Digital urban and rural comprehensive application platform based on artificial intelligence

Abstract

The invention relates to the technical field of digital urban and rural, in particular to a digital urban and rural comprehensive application platform based on artificial intelligence. The system comprises a data acquisition module used for acquiring urban and rural data; the risk assessment module is used for providing entrepreneurship risk assessment for entrepreneurs; the resource matching module is used for accurately matching the entrepreneurship project with urban and rural resources; the urban and rural development module is used for evaluating the potential influence of an entrepreneurship project on urban and rural development and integrating urban and rural resources; the ability construction module is used for providing entrepreneurship education training for entrepreneurs and urban and rural residents; and the monitoring and adjusting module is used for monitoring entrepreneurship project progress and urban and rural development change in real time. According to the invention, the entrepreneurs are endowed to stimulate the village development vitality and inject continuous power for village revitalization, and at the same time, the method is helpful to break the information barrier between urban and rural, promote the bidirectional flow of urban and rural elements, and realize urban and rural integrated development.

Description

Technical Field

[0001] The present invention relates to the field of digital urban and rural technology, and in particular to an artificial intelligence-based digital urban and rural comprehensive application platform. Background Art

[0002] In recent years, the integrated development of urban and rural areas has become an inevitable trend. However, there are still gaps between urban and rural areas in terms of resource endowment, development level and information acquisition, which restricts the coordinated development of urban and rural areas. This is particularly evident in the fields of entrepreneurship and industrial development.

[0003] Therefore, there is an urgent need to develop a digital urban and rural comprehensive application platform based on artificial intelligence to overcome the shortcomings of existing technologies. Summary of the invention

[0004] In order to solve the obvious deficiencies of the existing technology in urban and rural entrepreneurship support and risk assessment, the present invention provides such an artificial intelligence-based digital urban and rural comprehensive application platform, including a data acquisition module, a risk assessment module, a resource matching module, an urban and rural development module, a capacity building module and a monitoring and adjustment module;

[0005] A data acquisition module, used to acquire urban and rural data and perform preprocessing operations on the urban and rural data;

[0006] The risk assessment module is used to provide entrepreneurial risk assessment based on the acquired urban and rural data combined with market data;

[0007] Resource matching module, which is used to accurately match entrepreneurial projects with urban and rural resources based on the results of entrepreneurial risk assessment;

[0008] The urban and rural development module is used to evaluate the potential impact of entrepreneurial projects on urban and rural development and integrate urban and rural resources based on the development results of entrepreneurial projects and urban and rural data;

[0009] Capacity building module, which is used to provide entrepreneurship education and training for entrepreneurs and urban and rural residents based on the results of entrepreneurship risk assessment and resource matching;

[0010] The monitoring and adjustment module is used to monitor the progress of entrepreneurial projects and changes in urban and rural development in real time, and make timely adjustments.

[0011] Further, the data acquisition module includes a data acquisition unit and a data processing unit;

[0012] The data collection unit is used to collect urban and rural data, and the urban and rural data include entrepreneurial project data, urban and rural macro data and urban and rural micro data;

[0013] The entrepreneurial project data includes project plan, business model and team information;

[0014] The urban and rural macro data include urban and rural policies and regulations, market size, industrial structure, economic development level and floating population data;

[0015] The urban and rural micro data include information on transportation, logistics, infrastructure and human resources;

[0016] The data processing unit is used to clean the collected data, eliminate invalid data and duplicate data, and integrate and standardize data from different sources or in different formats.

[0017] Furthermore, the risk assessment module includes a risk identification unit, a risk assessment unit and a risk early warning unit:

[0018] The risk identification unit is used to perform semantic analysis on entrepreneurial project data in urban and rural data to identify potential entrepreneurial risk points;

[0019] The risk assessment unit is used to use machine learning algorithms to build a risk assessment model, conduct quantitative assessments of the market risk, operational risk, financial risk, and policy risk of entrepreneurial projects, and generate a comprehensive risk score;

[0020] The risk warning unit is used to monitor market environment changes and project operation data in real time, and to promptly issue warning information to users when risk factors arise.

[0021] Furthermore, the risk assessment model is constructed using a gradient boosting decision tree, and the specific risk assessment process is as follows:

[0022] S1: Feature engineering, extracting entrepreneurial risk features in four dimensions: market risk M, operational risk O, financial risk F, and policy risk N, to form a feature vector;

[0023] S2: Risk assessment, input the entrepreneurial risk feature vectors of the four dimensions into the risk assessment model to obtain the score of each risk dimension;

[0024] S3: Weighted summation. According to the different weights of each dimension, the weighted summation is calculated to obtain the comprehensive entrepreneurial risk score S:

[0025] S=ω 1 S M +ω 2 S O +ω 3 S F +ω 4 S F

[0026] Among them, S is the comprehensive entrepreneurial risk score; S M , S O , S F , S FThey are the market risk score, operational risk score, financial risk score and fiscal risk score obtained through the risk assessment model respectively; 1 ,ω 2 ,ω 3 ,ω 4 They represent the weight coefficients of market risk score, operational risk score, financial risk score and fiscal risk score respectively. The weight coefficients are adjusted using machine learning algorithms according to the different types and stages of entrepreneurial projects.

[0027] S4: Threshold determination: different entrepreneurial risk levels are defined according to preset thresholds, and the entrepreneurial risk level to which the entrepreneurial project belongs is determined.

[0028] Furthermore, the risk assessment model is personalized and assigned different weights according to entrepreneurial projects of different types and stages. The different types include traditional technology, high-tech and knowledge service, and the different stages include start-up, growth and maturity.

[0029] Furthermore, the resource matching module includes a capital matching unit, a talent introduction unit, a market expansion unit and an industrial chain integration unit:

[0030] The capital matching unit is used to match the corresponding government support funds, venture capital and bank loans based on the capital demand and risk level of the entrepreneurial projects to provide financial support for the entrepreneurial projects;

[0031] The talent introduction unit is used to match the urban and rural talent resource pools according to the talent needs of entrepreneurial projects, and assist entrepreneurial projects in introducing management and technical talents;

[0032] The market development unit is used to match the target market according to the product characteristics of the entrepreneurial project, connect with urban and rural sales channels, and expand the market;

[0033] The industrial chain integration unit is used to analyze the upstream and downstream relationships of the urban and rural industrial chains according to the industry to which the entrepreneurial project belongs, and to integrate the relevant resources of the upstream and downstream of the urban and rural industrial chains.

[0034] Furthermore, the urban and rural development module includes an economic benefit assessment unit, a social benefit assessment unit and a sustainable development assessment unit;

[0035] The economic benefit evaluation unit is used to evaluate the contribution of entrepreneurial projects to local economic growth, industrial upgrading and tax increase by statistically analyzing and comparing historical and current data in urban and rural areas;

[0036] The social benefit assessment unit is used to collect employment data and employee salary levels of entrepreneurial projects, and conduct social surveys to assess the impact of entrepreneurial projects on local employment, residents' income and quality of life;

[0037] The sustainable development assessment unit is used to evaluate the impact of entrepreneurial projects on local ecological environment protection and resource utilization efficiency by analyzing the energy consumption, waste emissions and resource utilization rate of entrepreneurial projects using a life cycle approach.

[0038] Furthermore, the urban and rural development module adopts a multi-dimensional indicator comprehensive evaluation method, combines quantitative analysis and qualitative analysis, constructs an urban and rural development evaluation indicator system, conducts a comprehensive evaluation of the economic benefits, social benefits and sustainable development of entrepreneurial projects, and generates a visual evaluation report.

[0039] Further, the capacity building module includes an entrepreneurship education unit and a vocational education unit;

[0040] The entrepreneurship education unit is used to provide entrepreneurship knowledge training for entrepreneurs based on the results of entrepreneurship risk and resource matching;

[0041] The vocational education unit is used to provide employment knowledge training for urban and rural residents based on entrepreneurial projects and the needs of urban and rural residents.

[0042] Furthermore, the monitoring and adjustment module includes an analysis and prediction unit, a decision support unit and an early warning reminder unit;

[0043] The analysis and prediction unit is used to use machine learning algorithms and big data analysis technology to analyze the progress of entrepreneurial projects and urban and rural resource allocation data in urban and rural data, and identify potential risks and opportunities in entrepreneurial projects and urban and rural development during the entrepreneurial process;

[0044] A decision support unit, used to generate adjustment measures based on the identification results in the analysis and prediction unit;

[0045] The early warning reminder unit is used to set early warning thresholds for key indicators in the implementation process of entrepreneurial projects and urban and rural development. When potential risks are identified, the corresponding early warning mechanism is activated according to different degrees of abnormal situations.

[0046] The beneficial effects of the present invention are:

[0047] 1. Through comprehensive collection and in-depth analysis of urban and rural data, the present invention can accurately identify the potential and needs of rural development, and use this as a basis for accurate matching and scientific allocation of resources. According to local resource endowments, industrial bases and market demands, it can recommend suitable entrepreneurial projects to entrepreneurs, which helps to reduce the entrepreneurial risks of entrepreneurs and increase the success rate of entrepreneurship.

[0048] 2. By empowering entrepreneurs, the present invention can effectively stimulate the vitality of rural development, attract more talents to return to their hometowns to start businesses, inject continuous impetus into rural revitalization, break down the information barriers between urban and rural areas, promote the two-way flow of urban and rural elements, and achieve urban and rural integrated development. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0050] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0051] As attached Figure 1 The embodiment of the present invention shows a digital urban and rural comprehensive application platform based on artificial intelligence, including a data acquisition module, a risk assessment module, a resource matching module, an urban and rural development module, a capacity building module and a monitoring and adjustment module; wherein,

[0052] The data acquisition module is used to acquire urban and rural data, including entrepreneurial project data, urban and rural macro data and urban and rural micro data, and then clean, integrate and standardize the collected urban and rural data;

[0053] The risk assessment module is used to provide entrepreneurial risk assessment for entrepreneurs based on pre-processed urban and rural data combined with market data;

[0054] Resource matching module, which is used to accurately match entrepreneurial projects with urban and rural resources based on the results of entrepreneurial risk assessment;

[0055] The urban and rural development module is used to evaluate the potential impact of entrepreneurial projects on urban and rural development and integrate urban and rural resources based on the development results of entrepreneurial projects and urban and rural data;

[0056] Capacity building module, which is used to provide entrepreneurship education and training for entrepreneurs and urban and rural residents based on the results of entrepreneurship risk assessment and resource matching;

[0057] The monitoring and adjustment module is used to monitor the progress of entrepreneurial projects and changes in urban and rural development in real time, and make timely adjustments.

[0058] In this embodiment, the data acquisition module includes:

[0059] A data collection unit is used to collect urban and rural data, including entrepreneurial project data, urban and rural macro data, and urban and rural micro data;

[0060] The entrepreneurial project data is obtained through manual input or API interface, including project plans, business models, and team information;

[0061] For urban and rural macro data, open data interfaces are used to obtain information on urban and rural policies and regulations, market size, industrial structure, economic development level, and migrant population data;

[0062] For urban and rural micro data, information such as transportation, logistics, infrastructure and human resources can be obtained through IoT devices or public data.

[0063] The data processing unit is used to clean the collected data, eliminate invalid data and duplicate data, and integrate and standardize data from different sources or in different formats.

[0064] In this embodiment, the risk assessment module includes:

[0065] The risk identification unit is used to use natural language processing technology to perform semantic analysis on entrepreneurial project data in urban and rural data and identify potential entrepreneurial risk points.

[0066] The risk assessment unit is used to build a risk assessment model based on urban and rural macro data, urban and rural micro data and entrepreneurial project data using machine learning algorithms, and to quantitatively assess the market risk, operational risk, financial risk and policy risk of entrepreneurial projects based on the risk points and market data provided by the risk identification unit, and generate a comprehensive risk score. At the same time, when conducting quantitative assessments of entrepreneurial projects, the risk assessment model is personalized and assigned different weights according to the different types and stages of entrepreneurial projects; among them, different types include traditional technology, high-tech and knowledge service; different stages include start-up, growth and maturity.

[0067] Specifically, the risk assessment model is constructed using a gradient boosting decision tree, and the process of entrepreneurial risk assessment is as follows:

[0068] S1: Feature engineering, extracting entrepreneurial risk features in four dimensions: market risk M, operational risk O, financial risk F, and policy risk N, to form a feature vector; the feature vector of market risk M includes but is not limited to target market size, number of competitors, product-market fit, and potential market expansion space; the feature vector of operational risk O includes but is not limited to entrepreneurial team experience, management capability score, operating cost, and talent fit; the feature vector of financial risk F includes but is not limited to capital demand, asset load ratio, successful capital matching, and profitability forecast; the feature vector of policy risk N includes but is not limited to government support, industry policy risk rating, and policy change risk forecast;

[0069] S2: Risk assessment, input the entrepreneurial risk feature vectors of the four dimensions into the risk assessment model to obtain the score of each risk dimension;

[0070] S3: Weighted summation. According to the different weights of each dimension, the weighted summation is calculated to obtain the comprehensive entrepreneurial risk score S:

[0071] S=ω 1 S M +ω2 S O +ω 3 S F +ω 4 S F

[0072] Among them, S is the comprehensive entrepreneurial risk score; S M , S O , S F , S F They are the market risk score, operational risk score, financial risk score and fiscal risk score obtained through the risk assessment model respectively; 1 ,ω 2 ,ω 3 ,ω 4 They represent the weight coefficients of market risk score, operational risk score, financial risk score and fiscal risk score respectively. The weight coefficients are adjusted using machine learning algorithms according to the different types and stages of entrepreneurial projects.

[0073] S4: Threshold judgment: According to the preset threshold, different entrepreneurial risk levels are defined to determine the entrepreneurial risk level to which the entrepreneurial project belongs.

[0074] The risk warning unit is used to monitor market environment changes and project operation data in real time. When risk factors appear, it will promptly issue warning information to users. It also provides personalized adjustment suggestions to entrepreneurs. For start-up projects, it provides suggestions on entrepreneurship guidance, team building, etc. For growth-stage projects, it provides suggestions on market expansion, financing channels, etc.; for mature projects, it provides suggestions on management consulting, strategic planning, etc.

[0075] In this embodiment, the resource matching module includes:

[0076] The funding matching unit is used to match corresponding government support funds, venture capital, bank loans and other resource information based on the funding needs and risk levels of entrepreneurial projects, so as to provide financial support for entrepreneurial projects.

[0077] The talent introduction unit is used to match urban and rural talent resource pools based on the talent needs of entrepreneurial projects, and to assist entrepreneurial projects in introducing management and technical talents.

[0078] The market development unit is used to match the target market according to the product characteristics of the entrepreneurial project, connect with urban and rural sales channels, and expand the market.

[0079] The industrial chain integration unit is used to analyze the upstream and downstream relationships of the urban and rural industrial chains according to the industry to which the entrepreneurial project belongs, and to integrate the relevant resources of the upstream and downstream of the urban and rural industrial chains.

[0080] In this embodiment, the urban and rural development module includes an economic benefit assessment unit, a social benefit assessment unit and a sustainable development assessment unit.

[0081] The economic benefit evaluation unit is used to evaluate the contribution of entrepreneurial projects to the local economy, industry, and taxation by statistically comparing the data before and after the start of entrepreneurial projects in urban and rural data. For the local economy, the economic growth model is used to predict the direct and indirect contribution of entrepreneurial projects to the local GDP. For the industry, through the analysis of industrial cluster effects, it is evaluated whether entrepreneurial projects can drive the development of the upstream and downstream of the urban and rural industrial chain and promote the optimization and upgrading of the industrial structure. For taxation, through local taxation and project financial data, the tax revenue that entrepreneurial projects can bring to urban and rural areas is analyzed.

[0082] The social benefit evaluation unit is used to collect employment data and employee salary levels of entrepreneurial projects, conduct social surveys, and evaluate the impact of entrepreneurial projects on local employment, residents' income, and quality of life. For the evaluation of local employment, it mainly analyzes how many jobs and job types the entrepreneurial projects can create, as well as the role they can play in improving the skills of the local workforce, through project plans, forecasting models, and data from similar projects. For residents' income, it mainly analyzes the impact of entrepreneurial projects on the income levels of urban and rural residents through questionnaires and relevant data after the start of entrepreneurial projects. For the evaluation of quality of life, it mainly analyzes how the projects improve the quality of life of urban and rural residents through social indicators and relevant data after the start of entrepreneurial projects.

[0083] The sustainable development assessment unit is used to evaluate the impact of entrepreneurial projects on local ecological environment protection and resource utilization efficiency by analyzing the energy consumption, waste emissions and resource utilization rate of entrepreneurial projects using a life cycle approach, including whether the entrepreneurial projects can improve environmental quality, protect biodiversity or the efficiency of the use of resources such as water, electricity and land.

[0084] Specifically, after evaluating the economic benefits, social benefits and sustainable development respectively, the urban and rural development module adopts a multi-dimensional indicator comprehensive evaluation method, combining quantitative analysis and qualitative analysis to construct an urban and rural development evaluation index system, and once again comprehensively evaluates the economic benefits, social benefits and sustainable development of entrepreneurial projects, and generates a visual evaluation report.

[0085] In this embodiment, the capability building module includes:

[0086] The entrepreneurship education unit is used to provide entrepreneurs with entrepreneurship knowledge training based on the results of entrepreneurship risk and resource matching; it provides courses covering basic entrepreneurship knowledge, marketing, financial management, etc., and provides relevant success and failure case analyses based on the different types and stages of entrepreneurial projects; at the same time, the entrepreneurship education unit can provide a virtual entrepreneurial environment, allowing entrepreneurs to make decisions and operate simulations in the virtual entrepreneurial environment and accumulate practical experience.

[0087] The vocational education unit is used to provide employment knowledge training for urban and rural residents, urban and rural migrant population or other practitioners according to entrepreneurial projects and the needs of urban and rural residents, so as to improve the employment ability and professional quality of urban and rural residents; and to evaluate the knowledge mastery and skill application ability of urban and rural residents, and provide personalized learning suggestions based on the evaluation results, so that urban and rural residents can master professional skills in a timely manner and improve their employment competitiveness; at the same time, the vocational education unit provides career planning guidance based on the personal circumstances and market demands of urban and rural residents, helping urban and rural residents find more suitable career development directions.

[0088] In this embodiment, the monitoring and adjustment module includes:

[0089] The analysis and prediction unit is used to use machine learning algorithms and big data analysis technology to analyze the progress of entrepreneurial projects and urban and rural resource allocation data in urban and rural data, and identify potential risks and opportunities in the entrepreneurial process, entrepreneurial projects and urban and rural development.

[0090] The decision support unit is used to generate adjustment measures based on the identification results in the analysis and prediction unit.

[0091] The early warning reminder unit is used to set early warning thresholds for key indicators in the implementation process of entrepreneurial projects and urban and rural development. When potential risks are identified, the corresponding early warning mechanism is activated according to different degrees of abnormal situations.

[0092] The above-mentioned embodiments only express the preferred implementation modes of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person skilled in the art, several modifications, improvements and substitutions can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the attached claims.

Claims

1. A digital urban and rural comprehensive application platform based on artificial intelligence, characterized by: It includes data acquisition module, risk assessment module, resource matching module, urban and rural development module, capacity building module and monitoring and adjustment module; A data acquisition module, used to acquire urban and rural data and perform preprocessing operations on the urban and rural data; The risk assessment module is used to provide entrepreneurial risk assessment based on the acquired urban and rural data combined with market data; Resource matching module, which is used to accurately match entrepreneurial projects with urban and rural resources based on the results of entrepreneurial risk assessment; The urban and rural development module is used to evaluate the potential impact of entrepreneurial projects on urban and rural development and integrate urban and rural resources based on the development results of entrepreneurial projects and urban and rural data; Capacity building module, which is used to provide entrepreneurship education and training for entrepreneurs and urban and rural residents based on the results of entrepreneurship risk assessment and resource matching; The monitoring and adjustment module is used to monitor the progress of entrepreneurial projects and changes in urban and rural development in real time, and make timely adjustments.

2. According to the artificial intelligence-based digital urban and rural comprehensive application platform of claim 1, it is characterized in that: The data acquisition module includes a data acquisition unit and a data processing unit; The data collection unit is used to collect urban and rural data, and the urban and rural data include entrepreneurial project data, urban and rural macro data and urban and rural micro data; The entrepreneurial project data includes project plan, business model and team information; The urban and rural macro data include urban and rural policies and regulations, market size, industrial structure, economic development level and floating population data; The urban and rural micro data include information on transportation, logistics, infrastructure and human resources; The data processing unit is used to clean the collected data, eliminate invalid data and duplicate data, and integrate and standardize data from different sources or in different formats.

3. According to the artificial intelligence-based digital urban and rural comprehensive application platform of claim 1, it is characterized in that: The risk assessment module includes a risk identification unit, a risk assessment unit and a risk early warning unit: the risk identification unit is used to perform semantic analysis on the entrepreneurial project data in the urban and rural data to identify potential entrepreneurial risk points; The risk assessment unit is used to use machine learning algorithms to build a risk assessment model, conduct quantitative assessments of the market risk, operational risk, financial risk, and policy risk of entrepreneurial projects, and generate a comprehensive risk score; The risk warning unit is used to monitor market environment changes and project operation data in real time, and to promptly issue warning information to users when risk factors arise.

4. According to the artificial intelligence-based digital urban and rural comprehensive application platform of claim 2, it is characterized in that: The risk assessment model is constructed using a gradient boosting decision tree, and the specific risk assessment process is as follows: S1: Feature engineering, extracting entrepreneurial risk features in four dimensions: market risk M, operational risk O, financial risk F, and policy risk N, to form a feature vector; S2: Risk assessment, input the entrepreneurial risk feature vectors of the four dimensions into the risk assessment model to obtain the score of each risk dimension; S3: Weighted summation. According to the different weights of each dimension, the weighted summation is calculated to obtain the comprehensive entrepreneurial risk score S: S=ω1S M +ω2S O +ω3S F +ω4S F Among them, S is the comprehensive entrepreneurial risk score; S M , S O , S F , S F They are the market risk score, operational risk score, financial risk score and fiscal risk score obtained through the risk assessment model; ω1, ω2, ω3 and ω4 represent the weight coefficients of the market risk score, operational risk score, financial risk score and fiscal risk score respectively. The weight coefficients are adjusted using machine learning algorithms according to the different types and stages of entrepreneurial projects; S4: Threshold determination: different entrepreneurial risk levels are defined according to preset thresholds, and the entrepreneurial risk level to which the entrepreneurial project belongs is determined.

5. According to the artificial intelligence-based digital urban and rural comprehensive application platform of claim 4, it is characterized in that: The risk assessment model is personalized and assigned different weights according to entrepreneurial projects of different types and stages. The different types include traditional technology, high-tech and knowledge service, and the different stages include start-up, growth and maturity.

6. The digital urban and rural comprehensive application platform based on artificial intelligence according to claim 1 is characterized in that: The resource matching module includes a capital matching unit, a talent introduction unit, a market expansion unit and an industrial chain integration unit: The capital matching unit is used to match the corresponding government support funds, venture capital and bank loans based on the capital demand and risk level of the entrepreneurial projects to provide financial support for the entrepreneurial projects; The talent introduction unit is used to match the urban and rural talent resource pools according to the talent needs of entrepreneurial projects, and assist entrepreneurial projects in introducing management and technical talents; The market development unit is used to match the target market according to the product characteristics of the entrepreneurial project, connect with urban and rural sales channels, and expand the market; The industrial chain integration unit is used to analyze the upstream and downstream relationships of the urban and rural industrial chains according to the industry to which the entrepreneurial project belongs, and to integrate the relevant resources of the upstream and downstream of the urban and rural industrial chains.

7. The digital urban and rural comprehensive application platform based on artificial intelligence according to claim 1 is characterized in that: The urban and rural development module includes an economic benefit assessment unit, a social benefit assessment unit and a sustainable development assessment unit; The economic benefit evaluation unit is used to evaluate the contribution of entrepreneurial projects to local economic growth, industrial upgrading and tax increase by statistically analyzing and comparing historical and current data in urban and rural areas; The social benefit assessment unit is used to collect employment data and employee salary levels of entrepreneurial projects, and conduct social surveys to assess the impact of entrepreneurial projects on local employment, residents' income and quality of life; The sustainable development assessment unit is used to evaluate the impact of entrepreneurial projects on local ecological environment protection and resource utilization efficiency by analyzing the energy consumption, waste emissions and resource utilization rate of entrepreneurial projects using a life cycle approach.

8. The digital urban and rural comprehensive application platform based on artificial intelligence according to claim 7 is characterized in that: The urban and rural development module adopts a multi-dimensional indicator comprehensive evaluation method, combines quantitative analysis and qualitative analysis, constructs an urban and rural development evaluation indicator system, conducts a comprehensive evaluation of the economic benefits, social benefits and sustainable development of entrepreneurial projects, and generates a visual evaluation report.

9. The digital urban and rural comprehensive application platform based on artificial intelligence according to claim 1 is characterized in that: The said capacity building modules include an entrepreneurship education unit and a vocational education unit; The entrepreneurship education unit is used to provide entrepreneurship knowledge training for entrepreneurs based on the results of entrepreneurship risk and resource matching; The vocational education unit is used to provide employment knowledge training for urban and rural residents based on entrepreneurial projects and the needs of urban and rural residents.

10. The digital urban and rural comprehensive application platform based on artificial intelligence according to claim 1 is characterized in that: The monitoring and adjustment module includes an analysis and prediction unit, a decision support unit and an early warning reminder unit; The analysis and prediction unit is used to use machine learning algorithms and big data analysis technology to analyze the progress of entrepreneurial projects and urban and rural resource allocation data in urban and rural data, and identify potential risks and opportunities in entrepreneurial projects and urban and rural development during the entrepreneurial process; A decision support unit, used to generate adjustment measures based on the identification results in the analysis and prediction unit; The early warning reminder unit is used to set early warning thresholds for key indicators in the implementation process of entrepreneurial projects and urban and rural development. When potential risks are identified, the corresponding early warning mechanism is activated according to different degrees of abnormal situations.

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