2772 results about "Continuous operation" patented technology

The invention provides a power plant system equipment fault diagnosis method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a continuous operation state data flow of key equipment of a power plant, then carrying out the feature extraction of the continuous operation state data flow, and generating a fusion feature set reflecting the equipment operation state coupling relation; calling a pre-trained fault dynamic diagnosis model to perform state evolution analysis processing on the fusion feature set to generate a diagnosis intermediate result which comprises a fault potential node and a fault evolution path, and performing fault attribute analysis processing based on the diagnosis intermediate result to obtain a fault attribute analysis result; according to the method, fault diagnosis information including fault trigger conditions, fault development trends and fault influence ranges is generated, and finally, an equipment diagnosis report including early warning strategies and maintenance priorities is generated according to the fault diagnosis information and is transmitted to a power plant intelligent operation and maintenance terminal, so that the accuracy and timeliness of power plant equipment fault diagnosis can be improved, and the power plant equipment fault diagnosis efficiency is improved. Safe and stable operation of a power plant is ensured.

The invention discloses a mainboard intelligent fault diagnosis method and system based on artificial intelligence, and relates to the technical field of fault diagnosis. Comprises: constructing a task number chain according to a calling sequence relationship; continuously sampling the running state parameters, and constructing a task state feature vector table in combination with the current task number; state feature vector offsets between adjacent task units are calculated pair by pair, and if the offsets exceed a set tolerance threshold value, the offsets are added into an offset anomaly table to serve as doubtful point marks; constructing an abnormal propagation path map through path reasoning, and calculating a path risk score based on the structure association degree and the historical state interaction frequency between the task units; and for the abnormal boundary with the path risk score exceeding a preset value, selecting an optimal alternative number under structural constraint from the task number chain, and deploying the optimal alternative number to a corresponding number position of the current task unit. According to the method, the dynamic substitution of the fault boundary and the local reconstruction of the system state are realized, and the robustness and the continuous operation capability of the mainboard task execution system are enhanced.

The invention discloses a servo motor dynamic overload detection and protection method based on a heat accumulation model, which relates to the technical field of servo system fault protection application, and comprises the steps of constructing a heat accumulation equivalent model, dynamically detecting an overload state, executing a heat dissipation recovery strategy, implementing a multi-stage protection mechanism, self-adaptive environment compensation and a historical data learning function. Through discretization processing of overload curve data and a nonlinear acceleration calculation strategy, the problems of protection lag and false triggering caused by traditional single-parameter detection are avoided, it is ensured that the motor can respond in time in light load, heavy load and load fluctuation scenes, the burn-out risk of the motor is reduced, and through integration of an environment temperature sensor and a heat dissipation condition evaluation unit, the reliability of the motor is improved. The heat accumulation coefficient and the heat dissipation attenuation parameter are dynamically adjusted, the problem of model deviation caused by environment interference is solved, when heat dissipation deterioration and temperature feedback abnormity are detected, the heat dissipation recovery period is automatically prolonged, calculation logic is corrected, and the continuous operation reliability of the system is improved.

The invention provides a data flow monitoring method and system based on a large model, and the method comprises the steps: obtaining a data flow record set generated by a to-be-monitored system in a continuous operation period, carrying out the correlation path construction of the data flow record set, generating a data flow topological graph containing a node interaction relation and a time sequence dependency relation, and carrying out the correlation path construction of the data flow record set; calling a pre-trained circulation behavior analysis large model to perform node sequence pattern recognition on the data circulation topological graph, and generating behavior abnormal confidence and abnormal pattern labels of each node in the data circulation topological graph; and according to the abnormal behavior confidence and the abnormal mode label, screening an abnormal interaction node cluster in the data flow topological graph. According to the method, relevance between abnormal nodes and time sequence relevance are considered, missing detection or false detection is avoided, and the reliability of the monitoring effect is improved.

The invention relates to the field of intelligent interaction, and discloses an intelligent hardware dynamic interaction system based on voice semantic fusion and multi-modal perception, which comprises the following steps of: constructing a context model of continuous operation by collecting continuous voice instructions, gesture actions and expression information of a user; semantic analysis and feature fusion are carried out on currently collected voice, gesture and expression features, meanwhile, credibility indexes of all modes are calculated through a weighting or deep learning model, weighting correction is carried out on a fusion result, a real-time feedback algorithm is adopted for weight adjustment for continuous optimization, the next operation intention of a user is predicted through deep learning, and the user experience is improved. And in combination with historical interaction data, online feedback and prediction errors, context management, modal weight and intention prediction strategies are adaptively optimized, and the updated strategies are used for next-round context acquisition and multi-modal fusion. The method has the advantage of improving the recognition accuracy in the continuous interaction scene.

The invention discloses a data center seismic isolation performance detection method based on AI intelligence, and particularly relates to the technical field of seismic isolation performance detection. Earthquake vibration characteristic data are collected in real time on a data center building and equipment, earthquake input characteristics are extracted in combination with a historical earthquake wave database, a seismic isolation performance prediction model is constructed and trained, a foundation shear wave velocity gradient abnormal value and a seismic isolation layer dynamic displacement response deviation ratio are further extracted, a deviation index is comprehensively calculated, and a seismic isolation performance prediction model is constructed and trained. And classification processing is carried out according to the deviation level, especially under the condition of a middle deviation level, foundation physical parameters are dynamically updated, and the model is optimized, so that the accuracy of the prediction model and the self-adaptive capability of the system are remarkably improved, and the shock isolation safety and the continuous operation capability of the data center in a strong shock environment are effectively guaranteed.

The invention discloses a deep foundation pit excavation deformation real-time monitoring system based on relative measurement. The deep foundation pit excavation deformation real-time monitoring system comprises a relative measurement sensor group, a data acquisition and preprocessing module, a wireless data transmission module, a cloud data processing and analysis module, an intelligent risk assessment module, a visual alarm module and a solar power supply and energy storage management module. The relative measurement sensor group collects deformation data of the deep foundation pit, the data acquisition and preprocessing module performs primary processing on the data, the wireless data transmission module stably transmits the data to the cloud, the cloud data processing and analysis module performs deformation prediction, and the intelligent risk assessment module performs risk grading. The visual alarm module gives an alarm in real time and carries out emergency notification according to different risk levels, and the solar power supply and energy storage management module guarantees continuous operation of the system. The system solves the problems of measurement precision, data transmission, risk assessment, power supply stability and the like of an existing monitoring system, provides powerful support for safe construction of the deep foundation pit, and has remarkable benefits.

The invention relates to the technical field of power supply system management, and discloses a centralized management method and management system for a power supply system. The method comprises the following steps: monitoring a current fluctuation sequence and voltage offset data of each node of a power supply network in a continuous operation period, and collecting an equipment state log text and load change time sequence information; identifying a time point of abnormal sudden change of current fluctuation, and extracting a characteristic parameter set of a power supply quality reduction event in combination with a voltage offset data change amplitude; matching a historical abnormal event library, positioning key operation record fragments strongly related to load change in the log, and generating a mapping relation table of equipment operation behaviors and power supply quality fluctuation through semantic analysis and time sequence alignment; and correcting the parameters of the load prediction model, outputting and executing a power supply priority dynamic adjustment strategy, and triggering automatic calibration of equipment operation parameters. According to the method, comprehensive control of the power supply system is realized, abnormity can be identified and processed in time, association between operation and quality is clear, and stability and flexibility of operation of the power supply system are improved.

The invention relates to the technical field of automatic control, in particular to a tower crane variable amplitude-rotation linkage self-adaptive control method oriented to a high wind load environment. The method comprises the following steps: step 1, acquiring an actually measured average wind speed at the height of the tower crane so as to determine a wind-induced coupling torque acting on a rotating shaft of the tower crane; 2, real-time equivalent rotational inertia generated by the dead weight of the cantilever crane, the suspended load mass and the relative position of the cantilever crane is calculated in real time; step 3, obtaining an original variable-amplitude torque instruction output by a variable-amplitude motor of the tower crane; and step 4, utilizing the rotating shaft to couple the rotational inertia so as to update the rotation angular velocity set value of the tower crane in the next control period in real time, and completing the amplitude variation-rotation linkage self-adaptive control of the tower crane. According to the invention, the safety, precision and continuous operation capability of the tower crane under complex construction conditions are improved.

The invention discloses an inspection method, system and device of an intelligent inspection robot and a medium, and belongs to the technical field of intelligent inspection, and the method comprises the steps that the intelligent inspection robot receives an inspection task instruction, and analyzes and obtains an inspection site, inspection time and inspection content; providing a navigation map; planning a plurality of polling paths to be selected, and calculating an optimal path through a dynamic planning algorithm; the intelligent inspection robot performs autonomous movement according to the optimal path, and performs data acquisition on an inspection object and an environment where an inspection point is located at a set point location; the intelligent inspection robot processes and analyzes the collected data, preliminarily recognizes potential abnormities, and transmits all the data to the cloud; generating alarm information and an inspection report; and the intelligent inspection robot executes automatic nest-returning charging operation according to the inspection period and the residual electric quantity state to guarantee continuous completion of the task. The task adaptability, the risk response efficiency and the continuous operation capability of the intelligent inspection robot in a complex environment are improved.

The invention relates to the technical field of wind power prediction, and particularly provides a generation power prediction method and system for a wind generating set, and the method comprises the steps: firstly obtaining a continuous operation data sequence of equipment state parameters and environment parameters containing timestamp marks, and then carrying out the time sequence feature analysis, generating an equipment state feature sequence and an environmental condition feature sequence, then performing correlation modeling on the two features through feature collaborative analysis operation to obtain a coupling feature sequence reflecting multi-factor collaborative influence, and inputting the coupling feature sequence into a pre-trained power prediction model to obtain a power prediction result; an initial power prediction sequence of a target time period is generated through time context learning and power value mapping operation, finally, an error calibration model is constructed based on historical data, the initial power prediction sequence is dynamically adjusted, and a calibration power prediction sequence is generated and output to a power dispatching system for power generation plan arrangement. And the accuracy of generating power prediction of the wind generating set is effectively improved.

The invention discloses a multi-dimensional space-time evidence chain analysis platform and method for sales data abnormal behavior recognition, and relates to the technical field of sales data processing. The analysis platform and method comprises the following steps: S1, collecting sales behavior data generated in the ordering process of a customer and the operation process of an employee through a sales order system, and preprocessing the sales behavior data; s2, constructing an employee continuous operation chain, extracting structured behavior characteristics, and quantifying the employee sales behavior deviation degree; s3, extracting a key field reconstruction operation chain of the employees with abnormal behaviors in the data source, and analyzing the overall risk intensity of behavior combination in the reconstruction operation chain; and S4, further evaluating consistency fluctuation of node behaviors in the abnormal operation chain, and generating a structured fund abnormality report and pushing the structured fund abnormality report to a financial platform interface. The problem that fund abnormity caused by the fact that salesmen make false sales through faking orders is difficult to recognize in a traditional detection method is solved.

The invention discloses an active prediction type modular battery thermal management system, which comprises the following steps: a liquid path and electrical connection module, a data acquisition module, a communication transmission module, a working condition identification module, a feedback control module, a cooperative control module, an execution adjustment module, a state feedback module, an exception handling module, a redundancy switching module and the like. A prediction control instruction is generated by combining a working condition recognition result, a fusion control quantity is formed by the prediction control instruction and temperature feedback, amplitude limiting control is conducted according to thermal power evaluation and a set threshold value, coordinated adjustment of a pump, a valve and a bypass assembly is achieved, and if recognition fails or data is abnormal, the system automatically freezes prediction control and executes closed-loop adjustment only depending on feedback. And the standby device takes over the fault after fault judgment, so that continuous operation and safety maintenance of the system are guaranteed. The system has a modular structure and complete maintenance process support.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses a high-precision hovering system and method for an omni-directional flying robot, and the method comprises the steps: collecting the connection state of a module assembly, and allowing a flying action when the connection state is judged to be normal; collecting GNSS signal intensity of the positioning sensor, determining a positioning decision mode according to the GNSS signal intensity, and generating flight attitude control data according to the positioning decision mode; according to the flight attitude control data, the flight attitude of the flying robot is adjusted based on closed-loop PID control of a harmonic reducer and a servo motor; obtaining predicted wind speed data, determining disturbance torque, and performing feed-forward compensation on the flight attitude according to the disturbance torque; and controlling the central double rotors of the flying robot to continuously operate, and controlling the outer side rotors in the flying robot to start and stop based on Q-learning. According to the invention, the anti-wind hovering performance is guaranteed, the energy utilization rate is improved, and the environmental adaptability, the anti-wind stability and the cruising ability of the aircraft are enhanced.

The invention relates to the technical field of power systems, and discloses a voltage regulation system of reactive power compensation equipment, which is characterized in that harmonic interference is filtered and eliminated through a voltage reactive power sensing unit, and accurate power grid parameters are acquired; a load trend pre-judgment unit analyzes the historical load curve and the real-time change trend to generate a future pre-judgment signal; the dynamic compensation calculation unit combines the current power grid state with the prediction to calculate the optimal compensation capacity and generate a switching instruction, after the hybrid switching actuator receives the instruction, a thyristor is triggered firstly to realize inrush current-free conduction, a contactor is switched to maintain low power consumption after the current is stable, and meanwhile, the compensation state monitoring unit performs closed-loop adjustment on parameter deviation, so that the optimal compensation capacity is obtained. The multi-dimensional protection unit prevents overvoltage, overcurrent and overheating, the double-path power supply guarantees continuous operation when the main power supply is abnormal, the intelligent communication unit realizes data uploading and remote cooperation, quick response switching of reactive compensation equipment and guarantee of safety of a large-scale power grid are realized, and inrush current impact in the switching process is effectively inhibited.

The invention provides an intelligent pressure fluctuation detection and regulation system for a building curtain wall, relates to the technical field of intelligent control and wind pressure regulation, and integrates five modules of data acquisition, credibility evaluation, dynamic wind pressure prediction, cooperative regulation and fault-tolerant guarantee to construct a high-precision wind pressure sensing and closed-loop control system. Rejecting failure nodes by evaluating the credibility of real-time data; a wind pressure trend is predicted based on an LSTM model in combination with a typhoon path, a current prediction error parameter Epr is calculated, and an error threshold E is dynamically compared to guarantee prediction reliability; a multi-objective optimization function is adopted to balance related parameters, a regulation and control instruction set is generated, and millisecond-level response and emergency switching are supported; the reconstruction module utilizes Dk1 (t), Dk2 (t) and Drec (t) to repair missing data, and a standby mechanism is started to guarantee continuous operation when necessary; meanwhile, a regulation and control instruction is dynamically and finely adjusted and closed-loop optimization is And the robustness, the response precision and the regulation and control stability of the system in high-humidity and strong-typhoon environments are remarkably improved.

The invention discloses a multi-robot task allocation and electric quantity management system for a semi-closed complex environment. The system is composed of a cloud server, an edge computing node and a robot end. The cloud server is used for formulating a global task plan according to the task type and the emergency degree and sending the global task plan to the edge computing node; the edge computing node is used for splitting and distributing a global task according to a real-time state of a robot cluster in the robot end and dynamically dispatching a robot in the robot end, and is internally provided with a computing gateway and a local cache; and the robot end is used for executing the subtasks and autonomously making decisions, and initiating task handover according to electric quantity pre-judgment. According to the method, navigation adaptation and a communication redundancy mechanism are fused, so that a robot system has the continuous operation capability in a complex, dynamic and weak communication tunnel environment, and powerful environment adaptation support is provided for collaborative operation of multiple robots.

The invention relates to the technical field of electrical measurement and power system monitoring, in particular to a cable insulation fault detection method. The method comprises the following steps: measuring an electric field signal and constructing an electric field distribution model in a cable to obtain a signal mapping result of a cable insulation state; the signal mapping result of the cable insulation state is mapped to a frequency domain, smoothing processing is carried out, multi-scale analysis is carried out on the signal after smoothing processing, fault features of the cable are extracted, the insulation state of the cable is quantified, the probability that the cable has an insulation fault is calculated, and a basis is provided for fault early warning and maintenance. The problems that most of traditional insulation fault detection methods depend on single electrical or spectral characteristic detection, so that detection information is incomplete; the stability is poor, and misjudgment or missed judgment is easily caused; in some methods, power failure or direct contact with a cable is needed for measurement, and continuous operation of the cable is affected. And complex electric field distribution and dynamic change thereof in the cable are difficult to accurately describe, and the fault development trend is difficult to predict.

The invention provides a highway electromechanical autonomous decision-making method and system based on big data and a knowledge graph, and relates to the technical field of highway traffic intellectualization, and the method comprises the steps: firstly obtaining an operation data set, including equipment state information, environment perception information and operation instruction records, of highway electromechanical equipment in a continuous operation period, and then carrying out the feature extraction of the operation data set; the method comprises the steps of obtaining a key feature set reflecting an equipment operation state, calling a pre-constructed knowledge graph to carry out association analysis on the key feature set, generating decision association information, and determining a current operation state evaluation result and a potential risk prediction result of the highway electromechanical system based on the decision association information. And finally, an autonomous decision instruction for triggering equipment adjustment or resource scheduling operation is generated, so that intelligent and automatic decision of the road electromechanical system is realized, and the operation efficiency and safety of the system are improved.

The invention relates to the field of server systems, and discloses a server cabinet system, which comprises a real-time power monitoring module used for collecting server current data at a frequency of once per second, calculating real-time power and dynamically adjusting power distribution; the intelligent prediction and optimization module is used for predicting future demands and generating an optimal distribution strategy based on the normalized historical power consumption data and load data; and the redundant power supply switching module comprises a main power supply and a standby power supply converted by the lithium battery pack through an inverter, and is used for switching to the standby power supply when the voltage of the main power supply is lower than 180V for 10ms. According to the method, the elastic power supply strategy is generated by fusing the prediction demand and the two-dimensional weight of the service priority, the continuous operation of the core service is ensured, the prior art depends on a single priority label to allocate resources, the burst load and the long-term stability are difficult to balance, and the problem of resource waste caused by separation of the two is solved.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may report, from a first layer of the UE to a second layer of the UE, a failure to receive an activation message or a deactivation message associated with a cell discontinuous operation in accordance with information differentiating a first communication during active periods of the cell discontinuous operation from a second communication outside of the active periods. The UE may transmit, to a network entity, a message indicating the failure to receive the activation message or the deactivation message. Additionally, or alternatively, the UE may change an activation status of a cell discontinuous transmission (DTX) or discontinuous reception (DRX) configuration at the UE based on the reporting of the failure to receive the activation message or the deactivation message.

The invention discloses a self-powered cholesteric phase display screen display driving system and a control method thereof, and belongs to the technical field of display. The system comprises a low-light energy acquisition module, an energy management module, a control module and a display driving module. The control module controls the turn-off and turn-on of the voltage output function of the energy management module by outputting a sleep / wake-up signal, so that the system is charged by using ambient light during sleep, and image refreshing is quickly completed and the system enters sleep again during wake-up. By setting a balance point of wake-up time and energy consumption, it is ensured that refreshing energy consumption is lower than charging energy, so that energy self-balance and continuous work of the system in a luminous environment are achieved, and external charging is not needed. The problems that an existing cholesteric phase display needs frequent external charging during continuous work and a traditional light energy power supply scheme is low in efficiency in the low-light environment are mainly solved, self-powered continuous operation of the cholesteric phase display in the indoor and outdoor low-light environment is achieved, and use convenience is improved.

The invention provides a building curtain wall deformation degree detection method and system, and relates to the technical field of intelligent building monitoring, and the method comprises the steps: obtaining multi-modal curtain wall data of a target building curtain wall based on a multi-modal data collection network; constructing a curtain wall deformation detection model, inputting the multi-modal curtain wall data into the curtain wall deformation detection model, and generating a curtain wall deformation detection result; based on a three-level alarm response mechanism, triggering an alarm response of a corresponding level according to a curtain wall deformation detection result; based on a model closed-loop optimization mechanism, parameters of the curtain wall deformation detection model are correspondingly adjusted, so that comprehensive, accurate and real-time detection and early warning can be performed on the deformation degree of the building curtain wall, the requirement of manual intervention is greatly reduced, the curtain wall detection efficiency is improved, meanwhile, the continuous operation capability and the immediate response characteristic of a detection system are guaranteed, and the detection accuracy is improved. And the safety and the reliability of the building curtain wall are improved.

The invention relates to the technical field of user intention analysis, in particular to a user intention analysis system and method based on artificial intelligence, and the method comprises the steps: generating a single-point intention recognition result representing the current instantaneous operation of a user through multi-modal behavior data at the current moment; generating a time sequence intention chain representing continuous operation of the user according to the multi-modal behavior data at the historical moment; when an intention conflict is detected, calculating an optimal transfer path for transferring a current conflict intention node in the time sequence intention chain to a candidate target intention node according to a transfer probability weight in a pre-constructed intention transfer probability graph; and determining a resource demand corresponding to the candidate target intention node of the optimal transfer path, dynamically allocating resources to the candidate target intention node according to the current available resource state and the resource demand, and performing intention analysis. Resource allocation is dynamically optimized after intention conflicts are effectively detected and solved, and accurate understanding and efficient execution of user intentions are achieved.

The invention provides an intelligent optimization control method for stable operation of a hydraulic system in a complex environment, which comprises the following steps of: aiming at an abnormal mode, optimizing power configuration parameters by adopting a genetic algorithm, determining a standby pump activation threshold value and a main pump switching condition, and obtaining a power redundancy scheme; according to the slow response reason, pipeline interface data are obtained, a feedback control mechanism is adopted to adjust valve group cooperation parameters, and a rapid pressure adjustment sequence is obtained; acquiring real-time feedback data according to the final optimization instruction, and processing high-temperature and high-dust influence through an iterative updating process to obtain stable operation configuration; according to the stable operation configuration, the state of the whole system is judged, and if the state meets the preset standard, a cooperative control result is output and used for maintaining continuous operation of the hydraulic system in the complex environment.

The invention relates to a user operation risk dynamic monitoring method and device based on data consanguinity and a medium, and the method comprises the following steps: obtaining operation behaviors of all users in real time, and extracting a segment of continuous operation behaviors of each user as a group of behavior fragments with semantic relevance; a plurality of continuous behavior segments of each user are matched with a preset behavior template, so that a plurality of intermediate behavior blocks are obtained through dimension reduction; according to a time sequence, traversing and extracting a predetermined number of intermediate behavior blocks from the plurality of intermediate behavior blocks, and respectively combining to generate a behavior fragment sequence; and respectively inputting the behavior fragment sequences into a pre-trained neural network model to obtain a risk classification result or a risk probability distribution result corresponding to each behavior fragment sequence. The method has the advantages that each operation behavior can be subjected to structured expression, the potential behavior chain can be reconstructed through the combination strategy, and the high-risk behavior path hidden in the data noise is recognized by means of the deep model.

The invention provides an edge calculation optimization method for a seawater parameter monitoring Internet of Things system, and belongs to the technical field of edge calculation, and the method comprises the steps: constructing a seawater parameter monitoring sensor network, and calculating an energy consumption evaluation reference value based on a data change rate and a disorder degree; performing feature extraction on the seawater parameter data; deploying a double-model collaborative architecture comprising a terminal lightweight model and a center complete model; setting a dynamic energy consumption threshold judgment mechanism to allocate calculation tasks; executing lightweight edge calculation at the sensing nodes; transmitting the processed data through a low-power wide area network; the calculation center verifies and fuses the data; constructing a seawater parameter space-time distribution model; dynamically adjusting an energy consumption evaluation reference value by using a Pareto optimal solution set method; and periodically evaluating system energy consumption efficiency and updating model parameters. According to the method, dynamic balance of energy consumption and calculation efficiency of the seawater parameter monitoring system is realized, and the continuous operation capability and the monitoring reliability of the system are remarkably improved.

The invention discloses an energy storage power adaptive coordination method for a photovoltaic portable energy storage power supply. The method is suitable for off-grid, emergency and outdoor scenes without power grid support. According to the method, photovoltaic output, load requirements, battery states and environmental parameters are collected in real time, the future photovoltaic output trend is predicted in combination with illumination and temperature data, and prospective power scheduling is achieved; charge and discharge power is dynamically adjusted based on the state of charge (SOC) of the battery, overcharge and overdischarge are avoided, and the service life of the battery is prolonged; when multiple paths of loads are connected, graded power supply is implemented according to a preset priority, and continuous operation of key equipment is ensured; and meanwhile, a lightweight machine learning algorithm is introduced, and a charging and discharging correction strategy and an expert rule base are optimized on line based on historical operation data, so that the system has adaptive evolution capability. According to the invention, efficient utilization of photovoltaic energy, load intelligent guarantee and energy storage safety collaboration are realized, and the intelligent level, the operation stability and the long-term reliability of the portable energy storage power supply are remarkably improved.

The invention relates to the technical field of safety valve fault monitoring, in particular to a safety valve full-life-cycle fault detection method and system, and aims to solve the problems that sudden safety accidents occur frequently due to the fact that a traditional method depends on leakage flow monitoring and cannot capture hidden faults such as valve element jamming and spring fatigue, a power supply system mostly adopts a single power generation mode, and the power consumption is low. In a low discharge scene, the electric quantity of the energy storage module is used up, and monitoring interruption is caused. According to the invention, the problems of safety valve hidden fault monitoring deficiency and unstable power supply are effectively solved. By means of multi-dimensional data collection, spring elasticity attenuation and valve element movement abnormity can be found in advance, and faults are prevented from being evolved into serious leakage. The composite power supply system maintains basic monitoring in the non-discharge stage, energy is quickly supplemented in the discharge stage, and continuous operation of the whole life cycle is guaranteed. A model analysis result can guide maintenance personnel to accurately position a fault part, and unnecessary shutdown maintenance is reduced.

The utility model provides a loading device for on-site rapid calibration of a multi-dimensional force sensor, which comprises a workbench, a loading chassis, a loading connecting piece, a top plate, a vertical loading mechanism and a horizontal loading mechanism, and is characterized in that the loading chassis is rotatably connected with the workbench, and the loading connecting piece is fixed at the top of the detected multi-dimensional force sensor; the vertical loading mechanism comprises a first lead screw loading assembly and a first sliding block, the first sliding block is connected with a first standard force sensor, and the first standard force sensor is connected with a first loading piece; the horizontal loading mechanism comprises a second lead screw loading assembly and a second sliding block, the second sliding block is connected with a second standard force sensor, and the second standard force sensor is connected with a second loading piece. The loading force applied to the loading connecting piece by the first loading piece and the second loading piece is changed along with continuous operation of the three motors, and the change of the loading force is continuous, so that continuous loading of any force is realized, and the loading precision is higher.