area and soil can be obtained in real time in a target monitoring area, so that pollution conditions can be found and responded in time, and potential hazards caused by environment pollution are reduced.

The invention discloses a hierarchical optimization-based water supply network optimization transformation method. The method comprises the following steps of: finding out a node with the lowest free water head in a water supply network as a control point; finding a shortest distance path from the control point to the water plant as a main control path; searching a pipe diameter optimization scheme of the pipe section on the main control path by taking the condition that delta FNPV is greater than 0 as a target; if the control points change after the current optimized pipe diameter optimization scheme is executed, finding out all points lower than the free water heads of the original control points as candidate control points, finding out the shortest distance path from the candidate control points to the water plant as a candidate control path, and searching the pipe diameter optimization scheme of the pipe section on the candidate control path; and forming a combined transformation scheme together with the current optimized pipe diameter optimization scheme. On the basis of the pipe diameter optimization scheme of the replacement pipeline, a parallel pipeline optimization scheme is selected. And finally outputting a feasible pipe diameter optimization scheme list which comprises a replacement pipeline scheme and a parallel pipeline scheme. The method is high in efficiency, and the deficiency of human experience can be avoided.