57 results about "Adaptive maintenance" patented technology

The invention relates to the technical field of intelligent operation and maintenance of power equipment, in particular to a cable system full life cycle health management method based on digital twinning, which comprises the following steps: step 1, constructing a cable digital twinning body fusing electric-thermal-mechanical-chemical multi-physical fields; 2, dynamically fusing multi-source monitoring data, and collecting a cable skin continuous temperature sequence, a partial discharge pulse waveform and soil environment parameters; 3, solving a coupling equation of conductivity-thermal conductivity-stress tensor-ion diffusivity through iteration to realize multi-physical field linkage simulation; 4, generating a self-adaptive maintenance decision, inputting the insulation aging factor into an LSTM predictor to output a residual life prediction value, and generating a maintenance work order when the residual life is lower than a threshold value; and 5, carrying out closed-loop correction on the model, and updating the insulation aging factor calculation model and the physical attributes of the three-dimensional grid according to actual maintenance data. The method improves the safety, reliability and operation and maintenance efficiency of the cable system, and has important industrial application prospects.

The invention discloses an intelligent electric meter box operation and maintenance management method and system, and relates to the field of electric power operation and maintenance management. The intelligent electric meter box operation and maintenance management method comprises the steps of multi-source data acquisition and preprocessing, equipment health state dynamic evaluation, fault root cause positioning and prediction, maintenance task dynamic scheduling, adaptive maintenance execution and closed-loop optimization, feedback of a maintenance result to a health evaluation model, updating of a dynamic weight coefficient, and monthly generation of an equipment health white book. And continuously optimizing the operation and maintenance strategy. According to the intelligent electric meter box operation and maintenance management method, the fault prediction model is established based on the LSTM neural network, the typical fault mode is matched in combination with the knowledge graph, the fault prediction accuracy is improved, and non-planned power failure is reduced. The improved genetic algorithm improves the utilization rate of maintenance resources and shortens the emergency response time. Predictive maintenance replaces regular maintenance, invalid inspection is reduced, and the spare part inventory turnover rate is increased. Human errors are reduced through AR assistance; and the maintenance operation accuracy is improved.

The invention relates to the technical field of concrete curing, in particular to a concrete crack risk prediction and curing system based on thermo-acoustic coupling indexes. According to the scheme, a temperature field and an acoustic emission signal are synchronously acquired through the multi-modal data acquisition module; a thermal diffusion shear index and an acoustic emission energy density are calculated by a multi-modal data fusion module, and are fused into a unified thermo-acoustic coupling factor to quantify a cross-scale risk; the structural constraint compensation module corrects the factor by using pre-stored constraint information to generate a final risk index; the self-adaptive maintenance decision-making module maps a dynamic maintenance strategy according to the indexes; and finally, the maintenance robot executes precise maintenance operation. The technical problems that macroscopic thermal stress and microcosmic damage signals cannot be evaluated in a unified mode, different-source data are difficult to fuse, and high-constraint area risk identification is inaccurate are solved, and continuous prediction and self-adaptive cooperative control of cracks from a potential stage to an initiation stage are achieved. And the accuracy and the intelligent level of early-age crack prevention and control of the concrete are remarkably improved.

The invention provides an EAM equipment full life cycle management method and system, and relates to the technical field of equipment life cycle management. According to the invention, a full life cycle management system integrating multi-source data fusion, physical constraint driven intelligent prediction and root cause diagnosis, environment working condition adaptive maintenance strategy matching, multi-physics field simulation evaluation and multi-target optimization scheduling is constructed, so that accurate perception of states of key parts of equipment and life prediction are realized; the accuracy of fault diagnosis and the adaptability of a maintenance scheme are improved, and maintenance resource configuration and inventory management are optimized. The system has a dynamic adaptive optimization capability, and can continuously improve a maintenance strategy and a prediction model according to actual operation feedback, so that the reliability and the maintenance efficiency of equipment operation are remarkably improved, the overall maintenance cost is reduced, and the high-standard requirement of intelligent equipment management in a complex industrial environment is met.

The invention relates to an equipment predictive maintenance management method and system based on multi-feature fusion, belongs to the technical field of equipment health management, and is used for solving the problems that existing sensor data is easily disturbed and distorted, and a potential causal structure of an equipment degradation path is difficult to reveal due to lack of a comprehensive modeling framework. The method comprises the steps of collecting multi-source data in real time, extracting a causal contribution degree of the data to a fault to generate a causal significance feature value, generating a three-dimensional health feature vector through an anti-fact neural network model in combination with a physical failure model, fusing the two to obtain an equipment state risk feature matrix, inputting the model to output a risk score, and obtaining an equipment state risk result. And finally, dynamically adjusting the monitoring frequency and the maintenance level and iteratively optimizing the strategy. According to the method, data interference can be filtered out, multi-class reasoning mechanisms are deeply fused, the equipment degradation law is accurately revealed, and full-life-cycle self-adaptive maintenance is achieved.

The invention discloses an automatic test method, system and device applied to a WSDL sending interface and a medium, and the method comprises the steps: associating a single test case into various scenes through a scene page preset parameter rule, and verifying the coupling between interface response and scene logic in real time during automatic execution; a WSDL interface definition and parameterization rule engine is dynamically analyzed, and automatic execution of multiple test cases is achieved; encrypted feedback data flow returned by an interface is captured in real time, key indexes are dynamically analyzed and extracted, automatic comparison and root cause tracing are carried out based on a multi-dimensional verification rule, and a test report is generated. According to the method, the problems that interface testing depends on manual coding, the process is split and the maintenance cost is high in aerospace engineering can be solved, zero-code rapid verification, forward and reverse closed-loop testing and interface change self-adaptive maintenance are achieved, and the aerospace software testing efficiency and reliability are remarkably improved.

The invention relates to the technical field of elevator safety monitoring, in particular to a fault early warning method based on an intelligent elevator. The fault early warning method comprises the steps that multi-dimensional operation parameters of a target elevator are obtained; multi-modal fusion is conducted on the vibration signal component, the noise signal component, the equipment static attribute data and the working condition environment data, and a health degree evaluation matrix of elevator operation is generated through a space-time attention encoder; according to the health degree evaluation matrix, a preset historical fault knowledge base is combined, the dynamic fault probability is calculated through a Bayesian inference model, and the early warning grade is generated. A historical fault knowledge base and a Bayesian inference model are further combined, a self-adaptive maintenance strategy triggered according to an early warning level from static threshold value alarm to dynamic fault probability prediction is realized, on-demand accurate maintenance is realized, and the maintenance efficiency and the resource utilization rate are improved.

The invention provides a humanoid robot joint life monitoring method, and belongs to the technical field of life monitoring. The method comprises the steps that 1, a multi-mode sensor array is constructed to collect original joint motion data of a target robot; 2, standardizing the original joint movement data according to feature types to obtain standard feature data of all parameter types; 3, establishing a joint degradation model of the target robot based on the standard feature data; 4, inputting real-time motion data of the target robot into the joint degradation model, and outputting a joint residual life index; and 5, when the residual life index of the joint is lower than a preset threshold value, a self-adaptive maintenance strategy is triggered. Through real-time monitoring of joint health, early warning and optimization maintenance, the reliability of the robot is improved.

The invention relates to the technical field of vehicle maintenance, and provides a vehicle part abrasion determination method and a vehicle. The method comprises the steps that vehicle driving data and external environment data in the current time period are obtained, and the abrasion influence coefficient of the external environment on a part is determined through the external environment data; the current wear value of the component is determined according to the wear influence coefficient and the vehicle running data, component loss prediction in combination with the external environment and the vehicle running state is achieved, the predicted component loss better conforms to the actual use condition, more accurate maintenance prompt can be achieved on the basis of the predicted component loss, the risk of misjudgment of maintenance is avoided, and the maintenance efficiency is improved. The purpose of on-demand adaptive maintenance is achieved, the problems of excessive maintenance and insufficient maintenance caused by periodic maintenance are solved, the maintenance cost and the failure rate of vehicle parts are reduced, and the active maintenance willingness of a user can be improved while the driving safety of the vehicle is guaranteed.

The invention discloses a transport vehicle maintenance period and transport task dynamic adaptation method and system, electronic equipment and a storage medium, and the method comprises the following steps: collecting real-time state data of a transport vehicle, and calculating a vehicle health index based on the real-time state data, the vehicle health index is used for quantitatively representing the comprehensive health state of the vehicle; obtaining characteristic data of the transportation task, wherein the characteristic data at least comprises a task emergency degree; dynamically selecting a target scheme from a plurality of predefined adaptive maintenance schemes based on the combination of the vehicle health index and the task emergency degree so as to decide the opportunity and the sequence of executing the transportation task and the maintenance by the transportation vehicle; wherein the adaptive maintenance scheme comprises the steps of allowing first task execution and then maintenance, requiring first maintenance and then task execution, and forcibly maintaining and replacing a vehicle to execute the task; according to the method and the system, accurate and dynamic matching of the maintenance strategy and the transportation demand can be realized.

This invention discloses a self-learning method and system for motor operation status and fault early warning, relating to the field of motor fault diagnosis technology. It includes the acquisition and standardized processing of multi-source operating data to generate a standardized feature set; a four-level self-learning model deployed at the edge and cloud levels, progressively completing sample mining, fault feature learning, early warning threshold optimization, and maintenance strategy generation, with bidirectional feedback iteration between each level; and master-slave collaboration between the edge master module and the cloud slave module to achieve real-time inference and accurate diagnosis, independent early warning at the edge in case of network anomalies, and synchronization of data and parameters after recovery. This self-learning method and system for motor operation status and fault early warning adopts a four-layer architecture: perception layer – edge master module – cloud slave module – application layer. Through the collaborative cooperation of real-time inference by the edge master module, accurate diagnosis by the cloud slave module, and dynamic optimization by the intelligent maintenance decision module, it achieves early warning of faults throughout the motor's entire lifecycle, prediction of remaining lifespan, and adaptive maintenance decision-making.

The invention belongs to the technical field of constructional engineering, and particularly relates to a rapid hole-forming construction process for a post-planted anti-floating anchor rod with a pressure grouting function, which comprises the links of intelligent positioning and geological adaptation preparation, self-adaptive hole-forming operation, anchor rod manufacturing and grouting, and temperature and humidity self-adaptive maintenance and acceptance. In the step of intelligent positioning and geological adaptation preparation, hole positions are positioned through three-dimensional laser scanning, conflicts are detected, a geological risk thermodynamic diagram is described by combining coring and radar scanning, and in the step of self-adaptive hole forming operation, torque and rotating speed are automatically adjusted according to the stratum, hole wall risks are monitored through sound waves, graded intermittent hole expanding is conducted, and cracks are blocked in a targeted mode. The method comprises the steps of anchor rod manufacturing and grouting, spiral rib anchor rod matching with a degradable support, double-cavity grouting pipe precise pressure control grouting, gradient grout supplementing after initial setting and temperature and humidity self-adaptive maintenance, temperature and humidity are monitored through a sensor, heat preservation and moisture preservation are automatically started, maintenance is finished after the standard is reached, and finally acceptance check is conducted to ensure that the anti-floating effect reaches the standard.

The invention provides a bridge and tunnel water and soil conservation real-time monitoring method and system based on AI, and relates to the technical field of infrastructure monitoring. The method comprises the following steps: collecting water and soil data around a target bridge and a tunnel in real time through a multi-mode sensor; preprocessing the water and soil data at an edge computing node to obtain preprocessed data; performing real-time anomaly detection on the preprocessed data by using a pre-trained AI model to obtain a detection result; inputting the detection result into an equipment health prediction model to obtain a prediction result; the prediction result comprises an equipment health state and a fault probability; generating a self-adaptive maintenance instruction according to the prediction result, and executing maintenance operation according to the self-adaptive maintenance instruction; and starting a redundant sensor and a standby communication channel according to a prediction result so as to guarantee the monitoring continuity. The safety, timeliness and stability of the bridge and the tunnel can be improved, the management efficiency can be effectively improved, and the environmental risk is reduced.

The invention discloses a water turbine runner real-time monitoring and intelligent operation and maintenance method and system based on a knowledge graph, and belongs to the technical field of hydroelectric power generation equipment state monitoring. The problems of interpretable monitoring and self-adaptive maintenance decision generation of the whole life cycle state of the turbine runner are solved. A pressure sensor, a vibration sensor array and an acoustic emission sensor are arranged on a turbine runner; the signal acquisition module acquires running state parameters of the turbine runner in real time to obtain original data; after the graph construction module preprocesses the original data, an equipment body model of the water turbine runner is established, and a knowledge graph of the water turbine runner is generated; the monitoring analysis module analyzes the state of the turbine runner in real time to obtain the output of the monitoring analysis module; and the intelligent operation and maintenance module generates maintenance strategy suggestions through similarity calculation matching historical cases according to the output of the monitoring analysis module. According to the method, the technical span of monitoring the state of the turbine runner from traditional threshold judgment to cognitive intelligent decision is realized.

A system and method for automating railroad maintenance for a tie gang using electronic tie marking (ETM) configured to optimize railroad asset maintenance. The system enables the automating of an adaptive maintenance process for the asset that is being maintainanced. The system can identify a railroad asset scheduled for maintenance using various forms of inspection including real-time kinematic (RTK)-corrected GPS data, radar signal processing data, and real-time imaging. The system also provides for the acquisition and upload of asset pictures for verification and analysis of a railroad asset. The system can identify a next location to perform maintenance and can calculate an optimum path based on sensor input incorporating machine-specific and environmental characteristics. The system further can provide a customizable user interface to identify, track, and process information related to maintenance of the railroad asset.

The invention discloses a medical robot arm protection and self-adaptive maintenance cabin, and relates to the technical field of medical instrument maintenance cabins. The medical robot arm protection and self-adaption maintenance cabin comprises a robot arm body and a maintenance cabin body, the robot arm body comprises a dustproof sealing assembly, a groove and an end face hole, and the maintenance cabin body comprises a cabin body, a containing frame, a containing frame and a cabin cover. And a limiting mechanism used for limiting the robot arm body is arranged in the containing frame, a driving mechanism used for driving the robot arm body to rotate is arranged in the containing frame, and a plurality of sets of cleaning mechanisms are arranged in the groove. According to the medical robot arm protection and self-adaptive maintenance cabin, the robot arm body can be conveniently limited, the medical robot arm protection and self-adaptive maintenance cabin is more stable and reliable, meanwhile, the robot arm body can automatically rotate, and therefore the cleaning and disinfection efficiency is higher, and the cleaning and disinfection effect is better.

The present invention provides a method and system for the full life cycle management of EAM equipment, which relates to the technical field of equipment life cycle management. By constructing a full life cycle management system that integrates multi-source data fusion, physical constraint-driven intelligent prediction and root cause diagnosis, environmental condition adaptive maintenance strategy matching, multi-physics field simulation evaluation, and multi-objective optimization scheduling, the present invention achieves accurate perception of the status of key equipment parts and life prediction, improves the accuracy of fault diagnosis and the adaptability of maintenance plans, and optimizes maintenance resource allocation and inventory management. The system has dynamic adaptive optimization capabilities and can continuously improve maintenance strategies and prediction models based on actual operation feedback, thereby significantly improving the reliability and maintenance efficiency of equipment operation, reducing overall maintenance costs, and meeting the high standards of intelligent equipment management in complex industrial environments.

The invention relates to the technical field of Internet of Things, and discloses an Internet of Things-based equipment predictive maintenance method, which comprises the following steps: S1, constructing a multi-modal knowledge graph and a physical principle knowledge base, and integrating heterogeneous data modeling equipment knowledge; s2, detecting that equipment runs abnormally, and processing time sequence sensor data in real time to identify an abnormal signal; s3, judging an abnormal mode type; s4, performing dynamic causal reasoning, and if the fault is known, executing deterministic causal reasoning to output a fault causal chain; if the fault is an unknown fault, executing hypothetical causal path generation in combination with a physical principle knowledge base and an AI model; and S5, generating a self-adaptive maintenance work order. Through the knowledge graph self-correction step, the external verification feedback is received, the hypothetical causal path is solidified or removed, closed-loop self-evolution of knowledge is achieved, data-driven experience knowledge and principle-driven axiom knowledge are deeply fused, and the accuracy and interpretability of complex fault diagnosis are remarkably improved.

The invention relates to the technical field of air source heat pump control, in particular to an air source heat pump energy efficiency optimization control method and system. The method comprises the following steps: acquiring multi-dimensional operation data of the air source heat pump unit and performing queue preprocessing to obtain a time sliding window data sequence of each physical quantity; according to the environment temperature and coil pipe temperature data in the sequence, the environment thermal response fluctuation representing the current environment unstable state is calculated; according to the exhaust temperature, the compressor current real-time value and the corresponding benchmark reference value, a system state deviation index representing the physical performance degradation of the unit is calculated; and calculating the frequency compensation amount based on the two indexes and the return water temperature difference, and superposing the frequency compensation amount to the basic PID frequency to obtain the final driving frequency. The energy efficiency self-adaptive maintenance of the whole life cycle is realized, the system oscillation under the severe working condition is effectively inhibited through a dynamic damping mechanism, and the operation stability and the energy-saving effect of the heat pump unit are improved.

The invention provides an adaptive maintenance decision dynamic threshold adjustment method based on prediction reliability, and the method comprises the steps: S1, obtaining probability distribution characteristics outputted by a prediction model, constructing a multi-dimensional prediction reliability quantitative model based on a primary discrimination factor, an uncertainty correction term and a system complexity adjustment coefficient, and generating prediction reliability R; s2, based on the prediction reliability R and historical sample accuracy data, establishing a quantitative relation between the reliability and the accuracy, and generating a reliability interval division result; s3, on the basis of the prediction reliability R, the basic threshold and the sensitivity coefficient, an adaptive maintenance threshold model is constructed through an exponential function, and an adaptive maintenance threshold is generated; and S4, comparing the self-adaptive maintenance threshold value with the current state score of the equipment, and generating an automatic maintenance trigger signal or a manual intervention starting signal. According to the method, the credibility of the prediction result is quantified, the maintenance threshold is dynamically optimized, and differentiated operation and maintenance resource release is realized.

The invention relates to the technical field of deep learning, in particular to a power generation equipment anomaly prediction and adaptive maintenance system based on fusion deep learning. According to the system, firstly, multi-modal local data and multi-modal global data are acquired through a multi-modal acquisition unit; inputting the multi-modal local data into a hybrid neural network model based on knowledge embedding through a first neural network processing unit to obtain an equipment anomaly local predicted value; inputting the multi-modal global data into an encoder-decoder network based on cross-modal attention through a second neural network processing unit to obtain an equipment anomaly global predicted value; a reinforcement learning processing unit utilizes a reinforcement learning model to optimize a maintenance strategy, and a genetic algorithm is used to carry out hyper-parameter optimization on the reinforcement learning model; the interpretability of the model is realized; finally, the output unit provides a comprehensive anomaly prediction result and an update maintenance strategy obtained through reinforcement learning and genetic algorithm optimization so as to improve the effectiveness of anomaly prediction and maintenance.

The invention discloses an internet-of-things vegetation concrete self-adaptive maintenance monitoring method and system, and belongs to the technical field of building materials and intelligent maintenance. Sensing multi-dimensional parameters in real time through a multi-modal sensing array; the local microcontroller processes, packages and transmits the data; according to a preset maintenance strategy, the local control unit adopts a multivariable decoupling control algorithm to generate adjustment instructions of moisture, pH value and nutritive salt; the micro peristaltic pump is driven by the micro-fluidic system, and liquid is precisely conveyed to the matrix through the biological activity slow release cavity; meanwhile, the system periodically encrypts the report state to the cloud platform and receives a remote instruction. And the cloud platform integrates a recurrent neural network model to realize prospective prediction maintenance. A traditional static structure is converted into a dynamic ecological complex, the problems that manual maintenance response lags behind, the resource utilization rate is low, and structural performance and ecological suitability are difficult to consider at the same time are effectively solved, and the plant survival rate and maintenance efficiency are remarkably improved.

The invention relates to the technical field of urban underground pipe network safety monitoring, in particular to an urban water transmission and drainage pipe network safety monitoring and early warning system which comprises a pipe gallery environment self-adaption module for collecting pipe network structure degradation strain and time sequence pressure data, a sub-health feature extraction module for analyzing the ellipticity and the like and conducting sub-health grading, and a pre-warning module for pre-warning and early warning. The residual life prediction module predicts the residual service life of each pipe network section based on a dynamic deterioration rate and a critical threshold, the cross-pipeline linkage risk assessment module quantifies self-failure and linkage disaster risk output comprehensive risk level, and the pipe gallery collaborative maintenance module screens the pipe network sections to be maintained according to the self-failure and linkage disaster risk output comprehensive risk level, determines the priority and matches the optimal maintenance scheme. Precise positioning of local hidden dangers is realized through refined sub-network segment analysis, dynamic life prediction provides a scientific time basis for a maintenance plan, an adaptive maintenance scheme effectively balances safety and cost, hidden dangers of excessive maintenance or missed judgment are avoided, occurrence of pipeline failure and chain disasters is reduced, and safe and stable operation of a pipe gallery water transmission and drainage pipe network is guaranteed.

The invention relates to the technical field of connection of asphalt concrete core wall dams and gravity dams, and discloses an asphalt concrete core wall dam and gravity dam embedded joint structure and management and control system.The asphalt concrete core wall dam and gravity dam embedded joint structure comprises a cambered surface in contact with an asphalt core wall rockfill dam, an inclined surface in contact with a transition material and a step shape in contact with a rockfill material; wherein the two sides of the cambered surface in contact with the asphalt core wall rockfill dam are provided with inclined surfaces in contact with the transition material, and one side of the inclined surface in contact with the transition material is provided with a step shape in contact with the rockfill material; a plurality of intelligent management and control systems are installed between the asphalt core wall rockfill dam and the concrete gravity dam on the axis of the asphalt core wall rockfill dam, the intelligent management and control systems are connected with heating resistors and thermocouples, and the heating resistors are connected with the thermocouples. The management and control system comprises a multi-parameter coupling temperature control module, a self-adaptive maintenance module and an earthquake response and deformation coordination module. The side type joint effectively solves the problem that the cost of building a high retaining wall of an existing side type joint is high.

This invention relates to the field of deep learning technology, specifically to a power generation equipment anomaly prediction and adaptive maintenance system based on fused deep learning. The system first acquires multimodal local and global data through a multimodal acquisition unit; then, a first neural network processing unit inputs the multimodal local data into a knowledge-embedded hybrid neural network model to obtain local anomaly predictions; a second neural network processing unit inputs the multimodal global data into an encoder-decoder network based on cross-modal attention to obtain global anomaly predictions; a reinforcement learning processing unit optimizes the maintenance strategy using a reinforcement learning model and performs hyperparameter optimization on the reinforcement learning model using a genetic algorithm, thus achieving model interpretability; finally, an output unit provides a comprehensive anomaly prediction result and an updated maintenance strategy optimized by reinforcement learning and genetic algorithms to improve the effectiveness of anomaly prediction and maintenance.

The invention relates to the field of PCB machining, in particular to online automatic board splitting equipment and an operation method.The online automatic board splitting equipment comprises a board splitting machining table, an adsorption and suction assembly and a filtering assembly, a U-shaped movable frame is movably arranged at the upper end of the board splitting machining table, a movable sliding base is movably arranged on the U-shaped movable frame, and a laser machining box is vertically arranged in the center of the movable sliding base; an adsorption and suction assembly is arranged on a movable sliding seat for laser board splitting and cutting, during laser board splitting and cutting, smoke dust generated by laser cutting is sucked when a suction guide pipe does not make contact with a PCB, normal negative pressure grabbing is carried out during grabbing and conveying of the PCB, then a filtering assembly is arranged in an air flow path, and the filtering assembly is arranged on the air flow path. Particle filtering treatment is carried out on airflow and smoke sucked by negative pressure, influence on use of negative pressure equipment such as a vacuum pump is avoided, the filtered airflow can carry out auxiliary heat dissipation on a laser acting element in the laser processing box, self-adaptive maintenance of a filtering assembly can be achieved in cooperation with high-pressure airflow communicated with the outside, and the laser processing box is worry-saving and convenient to use.

The invention discloses an intelligent equipment fault diagnosis and early warning system of an intelligent bus station, which is characterized in that equipment state data is acquired cooperatively through vibration, temperature, current, image and environment multi-source sensors, and equipment abnormity second-level detection and 98.2% precision fault positioning are realized by combining an edge computing node and a cloud dynamic weight multi-algorithm fusion engine; aR maintenance guidance, solar energy-LoRaWAN dual-channel redundant communication and a wide temperature range protection cabinet are innovatively adopted, a closed-loop system integrating real-time monitoring, intelligent diagnosis, graded early warning and adaptive maintenance is constructed, compared with a traditional scheme, the fault recognition rate is increased by 13%, the maintenance response time is shortened by 75%, and the maintenance cost is reduced by 30%. And the technical bottleneck of all-weather reliable operation and high-efficiency operation and maintenance of bus facilities is overcome.

The invention discloses a thermoelectric power generation system, and relates to the technical field of thermoelectric power generation, and the system comprises a thermoelectric conversion module which comprises a gradient doping thermoelectric unit, a multi-stage phase change unit, a thermoelectric interface adaption unit, an intelligent thermal management unit and an electric energy preprocessing unit; the parameter cooperative control module collects performance parameters of the thermoelectric conversion module in real time, and optimizes working parameters through parameter compensation of a gradient doping structure and dynamic adjustment of working temperature; a thermal efficiency monitoring module; the self-adaptive maintenance module is used for carrying out self-adaptive maintenance when the state of the power generation equipment triggers the early warning threshold value; an adaptation module; two modes of semiconductor thermoelectric power generation and liquid thermoelectric power generation are integrated, different scenes can be quickly matched through the adaptive mode switching unit, the low-power and portable requirements of wearable equipment, small monitoring terminals and the like can be met, and the high-power and continuous requirements of shore-based power stations, island large-scale power supply and the like can be met.

The invention belongs to the technical field of elevator control, discloses an intelligent scheduling and maintenance early warning system for an elevator, and relates to the technical field of elevator management. According to the system, technologies of passenger behavior prediction, multi-sensor health monitoring, self-adaptive maintenance scheduling, emergency dynamic priority adjustment and the like are integrated, and the problems of a traditional elevator system in the aspects of peak congestion, sudden failure, low maintenance efficiency and the like are solved. The system adopts an artificial intelligence algorithm to realize intelligent scheduling of the elevator, constructs a health assessment model through multi-dimensional sensor data, realizes early warning of faults, dynamically adjusts the maintenance period according to the actual operation state, and automatically optimizes the scheduling priority under the emergency condition. The elevator operation efficiency is remarkably improved, the fault occurrence rate is reduced, the maintenance resource configuration is optimized, and the method is suitable for elevator management systems of various high-rise buildings.

The invention discloses a PID control system real-time reliability prediction and adaptive maintenance method and system, electronic equipment and a program product, and relates to the technical field of industrial digital twin operation and maintenance. The method comprises the following steps: generating a probability digital twinborn body for each component according to current data of each component acquired from a PID physical system in real time; according to the historical data of each component, performing parameter calibration on the degradation process of the probability digital twinborn body corresponding to each component; predicting the reliability of the PID physical system at a certain time in the future by adopting the calibrated probability digital twinborn, and triggering maintenance early warning when a prediction result is smaller than a self-adaptive early warning threshold value; wherein the self-adaptive early warning threshold value is adjusted through the variance and gradient of the prediction result. According to the invention, the optimal balance between false alarm and missing alarm is realized.