800 results about "Production planning" patented technology

The invention provides a military clothing production scheduling optimization method and system based on an intelligent algorithm, and the method achieves the intelligent management of a production process through the construction of a dynamic state model and a disturbance cost evaluation system. Firstly, a reference production plan is generated by using a global optimization algorithm, and an event sensing module is deployed to monitor the production process in real time. When a task insertion event is captured, the system automatically collects related production parameters, triggers a disturbance cost evaluation mechanism, and dynamically predicts and quantifies the comprehensive influence of different interruption schemes. And based on the quantification result and a preset decision strategy, the system determines an optimal interrupt execution scheme, calls a rapid local rearrangement algorithm coupled with a disturbance cost index, adaptively adjusts the affected plan segments, and finally generates and issues an optimized production instruction sequence. According to the method, the whole process from disturbance identification to scheme execution is intelligentized, and the problem of dynamic scheduling for emergency tasks in military clothing production is effectively solved.

The invention discloses a supply chain intelligent management system based on dynamic collaborative optimization, and relates to the technical field of hotel supply chain management, and the system comprises a supply chain intelligent management platform which is in communication connection with the following modules: a multi-modal data sensing module, a multi-modal data processing module and a multi-modal data processing module. The multi-modal data acquisition module is used for acquiring multi-modal data in a hotel through a LoRaWAN + BLE hybrid sensor network, accessing an external data source and acquiring real-time information through an API (Application Program Interface); and the dynamic demand prediction module captures a time sequence trend through bidirectional LSTM based on an ASTGNN model. According to the method, external dynamic data such as social media public opinions and weather are fused, the multi-modal data analysis technology is combined, the accuracy of demand prediction is remarkably improved, the ASTGNN model is utilized, the time sequence trend and the demand association between the branches are analyzed in combination with bidirectional LSTM and GCN, and the feature weight is dynamically adjusted, so that the demand prediction error rate is greatly reduced, and the demand prediction efficiency is improved. A more reliable demand prediction basis is provided for enterprises, and optimization of inventory management and production plans is facilitated.

The invention relates to the technical field of industrial control, and discloses a dry-wet combined cooling tower operation state prediction and diagnosis system comprising a load prediction module used for obtaining production plan data and environment information, constructing a basic heat load prediction model, and outputting a total prediction heat load; the thermal modeling module is used for establishing a heat transfer model and an energy consumption model, the heat transfer model outputs cooling amounts in different operation modes, and the energy consumption model outputs total predicted power; the dry-wet decision module is used for constructing a multi-objective optimization function and solving the multi-objective optimization function to obtain a dry-wet switching strategy; and the control execution module is used for executing the dry-wet switching strategy. According to the method, the basic thermal load prediction model is established, so that the prediction precision of the thermal load is improved, and differentiated cooling strategies are provided for different production stages; on the basis of real-time load requirements and environmental conditions, a dry mode or a wet mode is intelligently selected for operation, and a multi-objective optimization function is constructed, so that the balance between cooling requirement meeting and system energy consumption minimization is realized.

The invention discloses a furniture production informatization management system based on data analysis, and relates to the technical field of production management, and the system comprises a production plan optimization module, a raw material cutting optimization module and a quality prediction center module. The production plan optimization module collects data by deploying an RFID sensing network, establishes a multi-dimensional evaluation matrix to determine order priority, optimizes a production plan by using a mixed integer programming model and an improved algorithm, and generates a production scheduling scheme after digital twinborn verification; the raw material cutting optimization module analyzes order data to construct a raw material model, optimizes component layout by applying a self-adaptive algorithm, and generates a cutting scheme with process constraint and an excess material map in combination with stress analysis; through an intelligent production management means, the production efficiency, the raw material utilization rate and the product quality stability are remarkably improved, and remarkable economic benefits are brought to enterprises.

The invention discloses a quick-freezing equipment fault prediction and health management system based on digital twinning, and particularly relates to the field of quick-freezing equipment operation and maintenance. The theoretical fault index generation module is used for generating a multi-dimensional theoretical fault index matrix; the real-time fault index calculation module generates a real-time fault index; the deviation threshold value library establishing unit forms a dynamically updated deviation threshold value library; the residual life prediction module calculates the residual running time of the equipment from the current state to the fault; the health management module calculates equipment health scores and divides equipment health states into four grades; the visual display module provides a visual interface for a user; compared with the traditional operation and maintenance that the residual life cannot be estimated, the method can enable the operation and maintenance personnel to plan maintenance and replace parts in advance and reasonably arrange a production plan, reduces the risk of sudden failure, and enables the equipment operation and maintenance to be changed from passive response to active prevention.

The invention discloses a flexible automatic production management and control system for a new energy high-power high-frequency transformer, and relates to the field of production management and control, the flexible automatic production management and control system comprises a production demand receiving module, a production resource management module, a production planning module, a production execution module and a quality control module, an order is received through an API or EDI, and a work order is generated; collecting equipment and material information in real time, and constructing a production resource association network; carrying out production scheduling and process matching by adopting an improved deep learning model; scheduling materials, procedures and equipment based on a multi-level analysis control model; the quality is evaluated by using a two-dimensional cloud model, and data recording is performed through a distributed database, so that the traceability of the quality problem is ensured. According to the system, the production efficiency and the quality management level are effectively improved, the problem that existing rigid automatic equipment lacks flexibility is solved, and efficient and accurate production of the new energy high-power high-frequency transformer is ensured through cooperative work of a plurality of modules so as to meet different production requirements.

The invention relates to an impregnated paper production process multi-parameter collaborative optimization control method based on reinforcement learning, and the method comprises the following steps: S1, building an omnibearing data collection network, achieving the panoramic perception of a production process, and obtaining a real-time data flow; s2, based on a data fusion technology, performing intelligent integration on different sources and different types of data to obtain a fused data set; s3, according to the fused data set and based on a process mechanism, constructing a knowledge graph of the impregnated paper production process; s4, constructing a digital twinborn model of the impregnated paper production line in combination with the fused data set and the knowledge graph; and S5, designing a multi-objective optimization decision framework according to the digital twinborn model, the production plan and the constraint condition, and realizing collaborative optimization of different time scales. According to the invention, multi-parameter collaborative optimization control of the impregnated paper production process is realized.

Disclosed in the present invention is a material picking method based on a mobile robot, comprising the following steps: step S1, acquiring a real-time production plan and a bill of materials, wherein the bill of materials comprises material codes, material names, material quantity and lead time; step S2, on the basis of the material codes, dividing the bill of materials into a plurality of task units suitable for single operation of the mobile robot, and assigning corresponding picking stations; step S3, performing picking state labeling on the task units on the basis of the lead time of materials in the task units, and assigning corresponding mobile robots on the basis of the labeled picking states; and step S4, the mobile robots performing path planning on the basis of the assigned task units, and executing the assigned task units on the basis of the planned path, so that racks to which the materials in the assigned task units belong are transported to specified picking stations for operators to pick the required materials.

The invention discloses an assembly equipment health management system based on industrial Internet of Things, and particularly relates to the field of intelligent maintenance of industrial equipment, comprising a distributed sensing module, a cross-production-line transfer learning module, a dynamic maintenance strategy optimization module, a multi-modal man-machine cooperation module and an enhanced execution terminal module, multi-source sensor clusters and intelligent edge nodes are deployed, multi-dimensional operation data are collected and preprocessed at high frequency, an encrypted global fault feature library is constructed by using a federal transfer learning algorithm, cross-production-line group intelligent advanced early warning is realized, and the equipment health degree, the production plan and the resource state are comprehensively considered based on a deep reinforcement learning decision-making agent. A priority maintenance strategy is output, a digital twinning and AR technology is fused to generate an enhanced maintenance guidance package, real-time operation verification and health re-evaluation are realized through an execution terminal, accurate and efficient closed-loop health management is formed, and the equipment reliability and the maintenance intelligence level are remarkably improved.

The invention discloses a smart factory energy efficiency optimization method, system and device based on the industrial Internet of Things, and relates to the technical field of the industrial Internet of Things, and the method comprises the steps: collecting production energy consumption data based on equipment operation parameters and production plan information; constructing a factory digital twinborn model, and determining an equipment start-stop time sequence and an energy distribution strategy; deploying edge computing nodes, monitoring the vibration spectrum of the equipment, generating a state prediction map in a time sliding window, and performing adaptive compensation optimization; and meanwhile, an energy flow network is drawn up based on the equipment start-stop time sequence and the energy distribution strategy, and energy dynamic adjustment is carried out. The technical problems of low energy utilization efficiency and insufficient equipment operation stability of a smart factory in the prior art are solved, and the technical effects of realizing smart factory energy efficiency optimization based on the industrial Internet of Things and improving the energy utilization efficiency and the equipment operation stability are achieved.

The invention provides an industrial manufacturing industry-oriented carbon asset full life cycle management method and system, and the method comprises the steps: carrying out the cross-link feature fusion processing through obtaining an original carbon activity data set of an industrial manufacturing whole process, and generating carbon footprint feature data; and calling a pre-trained deep learning model to perform carbon asset state evolution analysis on the carbon footprint feature data, predicting a carbon asset evolution path, generating a carbon asset optimization strategy set based on a prediction result, including equipment energy efficiency regulation and control and a production plan recombination strategy, and pushing the carbon asset optimization strategy set to an industrial manufacturing execution system. And triggering an automatic control instruction reconstruction operation to realize automation and intelligence of carbon asset management, thereby comprehensively and accurately mastering a carbon asset state and an evolution rule, formulating a scientific and effective optimization strategy, reducing carbon emission, improving energy utilization efficiency, and improving green production capacity of an enterprise.

The invention relates to the technical field of data management, in particular to an intelligent flow scheduling method for a high-performance oil way of a metal plate pipe network. The specific implementation process comprises the following steps: establishing a distributed intelligent executor and a resource optimization model based on a sheet metal pipe network comprising a schedulable unit and production management information comprising a historical performance baseline and a production plan; the distributed intelligent executor obtains and evaluates the operation data of the schedulable unit and generates real-time state data; combining the real-time state data and the historical performance baseline to analyze the drift trend and generate performance degradation data; based on the production plan and the performance degradation data, quantifying the comprehensive operation cost as a target function to calculate and generate a resource allocation strategy and an operation instruction; and the distributed intelligent executor combines the operation instruction, the real-time state data and the performance degradation data to execute dynamic compensation management to generate a scheduling instruction. According to the method, resource allocation is optimized by evaluating the real-time and long-term states of the equipment, so that risk avoidance and cost dynamic optimization are realized.

The invention discloses an intelligent cable production management and control method and system based on feature data analysis, and relates to the technical field of cable production management and control, and the method comprises the steps: firstly obtaining cable structure information to determine a type, and synchronously collecting historical production whole-process data of a corresponding type, including equipment operation, technological parameters and other data; key features are extracted from historical data, and a cable production quality index prediction model and a production equipment energy consumption prediction model are constructed; obtaining real-time production data, extracting key features, and inputting the key features into the model to obtain quality and energy consumption indexes; and finally, the production plan is dynamically adjusted based on the two types of indexes, and a set threshold value, multi-scene early warning and a plan maintaining or adjusting strategy are covered. The method has the advantages that intelligent control over the whole production process is achieved through data driving, the quality prediction accuracy can be improved, energy consumption management can be optimized, the production plan flexibility can be enhanced, and technical support is provided for efficient and accurate management of cable production.

The invention provides an MES-based solar cell production and manufacturing execution method and system, and the method comprises the steps: collecting equipment states and technological parameters in real time, combining a bar code system to achieve the single-chip-level metadata association, constructing a standardized data architecture, building a multi-dimensional index, converting the expert experience into reasonable rules, and carrying out the calculation of the reasonable rules. The method comprises the following steps: simulating the influence of parameter combination on product performance, triggering an edge computing node to call a knowledge graph to perform parameter optimization and production simulation based on a dynamic threshold early warning mechanism, identifying process improvement opportunities through a batch tracing report and clustering analysis, forming knowledge base iteration update, predicting inventory risks in combination with a production plan and equipment load, and performing production management. According to the method, intelligent purchasing is automatically triggered, raw material distribution is optimized, technological parameter self-optimization, abnormity self-healing, quality traceability and supply chain collaboration are achieved through data driving, the production efficiency, the product consistency and the resource utilization rate are improved, and a whole-process digital solution is provided for photovoltaic manufacturing.

The invention discloses a product production process scheduling method and system based on intelligent manufacturing, and belongs to the technical field of intelligent manufacturing. According to the method, rapid local rescheduling is realized through fault influence domain identification, a three-level response strategy and real-time equipment health assessment. When an equipment fault signal is detected, a to-be-executed task on the fault equipment is identified, a subsequent task chain is traced through the technological process dependency relationship, the boundary of a fault influence domain is determined, and the influence domain relates to 10%-20% of tasks in the whole production plan. And selecting a corresponding response strategy according to the predicted repair time, adopting a task delay strategy when the predicted repair time is less than 30 minutes, performing local redistribution on tasks in the influence domain when the predicted repair time is less than 30 minutes to 2 hours, and expanding the influence domain to the whole process for comprehensive optimization when the predicted repair time is more than 2 hours. Meanwhile, monitoring data such as equipment vibration, temperature, current and rotating speed are collected to calculate a health degree score, and 5%-10% of productivity redundancy is reserved for high-risk equipment.

The invention relates to a steel production-oriented multi-base multi-production-line multi-process global plan optimization method and system. The method comprises the following steps of: receiving and standardizing order demand data; executing hierarchical processing and distribution of multi-base orders, and outputting a base-level order set; deducting and marking the net production demand quantity of the base-level orders; executing multi-production-line order allocation in a single base, and generating a production line level order set; performing cross-process collaborative batch grouping on the production line level order set based on steel making-continuous casting-rolling-post-processing process constraints to generate a cross-process batch plan; constructing a global evaluation model, performing comprehensive evaluation on an intermediate result, and generating a parameter correction instruction based on an evaluation result for adjusting parameters, constraints or distribution strategies of subsequent modules; and finally, generating a production plan scheme with consistent cross-base, cross-production-line and cross-process, and triggering rolling optimization update when disturbance occurs. According to the invention, unified coordination and global optimization of multi-module plan results are realized.

The invention provides a manufacturing digital twin system, which is characterized in that a production process simulation module simulates different production scenes of a manufacturing process and production flow data under each production scene, and generates a simulation report; the workshop scheduling engine optimizes production task scheduling and resource allocation of a production workshop by using an operation planning optimization algorithm to generate a workshop task production scheduling scheme; obtaining a production scheduling result corresponding to the workshop task production scheduling scheme; a weekly production plan production scheduling module generates a weekly production plan according to the production scene provided by the production process simulation module and a production scheduling result corresponding to each workshop task production scheduling scheme; based on the enterprise resource state obtained in real time, dynamically adjusting the weekly production plan, and feeding back the dynamically adjusted weekly production plan to a production process simulation module and a workshop scheduling engine in real time, so that the production process simulation module performs simulation verification on the weekly production plan; and the workshop scheduling engine updates the workshop task production scheduling scheme according to the new weekly production plan.

The invention relates to the technical field of intelligent monitoring management, particularly discloses an intelligent assembly component whole-process management system, and aims to optimize production efficiency and product quality through real-time data acquisition and analysis. The system comprises a data acquisition module which monitors and records the switching time of each process and the quality inspection qualified rate of each component in real time through a sensor and automation equipment on an integrated production line, a production plan evaluation module which calculates a switching time abnormal coefficient, identifies a bottleneck or low-efficiency operation in the process switching time, and evaluates the quality inspection qualified rate of each component according to the bottleneck or low-efficiency operation. And the process switching time is dynamically adjusted based on the specific numerical values of the influence interference factors and the positive and negative characteristics of the influence interference factors, the production quality evaluation module calculates a quality abnormal coefficient according to the quality inspection qualified rate and judges the quality fluctuation condition of each component, and the influence analysis and adjustment module predicts the quality qualified rate by using a gradient enhanced regression model. The process switching time is dynamically adjusted to eliminate potential quality problems.

The invention relates to the technical field of intelligent manufacturing, and discloses a production scheduling system of a production line and a scheduling method thereof. According to the system and method, key information such as production orders, equipment states and raw material inventory is collected in real time through the data collection unit, basic data is provided for subsequent analysis, the data analysis unit can rapidly evaluate the production capacity and the resource utilization rate, the orders are subjected to priority ranking, and it is ensured that the production efficiency is improved in the dynamically changing market environment. The production plan can preferentially meet the most emergency and important requirements, the scheduling decision-making unit rapidly generates an optimized preliminary schedule by applying an advanced scheduling algorithm, the manual intervention time is shortened, the response speed is improved, the scheduling execution unit ensures that a production instruction is issued and executed in time, the production progress is monitored in real time, and the production efficiency is improved. And the optimization unit continuously improves a scheduling strategy by evaluating and feeding back an execution result, so that the production efficiency is improved, the cost is reduced, and meanwhile, the timeliness of delivery and the customer satisfaction are ensured.

The invention relates to the technical field of casting sand production, and discloses an artificial intelligence-based casting waste sand recycling intelligent manufacturing management system, which comprises a data acquisition module for acquiring production data in a casting waste sand treatment process; the data processing and analyzing module is used for receiving the data transmitted by the data acquisition module and processing and analyzing the data; the intelligent control module is used for adjusting operation parameters of crushing, grinding, sorting and roasting equipment; the production scheduling and management module is used for receiving the control information, making a production plan and performing inventory management; and the quality detection and tracing module is used for establishing a quality tracing system and ensuring the traceability of the data. By constructing a data processing and modeling module based on artificial intelligence, dynamic identification and adjustment optimization of a key process state in a foundry waste sand treatment process are realized, so that the adaptability of the system to complex working conditions is improved.

The invention discloses an equipment end wafer caching system for semiconductor production, and particularly relates to the field of semiconductor manufacturing equipment, which comprises a task initialization module, an intelligent scheduling module, a round box transportation module, a caching scheduling module, an exception handling module and a task feedback module, according to the task initialization module, a production management system MES generates a wafer box carrying task according to the production plan; according to the intelligent scheduling module, the core scheduling control unit generates an optimal carrying strategy based on the carrying task; the intelligent scheduling system MCS monitors the state and position of the wafer box and the requirements of a production line in real time, the optimal wafer box carrying path and storage strategy can be selected, the carrying task is issued, it is ensured that the wafer box efficiently flows among the cache groove, the STK storage area, the SMIF equipment and the OHS air shuttle vehicle, the waiting time and invalid carrying are reduced, and the production efficiency is improved. And the overall production efficiency is improved.

The invention relates to a production environment parameter control method and system based on industrial Internet of Things. The method comprises the following steps: collecting a first real-time production environment parameter and a subsequent planned production task, carrying out production environment parameter prediction, obtaining a prediction time sequence production environment parameter set, and determining a critical time point; acquiring response time characteristics of the environment controller, and determining a control time point for the critical time point; and at the control time point, collecting a second real-time production environment parameter of the target workshop, generating an environment control parameter of the environment controller, and controlling the environment controller to execute production environment control based on the environment control parameter. Intelligent prediction of production environment parameters, critical point identification and optimal opportunity intervention control are realized through an Internet of Things sensing network and a production plan, so that the stability of the production environment is ensured, and meanwhile, the energy consumption is reduced.

The invention discloses a self-adaptive scheduling method and system for multi-process production, and the method comprises the steps: generating a production plan according to basic data of a multi-process production task, executing the plan, monitoring a production field data flow in real time, and when an abnormal event is monitored, extracting abnormal event information including an abnormal source, abnormal occurrence time and predicted duration; inputting the abnormal event information into a preset rule engine, positioning an influenced scheduling block set according to a preset influence evaluation rule, marking the influenced scheduling block set as a to-be-rearranged task, and outputting the to-be-rearranged task; local rescheduling optimization is carried out in the affected time-resource range, and candidate rescheduling schemes are generated; and screening out an optimal scheme from the candidate rescheduling schemes, updating and integrating the optimal scheme into the production plan, and automatically updating the associated productivity load analysis data. According to the method, the scheduling block set influenced by abnormity can be accurately positioned, local rearrangement optimization is carried out, and the stability of a production plan is guaranteed on the basis of low computing power consumption.

According to the real-time production scheduling method provided by the invention, the material demand is triggered based on the production plan, the MES and the WMS collaboratively complete the global scheduling of the raw materials, after the raw materials are delivered to the production line, the material batch segmentation or combination or rework path optimization is executed in real time according to the abnormal condition, and the real-time production scheduling is realized through dynamically triggering the material demand, collaboratively and globally scheduling the MES and the WMS and a real-time abnormal response mechanism. And efficient configuration of production resources and rapid closed loop of abnormal working conditions are realized, so that the flexibility and intelligence level of the manufacturing process is improved.

The invention provides a carbon energy efficiency dual-objective optimization analysis method and system based on digital twinning, and relates to the technical field of carbon energy efficiency optimizing.The method comprises the steps that simulation modeling is conducted on an air compressor system and a sterile cold filling line of a beverage factory based on digital twinning, a filling twinning simulation space is constructed, and a production planning system is accessed; a bottle blowing machine beat signal, filling bottle attribute characteristics and filling bottle sterile blowing requirements in a preset time zone are obtained through a production planning system; in the filling twinborn simulation space, with minimization of carbon emission intensity and maximization of production stability as double optimization targets, iterative optimization search is conducted on the air compression control scheme of the air compressor system, and the optimal air compression control scheme is output; and the filling operation in the preset time zone is executed according to the optimal air pressure control scheme. The technical problems that in the prior art, an air compressor system adopts a fixed high-pressure control strategy, dynamic management and control cannot be conducted according to actual production requirements, and energy consumption is wasted are solved.

The invention relates to the technical field of manufacturing execution systems, in particular to an intelligent scheduling method integrating demand prediction and supply matching, which comprises the following steps of: acquiring to-be-scheduled order information and a workshop real-time resource state in a manufacturing execution system, constructing an order demand feature vector, the method comprises the following steps: acquiring an equipment precision retentivity value, a current load rate value, an energy consumption level value and an operator skill proficiency value, and constructing a resource supply capability vector; according to the method, when an urgent order insertion instruction is received, a clear decision basis is provided, a rigid scheduling locking block which must be kept unchanged and an adjustable liquid adjustable scheduling block which can be adjusted can be distinguished, only the latter is subjected to accurate splitting and backward moving operation, and the target-clear local adjustment mode is adopted, so that the operation efficiency is greatly improved. The huge calculation overhead and the violent fluctuation of the production plan caused by the traditional global rescheduling are avoided, and the agility of the manufacturing system to deal with the market change is enhanced.

The invention discloses a strip mine production management and control optimization system based on a full life cycle management idea, and belongs to the technical field of mine production management, and the system comprises the steps: collecting geological exploration data, production plan data and equipment state data, and carrying out the integration to generate production management and control data; the method comprises the following steps: preprocessing production management and control data to obtain production management and control processing data; a model is constructed by using big data analysis and a machine learning algorithm, and a scientific basis is provided for decision making; the ore grade prediction is obtained, the mining priority of each mining area is determined in combination with the market demands and mining costs of different ores, and the dynamic adjustment of the mine production plan is realized; the mining operation including the equipment state, the work progress and the safety condition is comprehensively monitored by adopting the Internet of Things technology, and an early warning prompt is sent out in time to respond to the abnormal condition; and performing environmental influence evaluation on each stage of the mine life cycle by calculating an environmental damage index, making and executing a reclamation plan, and promoting ecological restoration of the mining area.

The invention discloses an integrated management system for a concrete mixing plant, and relates to the technical field of mixing plant management, a data acquisition unit is accessed to a raw material supplier system to acquire raw material supply data and concrete usage plans in different time periods, and a demand prediction unit introduces meteorological information and construction site states to predict the concrete usage plans in different time periods. And the dynamic optimization module substitutes the raw material supply data and the concrete dosage demand result into a dynamic adjustment model, the dynamic adjustment model dynamically adjusts the stirring progress, a stirring task queue is established, and the production sequence is intelligently sorted according to the material supply condition and the delivery priority. The management system not only can automatically generate a stirring task queue according to the material arrival condition and the construction usage priority, effectively avoid raw material waste and production plan conflicts, but also can adjust the delivery time, the loading sequence and the vehicle scheduling path in a linkage manner, break through the management breakpoint between production and transportation, and improve the production efficiency. And the overall cooperation efficiency and the customer response speed are improved.

The invention relates to the technical field of intelligent manufacturing and supply chain management, in particular to an intelligent management system for titanium plate inventory and production plan collaboration. Comprises: a multi-source data fusion center for a supply chain management system to obtain operation data and generate a collaborative situation original data set; the situation quantitative analysis unit is used for receiving the collaborative situation original data set and generating a market agility demand score and an inventory liquidity current situation score; the collaborative conflict evaluation unit is used for calculating a collaborative conflict index and determining a collaborative conflict situation level; the evolution trend prediction unit is used for generating an evolution sequence of future collaborative conflict indexes; and a dynamic decision management unit for generating an immediate conversion policy in response to the collaborative conflict context level and generating a predictive inventory structure pre-configuration decision. According to the method, the accuracy and objectivity of decision making are remarkably improved, and enterprises can deeply insight into operation core contradictions.

The invention provides a tire factory multi-dimensional dynamic collaborative production command method and system, and relates to the field of industrial manufacturing informationization, and the method comprises the steps: collecting equipment layer sensor data, MES system production plan data and quality detection system data in real time through a protocol conversion module, and extracting the input of a frequency domain feature process conduction matrix; the method comprises the following steps: constructing a process conduction matrix of banburying, forming and vulcanizing, inputting current process abnormal data containing a vibration spectrum abnormal value into the process conduction matrix, outputting a prediction index set, and dynamically correcting a process alarm threshold value through an LSTM model based on the prediction index set output by the process conduction matrix, environment temperature and humidity and an equipment aging coefficient. A parameter adjustment control is embedded in the visual billboard, the timeliness and accuracy of a production plan can be improved according to comparison of a corrected process alarm threshold value and actual production data, the labor and time cost is reduced, comprehensive control of real-time data related to production is achieved, and the production efficiency and quality are improved.