3525 results about "Spare part" patented technology

The invention discloses a new energy power generation equipment health management platform based on a large model, and relates to the technical field of power generation equipment health management. The system comprises a multivariate data acquisition and region identification module, a data preprocessing and region adaptation unit, a region perception large model analysis module, an intelligent operation and maintenance decision and scheduling module and a spare part inventory optimization sharing module. The multivariate data acquisition and area identification module deploys multiple types of sensors at key equipment parts of photovoltaic power generation stations in different areas, acquires equipment operation parameters and environment state data, endows the areas with unique codes and feature tags, and ensures that the data are traceable and are associated with area characteristics; according to the invention, the health management efficiency and accuracy of the new energy power generation equipment are significantly improved; the system not only realizes real-time monitoring and fault early warning of the equipment state, but also reduces the maintenance cost and improves the system operation efficiency by optimizing the operation and maintenance resource configuration and intelligent spare part inventory management.

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 discloses a real-time pressure compensation method for a multi-axis linkage precision press, which relates to the technical field of control of a press, and comprises the following steps of: when a coefficient is detected to exceed a warning threshold value, executing abnormal identification and generating abnormal axis information; a short-stroke reciprocating test is carried out on an abnormal shaft under a safe working condition, force-displacement and force-speed curve characteristics are recorded through high-frequency sampling, and friction model parameters are updated by utilizing a self-learning fitting algorithm. And calculating a life degradation index by comparing a historical parameter sequence with the updated friction model parameters, and formulating a pre-maintenance strategy including a replacement period, spare part requirements and priorities. If the pre-maintenance strategy is not executed in time and it is monitored that the friction increasing coefficient continues to rise or the compensation amount is close to the upper limit, real-time compensation is applied, and the speed-limiting and load-limiting or emergency shutdown process is triggered. Through a multi-level safety control strategy, sudden friction abnormity can be accurately captured, and reliability and maintainability are remarkably improved.

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 relates to the technical field of supply chain management, in particular to a part supply chain full-process digital collaborative management system and method, and the method comprises the steps: evaluating supplier data through constructing a dynamic scoring model, and obtaining a supplier score; when the supplier score is lower than a predetermined threshold value, generating a multi-source purchase plan, and dynamically adjusting purchase distribution according to the supplier score; constructing a warehouse-in and warehouse-out part intelligent verification model to analyze warehouse-in and warehouse-out parts, generating an acceptance report and block chain record association, and triggering an alarm and freezing an order when a difference is found; a supply chain risk topological graph is constructed through the supplier fulfillment rate, the logistics data and the market demand fluctuation, and high-risk nodes are identified and early warned in real time; initiating emergency purchase to suppliers with high supplier scores based on high-risk nodes, and optimizing inventory scheduling based on geographic proximity; the inventory level is optimized in real time based on the production line state and the in-transit logistics data, and the safe inventory threshold is dynamically adjusted to cope with supply chain interruption.

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 a demand prediction method and system for aerial material spare parts, and relates to the field of aerial material management. The invention aims to provide the demand prediction system and method for the aerial material spare parts specially for the scene of demand prediction of the aerial material spare parts, the method is improved on the basis of the existing statistical analysis technology, the influence of various factors on the demand is considered in the demand change, the prediction deviation caused by human intervention is reduced, and the prediction accuracy is improved. Based on a time sequence method, a reasonable and effective classification mode and a scientific and accurate demand prediction method for different demand characteristic aviation materials are researched, a more accurate maintenance aviation material spare part consumption prediction model is obtained, an efficient prediction method suitable for aviation material management is provided, the purpose of reducing aviation material waste is achieved, and finally the reduction of aviation material cost is achieved. The method is of great significance to aviation material management and maintenance plan optimization of airline companies.

The invention relates to the technical field of maintenance work order management, in particular to an intelligent management system for after-sales maintenance work orders, which realizes equipment clustering based on a K-means algorithm, considers different user habits and use environment differences, and improves the fault diagnosis precision. A fault propagation network is constructed in combination with association rule mining and a graph convolutional network, and a fault influence path between hardware is described, so that accurate identification of target fault hardware is realized, alternative hardware is intelligently determined in combination with recent maintenance records, and misjudgment and repeated maintenance are avoided. And the intelligent order dispatching module is used for preferentially dispatching the nearby maintenance personnel with the required spare parts by matching the position information of the maintenance personnel with the real-time spare part inventory, so that the maintenance response efficiency is improved. And the model error correction module continuously monitors the operation state of the equipment after maintenance is completed, automatically adjusts node features and edge weight parameters if a prediction result does not accord with actual maintenance, updates a graph convolution model through an incremental learning mode, and improves the self-adaption and evolution capability of the model.

The invention discloses an intelligent spare part warehouse inventory dynamic monitoring and supplementary optimization system, and relates to the technical field of inventory management, and the system comprises the steps: collecting environment parameters, equipment operation data, inventory spare part images and historical supply data, and carrying out the preprocessing; analyzing the preprocessed environment parameters, the equipment operation data and the inventory spare part images by using an artificial intelligence algorithm to obtain the spare part demand quantity and the spare part inventory quantity; the inventory state is judged according to the spare part demand quantity and the spare part inventory quantity, and a difference report is generated; constructing a supply cycle model by using the preprocessed historical supply data; performing purchase replenishment according to the difference report, the supply cycle model and the budget to obtain a replenishment record table; and utilizing a block chain to perform uplink storage on the spare part inventory, the spare part demand, the difference report and the replenishment record chart. According to the invention, through data acquisition and algorithm analysis, the spare part demand is dynamically predicted, the replenishment strategy is optimized, and block chain storage is carried out to enhance the data transparency.

The invention belongs to the technical field of hydropower station operation and maintenance management, and relates to a hydropower station spare part inventory intelligent prediction method and system based on digital twinning. According to the method, the residual life of the key component of the equipment is predicted according to the historical fault data and the real-time monitoring data of the hydropower station equipment, and the operation condition and the health degree of the key component of the equipment can be known more comprehensively and accurately by fusing the historical fault data and the real-time monitoring data. According to the residual life of the key parts of the equipment, in combination with the equipment maintenance plan and the supply chain period, the expected demand of each spare part in the future time period is output, multiple factors such as the residual life of the key parts of the equipment, the equipment maintenance plan and the supply chain period are comprehensively considered, and prediction of the expected demand of the spare part is more comprehensive and reasonable. The safety inventory threshold value and the purchase priority are dynamically adjusted according to the expected demand of each spare part, matching of equipment loss prediction and the spare part demand is realized, the inventory structure is optimized, the spare part fund occupation is reduced, and the inventory management is more flexible and efficient.

The utility model discloses a single nut fixing buckle punch forming device, and belongs to the field of spare and accessory part production equipment. The stamping and reaming die comprises an upper die base, a lower die base and a stamping and reaming assembly. A punching and trimming assembly, an edge folding and bending assembly and a tapping assembly are sequentially arranged between the upper die base and the lower die base in the advancing direction of a plate. The stamping and reaming assemblies are arranged beside the punching and trimming assembly and the edge folding and bending assembly correspondingly, and the connecting line of the centers of the stamping and reaming assemblies and the center of the tapping assembly is parallel to the center line of the plate. By means of the device, the threaded hole can be machined in the single nut fixing buckle more conveniently, the occupied area is small, and the working efficiency is high.

The invention relates to the technical field of on-line monitoring, in particular to an automobile industry part on-line monitoring method and system, and the method comprises the following steps: based on collected part production line data, extracting the assembly data flow of parts, carrying out the quality screening of the real-time assembly data flow, eliminating the noise and invalid data in the data flow, and carrying out the real-time quality screening of the real-time assembly data flow; and obtaining the screened assembly data. According to the method, early recognition and early warning of abnormal conditions in the production process are achieved by analyzing and predicting potential production deviation in real time, data points deviating from a standard process can be accurately recognized and rapidly adjusted by comprehensively evaluating assembly data and a production trend, production quality and improvement of efficiency are ensured, and the production efficiency is improved. Through continuous monitoring and analysis of key performance indexes, machine setting is optimized, the stability of the production process is ensured, meanwhile, resource waste is remarkably reduced, maximization of cost benefits is achieved, and control over a production line is enhanced.

The invention discloses a management method and system for guaranteeing the purchase quality of spare parts of a nuclear power plant, and relates to the technical field of equipment reliability and maintenance, and the method comprises the steps: collecting and coding the information of nuclear power spare parts; before the spare parts are purchased, the qualification of the supplier is verified; a demand list is automatically generated in the purchasing stage according to the coding information of the spare parts; matching suppliers meeting conditions in a supplier database according to the content of the demand order, and generating a purchase order; the unique code of the safe and important spare part is collected, and an RFID label or a two-dimensional code label is pasted on the spare part; spare part codes and supplier information are checked by scanning RFID or two-dimensional code labels, it is ensured that the spare part information is consistent with a purchase order, and then the spare part information is put in storage. According to the invention, spare part purchasing quality process control is realized, technical clarification is greatly reduced, one-code multi-object problems are reduced, purchasing efficiency is improved, and counterfeiting is prevented, so that quality of safe and important spare parts is ensured, nuclear power plant equipment reliability is improved, and safe and stable operation of a unit is ensured.

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 press machines, and provides a press machine for machining automobile spare parts, which comprises a rack, a lifting part, a pressing block, a bearing block and an air supply unit, the lifting part is arranged on the rack in a lifting and sliding manner, the pressing block is arranged on the lifting part, the bearing block is arranged on the rack, a product is placed between the pressing block and the bearing block, and the pressing block is provided with a through air blowing hole; the air supply unit is arranged on the rack, one end of the blowing hole is communicated with the air supply unit, the other end of the blowing hole faces the product, and after the pressing block ascends, the air supply unit supplies air to the blowing hole and is used for spraying air to the product. By means of the technical scheme, the problem that in the prior art, when metal sheet type raw materials are machined, the upwards-bent edges of the metal sheet type raw materials are prone to being clamped on the male die is solved.

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 motor spare and accessory part punching, and particularly discloses anti-deformation punching equipment for motor spare and accessory part production, the top of a base is fixedly connected with a workbench, the top of the workbench is uniformly provided with clamping parts, the top of the base is uniformly provided with supporting rods, and the supporting rods are fixedly connected with the workbench. The bottom of the supporting rod is fixedly connected with the top of the workbench, the top of the supporting rod is fixedly connected with a top plate, and the top of the top plate is fixedly connected with a driving component. According to the anti-deformation punching equipment for motor part production, the punching component is arranged, a drill bit stably moves downwards in the vertical direction under the guidance of the stopping mechanism in the middle of the buffering frame, the stopping mechanism provides guidance for the drill bit, the drill bit is prevented from deviating at the moment when making contact with a part, and the rotating drill bit penetrates through the buffering frame and the stopping mechanism; accurate punching operation on the motor spare and accessory parts on the workbench is completed.

The invention relates to a ship spare part reliability evaluation method based on failure-free interval time failure data, which comprises the following steps: determining ship spare part field use data D at a failure-free interval time as (Ti, si, ri), Ti being the working time of equipment in an ith task, si being the number of parts installed by the equipment, ri being the total failure frequency of the equipment in the ith task, i being equal to 1, 2,..., n; obtaining a likelihood function Lp (lambda) of the failure rate lambda based on the ship spare part field use data D and the Poisson distribution principle; determining prior distribution of the failure rate lambda; the priori distribution is weighted to obtain a weighted comprehensive priori distribution type pi < s > (lambda), the weighted comprehensive priori distribution type pi < s > (lambda) is combined with a likelihood function Lp (lambda), a Bayesian formula is utilized to obtain posterior distribution of the failure rate lambda, and ship spare part reliability evaluation is carried out based on the posterior distribution of the failure rate lambda; fault-free interval time failure data in field use information is effectively utilized, and a proper likelihood function is constructed according to the characteristics of the fault-free interval time failure data to carry out Bayesian reliability evaluation on the ship spare parts.

The invention discloses a ship multi-source data integration method based on a Word2Vec model. The method comprises the following steps: 1) collecting ship spare part data from different sources; 2) preprocessing the collected ship spare part data; 3) arranging the preprocessed multi-source ship spare part data, and forming a standard ship target database in combination with a ship database standard; 4) constructing a ship spare part word source according to the spare part name information extracted from the ship target database in the step 3), and constructing a word vector model based on the standardized ship spare part word source; 5) extracting ship spare part names needing to be compared from spare part data from different sources, comparing the ship spare part names with spare part names in the target database, and calculating cosine similarity; and 6) fusing the spare part names to be compared with the spare part names in the target database, and finally generating uniform data representation. The word vector model is used for data matching and fusion, manual intervention can be reduced, and the data integration efficiency is improved.

The utility model provides a novel structure trunnion and grinding roller device, which comprises a grinding roller head and two trunnions respectively arranged on two sides of the grinding roller head, a connecting sleeve is arranged on each grinding roller head, and the trunnions are inserted into the connecting sleeves; in other words, the grinding roller head and the trunnion adopt a socket and spigot type flexible connection structure, so that the bearing capacity requirement of the trunnion and the grinding roller head in the operation working state is met, and the problems that the joint of the grinding roller head and the trunnion is cracked and a bolt is easy to break in the prior art are thoroughly solved; the trunnion and the grinding roller head can be directly mounted and used and can be universally exchanged with other parts, so that the requirements on safety performance and economic performance are met; the bushing is arranged between the trunnion and the grinding roller head to serve as a damping structure, and the bushing is made of a high-temperature-resistant and wear-resistant flexible material, so that the grinding roller head and the trunnion are connected more stably, the matching degree is higher, and the service life is greatly prolonged; even, the service life of the product can be greatly prolonged, the replacement period of spare parts of the coal mill is greatly prolonged, and the operation and maintenance cost of the coal mill can be obviously reduced.

The invention relates to the technical field of die-casting forming dies, and discloses an automobile part die-casting forming die which comprises a lower die block, four sets of stand columns are fixedly arranged at the upper end of the lower die block, an upper die block is fixedly arranged on the upper sides of the four sets of stand columns, and an air cylinder is fixedly arranged at the upper end of the upper die block. A sealing module is fixedly arranged at the lower end of a telescopic rod of the air cylinder, a forming cavity die holder is fixedly arranged among the four sets of stand columns at the upper end of the lower module, and cleaning assemblies are arranged at the positions, close to the positions between the sealing module and the forming cavity die holder, of the outer sides of the four sets of stand columns. According to the die-casting forming die for the automobile parts, through cooperative use of the sweeping assembly, the collecting assembly and the cleaning assembly, a brush deforms and effectively enters the inner side of a cavity to conduct sweeping work, and two movable sleeves and two sweeping shafts are driven to move along with work of two sliding tables; and the cleaning effect on the sealing module and the cavity of the forming cavity die holder 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.

The invention relates to the technical field of inventory data prediction, and provides a spare part inventory data prediction method, system and device and a medium, and the method comprises the steps: constructing a prediction model, and predicting the future condition according to the previous spare part inventory data; the model comprises a plurality of modules: a convolutional neural network used for mining local features of past inventory data and outputting corresponding representations; the LSTM network is used for generating a current hidden state containing long-term dependence information by combining the local features with the hidden state in the previous step; and the Transform neural network is used for converting a hidden state into an embedded vector, calculating a context vector containing global dependence through a self-attention mechanism, performing multi-head attention enhancement after adding position codes, then sending the context vector into the feedforward neural network, and outputting a prediction result. And obtaining the current inventory data and inputting the current inventory data into the trained model to obtain future inventory prediction data. According to the method, the inventory data of the spare parts can be predicted.

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 provides automobile spare part hoisting equipment, and belongs to the technical field of automobile spare part hoisting, the automobile spare part hoisting equipment comprises an L-shaped hoisting plate, a sleeve and a hoisting ring assembly, and further comprises arc-shaped plates symmetrically arranged in the sleeve, the opposite faces of the two arc-shaped plates are connected with matched cushion covers, a pair of shifting plates is arranged outside the sleeve, and the shifting plates are connected with the L-shaped hoisting plate. The shifting plate is provided with guiding faces inclining downwards, the sleeve is provided with a connecting piece connected between the shifting plate and the arc-shaped plate, the L-shaped hanging plate is provided with an adjusting groove, and an adjusting assembly arranged on the adjusting groove is arranged between the two guiding faces in a linkage mode. The guide wheels in the adjusting assemblies move along the guide faces, the positions of the two arc-shaped plates are flexibly adjusted in the sleeves, and therefore the motor rotor shaft lifting device adapts to motor rotor shafts of different sizes, the sleeves do not need to be replaced, the structure is simplified, the maintenance cost is reduced, meanwhile, it is ensured that the motor rotor shafts are tightly clamped, and lifting safety and reliability are improved.