339 results about "Statistical analyses" patented technology

The invention provides a cross-border e-commerce big data analysis system and method, and the method comprises the steps: obtaining multi-dimensional original business data of a cross-border e-commerce platform, carrying out the real-time collection through a distributed data collection system, and obtaining a structured business data set; data collected in real time is cleaned, converted and structured, a unified data model is constructed, and integration of heterogeneous data sources is achieved; storing the processed structured data in a time sequence database to provide data support for real-time analysis; performing real-time aggregation and statistical analysis on the newly added data to generate business index and key performance index data; a Lambda framework is adopted, batch processing and stream processing are combined, the historical data and the real-time data are subjected to full-amount analysis, and an analysis result is obtained; based on an analysis result, a data mining algorithm is adopted to identify a service mode and predict a future trend; a multi-dimensional data cube is constructed, multi-dimensional and multi-granularity data exploration and interactive analysis are supported, and management decision making is assisted.

The invention discloses a box-type transformer substation monitoring method based on unsupervised learning, and the method comprises the following steps: S1, collecting multi-source sensing data in the operation process of a box-type transformer, and generating a standardized input data set; s2, selecting a data sample in a normal operation state, constructing an unsupervised learning model, and obtaining a feature representation set; s3, inputting data, extracting current operation state characteristics, and recognizing a voiceprint abnormal state by combining outlier detection; s4, performing window sliding and statistical analysis on the time sequence data, and outputting a trend abnormal interval and an abnormal index type; s5, constructing a variable association graph structure, and identifying potential abnormal variables and propagation paths; s6, integrating the various types of abnormal information and the feature representation set, and constructing a voiceprint map library; and S7, integrating and processing the abnormal result and the voiceprint map database, and executing visual display and intelligent early warning. According to the invention, box transformer substation abnormity monitoring and early warning based on unsupervised learning are realized, and the fault identification accuracy and response efficiency are improved.

The invention discloses a medical consumable monitoring system based on data analysis, and relates to the field of medical consumable monitoring based on data analysis. Comprising a data acquisition module, a medical consumable consumption analysis module, a medical consumable level analysis consumable module, a medical consumable demand analysis module, a department consumable consumption analysis module, a consumable monitoring and early warning module and a database. The consumption record of each medical consumable, the consumption of each medical consumable in each quarter and the consumption of each medical consumable of different epidemic diseases are judged and analyzed, and then the supply parameters of the medical consumables, the storage parameters of the medical consumables and the consumption parameters of the consumables in departments are subjected to statistical analysis; according to the method, the required amount of each month time period corresponding to each medical consumable and the consumption value of each consumable corresponding to each department are obtained, various conditions such as seasons and diseases can be considered in multiple aspects, the consumable cost can be reduced, meanwhile, the consumable use condition of each department can be monitored, and early warning can be given out for abnormal conditions.

The invention discloses an intelligent scalp detection system and a detection method thereof, relates to the technical field of intelligent scalp detection, and aims at obtaining shooting conditions and definition evaluation through real-time environment perception and quality detection, solving hair occlusion and image noise by using deep learning segmentation and directional enhancement, showing a scalp deep structure by means of multi-modal fusion and splicing, and improving the detection accuracy. A diagnosis and optimization strategy is output in comprehensive analysis and self-adaptive closed-loop feedback; through software algorithm level expansion, online training and multi-label pathological expansion can be performed on multi-user long-period data, and adaptive evolution and group statistical analysis are realized; according to the method, the image acquisition stability and segmentation accuracy can be effectively improved, the adaptability of detection to complex illumination and multi-band information is enhanced, the risk of pathological misjudgment such as grease, inflammation and fungi is reduced, and personalized nursing and wide health management application are supported.

The invention belongs to the field of air pollution prevention and control, and particularly discloses an urban air pollution transmission path and source contribution evaluation system and method based on machine learning and a numerical mode, and the evaluation system comprises a meteorological simulation module, a pollution emission list module, a numerical traceability module and a machine learning transmission path evaluation module. According to the method, machine learning and a numerical mode are combined, the automation degree is high, automatic downloading, automatic running and online data analysis can be achieved without manual data statistics and analysis, operation is convenient, data accuracy is high, the response speed is high, and important support is provided for work such as atmospheric pollutant cause tracing and atmospheric pollution control scheme making.

The invention discloses an IMU and navigation collaborative energy-saving method and device, vehicle-mounted navigation equipment and a storage medium, and the method comprises the steps: obtaining original data, carrying out the data collection of the original data, and obtaining an initial sampling sequence; carrying out segmentation processing on the initial sampling sequence, and carrying out statistical analysis on energy consumption distribution characteristics in different motion states; the working state of the system is divided into different energy consumption areas based on the energy consumption distribution characteristics, and graded sampling configuration schemes of the IMU sensor and the navigation module are formulated according to the different energy consumption areas; calculating an energy consumption parameter combination of the IMU sensor and the navigation module in each working state according to the hierarchical sampling configuration scheme to obtain an energy consumption control parameter of the system; and adjusting the IMU sensor and the navigation module to corresponding working frequencies according to the energy consumption control parameters based on a real-time working state detection result of the system. According to the technical scheme, on the premise of ensuring the positioning precision, the power consumption of the vehicle-mounted navigation equipment is reduced, and the energy utilization rate of an automobile is improved.

The invention discloses a robot cluster management method, device and equipment, a storage medium and a program product, and relates to the technical field of cloud computing, the method is applied to a conch robot automation process platform, and the method comprises the steps of obtaining cluster database change log data; performing anomaly monitoring on the cluster database change log data based on a pre-generated monitoring early warning strategy to obtain an anomaly monitoring result; and performing statistical analysis according to the abnormal monitoring result, generating an abnormal statistical chart, and obtaining a cluster management result according to the abnormal statistical chart. Through the conch robot automatic process platform, a set of monitoring and early warning system is provided, the monitoring and early warning system comprises various task state circulation monitoring, business detail processing monitoring and the like, problems are found in time in the monitoring process, early warning is given to users through multiple channels, the technical problem that the management capacity of a large-scale robot cluster is poor is solved, and the management efficiency of the large-scale robot cluster is improved. The state of each robot can be reflected in real time, and the overall working efficiency of the cluster is optimized.

The invention discloses a seawater parameter correlation analysis method and device, electronic equipment and a storage medium, and relates to the technical field of ocean monitoring. The method comprises the following steps: acquiring satellite remote sensing data of a target sea area and performing space-time quality screening; preprocessing the satellite remote sensing data to generate correction data for temperature inversion; analyzing tidal dynamic characteristics of the target sea area; inverting a sea surface temperature field based on the pre-processed data, and performing enhancement analysis by fusing tidal dynamic characteristics; establishing a coupling strength quantitative index of the tidal power and the temperature field; collecting and complementing historical observation data of the power plant; the relevance between the temperature and the liquid level parameters is counted and analyzed; and the correlation of the seawater parameters is judged by integrating the coupling strength quantitative index and the data statistical result. The invention provides a two-channel decision-making mechanism with cooperation of a physical mechanism and operation data statistics, and the mechanism not only improves the operation efficiency of the cooling system of the power plant, but also reduces the misjudgment rate of abnormal working conditions, and provides an enforceable optimization strategy for warm drainage management.

The invention relates to the technical field of measuring instruments, in particular to a wireless transmission digital pressure transmitter, which comprises a pressure transmitter, and is characterized in that the pressure transmitter comprises a reset magnet, an antenna, an LED indicating lamp, a right-angle antenna turning joint, a liquid crystal display screen, a toughened glass window, a hanging screw, an M6 bracket fixing hole, an instrument key and a process connecting interface; according to the scheme, by integrating multiple functional modules, temperature and pressure integrated measurement, flexible setting of multiple parameters and efficient processing and management of data are achieved; the pressure transmitter of the system supports the temperature and pressure integrated function, and different measurement requirements are met; instrument keys and upper computer software enable parameter setting to be simple and rapid; the data analysis unit of the upper computer performs deep mining on the data by using various algorithms, so that not only can pressure data be counted and analyzed, but also trends can be predicted, faults can be diagnosed, powerful support is provided for equipment maintenance and production scheduling, and the defects of a traditional system are effectively overcome.

The invention relates to the technical field of carbon resource management, in particular to a carbon emission management system for a building energy carbon supervision platform, which comprises a carbon emission statistics module, a carbon emission calculation and evaluation module and a carbon planning module, the carbon emission statistics module comprises a carbon emission factor management sub-module, a real-time data sub-module, a historical data sub-module and a system data sub-module, the carbon emission calculation and evaluation module comprises a carbon emission calculation sub-module and a carbon emission reduction benefit analysis sub-module, and the carbon planning module comprises a carbon neutralization calculation sub-module and a dual-carbon process analysis sub-module. The invention provides a carbon emission management system suitable for a building energy and carbon supervision platform, which can connect the computational statistics, analysis and evaluation and planning and expectation processes of building carbon emission to form a complete building carbon emission management system, and on the basis of the basic data and functions of the energy and carbon supervision platform, according to the actual project requirements, the energy and carbon supervision platform can be used for energy and carbon supervision. And a comprehensive and clear carbon emission management service is provided for the user.

The invention discloses a pre-splitting blasting half-wall porosity intelligent identification system and method, and the system comprises a front-end display platform and a rear-end operation system which carry out data interaction through a computer, and the operation result information is displayed and checked through the front-end display platform; the front-end display platform consists of a menu bar, a toolbar, a display window and a command line; the rear-end operation system mainly comprises a half-wall hole image database, an image denoising module, an image segmentation module, a data statistical analysis module, an identification calculation module and the like, half-wall hole photo images are collected on steps on the two sides of the platform through the unmanned aerial vehicle oblique photography technology with or without the RTK function, a three-dimensional image database marked with half-wall hole information is established, and the three-dimensional image database is used for storing the half-wall hole information. An intelligent half-wall porosity recognition algorithm based on computer deep learning is developed, accurate recognition of the half-wall porosity is achieved, the traditional presplitting blasting quality evaluation efficiency can be greatly improved, and the method can be widely applied to slope presplitting blasting effect evaluation in the fields of surface mines, water conservancy and hydropower, transportation and the like.

The invention relates to the technical field of data analysis, and discloses a business analysis and decision support method and system, and the method comprises the steps: collecting purchase-related business data from a plurality of data sources, and carrying out the preprocessing; storing the preprocessed data in a centralized data warehouse, and performing management based on a preset data governance strategy; performing multi-dimensional analysis on the structured data based on a statistical analysis model and a machine learning algorithm, and generating a key performance indicator KP I and a trend prediction result; providing a customized visual display interface according to a user role, and presenting the KP I and a trend prediction result through an interactive instrument panel; and generating a purchase optimization suggestion based on the analysis result, evaluating the influence of different purchase strategies through analogue simulation, and outputting corresponding decision support information. The system effectively solves the problems of weak analysis capability, insufficient decision support and the like in an existing purchase decision system, and is suitable for multiple application scenes such as enterprise purchase management and the like.

The invention discloses an AI visual passenger flow volume statistics and analysis system based on YOLOv7, and belongs to the field of computer vision and data statistical analysis. Through multi-model collaborative optimization, the system realizes the functions of passenger flow statistics, personnel attribute analysis, abnormal behavior detection and multi-dimensional data mining. The method specifically comprises the steps of real-time passenger flow counting, peak period distribution and hot spot area analysis based on YOLOv7 and SORT algorithms; utilizing a PaddleClas module to extract the gender, age and clothes attributes of the pedestrian so as to construct a user portrait; and early warning is carried out on abnormal behaviors such as running, gathering and object throwing in combination with an ASTNet network. The system adopts a mixed data set training model, hardware resource consumption is reduced through a multi-task parallel processing architecture, and the real-time performance is improved while the detection precision is guaranteed. The multi-dimensional data analysis capability can provide accurate support for commercial operation optimization and public safety early warning, and the method is suitable for intelligent management of retail, scenic spots, transportation hubs and other scenes.

The invention relates to the technical field of data processing, and discloses an unmanned aerial vehicle photo and video management method and system based on a geographic position. The method comprises the following steps: extracting geographic attribute data from an unmanned aerial vehicle photo EXIF and video subtitles; establishing a spatial database, storing photos as a point graph layer, and storing videos as a line graph layer; renaming the file according to the shooting time and the camera model; importing the point-line graph layer into GIS software to generate a visual graph; searching and screening target area media through spatial analysis; and performing statistical analysis on the media set to generate a comprehensive report. According to the method, unmanned aerial vehicle photo and video data can be managed at the same time, geographic position information is fully utilized, multi-dimensional space retrieval is supported, and the method is suitable for various application scenes, so that the complexity and errors of manual arrangement are eliminated, and the management efficiency and retrieval precision of unmanned aerial vehicle media data are improved.

The invention discloses an AI capability integrated multifunctional enterprise communication business card implementation method, which comprises the following steps that: an enterprise user registers an account in a system, logs in, enters an enterprise management interface and uploads enterprise data; the system stores the uploaded data to an enterprise knowledge base module, performs initialization processing on the data, and establishes indexes and classifications; an enterprise employee fills in own identity information and things needing to be processed in the call on a dial plate of the intelligent communication system, and an incoming call business card pushing module pushes related information to a called terminal through a three-network flash message; enterprise employees communicate with clients through the intelligent communication system, the employees can send AI business cards and incoming call business cards, and the data statistical analysis module records the business card delivery condition of the employees and the watching behavior data of the clients to the AI business cards in real time. According to the invention, the information transmission efficiency can be improved, so that the business activities of employees, especially sales-type employees, are effectively managed, the operation cost is reduced, the propaganda efficiency is improved, and the labor cost is reduced.

The invention provides an AI-based shipbuilding man-hour unification method and system, a medium and a terminal, and the method comprises the steps: data collection and preprocessing, feature extraction, clustering algorithm application, model generation and the like. Three-dimensional model data and attribute information of ship parts of design end production design modeling are analyzed, an unsupervised learning algorithm is used for automatic classification, the parts with similar characteristics are classified into one class, and the average working hours of the parts of the same class are statistically analyzed in combination with currently recorded part working hour information. Part working hours of similar categories are unified into one datum, part categories in actual construction working hour management are reduced, working hour data are standardized, and field production is guided.

The invention provides an intelligent supervision system and method for safety production liability insurance, and is applied to the technical field of supervision science and technology. The system comprises an enterprise port, an insurance institution port, a supervision department port and a background port, and integrates four modules: an automatic safety insurance data matching module synchronizes insurance buying information in real time and generates a supervision form; the safety production state monitoring module collects enterprise data to support risk assessment and provide accident prevention service; the accident prevention service evaluation module judges validity through data comparison before and after service and tracks and rectifies the data; and the data statistical analysis module realizes multi-dimensional query, dynamic ranking and visualization. According to the method, data collection, state monitoring, effect evaluation and rectification closed-loop management are realized through multi-port deployment, and a supervision mechanism of enterprise evaluation and supervision reward and punishment linkage is established. According to the invention, through cross-department data intercommunication, the problems of information isolated island and supervision lagging of the traditional responsibility insurance are effectively solved, and the supervision efficiency of the responsibility insurance is remarkably improved.

The invention discloses an employment guidance method based on enterprise recruitment data and college entrance examination application filling linkage. The method comprises the steps of S1, collecting social enterprise recruitment data, public unit plan recruitment data and public unit actual admission data in real time; s2, integrating the cleaned data into a unified analysis data set through a data tagging technology; s3, constructing an occupational development prediction model; s4, generating an examinee personalized portrait model of each college entrance examination volunteer filling examinee by using a multi-dimensional data analysis method; s5, dynamically generating a volunteer filling professional selection suggestion for each college entrance examination volunteer filling examinee based on the vocational development prediction model and the examinee personalized portrait model; s6, constructing a Pareto optimal solution set by using a multi-objective optimization algorithm based on the volunteer filling professional selection suggestion; and S7, carrying out statistical analysis on the Pareto optimal solution set and the selection result of the college entrance examination volunteer filling examinees. According to the method, the scientificity and practicability of volunteer filling guidance are remarkably improved.

The invention belongs to the technical field of fire-fighting supervision, and particularly relates to a fire-fighting supervision and inspection management system, which comprises a fire-fighting supervision and inspection management subsystem, comprising a fire-fighting specification standard knowledge base, a fire-fighting facility equipment graph identification base, a supervision rule model base, a supervision algorithm model base, a supervision place basic information management unit, a supervision inspection content management unit, a supervision inspector information management unit and an inspection record information management unit. A fire-fighting supervision auxiliary decision-making subsystem; an intelligent monitoring and early warning module; an Internet of Things integration module; a cloud computing and data sharing module; and a system development and deployment platform. The problems that traditional fire-fighting supervision and inspection mainly depends on manual work, inspection task distribution is not scientific, inspection efficiency is low, accurate prediction capacity for fire risks is lacked, fire-fighting supervision and inspection data records are scattered, a unified management platform is lacked, data query and statistical analysis are difficult, and intelligent technical support is lacked are solved.

The invention discloses an efficient and intelligent production scheduling method for circuit boards, and belongs to the field of printed circuit board production and manufacturing. According to the high-efficiency intelligent production scheduling method for the circuit board provided by the embodiment of the invention, a feeding package and a process production scheduling rule are set through basic standard data such as front equipment, a production cycle, WIP balance, a production calendar, team members, product design, an order state and the like. Data obtained through ERP and MES statistical analysis are summarized into standard files of internal equipment, productivity, process, production cycle and process settlement, a system is guided to perform production order scheduling, and the method has the following advantages that the system automatically generates and continuously optimizes an order package for feeding and manufacturing, and the overall productivity is maximized; the shortest order production cycle and the minimum WIP settlement are realized; automatic transportation across workshops is realized; the production progress and abnormal conditions are monitored in real time, the plan is adjusted in time, and the production efficiency is improved.

A system and method for data storage, transfer, synchronization, and security using automated system efficacy monitoring and model training, wherein statistical analyses of test datasets are used to determine if the probability distribution of two datasets are within a pre-determined range, and responsive to that determination new encoding and decoding algorithms may be retrained in order to produce new data chunklets. The new data chunklets may then be processed and assigned new codewords which are compiled into an updated codebook which may be distributed back to encoding and decoding systems and devices.

The invention discloses a grain quality safety data storage method and system based on optomagnetic fusion, and the method comprises the steps: collecting grain quality data from multiple sources, collecting the collected multi-source heterogeneous data in a unified data access mode, and carrying out the standardization and modularization processing of the multi-source heterogeneous data; classifying the processed multi-source heterogeneous data, determining data from key business nodes and non-tampered data in supervision abnormal events as high-value data, and determining data needing frequent access as hot data; the high-value data is stored in an optical disk, and the hot data is stored in a hard disk; integrating the high-value data stored in the optical disk and the hot data stored in the hard disk, and establishing a grain quality safety database; statistical analysis is carried out on the grain quality data through a calculation layer of the grain quality safety database, key indexes for identifying the grain quality data are determined, and key factors influencing the grain quality safety are determined.

The invention relates to the technical field of power grid energy storage, and discloses a breeze power generation energy storage management method and system based on big data analysis. The method comprises the steps of obtaining future wind speed and other environmental data of an area where breeze power generation equipment is located; monitoring the current state of the energy storage module in real time, and extracting historical power generation data and a power grid transmission power sequence; obtaining a wind power parameter value through statistical analysis of the wind speed, setting a reference value of the energy storage module according to the wind power parameter value, and judging the variation tendency of the generating capacity; combining the current energy storage value, the reference value, the change tendency and historical data to formulate a charging and discharging state; predicting an electric power prediction value of the breeze power generation equipment, and calculating a first plan value of a second energy storage parameter of the energy storage module in combination with the historical transmission power sequence and the electric power parameter change interval; according to the charging and discharging state and the first planned value, a first charging and discharging strategy of the energy storage module in the first preset time is generated, the accuracy of breeze power generation energy storage management and the stability of a power grid are improved, and refined charging and discharging management is achieved.

The present invention provides a hospital hazardous chemicals safety management system and method, and discloses a hospital hazardous chemicals safety management system and method. The present invention is based on Php+Nodejs as a technical architecture, uses the Internet of Things and cloud servers as operating platforms, and can be accessed through mobile phone APP and PC terminals, realizing the full process management of hazardous chemicals purchase approval, safe storage, collection and return, waste disposal, supervision and management, and statistical analysis, timely data interaction through the network, sharing the database in the hospital hazardous chemicals safety management system, and managing hazardous chemicals in a traceable manner. All operation behaviors and operation data are visualized and traceable; all operation behaviors are standardized in accordance with the specifications, hierarchical management, and responsibility at all levels, which solves the current problem of hospitals' lack of intelligent management of hazardous chemicals, thereby meeting the requirements of high-quality hospital hazardous chemicals safety management in a complex real environment.

The invention discloses a centralized monitoring management method and system for special software licensing, and the method comprises the steps: collecting a source data sample used by the special software licensing, obtaining the basic data of the use state of the special software licensing, and storing the basic data of the use state of the special software licensing in the centralized monitoring management system for the special software licensing, according to the centralized monitoring and management system for the special software licenses, the monitoring and management system carries out statistical analysis and visual presentation on historical basic data, the use rule of the software licenses is mined, instructive suggestions are provided for increasing and decreasing work of the future licenses, and the centralized monitoring and management system for the special software licenses provides a flexible and elastic grouping authorization management strategy; and an idle permission elastic release mechanism based on authorization use activeness can effectively improve the use efficiency of the software and realize rapid, centralized and efficient management of the existing software authorization.

The invention provides a material management method and system suitable for an offline multi-terminal environment, and relates to the technical field of material management, and the method comprises the steps: obtaining network data; obtaining material data of a plurality of terminals in an offline state; performing statistical analysis on the material data to generate a material operation statistical index; carrying out visual analysis on the material operation statistical indexes to obtain a material consumption visual chart; according to the material consumption visualization chart, predicting a material demand index through a material prediction model; determining an optimal material distribution scheme through a material distribution algorithm according to the predicted material demand indexes in combination with inventory constraints and demand constraints; and based on the optimal material distribution scheme, material distribution is carried out through an automatic material distribution process so as to realize material management. According to the invention, the efficiency and accuracy of material management can be improved, so that managers can make scientific and reasonable decisions even in a network-free environment.

The invention relates to the technical field of intelligent network detection, in particular to an intelligent early warning method and device for optical power fluctuation based on wide fixed network FTTH, and the method comprises the following steps: S1, data collection: collecting an optical power value, a device ID and a timestamp of an optical port device; s2, data preprocessing, including data cleaning, format conversion, data standardization and data enhancement; s3, data analysis, including data mining, statistical analysis, anomaly detection and trend analysis; s4, fault prediction, including feature extraction, model training, model verification and model optimization; s5, intelligent early warning, including real-time data acquisition, real-time prediction, multi-level early warning mechanism, automatic order sending and closed-loop feedback; and S6, data visualization and report generation, including real-time interface monitoring, historical data query and automatic report generation, solving the problems of untimely monitoring and low processing efficiency of wide fixed network-based FTTH optical power fluctuation in the prior art.

The invention relates to the technical field of computer data processing, and discloses an immigrant archive multi-dimensional information integration and intelligent query method, which comprises the following steps: collecting and standardizing multi-source heterogeneous immigrant archive data to generate to-be-processed data; based on the to-be-processed data, constructing an initial event knowledge graph containing element nodes, event nodes and relationships; performing causal relationship discovery and correction on the initial event knowledge graph, and performing mining, confidence updating and logic completion on the causal relationship between events by fusing data statistical analysis and a business process normal form learned from historical data to generate a time sequence causal graph; and receiving a natural language query request of a user, and executing query and reasoning on the time sequence causal atlas. According to the method, the business process logic hidden behind the data can be revealed, intelligent questions and answers for complex problems such as event reasons and influences are supported, and the intelligent level of query and the interpretability of results are improved.

The invention belongs to the technical field of intelligent operation inspection of distribution lines, and particularly relates to a distribution line hidden danger alarm intelligent noise reduction method based on multi-source data fusion, electronic equipment and a storage medium. The method comprises the following steps: realizing data acquisition by adopting an intelligent sensor and a visual monitoring and shooting device; constructing a space-time alignment engine to perform multi-source data fusion on the data acquired on the distribution line; the improved YOLOv5s model is used for counting, analyzing and cleaning the hidden danger alarm data of the distribution line; based on an XGBoost algorithm, constructing label data for the hidden danger alarm features of the distribution line; carrying out alarm feature model training, and constructing a multi-dimensional feature vector of the hidden danger alarm; first-stage alarm identification is carried out, and effective alarm and invalid alarm noise data of hidden dangers of the distribution line are identified; and carrying out second-stage repeated warning, noise reduction and filtering treatment on the distribution line. The problems that the number of invalid alarms of hidden dangers of the power distribution line is large, the operation and maintenance efficiency of power distribution operation and maintenance personnel is low and the effective alarm processing efficiency is low are solved.

The invention relates to the technical field of source network load storage, transportation and maintenance management, and discloses an intelligent operation and maintenance management system for a source network load storage multi-energy system, and the system comprises a data collection module which is used for collecting the real-time operation data, equipment state data and historical operation and maintenance data of the source network load storage multi-energy system, the real-time operation data comprises generating capacity, load capacity, energy storage state and energy transmission efficiency, the equipment state data comprises equipment operation parameters, fault early warning information and health degree indexes, and the data acquisition module is further used for acquiring the real-time operation data and the equipment state data of the building energy management system. Through a cross-system data integration and fusion statistical analysis and machine learning feature extraction technology, collaborative operation and maintenance of a source network load storage system and a building energy management system are realized, an operation and maintenance strategy is dynamically optimized based on key parameters such as an equipment health index and load fluctuation, and resource investment is precisely regulated and controlled in combination with an operation and maintenance cost optimization algorithm. And the total operation and maintenance cost and the fault recurrence rate are reduced.