2396 results about "Spare part" patented technology

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

The invention relates to a spare part service life prediction method, device and equipment and a computer readable medium. The method comprises the steps of collecting multi-mode state data of a target spare part; verifying the historical consistency of the multi-modal state data; and under the condition that the historical consistency verification of the multi-modal state data is passed, inputting the multi-modal state data into a target residual life prediction model so as to predict a degradation track of the target spare part based on the multi-modal state data by using the target residual life prediction model, the target residual life prediction model is a neural network model obtained by training by taking a physics degradation mechanism of the spare part as priori knowledge; and determining the predicted remaining life of the target spare part based on the degradation trajectory. According to the method, the evaluation one-sidedness caused by insufficient single data dimension is avoided, the prediction credibility is improved through a data verification and physical mechanism constraint model, and the technical problem of low life prediction accuracy caused by spare part life counterfeiting is effectively solved.

The invention discloses a product full-life-cycle traceability after-sales service system based on multi-source heterogeneous data, and relates to the technical field of product traceability after-sales management. Manufacturing resumes, work orders, spare part warehouse-in and warehouse-out and equipment logs are accessed, mapping of product instances, parts and software primary keys is established, and the product traceability after-sales service system is established. And normalizing the configuration change event into a configuration change event containing double time axes of effective time and warehousing time, and writing the configuration change event carrying differential operation, an evidence packet and an idempotent event identifier into an immutable event library. And constructing a conflict set for the same configuration target, executing uniqueness, causality and compatibility constraint verification, determining an effective event based on evidence packet arbitration, and outputting an arbitration record and confidence. And deriving a configuration version chain according to the effective event and generating a snapshot, performing incremental playback on the late or error correction event and generating an influence propagation list. And when any time point configuration is queried, playback restoration is performed, an evidence chain and confidence are output, an active verification backflow error correction event is triggered if the confidence is insufficient, and the accuracy of quality guarantee judgment and spare part matching is improved.

The invention relates to the technical field of industrial equipment predictive maintenance and asset management, in particular to a machine vision equipment operation and maintenance cost analysis intelligent management method, which comprises the following steps of: acquiring equipment operation state, external environment and historical operation and maintenance work order data through a sensor group and an equipment log interface, and fusing and removing redundancy to form a multi-source data stream; static and dynamic features are extracted by using a pre-trained health state evaluation model and fused by means of an attention mechanism, and a real-time health state index in a 0-1 interval is output; constructing a dynamic cost prediction model, taking health related parameters, spare parts, manpower and depreciation cost as input, and predicting expected operation and maintenance cost of a specific time window in the future; establishing an optimization decision model by taking minimization of the total operation and maintenance cost and maximization of the equipment availability rate as double targets, and generating an optimal maintenance, spare part purchasing and scheduling scheme; and executing the scheme and acquiring actual data, comparing the actual data with a predicted value for feedback, and iteratively optimizing the core model. The operation and maintenance management accuracy and economy are improved, and the method is suitable for intelligent operation and maintenance of the machine vision equipment.

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 discloses an optimization method and system for aviation equipment storage and a medium, and the method comprises the steps: collecting multi-dimensional dynamic data, carrying out the fusion cleaning and structural feature extraction, and generating a high-dimensional feature set; outputting a dynamic demand prediction value and uncertainty measurement by using a hybrid intelligent prediction model fusing time sequence prediction and ensemble learning; calculating an optimal inventory control parameter through a stochastic optimization model in combination with the aviation material key grade and the stockout loss cost; and generating an inventory operation instruction according to the optimal inventory control parameter and the real-time inventory state, and outputting a strategy report with confidence evaluation and key influence factor analysis. According to the method, multi-dimensional accurate prediction of the aerial material demand and dynamic optimization of the inventory strategy are realized, the inventory control precision and the resource utilization efficiency are remarkably improved, the stockout risk and the overstocked cost are effectively reduced, and the reliability and the economical efficiency of aviation equipment guarantee are enhanced.

The invention discloses a workshop part declaration management system, and relates to the technical field of production workshop part inventory management. The workshop spare and accessory declaration management system comprises an acquisition and division unit used for analyzing data of a comprehensive production evaluation index of each part in each part type after a preset workshop production cycle is finished; the defect judgment unit is used for judging and screening production defect parts based on the comprehensive production evaluation index and a preset production evaluation interval; the maintenance analysis unit is used for analyzing the maintenance evaluation index; and the maintenance judgment unit is used for dividing maintainable defective parts and scrapped defective parts based on the maintenance evaluation indexes and a preset maintenance evaluation interval and generating a part declaration report. Each part is comprehensively analyzed through the comprehensive production evaluation indexes, so that the production quality of the parts can be accurately evaluated, and the production efficiency of the parts is improved. A comprehensive evaluation result is provided for each part type, and potential problems of the parts can be identified more comprehensively and meticulously.

The invention relates to the technical field of computer-aided engineering, in particular to an integrated design method for a three-dimensional simulation model of spare and accessory parts. The method comprises the following steps: obtaining a part structure drawing, and carrying out part interference analysis to obtain part interference data; reconstructing an assembly path according to the part interference data to obtain assembly path data; constructing a part three-dimensional simulation model according to the assembly path data; performing fatigue simulation according to the three-dimensional simulation model of the spare part to obtain fatigue life data; performing fracture area analysis based on the fatigue life data to obtain part fracture area data; performing grain boundary cracking detection according to the part fracture area data to obtain grain boundary cracking data; and performing grain boundary hydrogen permeation detection according to the grain boundary cracking data to obtain grain boundary hydrogen permeation data. Based on the computer-aided engineering technology, the accuracy of part structure design and the reliability of service life prediction are improved.

The invention, which relates to the technical field of intelligent inventory management, discloses an intelligent inventory management system for household appliance spare and accessory parts, comprising a problem diagnosis module used for generating an inventory management first-level problem label and an inventory management second-level problem label; the intelligent demand prediction module is used for carrying out classification demand prediction according to the problem label generated by the problem diagnosis module and carrying out dynamic parameter adjustment on a prediction value; the dynamic classification management module is used for carrying out intelligent warehouse division, supplier collaborative optimization and digital agreement on the household appliance spare and accessory parts; and the intelligent storage module is used for upgrading hardware and carrying out software integration operation. According to the invention, historical data, market trend and seasonal fluctuation are utilized to accurately predict the requirements of spare and accessory parts. Classification is performed according to the value and demand fluctuation of the spare and accessory parts, and fine management is implemented. Through a VMI + JMI mixed mode, a supply chain is optimized to be cooperatively combined with an RFID technology and automatic storage equipment, so that the warehouse management efficiency is improved.

The invention relates to the technical field of mine equipment intelligent operation and maintenance and augmented reality, in particular to a forklift predictive maintenance and repair guidance system based on augmented reality, which comprises a multi-source data acquisition module, a mining area operation analysis module, a predictive maintenance engine, an AR intelligent repair guidance module and a resource dynamic scheduling center, a multi-source sensor is used for collecting equipment state and environment data, and mineral characteristics, path characteristics and load changes are dynamically analyzed in combination with working conditions of a mining area; the predictive maintenance engine constructs a fault prediction model based on a deep belief network, and dynamically corrects a working condition adaptive threshold; the AR module is used for positioning and superposing three-dimensional maintenance guidance through SLAM (Simultaneous Localization and Mapping), and adapting to the skill And the resource scheduling center generates spare part, personnel and alternative equipment scheduling schemes as required. According to the method, the whole process from fault prediction to maintenance execution is intelligentized, the fault early warning accuracy and the maintenance efficiency are improved, and the method is suitable for operation and maintenance management of mining forklifts under complex working conditions.

Provided is a technique for evaluating the quantity of possessed spare parts in consideration of probability distribution characteristics of the failure frequency of a component. A possessed spare part quantity calculation device according to the present invention comprises: a means for calculating, with regard to a component constituting a system to be evaluated and on the basis of actual time-series data regarding failure and maintenance of the component, the failure frequency of the component during the evaluation period and a failure frequency-based failure probability for that failure frequency indicating the probability that a failure will occur; and a means for calculating, on the basis of the failure frequency-based failure probability, a possessed spare part quantity of the component, said quantity being that necessary for achieving a target value for the probability that the component will not be out of stock in the event of a failure.

The invention discloses a novel quick-change mechanism with a flow control function for a tundish. The novel quick-change mechanism comprises a mounting part, a sliding part, a pressing part, a first driving part and a second driving part. And the functions of the stopper rod opening and closing device are integrated on the tundish quick-change nozzle device, so that the investment, the installation site, the maintenance cost, the spare part site and the cost of a set of equipment are saved. The two sets of equipment are combined into a whole, so that the number of operators in a steel mill is reduced, and meanwhile, preparation is made in advance for the subsequent steel mill if intelligent and automatic control over steel-making continuous casting is adopted, and the positive effect is achieved.

The invention relates to a coal machine spare part similarity identification and coding unification system and method and a storage medium. The system comprises a data acquisition module for extracting coal machine spare part data from a plurality of systems; the cleaning and preprocessing module is used for carrying out unified formatting processing on the spare part data; the feature extraction and matching engine is used for performing multi-dimensional feature extraction on the preprocessed data, including text similarity features, semantic features, structural features and attribute features; the similarity calculation and clustering module is used for calculating the comprehensive similarity of spare part data through a deep fusion network according to the multi-dimensional features, and performing automatic clustering according to the similarity; and the master data generation and system docking module is used for generating unified spare part master data for each cluster and synchronizing the spare part master data to a plurality of systems. According to the method, a large model and deep learning are utilized, multi-dimensional features are extracted and fused for multi-source spare part data, spare part similarity matching is carried out, dependence on a spare part coding format is broken through, and the accuracy and robustness of cross-system matching are improved.

The invention relates to the technical field of inventory optimization, in particular to a multilevel guarantee-based maintenance resource optimization method, which comprises the following steps of: determining a resource allocation quantity based on a guarantee system and a guarantee resource total number, and determining a maintenance function of each base station in combination with spare part historical use data; determining the annual average demand quantity of the superior replacement unit based on the base-level replacement unit, and further determining a superior supply channel mean value and a superior expected stockout number; determining a basic-level supply channel mean value and a basic-level expected stockout number based on the annual average demand quantity, the maintenance function and the superior expected stockout number so as to determine the availability of each basic-level equipment and the total availability, and constructing a resource allocation optimization model based on the total availability and the resource allocation quantity. And performing calculation based on the model to obtain the maximum total availability, the maintenance resource allocation quantity and the inventory configuration. According to the method, dynamic allocation of resources can be guaranteed, and the problem of marginal effect decline existing in single-way improvement is solved, so that the availability level is improved.

The invention relates to the technical field of automobile part cutting, in particular to an automobile part cutting table which comprises a control cabinet, a first transferring set, a second transferring set and a lifting mechanism are arranged on the upper side of the control cabinet, a cutting assembly is arranged on the side face of the lifting mechanism, and a clamping assembly is arranged on the upper side of the control cabinet in a mirror image mode. The clamping assembly comprises an electric rotary table, a multidirectional mechanical arm and an electric clamping jaw, and clamping blocks are arranged on the side face of the electric clamping jaw in a mirror image mode. According to the cutting table module assembly, the clamping assembly and the cutting assembly are arranged, the cutting table module assembly with multi-state cutting and self-adaptive clamping can be integrated, single-laser, single-water-jet-cutter and water-laser mixed cutting operation can be carried out on spare and accessory parts made of different materials and in different shapes, meanwhile, the spare and accessory parts made of different materials and in different shapes can be adaptively clamped, and the cutting table module assembly is convenient to use. The clamped materials can be subjected to multidirectional position adjustment to be matched with cutting, in addition, independent adjustment operation can be carried out during single-laser cutting and single-water-jet cutting, and efficient and high-precision machining operation of spare and accessory parts is met.

The invention provides a property equipment spare part inventory dynamic prediction method based on an RFID technology, and the method comprises the steps: 1, collecting information in real time through an RFID reader-writer, and forming a historical data chain; step 2, cleaning and feature extraction are performed on historical data, and equipment fault rules and spare part consumption modes are mined in combination with time sequence analysis and a machine learning algorithm; 3, judging whether data returned by the RFID in real time is received or not, if so, returning to the step 1, and if not, executing the step 4; and Step 4, dynamically adjusting prediction model parameters according to the equipment operation condition and the spare part remaining quantity, and realizing accurate prediction of the spare part future demand quantity and time by using a dynamic weight optimization algorithm. And in combination with time sequence analysis and a machine learning algorithm, mining an equipment fault rule and a spare part consumption mode, continuously receiving equipment and spare part data returned by the RFID in real time, dynamically adjusting prediction model parameters according to the equipment operation condition and the spare part remaining quantity, and realizing accurate prediction of the spare part future demand quantity and time by using a dynamic weight optimization algorithm.

The present application relates to the technical field of temperature control, in particular to a temperature control method for injection mold of automobile spare parts production, comprising: according to the difference between the temperature value of the cavity and the core and the required temperature value at the current time, respectively completing the first temperature adjustment of the cavity and the core, after the first temperature adjustment of the cavity and the core, obtaining the temperature value of each monitoring point in the cavity and the core at the next time of the current time, and the flow rate of the cooling liquid in the cooling system of the cavity and the core, so as to determine the local temperature difference consistency of the temperature zone of the cavity and the core, and respectively complete the second temperature adjustment of the cavity and the core. The present application can improve the precision of injection mold temperature control, and is beneficial to the high-precision production of high-precision automobile spare parts.

The invention provides a maintenance spare part prediction system based on a big language model plug-in knowledge base, and the system comprises the steps: carrying out the information data extraction and preprocessing of a historical work order, and obtaining a preprocessing text; performing semantic vector conversion and association binding processing on the preprocessed text to obtain a vector database; extracting attribute information from the new work order, and performing semantic vector conversion on the attribute information to obtain a query vector; retrieving a work order related to the query vector from a vector database; semantic reasoning is carried out on a new work order and a related work order, a maintenance spare part prediction result is generated, natural language fault description and equipment model information in a maintenance record are fully utilized, natural language information is fully understood in the face of complexity and diversity, the spare part prediction accuracy and practicability are improved, reasonable configuration of maintenance resources is achieved, and the maintenance efficiency is improved. Spare part inventory cost and inventory waste are reduced, and maintenance efficiency is improved.

The invention provides a power equipment spare part allocation method and system based on an intelligent agent. The method comprises the following steps: acquiring multi-source operation and maintenance data of fault power equipment; performing natural semantic analysis on the multi-source operation and maintenance data by utilizing a large language model to obtain deployment task description of the fault power equipment; according to the deployment task description of the fault power equipment, screening out a target spare part associated with the deployment task description from pre-acquired spare part inventory information; based on the spare part inventory information of the target spare part, generating an allocation instruction of the target spare part by using a pre-constructed allocation intelligent agent; based on the power equipment operation and maintenance scene in the multi-source heterogeneous environment, the large language model is packaged in the intelligent agent, and the task context and the strategy rule are fused, so that intelligent allocation of power equipment spare parts is facilitated, and the power equipment operation and maintenance efficiency is improved. And the efficiency bottleneck and the adaptability obstacle faced by a traditional allocation mode based on manual rules and static flow driving are broken through.

The invention provides a spare part prediction method and device based on intelligent operation and maintenance and large model fusion, and relates to the field of data processing. In the method, acquisition data sent by edge operation and maintenance equipment is acquired, and the acquisition data comprises a field operation signal, a work order text and inventory data; generating fusion features based on the collected data; the fusion features are input into a Transform + LLM expert model, prediction data for the spare parts are determined, and the prediction data comprise future demand distribution, in-transit inventory and supply time delay; the prediction data and the collection data are linked to an alliance block chain, and a prediction deviation and an inventory value are monitored through an intelligent contract of the alliance block chain; if it is determined that the predicted deviation exceeds the limit and / or the inventory value is lower than the preset threshold value, a dynamic optimization strategy is generated, and the dynamic optimization strategy is used for conducting dynamic order supplement optimization on the spare parts. By implementing the technical scheme provided by the invention, the accuracy of predicting the spare parts is improved.

The design features for which protection is sought include the pattern and / or shape and / or configuration and / or decoration of the spare part.

The invention provides an assembling method for a ship hanging beam. The assembling method comprises the steps that S1, a plurality of positioning parts are arranged at preset positions according to the length and specification of the hanging beam; s2, the hanging beam comprises a plurality of hanging beam sections, and the multiple hanging beam sections are arranged on the multiple positioning parts in a hanging mode; s3, the position of each hanging beam segment is adjusted in sequence through a positioning part; and S4, the multiple hanging beam sections are pre-fixed through U-shaped clamping plates, the splicing positions of every two adjacent hanging beam sections are welded in sequence, and splicing of the hanging beam is completed. According to the device, super-long spare parts of the ship can be accurately assembled and welded, the assembling and welding quality of the hanging beam is guaranteed, the assembling and welding efficiency of the hanging beam is improved, the workload of constructors is relieved, the building efficiency and building quality of the ship are improved, and application and popularization are facilitated.

The utility model belongs to the technical field of regulating valves, and particularly relates to a regulating valve convenient for online maintenance, which comprises a valve body and a valve cover, a valve seat is arranged in the valve body, a valve rod is mounted on the valve seat, a valve core is arranged at the top end of the valve rod, and a valve seat sleeve is mounted on the valve core. Two valve element sealing rings and a sealing ring spare part which are oppositely arranged are arranged on the valve seat sleeve, a hexagon nut is arranged at the top end of the valve element, the valve seat sleeve is detachably installed on the valve element through the hexagon nut, the two same valve element sealing rings are arranged in the same valve element, one valve element sealing ring makes contact with the valve seat ring, and the other valve element sealing ring makes contact with the sealing ring spare part. And the design and use requirements of the regulating valve are met. And the other valve core sealing ring does not participate in the flow regulation work. The valve element sealing ring is only used as a spare part to be attached inside the valve element, when the valve has faults such as large leakage amount, the two valve element sealing rings on the valve element are exchanged, the leakage problem of the valve can be repaired, the whole valve does not need to be disassembled, maintenance is convenient, and working efficiency is greatly improved.

The invention discloses a fan fault analysis and operation and maintenance decision support method based on a new energy monitoring system, belongs to the field of new energy power generation monitoring, and aims at solving the problems that an existing system is low in fault recognition precision, depends on artificial experience and is not fully mined in data value. According to the method, a wind turbine generator SCADA system, meteorological data and operation log multi-source data are integrated, and a five-dimensional fault feature tag set of electrical and mechanical faults is constructed; after module pre-classification, a DBSCAN clustering algorithm is adopted for analysis, and samples with the similarity matching degree smaller than 60% of a new clustering center and an original clustering center serve as newly-added typical cases to update a fault processing knowledge manual; when the matching between the unit parameters and typical faults is greater than or equal to 80%, automatic early warning is carried out and a processing scheme is pushed; carrying out preventive maintenance in combination with a time sequence model, optimizing a spare part inventory according to a weighted model, and pushing a case when the matching between a fault and a historical case is greater than 90%; the method improves the fault diagnosis precision, shortens the processing time, reduces the operation and maintenance cost, and is suitable for various manufacturer models.

The invention discloses a management method and system for improving the receiving rate of spare parts purchased in the engineering period of a nuclear power plant, and relates to the technical field of industrial automation and monitoring, and the method comprises the steps: extracting consumption records after commercial transportation for units of the same supplier in a group, generating a list of spare parts purchased in the engineering period, and removing low-failure-rate parts; high-frequency consumables are retained to improve pertinence. And secondly, for spare parts which are not present, not scheduled and wide in coverage, a framework protocol is established with a manufacturer to lock unit prices and agree on demand supply, so that inventory occupation caused by mass transfer is avoided. And finally, executing a four-consistency principle on the on-site spare parts, namely checking an EOMM manual, and finally confirming a list, spare part real objects and on-site installation real objects. When the model or the code is upgraded, the specification is upgraded and solidified in combination with the commitment function of the manufacturer and the EOMM manual; when differences occur, interchangeability argumentation is carried out through technical analysis and an item replacement process, spare parts which are argumented to be available are put in storage and updated to an equipment spare part BOM and a template work order, and verifiability, traceability and efficient receiving of the spare parts are achieved.

The invention relates to the technical field of air cylinder machining, and discloses a part cutting device for air cylinder machining, the part cutting device for air cylinder machining comprises a machining table, a telescopic air cylinder is installed at the top of the machining table, according to the part cutting device for air cylinder machining, a movable assembly is arranged, a controller starts and controls a motor, and an output shaft of the motor drives a first cam to rotate; a gear on one surface of a cam is meshed with an inner gear ring and synchronously rotates during revolution, a fixed rod on the gear drives a movable rod to act, the movable rod is guided by a sliding block, slides downwards along with the gear in a self-steering mode, drives the sliding block to transversely slide in a reciprocating mode along revolution and moves in a triangular path, and then a cleaning plate is driven to conduct reciprocating sweeping along a conveying belt. The scraping plate at the bottom of the cleaning plate scrapes large chippings, and the internal cleaning block is attached to the conveying belt under the action of the fixing spring, so that part positioning deviation and cut-off deviation caused by the chippings are prevented, machining consistency is guaranteed, the rate of unqualified products is reduced, and the service life of equipment is prolonged.

The invention discloses a new unit equipment risk management and control and spare part demand optimization method and system, and relates to the technical field of intelligent operation and maintenance, and the method comprises the steps: collecting operation condition signals in real time, constructing a digital twin model of each part of unit equipment, and forming a unified discrete state vector; performing feature extraction on the operation condition signal according to a fixed window, and constructing a Bayesian network of a hierarchical causal structure in combination with process attributes; performing posterior reasoning on the Bayesian network through belief propagation to obtain a fault posterior probability of a causal node, and calculating a risk score according to a weight and a consequence cost; and life parameter estimation is carried out, Monte Carlo simulation is used to predict the demand quantity, and the optimal spare part order quantity is calculated in combination with inventory constraints. According to the method, potential risks can be found in time, non-planned shutdown is reduced, inventory redundancy and capital occupation are reduced, and the safety, reliability and economical efficiency of operation of a new unit are improved.

The invention relates to the technical field of intelligent manufacturing, in particular to a part defect detection method and system based on an image model. The method comprises the following steps: acquiring a spare part image; performing part edge defect detection according to the part image to obtain edge defect data; constructing an image defect model according to the edge defect data; performing crack growth simulation according to the edge defect data to obtain crack data; performing metal grain coarseness detection based on the crack data to obtain metal grain coarseness data; evaluating the thermal stability of the spare and accessory parts according to the metal grain coarseness data to obtain thermal stability data of the spare and accessory parts; performing rib position area warping detection according to the thermal stability data of the spare and accessory parts to obtain rib position warping data; and carrying out warping form classification based on the rib position warping data to obtain rib position upwarp data and rib position concave data. Based on the intelligent manufacturing technology, the accuracy rate of part multi-type defect identification and the adaptability rate of defect detection under complex working conditions are improved.

The utility model relates to an aeronautical turbine disc blade centering device, which belongs to the technical field of aeronautical parts, and comprises a chassis, a centering assembly and a positioning assembly, the positioning assembly comprises a positioning block and a plurality of pressing assemblies, the pressing assemblies are arranged along the radial direction of the positioning block and are in sliding fit with the positioning block, and the pressing assemblies are arranged along the circumferential direction of the positioning block at intervals and are in sliding fit with the positioning block. One end of the centering assembly is connected with the base plate, and the other end of the centering assembly penetrates through the positioning block and is in sliding fit with the positioning block. The turbine disc blade centering device has the beneficial effects that the turbine disc blade is placed on the base plate, the centering assembly is driven to move up and down in the vertical direction, then the positioning assembly is driven to move up and down in the vertical direction till the end, in the circumferential direction, of the positioning assembly moves to abut against the inner wall of the turbine disc blade and is pressed tightly, and a positioning point is formed at the abutting position; the center of a circle formed by the multiple positioning points is the circle center of the turbine disc blade, and the circle center of the turbine disc blade is accurately positioned.

The invention relates to the technical field of charging pile management, in particular to an intelligent management method for charging piles, which comprises the following steps of: acquiring a fault reporting position, a fault type and a fault priority of each charging pile, simultaneously acquiring professional skills, a current position, a working time window and a central spare part library position of a maintenance engineer, and constructing a to-be-scheduled maintenance task list. According to the method, the fault position and the fault priority are mapped to the urban map grid and weighted counting is carried out to form the demand side pressure expression, the relationship between the service radius and the position of an engineer is introduced, the supply side space distribution is formed through superposition, the regional maintenance resource supply and demand situation is dynamically constructed in a quantifiable mode, and the task allocation efficiency is improved. According to the resource supply and demand diagram, an initial task sequence is allocated for an engineer, and sequence adjustment and task exchange are performed, so that a scheduling scheme can be optimized according to actual capability, path accessibility and a time window.