290results about How to "Realize refined management" patented technology

The invention provides an inventory optimization method and system based on a deep learning model, and relates to the technical field of deep learning, and the method comprises the steps: obtaining the multi-dimensional data of a commodity, constructing a dynamic convolution kernel through employing a graph neural network, constructing a soft clustering distribution matrix in combination with a multi-layer perceptron, and obtaining a long-term dependence feature fusing a space dimension and a time dimension. And fusing the short-term and long-term features, inputting the fused short-term and long-term features into a bidirectional long-short-term memory network to generate a sales prediction sequence, and calculating a prediction confidence interval to obtain a final sales prediction result. And calculating a safe inventory level based on a sales prediction result, generating a replenishment decision by using a dynamic planning model based on deep reinforcement learning in combination with an inventory cost function, and finally sending the decision to a supply chain execution system. The sales volume is accurately predicted through the deep learning model, the inventory management is optimized, the storage cost, the stockout cost and the overstock cost are reduced, and the supply chain efficiency is effectively improved.

The invention relates to a multi-channel firmware upgrading method and system supporting breakpoint resume, and the method comprises the steps: segmenting a firmware file into a plurality of firmware data blocks, and generating a unique identifier and a checksum for each firmware data block; acquiring a network transmission channel, performing task matching with the network transmission channel according to the unique identifier to obtain a corresponding distribution transmission task, and establishing a corresponding initial record document; transmitting the firmware data block through the network transmission channel according to the distribution transmission task, monitoring the transmission state in real time, and carrying out breakpoint record updating on the initial record document to obtain a corresponding updated document; when it is detected that transmission is completed, verifying the firmware data block according to the checksum, and recombining the firmware data block to obtain the firmware file; and generating a corresponding upgrading report based on the updating document and the upgrading information. The upgrading strategy can be continuously optimized, and the system management efficiency can be improved.

The invention discloses a motor operation cycle monitoring and warning method and system, and relates to the technical field of motor monitoring, and the method comprises the steps: collecting motor operation data in real time through employing a distributed sensor network, and carrying out the analysis and processing of the collected original data through employing an edge computing device; transmitting the analyzed and processed data to a cloud data center, carrying out deep learning model training and feature extraction, and dividing a motor operation cycle based on the extracted features; and performing multi-dimensional anomaly detection on each divided operation cycle to generate an anomaly index, and performing motor operation cycle monitoring alarm according to the obtained anomaly index. The real-time performance and the reliability of the system can be improved. Compared with a traditional rule-based method, the method is more flexible and accurate, state changes of the motor can be evaluated more comprehensively, an early warning signal is sent out in time, and unnecessary downtime and maintenance cost are reduced.

The invention relates to a GNSS-based water conservancy facility deformation monitoring and early warning method, system and device and a medium, and the method comprises the steps: obtaining GNSS monitoring network data of a target part of a water conservancy facility, and carrying out the real-time dynamic differential processing, and obtaining corresponding three-dimensional coordinate data; acquiring environmental factor data, and performing multi-source feature fusion on the environmental factor data and the three-dimensional coordinate data to obtain a corresponding deformation feature vector; performing prediction analysis on the deformation feature vector according to a preset whale optimization algorithm to obtain a corresponding deformation trend prediction result; setting a multi-level early warning threshold according to the deformation feature vector, and performing early warning analysis on a deformation trend prediction result to obtain a corresponding initial early warning level; and obtaining standard deformation characteristic information of the deformation rate and the acceleration of the water conservancy facility, and performing comprehensive evaluation on the standard deformation characteristic information based on a deformation trend prediction result to obtain a corresponding early warning index. Potential risks can be found and processed in time, and the possibility of hydraulic engineering accidents is reduced.

The invention discloses an intelligent logistics system based on optimal scheduling, and relates to the field of logistics management, and the system comprises the steps: arranging environment sensors at key nodes of a logistics network, and transmitting collected real-time data to edge computing equipment for preprocessing; analyzing the preprocessed data by using a deep learning model, and predicting an environment change trend in a non-period of time; dynamically adjusting a logistics scheduling strategy based on the prediction result; after receiving the adjusted scheduling strategy, defining a state space, an action space and a reward function of the logistics system; training a reinforcement learning algorithm model, and continuously optimizing a decision strategy through interaction with the environment; and calling the reinforcement learning model through an API interface to make a real-time decision, and generating an optimal scheduling scheme. According to the invention, by integrating environment perception, deep learning prediction, adaptive scheduling and reinforcement learning, the defects of an existing intelligent logistics system in the aspects of environment perception, prediction and dynamic scheduling optimization are overcome, and the intelligent level and operation efficiency of logistics management are significantly improved.

The invention relates to the technical field of boiler heating surface fault diagnosis, in particular to a heating surface fault diagnosis prediction method and system based on boiler flow field analysis. The method comprises the following steps: collecting boiler multi-dimensional data; analyzing a flow field in the boiler; constructing a heating surface fault diagnosis model; coupling the flow field simulation result and the fault diagnosis model to construct a fault prediction model; extracting features and reducing dimensions; performing fault mode identification by using a Gaussian mixture model; and calculating a comprehensive fault score and carrying out risk grading. Through fusion of flow field analysis, deep learning and multi-source data, the accuracy of fault diagnosis is remarkably improved, and the early warning time is remarkably shortened. Experimental results show that compared with a traditional method, the fault detection rate is increased to 96.7%, the false alarm rate is reduced to 3.2%, the early warning time is prolonged to 12.3 hours, and the diagnosis accuracy rate reaches 94.1%. The method provides powerful support for safe and efficient operation of the boiler, and has important engineering application value.

The invention discloses a life cycle visual management method based on data, and relates to the technical field of data management. The method comprises the following steps: 1, data acquisition and identification; 2, classifying and dividing data life cycle stages; 3, data state monitoring and updating; 4, recording and associating data operation; step 5, constructing a data life cycle visual framework; step 6, data life cycle decision analysis optimization; 7, performing data archiving and deletion management; according to the invention, fine management of the whole life cycle of the data is realized, and the systematic method not only improves the efficiency of data processing, but also enhances the transparency of data management, so that managers can monitor the data state in real time, quickly respond to the data problem, optimize the data circulation process and improve the overall operation efficiency. Through application of a data visualization technology, complex data information is displayed in a visual and understandable form, and managers are helped to better understand data dynamics.

The invention relates to the technical field of environment monitoring, in particular to a coal mining environment sensing system based on multi-sensor fusion, which comprises a coal mining environment analysis module, an environment detection module, a regional analysis module, an environment sensing module and a coal mining environment early warning module, the coal mining environment analysis module determines a coal mining environment to be perceived and determines the function type of each perceived area; the environment detection module detects detection information of each sensing area; the regional analysis module determines the potential safety hazard type of each region; the environment sensing module determines an environment sensing strategy according to the potential safety hazard type of each sensing area; the coal mining environment early warning module is used for sending out early warning information; according to the method, the coal mining area is divided into a plurality of functional areas, and corresponding sensors are deployed according to the characteristics and safety requirements of each area, so that the correlation and accuracy of monitoring data are ensured, and the pertinence of data acquisition and the accuracy of coal mining environment perception are improved.

The invention is suitable for the technical field of public rental housing management, and provides a public rental housing inspection management system, which comprises a hardware equipment layer used for collecting environment data, equipment state data and inspection data of a public rental housing, and the hardware equipment layer comprises a plurality of sensor equipment, a wireless communication module and mobile inspection equipment; the data processing layer is used for receiving, storing and processing data collected by the hardware equipment layer, and the data processing layer comprises a data receiving module, a data processing module, a data storage module, a data analysis module and an early warning module; and the application service layer is used for providing user interaction and system management services according to the data acquired by the hardware equipment layer and the data processed by the data processing layer, and the application service layer comprises a housing resource management module, an inspection task distribution module, an inspection management module, an early warning management module, a contract management module and a housing maintenance management module. The problem of low inspection efficiency in the prior art is solved.

The invention relates to the field of intelligent control and software management, and discloses a carrier rocket software control method and decision making device based on SAC, and the method comprises the following steps: obtaining the state information of a carrier rocket software system, and constructing a unified expression; an optimal action is generated based on an SAC algorithm; executing the action and collecting feedback information after the action is executed; utilizing feedback information to optimize an actor network and a commentator network in the SAC model, and continuously updating a decision strategy; based on the optimized strategy, the task priority, the running state of the software module and the abnormal recovery operation are dynamically adjusted, and the system state is collected again to optimize the model in a closed-loop mode. The system operation state can be adjusted in real time, the task scheduling efficiency, the resource utilization rate and the exception handling capacity are improved, intelligent management of a carrier rocket software system is achieved, and the method is suitable for complex task environments and high-dynamic operation scenes.

The invention relates to the technical field of smart communities, and discloses a smart community intelligent security monitoring management method based on data interconnection, which comprises the following steps: acquiring real-time monitoring data, reading a preprocessed monitoring data set from a database, extracting feature parameters related to community security patrol, and determining the real-time monitoring data; the characteristic parameters comprise road length, road width, building height and building density, and constructing a community security patrol environment model based on the characteristic parameters; and aiming at the community security patrol route optimization model, solving by adopting an ant colony algorithm to obtain a plurality of candidate optimal patrol routes, evaluating the candidate optimal patrol routes according to historical patrol data, and selecting the patrol route with the optimal comprehensive evaluation index as the optimal patrol route. According to the invention, intelligent and fine management of community security patrol is realized, the patrol efficiency and the safety prevention and control level are improved, and powerful decision support is provided for community security management.

The invention provides a bidirectional parallel storage battery capacity checking system and method, and relates to the field of battery capacity checking, and the system comprises a station control system, a plurality of direct current buses, an alternating current bus, a plurality of storage battery packs, a battery optimizer and a bidirectional charging and discharging module, and is used for realizing storage, charging and discharging management and online capacity checking of storage batteries. The storage battery pack comprises a plurality of battery modules connected in parallel, and each battery module comprises storage batteries managed in pairs and a charging and discharging control circuit and is used for managing the charging and discharging states of the storage batteries. According to the capacity checking system and method provided by the invention, capacity checking is allowed to be carried out on a single battery pack, the storage batteries needing capacity checking can exit independently, and other battery packs continue to supply power normally under the condition that the overall power supply is not influenced, so that the parallel connection mode not only avoids power interruption and resource waste, but also realizes fine management of the battery packs, and the power supply efficiency is improved. The service life of the storage battery is prolonged, and the continuity and the stability of a power system are ensured.

The invention relates to the technical field of electric power inspection, in particular to an intelligent inspection data acquisition system, which comprises a route planning module for generating a flight path diagram based on the position of an electric power line; the unmanned aerial vehicle module is used for adopting an unmanned aerial vehicle to fly based on the flight path diagram; the line data acquisition module is used for being installed on the unmanned aerial vehicle module to capture line images of the power line and the surrounding environment thereof; the GPS positioning module is used for recording geographic position information in real time during line image acquisition; the image blurring detection module is used for automatically marking an image blurring area on the image by using GPS positioning data and an image analysis technology to obtain a blurred image and corresponding geographical location information; and the rechecking module is used for recollecting the corresponding line image based on the geographic position information corresponding to the blurred image. Therefore, the power line data can be collected more accurately, the labor burden is reduced, and the working efficiency is improved.

The invention relates to the technical field of behavior recognition, in particular to a construction site safety behavior recognition method based on deep learning, which comprises the steps of collecting environmental data of a construction site in real time, preprocessing the environmental data to improve data quality and diversity, extracting feature vectors, recognizing specific behaviors and identities of construction site personnel, and predicting personnel behaviors. Potential dangerous behaviors are identified, multi-level risk assessment is carried out according to risk levels, after high-risk behaviors are identified, the system not only triggers an alarm, but also automatically takes intervention measures, and through linkage with construction site mechanical equipment, operation of dangerous equipment is automatically stopped so as to prevent accident expansion. According to the invention, through multi-modal data acquisition and fusion processing, the accuracy of behavior identification is improved, refined multi-level risk management is realized, the system can rapidly trigger alarm and automatically take intervention measures, and the emergency processing efficiency and the overall safety level of a construction site are significantly improved.

According to the artificial intelligence-based antibacterial drug administration purpose judgment method and the automatic judgment system disclosed by the invention, after the acquired medical data of the patient, the clinical application guide and literature of the antibacterial drug and the clinical application guide principle of the antibacterial drug are preprocessed, the historical medical data set of the patient is constructed; a historical medical data set of a patient is randomly divided into a training set, a verification set and a test set according to a set proportion for training, and a trained antibacterial drug use purpose judgment model is converted into an antibacterial drug use purpose judgment rule. After an antibacterial drug order making request is received, an antibacterial drug medication purpose judgment rule is called, a matched antibacterial drug medication purpose is generated according to collected antibacterial drug order making information and current medical data of a corresponding patient, and automatic judgment of the antibacterial drug medication purpose is achieved. And selecting a corresponding antibacterial drug management and control rule and path according to the matched medication purpose.

The invention relates to the technical field of Chinese rose production monitoring, and particularly discloses a fine management method for Chinese rose production monitoring based on big data. According to the invention, the basic parameters of the soil environment of each target area in the planting base corresponding to the current monitoring time period are monitored and analyzed, and then the abnormal values of the soil conditions of each target area in the planting base corresponding to the current monitoring time period are analyzed, so that the defect of real-time monitoring of growth and production of Chinese roses in the prior art is overcome; meanwhile, the reliability and the accuracy of a Chinese rose growth soil environment monitoring analysis result are improved to the maximum extent, reliable data support can be effectively provided for follow-up soil improvement and Chinese rose growth regulation and control work, and the oneness and the subjectivity of Chinese rose growth production monitoring in the prior art are further avoided; and the accuracy of real-time monitoring of Chinese rose production is greatly improved.

The invention discloses a point shopping mall service method and system based on user behaviors, and relates to the technical field of point shopping malls. Comprising the steps of collecting behavior data of a user in real time and generating a behavior feature vector; on the basis of the behavior feature vector and the life cycle stage of the user, personalized integral commodities and activities are recommended; user integral history is analyzed, an integral strategy is dynamically adjusted, and customized incentives are provided for different types of users; automatically managing the period of validity of the points, reminding the user of the points to be expired, and guiding the user to consume the points in time in combination with the personalized point commodities and activities; the integral strategy is updated regularly, the excitation mode is optimized according to the user behavior change, and the adaptability and the user experience are improved. According to the invention, by dynamically adjusting the point strategy and combining point validity period management, the user is helped to timely consume points about to be expired, and point loss is reduced.

The invention discloses a spinning production process optimization method based on an intelligent terminal, and relates to the technical field of spinning production. According to the spinning production process optimization method based on the intelligent terminal, a sensor is integrated through the intelligent terminal, process parameters and yarn surface image data are obtained in real time and input into a spinning production process monitoring model, the matching degree of the process parameters and the yarn surface state is analyzed, and a process adaptation index is obtained. According to the process adaptation index, the yarn tension and the spinning speed are dynamically adjusted through a fuzzy control algorithm, and therefore the production process is optimized. Whether the textile equipment is abnormal or not is judged through the equipment position information and the process adaptation index, the inspection path is optimized through the A star algorithm, and it is ensured that the equipment is inspected and maintained in time. Intelligent monitoring and optimization adjustment in the spinning production process are achieved, the production efficiency, the yarn quality and the equipment operation stability are improved, and manual intervention and fault risks are reduced.

The invention provides a personalized settlement method, system and device, and relates to the technical field of information, and the method comprises the steps: training and generating a power utilization optimization model through combining a user power utilization behavior portrait, a device adjustable space, an unadjustable device power supply scheme and an electricity price adjustment scheme; determining an optimal equipment power utilization scheduling scheme and a differentiated electricity price adjustment strategy under different power utilization time periods by utilizing the power utilization optimization model and aiming at the adjustable equipment of each user, and generating a personalized power utilization plan; real-time power grid load data is continuously monitored, when the load data exceeds a load threshold value, personalized power utilization plan dynamic optimization is triggered, and according to the optimized personalized power utilization plan, differentiated electricity price adjustment and equipment power utilization scheduling are executed on each adjustable device. Personalized requirements of users are met, the load of the power grid is effectively balanced, and fine management and optimization of power utilization of the smart home are realized.

The invention discloses an LED display screen partitioning method, which comprises the following steps: obtaining content information to be displayed, and performing feature extraction and classification on display content to obtain display feature parameters of different regions; for each partition, calculating an optimal display parameter by adopting a parameter optimization algorithm according to the display characteristic parameter of the partition; interpolation and transition processing are carried out on parameters of adjacent partitions at the boundary of the partitions, and abrupt and flicker during partition switching are avoided; through a historical data analysis and prediction algorithm, the display content in a period of time in the future is predicted, and partitioning and parameter optimization are carried out in advance; according to the characteristics and changes of the display content, the display effect is adaptively optimized, hardware resources are saved to the maximum extent while the display quality is guaranteed, and intelligent and fine management of the display content is achieved through comprehensive application of technologies such as dynamic partitioning, parameter optimization, smooth transition and predictive analysis.

The invention relates to the technical field of circuit testing, and particularly discloses an online circuit testing method which comprises the following steps: starting communication control, issuing an electric signal to a detonator by a microprocessor through a communication circuit, releasing ignition capacitance energy by the detonator, presetting low and high voltage to measure the current of the detonator, adjusting a gun line voltage difference value, starting a current detection circuit, and closing an ignition and communication circuit. Analyzing and controlling branch states (normal, short circuit and open circuit) and reporting the states to a controller, considering the influence of distributed parameters to judge the short circuit state of the system, reporting circuit damage to a microprocessor based on damage signals, closing related enabling of the system at the same time, and forbidding detonating and discharging of detonators; comparing with a rated current to generate an analysis signal, calculating an early warning coefficient, calculating a high-risk coefficient based on the high-risk analysis signal, drawing a curve, dividing detonator risk levels, and taking corresponding measures for detonators with different risk levels.

The invention provides a yarn surface treatment method which comprises the following steps: aiming at coated yarns, adopting an orthogonal test design method, preferably selecting the type and concentration of a rinsing agent, and determining the optimal rinsing temperature, time and bath ratio, so that residual polymers and impurities on the surfaces of the yarns are removed, the damage and discoloration of the yarns caused by the rinsing agent are reduced to the greatest extent, and the quality of the yarns is improved. The cleanliness and the dyeing uniformity of the yarns are improved; for the rinsed yarn, according to different yarn varieties and filling requirements, the variety and particle size of a filling material are screened, filling process parameters such as filling temperature, time and pressure are optimized, so that the filling material is uniformly dispersed in the yarn, the heat retention property and the bulkiness of the yarn are improved, and meanwhile, the mechanical property and the dyeing property of the yarn are considered.

The invention provides a data acquisition and monitoring analysis system based on an MES platform, and relates to an industrial software application system. Comprising a low-code development module, a factory modeling module, a monitoring module and a data acquisition module, the low-code development module realizes cross-device linkage development and control program generation through combination of a dragging interface and a device API library in an MES platform integration environment, and guarantees efficient communication control of the device, the factory modeling module integrates two production modes, and the monitoring module is connected with the monitoring module. The monitoring module is used for realizing dynamic modeling and optimizing a production process according to multi-dimensional parameters, the monitoring module is used for realizing ultra-low-delay transmission processing and real-time monitoring of data under high concurrency by applying a dual-cache and message middleware mechanism, and the data acquisition module is used for realizing real-time monitoring of the data based on a machine learning algorithm and a complex event processing technology. Dynamically adjusting the acquisition frequency precision based on the production state; the system aims at improving the efficiency, quality and management level of industrial production, and has wide application prospects and important industrial software application value.

The invention provides an intelligent fault diagnosis and early warning system for digital mechanical equipment. The system comprises a data acquisition module, a feature extraction module, an optimization training module and a diagnosis and early warning module. The data acquisition module collects historical operation data such as equipment state parameters and fault characteristic parameters. The feature extraction module inputs historical data to a network, outputs equipment state features and fault features, and further obtains a fault diagnosis probability, an evaluation value, an early warning level and an evaluation value. And the optimization training module optimizes the network based on the evaluation value and the probability until a preset number of iterations is reached, and a trained diagnosis decision network is obtained. And the diagnosis and early warning module inputs the current equipment operation data into the network to realize fault diagnosis and early warning. According to the invention, accurate diagnosis and real-time early warning of mechanical equipment faults can be realized, the reliability and maintenance efficiency of equipment operation are improved, and the downtime and maintenance cost of faults are reduced.

The invention discloses a power transmission line foreign matter identification method and system based on an improved YOLOv10 model. Relates to the technical field of power transmission line detection. According to the method, the method is improved on the basis of the existing target detection technology in the scene of a special power transmission line, the structure of a YOLOv10 model is optimized and improved by introducing an ODConv dynamic convolution module, a CAFM hybrid convolution attention mechanism module and an NWD loss function on the basis of the YOLOv10 model, in the detection stage, foreign matters are recognized through a foreign matter detection model, and the foreign matters are recognized through a foreign matter recognition module. Inputting the image containing the foreign matter into a component detection model to determine the position of the component, and finally performing hazard assessment and making a response according to the relative position of the foreign matter and the component. The detection precision of the YOLOv10 model is effectively improved, the hazard assessment capability is improved, and the detection efficiency is improved, so that the efficiency of foreign matter removal work of the power transmission line is improved, data management can be performed, powerful support is provided for operation and maintenance of the power transmission line, and safe and stable operation of electric power is guaranteed.

The invention provides an elevation data measurement method, device and equipment for a large-fall area, and relates to the technical field of engineering survey and geological survey. The method comprises the following steps: acquiring level gauge height difference data and measurement gravity data of each measurement point in a large-fall measurement area; carrying out regional division on the large-fall measurement region to obtain measurement sub-regions, and determining a gravity interference coefficient corresponding to a measurement point according to measurement gravity data and regional environment data of the measurement sub-regions; determining corrected gravity data based on the gravity interference coefficient and the measured gravity data, and determining an elevation correction item and measured elevation data; and correcting the measured elevation data according to the elevation correction item to obtain corrected elevation data corresponding to the large-fall measurement area. According to the technical scheme, the reliability and precision of elevation data measured by the level gauge in a large-fall area can be effectively improved.

The invention discloses a medical equipment management and evaluation platform and method and a computer program product, and the platform comprises a data collection module which is used for obtaining the real-time data and information system data of medical equipment; the use efficiency analysis module is used for carrying out use efficiency analysis on the collected data to obtain a use efficiency analysis result of the medical equipment and storing the use efficiency analysis result to the platform database; the single-machine benefit analysis module is used for performing single-machine benefit analysis on the collected data to obtain a single-machine benefit analysis result and storing the single-machine benefit analysis result to the platform database; the performance evaluation module is used for performing performance evaluation on the data in the platform database by using a performance index evaluation system to obtain a performance evaluation result; and the simulation prediction module is used for performing simulation prediction according to the data in the platform database. According to the method and the system, fine management of the medical equipment is realized, the operation efficiency is improved, the rationality of resource allocation is enhanced, the equipment operation cost is reduced, and a powerful grasp is provided for management and monitoring of medical equipment assets.

The invention relates to the technical field of parking management, in particular to a parking lot complex charging management system based on artificial intelligence, and the system comprises a data collection module which is used for collecting the parking data of a user, and the parking data comprises the vehicle entering and leaving time, the vehicle type and the position of a parking space of each user; the behavior recognition module is used for generating a user behavior portrait corresponding to each user according to the parking data of the user; the portrait analysis module is used for analyzing the user behavior portrait, determining a parking strategy combination corresponding to the user behavior portrait, and determining differentiated earnings of the user under different behavior decisions and time sequence characteristics under the differentiated earnings according to the obtained parking strategy combination; and intelligence and refinement of charging management of the parking lot are realized.

The invention discloses a real estate registration-oriented business data integration method and system, and relates to the technical field of real estate registration data integration processing. The real estate registration-oriented business data integration method comprises the following steps of: obtaining identification information, property proving information, land use information and transaction record information of a real estate to be registered, and establishing a real estate initial registration data table; acquiring current state information of the real estate to be registered, analyzing a terrain development index, a land area development index, an environment quality index, an infrastructure convenience index, a regional market economic index and a comprehensive development index, and fusing and storing the indexes into a real estate initial registration data table; according to the method, the comprehensive development index is constructed by calculating multiple indexes such as the terrain development index, the land area development index, the environment quality index, the infrastructure convenience index and the regional market economic index, the development potential of the real estate is systematically quantified, and the comprehensive value and the development potential of the real estate can be reflected.

The invention belongs to the technical field of urban intelligent management, and relates to an urban intelligent management platform based on video monitoring. According to the invention, by analyzing the sanitation worker scheduling requirements of the sub-regions to which the various types of regions of the target city belong, the regions needing to be cleaned preferentially can be accurately judged, the waste of manpower and material resources is avoided, the environmental pollution is reduced, and the living quality of residents is improved. By analyzing the health evaluation feedback index of each sub-region to which each type of region of the target city belongs, the fine management of urban environmental sanitation is realized, the efficiency and quality of environmental sanitation work are improved, problems can be found in time, targeted measures can be taken, and the economic benefit is improved. By analyzing the work evaluation feedback index of each sanitation worker in each sub-region to which each type of region of the target city belongs, the work quality and efficiency of the sanitation workers are intuitively known, and problems and defects can be found, so that targeted measures are adopted for improvement, and the overall sanitation work quality is improved.