45 results about "Dynamic maintenance" patented technology

The invention discloses an intelligent concrete automatic curing system and method based on real-time monitoring, and the system comprises a multi-parameter sensing module which collects the environment parameters and internal structure parameters of a concrete structure through a plurality of sensors and label identification codes, and an edge calculation module which is used for carrying out the preprocessing, feature extraction and emergency decision-making of collected data. The cloud intelligent module is used for storing data, establishing a prediction model and generating a dynamic maintenance strategy; the maintenance execution module is used for adjusting and executing spraying, temperature control or crack repair operation according to the maintenance strategy; by integrating structural strain, crack detection and environment air pressure monitoring, the limitation of traditional single temperature and humidity monitoring is broken through, multi-dimensional sensing and accurate diagnosis of concrete are achieved, meanwhile, the system combines a physical model and a data driving model, the self-adaptive optimization capability is achieved, and the system is suitable for large-scale popularization and application. And the edge gateway is adopted to process emergencies, and the cloud platform is responsible for global optimization, so that the system gives consideration to the real-time performance and the calculation depth, and the maintenance efficiency and the concrete quality are effectively improved.

The invention discloses an equipment full-life-cycle intelligent operation and maintenance management method and system based on AI deep learning. The method comprises the following steps: collecting multi-source heterogeneous data generated in each stage in a full life cycle of equipment, deeply exploring a knowledge network for the heterogeneous data through semantics, converging and fusing associated equipment entities, constructing a triple knowledge network, and obtaining an equipment knowledge graph network; constructing an equipment digital twinborn body corresponding to the equipment entity in a high-fidelity manner through the digital twinborn virtual mapping and the equipment experiment sand table; carrying out equipment operation simulation deduction according to the real-time monitoring data to obtain digital twin simulation deduction data; machine learning deep intelligent analysis is carried out according to the digital twinborn simulation deduction data, the equipment health state is evaluated and predicted, and equipment degradation track life prediction information is obtained; and querying equipment knowledge graph information according to an equipment knowledge graph network, calibrating digital twin simulation deduction data, and reasoning and generating an equipment dynamic maintenance decision by referring to equipment degradation track life prediction information.

The invention relates to the technical field of industrial equipment control, and discloses a dynamic maintenance management method and system based on the full life cycle of test equipment, and the method comprises the steps: obtaining a time domain vibration signal and torque current data during the operation of the equipment through a high-frequency vibration sensor and a current loop feedback interface; calculating an accumulated damage gradient parameter representing the physical recession degree of the mechanical transmission chain; the parameters are input into a preset damage stress coupling model to solve the maximum radial impact force allowed to be borne by the damaged part, and the impact force is mapped into a dynamic capability boundary for carrying out logic constraint on the motion planning instruction; and the motion controller reconstructs an ideal speed curve by using the boundary in an interpolation period, and reshapes a torque load spectrum output to a servo motor by reducing the jump of acceleration and deceleration sections. The problem that an existing static control strategy is mismatched with the dynamic aging characteristic of equipment is solved.

The invention relates to the technical field of predictive maintenance and health management of industrial equipment, in particular to an intelligent electromechanical integrated monitoring system based on the industrial internet, which comprises a data acquisition module, a data processing module, a data processing module and a data processing module, and is characterized in that the data acquisition module acquires an equipment dynamic operation parameter set, inherent physical attribute parameters and historical maintenance data of target electromechanical equipment; calling preset process topology data, economic loss data and maintenance cost data; the state evaluation module iteratively calculates an accumulated degradation index representing the health condition of the equipment; the importance quantification module calculates a process importance coefficient representing the criticality of the equipment in the production line based on the process topology data; the risk calculation module generates a production risk degree which quantifies the shutdown influence; the decision generation module generates a dynamic maintenance decision instruction; according to the invention, systematic risk cognition from the individual equipment to the overall production line is realized, extensive management of all equipment is avoided, and allocation of maintenance resources is more strategic and targeted.

The invention discloses a dynamic maintenance method for a distribution network transient recording event sample library and computer equipment. The method comprises the steps of obtaining a first preset database; receiving a maintenance operation executed on the first preset database based on the target account; based on the maintenance operation, a log is generated and stored in a second preset database, and the log comprises operation time corresponding to the maintenance operation, an identifier corresponding to the power distribution network transient recording event sample and a target account identifier; based on the preset interval duration, whether an abnormal identifier exists in a first preset database is detected, and the abnormal identifier represents that the identifier of the corresponding power distribution network transient recording event sample does not exist; and under the condition that the abnormal identifier does not exist in the first preset database, determining that the maintenance operation is executed successfully. According to the method, the technical problem that certain risks exist in data errors due to implicit association of waveform data and label information when a power distribution network transient recording event sample library is maintained at present is solved.

The invention relates to the field of elevator maintenance, and discloses an elevator dynamic maintenance period optimization method and system combined with Internet of Things analysis, and the method comprises the following steps: collecting multi-modal data of an elevator in a manner of deploying a heterogeneous sensor array, and determining elevator part damage root cause associated nodes; and the aim of constructing an elevator fault prediction model is fulfilled by combining a federal learning mode. And through the elevator fault prediction model, risk simulation analysis is carried out in combination with a parallel Monte Carlo simulation method, and a periodic optimization optimal solution of elevator dynamic maintenance is output according to an analysis result. According to the method, the continuity and intelligence of data inspection can be realized in combination with an advanced data analysis algorithm, the operation and maintenance cost is remarkably reduced, and the resource utilization efficiency is improved.

The invention belongs to the field of hydroelectric generating set RCM maintenance, and discloses a hydroelectric generating set RCM maintenance strategy determination method and system based on risk priority, and the method comprises the steps: building an FMEA analysis database based on the historical operation data, fault event ledger and equipment state monitoring data of a hydroelectric generating set; performing quantitative evaluation on each fault mode of the FMEA analysis database from occurrence degree O, severity S and undetectability D, and calculating a risk priority number RPN of each fault mode; risk grades are divided according to the risk priority number RPN, a dynamic maintenance strategy is generated based on the risk grades, the dynamic maintenance strategy is output to the operation and maintenance management system, the actual operation condition and fault risks of equipment are fully considered, the reliability and safety of the hydroelectric generating set are effectively improved, the maintenance cost is reduced, and the non-planned downtime is shortened.

The invention discloses a die full life cycle management system based on the Internet of Things, and relates to the technical field of die management, and the die full life cycle management system comprises the steps: obtaining a pre-correlation parameter and a post-correlation parameter of a process in each stage in a die full life cycle, and obtaining a pre-correlation feature and a post-correlation feature; dynamically maintaining equipment in the process based on the real-time parameters; the method is used for solving the problems that in an existing mold full-life-cycle management method, the state of a mold is predicted in a one-sided mode only through collection of single data, and when the process where the mold is located changes and other variables influence the state of the mold, the relation between the single data and the variables cannot be effectively judged, and the mold state cannot be effectively predicted. Therefore, the problem that effective management cannot be performed based on the subsequent state of the mold is solved.

This invention provides a dynamic management method and system for multi-level assessment indicators of the resilience of nearshore bridges throughout their entire life cycle, relating to the field of structural resilience assessment and management technology. The method includes: acquiring multi-source heterogeneous maintenance data streams throughout the entire life cycle of the nearshore bridge; processing these data streams through time-series alignment and feature dimensionality reduction mapping to obtain a basic feature matrix of the bridge's service status; and performing conditional routing and data stream processing based on this basic feature matrix to obtain corrosion evolution calculation potential field distribution data, structural dynamic stiffness attenuation tensor, and structural life-cycle degradation confidence distribution vector. This invention accurately obtains resilience indices that truly reflect the bridge's resistance to degradation through multi-step quantification calculations, clarifies the sensitivity differences of assessment nodes, formulates differentiated dynamic maintenance strategies, and constructs a closed-loop management system throughout the entire process, thereby achieving intelligent bridge maintenance, improving maintenance efficiency, and reducing costs.

This invention presents an intelligent global dynamic maintenance method for multi-component systems considering imperfect maintenance, based on deep reinforcement learning. First, it acquires the degradation observation sequence of each component through periodic inspections, establishes a stochastic process model of component degradation, and achieves iterative RUL prediction through offline training and online Bayesian updates. Then, it determines the baseline predictive maintenance timing and, considering imperfect maintenance improvement factors, determines the baseline delayed maintenance time. Simultaneously, it constructs a maintenance timing deviation penalty cost model for group maintenance. Next, it uses the PPO algorithm to perform rolling optimization on the dynamic global group maintenance (DGGM) to output maintenance combinations and maintenance execution times. When a sudden failure occurs during operation, dynamic opportunistic imperfect maintenance (DOIM) is triggered, performing corrective maintenance on the failed component and determining the opportunistic maintenance component set, synchronously updating subsequent maintenance plans, thus achieving intelligent global dynamic maintenance decision-making in an infinite time domain. This invention realizes joint optimization of predictive and opportunistic maintenance for multi-component systems.

The invention discloses a wind power intelligent operation and maintenance management method and system based on deep learning. The method comprises the following steps: acquiring a graph structure data set; based on the graph structure data set, modeling a space-time dependency relationship in the fan group and between the fan and the environment by using a space-time graph neural network combined with a multi-head attention mechanism and a GCN graph convolutional network to obtain space-time features; performing feature alignment and fusion through a cross-modal attention mechanism to obtain fusion features; inputting the fusion features into a GRU gating circulation network, outputting residual effective life probability distribution of the wind turbine generator components and dynamic risk levels of multiple types of faults in future 72 hours, and obtaining a prediction result; and based on a deep reinforcement learning model, taking the prediction result as input, taking multi-objective optimization as rewards, and generating a dynamic maintenance decision scheme. The operation and maintenance cost is reduced, the unit availability rate is improved, and a large-scale wind power cluster operation and maintenance scene is adapted.

The invention relates to the technical field of roadway support engineering, and discloses an intelligent adaptive roadway support and dynamic maintenance integrated process. According to the technology, through geological environment and stress global detection, a multi-dimensional roadway digital model is constructed, support parameters are optimized based on historical data and real-time monitoring data, a regional stress discretization sorting support strategy is adopted, and dynamic monitoring feedback and intelligent maintenance decision are combined. And accurate adaptation of a supporting scheme and full-life-cycle dynamic management of a supporting structure are realized. The technical problems that in the prior art, support parameter determination is insufficient in scientificity, maintenance lacks pertinence, and roadway stress changes cannot be dynamically adapted are solved, the stability and safety of roadway support are remarkably improved, the maintenance cost and the accident risk are reduced, and the method is suitable for roadway engineering under various complex geological conditions.

The present application relates to the technical field of equipment maintenance, and more particularly to an intelligent predictive equipment maintenance system, comprising a data acquisition module, a data processing module, a maintenance decision module, a maintenance execution module, a user interaction module and a closed-loop optimization module. The data acquisition module collects equipment operating parameters through the deployment of a sensor array to generate standardized data streams; the data processing module extracts time sequence features and performs state deduction through a virtual model to output fault analysis results; the maintenance decision module calls enterprise resource information for multi-objective optimization to generate a dynamic maintenance plan; the maintenance execution module assists in guiding the execution of maintenance through augmented reality and records data; the user interaction module dynamically displays equipment status and risk information; and the closed-loop optimization module updates model parameters based on feedback data. The system realizes predictive maintenance under data driving through module collaboration, effectively reduces unplanned downtime and resource waste, and improves fault diagnosis accuracy and operation and maintenance efficiency.

Techniques for autonomous maintenance management (AMM) of an industrial facility are described. In one aspect, an AMM system monitors operational data values and an event associated with an equipment to determine key performance indicators and trend patterns associated with the equipment, where the event is characterized by deviations in the operational data values from a pre-determined threshold value, and where the trend pattern is indicative at least of operational history of the equipment. The KPIs and trend patterns are analysed to predict a behaviour of the equipment, based on which a dynamic maintenance schedule is generated, where the dynamic maintenance schedule includes a task to be executed for maintenance of the equipment. Further, the system obtains personnel information of the industrial facility to identify personnel to perform the task and accordingly issues a maintenance work order in correspondence to the dynamic maintenance schedule for maintenance of the equipment.

The embodiment of the invention provides a storage resource switching system, method and equipment, a storage medium and a computer program product, and is applied to the technical field of computers. In the embodiment of the invention, in the process of switching the storage resources for the application program, the incidence relation between the PV and the storage resources is decoupled through the dynamic storage object, so that the incidence relation between the dynamic storage object and the storage resources can be dynamically maintained under the condition of ensuring that the incidence relation among Pod, PVC, PV and the storage resources is not changed, and the user experience is improved. Therefore, one PV can be bound with different storage resources according to needs, the capacity of dynamically switching the storage resources for the application program is achieved under the condition that Pod, PVC and PV are not reconstructed, the problem that Pod, PVC and PV need to be reconstructed for updating the storage resources for the application program is solved, a switching link for switching the storage resources for the application program is simplified, the switching process is made to be lighter, and the switching efficiency is improved. And the switching efficiency is improved.

A plurality of log entries for a respective plurality of modules of a cloud computing platform are processed with a machine-learned Large Foundational Model (LFM) to obtain a prediction output. The prediction output is indicative of a predicted outage event for the cloud computing platform and a corresponding degree of severity. Based on the prediction output, a target module of the plurality of modules of the cloud computing platform is identified. A plurality of modifications is generated for a configuration of the target module with the machine-learned LFM. The plurality of modifications is configured to mitigate the predicted outage event. The plurality of modifications is based at least in part on the degree of severity. The plurality of modifications is deployed to the configuration of the target module.

The invention discloses a special-shaped steel reinforced concrete beam mold for an assembly type subway station and a construction method. The special-shaped steel reinforced concrete beam mold comprises a modular curved surface combination mechanism, a steel bar positioning guiding and preformed hole integration structure, a vacuum auxiliary vibration compacting system and a mold rigidity self-adaptive adjusting device. The modular curved surface combination mechanism is used for achieving accurate forming and efficient disassembly and assembly of special-shaped curved surfaces with different curvatures, the steel bar positioning and guiding and preformed hole integration structure is used for achieving accurate positioning of steel bars in multiple directions and leakage prevention of preformed holes, and the vacuum auxiliary vibration compacting system is used for improving the concrete pouring compactness. The mold rigidity self-adaptive adjusting device is used for dynamically compensating deformation in the mold pouring process, and the geometric dimension precision of a component is guaranteed. According to the device, deformation of the mold is controlled through strain monitoring and dynamic compensation of the hydraulic supporting rod, the process of step-by-step pouring, gradient vibration and dynamic maintenance is adopted, high-precision, high-efficiency and high-quality production of components is achieved, the mold reuse rate is high, and the construction requirements of the fabricated subway station are met.

The invention discloses an equipment state trend prediction and maintenance decision-making system and method for a new energy power station, and the system is deployed based on a cloud-side-end collaborative architecture, and a full-process closed-loop logic of data governance, trend prediction, uncertainty calibration, life evaluation, causal analysis, maintenance optimization, feedback execution, and safety audit is constructed. Accurate sensing, trend pre-judgment and dynamic maintenance decision making of the equipment operation state are achieved. According to the system, multi-source heterogeneous operation data and a physical mechanism model are fused, a cooperative work mechanism of data input-model operation-decision output-feedback iteration is formed by means of core technologies such as space-time diagram prediction, causal reasoning and risk optimization, all the modules are in close linkage with a logic dependency relationship through a standardized data interface, and the reliability of the system is improved. According to the method, the professionality of a single-module function is ensured, the collaboration of the whole scheme is also realized, the active maintenance, risk self-adaption and economical efficiency optimization of new energy power station equipment are finally achieved, and the operation safety and the power generation efficiency of the equipment are remarkably improved.

The invention relates to the field of organization and scheduling of computing resources, in particular to a computing resource scheduling allocation method based on self-organization. The method comprises the following steps that a resource tree network model based on self-organization is constructed, computing resources in the resource tree network model are spontaneously added into a resource set according to the processing capacity and availability of the computing resources, management nodes are generated through a distributed election algorithm, and a system updates a scheduling allocation strategy in real time according to dynamic changes of the computing resources. According to the self-organization-based computing resource scheduling and allocation method provided by the invention, the self-organization-based resource tree network model is constructed, the management nodes and the index nodes are utilized to efficiently schedule and allocate the computing resources, and meanwhile, each computing resource is dynamically maintained in real time; and fault-tolerant processing is carried out on abnormal situations possibly occurring in the resource scheduling and allocation process, so that efficient positioning, scheduling and allocation and collaborative sharing of the computing resources are realized.

The invention provides a medical equipment full-process maintenance management method and system based on multi-terminal cooperation. The method comprises the following steps: firstly, acquiring unique identification information of medical equipment, sending the unique identification information to a maintenance management server to form a collaborative verification result, and when the collaborative verification result indicates that a maintenance process is allowed to be initiated, retrieving real-time maintenance plan data associated with the medical equipment and generating a dynamic maintenance task instruction; the method comprises the following steps: receiving a dynamic maintenance task instruction, distributing the dynamic maintenance task instruction to a maintenance terminal to execute field maintenance operation, performing data exchange in an operation process to cooperatively complete a maintenance process and output maintenance result data, finally feeding back the maintenance result data to a maintenance management server, and updating a full-process maintenance record of the medical equipment; according to the technical scheme provided by the invention, the efficiency of maintenance response and the collaboration level of each link are improved, the adaptive optimization of the maintenance plan is also realized, and the refinement and intelligence level of the full life cycle management of the medical equipment is improved.

The invention relates to the technical field of water conservancy and hydropower engineering construction, and discloses a concrete dam vibration parameter test method suitable for multiple vibration working conditions, and the method comprises the following steps: constructing a transparent visual simulation device comprising key grooves and inclination characteristics, and deploying a multi-modal sensing network with a unified time base; in the concrete warehousing and vibrating process, multi-modal data representing the vibrating state are synchronously collected; then, an environment control device is used for conducting dynamic maintenance on the test body by simulating the actual construction environment, and physical coring detection is conducted on different feature parts at the design age; and finally, based on correlation analysis of process data and a detection result, establishing a mapping relation to determine an optimal vibration parameter. According to the invention, the transparent visual simulation device containing the transverse joint key groove and the inclined dam face characteristics is constructed, and the concrete rheological behavior and the bubble migration path under the complex boundary constraint are visually observed by using the optical perspective principle.

Techniques for autonomous maintenance management (AMM) of an industrial facility are described. In one aspect, an AMM system monitors operational data values and an event associated with an equipment to determine key performance indicators and trend patterns associated with the equipment, where the event is characterized by deviations in the operational data values from a pre-determined threshold value, and where the trend pattern is indicative at least of operational history of the equipment. The KPIs and trend patterns are analysed to predict a behaviour of the equipment, based on which a dynamic maintenance schedule is generated, where the dynamic maintenance schedule includes a task to be executed for maintenance of the equipment. Further, the system obtains personnel information of the industrial facility to identify personnel to perform the task and accordingly issues a maintenance work order in correspondence to the dynamic maintenance schedule for maintenance of the equipment.

Dynamic maintenance interval planning in a machine (10) involves receiving monitoring signals for a plurality of components (64) in the machine, each of which specifies an operating characteristic on which an operating state (SOH) of one of the plurality of components depends. For each of the components, an SOH characteristic value is calculated based on the monitoring signals and a stored SOH wear progression profile. SOH message signals are output based on one of the respective SOH characteristic values. The message signals can specify a plurality of different continuous operating capacities between the components, at least some of which are independent of the runtime of one or more components in a drive system in the machine. A corresponding device and control logic are also disclosed.

The invention relates to an offshore wind turbine generator dynamic maintenance decision-making method considering state prediction uncertainty, and the method comprises the steps: obtaining the historical state transition prediction data of a plurality of prediction methods, and considering the local density information of a nearest neighbor set, based on a K-nearest neighbor method, basic probability distribution of historical state prediction results under multiple prediction methods of the unit is dynamically updated through dynamic Bayesian, and a unit component state transition probability interval is obtained through a combined reliability model of unit component state prediction based on a prediction result conflict degree; and constructing a stochastic programming model of a maintenance dynamic decision considering the uncertainty of component state prediction, and optimizing and solving to obtain a unit dynamic maintenance strategy by taking the minimum total maintenance cost as a target. Compared with the prior art, the method can reduce the influence of the uncertainty of state prediction on the maintenance decision, reduces the maintenance cost, and improves the maintenance efficiency.

The invention relates to the technical field of product data management, and discloses a maintenance method and system for a corner module configuration chassis based on product data management. According to the method, digital twin bodies are introduced, multiple pieces of real-time working data can be managed and controlled, and the method specifically comprises the steps that a product data combination of the functional part is determined based on the working state of the functional part and the use scene of an angle module configuration chassis; a product data management system of the corner module configuration chassis is constructed according to the product data combination of each functional part and the corresponding functional type, so that the accuracy of the product data management system of the corner module configuration chassis is improved; meanwhile, the corresponding abnormal area is constructed based on the abnormal data of each abnormal part and the corresponding spatial position, and the corresponding dynamic maintenance event is determined according to the area position of the abnormal area, the corresponding influence range and the task list of the angle module configuration chassis, so that the accuracy of the dynamic maintenance event is improved.

The invention relates to the technical field of cutting fluid maintenance for intelligent manufacturing, and particularly discloses an intelligent cutting fluid maintenance machine and a dynamic maintenance method. The intelligent maintenance machine is provided with a liquid supplementing main machine, and the liquid supplementing main machine is connected with a cutting fluid box of the corresponding machine tool through a liquid supplementing pipeline. A stock solution tank and a water tank which are relatively independently arranged, and a control host which is used for acquiring real-time detection data of the cutting fluid in the cutting fluid tank and controlling the stock solution tank and the water tank to discharge the fluid in proportion according to a set relation are arranged at the fluid supplementing host so as to dynamically supplement the cutting fluid to the cutting fluid tank. On the basis of the current situation that a single machine tool exists in a machining scene, the maintenance machine which intelligently and dynamically prepares finished product cutting fluid through carried water and stock solution and dynamically supplements the cutting fluid to the cutting fluid box of the corresponding machine tool is formed; indexes such as the liquid level and the concentration of the cutting fluid in the cutting fluid tank can be stably kept relative to the set corresponding threshold values all the time, manual participation is basically not needed in the whole cutting fluid maintenance process, and the intelligent degree is high.

The application belongs to the technical field of blockchains, and particularly relates to a blockchain smart contract deployment method. The method first acquires a transaction tendency node list with transaction tendency; then calculates system credit and direct credit of each node in the transaction tendency node list, and comprehensively utilizes the system credit and the direct credit to update the node credit of each node, wherein the system credit is the node credit calculated according to historical transaction records of the smart contract, and the direct credit is the node credit calculated according to interaction records with past nodes; finally, according to the updated node credit of each node, a node with higher node credit is selected to initiate a transaction, so as to realize smart contract deployment. The method not only can support dynamic deployment of the smart contract, but also can dynamically maintain and manage the blockchain system node, that is, the node credit can be inquired at fixed periods, and nodes with lower credit are removed to ensure stability of the system and reliability of the smart contract.

The application discloses a mold full life cycle management system based on Internet of Things and relates to the technical field of mold management, and comprises the following steps: acquiring front and rear associated parameters and front and rear associated features of processes in each stage of the full life cycle of a mold; and dynamically maintaining equipment in the processes based on real-time parameters; the mold full life cycle management system is used to solve the problem that the existing mold full life cycle management method is one-sided in that the state of the mold is only predicted from the collection of single data, and when the process in which the mold is located changes and other variables affect the state of the mold, the relationship between the single data and the variables cannot be effectively judged, the state prediction of the mold is deviated, and the mold cannot be effectively managed based on the subsequent state of the mold.

The invention discloses an SoC chip anomaly identification method and system based on multi-modal data, and relates to the technical field of SoC chips, a dynamic monitoring system of the SoC chip is determined according to a digital twin model, the multi-modal data and a chip framework diagram of the SoC chip, multiple pieces of anomaly data are determined based on the identification of the dynamic monitoring system, and the anomaly identification of the SoC chip is realized. And according to the plurality of abnormal data, the corresponding abnormal dimensions and the current working state of the SoC chip, determining the abnormity identification system of the SoC chip, thereby improving the accuracy of the abnormity identification system of the SoC chip. Meanwhile, determining an exception maintenance event of the SoC chip according to the control content of each exception control node, the corresponding exception control level and the data load condition of the SoC chip; the plurality of abnormal maintenance items are determined according to the identification of the abnormal maintenance event, and the dynamic maintenance route is determined based on the item content of each abnormal maintenance item, the corresponding maintenance logic and the current work list of the SoC chip, so that the accuracy of the dynamic maintenance route is improved.

This invention discloses a method for intelligent generation, dynamic maintenance, and closed-loop restoration of spot contracts based on coil hash values, belonging to the field of Enterprise Resource Planning (ERP) technology. This invention enables batch selection of coils and one-click contract generation. The coil hash values ​​are generated and automatically grouped based on four core attributes: material, thickness, width, and zinc layer. Unit prices and freight are set in batches by hash group. Coil materials are dynamically added and deleted before financial review. After a contract is deleted, the associated coil materials automatically restore their original inventory state. This invention focuses on the core need for "rapid order completion" in spot trading, with ≤3 operation steps and a 95% improvement in order completion efficiency. It solves the problems of cumbersome operation, error-prone manual calculation, inconvenient adjustment, and data corruption after deletion in traditional spot contracts. The operation is extremely simple, the calculation is accurate, the dynamic adjustment is possible, and the closed-loop system is secure. It is suitable for high-frequency, high-volume business scenarios in spot trading such as steel coils. This invention has been fully implemented in a real industrial ERP system and can run stably in a real production environment for a long time, possessing mature engineering value and industrial promotion capabilities.