907results about How to "Timely response" patented technology

The invention provides a multi-modal large model construction and operation method and system for reservoir dam safety. The method comprises the following steps: constructing a training set, wherein the training set comprises a single sample; multi-modal data as model input: sensor data arranged on the dam, historical hidden danger records, engineering technology documents formed in various stages of planning, design, construction and operation of the dam, information resources related to dam safety, geometric and material information of the BIM model, timestamps and spatial position information; the probability value of occurrence of the hidden danger event serving as the training label is obtained; the training set is adopted to train a multi-mode pre-training model based on a Transform framework; performing real-time prediction and early warning on potential safety hazards of the dam; and calculating the actual influence degree, the comprehensive risk score, the dynamically set early warning threshold and the emergency measure suggestion of the hidden danger event according to the probability value of whether the hidden danger event occurs or not output by the model. The scientificity and effectiveness of dam safety monitoring are comprehensively improved.

The invention relates to the field of intelligent traffic management, and discloses a big data intelligent real-time processing analysis method and system, and the method comprises the following steps: obtaining structured, semi-structured and unstructured multi-modal traffic data; preprocessing the data and extracting feature vectors; fusing the feature vectors of different modes through a self-attention mechanism to generate a unified fusion feature; performing time sequence modeling on the fusion features by using a long short-term memory network, and predicting traffic flow; based on a traffic flow prediction result, a deep reinforcement learning algorithm is adopted to dynamically optimize the duration and phase of a traffic signal lamp, and meanwhile, an optimal path is provided for a vehicle in combination with a path recommendation algorithm; and applying the optimized signal control and path recommendation to a real-time traffic management system. According to the invention, intelligent fusion of multi-modal data, accurate traffic flow prediction and intelligent traffic signal control and path recommendation can be realized, and the real-time performance and efficiency of traffic management are improved.

The invention relates to the technical field of fire monitoring and prediction, in particular to a multi-source fire data fusion and fire analysis and prediction system which comprises a data acquisition module, a data preprocessing module, a data fusion module, a fire analysis module and an early warning module. Wherein the data acquisition module is used for acquiring fire data from various sources; the data preprocessing module is used for preprocessing the fire data; the data fusion module is used for carrying out fusion processing on the multi-source data output by the data preprocessing module; the fire analysis module is used for identifying the current situation of the fire and the position of a fire source and predicting the spreading direction of the fire; and the early warning module is used for sending out an early warning signal in time. According to the invention, through the multi-source fire data fusion and intelligent analysis and prediction technology, the occurrence, spreading and risk level of the fire can be accurately monitored in real time, so that the timeliness and accuracy of fire emergency response are effectively improved.

The invention discloses an emotional understanding and feedback method for a full-duplex interactive question-answering digital person, and the method comprises the steps: S1, arousing interaction: enabling a digital person of a digital person system to enter an interaction state in a human body detection arousing or voice instruction arousing manner by a user; s2, state analysis: the digital human system judges the state of the user in real time by collecting image data and audio data of the user, and identifies and analyzes the direction, emotion and speech content of the user in real time; and S3, feedback output: the digital human system drives the digital human in voice, expression and body movement through a screen in real time according to the analysis result of the direction, emotion and speech content of the user, and feeds back the result to the user in a manner of adding auxiliary characters, images and videos. The method has the advantages that the emotional state of the user is accurately judged through an advanced algorithm, digital human expressions, body actions and voice feedback are driven in real time according to the emotion and speech content of the user, response is made in time, and a real-person-like real-time interaction scene is achieved.

The invention discloses a cloud computer end-to-end fault root cause analysis method and system, and relates to the technical field of operation and maintenance data analysis, and the method comprises the steps: deploying data collectors on a virtual resource pool, a multi-stage cloud management platform and a cloud computer terminal, and monitoring and collecting various operation and maintenance data in real time; a unified operation and maintenance management center is introduced to complete the following work: providing management and configuration services of the data collector; receiving and summarizing operation and maintenance data; various AI models are accessed, operation and maintenance data from different sources are distributed based on the types of the AI models, the AI models perform deep analysis and diagnosis on the operation and maintenance data, and potential fault modes or abnormal behaviors are identified; customizing an alarm strategy, an alarm processing flow and an alarm notification mode; and introducing an AIOps service to generate an alarm and fault analysis report according to the analysis and diagnosis results of the AI model. According to the method, the service quality of the end-to-end full link of the cloud computer is monitored in real time, so that faults can be quickly positioned, early warning can be performed in advance, and the operation and maintenance cost and the fault risk are reduced.

The invention discloses a platform and a monitoring method for monitoring the home safety of old people based on decision engine multi-device linkage, and the method comprises the steps: carrying out the data preprocessing according to the human body home data collected by an edge computing device; according to the human body home data, multi-modal data fusion is carried out to generate behavior identification data, and the multi-modal data fusion comprises one or more of time sequence alignment, modal completion and feature extraction; inputting the behavior recognition data into a local recognition model for real-time behavior analysis, and inputting the behavior recognition data into a cloud decision engine for multi-source data comprehensive deep analysis; and according to an output result of the local identification model and / or the cloud decision engine, performing operation of matching the smart home equipment and / or home behavior alarm processing. According to the invention, multi-device data integration and cooperative linkage measurement can be adopted to monitor the home safety of the elderly.

The invention relates to the technical field of financial data processing, in particular to a data processing method and system based on financial information bills. The data processing method comprises the following steps: acquiring multi-modal financial information bill data; and performing bill modal decomposition on the multi-modal financial information bill data to generate financial information bill modal decomposition data. Bond information price income prediction needs a large amount of financial data, such as market interest rate, bond expiration date, face interest rate and the like, the data are similar to the data form of bill modal decomposition, and a more accurate model can be obtained through standardization and feature extraction of multi-modal data. Therefore, valuable input is provided for bond price and income prediction. According to the invention, through multi-modal data fusion, counterfeited feature comparison modeling, abnormal bill clustering analysis and dynamic risk assessment, the security and accuracy of financial bill identification are improved.

The invention relates to the technical field of instrument calibration, and particularly discloses a gas detection instrument prediction calibration system and method. The analysis module is used for determining a detection method of the zero drift phenomenon of the electrochemical gas sensor to be calibrated based on the use environment type; the calibration module is used for determining real-time calibration or periodic calibration of the electrochemical gas sensor in response to the occurrence of the zero drift phenomenon and the abnormal opening state of the electrochemical gas sensor; the evaluation module is used for determining the long-term zero point stability degree or the real-time signal fluctuation degree to evaluate the calibration precision according to the calibration method; the adjusting module is used for determining whether to update the environment compensation coefficient and adjust the calibration interval duration for the next calibration according to the evaluation result of the evaluation module; according to the invention, the accuracy of analyzing the use environment of the electrochemical gas sensor is improved, so that the accuracy of analyzing the zero drift phenomenon is improved, and accurate calibration is realized.

The invention relates to the technical field of environment automatic control, in particular to an environment regulation and control method and system for automatic control of a breeding house, and the method comprises the following steps: setting the operation parameters of a temperature and humidity controller and lighting equipment in the breeding house, adjusting the equipment to an initial state, checking that the parameters meet the standard during starting, and generating a baseline environment configuration. According to the invention, through environment monitoring and dynamic adjustment strategies, accurate and automatic breeding house environment control, management efficiency and animal welfare are improved, operation parameters are set and verified to be in a standard range during starting, errors are reduced, an optimal environment is maintained, key parameters such as temperature and humidity are continuously tracked, future changes are reflected and predicted in time, and the system is suitable for large-scale popularization and application. The control strategy is adjusted in advance, potential hazards to animals are avoided, equipment is adjusted according to the change trend, resource use is optimized, energy consumption is reduced, the environment needed by the animals is guaranteed, diseases are reduced, higher flexibility and accuracy are achieved for coping with sudden changes, and breeding efficiency and animal welfare improvement are guaranteed.

The invention provides an LED spectrum fingerprint open set identification method based on Transform and support vector data description and related equipment. The method comprises the following steps: collecting an optical signal emitted by at least one LED device, converting the optical signal into spectral data, and dividing the spectral data into a training set and a test set; inputting the training set into a preset feature extraction network to carry out network training so as to learn and obtain a spectral feature vector; wherein the feature extraction network comprises a Transform branch, a CNN (Convolutional Neural Network) branch and a feature interaction module; based on the spectral feature vectors of the training set, constructing a hypersphere model by using an SVDD algorithm, and optimizing parameters of the hypersphere model to obtain an optimal hypersphere model; and inputting test samples in a test set into the trained feature extraction network to obtain corresponding spectral feature vectors, and inputting the spectral feature vectors of the test samples into the optimal hypersphere model for classification.

The invention discloses a gas leakage point leakage detection system and method based on an e-dog, and belongs to the technical field of gas leakage detection. Through the autonomous navigation capability of the mobile platform module, the system can automatically avoid obstacles in a complex environment and inspect along a predetermined path, optimizes the inspection path in real time after finding a gas leakage point, improves the inspection efficiency, guarantees the high-density inspection of a key area, and the rapid movement and precise positioning capability of an e-dog, and improves the inspection efficiency. The gas leakage detection is quicker and more efficient, the risk that personnel enter a dangerous environment is reduced, the system has the functions of autonomous navigation, obstacle avoidance, remote control and the like, the intelligent level of detection is improved, the system is suitable for detection requirements of various complex environments and different types of gases, and through cooperative work of the gas detection module and the data acquisition and processing module, the detection efficiency is improved. The gas concentration data can be accurately acquired, the position of the leakage point can be judged, and the leakage point can be quickly and accurately positioned based on the combination of the GPS and the gas concentration data.

The invention discloses a transcutaneous electrical stimulation device based on a large language model, and relates to the technical field of wearable devices. The transcutaneous electrical stimulation equipment based on the large language model comprises a data acquisition and preprocessing module, a feature extraction and fusion module, a model training and prediction module and a real-time information processing and response module, real-time data processing and intervention capabilities are integrated, human body data can be analyzed in real time, electrical stimulation parameters can be automatically adjusted, an instant treatment or adjustment scheme is provided for the problems of arrhythmia, chronic pain and the like, and the timeliness and effectiveness of treatment are greatly improved. And secondly, by fusing the physiological signal features and the symptom text features, the equipment can more comprehensively understand the health condition of the user, so that a more accurate treatment scheme is provided. Besides, the equipment also has the functions of anomaly detection and trend analysis, can predict potential health risks, and triggers an alarm in time when a serious anomaly is detected, thereby effectively guaranteeing the life safety of the user.

The invention discloses an intelligent security monitoring and early warning method and system for an industrial control network, relates to the technical field of network security monitoring, and can collect multi-dimensional data such as network traffic, user behaviors and equipment states in real time through a data acquisition module and a system to ensure the timeliness and comprehensiveness of monitoring. Therefore, potential security threats can be quickly identified, and the overall security protection capability is improved. And the data preprocessing module cleans and standardizes the collected data, so that the data quality and consistency are improved, a reliable basis is provided for subsequent risk assessment, and misjudgment caused by data inconsistency is reduced. And the risk assessment module calculates a comprehensive risk index through a multi-dimensional assessment formula, different types of security threats can be identified more accurately, and a scientific basis is provided for a subsequent early warning mechanism. The multi-level early warning mechanism module divides the risk into a low level, a middle level and a high level according to the comprehensive risk index, and a targeted response strategy can be realized.

The invention provides a multi-dimensional fuel gas alarm system for a workshop production line, and relates to the technical field of fuel gas alarm. The multi-dimensional fuel gas alarm system for the workshop production line comprises a fuel gas sensor module, a positioning module, a valve cut-off module, a data acquisition module, a data processing module, an alarm module and a standby power supply module, the gas sensor module comprises a gas sensor installation unit, a sensor node unit, a wireless communication module and a threshold setting unit. According to different levels of gas concentrations, alarms (early warning, alarm and emergency alarm) of different levels are triggered, and corresponding countermeasures are taken, so that when a gas leakage event occurs, gas supply can be quickly cut off, gas accumulation and potential safety hazards are reduced, and after an alarm signal is sent out, a related person in charge is immediately notified, so that the safety of gas leakage is ensured. And timely response is ensured through various modes of short messages, calls and WeChat, so that potential risks and loss are reduced to the greatest extent.

The invention discloses an intelligent glasses streaming voice dialogue interaction system and method based on a large language model, and the system comprises a data collection module which is used for collecting the interaction information of a voice dialogue between a user and intelligent glasses and the resource condition information of the intelligent glasses; the data conversion module is used for processing the interaction information by adopting a real-time streaming processing technology, adaptively selecting an interaction information identification model of a corresponding type, and converting the identified content into text information; the data processing module is used for receiving the text information, calculating a comprehensive score according to a preset scoring rule, matching and loading large language models configured by different models according to the comprehensive score, performing intention recognition and generating a reply text; the data interaction module is used for converting the generated reply text into voice and outputting the voice; and the data feedback module is used for receiving the feedback user interaction satisfaction so as to optimize the interaction information identification model or the large language model. According to the invention, efficient, accurate and smooth voice interaction experience can be realized.

The invention discloses a database security secrecy system based on storage encryption, and relates to the technical field of information security. The database encryption platform is in communication connection with a key management module, an encryption and decryption management module, a performance monitoring module, a load evaluation module, a distributed storage optimization module, an early warning analysis module and an audit log module. According to the system, the early warning analysis module is combined with burden evaluation and security vulnerability detection of encryption and decryption performance, the abnormal degree of encryption and decryption processes is comprehensively analyzed, the early warning level is set, potential abnormal conditions are analyzed, and early warning information is sent out, so that a system administrator can respond in time, and the system safety is improved. A preposed risk management strategy enables a system administrator to take corresponding countermeasures before the risk actually occurs, thereby effectively reducing the influence of security threats, and meanwhile, according to different early warning levels, the countermeasures can be flexibly adjusted, and reasonable allocation of resources is ensured.

The invention relates to the technical field of safety engineering, in particular to an intelligent high-altitude and fire operation safety management system based on risk assessment, which comprises a safety helmet module, a data acquisition and transmission module, an edge calculation and processing module, a risk assessment and decision module, an emergency response module and a data analysis and optimization module. The intelligent safety helmet worn by each operator is integrated with a flame sensor and a height sensor, and flame information in an operation environment and height changes of the operators can be collected in real time. Through the low-power-consumption wireless communication technology, the collected data is transmitted to the edge computing device in real time, and the edge computing device calculates the comprehensive risk index through multi-sensor data fusion. And when the risk index exceeds a preset threshold value, the system automatically triggers alarm, sends early warning information to operators and a management platform, and takes emergency measures, so that the accident rate in high-altitude operation and fire operation can be effectively reduced, and the safety management level of the operation environment is improved.

The invention discloses an outdoor fire hydrant water pressure real-time monitoring system, and relates to the technical field of fire fighting. When the system operates, a data acquisition and preprocessing module performs efficient fusion of multi-source data, and provides accurate water pressure data Px (t); the correlation analysis module evaluates the relationship between adjacent fire hydrants by using the geographic position data and the correlation coefficient, and can discover potential risks in time and generate an early warning identifier; the anomaly detection and evaluation module dynamically analyzes the water pressure data and the early warning identifier to provide an accurate fire hydrant anomaly risk evaluation strategy scheme; the path analysis module accurately judges an abnormal propagation path by analyzing the abnormal index gradient Gxy (t) of the adjacent fire hydrants, and reveals a potential regional problem; according to the method, the effective regional exception handling strategy is generated and executed immediately, the response speed and the decision-making ability are enhanced, and particularly, when regional or systematic problems are handled, early warning can be performed in advance, and optimal countermeasures can be taken.

The invention relates to the technical field of energy storage system anomaly detection, and particularly discloses an abnormal state detection method for a liquid-cooled energy storage system, and the method comprises the steps: analyzing the environment where the liquid-cooled energy storage system is located, obtaining the feature value of the environment, analyzing the use state of a cooling system of the liquid-cooled energy storage system, and obtaining the feature value of the environment; judging whether the cooling system is abnormal or not and determining a cooling threshold value of the liquid-cooled energy storage system, analyzing an actual operation state of the liquid-cooled energy storage system, judging whether the liquid-cooled energy storage system is abnormal or not and performing abnormal detection on a battery unit of the liquid-cooled energy storage system, and determining an overheating detection abnormal level of the battery unit; the problem that traditional liquid cooling system anomaly detection depends on a fixed rule or a preset threshold value and lacks intelligent data processing and self-adaptive adjustment capability is solved, the intelligent level of system operation is improved, and the stability and safety of the system are further improved.

The invention belongs to the technical field of digital twinning, particularly relates to an intelligent monitoring and adjusting method, system and equipment based on the digital twinning technology, and aims to solve the problems that in the prior art, efficient and accurate fault prediction cannot be carried out on production equipment, the fault position is difficult to locate accurately, and the maintenance efficiency is affected. The method comprises the following steps: acquiring operation state data, working condition data and environmental condition data of to-be-monitored production equipment as input data; preprocessing the input data, and constructing a digital twin model; real-time monitoring is carried out through the digital twin model, and monitoring data are obtained; based on the monitoring data, fault prediction is carried out through an adaptive threshold fault prediction method, and a fault prediction result is obtained; and the digital twinborn model performs live-action simulation according to a fault prediction result, locates a fault position and gives an alarm to monitoring personnel. According to the invention, efficient and accurate fault prediction of the production equipment is realized, the accuracy of fault location positioning is improved, and the maintenance efficiency is improved.

The invention discloses a construction site inspection method, inspection system and equipment and a nonvolatile storage medium, and relates to the technical field of intelligent construction, and the method specifically comprises the steps: carrying out the inspection planning according to the inspection requirements of a construction site; based on the inspection plan, executing an inspection task, and obtaining inspection data of a target area; and analyzing the inspection data by constructing a deep learning algorithm model, and carrying out risk event classification based on an analysis result. According to the invention, a closed-loop processing mechanism of data in a platform-nest-unmanned aerial vehicle-platform is driven through control of the unmanned aerial vehicle cruise by the intelligent construction transportation and management platform for construction engineering, and a mobile supervision mode for engineering quality and safety is innovated.

The invention discloses a cluster remote real-time state monitoring and fault diagnosis method and platform, and belongs to the technical field of thermal power. The method solves the problem that the existing method is poor in machine group judgment accuracy, analyzes the collected operation data through the data processing center, extracts the characteristics of the operation data and compares the characteristics with the fault database, so that the fault type and position of the thermal power plant equipment can be judged, and meanwhile, the fault judgment accuracy of the thermal power plant equipment is improved. By performing trend analysis on the historical operation data and constructing the fault prediction model based on the historical operation data, the fault of the thermal power plant equipment can be predicted, and through the above process, the operation state of the thermal power plant equipment can be monitored in real time and the fault can be accurately diagnosed. According to the device, the fault early warning can be given out when a fault is found, and the response of the worker to the fault early warning is monitored in real time, so that the timely response of the worker to the fault early warning is improved.

The invention discloses an industrial chain node development evaluation method and system based on a new quality productivity index, and the method comprises the steps: obtaining industrial chain power data and social data, and building a basic database; constructing an industrial upstream and downstream network relation graph based on the basic database; new quality production evaluation indexes are constructed from different dimensions, and a new quality productivity index evaluation system is formed; screening indexes in the new quality productivity index evaluation system, and calculating a new quality productivity index of each node of the industrial chain; and evaluating the high-quality development condition of each node of the industry chain based on the new-quality productivity index and by considering the influence of upstream and downstream fluctuation transmission. The method can evaluate the high-quality development condition of each node of the industrial chain in combination with the upstream fluctuation influence of the industrial chain, analyzes the advantage nodes and weak links of the industrial chain, and facilitates the dynamic monitoring of the full-chain development of the industrial chain.

The invention discloses a road obstacle recognition method based on machine vision, and the method comprises the steps: carrying out the precise positioning of the spatial position of an obstacle through a multi-sensor information fusion algorithm according to the azimuth and distance information of the obstacle estimated through a sound source positioning algorithm and the position information outputted by an obstacle detection model, and improving the positioning precision; the method comprises the following steps: acquiring guide mark information, such as road signs and lane lines, extracting guide mark features through a semantic segmentation algorithm, carrying out dynamic fusion on the guide mark features and an obstacle recognition result, and improving the accuracy and robustness of obstacle recognition by using prior information; in order to solve the problem of insufficient parallax analysis precision in long-distance, high-speed motion and other scenes, a monocular depth estimation algorithm based on deep learning is introduced, pixel-level depth information is predicted from a single image through a convolutional neural network, and the precision of obstacle distance estimation is improved.

The invention discloses a mountainous region photovoltaic array remote monitoring and fault early warning installation system which comprises a monitoring unit used for collecting operation data of a mountainous region photovoltaic array in real time, and a fault early warning unit used for judging whether the mountainous region photovoltaic array breaks down or not according to the data collected by the monitoring unit and sending out an early warning signal when the fault occurs. The remote monitoring center is used for receiving data transmitted by the monitoring unit and monitoring and managing the running state of the mountain photovoltaic array in real time, the installation support is used for installing the mountain photovoltaic array, the monitoring unit collects photovoltaic assembly and environment data in real time through various sensors, the data processing module carries out preprocessing and analysis, and the data processing module carries out real-time monitoring and management on the running state of the mountain photovoltaic array. And the data transmission module transmits data to a remote monitoring center in time, so that omnibearing and real-time monitoring of the running state of the mountain photovoltaic array is realized, the running condition of the mountain photovoltaic array can be mastered in time, and potential problems can be found.

The invention provides a robot customer service question intelligent processing method and system. The method comprises the steps that user questions are acquired, and feature extraction and integration are conducted on the user questions; constructing a user dynamic causal reasoning model, and generating a multi-level problem solving strategy according to user problems; constructing a self-adaptive response strategy generation model according to the generated multi-level problem solving strategy and the integrated feature vector to generate a response strategy most adaptive to the current corresponding context; executing the response strategy and judging whether to perform learning triggering according to the feedback of the user; performing real-time adaptive learning and optimization on the adaptive response strategy generation model through user feedback and historical user feedback; and analyzing the long-term trend of the historical user feedback and the model parameters of the adaptive response strategy generation model after multiple times of optimization, performing global optimization in combination with the long-term trend, and generating a final response strategy. The method and the device can be excellently expressed in a complex user service scene.

The invention discloses an urban green land plant growth state monitoring and analyzing system and method, relates to the technical field of big data analysis, and can accurately track the health condition of plants in each sub-region and improve the scientificity and effectiveness of urban green land management through detailed region division and coordinate marking. By means of the plant growth simulation model and the ideal environment growth model which are constructed through the neural network, the system can dynamically predict the plant growth state and conduct personalized management on plant growth conditions in different areas. Various collection means such as environmental factors and remote sensing data are integrated, the growth state of urban green land plants is comprehensively monitored, and the limitation of a traditional method is overcome. By setting the state abnormality threshold and combining the growth deviation trend chart, the system can realize a real-time early warning mechanism, quickly identify potential growth abnormality and ensure that problems are found and solved in time, so that the monitoring error of the remote sensing technology is reduced, and the management efficiency is improved.

The invention relates to the technical field of digital video devices, in particular to a digital video device control method and device. The method comprises the following steps: acquiring digital monitoring data, performing real-time scene acquisition and dynamic region optimization according to the data, and generating optimized dynamic region data; video frames are read based on the dynamic region optimization data, and abnormal behavior analysis and behavior intention evaluation are carried out; calculating the complexity of monitoring behaviors by combining video frame data and behavior intention data, and evaluating resource consumption increase; the processing bottleneck is evaluated according to the resource consumption growth data, and the processing capability attenuation analysis is carried out, so that the monitoring device is adjusted, and the system performance is optimized; according to the method, the resource allocation and processing capability of the video device is dynamically adjusted through real-time analysis and processing of monitoring data. According to the invention, the digital video device is adjusted, so that the prediction identification of the digital video device is more accurate and efficient.

The invention relates to the technical field of Internet of Things, in particular to a method and system for realizing security authentication of Internet of Things equipment based on a block chain technology, and the method comprises the following steps: S1, equipment registration: generating a unique identifier for each piece of Internet of Things equipment on a block chain, and storing the basic information and authentication information of each piece of Internet of Things equipment into the block chain; s2, identity verification: verifying the authenticity of the identity of the equipment through a block chain when the equipment is accessed to the Internet of Things; s3, block chain storage: safely storing identity authentication information of the equipment by using a distributed account book technology of the block chain to ensure that the information cannot be tampered; and S4, smart contract: the smart contract is responsible for an automatic equipment identity verification process to ensure transparency and high efficiency of an authentication process.

The invention discloses a smart city environment monitoring system based on the Internet of Things, and relates to the technical field of the Internet of Things. When the system operates, the system can continuously acquire accurate hydrological data through fixed-period data acquisition of an intelligent water meter and a sensor, and the influence of noise and data missing is reduced through data cleaning and statistical analysis; and thus, reliable hydrological anomaly factors are generated. The water quality evaluation module further calculates a water quality evaluation index Ew, and performs quality judgment in combination with a water quality evaluation threshold value SZ to ensure that a water quality problem is found in time. And the water pollution risk calculation module calculates the influence degree of pollution diffusion according to the evaluation result, and quantifies the influence degree as a water pollution risk coefficient Rw to provide a basis for early warning decision. The early warning module generates an effective early warning strategy scheme and notifies related personnel through matching of an upper limit value Rmax and a lower limit value Rmin of water pipe network diffusion early warning, thereby ensuring that measures are taken in time before pollution diffusion, and avoiding event deterioration.