An agricultural environment comprehensive monitoring support system based on multi-source data fusion

Abstract

The present invention relates to the technical field of environmental data monitoring, and discloses an agricultural environment comprehensive monitoring support system based on multi-source data fusion, constructs an environmental monitoring blockchain, divides the environmental monitoring blockchain into multiple planting management areas through vegetation area deployment, collects plant planting information with timestamps in the planting management areas, processes the plant planting information and divides it into multiple plant species fields, and respectively sets storage nodes based on the plant species fields; hierarchically sets the data in the plant species fields based on the storage nodes, collects the monitoring information of the current plant species in real time, and extracts planting feature data from the monitoring information according to the timestamp; predicts the crop biomass and the corresponding yield estimate of the next stage based on the monitoring information in the plant species fields; evaluates the growth status of the current crop based on the yield estimate, writes an intelligent contract based on the growth status of the crop, and establishes a crop management strategy based on the intelligent contract.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental data monitoring, and more specifically, to an agricultural environment comprehensive monitoring support system based on multi-source data fusion. Background Art

[0002] Agriculture occupies an important position in China's national economy. Computer-supported systems that use computers to store, query and display data related to geography and spatial locations are mainly used to describe the spatial distribution and properties of land objects in the real world. Standardized data analysis is used to support data fusion, which is updated more timely and has a wider range of applications, thereby achieving efficient and sustainable agricultural development.

[0003] In the field of agricultural environment, current data sources can obtain environmental parameters, crop growth data, meteorological data, remote sensing data, socio-economic data, agricultural production management data and spatial node data; when there are too many data sources, each time the multi-source data of monitoring is evaluated, the source of the multi-source data should be considered. Even in the same area and the same piece of land, most of the multi-source data have commonalities, but there are still differences. When analyzing the differences, we should consider not only the problem of temporal discontinuity, but also the problem of spatial discontinuity.

[0004] In view of this, the present invention proposes an agricultural environment comprehensive monitoring support system based on multi-source data fusion. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, the present invention provides an agricultural environment comprehensive monitoring support system based on multi-source data fusion, which has the advantage of more precise operation.

[0006] In a first aspect, the present invention provides an agricultural environment comprehensive monitoring support system based on multi-source data fusion, including a blockchain construction module, a storage partitioning module, a data processing module, a data analysis module and a control support module, wherein each module is connected by wire or wirelessly;

[0007] Blockchain construction module, builds environmental monitoring blockchain, deploys vegetation area on the environmental monitoring blockchain and divides it into multiple planting management areas, and deploys each planting management area as an independent node on the environmental monitoring blockchain;

[0008] A storage division module collects plant planting information with timestamps in the planting management area, pre-processes the plant planting information, obtains first target data, divides the first target data into multiple plant species fields based on a fuzzy function, and sets storage nodes based on the plant species fields;

[0009] The data processing module performs hierarchical setting of the data in the field of plant species based on the storage node, obtains the first layer data and the second layer data, clarifies the basic information of the current plant species based on the first layer data, and collects the monitoring information of the current plant species in real time based on the second layer data, and the monitoring information extracts the planting characteristic data according to the timestamp;

[0010] The data analysis module predicts the crop biomass in the next stage based on the monitoring information of the plant species field, and obtains the estimated yield of the crop corresponding to the node time period within the preset spatial range based on the crop biomass;

[0011] The control support module evaluates the current crop growth status based on the yield estimate, writes smart contracts based on the crop growth status, and establishes crop management strategies based on the smart contracts.

[0012] As a preferred technical solution of the first aspect of the present invention, the application logic of the planting management area includes:

[0013] Pairing suitable plant species based on regional geographic information, and dividing the vegetation area into multiple planting management areas based on the plant species, wherein the regional geographic information includes geographic location, climate conditions and soil information;

[0014] Each planting management area independently records and stores plant information, and deploys the corresponding plant information as an independent node on the blockchain, while connecting to nodes in other areas to form a distributed network;

[0015] IoT sensors are deployed in each planting management area. The IoT sensors include sensor networks and remote sensing detection equipment. The plant growth environment data collected in real time by the sensor network and the plant growth image data collected by the remote sensing detection equipment are used to monitor crop growth and pest and disease conditions and to verify the accuracy of the system's analysis and evaluation of the sensor network.

[0016] As a preferred technical solution of the first aspect of the present invention, the acquisition logic of the plant species field is:

[0017] Analyze a large number of plant species images through deep learning algorithms to obtain species distinguishing features;

[0018] Construct a fuzzy function, use species distinguishing features as fuzzy input variables, use fuzzy degree functions to obtain the membership degree of certain variables and uncertain variables relative to the plant species domain, and determine the correlation between state variables based on the membership degree;

[0019] The degree of membership of all current species distinguishing features is statistically mapped to the fuzzy numerical interval corresponding to the plant species domain; thereby determining the plant species domain.

[0020] As a preferred technical solution of the first aspect of the present invention, the basic information includes social and economic data, agricultural production management data and crop growth data, and the basic information is displayed on the introduction page of the corresponding storage node. After understanding the basic information of the storage node, the contact point is triggered to view the monitoring information, and the monitoring information includes spatial node data, plant growth environment data, meteorological station data and remote sensing data, wherein:

[0021] Spatial node data, the farmland location, area and terrain information obtained by the GIS positioning system, combines the spatial node data of multiple spatially continuous nodes to form regional spatial data, and marks the geographical location based on the regional spatial data;

[0022] The plant growth environment data includes crop biomass corresponding to different soil information; the soil information includes soil type, soil moisture, soil temperature, soil pH value, soil salinity and soil nutrient content;

[0023] Weather station data, including light intensity, ambient temperature, precipitation, ambient humidity, carbon dioxide concentration, wind speed and solar radiation data; the average value of the weather station data within a preset time interval is used as the climate condition for the preset time interval, and the preset time interval includes a monthly, quarterly or annual duration;

[0024] Remote sensing data, based on plant growth image data collected by remote sensing monitoring equipment, obtain crop coverage, growth status and pest and disease conditions based on plant growth image data.

[0025] As a preferred technical solution of the first aspect of the present invention, the logic for obtaining the planting feature data is as follows:

[0026] Acquire spatial node data, plant growth environment data, and meteorological station data according to preset time intervals, associate the spatial node data, plant growth environment data, and meteorological station data to form data monitoring information, and predict the data monitoring information through a deep learning algorithm to obtain first growth information;

[0027] remote sensing data is acquired within a preset time interval, image detection information is extracted based on the remote sensing data, and feature matching is performed between the image detection information and plant species features in a plant species feature library; when the feature matching is successful, the plant species information of the specific object corresponding to the successfully matched plant species features is used as the second growth information;

[0028] Based on the corresponding plant species features in the second growth information, corresponding feature labels are assigned, and plant species features with the same feature labels in the first growth information are fused and calculated to update the plant species features;

[0029] For each plant species feature with the same feature label, the corresponding feature similarity is obtained, and the fused plant species feature is obtained by weighted average of the feature similarities of all feature labels corresponding to the same plant species. The fused plant species feature is marked as planting feature data.

[0030] As a preferred technical solution of the first aspect of the present invention, the logic for obtaining the estimated output value is:

[0031] Based on the plant species feature library, the growth progress of crops corresponding to the plant species field is obtained and divided into multiple stage growth intervals, each stage growth interval is set to determine the stage growth information, and the progress of crops is tracked based on the stage growth interval to obtain the detection growth information;

[0032] Building a biomass prediction model based on historical data and machine learning algorithms, the biomass prediction model predicts the crop biomass in the next stage based on current detected growth information;

[0033] Based on the predicted results of crop biomass and the actual growth information obtained, the monitoring data will be fed back in real time, the biomass prediction model will be updated, and the crop management strategy will be adjusted;

[0034] The predicted crop biomass and crop yield are used to establish a relationship model between biomass and yield, thereby obtaining an estimated yield.

[0035] As a preferred technical solution of the first aspect of the present invention, the updating logic of the stage growth information is:

[0036] Based on prior knowledge and artificial intelligence technology, a large amount of collected planting data is analyzed to obtain the preferred interval of planting characteristic data, and the detected growth information and the stage growth information are compared and analyzed in the preferred interval. If the planting characteristic data in the detected growth information is better than the planting characteristic data in the stage growth information, the planting characteristic data in the detected growth information is replaced by the planting characteristic data in the stage growth information of the current stage growth interval, and the stage growth information of the stage growth interval is updated.

[0037] In a second aspect, the present invention provides an agricultural environment comprehensive monitoring support method based on multi-source data fusion, based on the implementation of an agricultural environment comprehensive monitoring support system based on multi-source data fusion described in the first aspect, comprising the following steps:

[0038] Build an environmental monitoring blockchain, deploy vegetation areas on the environmental monitoring blockchain and divide it into multiple planting management areas, and deploy each planting management area as an independent node on the environmental monitoring blockchain;

[0039] Collecting plant planting information with timestamps in the planting management area, preprocessing the plant planting information to obtain first target data, dividing the first target data into multiple plant species fields based on a fuzzy function, and setting storage nodes based on the plant species fields;

[0040] The data in the field of plant species are hierarchically arranged based on the storage node to obtain the first layer data and the second layer data, the basic information of the current plant species is clarified based on the first layer data, and the monitoring information of the current plant species is collected in real time based on the second layer data, and the monitoring information extracts the planting characteristic data according to the timestamp;

[0041] Based on the monitoring information of plant species, the crop biomass of the next stage is predicted, and the yield estimate of the crop corresponding to the node time period within the preset spatial range is obtained based on the crop biomass;

[0042] Evaluate the current crop growth status based on the yield estimate, write smart contracts based on the crop growth status, and establish crop management strategies based on smart contracts.

[0043] In a third aspect, the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0044] The processor executes the agricultural environment comprehensive monitoring support system based on multi-source data fusion described in the first aspect by calling the computer program stored in the memory.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the agricultural environment comprehensive monitoring support system based on multi-source data fusion as described in the first aspect.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] Through blockchain technology and smart contracts, the present invention achieves data transparency and security, improves the efficiency of agricultural production and management, makes resource utilization more precise, optimizes crop management, reduces risks, and provides accurate yield estimates, which is helpful for market planning; it promotes agricultural modernization, provides data support for policy making, improves the resilience of the agricultural system, and provides strong technical support for the sustainable development of agriculture. It increases food traceability capabilities, enhances consumer trust, and at the same time standardizes the agricultural environment to make real-time adjustments to better support agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the framework of the agricultural environment comprehensive monitoring support system of the present invention;

[0049] Figure 2 A schematic diagram of the division method of the planting management area in the agricultural environment of the present invention;

[0050] Figure 3 It is a schematic diagram of the agricultural environment comprehensive monitoring support method of the present invention;

[0051] Figure 4 The figure is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0053] Example 1: Figure 1-2 As shown, the present invention provides an agricultural environment comprehensive monitoring support system based on multi-source data fusion, including a blockchain construction module 100, a storage partitioning module 200, a data processing module 300, a data analysis module 400 and a control support module 500, and each module is connected by wire or wirelessly;

[0054] The blockchain construction module 100 constructs an environmental monitoring blockchain, divides the environmental monitoring blockchain into multiple planting management areas by vegetation area deployment, deploys each planting management area as an independent node on the environmental monitoring blockchain, and sends the plant planting information in the planting management area to the storage division module 200;

[0055] It should be noted that the construction of environmental monitoring blockchain, based on the distributed ledger technology of blockchain, ensures that all monitoring data cannot be tampered with once it is on the chain, ensuring the authenticity and reliability of the data. A shared environmental monitoring platform is established based on blockchain. The smart contracts on the blockchain can automatically perform the recording and monitoring of agricultural activities such as crop planting, fertilization, and harvesting, and can also monitor and analyze data in real time. The blockchain system can predict and warn of risks in agricultural production such as pests and diseases and climate change, take timely measures, and realize the rapid transmission and interoperability of data. Record every link of agricultural products from planting, processing to sales, realize full traceability, enhance consumers' trust in the quality of agricultural products, promote information sharing and collaboration among various entities in the agricultural industry chain, and improve the operating efficiency and governance level of the entire industry chain.

[0056] Specifically, the application logic of the planting management area includes:

[0057] Pairing suitable plant species based on regional geographic information, and dividing the vegetation area into multiple planting management areas based on the plant species, wherein the regional geographic information includes geographic location, climate conditions and soil information;

[0058] It should be noted that: to a certain extent, geographical location, climate conditions and soil information do not change much, but in actual applications, especially in the era of modern mechanized technology, the terrain requirements for growing different crops are different, and the terrain of farmland locations can change. Therefore, the regional geographic information represents the geographic information of the divided area within a preset time interval.

[0059] Each planting management area independently records and stores plant information, and deploys the corresponding plant information as an independent node on the blockchain, while connecting to nodes in other areas to form a distributed network;

[0060] IoT sensors are deployed in each planting management area. The IoT sensors include sensor networks and remote sensing detection equipment. The plant growth environment data collected in real time by the sensor network and the plant growth image data collected by the remote sensing detection equipment are used to monitor crop growth and pest and disease conditions and to verify the accuracy of the system's analysis and evaluation of the sensor network.

[0061] The storage division module 200 collects plant planting information with timestamps in the planting management area, pre-processes the plant planting information, obtains first target data, divides the first target data into multiple plant species fields based on a fuzzy function, sets storage nodes based on the plant species fields, and sends different plant species fields of the storage nodes to the data processing module 300;

[0062] It should be noted that in actual operations, botanists usually combine multiple aspects to distinguish and identify plant species. With the development of technology, image recognition technology is used to identify plant species.

[0063] Specifically, the acquisition logic of the plant species field is:

[0064] Analyze a large number of plant species images through deep learning algorithms to obtain species distinguishing features;

[0065] Construct a fuzzy function, use species distinguishing features as fuzzy input variables, use fuzzy degree functions to obtain the membership degree of certain variables and uncertain variables relative to the plant species domain, and determine the correlation between state variables based on the membership degree;

[0066] The degree of membership of all current species distinguishing features is statistically mapped to the fuzzy numerical interval corresponding to the plant species domain; thereby determining the plant species domain.

[0067] The data processing module 300 hierarchically arranges the data in the field of plant species based on the storage node, obtains the first layer data and the second layer data, clarifies the basic information of the current plant species based on the first layer data, and collects the monitoring information of the current plant species in real time based on the second layer data. The monitoring information extracts the planting feature data according to the timestamp, and sends the planting feature data to the data analysis module 400;

[0068] It should be noted that the basic information includes social and economic data, agricultural production management data and crop growth data. The basic information is displayed on the introduction page of the corresponding storage node. After understanding the basic information of the storage node, the contact point is triggered to view the monitoring information. The monitoring information includes spatial node data, plant growth environment data, meteorological station data and remote sensing data, among which:

[0069] Spatial node data, farmland location, area and terrain information obtained through GIS positioning system; spatial node data of multiple spatially continuous nodes are combined to form regional spatial data, and regional spatial data are marked as geographic locations;

[0070] Plant growth environment data, including soil information within a preset time interval, wherein the soil information includes soil type, soil moisture, soil temperature, soil pH value, soil salinity and soil nutrient content;

[0071] Meteorological station data, including light intensity, ambient temperature, precipitation, ambient humidity, carbon dioxide concentration, wind speed and solar radiation data; the average value of the meteorological station data within a preset time interval is used as the climatic condition for the preset time interval, and the preset time interval includes a monthly, quarterly or annual duration.

[0072] Remote sensing data, based on plant growth image data collected by remote sensing detection equipment, obtains crop coverage, growth status and pest and disease conditions based on plant growth image data. Remote sensing detection equipment includes satellites or drones; plant growth image data are high-resolution images.

[0073] Specifically, the logic for obtaining planting feature data is as follows:

[0074] Acquire spatial node data, plant growth environment data, and meteorological station data according to preset time intervals, associate the spatial node data, plant growth environment data, and meteorological station data to form data monitoring information, and predict the data monitoring information through a deep learning algorithm to obtain first growth information;

[0075] remote sensing data is acquired within a preset time interval, image detection information is extracted based on the remote sensing data, and feature matching is performed between the image detection information and plant species features in a plant species feature library; when the feature matching is successful, the plant species information of the specific object corresponding to the successfully matched plant species features is used as the second growth information;

[0076] Based on the corresponding plant species features in the second growth information, corresponding feature labels are assigned, and plant species features with the same feature labels in the first growth information are fused and calculated to update the plant species features;

[0077] For each plant species feature with the same feature label, the corresponding feature similarity is obtained, and the fused plant species feature is obtained by weighted average of the feature similarities of all feature labels corresponding to the same plant species. The fused plant species feature is marked as planting feature data.

[0078] The data analysis module 400 predicts the crop biomass of the next stage based on the monitoring information of the plant species field, obtains the yield estimate corresponding to the crop in the node time period within the preset spatial range based on the crop biomass, and sends the yield estimate to the control support module 500;

[0079] Specifically, the logic for obtaining the output estimate is as follows:

[0080] Based on the plant species feature library, the growth progress of crops corresponding to the plant species field is obtained and divided into multiple stage growth intervals, each stage growth interval is set to determine the stage growth information, and the progress of crops is tracked based on the stage growth interval to obtain the detection growth information;

[0081] Building a biomass prediction model based on historical data and machine learning algorithms, the biomass prediction model predicts the crop biomass in the next stage based on current detected growth information;

[0082] Based on the predicted results of crop biomass and the actual growth information obtained, the monitoring data will be fed back in real time, the biomass prediction model will be updated, and the crop management strategy will be adjusted to improve the prediction accuracy;

[0083] The predicted crop biomass and crop yield are used to establish a relationship model between biomass and yield, thereby obtaining an estimated yield.

[0084] The update logic of the growth information in the above stage:

[0085] Compare and analyze the detected growth information with the stage growth information. If the planting feature data in the detected growth information is better than the planting feature data in the stage growth information, replace the planting feature data in the detected growth information with the planting feature data in the stage growth information of the current stage growth interval, and update the stage growth information of the stage growth interval;

[0086] It should be noted that: based on prior knowledge and artificial intelligence technology, a large amount of collected planting data is analyzed to obtain the optimal interval of planting characteristic data, and the quality of the current planting characteristic data is judged based on the optimal interval.

[0087] The control support module 500 evaluates the current growth status of the crop based on the yield estimate, writes a smart contract according to the growth status of the crop, and establishes a crop management strategy based on the smart contract.

[0088] It should be noted that the crop management strategy includes sowing management, fertilization management, disease control management and harvesting management. These activities are recorded on the blockchain. Based on smart contracts, the execution can be automatically controlled, and all modules can be integrated into one system to provide users with data access and management interfaces, thereby reducing manual intervention and improving efficiency.

[0089] Example 2: Please refer to Figure 3 As shown, the part not described in detail in this embodiment is described in Example 1. This embodiment provides an agricultural environment comprehensive monitoring support method based on multi-source data fusion, including the following steps:

[0090] Build an environmental monitoring blockchain, deploy vegetation areas on the environmental monitoring blockchain and divide it into multiple planting management areas, and deploy each planting management area as an independent node on the environmental monitoring blockchain;

[0091] Collecting plant planting information with timestamps in the planting management area, preprocessing the plant planting information to obtain first target data, dividing the first target data into multiple plant species fields based on a fuzzy function, and setting storage nodes based on the plant species fields;

[0092] The data in the field of plant species are hierarchically arranged based on the storage node to obtain the first layer data and the second layer data, the basic information of the current plant species is clarified based on the first layer data, and the monitoring information of the current plant species is collected in real time based on the second layer data, and the monitoring information extracts the planting characteristic data according to the timestamp;

[0093] Based on the monitoring information of plant species, the crop biomass of the next stage is predicted, and the yield estimate of the crop corresponding to the node time period within the preset spatial range is obtained based on the crop biomass;

[0094] Evaluate the current crop growth status based on the yield estimate, write smart contracts based on the crop growth status, and establish crop management strategies based on smart contracts.

[0095] The application logic of the planting management area includes:

[0096] Pairing suitable plant species based on regional geographic information, and dividing the vegetation area into multiple planting management areas based on the plant species, wherein the regional geographic information includes geographic location, climate conditions and soil information;

[0097] Each planting management area independently records and stores plant information, and deploys the corresponding plant information as an independent node on the blockchain, while connecting to nodes in other areas to form a distributed network;

[0098] IoT sensors are deployed in each planting management area. The IoT sensors include sensor networks and remote sensing detection equipment. The plant growth environment data collected in real time by the sensor network and the plant growth image data collected by the remote sensing detection equipment are used to monitor crop growth and pest and disease conditions and to verify the accuracy of the system's analysis and evaluation of the sensor network.

[0099] The acquisition logic of the plant species field is:

[0100] Analyze a large number of plant species images through deep learning algorithms to obtain species distinguishing features;

[0101] Construct a fuzzy function, use species distinguishing features as fuzzy input variables, use fuzzy degree functions to obtain the membership degree of certain variables and uncertain variables relative to the plant species domain, and determine the correlation between state variables based on the membership degree;

[0102] The degree of membership of all current species distinguishing features is statistically mapped to the fuzzy numerical interval corresponding to the plant species domain; thereby determining the plant species domain.

[0103] The basic information includes social and economic data, agricultural production management data and crop growth data. The basic information is displayed on the introduction page of the corresponding storage node. After understanding the basic information of the storage node, the contact point is triggered to view the monitoring information. The monitoring information includes spatial node data, plant growth environment data, weather station data and remote sensing data, among which:

[0104] Spatial node data, the farmland location, area and terrain information obtained by the GIS positioning system, combines the spatial node data of multiple spatially continuous nodes to form regional spatial data, and marks the geographical location based on the regional spatial data;

[0105] The plant growth environment data includes crop biomass corresponding to different soil information; the soil information includes soil type, soil moisture, soil temperature, soil pH value, soil salinity and soil nutrient content;

[0106] Weather station data, including light intensity, ambient temperature, precipitation, ambient humidity, carbon dioxide concentration, wind speed and solar radiation data; the average value of the weather station data within a preset time interval is used as the climate condition for the preset time interval, and the preset time interval includes a monthly, quarterly or annual duration;

[0107] Remote sensing data, based on plant growth image data collected by remote sensing monitoring equipment, obtain crop coverage, growth status and pest and disease conditions based on plant growth image data.

[0108] Logic for obtaining planting feature data:

[0109] Acquire spatial node data, plant growth environment data, and meteorological station data according to preset time intervals, associate the spatial node data, plant growth environment data, and meteorological station data to form data monitoring information, and predict the data monitoring information through a deep learning algorithm to obtain first growth information;

[0110] remote sensing data is acquired within a preset time interval, image detection information is extracted based on the remote sensing data, and feature matching is performed between the image detection information and plant species features in a plant species feature library; when the feature matching is successful, the plant species information of the specific object corresponding to the successfully matched plant species features is used as the second growth information;

[0111] Based on the corresponding plant species features in the second growth information, corresponding feature labels are assigned, and plant species features with the same feature labels in the first growth information are fused and calculated to update the plant species features;

[0112] For each plant species feature with the same feature label, the corresponding feature similarity is obtained, and the fused plant species feature is obtained by weighted average of the feature similarities of all feature labels corresponding to the same plant species. The fused plant species feature is marked as planting feature data.

[0113] The logic for obtaining the output estimate is as follows:

[0114] Based on the plant species feature library, the growth progress of crops corresponding to the plant species field is obtained and divided into multiple stage growth intervals, each stage growth interval is set to determine the stage growth information, and the progress of crops is tracked based on the stage growth interval to obtain the detection growth information;

[0115] Building a biomass prediction model based on historical data and machine learning algorithms, the biomass prediction model predicts the crop biomass in the next stage based on current detected growth information;

[0116] Based on the predicted results of crop biomass and the actual growth information obtained, the monitoring data will be fed back in real time, the biomass prediction model will be updated, and the crop management strategy will be adjusted;

[0117] The predicted crop biomass and crop yield are used to establish a relationship model between biomass and yield, thereby obtaining an estimated yield.

[0118] The update logic of the growth information in the above stage:

[0119] Based on prior knowledge and artificial intelligence technology, a large amount of collected planting data is analyzed to obtain the preferred interval of planting characteristic data, and the detected growth information and the stage growth information are compared and analyzed in the preferred interval. If the planting characteristic data in the detected growth information is better than the planting characteristic data in the stage growth information, the planting characteristic data in the detected growth information is replaced by the planting characteristic data in the stage growth information of the current stage growth interval, and the stage growth information of the stage growth interval is updated.

[0120] Example 3

[0121] An electronic device according to an exemplary embodiment includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0122] The processor executes the above-mentioned agricultural environment comprehensive monitoring support system based on multi-source data fusion by calling the computer program stored in the memory.

[0123] Figure 4 It is a structural diagram of an electronic device provided in an embodiment of the present application. The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to implement an agricultural environment comprehensive monitoring support system based on multi-source data fusion provided in the above-mentioned various method embodiments. The electronic device may also include other components for realizing the functions of the device. For example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface for input and output. The embodiments of the present application will not be described in detail here.

[0124] Example 4

[0125] According to an exemplary embodiment, a computer-readable storage medium is shown, on which a rewritable computer program is stored;

[0126] When the computer program runs on a computer device, the computer device executes the above-mentioned agricultural environment comprehensive monitoring support system based on multi-source data fusion.

[0127] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including at least one computer program, and the at least one computer program is executable by a processor to complete an agricultural environment integrated monitoring support system based on multi-source data fusion in the above embodiment. For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0128] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program includes one or more program codes, and the one or more program codes are stored in a computer-readable storage medium. One or more processors of an electronic device can read the one or more program codes from the computer-readable storage medium, and the one or more processors execute the one or more program codes, so that the electronic device can execute the above-mentioned agricultural environment comprehensive monitoring support system based on multi-source data fusion.

[0129] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0130] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0131] A person of ordinary skill in the art will understand that all or part of the steps to implement the above embodiments can be completed by hardware, or can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a disk or an optical disk, etc.

[0132] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0133] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0134] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0135] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0136] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An agricultural environment comprehensive monitoring support system based on multi-source data fusion, characterized in that: It includes a blockchain construction module (100), a storage partitioning module (200), a data processing module (300), a data analysis module (400) and a control support module (500), wherein each module is connected via wired or wireless connections; The blockchain construction module (100) constructs an environmental monitoring blockchain, divides the environmental monitoring blockchain into multiple planting management areas by vegetation area deployment, and deploys each planting management area as an independent node on the environmental monitoring blockchain; A storage division module (200) collects plant planting information with a time stamp in a planting management area, pre-processes the plant planting information, obtains first target data, divides the first target data into a plurality of plant species fields based on a fuzzy function, and respectively sets storage nodes based on the plant species fields; The data processing module (300) performs hierarchical arrangement of data in the field of plant species based on the storage node to obtain first-layer data and second-layer data, clarifies basic information of the current plant species based on the first-layer data, and collects monitoring information of the current plant species in real time based on the second-layer data, wherein the monitoring information extracts planting characteristic data according to a timestamp; Logic for obtaining planting feature data: Acquire spatial node data, plant growth environment data, and meteorological station data according to preset time intervals, associate the spatial node data, plant growth environment data, and meteorological station data to form data monitoring information, and predict the data monitoring information through a deep learning algorithm to obtain first growth information; Acquire remote sensing data within a preset time interval, extract image detection information based on the remote sensing data, and perform feature matching between the image detection information and plant species features in a plant species feature library; When the feature matching is successful, the plant species information of the specific object corresponding to the successfully matched plant species feature is the second growth information; Based on the corresponding plant species features in the second growth information, corresponding feature labels are assigned, and plant species features with the same feature labels in the first growth information are fused and calculated to update the plant species features; For each plant species with the same feature label, the corresponding feature similarity is obtained, and the fused plant species feature is obtained by weighted average of the feature similarities of all feature labels corresponding to the same plant species, and the fused plant species feature is marked as planting feature data; A data analysis module (400) predicts the crop biomass of the next stage based on the monitoring information of the plant species field, and obtains the estimated yield value of the crop corresponding to the node time period within the preset spatial range based on the crop biomass; The control support module (500) evaluates the current growth status of the crop based on the yield estimate, writes a smart contract according to the growth status of the crop, and establishes a crop management strategy based on the smart contract.

2. The agricultural environment comprehensive monitoring support system based on multi-source data fusion according to claim 1 is characterized by: The application logic of the planting management area includes: Pairing suitable plant species based on regional geographic information, and dividing the vegetation area into multiple planting management areas based on the plant species, wherein the regional geographic information includes geographic location, climate conditions and soil information; Each planting management area independently records and stores plant information, and deploys the corresponding plant information as an independent node on the blockchain, while connecting to nodes in other areas to form a distributed network; IoT sensors are deployed in each planting management area. The IoT sensors include sensor networks and remote sensing detection equipment. The plant growth environment data collected in real time by the sensor network and the plant growth image data collected by the remote sensing detection equipment are used to monitor crop growth and pest and disease conditions and to verify the accuracy of the system's analysis and evaluation of the sensor network.

3. The agricultural environment comprehensive monitoring support system based on multi-source data fusion according to claim 2 is characterized by: The acquisition logic of the plant species field is: Analyze a large number of plant species images through deep learning algorithms to obtain species distinguishing features; Construct a fuzzy function, use species distinguishing features as fuzzy input variables, use fuzzy degree functions to obtain the membership degree of certain variables and uncertain variables relative to the plant species domain, and determine the correlation between state variables based on the membership degree; The degree of membership of all current species distinguishing features is statistically mapped to the fuzzy numerical interval corresponding to the plant species domain; thereby determining the plant species domain.

4. The agricultural environment comprehensive monitoring support system based on multi-source data fusion according to claim 3 is characterized by: The basic information includes social and economic data, agricultural production management data and crop growth data. The basic information is displayed on the introduction page of the corresponding storage node. After understanding the basic information of the storage node, the contact point is triggered to view the monitoring information. The monitoring information includes spatial node data, plant growth environment data, weather station data and remote sensing data, among which: Spatial node data, the farmland location, area and terrain information obtained by the GIS positioning system, combines the spatial node data of multiple spatially continuous nodes to form regional spatial data, and marks the geographical location based on the regional spatial data; The plant growth environment data includes crop biomass corresponding to different soil information; the soil information includes soil type, soil moisture, soil temperature, soil pH value, soil salinity and soil nutrient content; Weather station data, including light intensity, ambient temperature, precipitation, ambient humidity, carbon dioxide concentration, wind speed and solar radiation data; the average value of the weather station data within a preset time interval is used as the climate condition for the preset time interval, and the preset time interval includes a monthly, quarterly or annual duration; Remote sensing data, based on plant growth image data collected by remote sensing monitoring equipment, obtain crop coverage, growth status and pest and disease conditions based on plant growth image data.

5. The agricultural environment comprehensive monitoring support system based on multi-source data fusion according to claim 4 is characterized by: The logic for obtaining the output estimate is as follows: Based on the plant species feature library, the growth progress of crops corresponding to the plant species field is obtained and divided into multiple stage growth intervals, each stage growth interval is set to determine the stage growth information, and the progress of crops is tracked based on the stage growth interval to obtain the detection growth information; Building a biomass prediction model based on historical data and machine learning algorithms, the biomass prediction model predicts the crop biomass in the next stage based on current detected growth information; Based on the predicted results of crop biomass and the actual growth information obtained, the monitoring data will be fed back in real time, the biomass prediction model will be updated, and the crop management strategy will be adjusted; The predicted crop biomass and crop yield are used to establish a relationship model between biomass and yield, thereby obtaining an estimated yield.

6. The agricultural environment comprehensive monitoring support system based on multi-source data fusion according to claim 5 is characterized by: The update logic of the growth information in the above stage: Based on prior knowledge and artificial intelligence technology, a large amount of collected planting data is analyzed to obtain the preferred interval of planting characteristic data, and the detected growth information and the stage growth information are compared and analyzed in the preferred interval. If the planting characteristic data in the detected growth information is better than the planting characteristic data in the stage growth information, the planting characteristic data in the detected growth information is replaced by the planting characteristic data in the stage growth information of the current stage growth interval, and the stage growth information of the stage growth interval is updated.

7. A method for supporting integrated monitoring of agricultural environment based on multi-source data fusion, based on the implementation of an integrated monitoring support system for agricultural environment based on multi-source data fusion according to any one of claims 1 to 6, characterized in that: The following steps are involved: Build an environmental monitoring blockchain, deploy vegetation areas on the environmental monitoring blockchain and divide it into multiple planting management areas, and deploy each planting management area as an independent node on the environmental monitoring blockchain; Collecting plant planting information with timestamps in the planting management area, preprocessing the plant planting information to obtain first target data, dividing the first target data into multiple plant species fields based on a fuzzy function, and setting storage nodes based on the plant species fields; The data in the field of plant species are hierarchically arranged based on the storage node to obtain the first layer data and the second layer data, the basic information of the current plant species is clarified based on the first layer data, and the monitoring information of the current plant species is collected in real time based on the second layer data, and the monitoring information extracts the planting characteristic data according to the timestamp; Based on the monitoring information of plant species, the crop biomass of the next stage is predicted, and the yield estimate of the crop corresponding to the node time period within the preset spatial range is obtained based on the crop biomass; Evaluate the current crop growth status based on the yield estimate, write smart contracts based on the crop growth status, and establish crop management strategies based on smart contracts.

8. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the agricultural environment comprehensive monitoring support system based on multi-source data fusion as described in any one of claims 1 to 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute an agricultural environment comprehensive monitoring support system based on multi-source data fusion as described in any one of claims 1-6.

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