Self-service decision-making method and system based on industrial brain

Abstract

The invention provides a self-service decision-making method and system based on an industrial brain. The method comprises the following steps: obtaining multi-source data updated in real time in an industrial area, carrying out data fusion, constructing a thinking map of the industrial area, carrying out trend analysis on the thinking map by utilizing an artificial intelligence technology, the method comprises the steps of obtaining a development trend corresponding to each industrial link in an industrial area, respectively obtaining resource configuration corresponding to each industrial link, performing simulation decision making on the industrial area in combination with the corresponding development trend, generating a plurality of development decision-making schemes, and respectively evaluating a decision-making effect corresponding to each development decision-making scheme. The production benefit of the industrial area is deduced, the industrial production center of gravity of the industrial area is adjusted according to the production benefit, an intelligent decision process is provided for the industry by using the industrial brain, each link in the industry is analyzed and optimized, and the competitiveness of the industry in the complex market is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial self-service decision-making, and in particular to a self-service decision-making method and system based on an industrial brain. Background Art

[0002] Industrial management decisions are a series of strategic and operational decisions made by managers during industrial development and business operations based on analysis of market, resource, technology, policy, and other information. The purpose of these decisions is to optimize resource allocation, enhance corporate competitiveness, and achieve sustainable development. With the advancement of technology and the progress of the times, the workload of management decisions within the industry is increasing. This has led to bottlenecks such as poor process flow, blockages, and inefficiencies in the "stabilizing, protecting, and consolidating" processes on both the enterprise and management sides of the existing industrial chain. This requires strong decision-making to overcome these shortcomings and help the industry gain a competitive advantage in this complex environment. Therefore, how to quickly and comprehensively consider multiple factors, such as market dynamics, corporate resources, and technological development, to assist the industry in making effective development decisions has become a pressing issue.

[0003] Therefore, the present invention provides a self-service decision-making method and system based on the industrial brain. Summary of the Invention

[0004] The present invention is based on a self-service decision-making method and system based on the industrial brain, which uses the industrial brain to provide an intelligent decision-making process for the industry, analyze and optimize each link in the industry, and enhance the competitiveness of the industry in a complex market.

[0005] The present invention provides a self-service decision-making method based on the industrial brain, including:

[0006] Step 1: Obtain multi-source data updated in real time in the industrial zone and perform data fusion to construct a mind map of the industrial zone;

[0007] Step 2: Using artificial intelligence technology to perform trend analysis on the mind map to obtain the development trend corresponding to each industrial link in the industrial zone;

[0008] Step 3: Obtain the resource allocation corresponding to each industrial link, conduct simulation decision-making on the industrial zone in combination with the corresponding development trend, and generate several development decision plans;

[0009] Step 4: Evaluate the decision effect corresponding to each development decision plan respectively, deduce the production efficiency of the industrial zone, and adjust the industrial production focus of the industrial zone according to the production efficiency.

[0010] In one practicable manner,

[0011] The step 1 comprises:

[0012] Step 11: Acquire multiple multi-source data generated by the industrial zone at different update times, identify the source of each multi-source data, and segment the traffic of each multi-source data to obtain multiple real-time data segments of each data source, and determine the segment weight corresponding to each real-time data segment according to the data volume corresponding to each real-time data segment;

[0013] Step 12: Determine the centroid data segment of each multi-source data according to the segment weight to obtain several fusion centers, obtain data-related features between each multi-source data and different fusion centers, and fuse the corresponding multi-source data according to the data-related features corresponding to each fusion center to obtain the overall fusion data of the industrial zone;

[0014] Step 13: Determine the number of thinking branches of the industrial zone based on the number of the fusion centers, determine several thinking themes of the industrial zone in combination with the data-related characteristics to obtain the thinking framework of the industrial zone, and map the overall fusion data to the thinking framework to obtain the thinking map of the industrial zone.

[0015] In one practicable manner,

[0016] The step 2 comprises:

[0017] Step 21: Using artificial intelligence technology to split the mind map into several mind branches, and summarizing the branch keywords corresponding to each mind branch, the mind branches are layered according to the mind core of the mind map to obtain the industrial hierarchical structure of the industrial zone;

[0018] Step 22: Determine the first correlation feature corresponding to each of the thought branches in the industry hierarchical structure, search the thought map for visual content corresponding to each of the branch keywords, and determine the second correlation feature corresponding to each of the thought branches;

[0019] Step 23: Using the artificial intelligence technology to identify the industrial link corresponding to each of the thought branches, and simulating the current link behavior corresponding to each of the industrial links based on the thought branches, obtaining the synchronous transformation content corresponding to each of the thought branches, and determining several synchronous transformation details of the thought map;

[0020] Step 24: Based on the first association feature and the second association feature, derive the detail association result corresponding to each of the synchronous transformation details, and feed back each of the detail association results to the industrial link to derive the operation status of the corresponding industrial link, and obtain the development trend corresponding to each of the industrial links.

[0021] In one practicable manner,

[0022] Also includes:

[0023] respectively obtaining details of each synchronous transformation and determining current thinking development information of the thinking map;

[0024] Determining the industrial development areas of the industrial zone according to the industrial needs of the industrial zone;

[0025] When the current thinking development information is inconsistent with the industrial development field, a development guidance decision is made for the industrial zone and a corresponding guidance decision plan is generated.

[0026] In one practicable manner,

[0027] The step 3 comprises:

[0028] Step 31: Obtain the link sub-graph corresponding to each of the industrial links in the mind map, identify a number of graph nodes and graph edges contained in each of the link sub-graphs, and construct an adjacency list and adjacency matrix of the link sub-graph;

[0029] Step 32: Identify the primary node corresponding to the link subgraph in the adjacency list, search for the primary resource in the industrial zone that is consistent with the primary node, identify the resource-related characteristics between each element and the primary resource in the adjacency matrix, and determine the resource configuration corresponding to the industrial link;

[0030] Step 33: performing real-time synchronous simulation on the industrial zone according to the resource allocation and development trend. When the real-time synchronous simulation result is consistent with the actual operation result of the industrial zone, performing prospective analysis on the real-time synchronous simulation result to obtain several prospective estimation results of the industrial zone.

[0031] Step 34: Derive the overall development direction and overall development rate of the industrial zone based on the expected estimation results, perform development simulation decisions based on each overall development direction and development focus, and obtain several development decision plans for the industrial zone.

[0032] In one practicable manner,

[0033] The step 4 comprises:

[0034] Step 41: Searching for the industrial function corresponding to each resource in the big data, simulating each development decision plan, determining the desired function corresponding to each resource, and performing a first evaluation of each development decision plan based on the functional matching characteristics between the industrial function corresponding to the same resource and the desired function;

[0035] Step 42: Simulate each of the development decision plans separately to obtain the risk characteristics and operating cost characteristics of the industrial zone when implementing different development decision plans, conduct a second evaluation of each of the development decision plans, and deduce the decision effect corresponding to each of the development decision plans based on the first and second evaluation results;

[0036] Step 43: Derive the corresponding production benefits when the industrial zone executes each of the development decision plans based on the decision effects, retrieve the target development decision plan with the highest production benefits, and adjust the industrial production focus of the industrial zone according to the target development focus corresponding to the target development decision plan.

[0037] In one practicable manner,

[0038] Also includes:

[0039] Using digital twin simulation technology to simulate the decision effect corresponding to each development decision plan;

[0040] And use the digital twin simulation technology to simulate the corresponding production benefits when the industrial zone implements each of the development decision plans.

[0041] In one practicable manner,

[0042] Also includes:

[0043] Feeding back the production benefits corresponding to each of the development decision plans to the industrial zone;

[0044] Matching an optimal development decision plan for the industrial zone according to the real-time resource transformation of the industrial zone;

[0045] When the decision maker selects the preferred development decision plan, the target development decision plan is replaced by the preferred development decision plan.

[0046] The present invention provides a self-service decision-making system based on the industrial brain, including:

[0047] A map generation module is used to obtain multi-source data updated in real time in the industrial zone and perform data fusion to construct a mind map of the industrial zone;

[0048] A trend analysis module, configured to use artificial intelligence technology to perform trend analysis on the mind map to obtain the development trend corresponding to each industrial link in the industrial zone;

[0049] A decision analysis module is used to obtain the resource allocation corresponding to each of the industrial links, conduct simulation decisions on the industrial zone in combination with the corresponding development trends, and generate several development decision plans;

[0050] The decision execution module is used to evaluate the decision effect corresponding to each development decision plan, deduce the production efficiency of the industrial zone, and adjust the industrial production focus of the industrial zone according to the production efficiency.

[0051] In one practicable manner,

[0052] The decision execution module includes:

[0053] a function evaluation unit for searching the big data for the industrial function corresponding to each resource, simulating each development decision plan, determining the desired function corresponding to each resource, and performing a first evaluation of each development decision plan based on the functional matching characteristics between the industrial function corresponding to the same resource and the desired function;

[0054] an effect evaluation unit, configured to simulate each of the development decision plans separately, obtain risk characteristics and operating cost characteristics when the industrial zone implements different development decision plans, conduct a second evaluation of each of the development decision plans, and derive the decision effect corresponding to each of the development decision plans based on the first and second evaluation results;

[0055] The decision execution unit is used to deduce the corresponding production benefits when the industrial zone executes each development decision plan based on the decision effect, retrieve the target development decision plan with the highest production benefits, and adjust the industrial production focus of the industrial zone according to the target development focus corresponding to the target development decision plan.

[0056] The achievable beneficial effects of the above technical solution are: in order to improve the decision-making ability of the industrial zone and achieve fast and effective decision-making, the multi-source data in the industrial zone are first integrated to construct a mind map, and then artificial intelligence technology is used to analyze the trend of the mind map, and the development trend corresponding to each industrial link in the industrial zone is determined. Then, combined with the resource allocation of the industrial link, the industrial zone is simulated and the corresponding development decision-making plan is obtained, achieving the purpose of rapid decision-making, providing multiple effective decision-making plans for the industrial zone, and each decision-making plan is simulated to determine its decision-making effect, and the production efficiency of the industrial zone is derived. At this time, the development decision-making plan for the next step can be selected according to the production efficiency, achieving the purpose of accurate and effective decision-making, and different development decision-making plans correspond to different industrial production centers, which can guide and adjust the development of the industry to adapt to changes in market demand.

[0057] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0058] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0060] Figure 1 Schematic diagram of the workflow of the self-service decision-making method based on the industrial brain in an embodiment of the present invention;

[0061] Figure 2 This is a schematic diagram of the composition of the self-service decision-making system based on the industrial brain in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0063] Example 1

[0064] This embodiment provides a self-service decision-making method based on the industrial brain, such as Figure 1 Shown, including:

[0065] Step 1: Obtain multi-source data updated in real time in the industrial zone and perform data fusion to construct a mind map of the industrial zone;

[0066] Step 2: Using artificial intelligence technology to perform trend analysis on the mind map to obtain the development trend corresponding to each industrial link in the industrial zone;

[0067] Step 3: Obtain the resource allocation corresponding to each industrial link, conduct simulation decision-making on the industrial zone in combination with the corresponding development trend, and generate several development decision plans;

[0068] Step 4: Evaluate the decision effect corresponding to each development decision plan respectively, deduce the production efficiency of the industrial zone, and adjust the industrial production focus of the industrial zone according to the production efficiency.

[0069] In this example, multi-source data includes: industry data, market dynamics data, industry development data, etc.

[0070] In this example, the development trend indicates the next development trend of each industrial link in the industrial zone;

[0071] In this example, artificial intelligence models are used to predict development trends, reinforcement learning is used to implement resource allocation, and digital twin simulation technology is used to make decisions;

[0072] In this example, the decision effect refers to the effect produced when the industrial zone implements a development decision plan;

[0073] In this example, the industrial production center represents the focus of production work in the industrial zone.

[0074] The working principle and beneficial effects of the above technical solution: In order to improve the decision-making ability of the industrial zone and achieve fast and effective decision-making, the multi-source data in the industrial zone are first integrated to construct a mind map, and then artificial intelligence technology is used to analyze the trend of the mind map, and the development trend corresponding to each industrial link in the industrial zone is determined. Then, combined with the resource allocation of the industrial link, the industrial zone is simulated and decided, and the corresponding development decision-making plan is obtained, achieving the purpose of rapid decision-making, providing multiple effective decision-making plans for the industrial zone, and each decision-making plan is simulated to determine its decision-making effect, and the production efficiency of the industrial zone is derived. At this time, the development decision-making plan for the next step can be selected according to the production efficiency, achieving the purpose of accurate and effective decision-making, and different development decision-making plans correspond to different industrial production centers, which can guide and adjust the development of the industry to adapt to changes in market demand.

[0075] Example 2

[0076] Based on Example 1, the self-service decision-making method based on the industrial brain, step 1, includes:

[0077] Step 11: Acquire multiple multi-source data generated by the industrial zone at different update times, identify the source of each multi-source data, and segment the traffic of each multi-source data to obtain multiple real-time data segments of each data source, and determine the segment weight corresponding to each real-time data segment according to the data volume corresponding to each real-time data segment;

[0078] Step 12: Determine the centroid data segment of each multi-source data according to the segment weight to obtain several fusion centers, obtain data-related features between each multi-source data and different fusion centers, and fuse the corresponding multi-source data according to the data-related features corresponding to each fusion center to obtain the overall fusion data of the industrial zone;

[0079] Step 13: Determine the number of thinking branches of the industrial zone based on the number of the fusion centers, determine several thinking themes of the industrial zone in combination with the data-related characteristics to obtain the thinking framework of the industrial zone, and map the overall fusion data to the thinking framework to obtain the thinking map of the industrial zone.

[0080] In this instance, a data source can correspond to one or more multi-source data;

[0081] In this instance, traffic segmentation refers to the process of dividing multi-source data into data segments according to the traffic progress of data generation;

[0082] In this example, the fusion center represents the fusion point corresponding to when the data information in the real-time data segment cannot be changed when fusing multi-source data;

[0083] In this example, the overall fused data represents the result of fusing multi-source data;

[0084] In this example, the number of thinking branches represents the branches of the thinking map of one or more industrial links in the industrial zone.

[0085] The working principle and beneficial effects of the above technical solution: In order to ensure the accuracy and effectiveness of subsequent decision-making, the multi-source data of the industrial zone are first identified and the traffic is segmented, and several real-time data segments corresponding to each data source are determined. Then, the segment weight of each real-time data segment is determined according to its corresponding data volume, and the central data segment with a large segment weight is selected as a fusion center of multi-source data. The data fusion is performed by combining the data-related features between different multi-source data and the fusion center to obtain the overall fusion data. In this way, data fusion can be completed in a short time, and the obtained fusion data will not have fusion offset and inappropriate fusion phenomena. The number of thinking branches of the industrial zone is further determined according to the number of fusion centers, and the thinking framework is constructed in combination with the corresponding thinking themes. Finally, the thinking map of the industrial zone is constructed by mapping the overall fusion data. In this way, not only data fusion can be achieved, but also the framework integrity and data validity of the thinking map can be guaranteed. The real-time status of the industrial zone can be displayed by the thinking map, which improves the efficiency of subsequent reasoning.

[0086] Example 3

[0087] Based on Example 1, the self-service decision-making method based on the industrial brain, step 2, includes:

[0088] Step 21: Using artificial intelligence technology to split the mind map into several mind branches, and summarizing the branch keywords corresponding to each mind branch, the mind branches are layered according to the mind core of the mind map to obtain the industrial hierarchical structure of the industrial zone;

[0089] Step 22: Determine the first correlation feature corresponding to each of the thought branches in the industry hierarchical structure, search the thought map for visual content corresponding to each of the branch keywords, and determine the second correlation feature corresponding to each of the thought branches;

[0090] Step 23: Using the artificial intelligence technology to identify the industrial link corresponding to each of the thought branches, and simulating the current link behavior corresponding to each of the industrial links based on the thought branches, obtaining the synchronous transformation content corresponding to each of the thought branches, and determining several synchronous transformation details of the thought map;

[0091] Step 24: Based on the first association feature and the second association feature, derive the detail association result corresponding to each of the synchronous transformation details, and feed back each of the detail association results to the industrial link to derive the operation status of the corresponding industrial link, and obtain the development trend corresponding to each of the industrial links.

[0092] In this example, the branch keyword refers to a word used to express the key information of a branch of thinking;

[0093] In this example, the industrial hierarchical structure represents the hierarchical results of various links in the industrial zone;

[0094] In this example, the first associated feature represents the hierarchical level corresponding to each branch of thinking;

[0095] In this example, the visual content represents the content about the branch keywords that can be viewed in the mind map;

[0096] In this example, the second association feature represents the relationship between a thought branch and various links in the industrial zone;

[0097] In this instance, current link behavior refers to the behavior currently exhibited by an industrial link;

[0098] In this example, the synchronous transformation details represent the details generated when the mind map is synchronously operated due to the operation of the industrial link.

[0099] The working principle and beneficial effects of the above technical solution: In order to better deduce the development trend of each industrial link, artificial intelligence technology is first used to divide the mind map into several thinking branches, and the branch keywords of each thinking branch and the thinking core of the mind map are combined to layer the mind map, and the first correlation feature of each thinking branch is determined in the industrial layering structure, and the second correlation feature of each thinking branch is determined according to the visual content of each branch keyword in the mind map, and then the industrial link corresponding to each thinking branch is identified to simulate its current link behavior and use the obtained synchronous transformation content to determine the synchronous transformation details of the mind map, and in-depth deduction of the detailed correlation results corresponding to each synchronous transformation detail, and finally it is fed back to the industrial link to deduce its operation status, and the development trend of the industrial link is obtained. In this way, not only can an in-depth correlation analysis be performed on each thinking branch, but also an in-depth deduction of the development trend of the industry from multiple angles can be carried out, laying a solid foundation for decision-making.

[0100] Example 4

[0101] Based on Example 3, the self-service decision-making method based on the industrial brain further includes:

[0102] respectively obtaining details of each synchronous transformation and determining current thinking development information of the thinking map;

[0103] Determining the industrial development areas of the industrial zone according to the industrial needs of the industrial zone;

[0104] When the current thinking development information is inconsistent with the industrial development field, a development guidance decision is made for the industrial zone and a corresponding guidance decision plan is generated.

[0105] The working principle and beneficial effects of the above technical solution: When the development direction of the industrial zone is different from the predetermined development area, it can be determined that an emergency has occurred in the industrial zone, so timely guidance and decision-making can be made to reduce the impact of the emergency on the industrial zone and reduce the losses of the industrial zone.

[0106] Example 5

[0107] Based on Example 1, the self-service decision-making method based on the industrial brain, step 3, includes:

[0108] Step 31: Obtain the link sub-graph corresponding to each of the industrial links in the mind map, identify a number of graph nodes and graph edges contained in each of the link sub-graphs, and construct an adjacency list and adjacency matrix of the link sub-graph;

[0109] Step 32: Identify the primary node corresponding to the link subgraph in the adjacency list, search for the primary resource in the industrial zone that is consistent with the primary node, identify the resource-related characteristics between each element and the primary resource in the adjacency matrix, and determine the resource configuration corresponding to the industrial link;

[0110] Step 33: performing real-time synchronous simulation on the industrial zone according to the resource allocation and development trend. When the real-time synchronous simulation result is consistent with the actual operation result of the industrial zone, performing prospective analysis on the real-time synchronous simulation result to obtain several prospective estimation results of the industrial zone.

[0111] Step 34: Derive the overall development direction and overall development rate of the industrial zone based on the expected estimation results, perform development simulation decisions based on each overall development direction and development focus, and obtain several development decision plans for the industrial zone.

[0112] In this example, the graph nodes represent the nodes contained in the link subgraph, and the graph edges represent the edges contained in the link subgraph;

[0113] In this example, the adjacency list represents the result of storing each node and its adjacent nodes or edges in a list, and the adjacency matrix represents a two-dimensional matrix of the graph structure, where each element in the matrix represents an edge between two nodes in the graph;

[0114] In this example, the master node represents the main information node in the link sub-graph, and the master resource represents the resource with the same function as the master node.

[0115] The working principle and beneficial effects of the above technical solution: In order to obtain an effective development decision-making plan, first obtain the link sub-map corresponding to each industrial link in the mind map, then identify the nodes and edges to construct the corresponding adjacency list and adjacency matrix, further identify its main node in the adjacency list, find its main resources, and determine the resource allocation of the industrial link based on the resource-related characteristics between each element in the adjacency matrix and the main resource. Use lists and matrices to display the relationship between each node and edge, effectively improving the quality of subsequent expected estimates, and then simulate the operation results of the industrial zone through simulation, and deduce the expected estimation results of the industrial zone when the operation results are consistent with the simulation results. Finally, deduce the overall development direction and overall development rate of the industrial zone, and perform simulation decision-making again to obtain the development decision-making plan of the industrial zone. In this way, it can ensure that the decision-making work of the industrial zone is completed in a short time, and the overall development direction and development focus of the industrial zone can also be deduced, which is convenient for subsequent management of the industrial zone to adjust its development direction.

[0116] Example 6

[0117] Based on Example 1, the self-service decision-making method based on the industrial brain, step 4, includes:

[0118] Step 41: Searching for the industrial function corresponding to each resource in the big data, simulating each development decision plan, determining the desired function corresponding to each resource, and performing a first evaluation of each development decision plan based on the functional matching characteristics between the industrial function corresponding to the same resource and the desired function;

[0119] Step 42: Simulate each of the development decision plans separately to obtain the risk characteristics and operating cost characteristics of the industrial zone when implementing different development decision plans, conduct a second evaluation of each of the development decision plans, and deduce the decision effect corresponding to each of the development decision plans based on the first and second evaluation results;

[0120] Step 43: Derive the corresponding production benefits when the industrial zone executes each of the development decision plans based on the decision effects, retrieve the target development decision plan with the highest production benefits, and adjust the industrial production focus of the industrial zone according to the target development focus corresponding to the target development decision plan.

[0121] In this example, industrial function represents the function that a resource can achieve, and desired function represents the function that the resource needs to achieve when implementing the development decision plan;

[0122] In this example, the first assessment represents the process of evaluating the functions that can be achieved by resources, and the second assessment represents the process of evaluating the risks and operating costs of development decision options;

[0123] In this example, production benefit refers to the industrial profit that can be achieved when an industrial zone implements a development decision plan.

[0124] The working principle and beneficial effects of the above technical solution are as follows: by searching for the industrial function of each resource in big data, and simulating each development decision plan to determine the desired function of each resource, the development decision plan is evaluated for the first time based on the matching relationship between the two, and then each development decision plan is simulated to determine its risk characteristics and operating cost characteristics, completing the second evaluation of the development decision plan, thereby deducing the decision effect of the development decision plan, and finally deducing the production benefits of the industrial zone when implementing different development decision plans through simulation, thereby selecting the target development decision plan with the best benefits, and adjusting the industrial production focus of the industrial zone according to the plan. In this way, the benefits of each development decision plan are analyzed in advance through simulation, thereby improving the efficient guiding role of decision-making work.

[0125] Example 7

[0126] Based on Example 6, the self-service decision-making method based on the industrial brain further includes:

[0127] Using digital twin simulation technology to simulate the decision effect corresponding to each development decision plan;

[0128] And use the digital twin simulation technology to simulate the corresponding production benefits when the industrial zone implements each of the development decision plans.

[0129] Example 8

[0130] Based on Example 6, the self-service decision-making method based on the industrial brain further includes:

[0131] Feeding back the production benefits corresponding to each of the development decision plans to the industrial zone;

[0132] Matching an optimal development decision plan for the industrial zone according to the real-time resource transformation of the industrial zone;

[0133] When the decision maker selects the preferred development decision plan, the target development decision plan is replaced by the preferred development decision plan.

[0134] The working principle and beneficial effects of the above technical solution are as follows: when the decision maker selects the preferred development decision solution, the auxiliary industrial zone adapts to the solution.

[0135] Example 9

[0136] This embodiment provides a self-service decision-making system based on the industrial brain, such as Figure 2 Shown, including:

[0137] A map generation module is used to obtain multi-source data updated in real time in the industrial zone and perform data fusion to construct a mind map of the industrial zone;

[0138] A trend analysis module, configured to use artificial intelligence technology to perform trend analysis on the mind map to obtain the development trend corresponding to each industrial link in the industrial zone;

[0139] A decision analysis module is used to obtain the resource allocation corresponding to each of the industrial links, conduct simulation decisions on the industrial zone in combination with the corresponding development trends, and generate several development decision plans;

[0140] The decision execution module is used to evaluate the decision effect corresponding to each development decision plan, deduce the production efficiency of the industrial zone, and adjust the industrial production focus of the industrial zone according to the production efficiency.

[0141] In this example, multi-source data includes: industry data, market dynamics data, industry development data, etc.

[0142] In this example, the development trend indicates the next development trend of each industrial link in the industrial zone;

[0143] In this example, artificial intelligence models are used to predict development trends, reinforcement learning is used to implement resource allocation, and digital twin simulation technology is used to make decisions;

[0144] In this example, the decision effect refers to the effect produced when the industrial zone implements a development decision plan;

[0145] In this example, the industrial production center represents the focus of production work in the industrial zone.

[0146] The working principle and beneficial effects of the above technical solution: In order to improve the decision-making ability of the industrial zone and achieve fast and effective decision-making, the multi-source data in the industrial zone are first integrated to construct a mind map, and then artificial intelligence technology is used to analyze the trend of the mind map, and the development trend corresponding to each industrial link in the industrial zone is determined. Then, combined with the resource allocation of the industrial link, the industrial zone is simulated and decided, and the corresponding development decision-making plan is obtained, achieving the purpose of rapid decision-making, providing multiple effective decision-making plans for the industrial zone, and each decision-making plan is simulated to determine its decision-making effect, and the production efficiency of the industrial zone is derived. At this time, the development decision-making plan for the next step can be selected according to the production efficiency, achieving the purpose of accurate and effective decision-making, and different development decision-making plans correspond to different industrial production centers, which can guide and adjust the development of the industry to adapt to changes in market demand.

[0147] Example 10

[0148] Based on Example 9, the self-service decision-making system based on the industrial brain, the decision execution module includes:

[0149] a function evaluation unit for searching the big data for the industrial function corresponding to each resource, simulating each development decision plan, determining the desired function corresponding to each resource, and performing a first evaluation of each development decision plan based on the functional matching characteristics between the industrial function corresponding to the same resource and the desired function;

[0150] an effect evaluation unit, configured to simulate each of the development decision plans separately, obtain risk characteristics and operating cost characteristics when the industrial zone implements different development decision plans, conduct a second evaluation of each of the development decision plans, and derive the decision effect corresponding to each of the development decision plans based on the first and second evaluation results;

[0151] The decision execution unit is used to deduce the corresponding production benefits when the industrial zone executes each development decision plan based on the decision effect, retrieve the target development decision plan with the highest production benefits, and adjust the industrial production focus of the industrial zone according to the target development focus corresponding to the target development decision plan.

[0152] In this example, industrial function represents the function that a resource can achieve, and desired function represents the function that the resource needs to achieve when implementing the development decision plan;

[0153] In this example, the first assessment represents the process of evaluating the functions that can be achieved by resources, and the second assessment represents the process of evaluating the risks and operating costs of development decision options;

[0154] In this example, production benefit refers to the industrial profit that can be achieved when an industrial zone implements a development decision plan.

[0155] The working principle and beneficial effects of the above technical solution are as follows: by searching for the industrial function of each resource in big data, and simulating each development decision plan to determine the desired function of each resource, the development decision plan is evaluated for the first time based on the matching relationship between the two, and then each development decision plan is simulated to determine its risk characteristics and operating cost characteristics, completing the second evaluation of the development decision plan, thereby deducing the decision effect of the development decision plan, and finally deducing the production benefits of the industrial zone when implementing different development decision plans through simulation, thereby selecting the target development decision plan with the best benefits, and adjusting the industrial production focus of the industrial zone according to the plan. In this way, the benefits of each development decision plan are analyzed in advance through simulation, thereby improving the efficient guiding role of decision-making work.

[0156] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A self-service decision-making method based on the industrial brain, characterized by: include: Step 1: Obtain multi-source data updated in real time in the industrial zone and perform data fusion to construct a mind map of the industrial zone; Step 2: Using artificial intelligence technology to perform trend analysis on the mind map to obtain the development trend corresponding to each industrial link in the industrial zone; Step 3: Obtain the resource allocation corresponding to each industrial link, conduct simulation decision-making on the industrial zone in combination with the corresponding development trend, and generate several development decision plans; Step 4: Evaluate the decision effect corresponding to each development decision plan respectively, deduce the production efficiency of the industrial zone, and adjust the industrial production focus of the industrial zone according to the production efficiency.

2. The self-service decision-making method based on the industrial brain according to claim 1 is characterized in that: The step 1 comprises: Step 11: Acquire multiple multi-source data generated by the industrial zone at different update times, identify the source of each multi-source data, and segment the traffic of each multi-source data to obtain multiple real-time data segments of each data source, and determine the segment weight corresponding to each real-time data segment according to the data volume corresponding to each real-time data segment; Step 12: Determine the centroid data segment of each multi-source data according to the segment weight to obtain several fusion centers, obtain data-related features between each multi-source data and different fusion centers, and fuse the corresponding multi-source data according to the data-related features corresponding to each fusion center to obtain the overall fusion data of the industrial zone; Step 13: Determine the number of thinking branches of the industrial zone based on the number of the fusion centers, determine several thinking themes of the industrial zone in combination with the data-related characteristics to obtain the thinking framework of the industrial zone, and map the overall fusion data to the thinking framework to obtain the thinking map of the industrial zone.

3. The self-service decision-making method based on the industrial brain according to claim 1 is characterized in that: The step 2 comprises: Step 21: Using artificial intelligence technology to split the mind map into several mind branches, and summarizing the branch keywords corresponding to each mind branch, the mind branches are layered according to the mind core of the mind map to obtain the industrial hierarchical structure of the industrial zone; Step 22: Determine the first correlation feature corresponding to each of the thought branches in the industry hierarchical structure, search the thought map for visual content corresponding to each of the branch keywords, and determine the second correlation feature corresponding to each of the thought branches; Step 23: Using the artificial intelligence technology to identify the industrial link corresponding to each of the thought branches, and simulating the current link behavior corresponding to each of the industrial links based on the thought branches, obtaining the synchronous transformation content corresponding to each of the thought branches, and determining several synchronous transformation details of the thought map; Step 24: Based on the first association feature and the second association feature, derive the detail association result corresponding to each of the synchronous transformation details, and feed back each of the detail association results to the industrial link to derive the operation status of the corresponding industrial link, and obtain the development trend corresponding to each of the industrial links.

4. The self-service decision-making method based on the industrial brain according to claim 3 is characterized in that: Also includes: respectively obtaining details of each synchronous transformation and determining current thinking development information of the thinking map; Determining the industrial development areas of the industrial zone according to the industrial needs of the industrial zone; When the current thinking development information is inconsistent with the industrial development field, a development guidance decision is made for the industrial zone and a corresponding guidance decision plan is generated.

5. The self-service decision-making method based on the industrial brain according to claim 1 is characterized in that: The step 3 comprises: Step 31: Obtain the link sub-graph corresponding to each of the industrial links in the mind map, identify a number of graph nodes and graph edges contained in each of the link sub-graphs, and construct an adjacency list and adjacency matrix of the link sub-graph; Step 32: Identify the primary node corresponding to the link subgraph in the adjacency list, search for the primary resource in the industrial zone that is consistent with the primary node, identify the resource-related characteristics between each element and the primary resource in the adjacency matrix, and determine the resource configuration corresponding to the industrial link; Step 33: performing real-time synchronous simulation on the industrial zone according to the resource allocation and development trend. When the real-time synchronous simulation result is consistent with the actual operation result of the industrial zone, performing prospective analysis on the real-time synchronous simulation result to obtain several prospective estimation results of the industrial zone. Step 34: Derive the overall development direction and overall development rate of the industrial zone based on the expected estimation results, perform development simulation decisions based on each overall development direction and development focus, and obtain several development decision plans for the industrial zone.

6. The self-service decision-making method based on the industrial brain according to claim 1 is characterized in that: The step 4 comprises: Step 41: Searching for the industrial function corresponding to each resource in the big data, simulating each development decision plan, determining the desired function corresponding to each resource, and performing a first evaluation of each development decision plan based on the functional matching characteristics between the industrial function corresponding to the same resource and the desired function; Step 42: Simulate each of the development decision plans separately to obtain the risk characteristics and operating cost characteristics of the industrial zone when implementing different development decision plans, conduct a second evaluation of each of the development decision plans, and derive the decision effect corresponding to each of the development decision plans based on the first and second evaluation results; Step 43: Derive the corresponding production benefits when the industrial zone executes each of the development decision plans based on the decision effects, retrieve the target development decision plan with the highest production benefits, and adjust the industrial production focus of the industrial zone according to the target development focus corresponding to the target development decision plan.

7. The self-service decision-making method based on the industrial brain according to claim 6 is characterized in that: Also includes: Using digital twin simulation technology to simulate the decision effect corresponding to each development decision plan; And use the digital twin simulation technology to simulate the corresponding production benefits when the industrial zone implements each of the development decision plans.

8. The self-service decision-making method based on the industrial brain according to claim 6 is characterized in that: Also includes: Feeding back the production benefits corresponding to each of the development decision plans to the industrial zone; Matching an optimal development decision plan for the industrial zone according to the real-time resource transformation of the industrial zone; When the decision maker selects the preferred development decision plan, the target development decision plan is replaced by the preferred development decision plan.

9. The self-service decision-making system based on the industrial brain is characterized by: include: A map generation module is used to obtain multi-source data updated in real time in the industrial zone and perform data fusion to construct a mind map of the industrial zone; A trend analysis module, configured to use artificial intelligence technology to perform trend analysis on the mind map to obtain the development trend corresponding to each industrial link in the industrial zone; A decision analysis module is used to obtain the resource allocation corresponding to each of the industrial links, conduct simulation decisions on the industrial zone in combination with the corresponding development trends, and generate several development decision plans; The decision execution module is used to evaluate the decision effect corresponding to each development decision plan, deduce the production efficiency of the industrial zone, and adjust the industrial production focus of the industrial zone according to the production efficiency.

10. The self-service decision-making system based on the industrial brain according to claim 9, characterized in that: The decision execution module includes: a function evaluation unit for searching the big data for the industrial function corresponding to each resource, simulating each development decision plan, determining the desired function corresponding to each resource, and performing a first evaluation of each development decision plan based on the functional matching characteristics between the industrial function corresponding to the same resource and the desired function; an effect evaluation unit, configured to simulate each of the development decision plans separately, obtain risk characteristics and operating cost characteristics when the industrial zone implements different development decision plans, conduct a second evaluation of each of the development decision plans, and derive the decision effect corresponding to each of the development decision plans based on the first and second evaluation results; The decision execution unit is used to deduce the corresponding production benefits when the industrial zone executes each development decision plan based on the decision effect, retrieve the target development decision plan with the highest production benefits, and adjust the industrial production focus of the industrial zone according to the target development focus corresponding to the target development decision plan.

Images