813 results about "Maintenance plan" patented technology

The invention discloses a concrete mixing plant automatic control system based on intelligence, and belongs to the technical field of automatic control. Comprising a multi-modal sensing data acquisition module, an intelligent batching optimization module, a digital twin simulation module, a self-adaptive energy consumption management module, a fault self-diagnosis and predictive maintenance module, a dynamic quality tracing module and a multi-target collaborative scheduling module. Real-time synchronization of sensor data and a virtual model is realized in combination with an edge computing technology, dynamic and visual technical support is provided for full-flow simulation of the concrete mixing plant, and complex working conditions in production are reflected more truly; the system predicts a potential problem through a machine learning algorithm, triggers an early warning signal based on a multi-dimensional threshold rule, and generates a preventive maintenance plan in advance; the digital twin platform supports AR and VR interaction interfaces, so that an operator can visually observe the operation states of a virtual model and actual equipment.

The invention relates to the technical field of data processing, and discloses a method and system for monitoring the running state of reflow soldering equipment. The method comprises the steps of collecting data through a multi-point sensor, extracting temperature gradient, tension fluctuation and gas concentration characteristics, constructing a state recognition model, calculating a health index and setting an early warning threshold value, constructing a fault precursor extraction model to predict a future state, and finally establishing a maintenance strategy optimization system and generating an equipment maintenance plan based on fault early warning information. Through multi-dimensional data fusion and intelligent analysis, early accurate identification of the abnormal state of the equipment, accurate prediction of the fault development trend and active maintenance decision based on quality influence and cost optimization are realized, so that the welding quality stability is improved, the non-planned downtime is shortened, and the maintenance cost is reduced.

The invention relates to the field of industrial robots, and discloses an industrial robot real-time maintenance system and method in combination with edge computing, and the method comprises the steps: obtaining the multi-source operation state data of an industrial robot, and constructing an operation feature data set of key parts of the robot in combination with a state coding mechanism of an edge end and a feature coupling analysis method; carrying out rapid distributed processing on the operation characteristic data set, constructing an equipment health state model based on a lightweight time sequence modeling algorithm, and introducing a multi-dimensional correlation analysis mechanism to carry out incremental learning on the model; judging whether the edge side state recognition result is stable or not based on the change trend of the trigger frequency; according to the corrected state mapping relation, performing response level division on the potential fault trend by applying a multi-scale fault prediction mechanism, and extracting matched maintenance plan parameters; and based on the maintenance scheduling plan, in combination with a preset fault handling knowledge base, performing automatic evaluation and optimization on the current maintenance strategy. The method has the advantage of improving the response speed.

The invention relates to the technical field of power system scheduling, and discloses a power distribution network simulation scheduling optimization method and system based on artificial intelligence, and the system comprises a data fusion module, a digital twin modeling module, an intelligent prediction module, a strategy optimization module, and a visual scheduling module. The whole scene of the power distribution network is simulated through the digital twin model, the operation state and fault influence of equipment are accurately simulated, a scientific basis is provided for making a maintenance plan, blind maintenance is avoided, and the maintenance and repair cost of the equipment is reduced; meanwhile, by optimizing a load transfer path and distributed power supply output, the network loss rate is reduced, and the utilization efficiency of electric power resources is improved; in addition, the knowledge graph and the LSTM deep learning algorithm are fused, the distribution network topology entity relation network is constructed, and multi-source data are trained, so that the fault prediction accuracy is improved, the power failure risk can be early warned in advance, the conversion from passive first-aid repair to active prevention is realized, and the power failure frequency outside a plan is reduced.

The invention discloses an intelligent service system and method for garden management. The intelligent service system comprises an Internet of Things sensing module, an edge computing module, a cloud service module and an application service module. And comprehensive intelligentization and refinement of garden management are realized. Through file recording, multi-module collaborative operation and deep analysis and intelligent decision of big data, the efficiency and quality of garden maintenance are improved, and the tourist experience and the garden safety management level are also significantly enhanced. The system can monitor the health state of plants in real time, accurately predict and prevent diseases and pests, optimize the irrigation strategy and guarantee the optimal growth conditions of the plants. Meanwhile, through the digital twinborn platform, a manager can deduce different maintenance schemes in a virtual environment, scientifically evaluate a long-term effect and make an optimal decision.

The invention relates to the technical field of intelligent management of storage equipment, and discloses a self-evolution digital twinning storage equipment fault prediction and health management system. The equipment twin modeling module constructs a behavior evolution model based on the vibration spectrum and the energy consumption data, and integrates multi-source data to generate a maintenance scheme; the collaborative awareness module generates a health state evolution table containing health indexes and calibration parameters in combination with the model; the mode mining module analyzes the log identification association factor; the health assessment module calculates a health state offset; the model iteration module updates the evolution table according to the offset; and the prediction execution module triggers fault early warning. The system is additionally provided with an operation instruction comparison unit to process conflicts and optimize scheduling, equipment state evaluation, fault prediction and maintenance optimization are achieved, and the intelligent management level of storage equipment is improved.

The invention provides an AI-based smart old-age care service method, system and device, and a storage medium, and relates to the technical field of smart old-age care. The method comprises the following steps: collecting vital sign data, behavior data and environment data of the elderly, and carrying out standardization processing on past case data to obtain standard case data; performing management fusion on the standard case data, the vital sign data, the behavior data and the environment data to obtain multi-dimensional health data; inputting the multi-dimensional health data into a preset health state evaluation model to obtain health state information; matching the health state information with maintenance knowledge in a preset knowledge base to obtain a plurality of alternative maintenance schemes, and inputting the plurality of alternative maintenance schemes into a preset maintenance effect prediction model to obtain a plurality of maintenance effect prediction results; and taking the alternative maintenance scheme with the highest evaluation score as a target maintenance scheme. Through the alternative scheme, the effect of the pension service is improved.

The invention relates to the technical field of host operation and maintenance, in particular to a cross analysis digital twin intelligent operation and maintenance method and system based on artificial intelligence. The method comprises the following steps that multi-mode sensing data and central air conditioner structure data are obtained, multi-mode air conditioner operation sensing is conducted, and multi-mode operation sensing data are obtained; carrying out feature weight dynamic assignment on the multi-modal operation sensing data to obtain a space-time interaction feature matrix; a real-time updating digital twinborn model is constructed based on the central air conditioner structure data; quantitatively evaluating the equipment degradation degree based on the real-time updated digital twin model to obtain a fault critical point prediction matrix; and performing double-layer game fault risk assessment on the fault critical point prediction matrix, and executing dynamic collaborative optimization of host operation parameters to obtain an intelligent operation and maintenance scheme of the central air conditioner. According to the invention, the operation and maintenance efficiency and the fault detection accuracy can be improved.

The invention provides a wind power mixed tower damage prediction method and system based on digital twinning. The method comprises the steps of obtaining multi-source data of a wind power mixed tower; wherein the multi-source data comprises structure response data, environment load data and historical operation and maintenance data; based on a building information model and finite element model fusion technology, constructing a digital twinborn model of the wind power mixing tower; inputting the multi-source data into a digital twinborn model to realize real-time mapping of the wind power mixed tower and the digital twinborn model, and obtaining real-time structure state data of the wind power mixed tower through the digital twinborn model; inputting the multi-source data and the real-time structure state data into a pre-trained LSTM-GRU hybrid neural network model, and predicting a damage evolution trend of the wind power hybrid tower in combination with an attention mechanism; and on the basis of the damage evolution trend, a maintenance scheme of the wind power mixed tower is generated by using a multi-target particle swarm algorithm, so that balanced optimization of maintenance cost, power generation loss and risk level is realized, and the operation and maintenance efficiency and safety of the wind power mixed tower are integrally improved.

The invention relates to the technical field of big data analysis, in particular to a contact defect detection system and method based on big data, and the system comprises a multi-dimensional data capture module, a time sequence analysis module, a trend evaluation and anomaly recognition module, a future state prediction module, and a fault classification and diagnosis module. According to the invention, by deeply capturing and screening contact operation state data, the accuracy of input data is ensured, abnormal interference of contact resistance is avoided, the data validity is improved, current impact fluctuation points are identified, the sensitivity to operation abrupt change is enhanced, the accuracy of time sequence analysis is ensured, long-term offset is tracked, the wear identification capability is improved, and the future state is accurately predicted. The method is advantaged in that maintenance plan precision is improved, contact pressure reduction trend is analyzed, fault classification is refined, defect identification accuracy is improved, data screening, trend tracking, offset characteristic calculation and fault classification are optimized, accurate detection is realized, and equipment reliability and operation safety are improved.

The invention provides an airport luggage system speed reducer fault prediction and diagnosis method based on digital twinning, which relates to the technical field of fault prediction and comprises the steps of acquiring real-time operation data of a speed reducer, establishing a digital twinning model and updating the model in real time, constructing a multi-dimensional feature vector and then performing feature extraction and fusion through deep learning, identifying a fault type and evaluating the degree. And optimizing model parameters by using a particle swarm optimization algorithm to establish a residual life prediction model, generating early warning information and determining a maintenance scheme. Accurate prediction and early warning of faults of the speed reducer are realized, the maintenance cost is reduced, and the operation reliability of an airport luggage system is improved.

The invention discloses an intelligent power grid operation and maintenance system based on federated learning and edge calculation, and the system comprises an intelligent electric meter enhancement module which is disposed at a power grid monitoring node and is used for collecting electric energy quality parameters and environment data in real time; the edge calculation layer is used for carrying out data preprocessing, power quality parameter anomaly detection and building a federated learning model; the secure communication framework is used for establishing a hybrid communication network and multi-level security protection, carrying out routing decision and carrying out encrypted transmission on interactive data among the modules; and the end analysis platform is used for integrating the multi-source heterogeneous data of the power grid, predicting the state of the power grid and generating a maintenance plan. According to the invention, comprehensive monitoring and predictive maintenance of the operation state of the power grid are realized.

The invention discloses a property maintenance prediction system based on big data, and the system comprises the steps: monitoring equipment and environment in real time through an intelligent sensor, transmitting the data to a cloud or local storage through a wireless network, and carrying out the historical data processing and cleaning through a distributed architecture; predicting the equipment fault risk through machine learning, establishing a customized prediction model for different equipment, analyzing the life cycle and predicting the service life of the equipment; for old equipment, constructing a life extension model by using historical fault data; the system generates intelligent decision suggestions, pushes equipment health states to property management personnel, automatically formulates maintenance plans and priorities, and dispatches maintenance personnel; during fault prediction, early warning is given out in real time, related personnel are reminded through a short message or an APP, meanwhile, maintenance operation is recorded, historical data are analyzed, and future maintenance plans and budget are optimized; the system dynamically adjusts the life prediction model of the old equipment by accumulating historical data, and ensures the accuracy and efficiency of equipment health management.

The invention discloses a boiler fault self-diagnosis method and related device, and the method comprises the steps: S1, collecting boiler operation parameters, generating virtual data in combination with a digital twinborn model, and constructing a multi-modal monitoring data set; s2, carrying out preprocessing and anomaly detection on the data set by utilizing an edge computing node, and obtaining a preliminary anomaly signal and a feature vector; s3, uploading the abnormal signal and the feature vector to a cloud end, and performing simulation verification through a digital twin engine; s4, inputting the feature vector and a verification result into a hybrid enhancement diagnosis model, and outputting a fault type and a probability; s5, reasoning a fault source and a propagation path in combination with the knowledge graph according to the fault type and the probability; and S6, generating a maintenance scheme based on the fault information, and optimizing the diagnosis model by using operation and maintenance feedback. According to the method, the boiler operation parameters are collected and combined with the digital twinborn model to generate the virtual sensor data, the multi-modal monitoring data set is constructed, synchronous monitoring of multiple data is achieved, and one-sidedness of single-parameter monitoring is avoided.

The invention relates to the technical field of intelligent control, in particular to a barrier gate remote monitoring system and method based on the Internet of Things, and the method comprises the steps: building a dual-channel communication link through a 4G module and a Bluetooth / WiFi module, achieving the synchronous collection and uploading of the current of a barrier gate motor, a position sensor and environment data, deploying an LSTM model at an edge calculation layer, and carrying out the remote monitoring of a barrier gate. Time sequence prediction and abnormal deviation quantification are carried out on the operation state, video acquisition is triggered, vehicle retention and rod body deformation are identified in combination with a lightweight YOLOv5 model, abnormal event grading judgment is realized, spare part inventory is associated according to event types, and a maintenance scheme is dynamically generated. An intelligent work order including a thermodynamic diagram, a video frame and a prediction curve is constructed by using a three-dimensional visualization template, evidence storage processing is completed through a block chain module, and finally optimal personnel scheduling and order dispatching are executed according to a maintenance scheme. According to the method, multi-source data fusion modeling, visual linkage anomaly recognition and full-link credible operation and maintenance are realized.

The invention provides a lean operation method based on production scheduling optimization and equipment OEE monitoring, and relates to the technical field of lean operation, and the method comprises the steps: deploying a multi-source heterogeneous data collection interface, and carrying out the data collection, the method comprises the following steps: acquiring customer order data, material supply state data, production task priority data, process time sequence requirement data and equipment maintenance plan data, performing data preprocessing based on data acquisition, generating a semantic tag set, acquiring the semantic tag set, configuring a production demand dynamic feature set based on the obtained semantic tag set, and generating a production demand dynamic feature set; and calculating a feature influence weight. By deploying a multi-source heterogeneous data acquisition interface, customer order data, material supply state data, production task priority data, process time sequence requirement data and equipment maintenance plan data are acquired at the same time, and a semantic tag set and a production demand dynamic feature set are constructed according to a time sequence structure.

The invention discloses an intelligent hardware equipment monitoring method based on the Internet of Things, which comprises the following steps of: acquiring data such as sensor signals, temperature power consumption of a processor, voltage and current of a power supply module and the like in real time, processing the data by applying technologies such as a filtering algorithm, a causal inference model and time sequence analysis and judging the performance change of a component; and inputting the processed data into a pre-training regression model, predicting the residual life of each component and generating a health score, integrating the health scores of each component, introducing a dynamic correlation analysis and adaptive weight adjustment mechanism, constructing a system-level health state report, and generating a priority maintenance plan for high-risk components based on the report. Intelligent evaluation and prediction of the health state of the hardware component are realized, interruption of operation of the monitoring system due to hardware faults is effectively avoided, and the reliability and maintenance efficiency of the system are improved.

The invention relates to the technical field of power distribution cabinet detection, and discloses an intelligent power distribution cabinet state dynamic monitoring method and system, and the method comprises the following steps: collecting multi-source data generated in the operation process of a power distribution cabinet; preprocessing the multi-source data to generate a standardized data set; a random field model is constructed and used for describing the spatial relevance between units in the power distribution cabinet and the relation between the state of each unit and multi-source data, and model parameters are estimated through the maximum logarithmic posterior probability. An intelligent power distribution simulation model and a multi-source data acquisition technology are adopted, multi-dimensional parameters are acquired in real time through a high-precision sensor, monitoring efficiency and decision accuracy are remarkably improved, data analysis integrity and model sensitivity are ensured through dynamic time modeling and capturing of space and time relevance of the power distribution cabinet, and the power distribution cabinet can be monitored more accurately. And the state distribution visualization and maintenance priority strategy generation module optimizes the maintenance plan, reduces the operation and maintenance cost, and guarantees the stability of the power distribution system.

The invention discloses a ship equipment maintenance support system and method based on artificial intelligence, and the system collects the operation data of equipment in real time through a data collection and integration module, and digitalizes and integrates the historical maintenance data into a unified database; a fault diagnosis model is constructed through the model diagnosis module to carry out fault diagnosis and output a diagnosis result; an optimal maintenance scheme is provided through a maintenance decision support system according to a fault diagnosis result in combination with multi-dimensional data; real-time monitoring and trend analysis are carried out on operation data of the equipment through the fault prediction module, potential faults of the equipment are predicted, and a maintenance plan is made in advance. According to the invention, equipment operation data is collected in real time through a sensor, accurate fault diagnosis and prediction are realized in combination with a deep learning algorithm, an optimal maintenance scheme is formulated in combination with multi-dimensional data, and a maintenance plan is dynamically adjusted.

The invention relates to the technical field of pipeline corrosion detection, in particular to a detection method and analysis system applied to the corrosion problem of a Texaco gasification device, and the method comprises the steps: selecting key monitoring points to monitor the corrosion condition of a target device, installing intelligent sensors at the points, collecting operation data in real time, and transmitting the operation data to a central processing unit; analyzing the data, identifying an area possibly having a corrosion risk and an abnormal change trend, and determining a suspected corrosion area; carrying out deep detection on the areas to obtain detection data; detection data, equipment operation data and maintenance records are encrypted and stored, the authenticity and traceability of the data are ensured, and a reliable basis is provided for subsequent analysis and decision making; and in combination with an intelligent manufacturing technology, operation parameters and maintenance plans of the device are optimized according to a corrosion analysis result, and accurate analysis and effective prevention and control of corrosion problems are realized. By means of the scheme, accurate analysis and effective prevention and control of the corrosion problem of the Texaco gasification device are achieved.

The embodiment of the invention discloses a flexible job shop scheduling method based on preference-driven graph reinforcement learning, and relates to the field of shop dynamic scheduling in an intelligent manufacturing technology. According to the method, by constructing a multi-objective optimization model, multiple objectives of the job shop can be optimized at the same time, including the minimum completion time, the total delay and the total cost. Wherein an imperfect maintenance model is constructed, and the maintenance demand and the maintenance opportunity of each machine are dynamically determined. And capturing a complex relationship between the operation and the machine by using an improved graph neural network. In combination with a preference-driven mechanism, a maintenance plan and workshop scheduling are adjusted in real time through a graph reinforcement learning method, and efficient priority scheduling rules under different preferences are learned, so that an integrated decision of machine allocation, an operation sequence and maintenance arrangement in dynamic scheduling is realized.

The invention discloses an internet of things data stable transmission optimization method, system and device and a medium, and relates to the field of internet of things, and the method comprises the steps: generating a plurality of test scenes through a multi-network simulation model; inputting the plurality of test scenes into the target Internet of Things to obtain Internet of Things test data; inputting the Internet of Things test data into the transmission performance prediction model to obtain performance prediction data; inputting the performance prediction data into a data mining and analysis model to obtain data mining information; generating a data stability analysis report according to the data mining information; and determining an abnormal point according to the data stability analysis report to form a detection and maintenance scheme. The stability, the reliability and the operation and maintenance efficiency of the Internet of Things system are effectively improved, and the method has wide application prospects and remarkable engineering practice values.

The invention discloses a new energy medium-voltage switch intelligent control method based on multi-data interaction, and particularly relates to the field of switch intelligent control. According to the method, electrical, mechanical and temperature multi-dimensional operation data are collected in real time through a multi-source sensor, fault diagnosis is performed by adopting a hybrid reasoning architecture, features are extracted in combination with Hilbert-Huang transform and Mel cepstrum coefficient algorithms, and a three-level reasoning mechanism is constructed to realize accurate classification; based on a diagnosis result, a system dynamically optimizes a control strategy, the control strategy optimizes a switch-on and switch-off time sequence based on phase-space reconstruction and LSTM prediction, plasma impedance closed-loop adjustment and a quantum genetic algorithm are combined to realize arc suppression and mechanical compensation, and a six-dimensional compensation vector is introduced to correct mechanical operation deviation; the self-learning unit is based on a depth deterministic strategy gradient and an online knowledge distillation technology, verifies the validity of parameter update by using digital twin simulation, and forms a fault feature-control strategy-maintenance scheme triple knowledge base.

The invention discloses a medical equipment full-life-cycle management system based on a digital twinning technology. The medical equipment full-life-cycle management system comprises four modules. The digital twinborn model construction module constructs and updates a simulation medical equipment digital model in real time; the intelligent data acquisition and monitoring module is responsible for data acquisition, transmission, monitoring and interface design; the intelligent decision and maintenance module realizes fault prediction, maintenance plan generation and record management; and the comprehensive analysis and optimization management module performs data analysis, optimization decision and life cycle planning. The system has the remarkable advantages that when equipment is used, the running state of the equipment is adjusted in time through real-time monitoring and intelligent analysis, and the fault rate and the maintenance and replacement frequency are reduced; in the aspect of cost control, a maintenance strategy and a purchase decision are optimized, and expenditure is reduced; at the medical service quality level, stable and efficient operation of equipment is guaranteed, patient safety is improved, and reasonable utilization of medical resources is achieved.

The invention discloses an intelligent maintenance system and method based on image analysis, and the system comprises a data collection module, a data fusion module, an intelligent analysis module, and a decision alarm module, the data collection module is used for collecting multi-dimensional image data and equipment data of a target power transformation main device in a target power system, obtaining first multi-dimensional image data and target equipment data; the data fusion module is used for fusing images in the first multi-dimensional image data to obtain a target fusion image; the intelligent analysis module is used for performing fault identification on the target power transformation main equipment according to the target fusion image and the target equipment data to obtain a target fault identification result; and the decision alarm module is used for determining a target maintenance plan and a target alarm operation corresponding to the target fault recognition result. According to the invention, the fault diagnosis accuracy of the power transformation main equipment can be improved.

The invention relates to the technical field of equipment maintenance, in particular to a distribution box remote diagnosis and maintenance system which comprises a voltage deviation detection module, a node hot stacking identification module, a channel stability classification module, a contact aging trend evaluation module and a period adjustment trigger module. According to the method, peak value change of effective voltage in each channel period in the distribution box is collected and compared with a preset criterion so as to accurately identify an abnormal discharge unit and accurately position a potential risk channel, and the change rate of equivalent series capacitance, contact conductance and contact resistance of a node is continuously sampled in a non-power-on state so as to accurately identify the potential risk channel. According to the method, multi-parameter same-trend change characteristics are determined, the contact aging process is accurately evaluated by integrating the parameter trend, the node trend and the maintenance plan period remaining time are subjected to cross comparison, the maintenance task advance opportunity is automatically judged, the maintenance plan is actively adjusted, the node fault missing detection risk is effectively reduced, and the maintenance timeliness and accuracy are remarkably improved.

The invention discloses a power grid maintenance plan reliability post-evaluation method based on Monte Carlo simulation and data driving, and belongs to the technical field of power system operation and reliability analysis. The method comprises the following steps: firstly, collecting multi-source data such as historical load, renewable energy output, equipment operation state and maintenance record of a power grid, constructing a time sequence database through cleaning, time alignment and feature extraction, and establishing a load and renewable energy probability model; constructing a maintenance plan model containing a state variable, a constraint condition and a peak clipping weight mechanism, and establishing a continuous time Markov chain state model for the key equipment to generate an availability sequence; generating a large-scale random operation scene set through a Monte Carlo method based on multiple models, and carrying out supply-demand balance and power flow analysis on each scene; multi-dimensional indexes of reliability, economy and safety are calculated and subjected to weighted fusion, a comprehensive post-evaluation report is generated after results are counted, and finally the maintenance plan is optimized according to the report. According to the method, the uncertainty of the power system can be comprehensively considered, multi-dimensional quantitative evaluation and closed-loop optimization of the maintenance plan are realized, intelligent support is provided for power grid maintenance decision making, and the method is suitable for a power transmission network, a power distribution network and a micro-grid.

The invention discloses a direct current equipment operation inspection intelligent agent, an evaluation method, a system and related equipment, and belongs to the technical field of electric power intelligent agents, and the method comprises the steps: collecting various types of multi-dimensional equipment information, searching and matching historical cases through the similarity of a plurality of evaluation dimensions based on the equipment information, and obtaining an evaluation result; therefore, corresponding fault history and disposal suggestions are obtained; and taking the corresponding fault history and the disposal suggestion as references to be input into the direct current equipment operation and maintenance intelligent agent, and generating an evaluation report and a maintenance plan. According to the method, the understanding capability, the decision-making capability and the generation capability of a direct-current equipment operation and maintenance intelligent agent in facing complex operation and maintenance tasks, particularly tasks relating to professional knowledge of direct-current equipment, are improved, and the interpretability of the model is improved.

The invention discloses an operation and maintenance method, system and device of a drainage pipe network and a medium. The method comprises the steps of obtaining historical monitoring data of the drainage pipe network in a target area; inputting the historical monitoring data into a preset pipe network risk identification model for training to obtain a trained pipe network risk identification model; acquiring real-time monitoring data of the drainage pipe network in the target area, and inputting the real-time monitoring data into the trained pipe network risk identification model to obtain a pipe network risk identification result of the target area; generating a health index of the drainage pipe network in the target area according to the real-time monitoring data; and generating an operation and maintenance scheme of the drainage pipe network according to the health index and the pipe network risk identification result. According to the method, the risk state in the pipe network is automatically recognized, multiple factors such as the pipe network topological relation and the flow load are combined and considered, the multi-dimensional operation and maintenance scheme is generated, secondary disasters such as urban waterlogging can be effectively avoided, and a scientific basis is provided for pipe network planning and investment decision making.

The invention discloses an elevator and building energy system fusion energy-saving management and control method based on the Internet of Things, and relates to the technical field of energy management. In order to solve the problems that traditional elevator operation management and building energy system control are mutually independent and lack of an effective cooperation mechanism, so that energy waste is serious, the operation states of an elevator and a building energy system are difficult to comprehensively master in real time, and an energy-saving strategy cannot be accurately formulated. Elevator operation and building energy system data are collected in real time through the sensor network, the remote supervision center deeply integrates, analyzes and excavates a correlation mode and a time sequence relation between operation characteristics of the elevator and the building energy system data, an operation characteristic analysis model is constructed, an accurate energy-saving strategy is formulated, energy consumption is remarkably reduced, and the energy utilization efficiency is improved; the energy-saving effect is quantitatively evaluated, a closed-loop optimization mechanism is formed, and the energy-saving management and control effect is continuously improved; by establishing a fault prediction model, equipment faults are predicted in advance, a maintenance scheme is generated, and stable operation of the system is guaranteed.