Search

CN-122022404-A - Industrial collaboration-oriented digital economic resource optimal allocation system and method

CN122022404ACN 122022404 ACN122022404 ACN 122022404ACN-122022404-A

Abstract

The invention provides an industrial collaboration-oriented digital economic resource optimal allocation system and method, which relate to the technical field of digital economic resource allocation, the invention adopts three-dimensional tensor operation, fuses real-time supply and demand and pre-calculated industrial multi-dimensional complementary relation, realizes the refinement and global quantification of the combined collaborative value of a supplier, a demander and a resource type, automatically outputs an optimal matching list which maximizes the overall collaborative value by constructing a binary integer programming model for solving, and finally, through model detection verification, the output scheme is fundamentally ensured to have no logic deadlock and meet all time sequence constraints, so that a high-quality and trusted resource allocation scheme is delivered.

Inventors

  • Xue Huanxia

Assignees

  • 安康学院

Dates

Publication Date
20260512
Application Date
20260413

Claims (10)

  1. 1. An industrial collaboration oriented digital economic resource optimization configuration method is characterized by comprising the following steps: s1, generating a standardized real-time supply matrix and a real-time demand matrix based on real-time business data of each industry; S2, performing tensor operation on the real-time supply matrix, the real-time demand matrix and the pre-constructed inter-industry resource complementation coefficient tensor to obtain a three-dimensional potential synergistic value tensor; S3, constructing a binary integer programming model based on the real-time supply matrix, the three-dimensional potential collaborative value tensor, the productivity upper limit vector and the reputation sub-threshold, and solving to obtain a collaborative matching pair list; S4, decomposing the collaborative matching pair list into a plurality of execution step nodes with time sequence dependency relationships based on a pre-constructed business collaboration flow library, and constructing an execution step dependency graph; S5, calculating the buffer time of each execution step node in the execution step dependency graph based on the time-consuming record of the historical execution step and a preset risk buffer coefficient, and generating an elastic scheduling plan through topological sequencing; And S6, compiling the flexible scheduling plan into a verifiable scheduling time sequence model, verifying through a model detection tool, and outputting a final resource allocation scheme after verification.
  2. 2. The method for optimizing configuration of industrial collaborative digital economic resources according to claim 1, wherein in step S1, generating a standardized real-time supply matrix and a standardized real-time demand matrix includes: Constructing a supply matrix frame and a demand matrix frame with blank initial values based on a predefined global industry set and a standard resource type set, wherein rows of the matrix frame correspond to industries and columns correspond to resource types; Collecting real-time business data of each industry, wherein the real-time business data comprises an industry identifier, a resource type identifier, a quantity, a measurement unit and a business type identifier; Cleaning the real-time service data, and uniformly mapping the resource type identifier and the measurement unit into a standard resource type identifier and a standard measurement unit to obtain a standardized data record; classifying the standardized data records into a supply data set and a demand data set based on the service type identification in the standardized data records, respectively; and filling the supply data set and the demand data set into a supply matrix frame and a demand matrix frame according to two dimensions of an industry identifier and a standard resource type identifier respectively to generate a real-time supply matrix and a real-time demand matrix.
  3. 3. The method for optimizing and configuring industrial-oriented collaborative digital economic resources according to claim 2, wherein in step S2, the constructing step of the pre-constructed inter-industrial resource complementary coefficient tensor includes: Constructing a three-dimensional inter-industry resource complementation coefficient tensor framework with an initial value of empty based on a predefined global industry set and a standard resource type set, wherein three dimensions respectively correspond to a supplier industry, a demander industry and a standard resource type; based on a historical industry collaborative transaction record library, calculating historical transaction success rate as a historical collaborative coefficient aiming at supplier industry, a demander industry and standard resource types corresponding to each element position in a three-dimensional inter-industry resource complementation coefficient tensor frame; Calculating the matching degree of technical parameters as a resource adaptation coefficient according to the supplier industry, the demander industry and the standard resource type corresponding to each element position in the resource complementation coefficient tensor frame between three-dimensional industries based on a resource attribute knowledge base; Calculating the association strength of an industry chain as an industry association coefficient for the industry of a supplier and the industry of a requester corresponding to each element position in the resource complementation coefficient tensor frame between three-dimensional industries based on the industry classification map; For each element position in the three-dimensional inter-industry resource complementation coefficient tensor frame, the corresponding historical synergetic coefficient, the resource adaptation coefficient and the industry association coefficient are fused and calculated according to preset weights, and the position is filled in to generate the inter-industry resource complementation coefficient tensor.
  4. 4. The method for optimizing and configuring industrial-collaboration-oriented digital economic resources according to claim 3, wherein in step S2, the step of obtaining the three-dimensional potential collaborative value tensor includes: Carrying out outer product operation on each column vector of the real-time supply matrix and the corresponding column vector of the real-time demand matrix to obtain a three-dimensional intermediate tensor; And multiplying the three-dimensional intermediate tensor by a pre-constructed inter-industry resource complementary coefficient tensor element by element, and taking the tensor obtained after element-by-element multiplication as a three-dimensional potential synergistic value tensor.
  5. 5. The method for optimizing and configuring industrial-collaboration-oriented digital economic resources according to claim 4, wherein in step S3, the step of obtaining the collaboration matching pair list includes: Defining a combination of a supplier industry, a demander industry and a standard resource type corresponding to each element in the three-dimensional potential collaborative value tensor as a binary decision variable, wherein a binary decision variable value of 1 indicates that the combination is selected to establish a collaborative relationship, and a value of 0 indicates that the combination is not selected; taking the sum of products of the maximum all binary decision variables and corresponding element values in the three-dimensional potential synergistic value tensor as an optimization target; Establishing capacity constraint conditions, namely, for each supplier industry and each standard resource type, all decision variables for selecting the supplier industry to provide the standard resource type, wherein the sum of the resource supply quantity in the associated real-time supply matrix cannot exceed the upper limit value specified for the industry and the standard resource type in the capacity upper limit vector; Establishing a reputation constraint condition, and allowing a decision variable taking the reputation constraint condition as a demand side to be set to 1 when the reputation score of the demand side industry is not lower than a reputation score threshold value; and solving a binary integer programming model consisting of an optimization target, a capacity constraint condition and a reputation constraint condition, and outputting the industry and resource combination corresponding to the decision variable with the value of 1 in the solving result to generate a collaborative matching pair list.
  6. 6. The method for optimizing and configuring digital economic resources for industry collaboration according to claim 5, wherein in step S4, standard task flow templates under different industry collaboration scenarios are stored in a business collaboration flow library, and each template defines a standard execution step sequence required for completing one collaboration and a logical dependency relationship among steps.
  7. 7. The method for optimizing and configuring industrial-collaboration-oriented digital economic resources according to claim 6, wherein in step S4, constructing the execution step dependency graph includes: According to the industry type and resource type related to the matching pair in the collaborative matching pair list, inquiring a standard task flow template matched in a business collaborative flow library, instantiating the template, taking the step after the instantiation as a node, and constructing an execution step dependency graph according to the dependency relationship connection in the template.
  8. 8. The method for optimizing configuration of industrial collaborative digital economic resources according to claim 7, wherein in step S5, calculating the buffering time of each execution step node in the execution step dependency graph includes: And obtaining the actual time consumption of all the historical steps with the same type as the node from the time consumption record of the historical execution step, calculating the standard deviation of the time consumption, and multiplying the standard deviation by a preset risk buffer coefficient to obtain the buffer time of the node.
  9. 9. The method for optimizing configuration of industrial collaborative digital economic resources according to claim 8, wherein in step S6, the verification by the model detection tool includes: The validation can verify whether the scheduling timing model satisfies two formalized protocol attributes that are free of deadlocks and that are satisfied by the time window constraints of all steps.
  10. 10. An industrial collaboration oriented digital economic resource optimal configuration system, characterized in that the system is applied to an industrial collaboration oriented digital economic resource optimal configuration method as claimed in any one of claims 1 to 9, and the configuration system comprises: The data standardization module is used for generating a standardized real-time supply matrix and a standardized real-time demand matrix based on real-time business data of each industry; the value quantization module is used for carrying out tensor operation on the real-time supply matrix, the real-time demand matrix and the pre-constructed inter-industry resource complementation coefficient tensor to obtain a three-dimensional potential cooperative value tensor; the matching optimization module is used for constructing a binary integer programming model and solving to obtain a collaborative matching pair list based on the real-time supply matrix, the three-dimensional potential collaborative value tensor, the productivity upper limit vector and the reputation sub-threshold; The task decomposition module is used for decomposing the collaborative matching pair list into a plurality of execution step nodes with time sequence dependency relationships based on a pre-constructed business collaboration flow library, and constructing an execution step dependency graph; The elastic scheduling module is used for calculating the buffer time of each execution step node in the execution step dependency graph based on the time consumption record of the historical execution steps and the preset risk buffer coefficient, and generating an elastic scheduling plan through topological sequencing; And the scheme verification module is used for compiling the elastic scheduling plan into a verifiable scheduling time sequence model, verifying through a model detection tool, and outputting a final resource allocation scheme after verification.

Description

Industrial collaboration-oriented digital economic resource optimal allocation system and method Technical Field The invention relates to the technical field of digital economic resource allocation, in particular to an industrial collaboration-oriented digital economic resource optimal allocation system and method. Background In the context of digital economy, industry collaboration is increasingly frequent, and how to efficiently allocate decentralized digital economic resources, such as computing power, data, software services, etc., to improve overall industry chain performance is a key challenge. The current common resource allocation method focuses on static or pairwise simple supply and demand matching, and lacks global dynamic quantization and optimization of complex synergistic relationship between multiple industries and multiple resource types. In the prior art, massive and heterogeneous industrial supply and demand data are difficult to fuse in real time, and multi-dimensional factors such as historical collaborative reputation, resource technical attribute matching degree, inter-industry association strength and the like cannot be deeply fused into a computable collaborative potential model. Meanwhile, when a configuration scheme is generated, the conventional method is used for matching and executing scheduling and splitting, the time sequence dependence logic conflict risk in the task execution process and the uncertainty caused by the historical execution volatility are not fully considered, so that the formulated plan is poor in executable performance and lacks in elasticity, and the maximization of the cross-industry cooperative value is difficult to achieve on the premise of meeting the multi-party capacity and reputation constraint. Therefore, a resource allocation method capable of realizing full-chain closed loop from real-time supply and demand sensing, global value quantization, intelligent matching optimization to flexible scheduling and formal verification is needed to solve the above problems. Therefore, it is necessary to provide a system and a method for optimizing and configuring digital economic resources for industry collaboration to solve the above technical problems. Disclosure of Invention In order to solve the technical problems, the invention provides an industrial collaboration-oriented digital economic resource optimal allocation system and method, which achieve the beneficial effect of intelligently and efficiently allocating digital economic resources. The invention provides an industrial collaboration oriented digital economic resource optimization configuration method, which comprises the following steps: s1, generating a standardized real-time supply matrix and a real-time demand matrix based on real-time business data of each industry; S2, performing tensor operation on the real-time supply matrix, the real-time demand matrix and the pre-constructed inter-industry resource complementation coefficient tensor to obtain a three-dimensional potential synergistic value tensor; S3, constructing a binary integer programming model based on the real-time supply matrix, the three-dimensional potential collaborative value tensor, the productivity upper limit vector and the reputation sub-threshold, and solving to obtain a collaborative matching pair list; S4, decomposing the collaborative matching pair list into a plurality of execution step nodes with time sequence dependency relationships based on a pre-constructed business collaboration flow library, and constructing an execution step dependency graph; S5, calculating the buffer time of each execution step node in the execution step dependency graph based on the time-consuming record of the historical execution step and a preset risk buffer coefficient, and generating an elastic scheduling plan through topological sequencing; And S6, compiling the flexible scheduling plan into a verifiable scheduling time sequence model, verifying through a model detection tool, and outputting a final resource allocation scheme after verification. Preferably, in step S1, generating the normalized real-time supply matrix and the real-time demand matrix includes: Constructing a supply matrix frame and a demand matrix frame with blank initial values based on a predefined global industry set and a standard resource type set, wherein rows of the matrix frame correspond to industries and columns correspond to resource types; Collecting real-time business data of each industry, wherein the real-time business data comprises an industry identifier, a resource type identifier, a quantity, a measurement unit and a business type identifier; Cleaning the real-time service data, and uniformly mapping the resource type identifier and the measurement unit into a standard resource type identifier and a standard measurement unit to obtain a standardized data record; classifying the standardized data records into a supply data set and a demand data set base