277 results about "Fault rate" patented technology

A predictive maintenance method based on an elevator operation and maintenance time sequence knowledge graph comprises the steps that firstly, the elevator operation and maintenance time sequence knowledge graph is constructed, predictive maintenance of electromechanical equipment parts is achieved based on elevator part maintenance period optimization, a theoretical distribution model based on Weibull distribution is constructed, and parameters of Weibull distribution are fitted through a least square method. Therefore, the model can accurately reflect the failure rule of the elevator parts; calculating an inference result of the elevator operation and maintenance time sequence knowledge graph through a graph convolution model and a Horkes process model, and carrying out recursive relation calculation of component fault rate functions in N maintenance cycles; an elevator component maintenance comprehensive cost simulation model is constructed, and the optimal maintenance cycle of elevator components is calculated through a continuous time Markov chain method; the fault prediction accuracy is improved.

The invention discloses a box-type substation remote monitoring method and system, and the method comprises the steps: obtaining and analyzing the historical operation monitoring data of a box-type substation, and determining the key operation parameters of the operation of the box-type substation; features of the key operation parameters are extracted, a prediction model is constructed based on the features and a preset model for prediction, and key operation parameter prediction data are obtained; analyzing the key operation parameter prediction data, and determining a fault mode of each key operation parameter; determining data features from the data change curve, and performing abnormal degree evaluation based on the data features to obtain an abnormal degree evaluation value; and determining the abnormal level of the box-type substation based on the fault mode and the abnormal degree evaluation value of each key operation parameter, and generating an early warning scheme based on the abnormal level and the fault mode. Remote monitoring data are analyzed, the operation state of the box-type substation is monitored in real time, abnormal conditions are found in time, early warning is carried out, and therefore the operation efficiency is improved, the fault rate is reduced, and safe and reliable operation of a power system is guaranteed.

A safety apparatus for a lifting machine platform and a three-dimensional goods shelf, said apparatus comprising a blocking assembly (100) that is located at the connection point between a lifting machine platform (10) and a three-dimensional goods shelf (30), a guide assembly (200), and a rotary bracket (300). The blocking assembly is rotatably arranged and is provided with a small blocking end (100a) and a large counterweight end (100b) which switch between high and low positions by means of rotation. When no external force is applied to the blocking assembly, under the action of gravity provided by the large counterweight end, the small blocking end is located at the high position in an upward orientation, so as to create blockage to a shuttle vehicle (40) in a direction of movement of the shuttle vehicle. The guide assembly and the blocking assembly are arranged to fit by means of offset collision in a direction of lifting, and in an offset collision state, the guide assembly collides with the large counterweight end, such that the blocking assembly rotates to switch the high and low positions of the small blocking end and the large counterweight end, and the small blocking end rotates to the low position to clear blockage to the shuttle vehicle. The described safety apparatus can effectively prevent losses such as falling of the shuttle vehicle caused by various uncertain factors in smart warehousing, thus reducing the fault rate and improving the working efficiency of three-dimensional warehousing.

The invention discloses a fault prediction method and system for deviation rectification of a laser die cutting and winding all-in-one machine, and belongs to the technical field of intelligent manufacturing and industrial equipment fault prediction.The method comprises the steps that multi-dimensional sensor data of a deviation rectification mechanism is collected and preprocessed; a sequence variability coefficient is calculated through a sliding window, and a high-risk abnormal time period is screened in combination with a dynamic threshold value; an improved Transform prediction model is constructed to carry out offset trajectory prediction; fault judgment is carried out, and the model is deployed in edge computing equipment or a server to realize efficient prediction; according to the fault prediction method and system for deviation correction of the laser die cutting and winding all-in-one machine, prediction errors can be reduced, the early warning advance and accuracy can be improved, redundant calculation can be reduced, and the model efficiency can be improved to adapt to industrial deployment; potential fault recognition and early warning are achieved, the equipment fault rate and the shutdown risk are reduced, and remarkable economic and social benefits are achieved.

The invention relates to the technical field of data processing, and particularly provides an automatic fault tracing method, system and device based on topological coding and a medium, and the method comprises the steps: distributing a unique topological code reflecting the hierarchical position of physical equipment for the physical equipment in a factory power distribution system; the method comprises the following steps: collecting real-time operation data through an edge collection terminal, and binding topological codes to form a real-time data stream with a label; constructing a topology dependency degree model, and defining the inherent failure rate of each node and the dependency degree of each node on a superior node; when the fault of the target equipment is detected, reversely traversing upstream nodes along a topological path based on the model, and calculating the fault suspicion probability of each upstream node by adopting a probability propagation algorithm; and carrying out normalization processing on the probabilities, fusing real-time state data, carrying out weighted correction, and finally generating a fault diagnosis report sorted according to the probabilities. According to the invention, by introducing a multi-level topological coding system and a probability propagation algorithm, rapid and accurate positioning and intelligent diagnosis of faults are realized.

The invention discloses an underground parking space new energy automobile lithium battery cooling and fire extinguishing device which comprises a double-control module, and the double-control module comprises a main control module A and a main control module B which are used for regularly sending detection signals between the main control module A and the main control module B, detecting faults, exchanging sensor data and synchronously storing the data in a local database; the multi-mode sensing layer is arranged at a parking space and used for detecting whether a vehicle is parked or not and whether the temperature is too high or not, and the multi-mode sensing layer is connected with the main control module A and the main control module B; and the spraying assembly is located at the parking space and connected with the fire fighting pipeline, an electromagnetic valve is arranged at the joint, and the electromagnetic valve is connected with the main control module A and the main control module B. The system has the effects of rapid intervention, accurate cooling, remote early warning closed loop and the like, meanwhile, the fault rate of the system is reduced to 0.0002 times per year by adopting the double main control modules, and the false alarm rate is only 0.1 times per thousand of vehicle days and is reduced by 87% compared with a single-point threshold value detection mode.

The invention provides an anomaly analysis method combining a fault propagation path and a topological structure, and the method comprises the steps: calculating an overall recovery time estimation value under different topological structures according to the business importance and a recovery time target, and obtaining a candidate topology scheme set meeting the recovery time requirement; for the candidate topology scheme set, key nodes and key links are extracted, a fault scene is simulated based on component fault rate and link fault rate parameters, a fault propagation path is analyzed, and the influence of faults on service quality recovery time is evaluated; obtaining an optimal topology scheme considering the recovery time and the dependency strength constraint through the service recovery time output by the service recovery time model; in the solving process, load balancing capacity and fault-tolerant backup capacity are introduced as constraint conditions.

The invention provides a method for predicting the working failure rate of a railway vehicle relay, and the method comprises the steps: classifying data according to a preset dimension to generate a structured feature set, constructing a time sequence feature matrix, and defining a multi-dimensional analysis coordinate system based on the classification dimension of the feature set; determining an initial prediction constraint condition according to the relay life distribution model and the fault mode library, executing dynamic prediction simulation on the feature set based on the time sequence feature matrix, simulating degeneration behaviors of the relay under various working conditions through a state transition simulation engine, generating an initial fault rate curve, and calculating the fault rate of the relay according to the initial fault rate curve. And finally, carrying out iterative correction on the initial fault rate curve by adopting an integrated optimization algorithm to generate a multi-dimensional fault rate prediction model. The invention further provides a system for predicting the working failure rate of the railway vehicle relay. According to the method, the adaptability and accuracy of prediction are greatly improved, and the degeneration behavior of the relay under the actual complex working condition can be simulated more truly.

The invention relates to the technical field of model training, in particular to a power transmission line fault rate prediction model training method and system based on machine learning. According to the method, an improved inverse distance weighted interpolation algorithm is adopted, timeliness weight, reliability weight and terrain constraint weight are introduced, regional meteorological grid point data are mapped to a tower coordinate position, and a time sequence is aligned; carrying out statistics on occurrence frequencies of meteorological factors in the fault samples and the non-fault samples, and calculating a single-factor association weight and a multi-factor collaborative association weight; combining the meteorological factor feature vector, the association weight and the fault tag to form a training data set; and constructing a neural network model containing an attention layer, calculating an attention weight based on a single-factor association weight, and training the model. According to the method, the attention mechanism is guided through the association weight, so that the model preferentially pays attention to the high-association-degree meteorological factors, and the prediction precision is effectively improved.

The invention relates to the technical field of intelligent control and predictive maintenance of heating water heaters, in particular to a wind pressure switch control system and method for a heating water heater. The method comprises the following steps of: extracting a starting pressure curve slope, a steady-state pressure fluctuation frequency spectrum entropy and a shutdown pressure decay time constant by collecting a time sequence pressure signal in a working period of a fan so as to generate a dynamic feature vector; comparing the vector with a preset reference health vector by the system, and resolving the health state confidence and the state deviation drift rate of the system in real time; when the health confidence coefficient is lower than a safety threshold value or the deviation drift rate is too high, the system executes forced safety locking; if the health degree is slightly reduced, the pre-purging time of the fan is actively prolonged as compensation; according to the method, conversion from fault response to predictive maintenance is achieved, early performance degradation symptoms such as fan abrasion and air duct dust deposition can be recognized in advance, post-maintenance is converted into beforehand intervention, and the fault rate and maintenance cost are effectively reduced.

The invention discloses a reliability index distribution method and system suitable for a ship power system. The objective of the invention is to overcome the defects of a traditional ship power system reliability evaluation method in the aspects of dynamic performance, real-time performance and adaptability. According to the method, real-time operation data, including temperature, pressure, vibration frequency, abrasion loss and other key parameters, of all subsystems in a ship power system are collected, and dynamic reliability influence factors are constructed based on the data. By introducing a deep reinforcement learning model, the system can adjust the weight coefficient of each subsystem according to the current dynamic reliability influence factor. In addition, in combination with historical fault data, a fault propagation path is modeled through a graph neural network (GNN), and a fault rate correction factor is calculated, so that the accuracy of reliability distribution is improved. And finally, verifying the rationality of a reliability distribution result through a Monte Carlo simulation method, and dynamically adjusting system parameters according to a simulation result to ensure stable operation of the ship power system.

The invention provides a knowledge graph construction method and device and a fault diagnosis method and device.The fault diagnosis method comprises the steps that problem information which is input by a user and associated with a family bandwidth fault is expanded; performing entity extraction from the expanded problem information based on the trained pointer network model; the extracted entities are mapped to a constructed knowledge graph to screen out an entity list, the entity list comprises one or more entities, and the similarity between each entity in the one or more entities and the extracted entities is larger than a preset threshold value; adjusting the path weight corresponding to the path between the entities in combination with the context information associated with the user; wherein the paths refer to the paths among the entities in the knowledge graph, and the larger the weight of the paths is, the higher the failure rate among the entities in the corresponding paths is; and screening out a target path from the entity list based on the adjusted path weight, and determining a corresponding family bandwidth fault from the knowledge graph based on an entity corresponding to the target path.

The invention discloses a PMSM driving system inverter open-circuit fault diagnosis method, and belongs to the technical field of motor driving system fault diagnosis, and the method comprises the steps: obtaining an ideal current value through a pre-constructed hybrid logic dynamic model of an inverter in a PMSM driving system; obtaining an observation current value through a pre-constructed improved super-spiral sliding mode observer; subtracting the ideal current value from the observation current value to obtain a three-phase current residual error; obtaining a fault diagnosis classification variable according to the three-phase current residual error; averaging the three-phase current residual errors in each period to obtain a three-phase residual error average value; obtaining a fault diagnosis detection variable according to the relationship between the three-phase residual mean value and a preset threshold value; and obtaining the fault position of the switching tube through the corresponding values of the fault diagnosis classification variable and the fault diagnosis detection variable in the fault table. According to the method, the problem that the fault rate is increased due to introduction of a sensor is solved, and accurate positioning of various fault types can be realized.

The invention discloses a power distribution network fault positioning method and device, equipment and a medium. The method comprises the following steps: S1, collecting multi-source data; s2, correcting a line impedance value by the meteorological attenuation factor; s3, outputting a health score of the switch equipment; s4, calculating a node fault confidence coefficient; s5, searching a fault interval; s6, generating an inspection instruction; s7, performing iterative training; according to the invention, the meteorological attenuation factor is introduced to correct the line impedance measurement value in real time, and the health score is combined to effectively solve the fault missed judgment problem caused by rainstorm or strong wind; an inspection instruction is generated according to the node fault confidence of the fault interval, the overall fault positioning time is shortened, and the power supply recovery speed is increased; the machine account and real-time data are fused, equipment state early warning is realized, high-risk equipment is accurately identified, expansion of a fault interval caused by false triggering of an old switch and participation of historical maintenance times in equipment health scoring are avoided, and the fault rate of a pilot line is effectively reduced.

The invention discloses a wire harness processing control method and system based on artificial intelligence, and the method comprises the steps: collecting sensor array data, carrying out the normalization processing, forming a standardized parameter set, precisely recognizing an abnormal mode through a classification algorithm, marking a potential fault point, extracting a time sequence feature sequence, and generating a prediction deviation vector through a prediction algorithm, analyzing dominant influence factors, grouping similar deviation modes through a clustering algorithm, and judging process adjustment requirements; and an optimization instruction sequence is generated, a production scene is matched based on historical data, the adjusted parameter configuration is dynamically injected into a control system, and the parameter model is continuously optimized through real-time monitoring and feedback circulation. According to the method, through data-driven anomaly detection and prediction adjustment, the stability and efficiency of the manufacturing process are remarkably improved, the fault rate is reduced, and intelligent machining state optimization is achieved.

The invention relates to the technical field of intelligent management of construction elevators, in particular to a digital intelligent management system for construction elevators, which comprises an operation monitoring module used for collecting operation state data of each construction elevator in real time through a sensor network, detecting fault conditions and performing early warning; the intelligent scheduling module is used for intelligently distributing transportation tasks based on the passenger flow data, the material transportation demand data and the operation state data of each construction elevator; the predictive maintenance module is used for acquiring key component parameters of each construction elevator, predicting the residual life according to the key component parameters in combination with a fault model, and generating a maintenance work order in advance so as to reduce the fault rate; the authority management module is used for limiting unauthorized personnel to use the construction elevator; and the digital archive management module is used for recording the operation state, the fault history and the maintenance record of the construction elevator and generating a complete equipment life cycle archive. According to the invention, fine and informatization management of the whole life cycle of the construction elevator can be realized.

A control method for the analog input / output setting mode of a hydro-generator monitoring system includes a newly added program for identifying input / output pulses. The control logic is as follows: ① If an input pulse command of 1 is detected, the analog input / output setting value = analog input / output setting value + fixed step size; otherwise, the analog input / output setting value remains unchanged. ② If an input pulse command of 1 is detected, the analog input / output setting value = analog input / output setting value - fixed step size; otherwise, the analog input / output setting value remains unchanged. A new analog input / output setting value initialization program is also included, employing the following control logic: When other control modes are switched to analog input / output setting mode, the current input / output setting feedback value sent by the governor is assigned to the analog input / output setting value as the initial value. This invention can improve the guide vane opening and active power regulation speed, enhance equipment stability and reliability, reduce intermediate links and fault points, lower equipment failure rate, and ensure the safe, stable, and high-quality operation of the generator unit and the power grid.

The application discloses a ship-boat coupling type inspection robot for offshore wind turbine generator units, which comprises a drone and an unmanned ship, and a ship body monitoring module for assisting the drone in monitoring tasks is arranged on the unmanned ship; the body of the drone is in a cylindrical frame structure, can conveniently pass through inside a wind turbine tower drum, and the body is provided with a drone monitoring module; the drone monitoring module comprises an image acquisition device, a sound and image acquisition device and a vibration acquisition device, and can effectively acquire various data inside the tower drum of the wind turbine generator unit. The ship-boat coupling type inspection robot can realize all-around state monitoring and fault diagnosis of offshore wind turbine generator units by adopting the combined operation mode of the unmanned ship and the drone, can timely master the operation state of the wind turbine generator unit, early discover potential fault signs, reduce the fault rate, ensure safe and efficient power generation operation of the wind turbine generator unit, simultaneously changes the previous manual inspection mode, greatly saves the operation and maintenance cost, and ensures personnel safety.

A system testability prediction method based on an aero-engine functional architecture comprises the following steps: determining system test architecture data according to the aero-engine functional architecture; based on the system test architecture data, obtaining a fault mode probability lambda Di capable of being correctly tested by the ith basic-level maintainable function unit and a fault rate lambda i of the ith basic-level maintainable function unit, and establishing a system fault detection rate rFD mathematical model; obtaining a function fault mode probability lambda Ii which can be isolated to an ith basic level maintainable function unit, and establishing a system fault isolation rate rFI mathematical model; obtaining a fault mode probability lambda Ai that the i-th basic-level maintainable function unit with the test function may generate a false alarm, and establishing a system inherent false alarm rate rFA mathematical model; and system testability prediction is completed. The method can be used for scheme design of aero-engines and other complex products, system testability prediction in the detailed design stage and evaluation of whether the scheme design of the aero-engines meets testability quantitative index requirements or not.

The utility model relates to the technical field of tool apron structures, in particular to a multifunctional cutting machine tool apron structure which comprises a connecting arm and a tool apron installed on one side of the connecting arm in a sliding mode, a sliding groove is formed in the connecting arm, a fixing part is arranged in the sliding groove, the tool apron comprises a first module and a second module, and a blade is rotationally arranged on the second module. The sliding groove is formed to quickly install the tool apron, installation can be completed only by aligning the first module of the tool apron to the inlet of the sliding groove and then pushing the first module, time is saved, the complexity and the error rate in the installation process are reduced, the tool apron is further locked and fixed through the bolt, and when a blade needs to be replaced or the tool apron needs to be maintained, the tool apron can be installed conveniently. Due to the design of the sliding groove, the downtime can be greatly shortened, replacement or maintenance work of the tool apron can be completed in a short time, the positioning groove is formed so that the blade can be conveniently and rapidly aligned with the output shaft of the micro motor, the equipment failure rate caused by improper alignment is reduced, the reliability and stability of equipment can be improved, and the installation efficiency is improved.

The invention discloses a transmission mechanism of a road marking machine. The device comprises a bottom plate, a driving transmission chain wheel and a transmission shaft which are used for forming a transmission relation with a driving mechanism are rotationally mounted on the bottom plate, and a transmission chain wheel forming a transmission relation with the driving transmission chain wheel is fixed on the transmission shaft; a walking transmission chain wheel and a transmission gear which are used for forming a transmission relation with the walking mechanism are further fixed to the transmission shaft. A movable plate is further installed on the bottom plate in a swinging mode, a lineation transmission gear used for forming a transmission relation with the lineation mechanism is rotationally installed on the movable plate, and the lineation transmission gear can selectively form a transmission relation with the transmission gear through swinging. The marking machine has the advantages that driving force from the driving mechanism is firstly transmitted to the transmission shaft and then transmitted to the walking mechanism and the marking mechanism, the structure is simple and compact, a hydraulic system or multi-motor driving is not needed, the fault rate is low, and therefore the overhaul period can be effectively prolonged, the operation efficiency is improved, and the service life is prolonged.

The invention provides a method for reducing the failure rate of a unit air cooling island speed reducer. The method comprises the following steps that S1, a multi-module cooperative prevention and control system is built; s2, configuring a parameter monitoring module assembly; s3, standardizing the installation standard of the sensor; s4, building a data transmission and analysis platform; s5, starting an intelligent diagnosis and data tracing function; s6, executing a monitor calibration and maintenance process; s7, carrying out preventive maintenance operation; s8, starting a working condition linkage adjusting mechanism; s9, activating an operation and maintenance training auxiliary module; and S10, a supplement lubricating oil leakage monitoring link. According to the method for reducing the failure rate of the unit air cooling island speed reducer, full-dimension real-time monitoring and intelligent diagnosis of operation parameters of the speed reducer are achieved by building a multi-module cooperative prevention and control system, and by combining graded early warning and preventive maintenance, fault hidden dangers are accurately positioned and handled; the working condition linkage adjusting mechanism can actively adjust the operation state when parameters are abnormal, and fault expansion is avoided.

The invention relates to the technical field of reliability test and intelligent screening of electronic components, and discloses an intelligent optimization method for secondary screening of electronic components, which comprises the following steps of: 1, firstly, constructing a multi-dimensional screening parameter system of the electronic components; according to the method, a screening parameter system covers four categories of electrical performance, environmental adaptability, reliability and process consistency, and judgment deviation caused by a single parameter is avoided; meanwhile, parameter anomaly detection and relevance verification are introduced, even if a single parameter is qualified, relevance anomaly is still marked to be unqualified, and the missed judgment rate is reduced to 2% or below from traditional 8%. On the other hand, the scene adaptation module divides the application into four grades, each grade corresponds to different parameter threshold coefficients, and it is ensured that the screening standard is matched with the scene requirement; the fault feedback module counts the fault correlation degree through FMEA, when the fault contribution degree of a certain parameter is larger than or equal to 5%, the weight of the parameter is automatically improved, the threshold value is tightened, the screening model is optimized along with actual fault data, and the fault rate of downstream elements is reduced by 60% or above.

The present disclosure relates to a kind of multi-extreme weather adapted offshore electric-hydrogen-alcohol system multi-energy flow failure modeling and supply-demand evaluation method and device.The method includes: constructing system success flow model based on GO method, identifying key equipment failure transmission chain;GO chart is converted into fault tree, combined with the minimum cut set method and sequential Monte Carlo method Quantification system failure probability and equipment contribution;Build wind and light hydrogen alcohol power model, introduce operation control strategy, dynamically evaluate power and fuel supply and demand reliability index;Establish fault-supply coupling mechanism, under the extreme weather such as typhoon, cold wave, strong wind and rain, the output of wind and light and equipment failure rate are doubly corrected, and a three-dimensional comprehensive reliability index system is constructed.The present application realizes the complete causal chain modeling from equipment failure to supply shortage, improves the accuracy and comprehensiveness of system comprehensive reliability evaluation under multi-extreme weather scenario, and can be used for system optimization design, operation decision and risk prevention and control.

The invention relates to the technical field of stamping part machining, and discloses an industrial manipulator feeding and discharging automatic stacking mechanism which comprises a mechanical arm, a second adsorption assembly connected with the mechanical arm, a table body arranged on one side of the mechanical arm, a transfer table and an adjusting assembly arranged on the top of the table body, and a first adsorption assembly arranged on the adjusting assembly. The second adsorption assembly comprises a box body, the box body is arranged on the mechanical arm, and a third rotating shaft is rotationally connected to the box body; the manipulator grabs the blanks from the blank placing area according to a set sequence, the blanks are sequentially processed by a plurality of punching machines, and finally finished products are orderly placed in the finished product placing area, so that automation and continuity of the production process are realized, and the production efficiency is greatly improved. And on the other hand, the automatic stacking structure is precisely matched with the manipulator, so that the error rate and the fault rate in the operation process are effectively reduced, production interruption and loss caused by human factors or mechanical faults are reduced, and the overall production quality and stability are improved.

The invention relates to a model determination method, a fault rate determination method and a related device, and relates to the technical field of electronic information, the method comprises the following steps: according to an update process corresponding to a terminal device, determining a statistical parameter corresponding to the update process, the update process being used for describing the number of times of fault occurrence of the terminal device. The statistical parameters are used for describing distribution of the number of times that the terminal equipment breaks down. And determining an undetermined fault rate model corresponding to the first equipment type of the terminal equipment according to the statistical parameters and the failure strength model. And obtaining equipment data corresponding to the first equipment type. The device data comprises a first device activation number and a first device failure rate corresponding to the first device type in at least one specified time period. And according to the undetermined fault rate model and the equipment data, determining a target fault rate model corresponding to the first equipment type, the target fault rate model being used for predicting the equipment fault rate of the first equipment type. According to the invention, the quantizability and interpretability of the model can be improved.

The invention belongs to the technical field of power grid fault analysis and early warning, and particularly relates to a power grid fault risk early warning method based on a fault quantitative relation model, a storage medium and electronic equipment. According to the scheme, a fault quantitative relation model of dynamic and non-linear correlation between the fault rates of various power grid equipment lines and the meteorological levels of various severe meteorological types is established through preferential function fitting, and meteorological level thresholds corresponding to various power grid equipment in various severe meteorological types are scientifically set; clear and quantitative decision criteria are provided for starting fault defense measures or not, power grid fault risk early warning analysis is carried out, and early risk early warning of power grid equipment line faults in a severe meteorological environment is accurately and quickly realized. And key data support can be provided for response measures such as preparation of a power grid fault defense plan in advance, calling of emergency standby resources such as energy storage, configuration scale and the like.

The invention discloses a power grid fault data processing method and device and a medium, and the method comprises the steps: carrying out the multi-dimensional detection data collection of a target power grid, obtaining a power grid image and sensing parameters, and collecting the environment parameters in a target power grid environment; acquiring historical detection data of a target power grid, extracting a fault sample proportion coefficient, performing combination division on the historical detection data, and performing training of an integrated image fault identification branch and an integrated sensing fault identification branch; performing fault rate influence analysis and detection data influence analysis according to the environmental parameters to obtain a fault rate influence coefficient, an image influence coefficient and a sensing influence coefficient; according to the fault rate, the image and the sensing influence coefficient, the number of image recognition branches and the number of sensing recognition branches are calculated and obtained, fault recognition branch calling and fault recognition are carried out, a power grid fault recognition result is obtained, and the technical problem that the machine learning recognition fault perception rate and accuracy are low due to the fact that power grid line inspection fault data samples are few is solved.

The invention relates to the technical field of power distribution networks, in particular to a power distribution equipment pressure assessment and risk prediction method, which comprises the following steps of: analyzing fault causes of three typical power distribution equipment, namely a power distribution transformer, an overhead line and switch equipment, extracting key characteristic quantities influencing the pressure state of the power distribution equipment; a power distribution equipment pressure index calculation model is established by adopting a subjective and objective combination weighting method, a fault rate model based on a pressure index is proposed, and a BP neural network is introduced to quantify the relationship between an overhaul strategy and equipment fault rate rollback in consideration of the influence of equipment overhaul on the fault rate. According to the method, the key characteristic quantity is extracted, and the pressure index calculation model, the fault rate model and the BP neural network fault rate curve correction model are established, so that accurate evaluation and risk prediction of the pressure of the power distribution equipment are realized, the method has relatively high accuracy and reliability, and a powerful guarantee is provided for safe operation of the power distribution network.

The application provides a control method of a refrigerant double control valve, comprising the following steps: after receiving a power-on signal of a vehicle, judging whether the refrigerant double control valve is suspected to be blocked in real time; if the refrigerant double control valve is suspected to be blocked, sending a target initialization instruction to the refrigerant double control valve, and judging whether the refrigerant double control valve can complete initialization according to the target initialization instruction; if yes, re-outputting a previous operation instruction, if the valve needle assembly normally reaches a target operation position corresponding to the previous operation instruction, judging that the refrigerant double control valve is normal; if no, outputting alarm information for indicating that the refrigerant double control valve is blocked; in the scheme, when the upper computer monitors that the refrigerant valve is blocked, the refrigerant valve is first regarded as being suspected to be blocked, and is not directly reported to be blocked, but is given an initialization instruction again, if the initialization is not completed, the upper computer outputs alarm information again, the fault reporting rate of the refrigerant valve is effectively reduced, and the refrigerant system is more stable and reliable.