CN113657751A - Wisdom agricultural is produced and is melted comprehensive service platform - Google Patents

Abstract

The invention belongs to the technical field of agriculture, and particularly relates to an intelligent agricultural production and integration comprehensive service platform which comprises an intelligent greenhouse module, a digital twin data analysis module, an operation module and a terminal. By establishing and connecting digital models of agricultural rural physical assets and processes, getting through scattered operation digital information, constructing and communicating a basic Internet of things, presenting the agricultural rural basic information through a cross-platform, cross-terminal, real-time and visual interactive interface, forming a comprehensive information command center for global examination, comprehensive management, unified scheduling and unified decision, and realizing real intelligent agricultural application management by using hand-grasping work.

Description

Wisdom agricultural is produced and is melted comprehensive service platform

Technical Field

The invention belongs to the technical field of agriculture, and particularly relates to an intelligent agricultural production and integration comprehensive service platform.

Background

Agriculture is the foundation of national economy, and the degree of agricultural modernization is one of the important marks of the national level of modernization. The development of intelligent agriculture is essentially the economic development problem of agricultural rural areas. At present, the digital economy development of China is short in rural areas, and the market with the most development prospect is also in rural areas. The vast rural areas become the main direction for the next industrial upgrading in China due to the weak construction of information infrastructures, the insufficient popularization of scientific and technological equipment and the insufficient depth of industrial integration development. The agricultural digitalization of China just started, and decentralized management is one of the reasons. But in the long term, the improvement of the agricultural operation concentration and the digital management are in great tendency.

Two major challenges facing the development of modern agriculture in China are: resource shortage and resource consumption are excessive. Therefore, an information sensing device, a sensing network, the Internet and agricultural data aggregation, connection and visual processing are taken as a comprehensive control platform of a core, global visualization, global integration and perception automation are provided for a modern agricultural production management technology system such as quantitative analysis, intelligent decision, variable input and positioning operation in the agricultural production process, guidance and command help of decision scientification is necessary to provide a new idea and a visual and effective means for solving problems.

Digital twin refers to a digital model of a physical asset or process. The method fully utilizes data such as a physical model, sensor updating, operation history and the like, integrates a multidisciplinary, multi-physical quantity, multi-scale and multi-probability simulation process, and finishes mapping in a virtual space so as to reflect the full life cycle process of corresponding entity equipment. The digital twin technical means is applied to the technical field of agriculture, and the further development of intelligent agriculture is promoted.

Disclosure of Invention

According to the defects of the prior art, the invention provides the intelligent agricultural production and integration comprehensive service platform, by establishing and connecting a digital model of agricultural rural physical assets and processes, getting through scattered management digital information, constructing and communicating a basic Internet of things, presenting the agricultural rural basic information through cross-platform, cross-terminal, real-time and visual interactive interfaces, forming a comprehensive information command center with global examination, comprehensive management, unified scheduling and unified decision, and realizing real intelligent agricultural application management by using hand-held work.

The invention relates to an intelligent agricultural production and integration comprehensive service platform which comprises an intelligent greenhouse module, a digital twin data analysis module, an operation module and a terminal;

the intelligent greenhouse module comprises a greenhouse body and a data acquisition submodule, wherein the data acquisition submodule is used for acquiring environmental data and crop growth data in the greenhouse body;

the data end of the digital twin data analysis module is in butt joint with the data acquisition sub-module in a wireless or wired mode, is in butt joint with meteorological environment data of an external network and is used for storing and simulating crop growth in the greenhouse body;

the operation module comprises a production submodule, a marketing submodule and a service submodule; the production submodule receives output data of the digital twin data analysis module and is used for managing crop research and development, a growth model, yield prediction and quality prediction in the greenhouse body; the marketing submodule is used for establishing a data-driven price element long-term and short-term prediction model by combining agricultural big data and agricultural product prices; the service submodule is used for talent culture, expert guidance and financial service;

the terminal is used for accessing the operation module and issuing a control instruction for the user.

Preferably, the data acquisition submodule includes, but is not limited to, a light intensity sensor, a carbon dioxide concentration sensor, an air humidity sensor, a temperature sensor, a soil PH sensor, a soil EC value sensor, a soil nutrient content sensor and a soil humidity sensor.

Preferably, the digital twin data analysis module comprises a computer cluster and a 5G-AR-VR device.

Preferably, the terminal includes, but is not limited to, a web end, a mobile phone client or a Wap end.

Preferably, the crop growth model includes, but is not limited to, environmental assessment, digital seed selection, cultivation supervision, water and fertilizer management, pest and weed management, harvest management, agricultural disaster early warning and emergency measures.

The intelligent agricultural digital twin platform is taken as an intelligent information aggregation center, an agricultural expert cross-platform system is taken as a leading factor, an agricultural Internet of things and an intelligent agricultural technology are taken as means, visual, sensible, interactive and high-level and multi-aspect agricultural experts and agricultural information are converged through a three-dimensional model and an artificial intelligent model, decision support is provided for a leader at the platform center, and various agricultural problem consultation services are provided for users or policy makers in an image and intuitive mode on each intelligent terminal. Under the relatively controllable environmental condition, the intensive, efficient and sustainable modern agricultural production mode is realized through industrial production, and a good demonstration effect is exerted for the modern agricultural development.

The invention has the advantages that:

(1) integrating digital infrastructure:

the project is used as a gripper, the information is paved according to the previous informatization work, the information entering household engineering is continuously and deeply implemented, a rural basic information service system is integrated, rural telecommunication infrastructure is fully utilized, rural agricultural basic communication and information networks including a 5G network are greatly improved, and a spatial transmission basis of a platform connection physical resource is integrated and established;

according to the fact that the mobile phone is popularized by the existing peasants, information such as scientific and technological knowledge, market information, life service and the like is sent to the mobile phone 'end' of the peasants, the use habits of agricultural rural information are developed, an agricultural information network combining 'cloud, network and end' is integrated, and the network topology mapping basis forming a digital twin platform is gradually constructed;

the project platform is established as a gripper, so that data gathering and sorting, resource catalogue and data hooking are accelerated, information resource integration and sharing are integrated and perfected, construction and application standards of data resources are established in a tightening mode, and basic support is provided for future agricultural big data construction.

(2) Integrating agricultural data management:

according to the implementation requirements of platform data, the network data structure is further improved, the data are processed and stored in the cloud, the transmission service is in the network, and the service and acquisition are in the terminal. Aiming at the three-agriculture service, a heavyweight cloud is established, a lightweight end is served, the agricultural data is globally visualized, comprehensively and perceptibly and cross-terminal in real time through the data integration work of a digital twin platform, an agricultural comprehensive data pool with clear hierarchy and logic is formed, and a solid foundation is laid for the data judgment and data application of the next step;

platform data is utilized to drive innovation, data service and data collection are combined, personalized service is provided for farmers, the original mode of simply selling information acquisition equipment is extended, more comprehensive long-term data management and use scheme service is provided for users, and for agricultural producers, optimization decision can be made according to recorded production data, so that greater economic benefit is obtained;

the agricultural Internet of things is further built and laid out according to the actual requirements of the system and the actual requirements of agricultural rural areas by taking a digital twin platform as a digital construction opportunity, the requirements of the Internet of things are integrated, the agricultural Internet of things is acquired, managed and used, and the platform can be connected with agricultural Internet of things enterprises and service providers to develop new industrial development business opportunities.

(3) Promote the development of wisdom agriculture:

according to the digital twin platform function, agricultural internet remote intelligent application is developed, in the aspect of facility agriculture, the greenhouse environment automatic monitoring and control technology is relatively mature, and the technologies such as water, fertilizer and pesticide intelligent management can be popularized and applied;

the core requirements of project areas and rural areas are taken as starting points. In five application directions, a technical practice direction most suitable for solving the local area is found according to specific requirements, the digital economic work of the agricultural rural areas in the whole area is pushed in a point-to-point manner and in stages and steps, and the development of the whole intelligent agriculture is further pulled;

the method comprises the steps of forming an icon image from factors such as administrative policy guarantee, equipment allocation, main body entrance rate, identification management and an entrance mechanism, carrying out visual management and interactive management and control through a digital twin platform, introducing advanced technologies such as a block chain and the like, promoting the establishment of a whole-course traceable mode, and providing a demonstration template for the next-step global traceable popularization.

Detailed Description

The present invention is further illustrated by the following examples.

Example 1:

an intelligent agricultural production and integration comprehensive service platform comprises an intelligent greenhouse module, a digital twin data analysis module, an operation module and a terminal;

the intelligent greenhouse module comprises a greenhouse body and a data acquisition submodule, wherein the data acquisition submodule is used for acquiring environmental data and crop growth data in the greenhouse body;

the data end of the digital twin data analysis module is in butt joint with the data acquisition sub-module in a wireless or wired mode, is in butt joint with meteorological environment data of an external network and is used for storing and simulating crop growth in the greenhouse body;

the operation module comprises a production submodule, a marketing submodule and a service submodule; the production submodule receives output data of the digital twin data analysis module and is used for managing crop research and development, a growth model, yield prediction and quality prediction in the greenhouse body; the marketing submodule is used for establishing a data-driven price element long-term and short-term prediction model by combining agricultural big data and agricultural product prices; the service submodule is used for talent culture, expert guidance and financial service;

the terminal is used for accessing the operation module and issuing a control instruction for the user.

The greenhouse body can realize monitoring and automatic control of illumination, temperature, humidity, carbon dioxide, soil and the like, so that the method leads the health development of modern agriculture to be one-time combination of the Internet of things technology and the agricultural field and is also an important mark of the modern agriculture. Through the construction of the intelligent greenhouse body based on the Internet of things, the vegetable planting technology has quantitative indexes as references, the fault tolerance rate of vegetable planting is improved, the vegetable planting has high operability, and an industrial chain can be formed while increasing production and creating income.

The data acquisition submodule comprises but is not limited to an illumination intensity sensor, a carbon dioxide concentration sensor, an air humidity sensor, a temperature sensor, a soil pH value sensor, a soil EC value sensor, a soil nutrient content sensor and a soil humidity sensor.

The digital twin data analysis module comprises a computer cluster and a 5G-AR-VR device. The method is used for carrying out the technical development of regional digital facility remote supervision, virtual reality operation, VR remote damage assessment and the like, and provides support for further deeply developing a quality tracing system of variety quality in the future. After all data are collected to a central node of the digital twin data analysis module, the data are connected with the Internet or a mobile network through a wireless gateway, multi-scale (individual domain, visual field, area and region) transmission of agricultural information is achieved, cross-terminal, cross-platform and remote real-time visualization technologies are comprehensively applied, a user can master the environmental information of a crop site in real time through a mobile phone or a computer, the system diagnoses the growth condition and the pest and disease condition of crops according to environmental parameters, and under the condition that the environmental parameters exceed the standard, the system can remotely control agricultural equipment, process monitoring of agricultural production is achieved, and the sustainable development targets of intensive, high-yield, high-quality, high-efficiency, ecological and safe are achieved. And large meteorological data issued by public meteorological service departments on project sites, nationwide and global in real time can be integrated by combining meteorological environment data, and an agricultural meteorological service algorithm module suitable for the flower industry is developed to support professional algorithms such as disaster insurance and green production environment assessment. And the system can also integrate all the image data of the growth monitoring of the planted crops and aquatic products and poultry cultivation, hyperspectral remote sensing data, AI yield evaluation, growth period evaluation and quality evaluation result data.

The terminal comprises but is not limited to a web end, a mobile phone client or a Wap end.

And the production submodule further expands the intelligent application technology according to the development requirement of the project location to form an intelligent application solution cluster. For example: in field planting, remote sensing monitoring, remote disease and pest diagnosis, accurate operation of agricultural machinery and the like are developed and applied, and the field production efficiency is improved in a large range; in the aspect of livestock and poultry breeding, technologies such as accurate feeding, estrus monitoring, automatic milking and the like are developed and applied; in the aspect of aquaculture, technical means such as water body monitoring and automatic bait feeding are developed, and a modern large-scale farm is built; through the integrated application of the intelligent agricultural science and technology, the comprehensive information interaction control path of the agricultural Internet of things is further expanded, the land output rate, the resource utilization rate and the labor productivity are further improved, and the development of agricultural rural economy to comprehension and intelligence is accelerated. Including but not limited to crop development, growth models, yield predictions, and quality predictions.

The research and development specifically includes that a direct communication platform is communicated with various quality resource companies and scientific research institutions through an intelligent agriculture digital twin platform, and first-hand information of new varieties and high-quality varieties can be obtained. Meanwhile, a scientific research institution can carry out new variety development together with farmers, and set up research and development rewards to stimulate the enthusiasm of the farmers. The method is effectively implemented to relieve the young agricultural scientist project.

The crop growth model includes but is not limited to environmental assessment, digital seed selection, cultivation supervision, water and fertilizer management, pest and weed management, harvest management, agricultural disaster early warning and emergency measures. The method specifically comprises the following steps:

(1) environmental assessment

a. Setting up an environment monitoring Internet of things: the method is used for carrying out data acquisition and whole-process monitoring and management on the growth factors affecting crops, such as soil temperature, humidity, nutrient content, pH value, EC value, water flow, relative humidity, carbon dioxide concentration, illumination intensity duration and the like. And automatically adjusting according to the set parameters. The temperature, the humidity and the illumination of the greenhouse are scientifically and accurately controlled, the most adaptive growth environment is created, the crop production is promoted, and the quality and the yield are improved.

b. The environmental disaster evaluation index is used for evaluating the severity of general environmental problems such as high temperature, low temperature and short exposure in facilities and for evaluating the quality of agricultural products.

c. And displaying the real-time data and the analysis result at the terminal.

(2) Digital seed selection

a. And intelligently recommending the crop species suitable for planting according to the environmental evaluation result.

b. The planting condition of farmers can be reflected on a command center platform in real time, the quantity of planted crops can be macroscopically regulated and controlled, and product delay is prevented.

(3) Cultivation supervision

a. According to the growth conditions of crops, the real-time regulation and control of intelligent heating and light supplement are realized by combining the actual environment.

b. And for part of crops needing to be trimmed, guiding farmers to scientifically trim, improving the quality of agricultural products and creating local high-quality brands.

c. The production management data acquisition is mainly used for systematically collecting management event data of pesticide operation in production, such as spraying, fertilizing, irrigating, harvesting, facility control operation and other links, and storing certificates through a digital platform to form key production and operation data of flower facilities to be filed from a production source for supervision and traceability service. The project group establishes the data acquisition standard through field recording of an experimental greenhouse and digitalization with experts through expert experience and the like, and integrates the data acquisition standard into an intelligent agriculture digital twin platform (for data distribution of peasant household APP).

d. Agricultural machine remote operation, unmanned aerial vehicle patrol and examine, gather daily growth data.

(4) Liquid manure management

a. And (4) automatically and remotely managing the liquid manure irrigation system, the shading system and the spray and drip irrigation system according to the real-time data analysis of the management center platform.

b. According to the intelligent water and fertilizer proportioning of the crops, the yield is improved, and the using amount of the fertilizer is reduced.

c. The straw returning to the field is guided by field crops, the biological fertilizer is prepared, the pollution of chemical fertilizer to the environment is reduced, and the ecological environment is protected.

d. And establishing a water and fertilizer management monitoring data evaluation index for evaluating the green degree of water and fertilizer management in growth and evaluating the product quality.

(5) Management of plant diseases and insect pests and weeds

a. And establishing a disease and pest and weed database, and identifying and predicting the disease and pest and weed AI. Early warning and timely treatment. And the loss is reduced.

b. The platform guides the type and dosage of pesticide for farmers after analyzing the data, and prevents pesticide from being sprayed excessively or being used without symptoms.

Mr remote expert guidance.

d. And establishing comprehensive environment evaluation indexes for evaluating the risk degree of plant diseases and insect pests and weed occurrence and evaluating quality standards such as pesticide residues and the like.

(6) Harvesting management

a. And (3) predicting the harvest period, establishing a growth model according to the types of crops and the environment, evaluating the maturity and the quality of agricultural products by a deep convolution network method, and reasonably arranging the operation time by combining weather prediction.

And b, uploading the actual harvest quantity by the APP terminal.

(7) Agricultural disaster early warning and emergency measure

a. Weather: and according to weather big data analysis and real-time weather forecast, carrying out early warning on agricultural weather disasters, and recommending and guiding a disaster prevention and loss prevention scheme. The weather can better serve agriculture.

b. Insect pest: according to the large-scale insect pest occurrence data and rule of the past year, the insect pest occurrence probability is intelligently analyzed for early warning, and an emergency department is connected to carry out real-time emergency plan planning.

c. And establishing a meteorological environment evaluation index for evaluating the production management or the good degree of production environment control in the intelligent greenhouse, and providing a basis for a government to evaluate the production management level of enterprises and farmers and to carry out policy compliance more accurately.

The yield prediction specifically comprises: (1) after the intelligent greenhouse automatic control core link, the production can be stabilized all the year round without being influenced by weather, the production time is accurate to the day, and the yield is stable. (2) And for fruit trees and field crops, the yield is predicted according to fruit bearing supervision.

The quality prediction specifically comprises: (1) data acquisition in the whole growth process, fertilizer and pesticide control and agricultural product quality improvement. (2) Intelligently analyzing nutrient components and transmitting analysis data to a detection mechanism through a digital twin platform. (3) And comparing the quality data of the same kind in other areas according to big data analysis. And a foundation is laid for local brands.

The marketing submodule includes but is not limited to forecasting market demand, creating regional brands, docking channels, and twinning of farm produce market. The method specifically comprises the following steps:

1. predicting market demand

And establishing a data-driven price element long-term and short-term prediction model by combining agricultural big data and agricultural product prices. The long-term prediction model can guide agricultural operation seeding in the next years, and loss risks are reduced; the short-term prediction can predict the inflection point of the price of the agricultural products and improve the short-term income of enterprises.

1) And (3) long-term trend prediction: potential, demand, supply, competition

2) Short-term fluctuation prediction: regression, trade, fluctuation, inflection points

2. Creating a geographical brand

1) Based on an intelligent agriculture digital twin platform, AI image recognition and APP data acquisition are applied to develop agricultural product pest and disease recognition, a trading volume and price prediction algorithm is developed according to yield and trading data, a prediction algorithm of the influence of other meteorological disasters on the productivity is developed according to climate environment monitoring data and an AI yield prediction algorithm, green production environment and quality authentication service of greenhouse crops is developed according to green production environment evaluation indexes, and the like.

2) And (3) decision guidance: and carrying out data comparison analysis and advantage analysis on products of the same type in the market.

3) Green production environment authentication: an industry authentication system which can support the development of high-quality agricultural product production enterprises and promote enterprise brands is established.

3. Butt channel

And connecting each platform data port, opening a data isolated island, driving data elements to flow, and creating a data value chain closed loop.

4. Twinning in the field agricultural product market

Space perception, three-dimensional visual, monitoring function are realized through the thing networking, can prevent to steal and pluck, build assembled light weight farm, daily fruit vegetables delivery.

The service sub-modules include, but are not limited to, sales experience, agricultural creation, talent training, MR expert guidance, enabling industry chain, and agricultural financial services. The method specifically comprises the following steps:

1. sales experience

1) Countryside happy

The intelligent management is formed through agricultural digitization, the intelligent management gradually develops to an industrial park, rural urbanization socialization is promoted, the rural basic operation system is consolidated and perfected, the quality of agricultural products is improved, and the method is popular in science and technology and brings a good way for farmers.

2) Development of travel

a. Creating local special agro-farming civilization and forming agro-farming cultural tourism industry. Such as: personal farm digital management.

b. The method combines the construction of characteristic small towns and beautiful villages, excavates characteristic culture, protects the original building landscape and village pattern, deeply excavates historical ancient rhymes, reproduces the scene of the original ecological field garden and enriches the cultural ecology of the villages.

c. Creating a special cultural industrial village, promoting the joy of the traditional process and promoting the inheritance of non-heritage culture. Forming an innovative mode of cultural tourism. Such as: and 4, rural nomadicity.

d. The offline agricultural parades realize AR self-help navigation through a digital twin platform.

3) Brand recognition

Production life cycle intelligent management, creates a green product brand for water, fertilizer and pesticide residue data management, and guides local farmers to go a green development way.

2. Agricultural wound

Through data distribution and analysis of the digital twin platform, decision guidance can be performed on the agricultural creatures, and loss of farmers is reduced. And creating county-area agricultural brand.

3. Talent culture

Cultivate novel professional peasant, increase the practical professional talent cultivation in agricultural field. Strengthen the support of the village talents, promote the village talents to be happy, and enable various talents to be capable of developing talents and showing hands in the great institute of the village.

1) VR training is bred in tradition planting

Any traditional innovation and advancement in agricultural technology requires an amount of time and money that is not imaginable. For common crops, one period is as long as one year, while fruit trees and animals need years, and experimental data needs to be adjusted for many times, and each adjustment needs the time of one growth period for verification. The conditional department of agriculture can comprehensively use new technologies such as XR and the like, simulate production environment parameters through an XR virtual simulation process, reduce the research and development cost of agricultural technologies, shorten the research time of important projects in the agricultural field, can be used in the fields of agricultural process research, agricultural technology training and the like, better realize the increase in production and value of agriculture and accelerate the agricultural modernization process. For example: in the field of virtual plants, a computer is applied to simulate the growth and development conditions of plants in a three-dimensional space, and the method is mainly characterized in that plant individuals are taken as a research center, the morphological structure of the plants is taken as a research focus, a three-dimensional visual plant model is established, the activity of pests and the change of weather and climate are simulated, and the optimal pesticide spraying mode, time and the like are determined. By utilizing the virtual plant system, farmers can obtain first-hand data information by only carefully observing with the help of equipment without high and deep experimental skills. In addition, a large amount of plant growth data can be collected within dozens of minutes, the characteristic that the traditional agriculture is difficult to quantify is changed, and a basis is provided for intelligent and fine agriculture; in the field of virtual breeding, accurate simulation of animals from organs, tissues and systems to the whole body is realized through a virtual environment, and the virtual animals can simulate real animals to make various reactions under the regulation and control of an operator. If the sound and force feedback device is arranged, visual, audible, tactile and other visual and natural real-time feelings can be provided. The method has great significance for simulating animal living environment, animal nutrition requirement, genetic resource fixation, variety breeding and the like; the virtual farm can be explored and established by utilizing the visual characteristics of the virtual animal and plant model, so that students and farmers can plant virtual crops and manage virtual farmlands on a computer, swim in the canopy of the crops from any angle and observe the growth conditions and dynamic processes of the crops, and can intuitively observe the change of the growth conditions of the crops by changing environmental conditions and cultivation measures, thereby obtaining the effect which cannot be achieved by the traditional mode, particularly for the popularization of agricultural scientific and technological achievements, farmers can easily understand and master advanced farmland management technologies, and the virtual farm also has the significance of education and sightseeing. If a virtual farm is developed to realize online field planting, vast farmers and students can master advanced farmland management technology and agricultural knowledge more quickly, and the creativity and imagination of intelligent agriculture are stimulated.

2) Vocational training

And creating a training course of related professional skills VR, optimizing the demonstration training of local talents, and cultivating rural tradesmen, agricultural professional managers and the like.

3) Talent introduction by reflux

The development of an industrial chain is enabled through a digital twin platform, the rural employment opportunities are widened, the industrial types are diversified, and the employment quality of rural labor force is improved. Talent introduction is stimulated, and the construction of the talent team in the rural areas is accelerated.

MR expert guidance

Through the connection of the intelligent agricultural digital twin platform and the expert database and the remote guidance function of the experts, the agricultural production can be remotely guided, and the problems of insufficient quantity of trans-regional experts, unmatched information levels and asymmetry are preliminarily solved. Through real-time remote guidance of experts, after agricultural production, science and technology and economic information are processed, the system can help producers and managers to make effective and rapid decisions, improve the scientific management level, change the blindness and subjectivity of basic leader decision making, reduce decision making errors, and simultaneously enable the experts to be directly communicated with farmers in the field to obtain obvious scientific application effect, thereby having important effect on improving the scientific and technological culture quality of the farmers. Promoting the development of agricultural production.

5. Enabling industrial chains

1) Producing: the rural township is realized, and the community is uniformly managed for rural home bases.

2) The processing industry: agricultural product sales distribution promotes the development of agricultural products and the development of processing industry.

3) Logistics: directly butt joint each big agricultural product supplies and sells platform, promotes the commodity circulation development. By enabling the logistics industry, the construction of a rural logistics infrastructure backbone network is accelerated, the terminal network is improved, and investors are attracted to construct distribution centers.

4) Water conservancy: promote novel irrigation mode, strengthen the modernized construction of hydraulic facilities, impel wisdom water conservancy to build and establish.

5) Energy sources: the intelligent greenhouse warming mode and the digital twin greenhouse promote rural energy structure optimization, and develop and utilize solar energy, shallow geothermal energy, biomass energy, water energy and wind energy. Promotes the rural energy consumption to be upgraded, develops intelligent energy and forms the rural energy revolution demonstration.

6) Scientific research: agricultural development is integrated through a digital twin platform to realize farm field, VR education popularization and MR remote guidance enable a large number of agricultural experts to reduce field guidance, and planting and breeding technologies, relevant parameters and the like can be optimized under data feedback and platform analysis of the Internet of things.

6. Agricultural financial service

The digital twin platform provides reliable basis for perfecting finance branch farmers.

1) The agricultural financial insurance business digitization is promoted, the intelligent agricultural digital twin platform can provide accurate user portrait and traceability data (meteorological disasters and plant diseases and insect pests) for insurance companies, damage assessment has objective basis, and the claim paying efficiency is improved.

2) Government intelligent supervision provides scientific basis for policy subsidy.

3) Providing price forecast and obtaining long-term and short-term benefits in the financial market; while hedge enterprise risks.

The foregoing is a detailed description of the invention, and specific examples are used herein to explain the principles and implementations of the invention, the above description being merely intended to facilitate an understanding of the principles and core concepts of the invention. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.

Claims (5)

1. The utility model provides an wisdom agricultural is produced and is melted comprehensive service platform which characterized in that: the intelligent greenhouse system comprises an intelligent greenhouse module, a digital twin data analysis module, an operation module and a terminal;

the intelligent greenhouse module comprises a greenhouse body and a data acquisition submodule, wherein the data acquisition submodule is used for acquiring environmental data and crop growth data in the greenhouse body;

the data end of the digital twin data analysis module is in butt joint with the data acquisition sub-module in a wireless or wired mode, and is in butt joint with meteorological environment data of an external network, and the data end is used for storing and simulating and analyzing crop growth in the greenhouse body;

the operation module comprises a production submodule, a marketing submodule and a service submodule; the production submodule receives output data of the digital twin data analysis module and is used for managing crop research and development, a growth model, yield prediction and quality prediction in the greenhouse body; the marketing submodule is used for establishing a data-driven price element long-term and short-term prediction model by combining agricultural big data and agricultural product prices; the service submodule is used for talent culture, expert guidance and financial service;

the terminal is used for accessing the operation module and issuing a control instruction for the user.

2. The intelligent agricultural production and integration integrated service platform of claim 1, wherein: the data acquisition sub-modules include, but are not limited to, a light intensity sensor, a carbon dioxide concentration sensor, an air humidity sensor, a temperature sensor, a soil pH sensor, a soil EC value sensor, a soil nutrient content sensor, and a soil humidity sensor.

3. The intelligent agricultural production and integration integrated service platform of claim 1, wherein: the digital twin data analysis module comprises a computer cluster and a 5G-AR-VR device.

4. The intelligent agricultural production and integration integrated service platform of claim 1, wherein: the terminal comprises but is not limited to a web end, a mobile phone client or a Wap end.

5. The intelligent agricultural production and integration integrated service platform of claim 1, wherein: the crop growth model includes but is not limited to environmental assessment, digital seed selection, cultivation supervision, water and fertilizer management, pest and weed management, harvest management, agricultural disaster early warning and emergency measures.