Intelligent management method and system for enterprise settlement in smart park

Abstract

The invention is suitable for the field of smart parks, and provides a smart park enterprise settlement intelligent management method and system, and the method comprises the following steps: building a supply chain relation graph based on the transaction data of an existing enterprise; obtaining application information of a settled enterprise, and generating an enterprise label of the settled enterprise according to the application information; matching nodes in the supply chain relation graph according to the enterprise label of the settled enterprise to obtain a plurality of alternative nodes; and generating a layout scheme about the settled enterprise location based on the plurality of alternative nodes. According to the method and the system, position selection help can be provided for entering of enterprises in the smart park, so that the entering enterprises can make transactions with other enterprises in the park at lower cost, and clustering development of the upstream and downstream of the industry is effectively promoted.

Description

Technical Field

[0001] The present invention relates to the field of smart parks, and specifically to an intelligent management method and system for enterprise settlement in a smart park. Background Art

[0002] A smart park refers to the use of new generation information technologies such as the Internet of Things (IoT), big data, cloud computing, artificial intelligence, etc. to digitally integrate and intelligently upgrade the infrastructure, services, management, and resources within the park, thereby improving the park's operation efficiency, service level, and sustainable development ability.

[0003] For the long-term development of the park, the settlement of enterprises is an essential part. Intelligent devices can bring great convenience to the park in aspects such as monitoring management, security, and life services, and can provide better services for enterprises.

[0004] When an enterprise chooses to settle in a park, the existing method is mostly for the person in charge of the enterprise to communicate with the park. Among them, the most important issue is the location selection. In this regard, the person in charge of the enterprise needs to conduct on-site inspections multiple times and also go through internal discussions before a decision can be made. The process is very cumbersome. And the park can only provide some suggestions for the enterprise regarding rent, surrounding facilities, etc. Therefore, an intelligent management method and system for enterprise settlement in a smart park are proposed to solve the above problems. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a production control and management system for a coal logistics park based on operation video recognition technology to solve the problems in the above background art.

[0006] The present invention is implemented as follows. An intelligent management method for enterprise settlement in a smart park, characterized in that the method includes the following steps:

[0007] Based on the transaction data of existing enterprises, establish a supply chain relationship map. The existing enterprises are those that have already settled in the park, and the supply chain relationship map is composed of multiple nodes with the settled enterprises as units;

[0008] Obtain the application information of the settling enterprise, and generate an enterprise label for the settling enterprise according to the application information;

[0009] Match the nodes in the supply chain relationship map according to the enterprise label of the settling enterprise to obtain multiple alternative nodes. The enterprises corresponding to the alternative nodes are in an upstream and downstream relationship with the settling enterprise in the supply chain;

[0010] Generate a layout plan for the location of the settling enterprise based on multiple alternative nodes.

[0011] As a further solution of the present invention: The step of establishing a supply chain relationship graph based on the transaction data of existing enterprises specifically includes:

[0012] Collect the transaction data of existing enterprises, where the transaction data includes information on both parties to the transaction, transaction amount, transaction frequency, product type, and transaction time;

[0013] Delete duplicate data in the transaction data and use a prediction model to complete it;

[0014] Format and unify all the transaction data;

[0015] Generate a supply chain relationship graph with information on both parties to the transaction as nodes and transaction amount, transaction frequency, product type, and transaction time as edges.

[0016] As a further solution of the present invention: The method further includes:

[0017] Traverse the supply chain relationship graph to select two related nodes;

[0018] Extract the transaction amount, transaction frequency, and transaction time in the transaction data of the two nodes, and calculate the cooperation time based on the transaction time;

[0019] Use normalization to process the transaction amount, transaction frequency, and cooperation time to obtain the intensity coefficient of enterprise cooperation;

[0020] Integrate the intensity coefficient into the supply chain relationship graph.

[0021] As a further solution of the present invention: The step of generating enterprise labels for the entering enterprises according to the application information specifically includes:

[0022] Check the integrity of the application information based on label classification, where the label classification includes industry label, scale label, function label, and demand label;

[0023] When the application information is incomplete, retrieve third-party data for the enterprise according to the existing application information;

[0024] Use the third-party data to complete the application information;

[0025] Use natural language processing technology to identify keywords in the processed application information and generate enterprise labels with reference to the label classification.

[0026] As a further solution of the present invention: The third-party data includes industrial and commercial registration information, industry reports, and enterprise public data.

[0027] As a further solution of the present invention: The step of generating a layout plan for the location of the settled enterprises based on multiple alternative nodes specifically includes:

[0028] Integrate the supply chain relationship graph with the virtual distribution model of the park to obtain a park distribution relationship model, and the virtual distribution model is constructed according to the original layout of the park;

[0029] Based on the park distribution relationship model, perform location marking with reference to the alternative nodes;

[0030] Select the optimal area according to the marked location, and the optimal area is the park location where no enterprise has settled and is the closest to the alternative node;

[0031] Calculate and generate operation cost data according to the optimal area, and generate a layout plan in combination with the location of the optimal area.

[0032] Another object of the present invention is to provide an intelligent management system for enterprise settlement in a smart park, and the system includes:

[0033] A relationship graph construction module, which establishes a supply chain relationship graph based on the transaction data of existing enterprises, and the existing enterprises are the enterprises that have settled in the park, and the supply chain relationship graph is composed of multiple nodes with the existing enterprises as units;

[0034] An enterprise label establishment module, which is used to obtain the application information of the settled enterprises and generate enterprise labels for the settled enterprises according to the application information;

[0035] A node matching module, which is used to match the nodes in the supply chain relationship graph according to the enterprise labels of the settled enterprises to obtain multiple alternative nodes, and the enterprises corresponding to the alternative nodes are in an upstream and downstream relationship with the settled enterprises in the supply chain relationship;

[0036] A plan generation module, which generates a layout plan for the location of the settled enterprises based on multiple alternative nodes.

[0037] As a further solution of the present invention: The relationship graph construction module includes:

[0038] A data collection unit, which is used to collect the transaction data of existing enterprises, and the transaction data includes information on both parties to the transaction, transaction amount, transaction frequency, product type, and transaction time;

[0039] A data processing unit, which is used to delete duplicate data in the transaction data and perform complementation processing on it using a prediction model;

[0040] A data standardization unit, which is used to uniformly format all the transaction data;

[0041] A spectrum generation unit, which is used to generate a supply chain relationship spectrum with the information of both trading parties as nodes and the trading amount, trading frequency, product type, and trading time as edges.

[0042] As a further solution of the present invention: The enterprise label establishment module includes:

[0043] An information verification unit, which conducts integrity verification on the application information based on label classification, and the label classification includes industry labels, scale labels, function labels, and demand labels;

[0044] An information retrieval unit, which is used to retrieve third-party data of the enterprise according to the existing application information when the application information is incomplete;

[0045] A data supplementation unit, which is used to supplement and process the application information by using the third-party data;

[0046] A label generation unit, which uses natural language processing technology to identify keywords in the processed application information and generates enterprise labels with reference to the label classification.

[0047] As a further solution of the present invention: The solution generation module includes:

[0048] A data integration unit, which is used to integrate the supply chain relationship spectrum with the virtual distribution model of the park to obtain a park distribution relationship model, and the virtual distribution model is constructed according to the original layout of the park;

[0049] A position marking unit, which marks positions with reference to the alternative nodes based on the park distribution relationship model;

[0050] A region selection unit, which is used to select the optimal region according to the marked positions, and the optimal region is the park position where no enterprise has settled in and is the closest to the alternative nodes;

[0051] An accounting and integration unit, which is used to calculate and generate operation cost data according to the optimal region and generate a layout plan in combination with the position of the optimal region.

[0052] Compared with the prior art, the beneficial effects of the present invention are:

[0053] The present invention first uses the transaction data of existing enterprises in the park to establish a supply chain relationship map in the park, which can more intuitively describe the fields and roles of enterprises in the park. When a new enterprise enters the park, according to the application information submitted by the enterprise to the park, natural language processing technology is used to extract the effective text in the application information to generate enterprise tags associated with the entering enterprise, so that the entering enterprise can be matched with the original supply chain relationship map, and then its best location in the park can be determined. In summary, the present invention can provide assistance in location selection for the entry of enterprises in the smart park, enabling the entering enterprises to conduct business transactions with other enterprises in the park at a lower cost and effectively promoting the clustered development of the upstream and downstream industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flowchart of an intelligent management method for the entry of enterprises in a smart park.

[0055] Figure 2 It is a flowchart of establishing a supply chain relationship map based on the transaction data of existing enterprises in an intelligent management method for the entry of enterprises in a smart park.

[0056] Figure 3 It is a flowchart of generating enterprise tags for entering enterprises according to application information in an intelligent management method for the entry of enterprises in a smart park.

[0057] Figure 4 It is a flowchart of generating a layout plan for the location of an entering enterprise based on multiple alternative nodes in an intelligent management method for the entry of enterprises in a smart park.

[0058] Figure 5 It is a schematic structural diagram of an intelligent management system for the entry of enterprises in a smart park.

[0059] Figure 6 It is a schematic structural diagram of a relationship map construction module in an intelligent management system for the entry of enterprises in a smart park.

[0060] Figure 7 It is a schematic structural diagram of an enterprise tag establishment module in an intelligent management system for the entry of enterprises in a smart park.

[0061] Figure 8 It is a schematic structural diagram of a scheme generation module in an intelligent management system for the entry of enterprises in a smart park. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0063] The following will describe in detail the specific implementation of the present invention in combination with specific embodiments.

[0064] As Figure 1 shown, an embodiment of the present invention provides an intelligent management method for enterprise settlement in a smart park. The method includes the following steps:

[0065] S100, establishing a supply chain relationship map based on the transaction data of existing enterprises. The existing enterprises are the enterprises that have already settled in the park. The supply chain relationship map consists of multiple nodes with the settled enterprises as units;

[0066] S200, obtaining the application information of the settling enterprise and generating an enterprise label for the settling enterprise according to the application information;

[0067] S300, matching the nodes in the supply chain relationship map according to the enterprise label of the settling enterprise to obtain multiple alternative nodes. The enterprises corresponding to the alternative nodes are in an upstream and downstream relationship with the settling enterprise in the supply chain relationship;

[0068] S400, generating a layout plan for the location of the settling enterprise based on multiple alternative nodes.

[0069] It should be noted that the supply chain relationship map is a network composed of nodes and edges. Each node represents an existing enterprise, and the edges represent the supply chain relationships between these enterprises (such as the supplier-customer relationship). This step helps to visualize and understand the complex supply chain connections between enterprises. The enterprise label includes industry type, product or service type, scale, etc., aiming to summarize the main characteristics of the enterprise and its role in the supply chain. The alternative nodes are the enterprises that may be associated with the settling enterprise in the supply chain. The main content of the enterprise layout plan obtained based on the alternative nodes is the location of the settling enterprise in the smart park. This location selection method can increase the probability of cooperation between the settling enterprise and the enterprises in the park, and at the same time reduce the transportation cost, thereby improving the overall operation efficiency.

[0070] In the embodiment of the present invention, the present invention first uses the transaction data of existing enterprises in the park to establish a supply chain relationship map in the park, which can more intuitively describe the fields and roles of enterprises in the park. When a new enterprise settles in, according to the application information submitted by the enterprise to the park, natural language processing technology is used to extract the effective text in the application information to generate an enterprise label associated with the settling enterprise, so as to match the settling enterprise with the original supply chain relationship map, and then determine its best location in the park. In summary, the present invention can provide assistance in location selection for enterprise settlement in a smart park, enabling the settling enterprise to conduct transaction activities with other enterprises in the park at a lower cost and effectively promoting the cluster development of the upstream and downstream industries.

[0071] As Figure 2 shown, as a preferred embodiment of the present invention, the steps of establishing a supply chain relationship graph based on the transaction data of existing enterprises specifically include:

[0072] S101, collecting the transaction data of existing enterprises, where the transaction data includes information on both parties to the transaction, transaction amount, transaction frequency, product type, and transaction time;

[0073] S102, deleting duplicate data in the transaction data and using a prediction model to complete it;

[0074] S103, formatting and unifying all the transaction data;

[0075] S104, generating a supply chain relationship graph with information on both parties to the transaction as nodes and transaction amount, transaction frequency, product type, and transaction time as edges;

[0076] S105, traversing the supply chain relationship graph to select two related nodes;

[0077] S106, extracting the transaction amount, transaction frequency, and transaction time in the transaction data of the two nodes, and calculating the cooperation time based on the transaction time;

[0078] S107, using normalization to process the transaction amount, transaction frequency, and cooperation time to obtain the intensity coefficient of enterprise cooperation;

[0079] S108, integrating the intensity coefficient into the supply chain relationship graph.

[0080] In the embodiments of the present invention, first, transaction data involving enterprises within the park needs to be collected from various sources. This data includes information about the two parties to the transaction (i.e., who is the buyer and the seller), transaction amount, transaction frequency (the number of transactions within a certain period of time), product type (the type of goods or services traded), transaction time (specific to the occurrence time of each transaction), etc. Since the data may come from multiple different systems or records, the probability of data duplication is extremely high. Therefore, duplicate items are deleted to ensure the uniqueness of the data. In actual situations, because the transaction data is collected, it means that not all information will be included. For the missing parts, they can be supplemented through a prediction model. The prediction model refers to a type of mathematical or statistical model used to process and complete the missing information in transaction data, such as regression analysis, time series analysis, etc. All transaction data is converted into a standard format for subsequent processing and analysis. This step may involve adjusting the date format, unifying the currency unit, standardizing the product type description, etc., with the aim of ensuring that data from different sources can be compared and analyzed under the same standard. Each enterprise participating in the transaction is regarded as a node, and the edges between the nodes are defined by the specific details of the transaction, such as transaction amount, transaction frequency, product type, and transaction time, so that the complex supply chain relationships between enterprises can be intuitively displayed. In addition, the calculation of the above strength coefficient can measure the closeness of the cooperation relationship between enterprises. Since the importance of transaction amount, transaction frequency, and cooperation time is directly compared, each indicator can be normalized and then the average value is obtained. The normalization formula is:

[0081]

[0082] where X is the original data, and X max and X min represent the maximum and minimum values of this indicator respectively, and X norm represents the normalized value. Taking the average of the three represents the strength coefficient of the transaction.

[0083] As Figure 3 shown, as a preferred embodiment of the present invention, the step of generating enterprise tags for the enterprises applying for entry according to the application information specifically includes:

[0084] S201, perform integrity check on the application information based on label classification, where the label classification includes industry label, scale label, function label, and demand label;

[0085] S202, when the application information is incomplete, retrieve third-party data for the enterprise according to the existing application information;

[0086] S203, use the third-party data to complete the application information;

[0087] S204. Use natural language processing technology to identify keywords in the processed application information and generate enterprise labels by referring to label classification.

[0088] In the embodiment of the present invention, first, a set of label classification criteria needs to be defined. Then, according to these classification criteria, check whether the application information submitted by the settled enterprises covers all necessary details. The purpose of this step is to confirm whether the application information of the settled enterprises is detailed enough to support subsequent label generation work. If it is found that the application information of the enterprises is missing or insufficient, additional measures need to be taken to supplement this information. This usually involves using the enterprise name, registration number or other available identification information to search for relevant enterprise materials in public databases, commercial information service providers (such as Tianyancha, Qichacha), government public resources and other channels. In this way, more industrial and commercial registration information, industry reports and enterprise public data of the enterprise can be obtained. After obtaining additional data from third-party sources, the next step is to integrate this newly obtained information into the original application information to fill the previous gaps. Data merging and cleaning can be achieved through automated scripts. Natural language processing technology can help identify key terms and phrases in the text, which is crucial for understanding the core business areas and special needs of the enterprise. For example, through NLP, it can be identified that a description mentions "software development", "hardware manufacturing" or "environmental protection products", and then map them to the corresponding enterprise labels.

[0089] As Figure 4 shown, as a preferred embodiment of the present invention, the step of generating a layout plan for the location of the settled enterprises based on multiple alternative nodes specifically includes:

[0090] S401. Integrate the supply chain relationship map with the virtual distribution model of the park to obtain a park distribution relationship model, and the virtual distribution model is constructed according to the original layout of the park;

[0091] S402. Based on the park distribution relationship model, perform location marking with reference to the alternative nodes;

[0092] S403. Select the optimal area according to the marked location, and the optimal area is the park location where no enterprise has settled and is the closest to the alternative node;

[0093] S404. Calculate and generate operation cost data according to the optimal area, and generate a layout plan in combination with the location of the optimal area.

[0094] In an embodiment of the present invention, the enterprise nodes in the supply chain relationship map are mapped to the corresponding geographic coordinates in the virtual distribution model to form a park distribution relationship model that includes supply chain relationships and geographic location information. This allows us to intuitively see which enterprises are located in the same area, how far apart they are, and the impact of these distances on inter-enterprise collaboration. The locations of all enterprises that may be associated with the newly settled enterprise in the supply chain are marked in the park distribution relationship model. Considering the synergy between upstream and downstream enterprises in the supply chain relationship, selecting the location closest to the existing supply chain node helps reduce logistics costs and improve response speed. Based on the distance between enterprises and the expected logistics volume, the transportation costs are estimated, and it is considered whether sufficient electricity, water and other resources can be provided in the optimal area to support the daily operations of the enterprise. Combined with the above calculation results, the total operating costs at different locations are analyzed, and the location with the lowest overall cost is selected as the recommended layout plan. In addition, it is also necessary to consider whether the location is conducive to future expansion and whether there are good transportation connections to ensure the long-term feasibility of the plan.

[0095] like Figure 5 As shown, an embodiment of the present invention further provides an intelligent management system for enterprise entry into a smart park, the system comprising:

[0096] A relationship map building module 100 is used to build a supply chain relationship map based on the transaction data of existing enterprises, wherein the existing enterprises are enterprises that have settled in the park, and the supply chain relationship map is composed of a plurality of nodes based on the settled enterprises;

[0097] The enterprise label establishment module 200 is used to obtain the application information of the settled enterprise and generate the enterprise label of the settled enterprise according to the application information;

[0098] A node matching module 300 is used to match the nodes in the supply chain relationship graph according to the enterprise labels of the settled enterprises to obtain multiple candidate nodes, wherein the enterprises corresponding to the candidate nodes are in an upstream and downstream relationship with the settled enterprises in the supply chain relationship;

[0099] The plan generation module 400 generates a layout plan for the location of the settled enterprises based on multiple candidate nodes.

[0100] In the embodiments of the present invention, first, the transaction data of existing enterprises in the park is used to establish a supply chain relationship map in the park, which can more intuitively describe the fields and roles of enterprises in the park. When a new enterprise enters the park, according to the application information submitted by the enterprise to the park, natural language processing technology is used to extract the effective text in the application information to generate enterprise tags associated with the entering enterprise, so that the entering enterprise can be matched with the original supply chain relationship map, and then its best location in the park can be determined. In summary, the present invention can provide assistance in location selection for the entry of enterprises in the smart park, enabling the entering enterprises to conduct transaction exchanges with other enterprises in the park at a lower cost and effectively promoting the clustered development of the upstream and downstream industries.

[0101] As Figure 6 shown, as a preferred embodiment of the present invention, the relationship map construction module 100 includes:

[0102] A data collection unit 101 for collecting the transaction data of existing enterprises, where the transaction data includes information of both parties to the transaction, transaction amount, transaction frequency, product type, and transaction time;

[0103] A data processing unit 102 for deleting duplicate data in the transaction data and performing a complement processing on it using a prediction model;

[0104] A data standardization unit 103 for formatting and unifying all the transaction data;

[0105] A map generation unit 104 for generating a supply chain relationship map with the information of both parties to the transaction as nodes and the transaction amount, transaction frequency, product type, and transaction time as edges.

[0106] As Figure 7 shown, as a preferred embodiment of the present invention, the enterprise tag establishment module 200 includes:

[0107] An information verification unit 201 for performing integrity verification on the application information based on tag classification, where the tag classification includes industry tags, scale tags, function tags, and demand tags;

[0108] An information retrieval unit 202 for retrieving information about the enterprise to obtain third-party data according to the existing application information when the application information is incomplete;

[0109] A data supplementation unit 203 for using the third-party data to perform a complement processing on the application information;

[0110] A tag generation unit 204 for identifying keywords in the processed application information using natural language processing technology and generating enterprise tags with reference to the tag classification.

[0111] AsFigure 8 As shown, as a preferred embodiment of the present invention, the solution generation module 400 includes:

[0112] A data association unit 401 for integrating the supply chain relationship graph with the virtual distribution model of the park to obtain a park distribution relationship model, where the virtual distribution model is constructed based on the original layout of the park;

[0113] A position marking unit 402 for marking positions with reference to the alternative nodes based on the park distribution relationship model;

[0114] A region selection unit 403 for selecting an optimal region according to the marked positions, where the optimal region is the park location that has no enterprises settled in and is the closest to the alternative nodes;

[0115] An accounting integration unit 404 for calculating and generating operation cost data according to the optimal region and generating a layout plan in combination with the position of the optimal region.

[0116] The above only describes the preferred embodiments of the present invention in detail and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0117] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown in sequence according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0118] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0119] After considering the specification and the disclosure of the embodiments, those skilled in the art will readily conceive of other embodiments of the present disclosure. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

Claims

1. An intelligent management method for enterprise settlement in a smart park, characterized in that, The method includes the following steps: Build a supply chain relationship graph based on the transaction data of existing enterprises, where the existing enterprises are the enterprises that have entered the park, and the supply chain relationship graph consists of multiple nodes with the existing enterprises as units; Obtain the application information of the entering enterprises, and generate enterprise labels for the entering enterprises according to the application information; Match the nodes in the supply chain relationship graph according to the enterprise labels of the entering enterprises to obtain multiple alternative nodes, and the enterprises corresponding to the alternative nodes are in an upstream and downstream relationship with the entering enterprises in the supply chain relationship; Generate a layout plan for the location of the entering enterprises based on the multiple alternative nodes.

2. The intelligent management method for enterprise settlement in the smart park according to claim 1, wherein The step of building a supply chain relationship graph based on the transaction data of existing enterprises specifically includes: Collect the transaction data of existing enterprises, where the transaction data includes information on both parties to the transaction, transaction amount, transaction frequency, product type, and transaction time; Delete the duplicate data in the transaction data and use a prediction model to complete it; Format and unify all the transaction data; Generate a supply chain relationship graph with information on both parties to the transaction as nodes and transaction amount, transaction frequency, product type, and transaction time as edges.

3. The enterprise settlement intelligent management method for the smart park according to claim 2, characterized in that, The method further includes: Traverse and select two related nodes based on the supply chain relationship graph; Extract the transaction amount, transaction frequency, and transaction time in the transaction data of the two nodes, and calculate the cooperation time based on the transaction time; Use normalization to process the transaction amount, transaction frequency, and cooperation time to obtain the intensity coefficient of enterprise cooperation; Integrate the intensity coefficient into the supply chain relationship graph.

4. The intelligent management method for enterprise settlement in the smart park according to claim 1, characterized in that, The step of generating enterprise labels for the entering enterprises according to the application information specifically includes: Check the integrity of the application information based on label classification, where the label classification includes industry label, scale label, function label, and demand label; When the application information is incomplete, retrieve information about the enterprise based on the existing application information to obtain third-party data; Use the third-party data to complete the application information; Use natural language processing technology to identify keywords in the processed application information and generate enterprise labels with reference to the label classification.

5. The enterprise settlement intelligent management method for the smart park according to claim 4, wherein, The third-party data includes industrial and commercial registration information, industry reports, and enterprise public data.

6. The enterprise settlement intelligent management method for an intelligent park according to claim 1, characterized in that The step of generating a layout plan for the location of the entering enterprises based on the multiple alternative nodes specifically includes: Integrate the supply chain relationship graph with the virtual distribution model of the park to obtain a park distribution relationship model, where the virtual distribution model is constructed according to the original layout of the park; Mark the positions based on the park distribution relationship model with reference to the alternative nodes; Select the optimal area according to the marked positions, where the optimal area is the park location where no enterprise has entered and is the closest to the alternative nodes; Calculate and generate operation cost data according to the optimal area, and generate a layout plan in combination with the location of the optimal area.

7. An intelligent management system for enterprise settlement in a smart park, characterized in that The system includes: A relationship graph construction module that builds a supply chain relationship graph based on the transaction data of existing enterprises, where the existing enterprises are the enterprises that have entered the park, and the supply chain relationship graph consists of multiple nodes with the existing enterprises as units; An enterprise label establishment module, which is used to obtain the application information of the settled enterprises and generate enterprise labels for the settled enterprises according to the application information; A node matching module, which is used to match the nodes in the supply chain relationship graph according to the enterprise labels of the settled enterprises to obtain multiple alternative nodes, and the enterprises corresponding to the alternative nodes are in an upstream and downstream relationship with the settled enterprises in the supply chain relationship; A solution generation module, which generates a layout solution for the location of the settled enterprises based on multiple alternative nodes.

8. The enterprise settlement intelligent management system of the smart park according to claim 7, characterized in that, The relationship graph construction module includes: A data collection unit, which is used to collect the transaction data of existing enterprises, and the transaction data includes information of both parties to the transaction, transaction amount, transaction frequency, product type, and transaction time; A data processing unit, which is used to delete duplicate data in the transaction data and perform completion processing on it using a prediction model; A data standardization unit, which is used to uniformly format all transaction data; A graph generation unit, which is used to generate a supply chain relationship graph with information of both parties to the transaction as nodes and transaction amount, transaction frequency, product type, and transaction time as edges.

9. The enterprise settlement intelligent management system for an intelligent park according to claim 7, wherein, The enterprise label establishment module includes: An information verification unit, which conducts integrity verification on the application information based on label classification, and the label classification includes industry label, scale label, function label, and demand label; An information retrieval unit, which is used to retrieve enterprise information to obtain third-party data according to the existing application information when the application information is incomplete; A data supplementation unit, which is used to supplement and process the application information using third-party data; A label generation unit, which uses natural language processing technology to identify keywords in the processed application information and generate enterprise labels with reference to the label classification.

10. The enterprise settlement intelligent management system for the smart park according to claim 7, characterized in that, The solution generation module includes: A data integration unit, which is used to integrate the supply chain relationship graph with the virtual distribution model of the park to obtain a park distribution relationship model, and the virtual distribution model is constructed according to the original layout of the park; A location marking unit, which conducts location marking based on the park distribution relationship model with reference to the alternative nodes; An area selection unit, which is used to select the optimal area according to the marked location, and the optimal area is the park location where no enterprise has settled and is the closest to the alternative nodes; An accounting integration unit, which is used to calculate and generate operation cost data according to the optimal area and generate a layout solution in combination with the location of the optimal area.

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