CN-121981580-A - Supply chain intelligent management and control system and method based on multi-source data fusion

CN121981580ACN 121981580 ACN121981580 ACN 121981580ACN-121981580-A

Abstract

The invention relates to the technical field of supply chain management and control, in particular to a supply chain intelligent management and control system and method based on multi-source data fusion, wherein the system comprises a data acquisition module, a data fusion processing module, a digital twin deduction module, a multi-target decision module and a strategy execution and feedback module, and the method completes corresponding management and control steps according to the system; the method comprises the steps of firstly collecting all-chain multi-source heterogeneous data of a supply chain, generating unified multi-dimensional state data through space-time alignment and multi-mode fusion, constructing a digital twin model to develop a multi-scenario deduction output candidate strategy, screening out a comprehensive optimal strategy through multi-objective decision, and finally analyzing and issuing the strategy, collecting and executing effect feedback to form closed-loop iterative optimization. The invention effectively solves the problems of island management and control data, subjective decision, no deduction system and lack of closed loop optimization of the traditional supply chain, improves the level of intellectualization and automation of management and control, enhances the toughness of the supply chain, optimizes the operation efficiency, reduces the operation cost and realizes the cooperative management and control of the whole chain.

Inventors

LI XINHUA
YE SONGSHOU
LIN JUNJIE
HUANG WEI

Assignees

福建中禾新材料有限公司
福州恒捷智控科技有限公司

Dates

Publication Date: 20260505
Application Date: 20260407

Claims (9)

1. A supply chain intelligent management and control system based on multi-source data fusion is characterized by comprising: the data acquisition module acquires multi-source heterogeneous data covering the whole chain of the supply chain in real time, wherein the multi-source heterogeneous data comprises internal service data, external supply chain node data, logistics dynamic data and market environment data; The data fusion processing module is connected with the data acquisition module, performs space-time alignment processing on the multi-source heterogeneous data, eliminates time delay and space coordinate differences among different data sources, extracts deep association features reflecting the running state of a supply chain through a pre-trained multi-mode fusion neural network and a cross attention mechanism, and generates supply chain multidimensional state data under a unified space-time reference; The digital twin deduction module is connected with the data fusion processing module, constructs a digital twin model dynamically mapped with the physical supply chain entity based on the supply chain multidimensional state data, and drives the digital twin model to carry out multi-scenario simulation deduction in the virtual space when a preset abnormal event is detected or a deduction instruction is received, and outputs a plurality of candidate regulation strategies and corresponding prediction effect indexes; The multi-objective decision module is connected with the digital twin deduction module, and is used for comprehensively evaluating and sequencing a plurality of candidate regulation strategies based on a preset multi-objective optimization function, taking the supply chain toughness as a core optimization target, considering the efficiency and the cost, and screening out a cooperative regulation strategy meeting the comprehensive optimization of the supply chain toughness, the efficiency and the cost; The strategy execution and feedback module is connected with the multi-objective decision module, analyzes the collaborative management and control strategy into atomic operation instructions for different supply chain execution systems, issues and executes the atomic operation instructions, simultaneously acquires actual effect data after strategy execution in real time, and feeds the actual effect data back to the data acquisition module so as to update the supply chain multidimensional state data to form closed loop iterative optimization of supply chain management and control.
2. The supply chain intelligent management and control system based on multi-source data fusion of claim 1, wherein the data acquisition module comprises an internal business data acquisition unit, an external supply chain node data acquisition unit, a logistics dynamic data acquisition unit and a market environment data acquisition unit, wherein: The internal business data acquisition unit acquires order data, inventory data and production scheduling data at a minute-level frequency through application programming interfaces of the enterprise resource planning system and the warehouse management system; The external supply chain node data acquisition unit acquires raw material inventory, production state and point-of-sale data of distributors in real time through a supplier collaborative platform interface and an Internet of things sensor network; The logistics dynamic data acquisition unit acquires the position coordinates, the cargo state and the expected arrival time of the transport vehicle at the second-level frequency through a vehicle-mounted terminal integrating the global positioning system device, the radio frequency identification reader-writer and the temperature and humidity sensor; The market environment data acquisition unit regularly acquires port congestion index, fuel price change, weather early warning information and industry policy dynamic through a web crawler technology and a third party data service interface; The data acquisition module also carries out time stamp calibration and outlier filtering on the acquired original data, generates preliminary cleaning data with uniform time identification, and sends the preliminary cleaning data to the data fusion processing module.
3. The intelligent supply chain management and control system based on multi-source data fusion of claim 1, wherein the data fusion processing module performs space-time alignment processing on multi-source heterogeneous data, eliminates time delay and space coordinate differences between different data sources, extracts deep association features reflecting the running state of the supply chain by combining a cross attention mechanism through a pre-trained multi-mode fusion neural network, and generates supply chain multidimensional state data under a unified space-time reference, and the system specifically comprises: The data fusion processing module receives the preliminary cleaning data with the unified time mark sent by the data acquisition module, and for the data acquisition frequency difference and network transmission delay of different data sources, the asynchronously-arrived multi-source heterogeneous data are synchronized to a unified time section by adopting a time registration algorithm based on event triggering, and meanwhile, the logistics coordinate data under different space reference systems are unified to a preset geographic coordinate system through a coordinate conversion model, so that a time-synchronous space-time unified data set with consistent space references is generated; the data fusion processing module invokes a pre-trained multi-mode fusion neural network, maps structured data, semi-structured data and unstructured data in a space-time unified data set to the same feature space, and extracts deep association feature vectors capable of representing the overall operation situation of a supply chain by mining implicit association and coupling rules among different mode data through a cross attention mechanism; The data fusion processing module is used for splicing and reconstructing the extracted deep association feature vector and the basic state data subjected to space-time alignment processing to form a supply chain multidimensional state data matrix containing the supply chain full-element operation parameters, the dynamic association relation and the derived statistical indexes, and sending the supply chain multidimensional state data matrix to the digital twin deduction module.
4. The intelligent supply chain management and control system based on multi-source data fusion of claim 1, wherein the digital twin deduction module constructs a digital twin model dynamically mapped with a physical supply chain entity based on the multi-dimensional state data of the supply chain, and drives the digital twin model to perform multi-scenario analog deduction in a virtual space when a preset abnormal event is detected or a deduction instruction is received, and outputs a plurality of candidate regulation and control strategies and corresponding prediction effect indexes thereof, and the intelligent supply chain management and control system specifically comprises: the digital twin deduction module receives the supply chain multidimensional state data matrix sent by the data fusion processing module, constructs a static geometry and a dynamic parameterization model of the digital twin model based on the whole element operation parameters of the supply chain, establishes causal logic links between different supply chain nodes and links through dynamic association relations, takes derived statistical indexes as boundary constraint conditions of model operation, and generates the digital twin model which keeps dynamic consistency with a physical supply chain entity in structure, behavior and rules; The digital twin deduction module is internally provided with an anomaly monitoring engine, compares the multidimensional state data of the supply chain with the theoretical state data simulated and output by the digital twin model in real time, automatically judges that a preset anomaly event is detected when the deviation exceeds a preset threshold value, or triggers a multi-scenario simulation deduction process when receiving an externally input manual deduction instruction, invokes an initial disturbance parameter and a candidate regulation and control action set from a strategy knowledge base according to the anomaly event type or a deduction target, loads a plurality of groups of different regulation and control action sequences on the digital twin model in parallel, and drives the digital twin model to accelerate and simulate the supply chain operation evolution process under different scenarios in a virtual space; After each group of simulation deductions is finished, the digital twin deduction module automatically collects key performance index change tracks output by the digital twin models, including but not limited to order delivery time, inventory turnover rate, logistics transportation cost and customer satisfaction, carries out statistics summarization on index performances of the same regulation and control action sequence under different scenes, generates a multi-dimensional prediction effect index set uniquely corresponding to each candidate regulation and control strategy, and sends all the candidate regulation and control strategies and the multi-dimensional prediction effect index sets thereof to the multi-objective decision module after being associated and packaged.
5. The multi-source data fusion based supply chain intelligent management and control system of claim 4, wherein the digital twin model comprises: Entity definition sub-model, mapping suppliers, manufacturers, warehouses, distribution centers and terminal clients in a physical supply chain into virtual entity objects in a digital twin space based on all-element operation parameters in a multi-dimensional state data matrix of the supply chain, and endowing each virtual entity object with static attribute labels comprising geographic positions, productivity, inventory capacity and operation efficiency and dynamic attribute parameters comprising real-time inventory quantity, in-transit order quantity and equipment operation state; The behavior logic sub-model is respectively connected with the entity definition sub-model and the data fusion processing module, analyzes business interaction rules and material flow logic among different virtual entity objects according to the dynamic association relation, constructs a behavior rule base covering the whole flows of purchasing, producing, warehousing, transporting and selling, and carries out real-time calibration on parameters in the behavior rule base based on derived statistical indexes so as to ensure that the behavior output of the virtual entity is consistent with the actual performance of the physical entity; The disturbance injection submodel is connected with the behavior logic submodel, when a multi-scenario simulation deduction instruction is received, a preset abnormal event script is activated in the behavior logic submodel or a candidate regulation and control action set is loaded according to initial disturbance parameters, and a risk event occurrence process including supply interruption, transportation delay and abrupt change of requirements of a supplier and a supply chain dynamic response process under the action of different regulation and control strategies are simulated by modifying parameters in a behavior rule base or temporarily changing causal logic links between virtual entity objects; The deduction engine submodel is respectively connected with the behavior logic submodel and the multi-target decision module, drives the behavior logic submodel loaded with disturbance parameters to run at a speed which is higher than the flow speed of the physical world time, records the change track of the state parameters of each virtual entity object in the deduction process in real time, extracts a multi-dimensional prediction effect index set corresponding to the candidate regulation strategy from the change track according to a preset statistical rule when the deduction is ended, binds the multi-dimensional prediction effect index set with the corresponding candidate regulation strategy identifier, and outputs the multi-dimensional prediction effect index set to the multi-target decision module.
6. The intelligent supply chain management and control system based on multi-source data fusion of claim 1, wherein the multi-objective decision module comprehensively evaluates and sorts a plurality of candidate regulation and control strategies based on a preset multi-objective optimization function, takes supply chain toughness as a core optimization target, considers efficiency and cost, and screens out a cooperative management and control strategy meeting comprehensive optimization of supply chain toughness, efficiency and cost, and specifically comprises the following steps: The multi-objective decision module receives a plurality of candidate regulation strategies and a multi-dimensional prediction effect index set which are respectively related to the candidate regulation strategies and the multi-dimensional prediction effect index set and are sent by the digital twin deduction module, wherein the multi-dimensional prediction effect index set comprises prediction effect data which at least reflects three dimensions of supply chain toughness, efficiency and cost; The multi-objective decision module normalizes each index in the multi-dimensional prediction effect index set, eliminates the dimension difference among different indexes, converts all the indexes into benefit indexes in the same direction, and generates a normalized decision matrix; The multi-objective decision module performs weighted transformation on the normalized decision matrix according to a preset toughness weight coefficient, an efficiency weight coefficient and a cost weight coefficient to construct a weighted normalized decision matrix; The multi-objective decision module calculates Euclidean distance between each candidate regulation strategy in the weighted normalized decision matrix and a positive ideal solution and a negative ideal solution by adopting an approximation ideal solution sorting method, and determines the relative closeness of each candidate regulation strategy based on the Euclidean distance, wherein the positive ideal solution is composed of optimal values of all indexes, and the negative ideal solution is composed of the worst values of all indexes; the multi-objective decision module sorts all candidate regulation strategies according to the sequence of the relative closeness from large to small, selects the candidate regulation strategy with the maximum relative closeness as a cooperative regulation strategy which meets the comprehensive optimization of the toughness, the efficiency and the cost of a supply chain, and sends the cooperative regulation strategy and a corresponding multidimensional prediction effect index set to the strategy execution and feedback module.
7. The supply chain intelligent management and control system based on multi-source data fusion of claim 1, wherein the policy enforcement and feedback module comprises a policy parsing unit and an instruction issuing unit, wherein: The strategy analysis unit receives a collaborative management and control strategy and a corresponding multidimensional prediction effect index set thereof, which are sent by the multi-objective decision module, identifies the category to which the collaborative management and control strategy belongs according to a preset strategy type classification rule, calls a corresponding instruction analysis template from a preset instruction template library based on the strategy category, and decomposes the collaborative management and control strategy into atomic operation instructions capable of being directly executed layer by layer, wherein each atomic operation instruction carries a target execution system identifier, an operation object identifier, an operation parameter and an expected execution time window; The strategy analysis unit also generates an instruction execution topological graph containing instruction execution sequence and parallel conditions according to the logic dependency relationship and time sequence constraint among the atomic operation instructions, the instruction issuing unit receives the instruction execution topological graph, converts the atomic operation instructions into data messages conforming to private protocols of each execution system by adapting standardized interface adapters of different supply chain execution systems according to the sequence and parallel conditions determined by the topological graph, issues the instructions in parallel or in series to a warehouse management system, a transportation management system, a purchasing system and a production execution system respectively, monitors the instruction issuing state in real time, issues the instructions which fail to issue again according to a preset retry strategy, and generates alarm information to be pushed to an operation and maintenance terminal when the retry times exceed a threshold value.
8. The intelligent supply chain management and control system based on multi-source data fusion as recited in claim 7, wherein said policy enforcement and feedback module further comprises an effect acquisition unit and a closed loop optimization unit: The method comprises the steps that after an atomic operation instruction is issued and executed, actual execution data are acquired in real time through feedback interfaces of each supply chain execution system according to a preset sampling period, the actual execution data comprise atomic operation instruction arrival time, atomic operation instruction completion time, operation execution result states, updated inventory quantity, on-road quantity and equipment operation parameters after execution, and the actual execution data are compared with corresponding predicted values in a multi-dimensional prediction effect index set corresponding to a collaborative management and control strategy item by item, so that an execution deviation vector is calculated; The closed loop optimization unit packages actual execution data, an execution deviation vector and a current timestamp to generate feedback data, the feedback data is sent to the data acquisition module through the data feedback interface, the data acquisition module inputs the feedback data serving as a newly added multi-source heterogeneous data source to the data fusion processing module after timestamp calibration and outlier filtration, the data fusion processing module is triggered to update the supply chain multidimensional state data based on the feedback data, the updated supply chain multidimensional state data is sent to the digital twin deduction module, and the digital twin deduction module recalibrates model parameters and carries out next deduction according to the latest state after actual execution, so that closed loop iterative optimization of the supply chain control strategy based on the atomic operation instruction execution effect is realized.
9. A supply chain intelligent management method based on multi-source data fusion, which is executed by the system of any one of claims 1-8, characterized in that the method comprises the following steps: Step S1, multi-source heterogeneous data covering the whole chain of a supply chain are collected in real time, wherein the multi-source heterogeneous data comprise internal service data, external supply chain node data, logistics dynamic data and market environment data; s2, carrying out space-time alignment processing on the multi-source heterogeneous data acquired in the step S1, eliminating time delay and space coordinate differences among different data sources, extracting deep association features reflecting the running state of a supply chain through a pre-trained multi-mode fusion neural network in combination with a cross attention mechanism, and generating supply chain multi-dimensional state data under a unified space-time reference; step S3, constructing a digital twin model dynamically mapped with the physical supply chain entity based on the supply chain multidimensional state data generated in the step S2, and driving the digital twin model to carry out multi-scenario simulation deduction in a virtual space when a preset abnormal event is detected or a deduction instruction is received, so as to output a plurality of candidate regulation strategies and corresponding prediction effect indexes; S4, comprehensively evaluating and sequencing the plurality of candidate regulation strategies output in the step S3 based on a preset multi-objective optimization function, taking the supply chain toughness as a core optimization target, considering efficiency and cost, and screening out a cooperative regulation strategy meeting comprehensive optimization of the supply chain toughness, the efficiency and the cost; And S5, analyzing the collaborative management and control strategy screened in the step S4 into specific operation instructions aiming at different supply chain execution systems, issuing and executing the specific operation instructions, simultaneously collecting actual effect data after strategy execution in real time, and feeding the actual effect data back to the step S1 so as to update the multidimensional state data of the supply chain, thereby forming closed loop iterative optimization of supply chain management and control.

Description

Supply chain intelligent management and control system and method based on multi-source data fusion Technical Field The invention relates to the technical field of supply chain management and control, in particular to a supply chain intelligent management and control system and method based on multi-source data fusion. Background In the current supply chain management and control field, the traditional management and control mode has a plurality of technical pain points. The data sources of the whole chain of the supply chain are distributed, the multi-source heterogeneous data are difficult to integrate effectively, the problem of data island is outstanding, and the deep association characteristics between the data cannot be mined due to the fact that time delay and space coordinate difference exist among different data sources, so that the management and control decision lacks comprehensive and accurate data source support. Meanwhile, a virtual deduction system dynamically mapped with a physical supply chain is lacking, running risks such as supply interruption, transportation delay and the like of a provider are difficult to predict in advance, a management and control strategy is formulated to be dependent on off-line trial-and-error and manual experience, the efficiency is low, the cost is high, and the response capability of the system to an emergency is insufficient. The multi-objective decision process lacks a standardized quantitative analysis method, so that three core dimensions of toughness, efficiency and cost of a supply chain are difficult to consider, decision results are easily influenced by subjective factors, and a screening strategy is difficult to adapt to comprehensive operation requirements of the supply chain. In addition, the control strategy execution and effect feedback links are disjointed, a complete closed-loop optimization system is not formed, actual effect data after strategy execution cannot be returned and analyzed in time, the model and the strategy cannot be dynamically adjusted according to the execution deviation, and the suitability and the effectiveness of control are greatly reduced. The execution systems of all links of the supply chain independently operate, the communication protocols are incompatible, the cooperative operation capability is weak, the resource allocation is unreasonable, and the operation efficiency of the whole chain is low, so that the operation cost is high. The whole control flow is intelligent, low in automation degree and many in manual intervention links, so that the management cost is increased, and operation errors are easy to generate, so that the whole toughness and dynamic adaptability of the supply chain are difficult to meet the complex change requirements of the market environment. Therefore, a supply chain intelligent management and control system and method based on multi-source data fusion are provided for the problems. Disclosure of Invention The invention aims to provide a supply chain intelligent management and control system and method based on multi-source data fusion, so as to solve the problems in the background technology. In order to achieve the above purpose, the present invention provides the following technical solutions: A supply chain intelligent management and control system based on multi-source data fusion, comprising: the data acquisition module acquires multi-source heterogeneous data covering the whole chain of the supply chain in real time, wherein the multi-source heterogeneous data comprises internal service data, external supply chain node data, logistics dynamic data and market environment data; The data fusion processing module is connected with the data acquisition module, performs space-time alignment processing on the multi-source heterogeneous data, eliminates time delay and space coordinate differences among different data sources, extracts deep association features reflecting the running state of a supply chain through a pre-trained multi-mode fusion neural network and a cross attention mechanism, and generates supply chain multidimensional state data under a unified space-time reference; The digital twin deduction module is connected with the data fusion processing module, constructs a digital twin model dynamically mapped with the physical supply chain entity based on the supply chain multidimensional state data, and drives the digital twin model to carry out multi-scenario simulation deduction in the virtual space when a preset abnormal event is detected or a deduction instruction is received, and outputs a plurality of candidate regulation strategies and corresponding prediction effect indexes; The multi-objective decision module is connected with the digital twin deduction module, and is used for comprehensively evaluating and sequencing a plurality of candidate regulation strategies based on a preset multi-objective optimization function, taking the supply chain toughness as a core optimizat