244results 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 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 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 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 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 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 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 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 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.

The invention discloses an alarm flow control method based on consistent Hash of alarm priority and related equipment, the cache size is firstly configured according to the actual physical memory size to ensure that alarm software can process alarm information as efficiently as possible on the premise of not exhausting system resources, then priority arrangement is carried out, and the alarm flow control efficiency is improved. Priority processing of high-priority alarms can ensure that key safety problems are responded in time, potential risks are reduced, setting of an avalanche value provides an early warning mechanism for the system, the system can respond in advance before the number of alarms increases sharply, the system state is automatically adjusted according to real-time changes of the number of alarms, and the system safety is improved. The method ensures that the processing capability can be quickly recovered after the system returns to normal, finally ensures that all nodes can obtain complete alarm information and keep data consistency, improves the stability and reliability of the system under extreme conditions through a series of optimization measures, preferentially processes high-priority alarms, and improves the reliability of the system. And key safety problems can be timely responded and processed.

The embodiment of the invention provides a rockburst dynamic early warning method and system based on micro-seismic information, and can solve the problem that in engineering scenes such as mining and tunneling, continuous vibration of equipment such as a drilling machine and a heading machine can generate a large amount of background noise, so that the difficulty of effectively extracting a micro-seismic fracture event in a rock mass is increased. The method comprises the following steps: acquiring operation disturbance information of vibration of an operation machine in a typical strong rockburst hole section rock mass construction process on surrounding rock masses, so as to extract operation disturbance characteristics; determining a continuous influence period of operation disturbance in an acquisition point range during operation and after the operation is stopped according to the acquisition point range of micro-seismic monitoring; and based on the operation disturbance characteristics, the continuous influence period and the working state information of the operation equipment, performing dynamic denoising on the acquired micro-seismic information so as to perform rockburst early warning through the denoised micro-seismic information.

The invention relates to the technical field of path planning, and discloses a trajectory path planning system for automation equipment, which is characterized in that map data is acquired through a map establishment module, and is processed and analyzed to establish a map model; the path planning module generates a path planning strategy based on the map model, the equipment initial position and the supply point; the motion control module generates a motion control instruction according to the result of the path planning strategy to control the automation equipment; the safety monitoring module continuously monitors the terrain environment, obstacles and equipment states, and evaluates the safety of the motion control instruction in real time; and when the motion control instruction is not safe, the dynamic adjustment module generates a correction signal and adjusts the motion control instruction, so that autonomous positioning, path planning, safe navigation and dynamic adjustment of the automation equipment in a complex environment are realized, the operation efficiency of the automation equipment is improved, the operation safety is guaranteed, and the adaptive capacity of the automation equipment is improved.

The invention relates to the field of network security monitoring, in particular to a network security threat monitoring method based on deep learning, which comprises the following steps: collecting flow data on a network node, and preprocessing the collected flow data; constructing a deep neural network model to analyze the flow data, and adjusting model parameters through an optimization algorithm to minimize a loss function; evaluating the network security threat according to an output result of the deep learning model; determining response measures according to the threat assessment result, and taking corresponding response operations for different threat levels; according to the method, the timeliness of the response is evaluated according to the time interval from threat detection to response measure implementation, the deep neural network is utilized, the deep features can be automatically extracted from the network flow, and the recognition precision of the complex attack mode is remarkably improved. Compared with a traditional rule-based detection method and a simple machine learning algorithm, the scheme can effectively reduce the false report rate and the missing report rate, and improve the accuracy of network threat monitoring.

The invention discloses a method for optimizing edge computing equipment based on a multi-modal large model in reservoir intelligent measurement, and the method achieves the comprehensive and real-time monitoring of a reservoir water source protection region through the combination of the multi-modal large model and an edge computing technology. A high-performance edge computing device is adopted as a platform, a multi-mode large model is deployed to perform real-time analysis on a monitoring video, and illegal behaviors such as burning prohibition, cultivation, water surface floating objects, catering business point garbage and personnel invasion are identified; meanwhile, a small model is designed for specific illegal behaviors for special identification, and the identification precision and efficiency are improved; the edge computing platform supports access of Internet of Things equipment, and integrated analysis of multi-dimensional data such as meteorological data and water quality data is achieved. According to the method, a manager is notified in time through an early warning mechanism, remote access and management are supported, and data transmission security is ensured; the whole scheme is efficient and accurate, is suitable for various reservoir intelligent monitoring scenes, and provides powerful support for reservoir management and protection.

The invention discloses an AI voice call method and system, a program product and a storage medium. In the method, after a user starts an outbound task, customer data is collected in real time to screen out-calling customers and select a proper robot to call; and after the call is started, judging whether the customer is interested in according to the voice characteristics and voice content of the customer, and if the customer is not interested in, interrupting the call in time and calling the next customer. By implementing the technical scheme provided by the invention, the efficiency of executing the outbound task by the AI voice robot is improved.

The invention relates to the field of bridge construction, and discloses a real-time monitoring system for a bridge construction process, and the system comprises a high-precision sensing module which is used for monitoring the data of all aspects in the bridge construction process in real time; the communication module is used for transmitting the data acquired by the sensor to a monitoring center in real time; the intelligent analysis module is used for preprocessing, storing, comprehensively analyzing and predicting the collected data and generating early warning information; the visual monitoring module is used for displaying an analysis result and real-time data to construction management personnel; the high-precision sensing module, the communication module, the intelligent analysis module and the visual monitoring module are in communication connection through a network. According to the invention, the real-time condition of the construction site can be comprehensively and accurately reflected through the high-precision sensing module, and compared with a traditional manual patrol and regular detection method, potential safety hazards can be quickly found and pre-warned, so that the safety and reliability of the construction process are ensured.

The present invention discloses a method and application of preparing a freshness indicator label by electrostatic spinning, which is obtained by the following steps: S1: adding bayberry anthocyanin to a matrix solution to obtain a spinning solution; S2: after ultrasonic filtration of the matrix solution, taking part of the solution and putting it into a 10ml syringe, fixing it on a propulsion pump, spinning using a high-voltage electrostatic spinning device under a specific voltage, receiving distance, flow rate, temperature and humidity to prepare a nanofiber membrane; S3: balancing the prepared nanofiber membrane for 48 hours, and cutting it into a specific size to prepare a freshness indicator label. The freshness indicator label prepared by the method of the present invention has a large specific surface area and porosity, and is sensitive to pH response. The bayberry anthocyanin freshness indicator label based on pullulan / polyvinyl alcohol has great application potential in the freshness monitoring and intelligent packaging of white shrimp.

The invention discloses a three-dimensional map collecting and updating method and device and electronic equipment, and belongs to the field of map updating. Only the data collected by the unmanned aerial vehicle can update the base map of the current three-dimensional map, so that when the position of the to-be-collected area is not the existing position on the current three-dimensional map, at least the data collected by the unmanned aerial vehicle is needed, and when the position of the to-be-collected area is the existing position on the current three-dimensional map, the base map does not need to be updated. Therefore, the unmanned aerial vehicle and the collection vehicle can be used as execution equipment. According to the scheme, after the position of the to-be-collected area is obtained, the execution equipment is automatically determined according to the area data of the to-be-collected area, the collection task is generated, the three-dimensional map is automatically updated based on the collected data, manual participation is not needed, cost is low, efficiency is high, rapid changes of the terrain and texture of a working site can be reflected in time, and working efficiency is improved. Reasonable planning and accurate scheduling of operators are effectively supported.

The invention relates to an air separation device internet of things monitoring method based on a computer serial port, and relates to the technical field of internet of things communication, and the method comprises the steps: monitoring and collecting air separation operation data in an air separation device through an air separation device operation station, carrying out the local abnormality analysis, and obtaining a local abnormality parameter; performing space division anomaly judgment according to the local anomaly parameters, if not, transmitting space division operation data to an Internet of Things communication module through a computer serial port, and if yes, directly transmitting the space division operation data to the Internet of Things communication module; if yes, sending the space division operation data to a cloud server, configuring an exception continuous identification coefficient according to data characteristics of the space division operation data and local exception parameters, performing exception continuous integration identification, and obtaining an exception continuous window; and configuring the air separation unit operation station according to the abnormal continuous window. According to the invention, the technical problem that network risks exist when the air distribution device monitors the Internet of Things in the prior art is solved.

The invention discloses an electric bicycle battery system compatible with thermal safety protection and a vehicle-mounted charger, and particularly relates to the technical field of new energy power batteries and power supply systems, and through an electric bicycle charger circuit structure, the temperature change condition of each hardware in the use process and the degree of influence on the charger safety are tested; the temperature monitoring area of the charger is determined, meanwhile, a battery sensing device is arranged in the charger, the current change situation in the charging process can be monitored in real time, and it is ensured that a battery system is damaged due to the fact that the current is too large; a charger performance evaluation and active protection strategy based on'electricity and heat 'coupling is designed, the problem of safe use of an electric bicycle charger is solved, and an electric bicycle battery system with'charger built-in, an internal battery management system and external safety protection' is built by establishing an'electricity and heat 'electric bicycle charging safety monitoring technology. The characteristics of the battery and the charger can be reflected timely and accurately.

The utility model discloses an IGBT (Insulated Gate Bipolar Transistor) protection circuit, which comprises a driving module, the input control end of the driving module is connected with the cathode of a Zener diode D0, and the cathode of the Zener diode D0 is also connected to the emitter E of an insulated gate bipolar transistor Q0. The grid electrode G of the insulated gate bipolar transistor Q0 is connected to the driving output end of the driving module through a resistor R6, and the collector electrode C of the insulated gate bipolar transistor Q0 is connected with the overcurrent detection output end of the driving module through a fast recovery diode FR107. According to the technical scheme, transient voltage spikes can be effectively restrained, the IGBT is protected against overvoltage damage, the anti-interference capacity of the circuit is enhanced, a fast recovery diode FR107 is further arranged at the overcurrent detection output end of the driving module, the switching speed can be increased, the overvoltage phenomenon during switching-off is reduced, the service life of the IGBT and the driving module is prolonged, and the service life of the IGBT and the driving module is prolonged. And the working reliability of the whole circuit is improved.

The invention relates to the technical field of image communication, in particular to an intelligent camera with active defense and a defense method thereof, and the intelligent camera comprises a data acquisition module, an extraction module, a judgment module, a determination module, a screening module and a defense module. According to the invention, through fusion of a multi-parameter analysis technology and combination of the target position, the attitude angle and the height of a real-time image and dynamic changes of sound intensity, accurate identification of an intrusion target is realized, and the judgment module adopts a multi-level logic of combination of boundary judgment and attitude angle change, so that the accuracy of abnormal target judgment is effectively improved; the screening module reduces erroneous judgment through comprehensive screening of sound intensity and position; the defense module calculates a launching track based on the real-time position and height of the target, fixed-point active defense is achieved through a water bomb, passivity of a traditional voice alarm mode is avoided, and the problem that the response speed is not timely due to single fingerprint recognition and vibration monitoring data is effectively solved.

The invention discloses a method for calculating a radiation dose rate based on pulse counting, which is used for filtering the influence of a large pulse generated under the condition of jitter or other interference in a dose rate calculation process on dose rate jitter calculation, and can effectively reduce the dose rate jitter caused by the interference. Meanwhile, pulse increase or decrease caused by normal rapid increase or decrease of radiation is responded in time, dose rate output is rapidly calculated, that is, when the radiation is rapidly increased or decreased, the dose rate change time is shortened, and therefore the current dose rate level is displayed more timely and stably; through the method provided by the invention, the calculation of the dose rate can effectively filter abnormal pulses and reduce fluctuations, and meanwhile, when the radiation intensity changes quickly, quick response can be realized, the timeliness of dose rate calculation can be improved, the stability and accuracy of dose rate output can be ensured, and reliable data support can be provided for safe operation in a nuclear radiation environment.

The invention provides a load balancing regulation and control method for a liquefied gas carrier liquid cargo operation system simulator, and belongs to the technical field of traffic transportation, and the method comprises the steps: firstly building a liquid tank pressure response model and a ship state monitoring model, and providing support for safe operation; and deploying a real-time load monitoring module to collect performance data, and carrying out preprocessing and load evaluation to comprehensively grasp the working state of the system. On the basis, dynamic task priority adjustment and load trend prediction are adopted, it is ensured that key tasks are processed in time, and the overall efficiency of the system is improved. And finally, making a dynamic load balancing strategy based on load distribution evaluation, realizing optimal configuration of resources, and monitoring and adjusting in real time to cope with load change so as to ensure stable and reliable operation of the system. According to the scheme, the technologies of modeling analysis, real-time monitoring, dynamic scheduling and the like are integrated, and the technical problems that a complex coupling relation exists among subsystems, local overload or resource waste is possibly caused by unbalanced load distribution, and the overall performance is affected are solved.