Supply chain module cooperation system for non-standardized customized production

Abstract

The invention discloses a non-standardized customized production-oriented supply chain module collaboration system, which comprises a basic data fusion module used for enterprise registration, data entry, enterprise collaboration network construction, and sensing and fusion processing of multi-source heterogeneous data; the supply task collaboration module is used for receiving a task request of non-standardized customization production, and performing optimization decision making, resource scheduling and intelligent allocation of tasks based on the data processed by the basic data fusion module; and the exception handling and collaborative value evaluation module is used for state tracking, exception handling and collaborative effect quantitative analysis of the whole task execution process. The system is suitable for the manufacturing industry of non-standardized customized production involving multi-enterprise participation and flexible process requirements, and the intelligent level of supply chain collaboration, the task execution stability and the overall strain capacity of the system can be remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing and supply chain collaboration technology, and in particular to a supply chain module collaboration system for non-standardized customized production. Background Art

[0002] With the continuous development of the global economy and increasingly fierce market competition, customized production is becoming a mainstream trend in the manufacturing industry. The home appliance industry is a traditional consumer goods industry characterized by fierce competition and a high degree of marketization. Home appliance companies' products have become relatively mature in terms of performance and technology. Competition among companies is gradually shifting from product competition to service competition, and from "mass production" to "customized production." Mass customization is an innovative customized service model that uses low-cost and high-efficiency large-scale production to meet users' unique needs. It primarily utilizes intelligent manufacturing systems and standardized structural dimensions to meet users' diverse customization requirements.

[0003] In the context of economic globalization, due to the divergence of national product technical standards, home appliance production is characterized by non-standardization and customization (e.g., voltage differences of 110V and 220V, inconsistent quality requirements for key components, and varying color preferences for appliances among customers in different countries). This custom production, tailored to specific customer needs and inconsistent with national or industry standards and specifications, places higher demands on supply chain collaboration and the timeliness and accuracy of supply.

[0004] In view of this, this invention is proposed. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of the existing technology and propose a supply chain module collaboration system for non-standardized customized production. It is suitable for the manufacturing industry with non-standardized customized production involving multiple enterprises and flexible process requirements. It can significantly improve the intelligence level of supply chain collaboration, task execution stability and the overall adaptability of the system.

[0006] In order to achieve the above object, the present invention also adopts the following technical solutions:

[0007] A supply chain module collaboration system for non-standardized customized production, characterized by including:

[0008] Basic data fusion module, used for enterprise registration, data entry, building enterprise collaborative networks, and sensing and fusion processing of multi-source heterogeneous data;

[0009] The supply task collaboration module is used to receive task requests for non-standardized customized production and make optimized task decisions, resource scheduling, and intelligent allocation based on the data processed by the basic data fusion module;

[0010] The exception handling and collaborative value assessment module is used for status tracking, exception response and quantitative analysis of collaborative effects throughout the entire task execution process.

[0011] The data center is used to store the enterprise data, task data, execution process data and evaluation results generated by the above modules, and supports data interaction calls between all modules.

[0012] Furthermore, the basic data fusion module includes:

[0013] The enterprise registration submodule is used to support the independent registration or entrusted registration of complete machine enterprises and supplier enterprises in the system;

[0014] An enterprise collaborative network construction submodule is used to automatically or manually construct an enterprise collaborative network;

[0015] The enterprise data aggregation submodule is used to collect structured and unstructured data from the operational data of registered enterprises and perform aggregation processing;

[0016] The data sharing incentive submodule is used to encourage registered companies to upload and update operational data.

[0017] Furthermore, the basic data fusion module also includes:

[0018] The enterprise multi-source heterogeneous data perception and fusion submodule is used to perceive and access the data sources of registered enterprises in real time, and extract and fuse the features of the data sources to obtain standardized enterprise features;

[0019] The enterprise-wide collaborative data fusion sub-module is used to conduct in-depth integration value mining of standardized enterprise characteristics and obtain multi-dimensional evaluation results of registered enterprises.

[0020] Furthermore, the supply task system module includes:

[0021] The task receiving and parsing submodule is used to receive task requests for non-standardized customized production, parse the task requests, and obtain task objectives, delivery conditions, and budget constraints;

[0022] The optimization decision submodule is used to build a task allocation optimization model that includes multi-objective optimization functions and constraints, and obtain the enterprise task assignment plan based on the data processed by the basic data fusion module;

[0023] The cooperation conflict analysis submodule is used to identify the task conflicts, resource preemption and assistance failure risks among enterprises in the enterprise task assignment plan and form constraint conditions.

[0024] The task allocation submodule is used to split the enterprise task assignment plan into subtasks based on the granularity of non-standardized customized production tasks and enterprise capabilities, and then assign them;

[0025] The resource scheduling submodule is used to coordinate the scheduling of human resources, production capacity, logistics and other resources across enterprises based on the resource types and quantities required for each subtask, as well as resource allocation;

[0026] The sequential decision-making submodule is used to continuously evaluate the current execution status during the execution of the enterprise capability-to-enterprise task assignment plan, and to fine-tune and optimize the subsequent sequence and resource allocation of subtasks that have not yet started and are in progress.

[0027] Furthermore, the exception handling and collaborative value assessment module includes:

[0028] The collaborative performance and value assessment submodule is used to comprehensively evaluate task execution effects, enterprise contribution, and benefit distribution, and generate a visual collaborative value map;

[0029] The task execution monitoring and exception handling submodule is used to continuously obtain status data during task execution and trigger task reconstruction and resource replacement strategies when an exception occurs.

[0030] Furthermore, the collaborative performance and value assessment submodule includes:

[0031] The collaborative effect evaluation submodule is used to build a multi-dimensional performance indicator system including task achievement, response timeliness, and collaboration efficiency, and conduct dynamic quantitative evaluation and trend analysis of the task execution process;

[0032] The enterprise revenue evaluation submodule is used to evaluate the input-output data of each enterprise, evaluate the revenue contribution of each enterprise, and construct a revenue distribution map of each enterprise in the entire supply chain;

[0033] The analysis result feedback submodule is used to transmit the evaluation results back to the basic data fusion module and the supply task collaboration module.

[0034] Furthermore, the task execution monitoring and exception handling submodule includes:

[0035] The status monitoring submodule is used to continuously obtain status data during task execution, including progress, quality, and delivery status information, and dynamically update key indicators;

[0036] The anomaly identification submodule is used to identify whether there are abnormal events based on rule templates or historical models. Abnormal events include task delays, production interruptions, and resource failures;

[0037] The task reconstruction and migration submodule is used to dynamically generate alternative enterprise or collaboration paths and execute task migration strategies when abnormal events occur.

[0038] Furthermore, it also includes a permission and security management module, which includes:

[0039] The role permission control submodule is used to assign function access rights and data operation permissions based on user types;

[0040] The data security encryption submodule is used to encrypt sensitive data during storage and transmission to prevent data leakage and tampering;

[0041] The operation audit log submodule is used to record various key data operation behaviors and realize the full-process traceable management of data addition, deletion and modification behaviors.

[0042] Furthermore, the system adopts a B / S architecture design with a front-end interface and back-end services separated, wherein:

[0043] The front-end interface is built based on the Vue3 framework;

[0044] The backend service is built based on the Spring Boot framework.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. The basic data fusion module addresses the real-time management of multi-source heterogeneous enterprise data in non-standardized customized production scenarios and builds an enterprise data perception and coding system. By standardizing modeling and dynamically updating key indicators such as supplier capacity, delivery time, cost, and quality, it enables intelligent matching and continuous integration of enterprise basic data, providing high-precision data support for upstream collaborative planning and downstream task scheduling.

[0047] 2. The supply task collaboration module is oriented towards the multi-objective optimization needs of task scheduling and resource allocation. It comprehensively considers order delivery time limits, manufacturing resource constraints and cooperation risks, and builds a dynamic task allocation optimization model based on sequential decision logic and conflict analysis mechanism. It realizes intelligent dispatching of outsourced tasks and optimal configuration of manufacturing resources, which can significantly improve task collaboration efficiency and execution success rate.

[0048] 3. The exception handling and collaborative value assessment module focuses on status monitoring and exception response management during task execution, and builds a collaborative assessment system covering multi-dimensional indicators such as progress, quality, and performance. In addition, real-time analysis and predictive warning of supply chain execution status, combined with an emergency reconstruction mechanism, quickly generates alternative solutions in situations such as collaborative interruption or supplier failure, ensuring supply chain resilience and task continuity.

[0049] 4. The above modules are based on the data center, combined with the functions of each module, and through unified interface specifications and process control mechanisms, they realize collaborative management of the entire process from basic data initialization to task execution closed loop, from collaborative plan generation to exception response processing.

[0050] 5. The basic data fusion module, supply task collaboration module and exception handling and collaborative value assessment module run through the entire process of enterprise registration, task collaboration, resource optimization, task execution and benefit evaluation, and build an intelligent collaborative system for multiple enterprises, dynamic adaptation, exception handling and value assessment; it can adapt to the non-standard customized production environment of multiple enterprises, dynamic and flexible collaboration, and can significantly improve the intelligence level of supply chain collaboration, task execution stability and the overall adaptability of the system, and has high industrial application value and promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a structural diagram of a supply chain module collaborative system for non-standardized customized production. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0053] Example 1:

[0054] The core requirements for collaborative production of non-standardized customized home appliances are timelines, quality, and cost. All aspects of supplier selection and order placement are based on these considerations. During production, some products and components require outsourcing, and a single appliance may contain thousands of parts. Therefore, collaborative production of non-standardized customized home appliances first requires a wealth of basic supplier data, such as price, quality, and production capacity. Secondly, during the order process, when outsourcing parts procurement or processing requires coordination with suppliers, the manufacturer initiates and leads the collaboration, coordinating the optimal combination of manufacturing resources across the supply chain based on timelines, quality, and cost. Thirdly, data needs to be extracted from the personalized order business of non-standardized customized production, and data services for these orders need to be established to achieve data-driven, parallel interaction throughout the entire process. This means that once a personalized order is initiated by a customer, the manufacturer, parts supplier, logistics, after-sales service, and other departments collaborate. Finally, to address potential chain disruptions and task reallocation (e.g., a supplier fire), task monitoring, timely detection of anomalies, and solution generation are required, coupled with effective algorithms to enable rapid and accurate decision-making.

[0055] Based on this, the following system is designed in this embodiment.

[0056] A supply chain module collaborative system for non-standardized customized production, such as Figure 1 Shown, including:

[0057] Basic data fusion module, used for enterprise registration, data entry, building enterprise collaborative networks, and sensing and fusion processing of multi-source heterogeneous data;

[0058] In this embodiment, the basic data fusion module is aimed at the real-time management of multi-source heterogeneous enterprise data in the scenario of non-standardized customized production, and has built an enterprise data perception and coding system. By standardizing modeling and dynamically updating key indicators such as supplier production capacity, delivery time, cost, and quality, it can achieve intelligent matching and continuous integration of enterprise basic data, providing high-precision data support for upstream collaborative planning and downstream task scheduling.

[0059] The supply task collaboration module is used to receive task requests for non-standardized customized production and make optimized task decisions, resource scheduling, and intelligent allocation based on the data processed by the basic data fusion module;

[0060] In this embodiment, the supply task collaboration module is oriented to the multi-objective optimization needs of task scheduling and resource allocation, comprehensively considers order delivery time limits, manufacturing resource constraints and cooperation risks, and builds a dynamic task allocation optimization model based on sequential decision logic and conflict analysis mechanism to achieve intelligent dispatching of outsourced tasks and optimal configuration of manufacturing resources, which can significantly improve task collaboration efficiency and execution success rate.

[0061] The exception handling and collaborative value assessment module is used for status tracking, exception response and quantitative analysis of collaborative effects throughout the entire task execution process.

[0062] In this embodiment, the exception handling and collaborative value assessment module is aimed at status monitoring and exception response management during task execution, and builds a collaborative assessment system covering multi-dimensional indicators such as progress, quality, and performance. In addition, real-time analysis and predictive warning of the supply chain execution status, combined with the emergency reconstruction mechanism, quickly generates alternative solutions in situations such as collaborative interruption or supplier failure, thereby ensuring supply chain resilience and task continuity.

[0063] The data center is used to store the enterprise data, task data, execution process data and evaluation results generated by the above modules, and supports data interaction calls between all modules.

[0064] In this embodiment, the above modules are based on the data center as the core, combined with the functions of each module, and through unified interface specifications and process control mechanisms, realize collaborative management of the entire process from basic data initialization to task execution closed loop, from collaborative plan generation to exception response processing.

[0065] In summary, the present embodiment is a supply chain module collaboration system for non-standardized customized production, which includes a basic data fusion module, a supply task collaboration module, and an exception handling and collaborative value assessment module. It runs through the entire process of enterprise registration, task collaboration, resource optimization, task execution, and benefit assessment, and builds an intelligent collaboration system for multiple enterprises, dynamic adaptation, exception handling, and value assessment. It can adapt to the non-standardized customized production environment of multiple enterprises, dynamic, and flexible collaboration, and can significantly improve the intelligence level of supply chain collaboration, the stability of task execution, and the overall adaptability of the system, and has high industrial application value and promotion prospects.

[0066] The basic data fusion module is the basis for building supply chain collaboration. In an optional embodiment, the basic data fusion module includes: an enterprise registration submodule, an enterprise collaboration network construction submodule, an enterprise data aggregation submodule, and a data sharing incentive submodule.

[0067] The enterprise registration submodule is used to support the independent registration or entrusted registration of complete machine enterprises and supplier enterprises in the system;

[0068] During specific implementation, the complete machine enterprise can first complete the enterprise registration and enter the enterprise product information in sequence; then, the complete machine enterprise can invite its cooperative supplier enterprises to complete the supplier's independent registration process. In addition, the complete machine enterprise can initiate a registration request on behalf of the supplier enterprise, that is, the supplier enterprise entrusts the complete machine enterprise to register to improve the efficiency of data initialization; after registration is completed, each enterprise needs to enter data such as product category, supplier module, key parameters, etc., and all data are uniformly connected through standardized interfaces.

[0069] An enterprise collaborative network construction submodule is used to automatically or manually construct an enterprise collaborative network;

[0070] During automatic construction, the enterprise collaboration network can be constructed based on factors such as the role of each enterprise, the frequency of cooperation between enterprises, and the level of collaboration. During specific implementation, the automatic construction is based on the role of each enterprise in the supply chain (such as main manufacturer, raw material supplier, parts manufacturer, etc.) and its historical collaboration records, to determine the strength of the collaborative relationship between enterprises and dynamically update the collaboration path. The cosine similarity algorithm can be used to calculate the similarity of the inherent attributes of enterprises (such as industry category, scale, technical capabilities, etc.) to infer the potential collaboration fit. Combined with the topological connection relationship of each enterprise in the supply chain network, the association strength of different relationship types is modeled by introducing a latent variable model, and the joint probability distribution of historical data is fitted to the maximum extent to calculate the comprehensive collaboration strength between enterprises.

[0071] The enterprise data aggregation submodule is used to collect structured and unstructured data from the operational data of registered enterprises and perform aggregation processing;

[0072] Operational data includes production capacity, price, delivery time, quality, etc. In addition, this submodule can collect operational data from the internal system or platform of each enterprise; the enterprise data aggregation submodule aggregates and processes structured data and unstructured data of multiple enterprises and types to achieve unified aggregation, structural transformation and node relationship mapping of supply chain data; in specific implementation, during the data aggregation process, data from different sources are first pre-processed by format standardization, cleaning, deduplication and missing filling; then, the comprehensive collaborative strength between enterprises and the results of enterprise group division obtained by the above calculations are used to map enterprise nodes and the various relationships between them into a computable graph data structure, forming the prototype of the supply chain collaborative knowledge map

[0073] The data sharing incentive submodule is used to encourage registered companies to upload and update operational data;

[0074] Registered companies are encouraged to upload and update operational data to improve overall data coverage and real-time performance. In specific implementation, a mechanism can be designed to encourage companies to upload and update operational data, and credit points or other rewards can be given to data providers to overcome the data silo problem in the traditional industrial chain. In addition, when registered companies upload updated data, the enterprise data aggregation submodule will immediately perform fusion updates to ensure that the global supply chain data in the data center remains up-to-date and complete, and create a positive incentive mechanism for data sharing between companies.

[0075] In an optional embodiment, the basic data fusion module further includes: an enterprise multi-source heterogeneous data perception and fusion sub-module and an enterprise multi-source heterogeneous data perception and fusion sub-module.

[0076] The enterprise multi-source heterogeneous data perception and fusion submodule is used to perceive and access the data sources of registered enterprises in real time, and extract and fuse the features of the data sources to obtain standardized enterprise features;

[0077] In specific implementation, the enterprise multi-source heterogeneous data perception and fusion sub-module can deploy the Transformer model to extract and fuse features of multi-source heterogeneous data; the self-attention mechanism in the Transformer model can consider the global information of the entire data sequence when processing each data, thereby capturing the complex dependencies between different data sources; through the self-attention layer, multimodal feature fusion of structured data and semi-structured data from various enterprises can be performed to obtain unified and information-rich enterprise features.

[0078] The enterprise-wide collaborative data fusion submodule is used to conduct in-depth integration and value mining of standardized enterprise characteristics to obtain multi-dimensional evaluation results of registered enterprises;

[0079] During specific implementation, the enterprise multi-source heterogeneous data perception and fusion sub-module can adopt a many-to-many group role assignment algorithm to build a multi-objective value mining model for the enterprise's multi-dimensional data; for example, with dimensions such as enterprise reputation, product quality, production cost, distribution efficiency, and service level as analysis targets, the perceived enterprise basic data is re-evaluated and integrated; indicators of different dimensions are weighed as multiple targets, and the data evaluation value of the enterprise under each target is obtained through iterative optimization, forming multi-dimensional evaluation results such as "enterprise product reputation data", "enterprise product quality data", "enterprise product cost data", "enterprise product distribution efficiency data" and "enterprise product service data". These evaluation results represent a quantitative characterization of the enterprise in different management aspects, and meet the managers' diverse needs for enterprise data in different scenarios.

[0080] Ultimately, the data generated by the basic data fusion module is uniformly stored in the data center, providing standardized, structured, and traceable basic data support for subsequent modules such as supply collaborative task distribution, conflict analysis, resource scheduling, and collaborative evaluation.

[0081] After the basic data fusion module completes enterprise information collection, collaborative network construction, and data fusion processing, the system enters the supply collaboration phase, focusing on intelligently distributing customized production tasks across enterprise groups and enabling efficient collaboration. In an optional embodiment, the supply task system module includes: a task reception and parsing submodule, an optimization decision submodule, a collaborative conflict analysis submodule, a task allocation submodule, a resource scheduling submodule, and a sequential decision submodule.

[0082] The task receiving and parsing submodule is used to receive task requests for non-standardized customized production, parse the task requests, and obtain task objectives, delivery conditions, and budget constraints;

[0083] During specific implementation, the task request information usually includes core constraints such as the product structure list, required production capacity and parts list, delivery cycle, quality requirements, and budget cap. After receiving the task request, the task reception and parsing submodule parses the task request to obtain the task objectives, delivery conditions, and budget constraints, and breaks down the subtasks involved and the corresponding resource requirements.

[0084] The optimization decision submodule is used to build a task allocation optimization model that includes multi-objective optimization functions and constraints, and obtain the enterprise task assignment plan based on the data processed by the basic data fusion module;

[0085] During specific implementation, the optimization decision submodule receives the content analyzed by the task reception and analysis submodule; based on the production capacity, cost, delivery cycle, credit rating, historical fulfillment rate and other data of enterprises in the supply chain, an optimization function is established to evaluate the adaptability of a certain enterprise or a combination of enterprises to the task; the optimization function covers multiple dimensions such as cost, time, quality, and risk, for example: the lowest total task cost, the shortest delivery cycle, the highest product quality index, and the lowest risk of cooperative breach of contract; because these goals often conflict with each other (such as the lowest cost may lead to extended delivery time), the optimization decision submodule can use weighted synthesis or multi-objective evolutionary algorithm to find a balanced solution for each goal.

[0086] The cooperation conflict analysis submodule is used to identify task conflicts, resource preemption, and cooperation failure risks among enterprises in the enterprise task assignment plan, and form constraint conditions;

[0087] During the solution process of the task allocation optimization model, the risk assessment data provided by the cooperation conflict analysis sub-module is simultaneously connected. During specific implementation, the cooperation conflict analysis sub-module can use the KB4 logic system to pre-calculate the potential cooperation conflict relationships between enterprises: by inputting multi-dimensional data such as the quality, cost, reputation, and delivery efficiency of the enterprises, logically infer which enterprise combinations are at risk of information asymmetry, historical disputes, or resource conflicts; and these conflict data are fed back into the task allocation optimization model as constraints for further optimization calculation, thereby avoiding assigning tasks to enterprise combinations that may have cooperation risks.

[0088] The task allocation submodule is used to split the enterprise task assignment plan into subtasks based on the granularity of non-standardized customized production tasks and enterprise capabilities, and then assign them;

[0089] After the task allocation optimization model outputs the optimization decision, it passes the overall task collaboration plan to the task allocation submodule; the task allocation submodule is responsible for breaking down the task into smaller parts according to the granularity of the task and the capabilities of the enterprise, and making specific assignments; if the received task contains multiple subtasks or requires collaboration among multiple enterprises, the task allocation submodule uses preset rules or algorithms to divide the total task into the smallest task units that can be completed by a single enterprise, and selects the most suitable execution enterprise for each subtask; in this process, a task assignment strategy that takes into account both fairness and efficiency can be introduced: by comparing the comprehensive efficiency indicators and current task loads of each candidate enterprise, the fairness index is calculated and the highest value is selected. The enterprise undertakes the current subtask; among them, the comprehensive benefit index is obtained by weighting factors such as the number of production lines, degree of equipment automation, monthly production capacity, and monthly production efficiency of the enterprise, reflecting the relative production capacity of the enterprise; the task load measures the ratio of the current number of tasks undertaken by the enterprise to its benefit index; the fairness index is defined as the enterprise's comprehensive benefit divided by the current task load. The higher the index, the more likely the enterprise is to have the spare capacity to fairly undertake new tasks while taking into account production capacity and existing load; the task allocation submodule executes the above enterprise selection process for each subtask, updates the enterprise's load immediately after selecting the enterprise, and then continues to select executors for the next subtask until all subtasks are allocated.

[0090] The resource scheduling submodule is used to coordinate the scheduling of human resources, production capacity, logistics and other resources across enterprises based on the resource types and quantities required for each subtask, as well as resource allocation;

[0091] After the task allocation optimization model outputs the optimization decision, the overall task collaboration plan is simultaneously delivered; during specific implementation, the resource scheduling submodule automatically matches and schedules resources among collaborative enterprises based on the resource types and quantities required for each subtask; first, the resource scheduling submodule obtains the task link information of each relevant enterprise and the resource supply and demand situation of the enterprise's current task; then, the resource supply and demand information sheets submitted by each enterprise are sorted, and resource requests that are urgent and have a high historical transaction success rate are given priority. The sorting adopts a multi-level feedback queue mechanism: resource requests are divided into levels according to task priority, with high-priority requests at the front; at the same time, the sorting is dynamically adjusted based on the success rate and response speed of each enterprise's past resource transactions; after the sorting is completed After completion, it enters the resource matching stage; the resource scheduling submodule will store the sorted supply orders and demand orders in the resource pool according to the resource type, and then match them pairwise according to the pre-defined matching rules; the matching algorithm comprehensively considers multiple dimensions: including whether the resource types are consistent, whether the supply and demand quantities are matched, whether the quotation overlaps with the expected price range, and the synergy of the supply and demand companies in the supply chain; through matching rules, it is ensured that the resource supply and demand pairs found not only meet the requirements in terms of quantity and category, but also that the transaction conditions are reasonable and the cooperation between the two companies is smooth, so that resources can be allocated to the required links in the best way at the right time; after the matching is completed, the relevant companies are notified to interact or trade resources, and the results are recorded.

[0092] The sequential decision-making submodule is used to continuously evaluate the current execution status during the execution of the enterprise capability-to-enterprise task assignment plan, and to fine-tune and optimize the subsequent sequence and resource allocation of unstarted and ongoing subtasks;

[0093] After task allocation and resource scheduling are completed, the sequential decision-making submodule is further called to dynamically optimize and adjust the entire task execution plan; since resource status changes, task priority adjustments, or external constraints may change during the production process, the original collaborative execution path may need to be adjusted accordingly; the sequential decision-making submodule continuously evaluates the current execution status through rolling optimization, and fine-tunes and optimizes the subsequent task sequence and resource allocation that have not yet started or are in progress; in particular, distributed reinforcement learning technology can be used to achieve the dynamic optimization of the above-mentioned sequential decisions: the main decision points in the supply collaboration process are modeled as sequential decision-making problems, and each collaborative enterprise or task node is an intelligent agent that learns the optimal strategy through repeated interactions with the environment; through this distributed reinforcement learning-driven sequential decision, the system gives the supply collaboration layer the ability to adaptively adjust, ensuring that the collaborative plan remains stable and efficient in a dynamic environment.

[0094] In addition, the supply task system module can be configured with a real-time feedback mechanism. When an enterprise encounters unexpected collaboration blockages (such as sudden production stoppages, logistics delays, cost fluctuations, etc.) during task execution, the exception handling process will be automatically triggered, and the exception handling and collaborative value assessment modules will be linked to perform intelligent diagnosis and task reconstruction to ensure system operation continuity and task delivery stability.

[0095] After the supply task system module completes task distribution and resource scheduling, it enters the task execution and evaluation phase, focusing on status tracking, exception handling, and quantitative analysis of collaborative effects throughout the entire task process. In an optional embodiment, the exception handling and collaborative value assessment module includes: a collaborative performance and value assessment submodule and a task execution monitoring and exception handling submodule.

[0096] The collaborative performance and value assessment submodule is used to comprehensively evaluate task execution effects, enterprise contribution, and benefit distribution, and generate a visual collaborative value map;

[0097] The task execution monitoring and exception handling submodule is used to continuously obtain status data during task execution and trigger task reconstruction and resource replacement strategies when an exception occurs.

[0098] In an optional embodiment, the synergy performance and value evaluation submodule includes: a synergy effect evaluation submodule, an enterprise benefit evaluation submodule, and an analysis result feedback submodule.

[0099] The collaborative effect evaluation submodule is used to build a multi-dimensional performance indicator system including task achievement, response timeliness, and collaboration efficiency, and conduct dynamic quantitative evaluation and trend analysis of the task execution process;

[0100] After the collaborative task begins, operational data from each executing enterprise is continuously collected. A multi-dimensional performance indicator system is established through the collaborative effect evaluation submodule to dynamically and quantitatively evaluate the task execution process. This performance indicator system covers aspects such as task achievement (such as on-time completion rate and delivery compliance rate), response timeliness (such as order response time and exception handling speed), and collaboration efficiency (such as the synchronization rate of multi-enterprise parallel operations and communication delay). Furthermore, to achieve a more fair and comprehensive evaluation, each participating enterprise is allowed to provide a fuzzy evaluation of the collaboration process, such as providing a fuzzy description of the smoothness of collaboration and the timeliness of information sharing. The collaborative effect evaluation submodule converts these collected fuzzy evaluations into a unified binary semantic representation, forming an evaluation matrix for each enterprise or task. Based on this, the collaborative effect evaluation submodule aggregates and analyzes the data from multiple evaluation dimensions, assigning different weights to the importance of each dimension and calculating the overall collaborative performance score for each participating enterprise. This score, a quantitative evaluation of the enterprise's overall performance in the collaboration, is not only provided to the enterprise itself but also serves as a reference for decision-makers, such as OEMs, to help them select partners or improve collaborative processes.

[0101] The enterprise revenue evaluation submodule is used to evaluate the input-output data of each enterprise, evaluate the revenue contribution of each enterprise, and construct a revenue distribution map of each enterprise in the entire supply chain;

[0102] Once the collaborative task is finally completed (whether completed normally on schedule or after exception handling), the collaborative value assessment phase begins, analyzing and quantifying the collaborative contributions and benefits of each participating enterprise. The enterprise benefit assessment submodule is responsible for this phase. It first obtains input-output data from each participating enterprise, including the human and material costs invested during the task, the workload completed, the performance indicators achieved, and the enterprise's operating status indicators (such as annual revenue, profit margin, and market share). The enterprise benefit assessment submodule then uses a pre-established TSK fuzzy inference model to assess each enterprise's benefit contribution. This model is established as follows: The alliance structure of participating enterprises is determined based on the task's specific circumstances. A pattern recognition algorithm is then applied to the benefit characteristics of each enterprise within the alliance to extract structured information that represents the enterprise's benefits. This extracted information is then interval-expanded based on the principle of reasonable granularity to obtain an interval value representation of each enterprise's benefit characteristics (given the uncertainty of benefits, using intervals rather than point values ​​is more robust). The benefit interval data for each enterprise within the alliance is then aggregated and integrated using fuzzy clustering to obtain a revenue distribution map for all enterprises within the entire supply chain.

[0103] The analysis result feedback submodule is used to transmit the evaluation results back to the basic data fusion module and the supply task collaboration module;

[0104] Based on this fused information, a TSK-type fuzzy reasoning model is established, and the historical revenue data of each enterprise is input to train and optimize the model parameters, ultimately forming a set of fuzzy reasoning rules that can be used for revenue evaluation and prediction. The core of this stage is to achieve the visualization, evaluability and traceability of task execution results, and truly build a closed-loop collaborative system with self-diagnosis, self-recovery and self-optimization capabilities, providing clear value feedback and behavioral incentives for upstream and downstream enterprises in the industrial chain.

[0105] If an abnormality is detected during task execution (e.g., a significant process delay, a key supplier's inability to fulfill a contract, or product quality consistently failing to meet standards), the task execution monitoring and exception handling submodule activates an emergency response mechanism. In an optional embodiment, the task execution monitoring and exception handling submodule includes: a status monitoring submodule, an anomaly identification submodule, and a task reconstruction and migration submodule.

[0106] The status monitoring submodule is used to continuously obtain status data during task execution, including progress, quality, and delivery status information, and dynamically update key indicators;

[0107] The anomaly identification submodule is used to identify whether there are abnormal events based on rule templates or historical models. Abnormal events include task delays, production interruptions, and resource failures;

[0108] The task reconstruction and migration submodule is used to dynamically generate alternative enterprise or collaboration paths and execute task migration strategies when abnormal events occur.

[0109] The task execution monitoring and exception handling submodule first conducts an intelligent diagnosis of the nature and scope of the current anomaly: identifying the enterprise or link with the problem, evaluating whether the remaining part of the task can be completed as originally planned, and determining whether it will cause disruption to the entire supply chain. Then, based on the diagnostic results and the enterprise capability model stored in the data center (including the spare resources available to each enterprise, production capacity redundancy, and their mutual assistance relationship in the collaborative network), the task execution monitoring and exception handling submodule enables a multi-mode task migration algorithm to generate a solution. This algorithm comprehensively considers the alliance and the status of the collaborative enterprise group in which the anomaly occurs, and reallocates the part of the abnormal task that cannot be completed by the original enterprise.

[0110] In an optional embodiment, a permission and security management module is further included, and the permission and security management module includes:

[0111] The role permission control submodule is used to assign function access rights and data operation permissions based on user types;

[0112] The data security encryption submodule is used to encrypt sensitive data during storage and transmission to prevent data leakage and tampering;

[0113] The operation audit log submodule is used to record various key data operation behaviors and realize the full-process traceable management of data addition, deletion and modification behaviors.

[0114] In an optional embodiment, the system adopts a B / S architecture design with a front-end interface and back-end services separated, wherein:

[0115] The front-end interface is built on the Vue3 framework, enabling visual interaction, supporting multi-terminal access and real-time data presentation. The platform supports both local and private cloud deployment modes, and can be connected to distributed databases and cache middleware.

[0116] The backend service is built based on the Spring Boot framework; it supports multi-device access and real-time data interaction.

[0117] In addition, the platform's underlying database supports local deployment and private cloud deployment, has horizontal expansion capabilities, and is compatible with distributed database structures to support high concurrency and large data volume scenarios.

[0118] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A supply chain module collaboration system for non-standardized customized production, characterized by: include: Basic data fusion module, used for enterprise registration, data entry, building enterprise collaborative networks, and sensing and fusion processing of multi-source heterogeneous data; The supply task collaboration module is used to receive task requests for non-standardized customized production and make optimized task decisions, resource scheduling, and intelligent allocation based on the data processed by the basic data fusion module; The exception handling and collaborative value assessment module is used for status tracking, exception response and quantitative analysis of collaborative effects throughout the entire task execution process. The data center is used to store the enterprise data, task data, execution process data and evaluation results generated by the above modules, and supports data interaction calls between all modules.

2. The supply chain module collaborative system for non-standardized customized production according to claim 1 is characterized in that: The basic data fusion module includes: The enterprise registration submodule is used to support the independent registration or entrusted registration of complete machine enterprises and supplier enterprises in the system; An enterprise collaborative network construction submodule is used to automatically or manually construct an enterprise collaborative network; The enterprise data aggregation submodule is used to collect structured and unstructured data from the operational data of registered enterprises and perform aggregation processing; The data sharing incentive submodule is used to encourage registered companies to upload and update operational data.

3. The supply chain module collaborative system for non-standardized customized production according to claim 2 is characterized in that: The basic data fusion module also includes: The enterprise multi-source heterogeneous data perception and fusion submodule is used to perceive and access the data sources of registered enterprises in real time, and extract and fuse the features of the data sources to obtain standardized enterprise features; The enterprise-wide collaborative data fusion sub-module is used to conduct in-depth integration value mining of standardized enterprise characteristics and obtain multi-dimensional evaluation results of registered enterprises.

4. The supply chain module collaborative system for non-standardized customized production according to claim 1 is characterized in that: The supply task system module includes: The task receiving and parsing submodule is used to receive task requests for non-standardized customized production, parse the task requests, and obtain task objectives, delivery conditions, and budget constraints; The optimization decision submodule is used to build a task allocation optimization model that includes multi-objective optimization functions and constraints, and obtain the enterprise task assignment plan based on the data processed by the basic data fusion module; The cooperation conflict analysis submodule is used to identify the task conflicts, resource preemption and assistance failure risks among enterprises in the enterprise task assignment plan and form constraint conditions. The task allocation submodule is used to split the enterprise task assignment plan into subtasks based on the granularity of non-standardized customized production tasks and enterprise capabilities, and then assign them; The resource scheduling submodule is used to coordinate the scheduling of human resources, production capacity, logistics and other resources across enterprises based on the resource types and quantities required for each subtask, as well as resource allocation; The sequential decision-making submodule is used to continuously evaluate the current execution status during the execution of the enterprise capability-to-enterprise task assignment plan, and to fine-tune and optimize the subsequent sequence and resource allocation of subtasks that have not yet started and are in progress.

5. The supply chain module collaborative system for non-standardized customized production according to claim 1 is characterized in that: The exception handling and collaborative value assessment module includes: The collaborative performance and value assessment submodule is used to comprehensively evaluate task execution effects, enterprise contribution, and benefit distribution, and generate a visual collaborative value map; The task execution monitoring and exception handling submodule is used to continuously obtain status data during task execution and trigger task reconstruction and resource replacement strategies when an exception occurs.

6. The supply chain module collaborative system for non-standardized customized production according to claim 5 is characterized in that: The collaborative performance and value assessment submodule includes: The collaborative effect evaluation submodule is used to build a multi-dimensional performance indicator system including task achievement, response timeliness, and collaboration efficiency, and conduct dynamic quantitative evaluation and trend analysis of the task execution process; The enterprise revenue evaluation submodule is used to evaluate the input-output data of each enterprise, evaluate the revenue contribution of each enterprise, and construct a revenue distribution map of each enterprise in the entire supply chain; The analysis result feedback submodule is used to transmit the evaluation results back to the basic data fusion module and the supply task collaboration module.

7. The supply chain module collaborative system for non-standardized customized production according to claim 5 is characterized in that: The task execution monitoring and exception handling submodule includes: The status monitoring submodule is used to continuously obtain status data during task execution, including progress, quality, and delivery status information, and dynamically update key indicators; The anomaly identification submodule is used to identify whether there are abnormal events based on rule templates or historical models. Abnormal events include task delays, production interruptions, and resource failures; The task reconstruction and migration submodule is used to dynamically generate alternative enterprise or collaboration paths and execute task migration strategies when abnormal events occur.

8. The supply chain module collaborative system for non-standardized customized production according to claim 1 is characterized in that: It also includes a permissions and security management module, which includes: The role permission control submodule is used to assign function access rights and data operation permissions based on user types; The data security encryption submodule is used to encrypt sensitive data during storage and transmission to prevent data leakage and tampering; The operation audit log submodule is used to record various key data operation behaviors and realize the full-process traceable management of data addition, deletion and modification behaviors.

9. A supply chain module collaborative system for non-standardized customized production according to any one of claims 1 to 8, characterized in that: The system adopts a B / S architecture design with a front-end interface and back-end services separated, where: The front-end interface is built based on the Vue3 framework; The backend service is built based on the Spring Boot framework.

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