Digital economy dynamic analysis method and system based on multi-modal large model

Abstract

The invention relates to a digital economy dynamic analysis method and system based on a multi-modal large model. The method comprises the following steps: acquiring multi-modal multi-source data in real time; substituting the multi-source data into an analysis module, and updating an economic activity feature set; in response to the update of the economic activity feature set, substituting the economic activity feature set into an economic dynamic analysis module to obtain a dynamically updated economic analysis template and an industry knowledge graph corresponding to each business district; and in response to an industry analysis instruction of the user terminal, generating a pre-estimated image-text report based on the latest version of economic analysis template and the industry knowledge graph, and feeding back the pre-estimated image-text report to the user terminal. According to the method, automatic analysis and fusion of the multi-modal data information are realized, the processing efficiency of the multi-modal data is improved, dynamic updating of the economic analysis template and automatic construction of the knowledge graph are realized, and a real-time pre-estimated image-text report is fed back to a user in time, so that the accuracy and dynamic pre-estimation capability of the pre-estimated image-text report are improved, and the user experience is improved. And the accuracy of dynamic analysis of digital economy is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to the technical field of digital economy dynamic analysis, and in particular to a digital economy dynamic analysis method and system based on a multi-modal large model. BACKGROUND

[0002] With the vigorous development of the digital economy, enterprises as market mainstays, their dynamic changes have become an important window for observing the development of the digital economy. However, different modal data information is scattered in various systems, forming a "data island", which makes it difficult to associate cross-field knowledge, and the data analysis technology in the prior art has the problems of rigid business process, low collaboration efficiency, and reliance on manual intervention for link connection, which is prone to information delay and decision bias, directly affecting the timeliness of economic analysis.

[0003] Therefore, a digital economy dynamic analysis method and system based on a multi-modal large model are needed, which can analyze and integrate cross-modal data, and enable real-time dynamic updating of digital economy analysis and industry knowledge. SUMMARY

[0004] Therefore, the present application proposes a digital economy dynamic analysis method and system based on a multi-modal large model, which solves the technical problems of rigid business process, low collaboration efficiency, and reliance on manual intervention for link connection in the prior art, which is prone to information delay and decision bias, directly affecting the timeliness of economic analysis.

[0005] According to a first aspect of the present application, a digital economy dynamic analysis method based on a multi-modal large model is provided, the method comprising the following steps: real-time acquisition of multi-modal multi-source data, wherein the multi-source data at least includes satellite remote sensing images, enterprise financial reports, and social media texts; substituting the multi-source data into an analysis module to update an economic activity feature set, wherein the economic activity feature set at least includes factory start-up features, commercial district flow density features, traffic features, service facility distribution features, and commercial district market features; in response to the update of the economic activity feature set, substituting the economic activity feature set into an economic dynamic analysis module to obtain a dynamically updated economic analysis template and an industry knowledge graph corresponding to each commercial district; in response to an industry analysis instruction of a user terminal, generating an estimated text report based on the latest version of the economic analysis template and the industry knowledge graph and feeding back to the user terminal.

[0006] According to an embodiment of the present application, substituting the multi-source data into the analysis module to update the economic activity feature set comprises: based on satellite remote sensing images and enterprise financial reports, obtaining factory start-up features; Based on satellite remote sensing images and social media texts, obtain business district passenger flow density characteristics, traffic characteristics, and service facility distribution characteristics; Based on enterprise financial reports and social media texts, obtain business district market characteristics; Based on the factory start-up characteristics, business district passenger flow density characteristics, traffic characteristics, service facility distribution characteristics, and business district market characteristics, update the economic activity feature set.

[0007] According to the embodiments of the present application, the economic activity feature set is substituted into the economic dynamic analysis module to obtain a dynamically updated economic analysis template and an industry knowledge graph corresponding to each business district, which includes: Based on the latest version of the economic activity feature set, obtain the basic feature comprehensive score of different industries; Obtain the current industry guidance feature set, wherein the feature types of the industry guidance feature set at least include policy response features, technology breakthrough features, industry correlation features, and industry chain transmission features; Based on the basic feature comprehensive score of different industries and the current industry guidance feature set, obtain the recommended index corresponding to different industries in each business district; Based on the recommended index corresponding to different industries in each business district, update the economic analysis template and the industry knowledge graph corresponding to each business district.

[0008] According to the embodiments of the present application, based on the latest version of the economic activity feature set, the basic feature comprehensive score of different industries is obtained, which includes: The basic feature comprehensive score of different industries is obtained by the following formula: Wherein, represents the basic feature comprehensive score of the i-th industry, represents the standardized value of the j-th feature of the i-th industry, represents the dynamic weight of the j-th feature of the i-th industry, represents the number of features, here ; ; Wherein, the dynamic weight of the j-th feature of the i-th industry is obtained by the following formula: Wherein, represents the PCA weight, represents the j-th feature of the i-th industry, represents the j-th feature of the i-th industry, represents the j-th feature of the i-th industry, represents the j-th feature of the i-th industry, ​​​​a raw feature weight of the feature, a policy response feature value of the first industry, an industry feature correlation parameter of the first feature of the first industry.

[0009] According to an embodiment of the present application, the current industry guidance feature set is obtained by: determining a policy response feature value of each industry based on social media text; obtaining current technology data of each industry; determining a technology breakthrough feature value of each industry based on the current technology data of each industry; obtaining industry chain correlation data between different industries; determining an industry correlation feature value of each industry based on the industry chain correlation data between different industries and enterprise financial reports; determining an industry chain transmission feature value of each industry based on social media text, enterprise financial reports and industry chain correlation data between different industries; to obtain the current industry guidance feature set.

[0010] According to an embodiment of the present application, based on the basic feature comprehensive score of each industry and the current industry guidance feature set, the recommended index corresponding to each industry in each business circle is obtained by: the recommended index corresponding to each industry in each business circle is obtained by the following formula: wherein, the recommended index corresponding to the first industry in the first business circle, , , , , all represent dynamic weight coefficients matched with the first industry, the policy response feature value of the first industry, the technology breakthrough feature value of the first industry, the industry correlation feature value of the first industry, the industry chain transmission feature value of the first industry, the basic feature comprehensive score of the first industry in the first business circle.

[0011] According to an embodiment of the present application, the updating of the economic analysis template corresponding to each business circle and the industry knowledge graph based on the recommended index corresponding to different industries in each business circle comprises: generating the prompt word engineering parameter corresponding to each business circle based on the recommended index corresponding to different industries in each business circle; updating the economic analysis template corresponding to each business circle based on the recommended index corresponding to different industries in each business circle and the prompt word engineering parameter corresponding to each business circle; updating the industry knowledge graph corresponding to each business circle based on the recommended index corresponding to different industries in each business circle and the prompt word engineering parameter corresponding to each business circle.

[0012] According to an embodiment of the present application, the generating of the prompt word engineering parameter corresponding to each business circle based on the recommended index corresponding to different industries in each business circle comprises: sorting the multiple industries in each business circle based on the recommended index corresponding to different industries in each business circle to obtain the arrangement of the multiple industries in each business circle; determining the number of prompt word parameters corresponding to each industry in each business circle based on the arrangement position of each industry in each business circle; generating the prompt word engineering parameter corresponding to each business circle based on the number of prompt word parameters corresponding to each industry in each business circle.

[0013] According to an embodiment of the present application, the obtaining of the recommended index corresponding to different industries in each business circle based on the basic feature comprehensive score of different industries and the current industry guidance feature set further comprises: obtaining the policy response feature value of the i-th industry through the following formula: wherein, the i-th policy corresponds to a policy intensity parameter value, the matching degree between the i-th industry and the j-th policy target, the policy timeliness decay parameter value, and the weight value corresponding to the preset different policy level. obtaining the technical breakthrough feature value of the i-th industry through the following formula: wherein, the dynamic weight coefficient, the patent quality parameter value, and the weight value corresponding to the preset different policy level. ​​​​​​​​​​​​​Represents the R&D output parameter value; The number is obtained through the following formula. The industry and the first Industry-related characteristic values ​​of individual industries : in, Representing the The industry and the first Covariance of returns for each industry Representing the The industry and the first The industrial chain linkage coefficient of each industry Representing the Variance of returns for each industry Representing the The profitability of individual industries Representing the Variance of returns for each industry Representing the The rate of return of each industry.

[0014] According to a second aspect of the present invention, a digital economy dynamic analysis system based on a multimodal large model is provided. The system includes a communication device, a data acquisition device, a parsing module, an economic dynamic analysis module, and a control device. The communication device is used to realize information interaction between several user terminals and the system. The data acquisition device is used to acquire multimodal multi-source data in real time, acquire the current industry guidance feature set, acquire the current technical data of each industry, and acquire the industrial chain correlation data between different industries. The parsing module is used to parse the multi-source data to update the economic activity feature set. The economic dynamic analysis module is used to obtain a dynamically updated economic analysis template and industry knowledge graph corresponding to each business district based on the economic activity feature set. The control device includes a processor and a memory. The memory is adapted to store multiple program codes, which are adapted to be loaded and run by the processor to execute the digital economy dynamic analysis method based on a multimodal large model as described in any of the above-described technical solutions.

[0015] As can be seen from the above technical solution, the digital economy dynamic analysis method based on a multimodal large model provided by the present invention has the following beneficial effects: The application provides a digital economic dynamic analysis method based on a multi-modal large model, which realizes automatic analysis and fusion of multi-modal data information by substituting real-time acquired multi-modal multi-source data into an analysis module, improves the processing efficiency of multi-modal data, and in response to the update of the economic activity feature set, substitutes the economic activity feature set into an economic dynamic analysis module to obtain a dynamically updated economic analysis template and an industry knowledge graph corresponding to each business circle, realizes dynamic update of the economic analysis template and automatic construction of the knowledge graph, ensures the timeliness of the economic analysis template and the industry knowledge graph, and in response to an industry analysis instruction of a user terminal, generates an estimated graphic-text report according to the latest economic analysis template and the industry knowledge graph and feeds back to the user terminal, realizes timely feedback of the real-time estimated graphic-text report to the user, and further improves the accuracy and dynamic estimation capability of the estimated graphic-text report, ensures the accuracy of the digital economic dynamic analysis, and avoids the technical problems of existing technologies, such as business process solidification, low collaborative efficiency, link connection relying on manual intervention, easy information delay and decision deviation, and direct influence on the timeliness of economic analysis. BRIEF DESCRIPTION OF DRAWINGS

[0016] The disclosure of the application will become more apparent with reference to the drawings. It should be understood by those skilled in the art that the drawings are only for illustrative purposes, and are not intended to limit the scope of protection of the application. In addition, similar numbers in the drawings are used to represent similar components, wherein: Figure 1 is a main step flow diagram of a digital economic dynamic analysis method based on a multi-modal large model according to an embodiment of the application; Figure 2 is a main structure block diagram of a digital economic dynamic analysis system based on a multi-modal large model according to an embodiment of the application.

[0017] LIST OF REFERENCE NUMBERS: 200: digital economic dynamic analysis system; 201: control device; 2011: processor; 2012: memory; 2013: program code; 202: analysis module; 203: acquisition device; 204: economic dynamic analysis module; 205: communication device. DETAILED DESCRIPTION

[0018] Some embodiments of the application will be described below with reference to the drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the application, and are not intended to limit the scope of protection of the application.

[0019] In the description of the present application, "module" and "processor" can include hardware, software or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and can also include a software portion such as program code, and can be a combination of software and hardware. The processor can be a central processor, a microprocessor, a graphics processor, a digital signal processor or any other suitable processor. The processor has data and / or signal processing functions. The processor can be implemented in software, hardware or a combination of both. The non-transitory computer readable storage medium includes any suitable medium that can store program code, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The term "at least one of A or B" or "at least one of A and B" has a similar meaning to "A and / or B", and can include only A, only B, or A and B. The singular form of the term "one", "this" can also include the plural form.

[0020] Some terms related to the present application are explained here.

[0021] PCA, Principal Component Analysis, principal component analysis.

[0022] Referring to the accompanying Figure 1 , Figure 1 is a main step flowchart of a digital economy dynamic analysis method based on a multi-modal large model according to an embodiment of the present application. As shown in Figure 1 , the digital economy dynamic analysis method based on a multi-modal large model in the embodiment of the present application mainly includes the following steps S101-S104.

[0023] Step S101: Real-time acquisition of multi-modal multi-source data, wherein the multi-source data at least includes satellite remote sensing images, enterprise financial reports, social media texts; Step S102: Substituting the multi-source data into the analysis module to update the economic activity feature set, wherein the economic activity feature set at least includes factory start-up features, commercial district passenger flow density features, traffic features, service facility distribution features and commercial district market features; Specifically, substituting the multi-source data into the analysis module to update the economic activity feature set includes: Based on satellite remote sensing images and enterprise financial reports, the factory start-up features are obtained; Based on satellite remote sensing images and social media texts, the commercial district passenger flow density features, traffic features and service facility distribution features are obtained; Based on enterprise financial reports and social media texts, the commercial district market features are obtained; Update the set of economic activity features based on the factory start-up features, the commercial district people flow density features, the traffic features, the service facility distribution features, and the commercial district market features.

[0024] Specifically, the factory start-up features are obtained based on satellite remote sensing images and enterprise financial reports, and include: Based on satellite remote sensing images, determine the in-and-out vehicle parameters inside each factory; Based on enterprise financial reports, determine the capacity utilization rate of each factory; Based on the in-and-out vehicle parameters inside each factory and the capacity utilization rate of each factory, determine the factory start-up features, wherein the factory start-up features = the in-and-out vehicle parameters inside the factory * a preset vehicle weight parameter corresponding to the factory + the capacity utilization rate of the factory * a preset capacity utilization rate parameter corresponding to the factory.

[0025] Specifically, the commercial district people flow density features, the traffic features, and the service facility distribution features are obtained based on satellite remote sensing images and social media texts, and include: Commercial district people flow density features Obtained by the following formula: Wherein, represents a social media check-in data parameter, represents a mobile phone signaling data parameter, represents a preset focusing weight coefficient of the commercial district people flow density features, and here, , represents a preset commercial district area parameter, represents a preset time window, represents a weather correction factor, represents a decay coefficient, and here 0.1; Traffic features Obtained by the following formula: Wherein, represents the number of vehicles detected in a preset time period in a satellite image, represents the maximum capacity of a road section, represents the number of negative road condition feedbacks in a social media text, represents the total number of feedbacks in a social media text, represents a preset focusing weight coefficient of the traffic features, and here ; Service facility distribution features Obtained by the following formula: wherein, represent the surrounding people flow density parameter of the th facility, represent the check-in data in the social media text, represent the preset service facility distribution feature emphasis weight coefficient, here .

[0026] Specifically, based on the enterprise financial report and the social media text, the business district market features include: Business district market features obtained by the following formula: wherein, represent the gross profit margin parameter in the business district in the enterprise financial report, represent the keyword frequency parameter of different consumptions in the social media text, represent the total number of keywords, represent the preset business district market feature emphasis weight coefficient, here 6.

[0027] Step S103: in response to the update of the economic activity feature set, substituting the economic activity feature set into the economic dynamic analysis module to obtain the dynamically updated economic analysis template and the industry knowledge graph corresponding to each business district; Specifically, substituting the economic activity feature set into the economic dynamic analysis module to obtain the dynamically updated economic analysis template and the industry knowledge graph corresponding to each business district includes: obtaining the basic feature comprehensive score of different industries based on the latest version of the economic activity feature set; obtaining the current industry guidance feature set, wherein the feature types of the industry guidance feature set at least include policy response features, technology breakthrough features, industry correlation features, and industry chain transmission features; obtaining the recommended index corresponding to different industries in each business district based on the basic feature comprehensive score of different industries and the current industry guidance feature set; updating the economic analysis template and the industry knowledge graph corresponding to each business district based on the recommended index corresponding to different industries in each business district.

[0028] Specifically, obtaining the basic feature comprehensive score of different industries based on the latest version of the economic activity feature set includes: The basic feature comprehensive score of different industries is obtained by the following formula: wherein, represent the basic feature comprehensive score of the th industry, Representing the The first industry Standardized values ​​of each feature Representing the The first industry Dynamic weights of each feature The number of representative features, here ; Among them, the The first industry The dynamic weights of each feature are obtained using the following formula: in, Represents PCA weights. Representing the The first industry The original feature weights of each feature. Representing the The first industry The industry-specific correlation parameters of each feature.

[0029] Specifically, no. The first industry The original feature weights of each feature are obtained using the following formula: in, This represents the total number of principal components retained. Representing the The first industry The feature in the first Loads in each principal component Representing the The first industry The feature in the first The eigenvalues ​​of each principal component.

[0030] Specifically, no. The first industry The industry-specific relevance parameters of each feature are obtained using the following formula: in, Representing the Target variables for each industry Representing the One feature, satisfying .

[0031] Specifically, no. The first industry The standardized values ​​of each feature are obtained using the following formula: wherein, the original value of the first feature of the first industry, the minimum value of the first feature among all industries, the maximum value of the first feature among all industries.

[0032] Specifically, obtaining the current industry guidance feature set comprises: determining the policy response feature value of each industry based on social media text; obtaining the current technical data of each industry; determining the technical breakthrough feature value of each industry based on the current technical data of each industry; obtaining the industrial chain correlation data between different industries; determining the industry correlation feature value of each industry based on the industrial chain correlation data between different industries and the enterprise financial report; determining the industrial chain transmission feature value of each industry based on social media text, enterprise financial report and industrial chain correlation data between different industries; to obtain the current industry guidance feature set.

[0033] Specifically, based on the basic feature comprehensive score of different industries and the current industry guidance feature set, the recommended index corresponding to different industries in each business circle is obtained, comprising: the recommended index corresponding to different industries in each business circle is obtained by the following formula: wherein, the recommended index corresponding to the first industry in the first business circle, , , , , , , , all represent the dynamic weight coefficient matched with the first industry, the policy response feature value of the first industry, the technical breakthrough feature value of the first industry, the industry correlation feature value of the first industry, the industrial chain transmission feature value of the first industry, the industrial chain transmission feature value of the first industry, the industrial chain transmission feature value of the first industry, the industrial chain transmission feature value of the first industry, the industrial chain transmission feature value of the first industry, the industrial chain transmission feature value of the first industry, ​​​​a number of industries in each business circle a comprehensive score of the basic features of each industry.

[0034] Specifically, based on the recommended index corresponding to each industry in each business circle, the economic analysis template corresponding to each business circle and the industry knowledge graph are updated, including: Based on the recommended index corresponding to each industry in each business circle, the prompt word engineering parameter corresponding to each business circle is generated; Based on the recommended index corresponding to each industry in each business circle and the prompt word engineering parameter corresponding to each business circle, the economic analysis template corresponding to each business circle is updated; Based on the recommended index corresponding to each industry in each business circle and the prompt word engineering parameter corresponding to each business circle, the industry knowledge graph corresponding to each business circle is updated.

[0035] Specifically, based on the recommended index corresponding to each industry in each business circle, the prompt word engineering parameter corresponding to each business circle is generated, including: Based on the recommended index corresponding to each industry in each business circle, the multiple industries in each business circle are sorted to obtain the arrangement of the multiple industries in each business circle; Based on the arrangement position of each industry in each business circle, the number of prompt word parameters corresponding to each industry in each business circle is determined; Based on the number of prompt word parameters corresponding to each industry in each business circle, the prompt word engineering parameter corresponding to each business circle is generated.

[0036] Specifically, the prompt word engineering parameter corresponding to each industry is generated through different industries, which can be generated through the existing prompt word engineering parameter generation method, or obtained through a trained neural network model. The selection of the prompt word engineering parameter acquisition method here is only exemplary, and in actual testing, a person skilled in the art can select according to actual needs, which will not be repeated here.

[0037] Specifically, based on the comprehensive score of the basic features of different industries and the current industry guidance feature set, the recommended index corresponding to each industry in each business circle is obtained, further including: The policy response feature value of the i-th industry is obtained by the following formula : Wherein, represents the policy strength parameter value corresponding to the i-th policy, represents the matching degree between the i-th industry and the j-th policy target, ​​​​This represents the value of the policy's timeliness decay parameter. These represent the weight values ​​corresponding to different preset policy levels; The number is obtained through the following formula. Technological Breakthrough Characteristics of Individual Industries : in, Represents dynamic weighting coefficients. Represents the patent quality parameter value. Represents the R&D output parameter value; The number is obtained through the following formula. The industry and the first Industry-related characteristic values ​​of individual industries : in, Representing the The industry and the first Covariance of returns for each industry Representing the The industry and the first The industrial chain linkage coefficient of each industry, among which... , Representing the The industry for the first Intermediate inputs in various industries Represents the total output value, Representing the Variance of returns for each industry Representing the The profitability of individual industries Representing the Variance of returns for each industry Representing the The rate of return of each industry.

[0038] Specifically, the first is obtained through the following formula. Industrial chain transmission characteristics of individual industries : in, Represents price transmission efficiency, among which, , Representing the Price changes in various industries Representing the Price changes in the upstream industries of each industry The value of the parameter representing the impact of inventory on fluctuations. This represents the network center parameter value.

[0039] Specifically, the patent quality parameter value is obtained by the following formula: wherein, , , represents a dynamic weight coefficient corresponding to each data volume, represents the number of citations, represents the number of international patent applications, represents an innovation parameter value.

[0040] Specifically, each of the above parameter values can be obtained by a trained machine learning model, or can be obtained by a trained neural network model. The selection of the acquisition method of each parameter value here is only an exemplary description. In actual testing, a person skilled in the art can select according to actual needs. Here, no further description is given.

[0041] Specifically, the economic dynamic analysis module also stores a real-time updated dynamic weight data table. The dynamic weight data table stores dynamic weight coefficients corresponding to each different data. The dynamic weight coefficients in the dynamic weight data table can be obtained by a trained machine learning model, or can be obtained by a trained neural network model. The selection of the acquisition method of each dynamic weight coefficient here is only an exemplary description. In actual testing, a person skilled in the art can select according to actual needs. Here, no further description is given.

[0042] Step S104: In response to the industry analysis instruction of the user terminal, based on the latest version of the economic analysis template and the industry knowledge graph, an estimated graphic-text report is generated and fed back to the user terminal.

[0043] Specifically, based on the latest version of the economic analysis template and the industry knowledge graph, an estimated graphic-text report is generated and fed back to the user terminal, including: Substitute the latest version of the economic analysis template and the industry knowledge graph into the economic dynamic analysis module to generate an estimated graphic-text report and feed it back to the user terminal.

[0044] Specifically, substituting the latest version of the economic analysis template and the industry knowledge graph into the economic dynamic analysis module to generate an estimated graphic-text report and feed it back to the user terminal includes: Based on the latest version of the economic analysis template and the industry knowledge graph, a prediction parameter set of each business circle is obtained, wherein the prediction parameter set at least includes a trend prediction parameter and a risk prediction parameter; Based on the prediction parameter set of each business circle, an estimated graphic-text report is generated.

[0045] Specifically, the prediction parameter set can be obtained by predicting the economic situation in each business circle through the economic prediction technology in the prior art, or can be obtained by predicting through a trained neural network model. Here, the selection of the acquisition method of the prediction parameter set is only an exemplary description, and in actual tests, a person skilled in the art can select according to actual needs, and details are not repeated here.

[0046] Specifically, the industry knowledge graph and the generation method of the estimated graphic report can adopt the generation method in the prior art, or can be generated by a trained neural network model. Here, the selection of the generation method of the industry knowledge graph and the estimated graphic report is only an exemplary description, and in actual tests, a person skilled in the art can select according to actual needs, and details are not repeated here.

[0047] Specifically, the construction method of the economic analysis template can be obtained by a trained machine learning model, or can be obtained by a trained neural network model. Here, the selection of the construction method of the economic analysis template is only an exemplary description, and in actual tests, a person skilled in the art can select according to actual needs, as long as the economic analysis template is constructed according to the recommended index corresponding to each industry in each business circle and the prompt word engineering parameter corresponding to each business circle. Details are not repeated here.

[0048] Based on the above steps S101-S104, by substituting the real-time obtained multi-modal multi-source data into the analysis module, the economic activity feature set is updated, the automatic analysis and fusion of multi-modal data information are realized, and the processing efficiency of multi-modal data is improved. In response to the update of the economic activity feature set, the economic activity feature set is substituted into the economic dynamic analysis module, the dynamically updated economic analysis template and industry knowledge graph corresponding to each business circle are obtained, the dynamic update of the economic analysis template and the automatic construction of the knowledge graph are realized, the timeliness of the economic analysis template and the industry knowledge graph is ensured. Through the industry analysis instruction of the user terminal, the latest economic analysis template and industry knowledge graph are used to generate an estimated graphic report and feed back to the user terminal, the real-time estimated graphic report is fed back to the user in time, the accuracy and dynamic estimation ability of the estimated graphic report are improved, the accuracy of digital economic dynamic analysis is ensured, and the technical problems of existing technologies, such as business process solidification, low coordination efficiency, link connection relying on manual intervention, easy to produce information delay and decision deviation, and affecting the timeliness of economic analysis are avoided.

[0049] It should be noted that although the above embodiments describe the steps in a specific order, those skilled in the art can understand that in order to achieve the effect of the present application, the different steps do not have to be executed in such an order, they can be executed simultaneously (in parallel) or in other orders, and these changes are within the protection scope of the present application.

[0050] Further, the present application also provides a digital economic dynamic analysis system based on a multi-modal large model.

[0051] Referring to the drawings Figure 2 , Figure 2 is a main structure block diagram of a digital economic dynamic analysis system based on a multi-modal large model according to an embodiment of the present application. As Figure 2 shown, the digital economic dynamic analysis system 200 based on a multi-modal large model in the embodiment of the present application mainly includes a communication device 205, a plurality of collection devices 203 of different collection data types, an analysis module 202, an economic dynamic analysis module 204 and a control device 201, the communication device 205 is used to realize the information interaction between a plurality of user terminals and the system, the collection device 203 is used to obtain multi-modal multi-source data in real time, obtain the current industry guidance feature set, obtain the current technical data of each industry and obtain the industry chain correlation data between different industries, the analysis module 202 is used to analyze the multi-source data to update the economic activity feature set, the economic dynamic analysis module 204 is used to obtain the dynamically updated economic analysis template corresponding to each business circle and the industry knowledge graph according to the economic activity feature set, and the control device 201 includes a processor 2011 and a memory 2012, the memory 2012 can be configured to store program codes 2013 of the digital economic dynamic analysis method based on a multi-modal large model of the above method embodiments, and the processor 2011 can be configured to execute the program codes 2013 in the memory 2012, the program codes 2013 include but are not limited to the program codes 2013 of the digital economic dynamic analysis method based on a multi-modal large model of the above method embodiments. For the convenience of description, only the parts related to the embodiments of the present application are shown, and the specific technical details not disclosed are referred to the method part of the embodiments of the present application. The control device 201 can be a control device device formed by various electronic devices.

[0052] Specifically, the collection device 203 can be various collection equipment of different collection types, can also be satellite remote sensing equipment, enterprise financial report acquisition equipment, internet acquisition equipment for collecting social media text, and the selection of the collection device 203 here is only exemplary description, and those skilled in the art can select according to actual needs in actual test, as long as the multi-modal multi-source data can be acquired in real time through the collection device 203, the current industry guidance feature set is acquired, the current technical data of each industry is acquired, and the industry chain association data between different industries is acquired, and details are not repeated here.

[0053] Specifically, the communication device 205 can be connected through wifi, can also be connected through Bluetooth, and can also be wired connection, and the selection of the communication device 205 here is only exemplary description, and those skilled in the art can select according to actual use requirements, as long as the mutual communication connection of the several user terminals and the system can be realized through the communication device 205, and the information interaction between the several user terminals and the system can be realized, and details are not repeated here.

[0054] In one embodiment, the description of the specific implementation function can be referred to the steps S101-S104.

[0055] The above-mentioned digital economic dynamic analysis system 200 based on a multi-modal large model is used to execute Figure 1 The technical principle, the technical problem solved and the technical effect of the multi-modal large model-based digital economic dynamic analysis method embodiment shown are similar, and those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related description of the multi-modal large model-based digital economic dynamic analysis system 200 can refer to the description of the multi-modal large model-based digital economic dynamic analysis method embodiment, and details are not repeated here.

[0056] Those skilled in the art can understand that all or part of the processes in the method of the above-mentioned embodiment of the present application can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor 2011. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable storage medium can include any entity or device, medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code. It should be noted that the contents included in the computer readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, according to legislation and patent practice in some jurisdictions, the computer readable storage medium does not include electrical carrier signals and telecommunication signals.

[0057] Further, the multi-modal large model-based digital economic dynamic analysis system 200 of the present application further includes a computer readable storage medium. In one computer readable storage medium embodiment according to the present application, the computer readable storage medium can be configured to store program code 2013 for executing the multi-modal large model-based digital economic dynamic analysis method of the above-mentioned method embodiments. The program code 2013 can be loaded and run by the processor 2011 to implement the above-mentioned multi-modal large model-based digital economic dynamic analysis method. For ease of illustration, only the parts related to the embodiments of the present application are shown, and the specific technical details that are not disclosed are referred to the method part of the embodiments of the present application. The computer readable storage medium can be a memory device formed by various electronic devices. Optionally, the computer readable storage medium in the embodiments of the present application is a non-transitory computer readable storage medium.

[0058] Further, it should be understood that, since the setting of each module is only for illustrating the functional units of the device of the present application, the corresponding physical device of the module can be the processor 2011 itself, or a part of software, a part of hardware, or a part of combination of software and hardware in the processor 2011. Therefore, the number of each module in the figure is only illustrative.

[0059] Those skilled in the art can understand that each module in the device can be adaptively split or combined. Such splitting or combining of specific modules does not cause the technical solution to deviate from the principles of the present application, and therefore, the technical solution after splitting or combining will fall within the protection scope of the present application.

[0060] The technical scheme of the present application has been described in combination with the preferred embodiments shown in the drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical schemes after the changes or replacements will all fall within the protection scope of the present application.

Claims

1. A method for digital economy dynamic analysis based on a multi-modal large model, characterized in that, The method comprises the following steps: Real-time acquisition of multi-modal multi-source data, wherein the multi-source data at least comprises satellite remote sensing images, enterprise financial reports, and social media texts; Substituting the multi-source data into an analysis module to update an economic activity feature set, wherein the economic activity feature set at least comprises factory start-up features, commercial district passenger flow density features, traffic features, service facility distribution features, and commercial district market features; In response to the update of the economic activity feature set, substituting the economic activity feature set into an economic dynamic analysis module to obtain a dynamically updated economic analysis template and an industry knowledge graph corresponding to each commercial district; In response to an industry analysis instruction of a user terminal, generating an estimated graphic-text report based on the latest version of the economic analysis template and the industry knowledge graph and feeding back to the user terminal.

2. The digital economy dynamic analysis method based on a multi-modal large model according to claim 1, characterized in that, Substituting the multi-source data into the analysis module to update the economic activity feature set comprises: Obtaining factory start-up features based on satellite remote sensing images and enterprise financial reports; Obtaining commercial district passenger flow density features, traffic features, and service facility distribution features based on satellite remote sensing images and social media texts; Obtaining commercial district market features based on enterprise financial reports and social media texts; Updating the economic activity feature set based on the factory start-up features, the commercial district passenger flow density features, the traffic features, the service facility distribution features, and the commercial district market features.

3. The digital economy dynamic analysis method based on a multi-modal large model according to claim 2, characterized in that, Substituting the economic activity feature set into the economic dynamic analysis module to obtain a dynamically updated economic analysis template and an industry knowledge graph corresponding to each commercial district comprises: Obtaining a basic feature comprehensive score of different industries based on the latest version of the economic activity feature set; Obtaining a current industry guidance feature set, wherein the feature types of the industry guidance feature set at least comprise policy response features, technology breakthrough features, industry correlation features, and industry chain transmission features; Obtaining a recommended index corresponding to different industries in each commercial district based on the basic feature comprehensive score of different industries and the current industry guidance feature set; Updating the economic analysis template and the industry knowledge graph corresponding to each commercial district based on the recommended index corresponding to different industries in each commercial district.

4. The digital economy dynamic analysis method based on a multi-modal large model according to claim 3, characterized in that, Obtaining a basic feature comprehensive score of different industries based on the latest version of the economic activity feature set comprises: The basic feature comprehensive score of different industries is obtained by the following formula: wherein, a composite score representing the underlying characteristics of the industry, a standardized value representing the characteristic of the industry, a dynamic weight representing the characteristic of the industry, representing the number of characteristics, here ; wherein the dynamic weight of the first characteristic of the first industry is obtained by the following formula: wherein, represents a PCA weight, represents a raw feature weight of a first feature of a first industry, represents a raw feature weight of a first feature of a first industry, represents a raw feature weight of a first feature of a first industry, represents an industry feature correlation parameter of a first feature of a first industry, represents an industry feature correlation parameter of a first feature of a first industry, represents an industry feature correlation parameter of a first feature of a first industry.

5. The digital economy dynamic analysis method based on a multi-modal large model according to claim 4, characterized in that, Obtaining a current industry guidance feature set comprises: Determining a policy response feature value of each industry based on social media texts; Obtaining current technical data of each industry; Determining a technology breakthrough feature value of each industry based on the current technical data of each industry; Obtaining industry chain correlation data between different industries; Determining an industry correlation feature value of each industry based on the industry chain correlation data between different industries and enterprise financial reports; Determining an industry chain transmission feature value of each industry based on social media texts, enterprise financial reports, and the industry chain correlation data between different industries; To obtain the current industry guidance feature set.

6. The digital economy dynamic analysis method based on a multi-modal large model according to claim 5, characterized in that, Obtaining a recommended index corresponding to different industries in each commercial district based on the basic feature comprehensive score of different industries and the current industry guidance feature set comprises: The recommended index corresponding to different industries in each commercial district is obtained by the following formula: Wherein, represent the corresponding recommended index of the first industry in the first business circle, represent the policy response characteristic value of the first industry, represent the technology breakthrough characteristic value of the first industry, represent the industry correlation characteristic value of the first industry, represent the industry chain conduction characteristic value of the first industry, represent the basic characteristic comprehensive score of the first industry in the first business circle.​​​​​​​​​​​​​​ 7. The digital economy dynamic analysis method based on a multi-modal large model according to claim 6, characterized in that, The updating of the economic analysis template and the industry knowledge graph corresponding to each business circle based on the recommendation index corresponding to different industries in each business circle comprises: Generating the prompt word engineering parameter corresponding to each business circle based on the recommendation index corresponding to different industries in each business circle; Updating the economic analysis template corresponding to each business circle based on the recommendation index corresponding to different industries in each business circle and the prompt word engineering parameter corresponding to each business circle; Updating the industry knowledge graph corresponding to each business circle based on the recommendation index corresponding to different industries in each business circle and the prompt word engineering parameter corresponding to each business circle.

8. The digital economy dynamic analysis method based on a multi-modal large model according to claim 7, characterized in that, The generating of the prompt word engineering parameter corresponding to each business circle based on the recommendation index corresponding to different industries in each business circle comprises: Ranking the multiple industries in each business circle based on the recommendation index corresponding to different industries in each business circle to obtain the arrangement of the multiple industries in each business circle; Determining the number of prompt word parameters corresponding to each industry in each business circle based on the arrangement position of each industry in each business circle; Generating the prompt word engineering parameter corresponding to each business circle based on the number of prompt word parameters corresponding to each industry in each business circle.

9. The digital economy dynamic analysis method based on a multi-modal large model according to claim 8, characterized in that, The obtaining of the recommendation index corresponding to different industries in each business circle based on the comprehensive score of the basic features of different industries and the current industry guidance feature set further comprises: The first industry policy response eigenvalue is obtained by the following equation: in, Representing the The policy strength parameter value corresponding to each policy. Representing the The industry and the first The degree of alignment between policy objectives This represents the value of the policy's timeliness decay parameter. These represent the weight values ​​corresponding to different preset policy levels; The number is obtained through the following formula. Technological Breakthrough Characteristics of Individual Industries : wherein, represents a dynamic weight coefficient, represents a patent quality parameter value, represents a research and development output parameter value; The industry correlation eigenvalue of the first industry and the second industry is obtained by the following formula: :​​ in, Representing the The industry and the first Covariance of returns for each industry Representing the The industry and the first The industrial chain linkage coefficient of each industry Representing the Variance of returns for each industry Representing the The profitability of individual industries Representing the Variance of returns for each industry Representing the The rate of return of each industry. 10.A digital economy dynamic analysis system based on a multi-modal large model, characterized in that, The system comprises a communication device, a collection device, an analysis module, an economic dynamic analysis module, and a control device. The communication device is used to realize information interaction between a plurality of user terminals and the system. The collection device is used to obtain multi-modal multi-source data in real time, obtain the current industry guidance feature set, obtain the current technical data of each industry, and obtain the industry chain association data between different industries. The analysis module is used to analyze the multi-source data to update the economic activity feature set. The economic dynamic analysis module is used to obtain the dynamically updated economic analysis template and the industry knowledge graph corresponding to each business circle based on the economic activity feature set. The control device comprises a processor and a memory. The memory is adapted to store a plurality of program codes. The program codes are adapted to be loaded and run by the processor to execute the digital economic dynamic analysis method based on the multi-modal large model according to any one of claims 1 to 9.

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