Characteristic rural industry green planning system combined with economic analysis

Abstract

The invention relates to the technical field of agricultural development, and discloses a characteristic rural industry green planning system combined with economic analysis, and the system comprises a data collection and analysis module which is used for collecting and integrating policy information, environment data, economic data and market demand information; the environment evaluation and grading module is used for dividing the rural industrial area into a green multi-dominant production area, a pollution-free multi-dominant production area, a green less-dominant production area and a pollution-free less-dominant production area; the industrial planning module is used for designing production, processing, packaging and supply chain schemes of characteristic agricultural products and constructing a brand construction scheme in combination with regional historical and cultural characteristics; the economic analysis module is used for analyzing economic benefits of the industry, evaluating risks and providing an optimization scheme; and the intelligent auxiliary decision-making module simulates economic and ecological influences of different planning schemes. According to the method, intelligent and dynamic planning is realized through reinforcement learning, linear planning and a system dynamics model, and the scheme is quickly adjusted according to real-time environment, economy and policy data.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural development, and in particular to a characteristic rural industry green planning system combined with economic analysis. Background Art

[0002] With the development of the global economy and society, the industrial structure of rural areas is facing profound changes. my country has put forward a rural revitalization strategy, emphasizing industrial revitalization as the core, and realizing the sustainable development of the rural economy through efficient use of resources, optimization of industrial layout and strengthening of ecological environment protection. In this context, characteristic rural industries, especially regional characteristic economies represented by landmark agricultural products, have become an important means to promote the development of rural economy.

[0003] However, existing rural industrial planning technologies often rely on traditional manual experience and static data, and lack comprehensive consideration of complex environmental conditions and economic variables. This planning method is difficult to dynamically reflect the actual characteristics of the region, such as real-time changes in soil, water resources and climate conditions, which can easily lead to irrational resource allocation and even the problem of planning goals being out of touch with the actual development needs of the region. In addition, traditional technologies usually use a single indicator (such as land area or output value) as the basis for decision-making, ignoring the ability to integrate and analyze multidimensional data, and cannot achieve accurate industrial layout and optimization. Summary of the invention

[0004] In order to make up for the above shortcomings, the present invention provides a characteristic rural industry green planning system combined with economic analysis, aiming to improve the problem that existing rural industry planning technology often relies on traditional manual experience and static data, and lacks comprehensive consideration of complex environmental conditions and economic variables.

[0005] In the first aspect, the present invention provides the following technical solutions, combining a characteristic rural industry green planning system with economic analysis, including:

[0006] Data collection and analysis module, used to collect and integrate policy information, environmental data, economic data and market demand information, and to update rural industrial environmental data in real time based on IoT devices and remote sensing technology;

[0007] Environmental assessment and classification module, which is used to classify rural industrial areas into green and more advantageous production areas, pollution-free and more advantageous production areas, green and less advantageous production areas, and pollution-free and less advantageous production areas according to the production environment characteristics of soil, water quality, climate and topography, and evaluate the ecological adaptability of the production areas;

[0008] The industrial planning module is used to design production, processing, packaging and supply chain plans for specialty agricultural products based on the results of the production environment assessment, and to build a brand building plan in combination with the historical and cultural characteristics of the region;

[0009] Economic analysis module, which is used to analyze the economic benefits of the industry, assess risks and provide optimization solutions based on production costs, market prices and policy subsidy information;

[0010] The intelligent decision-making support module is used to optimize industrial resource allocation through artificial intelligence algorithms, simulate the economic and ecological impacts of different planning schemes, and provide a visual interactive decision-making interface.

[0011] Preferably, the data acquisition and analysis module includes:

[0012] The policy information collection unit connects to the government data platform through the API interface to obtain policy information related to "one village one product" and "geographical indications of agricultural products" in real time;

[0013] Environmental monitoring unit, which uses IoT devices equipped with sensors to collect environmental parameters such as soil moisture, pH value and air quality in real time, and obtains regional climate and topographic information through remote sensing technology;

[0014] The data analysis unit, based on a distributed data processing platform, integrates raw information from different data sources and extracts environmental change trends and potential problems through big data analysis algorithms.

[0015] Preferably, the environmental assessment and classification module includes:

[0016] The production environment grading unit evaluates the content of heavy metals in the soil, irrigation water quality and air pollutants, and uses a grading algorithm to divide the environment into "high-quality area", "safe area", "still safe area" and "polluted area";

[0017] The ecological adaptability analysis unit uses GIS technology to overlay and analyze the distribution of climate, soil and water resources to generate a map of areas suitable for planting or breeding;

[0018] The environmental protection planning unit, through the environmental simulation model, proposes optimization plans based on regional soil improvement technology and pollution control measures, and outputs a visual environmental protection planning report.

[0019] Preferably, the industrial planning module includes:

[0020] The agricultural production planning unit develops efficient crop rotation and three-dimensional breeding models through rule-based planting models combined with regional environmental characteristics;

[0021] The processing and supply chain planning unit designs a distribution network supported by cold chain logistics technology and optimizes the storage and transportation efficiency of agricultural products through intelligent warehousing systems;

[0022] The culture and brand building unit uses natural language processing (NLP) technology to analyze the historical and cultural value of landmark agricultural products, generate brand stories and marketing copy, and use blockchain technology to authenticate brand logos.

[0023] Preferably, the economic analysis module includes:

[0024] The revenue analysis unit, based on the multiple regression analysis model, inputs the agricultural product market price, production cost, transportation cost and policy subsidy data to calculate the unit revenue and regional total revenue of each agricultural product;

[0025] The risk assessment unit uses Monte Carlo simulation methods to predict the impact of climate change, policy adjustments or market fluctuations on regional returns;

[0026] The policy support optimization unit outputs financial support optimization recommendations and subsidy plans for rural industries based on the results of benefit and risk analysis.

[0027] Preferably, the intelligent auxiliary decision-making module includes:

[0028] The optimization algorithm unit uses a resource allocation algorithm based on deep learning to optimize the yield ratio and regional configuration of different agricultural products;

[0029] The simulation and prediction unit simulates the economic benefits and environmental impacts of different planning schemes through the system dynamics model and generates a long-term benefit prediction curve;

[0030] The visual interactive interface uses front-end technology to build the user interface, which displays regional planning and economic analysis results through interactive maps, allowing users to modify parameters and view simulation effects in real time.

[0031] Preferably, it also includes utilizing cloud computing platforms to support massive data storage and computing, and achieving collaboration between modules through a distributed architecture.

[0032] In a second aspect, the present invention provides the following technical solution, which is a method for green planning of characteristic rural industries combined with economic analysis, comprising the following steps:

[0033] S1. Data collection and analysis

[0034] S101. Policy information collection:

[0035] Connect to relevant government platforms through API interfaces to collect policy data on "one village, one product" and "geographical indications of agricultural products" and establish a policy database;

[0036] S102, Environmental Data Collection:

[0037] Collect soil quality, water quality, climate and topography data through IoT devices, and obtain land use and vegetation coverage through remote sensing technology;

[0038] S103. Economic data collection:

[0039] Collect economic parameters of production costs, market prices, transportation costs and policy subsidies within the region;

[0040] S104, Data Integration and Analysis:

[0041] Use the distributed data processing platform to clean and integrate the collected data, extract key features and trends through big data analysis algorithms, and form a data foundation;

[0042] S2. Origin environment assessment and classification

[0043] S201, Environmental Quality Assessment:

[0044] Standardized assessments of soil, water, and air quality;

[0045] S202, Regional classification:

[0046] Based on the results of environmental quality assessment, the production environment is divided into "green more advantage area", "pollution-free more advantage area", "green less advantage area" and "pollution-free less advantage area";

[0047] S203, Ecological Adaptability Analysis:

[0048] Use GIS technology to overlay and analyze climate, soil, water resources and terrain data to generate ecological distribution maps of agricultural products suitable for planting or breeding;

[0049] S3. Industrial Planning

[0050] S301. Agricultural production planning:

[0051] Design planting and breeding models based on environmental assessment results and characteristics of landmark agricultural products;

[0052] S302, Processing and Supply Chain Design:

[0053] Develop agricultural product processing and packaging solutions, optimize distribution networks based on cold chain logistics technology, and design intelligent warehouse management systems;

[0054] S303, Brand and Culture Construction:

[0055] Explore the historical and cultural value of landmark agricultural products, combine natural language processing (NLP) technology to generate brand stories, build online and offline brand promotion plans, and use blockchain technology for brand anti-counterfeiting authentication;

[0056] S4. Economic Benefit Analysis

[0057] S401. Calculation of income:

[0058] A multiple regression analysis model was used to input market prices, production costs, transportation costs and policy subsidy data to calculate regional total benefits and unit benefits;

[0059] S402, Risk Assessment:

[0060] Using Monte Carlo simulation methods, we can predict the potential impact of market volatility and climate change uncertainty on regional economic returns;

[0061] S403, Policy support optimization:

[0062] Based on the results of benefit and risk assessment, output policy support optimization suggestions for rural industries, including subsidy adjustments and financial support directions;

[0063] S5. Intelligent decision-making and solution optimization

[0064] S501, Scheme simulation and prediction:

[0065] Use system dynamics models to simulate the performance of different industrial planning schemes in terms of economic benefits and ecological protection;

[0066] S502, resource allocation optimization:

[0067] Optimize resource allocation through deep learning algorithms to improve the overall benefits of planning solutions;

[0068] S503, Visual interaction adjustment:

[0069] Provides an interactive map interface to display regional planning and economic analysis results in real time, allowing users to adjust parameters and view instant forecast effects.

[0070] In the third aspect, the invention provides the following technical solution: a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned green planning method for characteristic rural industries combined with economic analysis when executing the computer program.

[0071] In a fourth aspect, the present invention provides the following technical solution: a readable storage medium having a computer program stored thereon, and the computer program, when executed by a processor, implements the above-mentioned method for green planning of characteristic rural industries combined with economic analysis.

[0072] The present invention has the following beneficial effects:

[0073] 1. In the present invention, intelligent and dynamic planning is achieved through reinforcement learning, linear programming and system dynamics models, and the plan is quickly adjusted according to real-time environmental, economic and policy data to avoid the lag of traditional planning; the intelligent decision-making support module integrates the evaluation and analysis results, simulates the long-term economic and ecological impacts of different plans, and users can adjust parameters in real time to intuitively understand the benefits of the plan, ensuring the accuracy and scientificity of the planning.

[0074] 2. In the present invention, the entire process from data collection to policy support optimization is covered, integrating the Internet of Things, remote sensing, GIS and cloud computing technologies to improve planning efficiency and coordination capabilities; each module operates in a coordinated manner, and data flows throughout environmental assessment, economic analysis and planning optimization, avoiding information islands, providing an integrated solution, and fully supporting the sustainable development of rural green industries.

[0075] 3. In the present invention, economic benefits and ecological protection are deeply integrated. Based on environmental classification and crop suitability analysis, an eco-friendly industrial layout plan is proposed, while optimizing economic benefits. By treating polluted areas and promoting high value-added industries, regional ecological security and economic growth are ensured to be synchronized, achieving a win-win situation of green development and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 This is a system architecture diagram of the characteristic rural industry green planning system combined with economic analysis proposed by the present invention;

[0077] Figure 2 This is a flow chart of the method for green planning of characteristic rural industries combined with economic analysis proposed in the present invention. DETAILED DESCRIPTION

[0078] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0079] Embodiment 1

[0080] Reference Figure 1 In the first embodiment of the present invention, the present invention provides a characteristic rural industry green planning system combined with economic analysis, including:

[0081] Data collection and analysis module, used to collect and integrate policy information, environmental data, economic data and market demand information, and to update rural industrial environmental data in real time based on IoT devices and remote sensing technology;

[0082] Environmental assessment and classification module, which is used to classify rural industrial areas into green and more advantageous production areas, pollution-free and more advantageous production areas, green and less advantageous production areas, and pollution-free and less advantageous production areas according to the production environment characteristics of soil, water quality, climate and topography, and evaluate the ecological adaptability of the production areas;

[0083] The industrial planning module is used to design production, processing, packaging and supply chain plans for specialty agricultural products based on the results of the production environment assessment, and to build a brand building plan in combination with the historical and cultural characteristics of the region;

[0084] Economic analysis module, which is used to analyze the economic benefits of the industry, assess risks and provide optimization solutions based on production costs, market prices and policy subsidy information;

[0085] The intelligent decision-making support module is used to optimize industrial resource allocation through artificial intelligence algorithms, simulate the economic and ecological impacts of different planning schemes, and provide a visual interactive decision-making interface.

[0086] Specifically, this system realizes the scientificity, comprehensiveness and sustainability of rural industrial planning through the innovative design of intelligence and dynamic optimization, full process coverage and dual integration of economy and ecology. The intelligent module uses reinforcement learning, linear programming and system dynamics models to integrate real-time environmental, economic and policy data, realize dynamic adjustment and optimal decision-making, and solve the lag problem of traditional planning; the system architecture covering the whole process integrates the Internet of Things, remote sensing, GIS and cloud computing technologies from data collection to policy support optimization, realizes efficient collaboration and data integration of various modules, and provides an integrated solution; at the same time, through the deep integration of economic benefits and ecological protection, the system proposes an eco-friendly industrial layout plan based on environmental classification and crop suitability analysis, and realizes the coordinated improvement of regional ecological security and economic benefits through pollution control and the development of high value-added industries, and truly realizes the green and high-quality development of rural industries.

[0087] Data collection and analysis modules include:

[0088] The policy information collection unit connects to the government data platform through the API interface to obtain policy information related to "one village one product" and "geographical indications of agricultural products" in real time;

[0089] Environmental monitoring unit, which uses IoT devices equipped with sensors to collect environmental parameters such as soil moisture, pH value and air quality in real time, and obtains regional climate and topographic information through remote sensing technology;

[0090] The data analysis unit, based on a distributed data processing platform, integrates raw information from different data sources and extracts environmental change trends and potential problems through big data analysis algorithms.

[0091] Specifically, the policy information collection unit

[0092] Implementation:

[0093] System connection: Connect to the government data open platform through the HTTPS protocol to obtain policy information flow in JSON format (such as the "one village, one product" policy support list).

[0094] Data cleaning: Use regular expressions to parse documents, remove noise data, and extract key fields such as policy name, subsidy conditions, and applicable areas.

[0095] Automatic update: Design a scheduled task (CronJob) to call the government policy interface every 24 hours to detect new or updated policy content.

[0096] Storage method: Store policy information in a relational database (such as MySQL), with fields including policy ID, policy type, scope of application, and subsidy amount.

[0097] Environmental Monitoring Unit

[0098] Implementation:

[0099] Sensor network layout: LoRa sensors are installed in the area to collect data such as soil moisture, pH value, temperature, etc. At the same time, automatic weather stations are installed at key locations to collect meteorological information such as wind speed, rainfall and light intensity.

[0100] Transmission and reception: The collected data is transmitted to the edge computing device through the LoRa gateway, and the real-time data is pre-processed locally (such as noise filtering).

[0101] High-precision remote sensing analysis: Combined with multispectral cameras mounted on drones, large-scale land use and vegetation index data are obtained to generate NDVI (normalized difference vegetation index) maps.

[0102] Storage and processing: The collected data is processed by the ETL (Extract-Transform-Load) process and stored in HDFS (distributed file system) to support subsequent analysis.

[0103] Data Analysis Unit

[0104] Implementation:

[0105] Data preprocessing: Use Python scripts to standardize multi-source data (such as normalizing the numerical range to 0-1) and unify units and coordinate systems.

[0106] Trend analysis: Use time series analysis algorithms (such as the ARIMA model) to predict future environmental trends, such as rainfall and soil nutrient fluctuations.

[0107] Classification and clustering: K-means clustering algorithm is used to perform cluster analysis on regional environmental data to identify areas with environmental similarity and provide reference for subsequent planning.

[0108] The environmental assessment and classification modules include:

[0109] The production environment grading unit evaluates the content of heavy metals in the soil, irrigation water quality and air pollutants, and uses a grading algorithm to divide the environment into "high-quality area", "safe area", "still safe area" and "polluted area";

[0110] The ecological adaptability analysis unit uses GIS technology to overlay and analyze the distribution of climate, soil and water resources to generate a map of areas suitable for planting or breeding;

[0111] The environmental protection planning unit, through the environmental simulation model, proposes optimization plans based on regional soil improvement technology and pollution control measures, and outputs a visual environmental protection planning report.

[0112] Specifically, the origin environment classification unit

[0113] Implementation:

[0114] Environmental quality sampling: Through zoning sampling design, one sampling point is set up every 10 square kilometers to collect soil samples and irrigation water samples.

[0115] Grading algorithm: A multi-dimensional evaluation model (such as the analytic hierarchy process (AHP)) is used to calculate the comprehensive environmental quality score by combining indicators such as soil heavy metal content and water quality chemical oxygen demand (COD).

[0116] Dynamic grading update: Rolling calculation of regional environmental monitoring data is carried out and the environmental quality level is updated every quarter.

[0117] Distribution visualization: Use GIS software (such as ArcGIS) to overlay the classification results onto the geographic layer to generate an environmental classification heat map.

[0118] Ecological Adaptability Analysis Unit

[0119] Implementation:

[0120] Crop adaptability model: Based on the FAO crop ecological adaptability theory, soil texture, climate conditions (temperature, precipitation) and terrain (slope) data are input to calculate the adaptability score of each crop.

[0121] Regional matching analysis: Using spatial analysis tools (such as QGIS), crop adaptability distribution and environmental classification maps are overlaid to identify areas suitable for crop planting.

[0122] Output: Generate a list of recommended habitats and a geographical distribution map, marking the best crops to grow and their potential yields in each area.

[0123] Environmental Protection Planning Unit

[0124] Implementation:

[0125] Selection of pollution control technologies: For heavy metal polluted areas, a variety of technical means can be selected for coordinated use to improve the remediation effect. Traditional methods include planting metal-enriched plants (such as blue grass, sunflower, etc.) to achieve remediation through plant absorption and accumulation of heavy metals; or using soil conditioners (such as biochar) to stabilize pollutants through physical and chemical effects. In addition, Chinese medicinal plant extracts (such as Schisandra chinensis and dandelion extracts) can also be used as auxiliary remediation materials. These extracts contain organic acids, polyphenols and flavonoids, which can reduce the mobility of heavy metals through chelation, while improving soil microbial communities and enhancing the ecological recovery capacity of contaminated soils.

[0126] Land resource optimization: The impact of unreasonable land use on ecology is simulated through the Land Use Dynamic Change Model (LUCC), and optimization solutions are proposed in combination with soil remediation measures. For example, in highly polluted areas, Chinese herbal medicine extracts are combined with traditional metal-enriching plant plantings to further improve soil remediation efficiency and optimize land use functions through the chemical stabilization of extracts and the biological absorption of plants.

[0127] Treatment plan output: The soil remediation planning report generated by the system may include the following: matching analysis between pollution type and remediation technology, pollution control schedule, remediation cost estimation, and expected ecological and economic benefit assessment. For example, in lead-contaminated farmland, the combined use of Schisandra extract and biochar can not only reduce the toxicity of lead, but also restore soil fertility within 2-3 production cycles, while significantly reducing the cost of remediation.

[0128] Practical application cases of Chinese herbal medicine extracts: In cadmium-contaminated farmland, dandelion extracts are used in combination with metal-accumulating plants to reduce the bioavailability of cadmium while enhancing soil fertility and microbial activity; in lead-contaminated areas, schisandra extracts are combined with traditional physical stabilizers to significantly reduce the environmental risks of lead. These practices show that Chinese herbal medicine extracts are not only green remediation materials, but can also complement existing technologies to significantly improve remediation efficiency and ecological restoration effects.

[0129] The industrial planning module includes:

[0130] The agricultural production planning unit develops efficient crop rotation and three-dimensional breeding models through rule-based planting models combined with regional environmental characteristics;

[0131] The processing and supply chain planning unit designs a distribution network supported by cold chain logistics technology and optimizes the storage and transportation efficiency of agricultural products through intelligent warehousing systems;

[0132] The culture and brand building unit uses natural language processing (NLP) technology to analyze the historical and cultural value of landmark agricultural products, generate brand stories and marketing copy, and use blockchain technology to authenticate brand logos.

[0133] Specifically, the agricultural production planning unit

[0134] Implementation:

[0135] Optimize cropping patterns: Through the rule engine (Drools), crop rotation plans are automatically generated based on soil fertility and climate conditions, such as alternating between grain crops and cash crops.

[0136] Optimization of breeding model: Combining the distribution of water resources and grassland resources, a three-dimensional ecological model of aquaculture-forage planting is recommended.

[0137] Output: Generate a regional production planning table, listing in detail the crop types, breeding objects and corresponding production cycles in each area.

[0138] Process and Supply Chain Planning Unit

[0139] Implementation:

[0140] Cold chain logistics optimization: Combined with the TSP (traveling salesman problem) optimization model, the shortest path from the processing point to the distribution point is calculated to reduce transportation costs.

[0141] Smart warehouse design: Deploy a sensor network to monitor the temperature and humidity of the warehouse environment, and use a dynamic shelf allocation algorithm to improve storage efficiency.

[0142] Output results: Generate agricultural product processing and transportation route map, clarify the layout of processing center and distribution plan.

[0143] Culture and Brand Building Unit

[0144] Implementation:

[0145] Cultural mining: Use natural language processing technology to extract the cultural stories of landmark agricultural products from local documents and historical archives.

[0146] Brand promotion: Generate brand positioning reports based on big data and recommend online and offline promotion channels.

[0147] Anti-counterfeiting certification: Each batch of products is uniquely identified through the blockchain platform and an anti-counterfeiting QR code is output.

[0148] The economic analysis module includes:

[0149] The benefit analysis unit, based on the multiple regression analysis model, inputs the agricultural product market price, production cost, transportation cost and policy subsidy data to calculate the unit benefit and regional total benefit of each agricultural product;

[0150] The risk assessment unit uses Monte Carlo simulation methods to predict the impact of climate change, policy adjustments or market fluctuations on regional returns;

[0151] The policy support optimization unit outputs financial support optimization recommendations and subsidy plans for rural industries based on the results of benefit and risk analysis.

[0152] Specifically, the revenue analysis unit

[0153] Implementation:

[0154] Multiple regression model: The system calculates the unit net income of each agricultural product in the region based on variables such as market price, production cost, and subsidy amount.

[0155] Dynamic earnings forecast: Combined with the ARIMA time series model, the earnings trend in the next 3-5 years is predicted.

[0156] Output results: Generate agricultural product income distribution map and regional economic forecast report.

[0157] Risk Assessment Unit

[0158] Implementation:

[0159] Risk factor simulation: Based on the Monte Carlo method, simulate scenarios such as climate anomalies and market price fluctuations to calculate the fluctuation range of returns.

[0160] Sensitivity analysis: Assess the impact of changes in key variables (such as precipitation and temperature) on returns and generate a risk priority list.

[0161] Output: Generate risk assessment report and response recommendations.

[0162] Policy Support Optimization Unit

[0163] Implementation:

[0164] Dynamic subsidy allocation: Calculate the optimal allocation strategy of fiscal subsidies based on dynamic programming algorithm.

[0165] Output: Generate a policy support optimization plan, including a regional priority subsidy list and funding allocation map.

[0166] The intelligent decision-making support module includes:

[0167] The optimization algorithm unit uses a resource allocation algorithm based on deep learning to optimize the yield ratio and regional configuration of different agricultural products;

[0168] The simulation and prediction unit simulates the economic benefits and environmental impacts of different planning schemes through the system dynamics model and generates a long-term benefit prediction curve;

[0169] The visual interactive interface uses front-end technology to build the user interface, which displays regional planning and economic analysis results through interactive maps, allowing users to modify parameters and view simulation effects in real time.

[0170] Specifically, the optimization algorithm unit

[0171] Implementation:

[0172] Resource allocation model: Optimize the allocation ratio of land, water resources, and labor through reinforcement learning algorithms (such as DQN).

[0173] Real-time adjustment mechanism: Design a feedback mechanism to dynamically adjust the allocation strategy based on the latest data.

[0174] Output results: Generate resource allocation plans and real-time adjustment suggestions.

[0175] Simulation and Prediction Unit

[0176] Implementation:

[0177] System dynamics modeling: Establish dynamic relationship models between land use, income changes and environmental quality, and predict long-term impacts.

[0178] Output: Generate a simulation report, including a comparison of the benefits and environmental impacts of different planning options.

[0179] Visual interactive interface

[0180] Implementation:

[0181] Interface design: Use a front-end framework (such as React or Vue) to build a map visualization interface that supports drag-and-drop parameter adjustment.

[0182] Output results: Display planning simulation results in real time for users to make decisions.

[0183] It also includes using cloud computing platforms to support massive data storage and computing, and coordinating between modules through a distributed architecture.

[0184] Specifically, cloud computing platforms support

[0185] Implementation:

[0186] Distributed computing architecture:

[0187] Use Hadoop Distributed File System (HDFS) to store data from IoT devices, policy databases, and user interactions.

[0188] Efficient distributed data processing is achieved through Spark clusters for tasks such as analyzing environmental changes, risk prediction, and resource optimization.

[0189] The system adopts a Master-Worker structure, where the Master node is responsible for task allocation and scheduling, and the Worker node performs specific data computing tasks.

[0190] Real-time data stream processing:

[0191] Use Kafka message queues to receive real-time data streams from IoT devices, such as soil temperature and humidity, and water quality monitoring values ​​collected by environmental sensors.

[0192] Cooperate with the Flink stream processing framework to realize real-time data processing and event response, such as automatically triggering an alarm when the data exceeds the preset threshold.

[0193] Elastic expansion capability:

[0194] Based on containerization technology (such as Docker), system services are deployed on cloud platforms (such as AWS, Google Cloud, or Alibaba Cloud), and services are automatically scaled through Kubernetes to cope with high-concurrency analysis requests.

[0195] High availability and disaster recovery mechanism:

[0196] Using a master-slave redundant architecture, the database adopts sharding and multi-copy strategies (such as MySQL or Cassandra) to ensure the stability and consistency of data storage.

[0197] Configure a cross-region backup plan to store data in an off-site data center every day to prevent data loss due to a single point of failure.

[0198] Multi-user collaboration support:

[0199] Design a user role management system to support multi-level permission access for government departments, enterprises and farmers.

[0200] By providing an external interface through the API gateway, users can access planning results and submit feedback through terminal devices.

[0201] Output:

[0202] Real-time data analysis results: The data collected by sensors is processed and analyzed in real time through the cloud computing platform, and a dynamically updated environmental status report is output.

[0203] Planning task distribution and monitoring: supports concurrent access by multiple users and provides a distribution status and progress monitoring interface for planning tasks.

[0204] Elastic expansion report: Under high load, the system records the elastic expansion process and generates a resource utilization report to provide a basis for subsequent optimization.

[0205] Report generation and sharing

[0206] Implementation:

[0207] Report generation system:

[0208] Use tools such as JasperReports to dynamically generate comprehensive planning reports in PDF format based on user needs, including regional environmental analysis, industrial layout, economic benefit forecasts, etc.

[0209] The report structure is automatically adjusted according to the user's selections, such as the option to generate a single region analysis report or a cross-region comparison report.

[0210] Report visualization output:

[0211] With the help of front-end chart tools such as ECharts or D3.js, the data results are presented in an interactive visual form, supporting various display methods such as bar charts, line charts, heat maps, etc.

[0212] Users can view detailed analysis results by sliding, clicking, and other operations (such as clicking on a certain area map to display the corresponding environmental quality and industrial planning details).

[0213] Report sharing mechanism:

[0214] The system uploads the generated report to the shared directory of the cloud platform (such as the government management system or enterprise collaboration platform) through the built-in file sharing module.

[0215] The report can be notified to relevant parties via email or SMS, and a link can be provided for users to view online or download.

[0216] User feedback module:

[0217] Provide a user feedback interface for report generation, allowing users to submit revisions or requests for further analysis.

[0218] The system automatically records user feedback and stores it in the back-end database for reference in subsequent planning and optimization.

[0219] Output:

[0220] PDF / HTML format report: Generates detailed downloadable and printable report files, including environmental ratings, economic analysis, policy recommendations, etc.

[0221] Visual report page: provides an online interactive report interface that supports switching between charts and maps.

[0222] Multi-channel sharing: Enable multi-party collaboration and information sharing through email, links or integration into third-party platforms (such as government systems).

[0223] Embodiment 2:

[0224] Reference Figure 2 In a second embodiment of the present invention, the present invention provides a characteristic rural industry green planning method combined with economic analysis, comprising the following steps:

[0225] S1. Data collection and analysis

[0226] S101. Policy information collection:

[0227] Connect to relevant government platforms through API interfaces to collect policy data on "one village, one product" and "geographical indications of agricultural products" and establish a policy database;

[0228] S102, Environmental Data Collection:

[0229] Collect soil quality, water quality, climate and topography data through IoT devices, and obtain land use and vegetation coverage through remote sensing technology;

[0230] S103. Economic data collection:

[0231] Collect economic parameters of production costs, market prices, transportation costs and policy subsidies within the region;

[0232] S104, Data Integration and Analysis:

[0233] Use the distributed data processing platform to clean and integrate the collected data, extract key features and trends through big data analysis algorithms, and form a data foundation;

[0234] S2. Origin environment assessment and classification

[0235] S201, Environmental Quality Assessment:

[0236] Standardized assessments of soil, water, and air quality;

[0237] S202, Regional classification:

[0238] Based on the results of environmental quality assessment, the production environment is divided into "green more advantage area", "pollution-free more advantage area", "green less advantage area" and "pollution-free less advantage area";

[0239] S203, Ecological Adaptability Analysis:

[0240] Use GIS technology to overlay and analyze climate, soil, water resources and terrain data to generate ecological distribution maps of agricultural products suitable for planting or breeding;

[0241] S3. Industrial Planning

[0242] S301. Agricultural production planning:

[0243] Design planting and breeding models based on environmental assessment results and characteristics of landmark agricultural products;

[0244] S302, Processing and Supply Chain Design:

[0245] Develop agricultural product processing and packaging solutions, optimize distribution networks based on cold chain logistics technology, and design intelligent warehouse management systems;

[0246] S303, Brand and Culture Construction:

[0247] Explore the historical and cultural value of landmark agricultural products, combine natural language processing (NLP) technology to generate brand stories, build online and offline brand promotion plans, and use blockchain technology for brand anti-counterfeiting authentication;

[0248] S4. Economic Benefit Analysis

[0249] S401. Calculation of income:

[0250] A multiple regression analysis model was used to input market prices, production costs, transportation costs and policy subsidy data to calculate regional total benefits and unit benefits;

[0251] S402, Risk Assessment:

[0252] Using Monte Carlo simulation methods, we can predict the potential impact of market volatility and climate change uncertainty on regional economic returns;

[0253] S403, Policy support optimization:

[0254] Based on the results of benefit and risk assessment, output policy support optimization suggestions for rural industries, including subsidy adjustments and financial support directions;

[0255] S5. Intelligent decision-making and solution optimization

[0256] S501, Scheme simulation and prediction:

[0257] Use system dynamics models to simulate the performance of different industrial planning schemes in terms of economic benefits and ecological protection;

[0258] S502, resource allocation optimization:

[0259] Optimize resource allocation through deep learning algorithms to improve the overall benefits of planning solutions;

[0260] S503, Visual interaction adjustment:

[0261] Provides an interactive map interface to display regional planning and economic analysis results in real time, allowing users to adjust parameters and view instant forecast effects.

[0262] Specifically, S1 data collection and analysis

[0263] S101 Policy Information Collection

[0264] Implementation refinement:

[0265] Interface connection: Connect to the government open data platform (such as the policy service system of the Ministry of Agriculture and Rural Affairs) through RESTful API to obtain structured policy data (JSON, XML format).

[0266] Document crawling: Use the Scrapy framework to crawl policy announcements on government portals, and combine regular expressions to extract key information such as policy names, subsidy conditions, applicable areas, etc.

[0267] Storage: Store structured data in a relational database (such as MySQL) and create indexes to improve query efficiency. Table fields include policy ID, release time, support amount, and coverage area.

[0268] S102 Environmental Data Collection

[0269] Implementation refinement:

[0270] Sensor deployment: IoT sensor networks are deployed in key areas to collect data such as soil quality (pH value, salinity), water quality (COD, BOD), and meteorology (temperature, humidity), and upload the data to the local gateway using the LoRa wireless communication protocol.

[0271] Remote sensing image acquisition: Obtain large-scale data such as land use and vegetation index (NDVI) through satellite images (such as Landsat8) or drones equipped with multispectral cameras.

[0272] Real-time transmission and storage: Use Apache Kafka message queue to process high-frequency collected data streams and store them in real time in the distributed file system (HDFS).

[0273] S103 Economic Data Collection

[0274] Implementation refinement:

[0275] Market data capture: Regularly obtain price data from the agricultural market price monitoring platform (such as the agricultural product wholesale price index system), and clean, transform and load it into the data warehouse through the ETL process (Extract-Transform-Load).

[0276] Production cost collection: Obtain detailed data on planting, breeding and processing costs through government research reports and production records provided by enterprises.

[0277] Dynamic subsidy information update: Use scheduled tasks (such as CronJob) to regularly synchronize policy subsidy related data from the government database.

[0278] S104 Data Integration and Analysis

[0279] Implementation refinement:

[0280] Data cleaning: The Pandas library in Python was used to standardize environmental and economic data (e.g., unit conversion, removal of outliers).

[0281] Time series analysis: predict future trends of soil nutrients, climate conditions, etc. through ARIMA model.

[0282] Cluster analysis: Use the K-means algorithm to classify the collected data and identify areas with similar environmental and economic characteristics.

[0283] S2 Origin Environment Assessment and Classification

[0284] S201 Environmental Quality Assessment

[0285] Implementation refinement:

[0286] Standardized data processing: According to the "Green Food Production Area Environmental Quality Standards", soil, water quality and air quality indicators are converted into standard scores (0-100 points).

[0287] Comprehensive scoring model: The analytic hierarchy process (AHP) is used to calculate the comprehensive environmental score, and the weight distribution is set according to expert opinions, such as soil indicators accounting for 50%, water quality indicators accounting for 30%, and air indicators accounting for 20%.

[0288] Output result: Generate an environmental quality grade report, which is divided into four grades: high quality, safe, moderately safe and polluted.

[0289] S202 Regional Classification

[0290] Implementation refinement:

[0291] GIS overlay analysis: ArcGIS software is used to overlay soil, water quality, and climate data onto geographic layers to generate environmental distribution maps.

[0292] Partitioning algorithm: Use a spatial clustering algorithm (such as DBSCAN) to group regions according to environmental characteristics and draw boundaries.

[0293] Output: Generate a partition heat map and mark the environmental level of each area.

[0294] S203 Ecological Adaptability Analysis

[0295] Implementation refinement:

[0296] Suitability modeling: Based on the FAO ecological suitability model, soil texture, water resource distribution and climate conditions are input to calculate the adaptability score of each crop.

[0297] Spatial matching: The crop suitability scores were mapped to the regional environmental distribution map using Python’s Geopandas library.

[0298] Output: Generate distribution map and recommended list of suitable crops.

[0299] S3 Industry Planning

[0300] S301 Agricultural production planning

[0301] Implementation refinement:

[0302] Production optimization model: Based on linear programming models (such as Simplex), the planting pattern is optimized under the goals of land use efficiency and maximum profit.

[0303] Production task allocation: Generate planting and breeding task schedules by region, including crop types, production cycles and expected outputs.

[0304] Output results: Output regional production planning report to clarify production tasks and schedules.

[0305] S302 Processing and Supply Chain Planning

[0306] Implementation refinement:

[0307] Logistics network design: Use the traveling salesman problem (TSP) algorithm to optimize cold chain logistics routes and reduce transportation time and costs.

[0308] Machining center layout: Select the location of the machining center based on geographical and economic conditions, and use the site selection model (FacilityLocationProblem) to calculate the optimal location.

[0309] Output results: Generate processing and distribution planning diagram, including processing center distribution and logistics routes.

[0310] S303 Brand and Culture Construction

[0311] Implementation refinement:

[0312] Cultural value extraction: Extract the cultural stories of specialty agricultural products from local historical documents through NLP technology.

[0313] Brand design: Combine consumer preference data to generate brand positioning reports and recommend appropriate promotional channels (such as social media and offline promotion).

[0314] Output results: Generate brand promotion plan and anti-counterfeiting certification report.

[0315] S4 Economic Benefit Analysis

[0316] S401 profit calculation

[0317] Implementation refinement:

[0318] Revenue prediction model: Based on the multiple regression model, input market prices, production costs, policy subsidies and other data to calculate unit revenue.

[0319] Dynamic forecasting: Combine the ARIMA model to predict future earnings trends.

[0320] Output: Generate a revenue forecast report showing regional revenue distribution.

[0321] S402 Risk Assessment

[0322] Implementation refinement:

[0323] Monte Carlo simulation: Simulate risk scenarios for market price fluctuations and climate change, and calculate the range of returns.

[0324] Sensitivity analysis: Evaluate the impact of different variables on revenue and generate a sensitivity priority report.

[0325] Output: Generate regional risk assessment report and response recommendations.

[0326] S403 Policy support optimization

[0327] Implementation refinement:

[0328] Dynamic programming algorithm: Calculate the optimal allocation plan for financial subsidies, giving priority to high-yield areas.

[0329] Output: Generate policy support optimization report.

[0330] S5 Intelligent Decision-making and Solution Optimization

[0331] S501 scheme simulation and prediction

[0332] Implementation refinement:

[0333] System dynamics modeling: Model the causal relationships between environmental quality, benefits, and policies and predict long-term impacts.

[0334] Output results: Generate a simulation comparison report to compare the benefits of different solutions.

[0335] S502 Resource Configuration Optimization

[0336] Implementation refinement:

[0337] Reinforcement learning algorithm: Optimize the distribution ratio of land, water resources and labor through the DQN algorithm.

[0338] Output result: Generate resource allocation plan.

[0339] S503 Visual Interactive Adjustment

[0340] Implementation refinement:

[0341] Interface development: Build an interactive map interface using React.js, supporting parameter adjustment.

[0342] Output results: Provide real-time updated planning result diagram.

[0343] Embodiment 3

[0344] The third embodiment of the present invention is based on the same inventive concept. The present invention proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method for green planning of characteristic rural industries combined with economic analysis of the above-mentioned embodiment.

[0345] Embodiment 4

[0346] The fourth embodiment of the present invention is based on the same inventive concept. A computer device proposed by the present invention comprises: a processor and a memory. The processor and the memory communicate with each other. The memory is used to store instructions. The processor is used to execute the instructions in the memory to execute the characteristic rural industry green planning method combined with economic analysis of the above embodiment.

[0347] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0348] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A characteristic rural industry green planning system combined with economic analysis, characterized by: include: Data collection and analysis module, used to collect and integrate policy information, environmental data, economic data and market demand information, and to update rural industrial environmental data in real time based on IoT devices and remote sensing technology; Environmental assessment and classification module, which is used to classify rural industrial areas into green and more advantageous production areas, pollution-free and more advantageous production areas, green and less advantageous production areas, and pollution-free and less advantageous production areas according to the production environment characteristics of soil, water quality, climate and topography, and evaluate the ecological adaptability of the production areas; The industrial planning module is used to design production, processing, packaging and supply chain plans for specialty agricultural products based on the results of the production environment assessment, and to build a brand building plan in combination with the historical and cultural characteristics of the region; Economic analysis module, which is used to analyze the economic benefits of the industry, assess risks and provide optimization solutions based on production costs, market prices and policy subsidy information; The intelligent decision-making support module is used to optimize industrial resource allocation through artificial intelligence algorithms, simulate the economic and ecological impacts of different planning schemes, and provide a visual interactive decision-making interface.

2. The characteristic rural industry green planning system combined with economic analysis according to claim 1 is characterized in that: The data acquisition and analysis module includes: The policy information collection unit connects to the government data platform through the API interface to obtain policy information related to "one village one product" and "geographical indications of agricultural products" in real time; Environmental monitoring unit, which uses IoT devices equipped with sensors to collect environmental parameters such as soil moisture, pH value and air quality in real time, and obtains regional climate and topographic information through remote sensing technology; The data analysis unit, based on a distributed data processing platform, integrates raw information from different data sources and extracts environmental change trends and potential problems through big data analysis algorithms.

3. The characteristic rural industry green planning system combined with economic analysis according to claim 1 is characterized in that: The environmental assessment and classification module includes: The production area environment grading unit evaluates the content of heavy metals in the soil, irrigation water quality and air pollutants, and uses a grading algorithm to divide the environment into "high-quality area", "safe area", "still safe area" and "polluted area"; The ecological adaptability analysis unit uses GIS technology to overlay and analyze the distribution of climate, soil and water resources to generate a map of areas suitable for planting or breeding; The environmental protection planning unit, through the environmental simulation model, proposes optimization plans based on regional soil improvement technology and pollution control measures, and outputs a visual environmental protection planning report.

4. The characteristic rural industry green planning system combined with economic analysis according to claim 1 is characterized in that: The industrial planning module includes: The agricultural production planning unit develops efficient crop rotation and three-dimensional breeding models through rule-based planting models combined with regional environmental characteristics; The processing and supply chain planning unit designs a distribution network supported by cold chain logistics technology and optimizes the storage and transportation efficiency of agricultural products through intelligent warehousing systems; The culture and brand building unit uses natural language processing (NLP) technology to analyze the historical and cultural value of landmark agricultural products, generate brand stories and marketing copy, and use blockchain technology to authenticate brand logos.

5. The characteristic rural industry green planning system combined with economic analysis according to claim 1 is characterized in that: The economic analysis module includes: The revenue analysis unit, based on the multiple regression analysis model, inputs the agricultural product market price, production cost, transportation cost and policy subsidy data to calculate the unit revenue and regional total revenue of each agricultural product; The risk assessment unit uses Monte Carlo simulation methods to predict the impact of climate change, policy adjustments or market fluctuations on regional returns; The policy support optimization unit outputs financial support optimization recommendations and subsidy plans for rural industries based on the results of benefit and risk analysis.

6. The characteristic rural industry green planning system combined with economic analysis according to claim 1 is characterized in that: The intelligent auxiliary decision-making module includes: The optimization algorithm unit uses a resource allocation algorithm based on deep learning to optimize the yield ratio and regional configuration of different agricultural products; The simulation and prediction unit simulates the economic benefits and environmental impacts of different planning schemes through the system dynamics model and generates a long-term benefit prediction curve; The visual interactive interface uses front-end technology to build the user interface, which displays regional planning and economic analysis results through interactive maps, allowing users to modify parameters and view simulation effects in real time.

7. The characteristic rural industry green planning system combined with economic analysis according to claim 1 is characterized in that: It also includes using cloud computing platforms to support massive data storage and computing, and coordinating between modules through a distributed architecture.

8. The characteristic rural industry green planning method combined with economic analysis is characterized by: The characteristic rural industry green planning system combined with economic analysis as described in any one of claims 1 to 7 comprises the following steps: S1. Data collection and analysis S101. Policy information collection: Connect to relevant government platforms through API interfaces to collect policy data on "one village, one product" and "geographical indication of agricultural products" and establish a policy database; S102, Environmental Data Collection: Collect soil quality, water quality, climate and topography data through IoT devices, and obtain land use and vegetation coverage through remote sensing technology; S103. Economic data collection: Collect economic parameters of production costs, market prices, transportation costs and policy subsidies within the region; S104, Data Integration and Analysis: Use the distributed data processing platform to clean and integrate the collected data, extract key features and trends through big data analysis algorithms, and form a data foundation; S2. Origin environment assessment and classification S201. Environmental quality assessment: Standardized assessments of soil, water, and air quality; S202, Regional classification: Based on the results of environmental quality assessment, the production environment is divided into "green more advantageous areas", "pollution-free more advantageous areas", "green less advantageous areas" and "pollution-free less advantageous areas"; S203, Ecological Adaptability Analysis: Use GIS technology to overlay and analyze climate, soil, water resources and terrain data to generate ecological distribution maps of agricultural products suitable for planting or breeding; S3. Industrial Planning S301. Agricultural production planning: Design planting and breeding models based on environmental assessment results and characteristics of landmark agricultural products; S302, Processing and Supply Chain Design: Develop agricultural product processing and packaging solutions, optimize distribution networks based on cold chain logistics technology, and design intelligent warehouse management systems; S303, Brand and Culture Construction: Explore the historical and cultural value of landmark agricultural products, combine natural language processing (NLP) technology to generate brand stories, build online and offline brand promotion plans, and use blockchain technology for brand anti-counterfeiting authentication; S4. Economic Benefit Analysis S401. Calculation of income: A multiple regression analysis model was used to input market prices, production costs, transportation costs and policy subsidy data to calculate regional total benefits and unit benefits; S402, Risk Assessment: Using Monte Carlo simulation methods, we can predict the potential impact of market volatility and climate change uncertainty on regional economic returns; S403, Policy support optimization: Based on the results of benefit and risk assessment, output policy support optimization suggestions for rural industries, including subsidy adjustments and financial support directions; S5. Intelligent decision-making and solution optimization S501, Scheme simulation and prediction: Use system dynamics models to simulate the performance of different industrial planning schemes in terms of economic benefits and ecological protection; S502, resource allocation optimization: Optimize resource allocation through deep learning algorithms to improve the overall benefits of planning solutions; S503, Visual Interaction Adjustment: Provides an interactive map interface to display regional planning and economic analysis results in real time, allowing users to adjust parameters and view instant forecast effects.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the characteristic rural industry green planning method combined with economic analysis as described in claim 8.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the characteristic rural industry green planning method combined with economic analysis as described in claim 8.

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