Industrial chain prosperity analysis method, equipment and medium

Abstract

The invention discloses an industrial chain prosperity analysis method and device and a medium, and the method comprises the steps: carrying out the data cleaning, data deduplication and standardization processing of all-link data of an enterprise industrial chain, and obtaining standard all-link data; inputting the standard full-link data into a pre-trained prosperity processing model for weighted summation, and generating an industrial chain prosperity score; according to a preset grid division standard, grid division is carried out on the enterprise location to generate a grid enterprise area; and associating the industry chain prosperity score with a corresponding enterprise in the enterprise area, determining an enterprise prosperity level, and carrying out resource allocation according to the enterprise prosperity level. According to the method, the full-link data of the enterprise industry chain is adopted, the prosperity characteristics of all links of the industry chain are comprehensively described, and misjudgment caused by data one-sidedness is avoided. And through real-time updating of the prosperity processing model, the decision timeliness is enhanced, and the resource allocation efficiency and the marketing success rate are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, and in particular to an industry chain prosperity degree analysis method, device and medium. BACKGROUND

[0002] In the current industrial economic field, industry prosperity evaluation and marketing decision-making face many challenges.

[0003] Most of the existing industry chain prosperity evaluation analysis methods rely on single-dimensional data such as financial indicators or policy documents, and lack multi-dimensional dynamic monitoring of the whole link of the industry chain, which leads to one-sidedness and hysteresis in the evaluation results; further leading to the difficulty of accurately positioning high-potential customers in marketing decision-making combined with the changes in the prosperity of the industry chain, and the irrationality in resource allocation. These problems lead to the fact that financial institutions, investment institutions and enterprises cannot fully and timely understand the actual situation of the prosperity of the industry chain when making decisions, which affects the efficiency of resource allocation and the success rate of marketing. SUMMARY

[0004] The embodiments of the present application provide an industry chain prosperity analysis method, device and medium, which are used to solve the problem of low resource allocation efficiency and marketing success rate caused by insufficient data analysis in the existing prosperity analysis method.

[0005] The embodiments of the present application adopt the following technical solutions:

[0006] On the one hand, the embodiments of the present application provide an industry chain prosperity analysis method, which comprises: performing data cleaning, data deduplication and standardization processing on the whole link data of the enterprise industry chain to obtain standard whole link data; inputting the standard whole link data into a pre-trained prosperity processing model for weighted summation to generate an industry chain prosperity score; according to a preset grid division standard, performing grid division on the location of the enterprise to generate a grid-based enterprise region; associating the industry chain prosperity score with the corresponding enterprises in the enterprise region to determine the enterprise prosperity grade, so as to allocate resources according to the enterprise prosperity grade.

[0007] In one example, the standard full-link data is input into a pre-trained industry chain prosperity processing model for weighted summation to generate an industry chain prosperity score, specifically including: classifying the standard full-link data according to data structure types to obtain standard structured data and standard unstructured data; performing hierarchical analysis on the standard structured data by an analytic hierarchy process, and assigning a corresponding weight to each index of the standard structured data according to a preset expert scoring table; performing NLP analysis on the standard unstructured data, extracting keywords of the standard unstructured data, and assigning a corresponding weight to each index of the standard unstructured data according to a correlation degree between the keywords and enterprise prosperity; and performing weighted summation on the corresponding weight of each index of the standard structured data and the corresponding weight of each index of the standard unstructured data to determine the industry chain prosperity score.

[0008] In one example, after the standard full-link data is input into the pre-trained industry chain prosperity processing model for weighted summation to generate the industry chain prosperity score, the method further includes: inputting the full-link data and historical industry chain prosperity scores into a pre-trained LSTM and Transformer architecture according to a preset prediction period to generate an industry chain prosperity prediction index within the preset period; and performing NLP analysis on the industry chain prosperity score and the industry chain prosperity prediction index to generate an industry chain prosperity analysis report.

[0009] In one example, after the standard full-link data is input into the pre-trained industry chain prosperity processing model for weighted summation to generate the industry chain prosperity score, the method further includes: acquiring new industry chain full-link data in real time according to a preset score update period; inputting the new industry chain full-link data into the industry chain prosperity processing model, updating the industry chain prosperity score by an online learning algorithm, and generating a heat map of the industry chain prosperity score; and performing three-dimensional modeling on the heat map by a virtual scene scanning technology to generate an AR three-dimensional model of the industry chain prosperity score.

[0010] In one example, the location of an enterprise is divided into a grid according to a preset grid division standard to generate a grid-based enterprise region, specifically including: dividing the location of the enterprise into a grid according to an administrative region range to generate a fixed enterprise grid region; and adjusting the boundary of the fixed enterprise grid region by a geographic clustering algorithm according to a preset prosperity fluctuation threshold to generate a dynamic grid-based enterprise region.

[0011] In one example, after the industry chain prosperity score is associated with corresponding enterprises in an enterprise region to determine an enterprise prosperity level, and resource allocation is performed according to the enterprise prosperity level, the method further includes: performing risk assessment and innovation assessment on all enterprises in the enterprise region in descending order of the enterprise prosperity level.

[0012] In one example, according to the enterprise prosperity level from high to low, risk assessment and innovation assessment are performed on all enterprises in the enterprise region, specifically including: comparing the scientific and technological innovation dimension index of the enterprise with the preset scientific and technological innovation index threshold, if greater than the threshold, the enterprise is given an innovation label; matching the enterprise with the innovation policy of the government, if the matching is successful, the enterprise is given an innovation policy bonus label; comparing the risk dimension index of the enterprise with the preset risk index threshold, if greater than the threshold, the enterprise is given a high-risk label; when the enterprise has a high-risk label, the affected degree of the upstream suppliers and downstream customers of the high-risk label enterprise is traced through the knowledge graph, a risk heat map is generated, and a resource avoidance path is provided.

[0013] In one example, after the risk assessment and innovation assessment are performed on all enterprises in the enterprise region according to the enterprise prosperity level from high to low, the method further includes: adding the enterprises in the enterprise region with the innovation policy bonus label and without the high-risk label to a preset marketing list library; generating a marketing personnel distribution table, a marketing progress follow-up table and an enterprise marketing record table of the enterprises in the list library according to the marketing list library.

[0014] In another aspect, the embodiments of the present application provide an industrial chain prosperity analysis device, comprising: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform any one of the industrial chain prosperity analysis methods.

[0015] In another aspect, the embodiments of the present application provide an industrial chain prosperity analysis nonvolatile computer storage medium, which stores computer executable instructions, and the computer executable instructions can execute any one of the industrial chain prosperity analysis methods.

[0016] The above at least one technical scheme adopted by the embodiments of the present application can achieve the following beneficial effects:

[0017] The present application comprehensively depicts the prosperity characteristics of each link of the industrial chain by using the full-link data of the enterprise industrial chain, avoids misjudgment caused by one-sided data; through real-time updating of the prosperity processing model, the timeliness of decision-making is enhanced, and the efficiency of resource allocation and the success rate of marketing are improved; by taking scientific and technological innovation qualification as the core evaluation dimension, precise resource inclination is realized, customized marketing strategies such as preferential matching of financing support and technical cooperation are realized for high scientific and technological innovation score enterprises, and the method helps to seize the commanding point of emerging industries; by taking enterprise risk as the core evaluation dimension, potential risk of violent explosion caused by blindly pursuing high growth is avoided, and the safety of resource allocation is ensured. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the present application, some embodiments of the present application will be described in detail below with reference to the accompanying drawings, in which:

[0019] Figure 1 A flowchart of an industry chain prosperity degree analysis method provided by an embodiment of the present application is shown in FIG. 1.

[0020] Figure 2 A module execution flowchart of an industry chain prosperity degree analysis method provided by an embodiment of the present application is shown in FIG. 2.

[0021] Figure 3 A structure diagram of an industry chain prosperity degree analysis device provided by an embodiment of the present application is shown in FIG. 3. DETAILED DESCRIPTION

[0022] In order to make the purposes, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described in detail below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0023] Some embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0024] Figure 1 A flowchart of an industry chain prosperity degree analysis method provided by an embodiment of the present application is shown in FIG. 1. The method can be applied to different business fields. Some input parameters or intermediate results in the flow allow manual intervention adjustment to help improve accuracy.

[0025] The implementation of the analysis method related by the embodiments of the present application can be a terminal device or a server, and the present application does not make special limitations thereon. For the convenience of understanding and description, the following embodiments are described in detail with the controller as an example.

[0026] Based on this, Figure 1 The flow in the method can include the following steps:

[0027] S101: Perform data cleaning, data deduplication and standardization processing on the full-link data of the enterprise industry chain to obtain standard full-link data.

[0028] In some embodiments of the present application, before the prosperity analysis of the enterprise industrial chain is carried out, the full-link data of the enterprise industrial chain needs to be obtained, and the specific full-link data includes but is not limited to: policy support index data, enterprise news public opinion index data (media reports, industry forum public opinion data), market demand index data (number of business opportunities, bidding information, order growth trend), enterprise basic information index data (number, scale, registered capital, type, establishment time), enterprise operation index data (revenue, gross profit, turnover efficiency, debt level), enterprise investment and financing (investment amount, financing times and single amount), enterprise qualification and intellectual property (number of patents, qualification certification, technology research and development investment), enterprise risk information (judicial risk, abnormal operation, negative public opinion), enterprise talent and recruitment (number of recruitment positions, talent flow rate), enterprise credit rating (third-party credit score and historical performance record).

[0029] The full-link data of the enterprise industrial chain is accessed through a public database, an API interface or a crawler, and further, after accessing the data, the full-link data of the enterprise industrial chain is subjected to data cleaning (removing noise, etc.), data deduplication (removing duplicate descriptions) and standardization processing (such as uniform format, etc.).

[0030] The present application comprehensively depicts the prosperity characteristics of each link of the industrial chain by using the full-link data of the enterprise industrial chain, avoids misjudgment caused by one-sided data, and increases the credibility and accuracy of the enterprise prosperity.

[0031] S102: inputting the standard full-link data into a pre-trained prosperity processing model for weighted summation to generate an industrial chain prosperity score; the prosperity processing model includes an analytic hierarchy process and a machine learning algorithm architecture.

[0032] In some embodiments of the present application, before the standard full-link data is input into the pre-trained prosperity processing model for weighted summation, the prosperity processing model needs to be constructed first. The industrial prosperity processing model is constructed by an analytic hierarchy process and a machine learning algorithm, and is trained using historical full-link data to obtain a trained prosperity processing model.

[0033] Further, the standard full-link data is input into the prosperity processing model, and the specific processing operation is to classify the full-link data of the enterprise industrial chain. According to the data structure type, the standard full-link data of the enterprise industrial chain is classified to obtain standard structured data and standard unstructured data. Then, the standard structured data is subjected to hierarchical analysis by an analytic hierarchy process, and each index of the standard structured data is given a corresponding weight according to a pre-set expert scoring table (for example, policy support weight 20%, market demand weight 15%).

[0034] At the same time, the NLP analysis is performed on the standard unstructured data, the keywords of the standard unstructured data are extracted, and each index of the standard unstructured data is given a corresponding weight according to the correlation degree of the keywords and the enterprise prosperity degree (for example, the correlation degree of high revenue is higher than that of low revenue).

[0035] Further, the corresponding weights of each index of the standard structured data and the corresponding weights of each index of the standard unstructured data are summed to determine the industry chain prosperity degree score (0-100 points).

[0036] It should be noted that the application also presets a prosperity degree score update period (for example, 1 day update 1 time), and when the preset prosperity degree score update period is reached, the new industry chain full-link data is obtained in real time through a public database, an API interface or a crawler, and then the new industry chain full-link data is input into the prosperity degree processing model, the industry chain prosperity degree score is updated through an online learning algorithm, and an AR three-dimensional model of the industry chain prosperity degree score is generated through a virtual scene scanning technology.

[0037] Further, according to a preset prediction period (for example, the industry chain prosperity degree in the next 6 months), the full-link data and the historical industry chain prosperity degree score are input into a pre-trained LSTM and Transformer architecture to generate an industry chain prosperity degree prediction index in the preset period, and the industry chain prosperity degree score and the industry chain prosperity degree prediction index are analyzed by NLP to generate an industry chain prosperity degree analysis report, wherein the report includes the future industry chain prosperity degree trend and investment suggestions.

[0038] The application expands the present situation evaluation to trend prediction through the prediction of the prosperity degree, improves the decision foresight through the time series prediction ability, and is different from the traditional static scoring model. And through the real-time update of the prosperity degree processing model, the decision timeliness is enhanced, and the resource allocation efficiency and the marketing success rate are improved.

[0039] S103: According to a preset grid division standard, the location of the enterprise is divided into a grid to generate a grid enterprise area.

[0040] In some embodiments of the application, while generating the industry chain prosperity degree score, the application also divides the location of the enterprise into a grid according to the administrative region range to generate a fixed enterprise grid area.

[0041] Then according to the preset prosperity fluctuation threshold, the fixed enterprise grid area is adjusted in boundary (for example, when the prosperity of new energy enterprises in a certain area rises sharply, the surrounding grids are dynamically merged to form a "high prosperity industrial belt"), and the traditional fixed administrative region division is replaced to generate a dynamic grid enterprise region.

[0042] Through the geographical clustering algorithm and the prosperity fluctuation, the grid division is upgraded from "static preset" to "dynamic adaptation", which is more in line with the actual evolution of the industrial chain space aggregation.

[0043] S104: associate the industry chain prosperity score with the corresponding enterprises in the enterprise region, determine the enterprise prosperity level, and allocate resources according to the enterprise prosperity level.

[0044] In some embodiments of the present application, after generating a dynamic grid enterprise region, the industry chain prosperity score is associated with the corresponding enterprises in the enterprise region to determine the enterprise prosperity level (S / A / B / C level). Further, according to the enterprise prosperity level from high to low, the risk assessment and innovation assessment of all enterprises in the enterprise region are carried out, and the specific steps are as follows:

[0045] The enterprise's scientific and technological innovation dimension index is compared with the preset scientific and technological innovation index threshold. If it is greater than the threshold, the enterprise is given an innovation label, wherein the scientific and technological innovation dimension index includes the number of patents and the technical certification level of the enterprise.

[0046] Then the enterprise with the innovation label is matched with the government innovation policy. If the matching is successful, the enterprise is given an innovation policy bonus label, which means that the enterprise meets the government's policy support.

[0047] At the same time, the risk dimension index of the enterprise is compared with the preset risk index threshold. If it is greater than the threshold, the enterprise is given a high-risk label, wherein the risk dimension index includes the number of cases involved and the abnormal operation records.

[0048] Then, when the enterprise has a high-risk label, the affected degree of the upstream suppliers and downstream customers of the high-risk label enterprise is traced through the knowledge graph to generate a risk heat map and provide a resource avoidance path.

[0049] Furthermore, enterprises within the enterprise's region that possess innovation policy benefits but lack high-risk labels are added to a pre-defined marketing list. Then, based on this list, a marketing personnel allocation table, a marketing progress tracking table, and an enterprise marketing record table are generated. The marketing personnel allocation table displays the assigned enterprises to specific marketing personnel, who can view the enterprise's basic information and business opportunities. The marketing progress tracking table displays the marketing follow-up progress for each enterprise, helping users manage the enterprises they are following. The enterprise marketing record table records the enterprise's marketing progress, helping users manage and track the enterprise's marketing progress.

[0050] This application uses science and technology innovation qualifications as the core evaluation dimension to achieve precise resource allocation, prioritizing customized marketing strategies such as financing support and technology cooperation for enterprises with high science and technology innovation scores, thus helping them seize the commanding heights of emerging industries. By using enterprise risk as the core evaluation dimension, it avoids ignoring potential risks of failure due to blindly pursuing high growth, thereby ensuring the safety of resource allocation.

[0051] It should be noted that, although the embodiments in this application are based on... Figure 1 Steps S101 to S104 will be described sequentially, but this does not mean that steps S101 and S104 must be performed in a strict order. The reason this embodiment follows this order is... Figure 1 The order in which steps S101 to S104 are described is provided to facilitate understanding of the technical solutions of the embodiments of this application by those skilled in the art. In other words, in the embodiments of this application, the order of steps S101 to S104 can be appropriately adjusted according to actual needs.

[0052] pass Figure 1 This application employs data from the entire enterprise value chain to comprehensively depict the prosperity characteristics of each link in the value chain, avoiding misjudgments caused by data bias. Real-time updates to the prosperity processing model enhance decision-making timeliness, improve resource allocation efficiency, and increase marketing success rates. By using scientific and technological innovation qualifications as a core evaluation dimension, precise resource allocation is achieved, prioritizing customized marketing strategies such as financing support and technological cooperation for enterprises with high scientific and technological innovation scores, helping them seize the commanding heights of emerging industries. Furthermore, by using enterprise risk as a core evaluation dimension, this application avoids ignoring potential risks of default due to blindly pursuing high growth, ensuring the safety of resource allocation.

[0053] Figure 2 This is a schematic diagram of the module execution flow of a method for analyzing the prosperity of an industrial chain provided in an embodiment of this application.

[0054] exist Figure 2In the embodiment, the functional modules involved in the application are shown, including a data acquisition module, an industry chain prosperity processing model, a grid marketing decision module, and a visualization and management module, and the specific implementation functions of each module in the application are shown in detail.

[0055] Figure 3 A structural schematic diagram of an industry chain prosperity analysis device provided by the embodiment of the application, comprising:

[0056] at least one processor; and

[0057] a memory in communication connection with the at least one processor; wherein

[0058] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform any one of the industry chain prosperity analysis methods.

[0059] Some embodiments of the application provide an industry chain prosperity analysis nonvolatile computer storage medium, which stores computer executable instructions, and the computer executable instructions can execute any one of the industry chain prosperity analysis methods.

[0060] Each of the embodiments in the application is described in a progressive manner, and the same or similar parts of each of the embodiments can be referred to each other. Each of the embodiments mainly describes the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, and thus the description is relatively simple, and the related parts can be referred to the part of the method embodiment.

[0061] The device and medium provided by the embodiments of the application correspond to the method, and thus the device and medium have similar beneficial technical effects as the method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device and medium will not be described here.

[0062] Those skilled in the art should understand that the embodiments of the application can be provided as a method, a system, or a computer program product. Therefore, the application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0063] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0064] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0065] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0066] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0067] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) about which the processor can execute instructions. The memory can also include non-volatile memory, such as read only memory (ROM) or flash RAM, about which too the processor can execute instructions. The memory thus is an example of computer-readable media.

[0068] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0069] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover non-exclusive inclusions, so that a process, method, article or apparatus that includes a list of elements does not only include those elements, but also includes other elements not explicitly listed, or further includes elements inherent in such a process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0070] The above is only an embodiment of the present application and is not intended to limit the present application. Those skilled in the art can make various modifications and changes to the present application. Any modification, equivalent replacement, improvement, etc. within the technical principles of the present application shall fall within the protection scope of the present application.

Claims

1. An industry chain prosperity analysis method, characterized in that, The method comprises: data cleaning, data deduplication and standardization processing are performed on full-link data of an enterprise industrial chain to obtain standard full-link data; the standard full-link data is input into a pre-trained prosperity processing model for weighted summation to generate an industrial chain prosperity score; the prosperity processing model comprises an analytic hierarchy process and a machine learning algorithm architecture; a grid division is performed on the location of the enterprise according to a preset grid division standard to generate a grid-based enterprise region; the industrial chain prosperity score is associated with the corresponding enterprise in the enterprise region to determine an enterprise prosperity level, and resource allocation is performed according to the enterprise prosperity level.

2. The method of claim 1, wherein, The standard full-link data is input into the pre-trained prosperity processing model for weighted summation to generate the industrial chain prosperity score, specifically comprising: standard full-link data is classified according to data structure types to obtain standard structured data and standard unstructured data; a hierarchical analysis is performed on the standard structured data by the analytic hierarchy process, and each index of the standard structured data is given a corresponding weight according to a preset expert scoring table; NLP analysis is performed on the standard unstructured data to extract keywords of the standard unstructured data, and each index of the standard unstructured data is given a corresponding weight according to the correlation degree of the keywords and the enterprise prosperity; the corresponding weights of each index of the standard structured data and the corresponding weights of each index of the standard unstructured data are summed to determine the industrial chain prosperity score.

3. The method of claim 1, wherein, After the standard full-link data is input into the pre-trained prosperity processing model for weighted summation to generate the industrial chain prosperity score, the method further comprises: According to a preset prediction period, the full-link data and the historical industrial chain prosperity score are input into a pre-trained LSTM and Transformer architecture to generate an industrial chain prosperity prediction index within the preset period; NLP analysis is performed on the industrial chain prosperity score and the industrial chain prosperity prediction index to generate an industrial chain prosperity analysis report.

4. The method of claim 1, wherein, After the standard full-link data is input into the pre-trained prosperity processing model for weighted summation to generate the industrial chain prosperity score, the method further comprises: According to a preset scoring update period, new industrial chain full-link data is obtained in real time; the new industrial chain full-link data is input into the prosperity processing model, and the industrial chain prosperity score is updated by an online learning algorithm, and a heat map of the industrial chain prosperity score is generated; a three-dimensional model of the industrial chain prosperity score is generated by a virtual scene scanning technology.

5. The method of claim 1, wherein, According to a preset grid division standard, the location of the enterprise is divided into a grid to generate a grid-based enterprise region, specifically comprising: According to the administrative region range, the location of the enterprise is divided into a grid to generate a fixed enterprise grid region; According to a preset prosperity fluctuation threshold, the fixed enterprise grid region is adjusted in boundary by a geographic clustering algorithm to generate a dynamic grid-based enterprise region.

6. The method of claim 1, wherein, The method further comprises: After the association of the industry chain prosperity score with the corresponding enterprises in the enterprise region and the determination of the enterprise prosperity level for resource allocation according to the enterprise prosperity level, the method further comprises:

7. The method of claim 6, wherein, According to the enterprise prosperity level from high to low, risk assessment and innovation assessment are performed on all enterprises in the enterprise region. The risk assessment and innovation assessment according to the enterprise prosperity level from high to low specifically include: Comparing the enterprise's scientific and technological innovation dimension index with the preset scientific and technological innovation index threshold, if greater than the threshold, the enterprise is given an innovation label; the scientific and technological innovation dimension index includes the number of patents and the technical certification level of the enterprise; Matching the enterprise with an innovation label with government innovation policies, if the matching is successful, the enterprise is given an innovation policy bonus label; Comparing the enterprise's risk dimension index with the preset risk index threshold, if greater than the threshold, the enterprise is given a high-risk label; the risk dimension index includes the number of cases involved and abnormal operation records; 8. The method of claim 6, wherein, When the enterprise has a high-risk label, the affected degree of the upstream suppliers and downstream customers of the high-risk label enterprise is traced through a knowledge graph to generate a risk heat map and provide a resource avoidance path. After the risk assessment and innovation assessment according to the enterprise prosperity level from high to low, the method further comprises: Enterprises in the enterprise region with an innovation policy bonus label and without a high-risk label are added to a preset marketing list library; 9. An industry chain mood analysis device characterized by comprising: According to the marketing list library, a marketing personnel allocation table, a marketing progress follow-up table, and an enterprise marketing record table of the enterprises in the list library are generated. It includes: At least one processor; And The memory is in communication connection with the at least one processor; wherein 10. An industry chain prosperity analysis storage medium storing computer executable instructions, characterized in that, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the industry chain prosperity analysis method of any one of claims 1-8. The computer executable instructions can perform the industry chain prosperity analysis method of any one of claims 1-8.

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