184results about How to "Improve management ability" patented technology

The invention discloses a data analysis method, system and device based on distributed communication and a medium, and relates to the technical field of data processing, and the method comprises the steps: monitoring a node state in real time based on a distributed communication network, obtaining a node resource state data set, building a node performance evaluation model through a machine learning algorithm, and obtaining a node performance evaluation result; computing resources of the data analysis tasks are dynamically allocated to optimize resource allocation, a task allocation strategy is designed according to requirements and node performance of the data analysis tasks, and a distributed data processing framework is established; the method comprises the following steps: performing distributed decomposition and executing a calculation data analysis task, aggregating and collecting distributed calculation results, and dynamically adjusting and optimizing resource task allocation to obtain a final data analysis result, so that a node state can be monitored in real time, and a resource allocation scheme can be intelligently adjusted; according to the advantages of dynamically optimizing task allocation according to the communication condition and the computing power of the node, the problems of insufficient dynamic resource management and poor task scheduling flexibility in the prior art are solved.

The invention belongs to the technical field of data detection, and particularly relates to an efficient data anomaly automatic detection and alarm system. The detection and alarm system comprises a sensor and data acquisition device, a data processing and analysis module, an alarm module, a monitoring center and visual interface, a communication and network module, a storage and backup module, an intelligent decision support module and a user feedback and adjustment module. According to the system, data can be monitored and analyzed in real time, abnormity can be quickly found, an alarm is given, manual monitoring requirements are reduced, data misjudgment is avoided, data accuracy is ensured through data cleaning and processing, an intelligent decision support module provides optimization suggestions based on AI, and a user can adjust rules to adapt to requirements. The system has the functions of data processing, anomaly recognition and trend prediction, the monitoring center visually displays the state, the storage and backup module ensures long-term storage of data, the automatic mechanism improves the response speed and the operation benefits, and the adaptability is high.

The invention provides a flow limiting method and system based on a distributed cache strategy, belongs to the technical field of distributed flow limiting, and is applied to a distributed cache system which comprises a plurality of cache partitions. The method comprises the following steps: acquiring real-time traffic and historical traffic of each cache partition; inputting the real-time traffic and the historical traffic into a traffic prediction model to obtain predicted traffic at the next moment; and determining a flow limiting parameter of the cache partition based on the predicted flow, and limiting the flow at the next moment based on the flow limiting parameter. According to the method, the flow limiting parameters are automatically adjusted by using the machine learning technology, the data flow can be accurately controlled, the system overload is prevented, the resource waste caused by excessive limitation is avoided, and the flow management efficiency and effect of the distributed cache system are remarkably improved.

The invention relates to a microbial index-based estuary and coastal environment ecological evaluation method, which comprises the following steps of: selecting representative sampling points, collecting sediment samples, analyzing and evaluating pressure gradients generated by human activities to estuary and coastal environments through physicochemical indexes, and constructing pressure indexes according to a reference standard, the comprehensive environment pressure index of each sampling point is constructed and calculated to reflect the overall condition of the target area environment; evaluating the health condition of large invertebrate communities in estuary and coastal environments and the response of the communities to human pressure, and calculating a marine organism index AMBI, so as to display the ecological health condition of a region; a high-throughput sequencing technology is used for identifying microorganism composition, pollution indicator bacteria are defined, a microorganism index M-AMBI value is calculated according to the pollution indicator bacteria, and the ecological health condition of each sampling point is graded; and finally, verifying the reliability of an ecological evaluation result through statistical analysis. According to the method, multi-dimensional data are integrated, the ecology of the estuary and coastal environment is comprehensively evaluated, a scientific basis is provided for revealing an environment pressure source and an ecological effect thereof, formulation of a precise environment management strategy is assisted, pollution risks are effectively prevented and controlled, and sustainable development of an estuary and coastal ecological system is promoted.

The invention relates to the technical field of process management, in particular to a sealing integrated collaborative process optimization method and system. The method comprises the following steps: obtaining sealing process data; performing sensor data acquisition according to the sealing process data to obtain a multi-modal sensor acquisition data set; performing data preprocessing on the multi-modal sensor acquisition data set to generate a standard multi-modal sensor acquisition data set; performing process flow analysis on the sealing process data to generate sealing process flow data, the sealing process flow data including a sealing flow, a cover closing flow and a cover pressing flow; and performing intelligent process modeling on the sealing process, the cover closing process and the cover pressing process to generate a process procedure mathematical model. According to the invention, through multi-modal data acquisition, detailed process flow analysis, real-time monitoring and dynamic adjustment strategies, the flexibility and adaptability of sealing process production process management are improved.

The invention relates to the technical field of data processing, and discloses a method, device and equipment for constructing a lightweight data warehouse based on an MPP architecture, and the method comprises the steps: collecting data in a plurality of mainstream databases through a DataX collection assembly, and writing the collected data into a Dores data lake to obtain offline data; the method comprises the following steps: acquiring data of a plurality of data sources through a RoutingLoad component and a Kafka component, and writing the data into a Dores data lake to obtain real-time data; establishing a data hierarchical structure; performing calculation and analysis processing to generate a data calculation result; constructing a data asset catalog based on a metadata system of a Dores data lake to perform classified management of libraries, tables and fields; the data service interface is constructed based on the data asset directory, the BI report tool is connected with the data service interface to generate the data analysis report, and the data calculation result and the data analysis report are output to the outside, so that the construction and maintenance cost of an enterprise is reduced, and the requirements in a big data environment can be better met.

The invention discloses a laboratory safety IOT (Internet of Things) intelligent early warning system, which comprises a laboratory environment early warning system, a laboratory equipment early warning system, a laboratory personnel behavior early warning system, a self-adaptive learning system and an integrated control system, wherein the laboratory environment early warning system, the laboratory equipment early warning system, the laboratory personnel behavior early warning system and the adaptive learning system are connected with the integrated control system through a wireless communication network; the adaptive learning system is connected with the laboratory environment early warning system, the laboratory equipment early warning system and the laboratory personnel behavior early warning system through a wireless communication network; the self-adaptive learning system is used for optimizing early warning models of the subsystems, and the integrated control system is used for efficient integration of the subsystems to ensure data sharing and cooperative work among the subsystems. According to the invention, the safety and management efficiency of the laboratory are effectively improved, and the potential risk is reduced.

The invention discloses a smart home management system and method, electronic equipment and a storage medium, and the method comprises the steps: receiving a home management control instruction through an application service layer, determining a corresponding target home management application service according to the home management control instruction, generating a corresponding service calling instruction, receiving the service calling instruction through a business logic layer, and transmitting the service calling instruction to a server; determining a corresponding target task processing logic and each target home management agent, calling each target home management agent to execute a home management task according to the target task processing logic, and storing each home management agent and a plurality of task processing logics corresponding to each home management agent through a service deployment layer, and managing each task processing logic and each home management agent. According to the invention, standardized smart home management can be realized, the smart home management efficiency is improved, the smart home management effect is improved, the smart home management requirement of a user is met, and the method can be widely applied to the technical field of artificial intelligence.

The invention relates to a method for actively collecting a micro-service error log, which comprises the following steps: collecting a micro-service log according to pre-configured task information, the pre-configured task information comprising a micro-service name and a log path, and the micro-service log comprising log content, a link tracking ID and log context information; filtering the micro-service log according to the filtering rule to obtain a key error log, wherein the key error log is used for representing generated important error information; filling the abnormal type, the log context and the link tracking ID analyzed based on the key error log into a structure template to generate a structured log file; and sending the structured log file to the target receiving end according to the configuration information. According to the method, a rear-end collection mode is adopted, the micro-service logs are automatically collected, developers do not need to manually log in a server or query log files, and time and energy are saved.

The invention provides a multi-source aggregation-based low-frequency and low-voltage load shedding device operation reliability studying and judging method, which is used for studying and judging the reliability of the operation state of a low-frequency and low-voltage load shedding device by combining the importance of operation management of the low-frequency and low-voltage load shedding device and generating a pressing plate factor, a constant value factor, a blocking signal factor and a load type factor. And further judging the load shedding capacity factor according to the generation factor, uniformly studying and judging the dispersed parameters of the low-frequency and low-voltage load shedding device, and judging the safety and health degree of the low-frequency and low-voltage load shedding device and the power grid. According to the invention, the pressing plate factor, the constant value factor, the blocking signal factor, the load type factor and the load shedding capacity factor are visually displayed through the large visual screen, so that a worker can visually grasp the safety condition of the low-frequency and low-voltage load shedding device and the safety condition of a power grid. According to the invention, the monitoring and management capabilities of the low-frequency load shedding device are improved, the safe and stable operation of the device is ensured, and the safety and reliability of power grid operation are improved.

The invention discloses an intelligent distribution box production management method and system based on a two-dimensional code and a storage medium, and relates to the technical field of production control, and the method comprises the steps: obtaining the basic information of a distribution box product and the production plan information of a target scene, and generating a unique identification code of the distribution box product; extracting flow link information of a target scene, analyzing a key link node set, configuring edge processing nodes, and constructing a scene block chain comprising a block main chain and a plurality of side chains; generating a two-dimensional code according to the unique identification code, extracting a product information dimension set to construct a product information database, and establishing an association relationship between the unique identification code and the database; identifying an exogenous product information database associated with the target scene, and subscribing and updating exogenous data to the product information database; the production information is monitored in real time through the scene block chain and synchronized to the product information database, and the control decision is supported, so that the technical effects of smooth information sharing, management capability expansion and user satisfaction improvement are achieved.

The invention relates to the technical field of the Internet of Things, in particular to an integrated parking space management system and method based on the Internet of Things. The system comprises an electronic parking space lock which is used for identifying vehicle license plate information of a vehicle owner and unlocking and locking the parking space lock after comparison according to input information; the parking space management all-in-one machine is used for storing vehicle information of a vehicle owner and controlling opening and closing of the electronic parking space lock; and the client is used for inputting the vehicle information of the vehicle owner into the parking space management all-in-one machine and remotely controlling the electronic parking space lock to be opened and closed. According to the technical scheme, in the equipment installation process, operation is easy and convenient, intelligent management can be effectively carried out on parking spaces, one-to-one independent equipment control and independent installation can be achieved, use is convenient, the trouble of getting on and off of an automobile owner is reduced, and a remote control key does not need to be kept all the time.

The invention discloses a two-stage data pool data loading method based on a CXL memory architecture. According to the method, a CXL memory serves as a cold data pool to store data with low use frequency, and a DRAM memory of a server node serves as a hot data pool to store data with high use frequency. When data in a CXL memory is used for many times, the data monitor judges whether the data can be transferred into hot data or not through a delay caching strategy and caches the hot data. In addition, a self-adaptive caching strategy is adopted to keep the hot data in a server for a certain time through a hot data pool, so that the application performance is improved. And dynamically learning a caching strategy by the hot data pool. And when the hot data is added or expelled every time, a caching strategy is selected according to the weight of the LRU-S, the weight of the LFU-S and the weight of the FIFO expert. And meanwhile, the hot data pool comprises a historical list, the decision regret expelling behavior of the cache strategy is allowed, and wrong decisions are punished.

The invention relates to a noninvasive blood glucose detection technology, in particular to a blood glucose detection method for a noninvasive blood glucose meter and a blood glucose detector. The invention discloses a blood glucose detection method for a noninvasive blood glucose meter. The blood glucose detection method comprises the following steps: acquiring near-infrared light data and mid-infrared light data and calculating corresponding weight coefficients; acquiring a predicted blood glucose value by using the blood glucose detection model, calculating credibility, comparing the credibility with a preset credibility threshold, and executing corresponding operation; and acquiring a blood glucose health assessment result by using the blood glucose health assessment model. The near-infrared spectrum technology and the mid-infrared spectrum technology are combined, the influence of environmental factors is considered in real time, the weight coefficients of the near-infrared light data and the mid-infrared light data are calculated respectively, analysis of the spectrum data is adjusted and optimized, a more accurate blood glucose detection result is provided, and the accuracy of blood glucose detection is improved. And reliable blood glucose measurement values can be obtained under different environmental conditions.

The invention discloses a supply chain logistics information management system based on artificial intelligence, and particularly relates to the technical field of artificial intelligence, and the system comprises a linear fitting module, a nonlinear fitting module, a dual-mode construction module, a distribution verification module, and a management intervention module. The non-linear fitting module obtains a non-linear special coefficient according to the non-linear scene information, the dual-mode construction module constructs a dual-mode management model according to the linear conventional coefficient and the non-linear special coefficient and outputs a time cost delay index, and the distribution verification module classifies delay states in a supply chain logistics information management process. According to the supply chain logistics information management system and method, time cost prediction is carried out on customs declaration and tax payment links which are most time-consuming and labor-consuming in the supply chain logistics information management process, and the management effectiveness and real-time performance of the whole logistics information management chain are improved.

The invention provides an energy equipment data communication method based on a block chain, and the method comprises the steps: obtaining energy data of energy equipment which completes registration in a block chain network, and writing the energy data into a sub-chain of the block chain network based on the type of the energy data; the target energy equipment responds to a preset data interaction condition meeting the target control mode, initiates a first identity authentication request to the block chain network to perform first identity authentication, generates a data sharing request for the target type data based on the preset data interaction condition of the target control mode when the first identity authentication is successful, and performs second identity authentication; and when the access is successful, processing the data sharing request for the target type data by a sub-chain matched with the type of the target type data in the block chain network based on an intelligent contract, determining target shared energy data conforming to the access rule, and sharing the target shared energy data to the target energy equipment, therefore, energy data interaction between household energy devices can be realized safely and efficiently.

The invention relates to a multi-level quality file management method based on big data, and the method comprises the steps: quantifying the relation between quality file information and a quality control result through building a random forest model, and recognizing key quality file information which has an important influence on the quality control result; a reference basis is provided for improving the quality file information for improving the quality control effect; the use frequency of the quality files is clustered by adopting a clustering analysis method, and the importance of the quality files is sorted according to a clustering result, so that classified storage of the importance of the quality files is realized, and the file management efficiency is improved. Through dual importance evaluation of key quality file information identification and importance grade division of the quality file, the problem of low management efficiency caused by incapability of evaluating the importance of the quality file in the prior art is solved.

The invention discloses an intelligent management system for green building supervision engineering, and belongs to the technical field of engineering management. The method is used for solving the technical problems of poor local supervision reliability analysis effect and poor overall supervision reliability analysis and management effect of green building supervision in an existing scheme. Performing data analysis, digital representation and combination on supervision implementation states corresponding to a plurality of supervision data items associated with all supervision indexes implemented by the supervisor to obtain a supervision implementation supervision table; corresponding local supervision reliability data processing and analysis are carried out on different supervision indexes according to a supervision implementation supervision table obtained through earlier-stage processing; and performing overall supervision reliability data calculation on all supervision indexes supervised by the supervisor according to a local supervision reliability processing sequence obtained through earlier-stage processing to obtain a supervision overall abnormal state identifier corresponding to the supervisor, performing data analysis on the supervision overall abnormal state identifier, and adaptively performing dynamic management on supervision of the supervisor.

The invention discloses an unmanned store risk identification intelligent management system based on big data analysis, and belongs to the technical field of risk identification management. The method and the device are used for solving the technical problem of poor commodity settlement and display and placement risk identification and management effects during unmanned store operation in the existing scheme. In the supervision processing unmanned store operation process, different users carry out settlement of different articles, supervision processing data of different users are analyzed and combined, supervision statistical data of all settlement commodities are processed and analyzed, and an existing settlement risk prevention scheme is dynamically managed according to an analysis result. According to the method, the falling conditions of all the placed commodities in the unmanned store are monitored and counted, data processing and analysis are carried out on the display abnormity degrees corresponding to all the display racks for placing the commodities in the unmanned store, the display abnormity degrees corresponding to different display racks are determined according to analysis results, and targeted local display rack commodity placement management is implemented.

The invention provides a pipeline defect detection method and system and electronic equipment, and the method comprises the following steps: collecting and preprocessing a pipeline video, and obtaining a preprocessed pipeline image; detecting and positioning pipeline defects existing in the preprocessed pipeline image by adopting a pipeline real-time detection algorithm; studying and judging the type of the pipeline defect to obtain a pipeline defect detection result; and generating a pipeline defect detection report according to the pipeline defect detection result. According to the scheme provided by the invention, the pipeline defect is detected by using the pipeline real-time detection algorithm, so that the detection accuracy is improved and the manual operation is reduced; a ChatGLM3-6B technology and an RAG technology are applied to generate a more accurate defect detection report, and a pipeline CCTV detection technology is combined to realize one-button generation of the pipeline defect detection report, so that resource configuration can be optimized, pipelines can be managed and maintained in a targeted manner, and the management capability of an urban drainage system is effectively improved while the cost is reduced.

The present invention relates to the field of blockchain technology, and in particular to a method and system for enterprise credit assessment based on blockchain and homomorphic encryption. The method is implemented by an enterprise credit assessment system, which includes a blockchain storage node, a government department node, a key management node, a supervisor node, and a credit assessment node. The enterprise credit assessment system mainly utilizes blockchain and additive homomorphic encryption technology, and can assess the credit of an enterprise without the original data being stored in the warehouse. At the same time, the assessment process will be automatically calculated and stored by a blockchain smart contract. Based on the decentralized tamper-proof feature of blockchain, the transparency and distributed trust of the assessment process can be achieved, so that data between multiple departments can be safely integrated and utilized, thereby improving the utilization rate and activity of data and enhancing the comprehensive management capabilities of government departments over enterprises.

The invention provides a diabetic patient life management method and system in combination with traditional Chinese medicine knowledge, and relates to the field of medical health data processing, the method comprises the following steps: obtaining multiple groups of sample data, the sample data at least comprising personal information of sample patients and diabetic traditional Chinese medicine syndromes, and the personal information of the sample patients at least comprising diabetes index data; based on the multiple groups of sample data, multiple key traditional Chinese medicine syndrome discrimination units are determined, and a diabetes traditional Chinese medicine syndrome diagnosis scale is generated; based on the multiple groups of sample data, determining music types matched with different diabetes mellitus traditional Chinese medicine syndromes; determining the diabetic traditional Chinese medicine syndrome of the current user based on the personal information of the current user and the filling information of the diabetic traditional Chinese medicine syndrome diagnosis scale; based on the diabetic traditional Chinese medicine syndromes of the current user and the music types matched with different diabetic traditional Chinese medicine syndromes, music, traditional Chinese medicine health maintenance skills and traditional Chinese medicine recipes are recommended, and the method has the advantage of improving the quality of life management of diabetic patients.

The present invention relates to the field of dangerous goods transportation supervision technology, and in particular to a waybill enhancement method based on trajectory data of dangerous goods transportation vehicles, the method comprising obtaining high-dimensional trajectory data of the transportation vehicle, time series trajectory data without missing values; identifying a valid stop point list of the transportation vehicle; performing behavioral feature analysis and duration analysis on the valid stop point list, correlating and comparing the classified stop information of the same transportation vehicle with the pre-filled waybill information, and generating an enhanced waybill based on the consistency between the classified stop information of the time series trajectory data and the pre-filled waybill information. The present invention significantly improves the monitoring and management capabilities of the dangerous goods transportation process by generating accurate and effective enhanced waybills, ensuring the timely identification and handling of abnormal situations, and the enhanced waybills also help to quickly trace the cause of the accident.

The invention discloses an electricity stealing user identification system and method based on big data analysis, and relates to the technical field of user power data analysis, and the system comprises a data collection module, a data processing module, an anomaly detection module, an intelligent identification module and an alarm module. The information acquisition module is used for acquiring power utilization information of a user in real time and sending the power utilization information of the user to the data processing module; the information acquisition module comprises a current sensor, a voltage sensor and a power sensor, and is used for acquiring performance information of power utilization of a user in real time, comprehensively analyzing the data by using the intelligent identification module, confirming an electricity stealing behavior by using a machine learning algorithm or a statistical model, and sending early warning information to a manager; the system is convenient for management personnel to carry out further verification and processing, measures for preventing electricity stealing are taken in time, and the screening accuracy is improved by collecting electricity utilization, power supply, loss and environment information in the power system and carrying out preprocessing and analysis by using the artificial intelligence module.

The invention relates to an intelligent safety management and control technology, in particular to an intelligent safety management and control system for a hydropower station, which solves the defects of the traditional method in the aspects of real-time performance, accuracy and intelligence by integrating advanced data acquisition, personnel identification and track recording and analysis technologies. According to the system, positioning information is collected through portable equipment, personnel images are collected through face recognition and video monitoring, tracks of internal personnel and tracks of external personnel are distinguished, and abnormal behaviors are recognized through historical track analysis and a set time threshold value. The monitoring of the behaviors of the external personnel is enhanced, the management capability of the abnormal behaviors of the internal personnel is also improved, the safety management and control efficiency and the response speed are effectively improved, and an efficient and intelligent solution is provided for the safety management of the hydropower station.

The utility model provides a compressor grounding assembly which comprises a grounding box, a grounding metal rod is fixedly installed in the grounding box, a ceramic plate is fixedly installed in the grounding box, a clamping groove is formed in the upper surface of the ceramic plate, a grounding plate is inserted in the clamping groove, and the grounding metal rod is fixedly installed in the grounding box. The upper surface of the grounding plate is provided with a through jack, and the interior of the jack is inserted into the top of the grounding metal rod. According to the utility model, one end of the grounding metal rod is provided with the grounding box and the ceramic plate, and the grounding plate and the nuts are fixed on the grounding metal rod and the ceramic plate, so that eight O-shaped grounding wires can be installed on the grounding box, and compressors in the same area can be connected to the same grounding box in a centralized manner; the grounding metal rod is simple to construct and install, centralized grounding is beneficial to later inspection and maintenance, the management capability of multiple groups of grounding wires is improved, and the labor intensity of maintenance is reduced.

The invention discloses a multi-type elevator risk management system, which belongs to the technical field of elevator management, and comprises a data acquisition module used for acquiring speed, load and door opening and closing times in the elevator running process and determining elevator running real-time state data; the wireless communication module is used for transmitting the elevator running real-time state data to the risk management terminal in a wireless network mode, and sharing of the elevator running real-time state data is achieved; and the risk management terminal is used for carrying out processing, risk analysis and early warning record management on the elevator operation real-time state data. The elevator risk monitoring and management system solves the problems that in the prior art, elevator risks cannot be monitored and controlled in real time, and various types of elevator risks cannot be effectively recorded and managed, so that the elevator use and management effect is poor. The elevator risk can be monitored and controlled in real time, multiple types of elevator risks can be effectively recorded and managed, and the elevator use and management effect can be improved.

The invention relates to the technical field of data asset management, in particular to a data asset management system and method. The method comprises the following steps: acquiring a data asset information set from different data sources in real time and performing tag structure sub-column division to obtain data asset information tag description structured sub-columns, and meanwhile, performing time sequence creation synchronization processing and integrating and storing the data asset information tag description structured sub-columns into a data asset management database; performing use frequency and sensitivity evaluation analysis on each piece of data asset sub-information in the data asset management database, and performing data asset access utility evaluation analysis to obtain data access utility of each piece of data asset sub-information; and performing dynamic access right control and full life cycle management design on each piece of data asset sub-information in the data asset management database, and performing automatic management and report feedback recording to generate a data asset information management report. The transparency, safety and efficiency of data asset management can be improved.

The invention belongs to the technical field of Internet of Things sensing systems, and particularly relates to a low-power-consumption Internet of Things sensing system based on thermoelectric power generation and energy supply, which comprises a power generation module, an electric energy management module, a data collection module and a wireless transmission and storage module. The power generation module comprises a heat dissipation unit, a thermoelectric power generation unit and a heat pipe heat conduction unit. The energy management module comprises a low-power-consumption low-voltage boosting unit, an electric energy collecting unit, an energy storage unit and a discharging voltage stabilizing unit. The data collection module comprises a microprocessor main control chip unit with a built-in Wi-Fi module, a serial port communication circuit unit, a temperature and humidity collection unit, a sound loudness collection unit and a working state warning unit. The wireless transmission and storage module comprises an MQTT wireless transmission network server unit, an equipment client unit, a WEB client unit and a database client unit. The problem of power supply of the wireless sensor working outdoors is solved, power consumption of system operation is reduced, and long-term effective operation of the system is guaranteed.

The invention relates to the technical field of asset management, discloses a specific model construction method and system based on an asset digital intelligent management platform, and aims at solving the problems that a traditional asset management mode is low in efficiency, inaccurate in prediction, incomplete in evaluation, not intelligent in decision making and the like. The method comprises the step of constructing an asset performance prediction model, an asset health degree evaluation model and an asset management decision model. The performance prediction model predicts future performance indexes based on asset configuration and environmental condition data; the health degree evaluation model comprehensively considers multiple factors to evaluate the current health state of the assets; the management decision model models a management process into a Markov decision process, and optimizes decision actions through a deep reinforcement learning algorithm. According to the system, effective integration and cooperative work of the models are realized, the intelligent level and decision-making efficiency of asset management are improved, and a brand-new and intelligent solution is provided for asset management.