CN119005716B - An agricultural environment comprehensive monitoring support system based on multi-source data fusion - Google Patents
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Abstract
The 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, which is used for constructing an environment monitoring blockchain, carrying out vegetation region deployment on the environment monitoring blockchain to divide a plurality of planting management regions, collecting plant planting information with time stamps in the planting management regions, processing the plant planting information to divide the plant planting information into a plurality of plant species fields, and respectively setting storage nodes based on the plant species fields; the method comprises the steps of carrying out layering arrangement on data in the field of plant species based on storage nodes, collecting monitoring information of current plant species in real time, extracting planting characteristic data according to time stamps, predicting crop biomass and corresponding yield pre-estimated values in the next stage based on the monitoring information in the field of plant species, evaluating the growth condition of current crops based on the yield pre-estimated values, writing intelligent contracts according to the growth condition of the crops, and establishing crop management strategies based on the intelligent contracts.
Description
Technical Field
The invention relates to the technical field of environmental data monitoring, in particular to an agricultural environment comprehensive monitoring support system based on multi-source data fusion.
Background
The system is mainly used for describing the spatial distribution and the attribute of ground objects in the real world, adopts standardized data analysis to support data fusion, is more timely in updating and wider in application range, and thus realizes efficient and sustainable development of agriculture.
In the field of agricultural environment, the current data source can acquire environmental parameters, crop growth data, meteorological data, remote sensing data, socioeconomic data, agricultural production management data and space node data, when the data source is too many, the monitored multi-source data are evaluated each time, the source of the multi-source data is considered, most of the multi-source data have common points even on the same area and a piece of land, but the differences exist, and when the differences are analyzed, the problem of time discontinuity is considered, and the problem of space discontinuity is also needed to be considered.
In view of the above, the invention provides an agricultural environment comprehensive monitoring support system based on multi-source data fusion.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an agricultural environment comprehensive monitoring support system based on multi-source data fusion, which has the advantage of more accurate operation.
In a first aspect, the invention provides an agricultural environment comprehensive monitoring support system based on multi-source data fusion, which comprises a block chain construction module, a storage division module, a data processing module, a data analysis module and a control support module, wherein the blocks are connected through wires or wirelessly;
The system comprises a blockchain construction module, a block chain management module and a control module, wherein the blockchain construction module is used for constructing an environment monitoring blockchain, carrying out vegetation region deployment on the environment monitoring blockchain to divide a plurality of planting management regions, and deploying each planting management region on the environment monitoring blockchain as an independent node;
The storage dividing module is used for collecting plant planting information with a time stamp in a planting management area, preprocessing the plant planting information to obtain first target data, dividing the first target data into a plurality of plant species fields based on a fuzzy function, and respectively setting storage nodes based on the plant species fields;
the data processing module is used for carrying out layered arrangement on the data in the plant species field based on the storage nodes to obtain first-layer data and second-layer data, determining the basic information of the current plant species based on the first-layer data, and acquiring the monitoring information of the current plant species based on the second-layer data in real time, wherein the monitoring information extracts planting characteristic data according to the time stamp;
The data analysis module predicts crop biomass in the next stage based on monitoring information in the plant species field, and acquires a yield estimated value corresponding to the crop in a node period in a preset space range based on the crop biomass;
and the control support module is used for evaluating the growth condition of the current crop based on the yield predicted value, writing an intelligent contract according to the growth condition of the crop and establishing a crop management strategy based on the intelligent contract.
As a preferred technical solution of the first aspect of the present invention, the application logic of the planting management area includes:
dividing a vegetation area into a plurality of planting management areas based on plant species, wherein the area geographic information comprises geographic positions, climate conditions and soil information;
Each planting management area independently records and stores plant information, and deploys the corresponding plant information on a block chain as an independent node, and is connected with nodes of other areas to form a distributed network;
The method comprises the steps that an Internet of things sensor is deployed in each planting management area, the Internet of things sensor comprises a sensor network and remote sensing detection equipment, plant growth environment data are collected in real time based on the sensor network, plant growth image data are collected based on the remote sensing detection equipment, the Internet of things sensor is used for monitoring crop growth and pest and disease conditions, and the accuracy of analysis and evaluation of the sensor network by a system is verified.
As a preferred technical solution of the first aspect of the present invention, the obtaining logic in the plant species field is:
analyzing a large number of plant species images through a deep learning algorithm to obtain species distinguishing features;
Constructing a fuzzy function, taking a species distinguishing feature as a fuzzy input variable, acquiring the membership degrees of a determined variable and an uncertain variable relative to the plant species field through the fuzzy degree function by the species distinguishing feature, and determining the relevance between state variables based on the membership degrees;
and carrying out statistics mapping on membership degrees of distinguishing features of all the current species to obtain fuzzy numerical intervals corresponding to the plant species field, thereby determining the plant species field.
As a preferred technical solution of the first aspect of the present invention, the basic information includes socioeconomic data, agricultural production management data, and crop growth data, the basic information is displayed on an introduction page corresponding to the storage node, and after knowing the basic information of the storage node, the contact point is triggered to view monitoring information, where the monitoring information includes spatial node data, plant growth environment data, weather station data, and remote sensing data, and the monitoring information includes:
Spatial node data, namely combining spatial node data of a plurality of spatially continuous nodes together to form regional spatial data through farmland position, area and terrain information acquired by a GIS positioning system, and marking the regional spatial data as geographic positions;
the plant growth environment data comprises crop biomass corresponding to different soil information, wherein the soil information comprises soil type, soil humidity, soil temperature, soil pH value, soil salinity and soil nutrient content;
Weather station data including illumination intensity, ambient temperature, precipitation, ambient humidity, carbon dioxide concentration, wind speed and solar radiation data, taking an average value of the weather station data in a preset time interval as a weather condition of the preset time interval, wherein the preset time interval comprises a month, a quarter or a year duration;
Remote sensing data, plant growth image data collected based on remote sensing monitoring equipment, and crop coverage, growth conditions and pest and disease damage conditions are obtained based on the plant growth image data.
As a preferred technical solution of the first aspect of the present invention, the acquiring logic of the planting feature data:
Acquiring space node data, plant growth environment data and weather station data according to a preset time interval, correlating the space node data, the plant growth environment data and the weather station data to form data monitoring information, and predicting the data monitoring information through a deep learning algorithm to acquire first growth information;
Acquiring remote sensing data in a preset time interval, extracting image detection information based on the remote sensing data, and carrying out feature matching on the image detection information and plant species features in a plant species feature library;
distributing corresponding characteristic labels based on the plant species characteristics corresponding to the second growth information, and carrying out fusion calculation on the plant species characteristics with the same characteristic label in the first growth information to update plant species characteristics;
and aiming at plant species characteristics of each plant species with the same characteristic tag, acquiring corresponding characteristic similarity, weighted averaging all characteristic tag characteristic similarity corresponding to the same plant species to obtain fused plant species characteristics, and marking the fused plant species characteristics as planting characteristic data.
As a preferred technical solution of the first aspect of the present invention, the obtaining logic of the yield pre-estimated value is:
Acquiring the growth progress of crops corresponding to the plant species field based on a plant species characteristic library, dividing the growth progress to acquire a plurality of stage growth intervals, setting and determining stage growth information for each stage growth interval, and tracking the progress of the crops based on the stage growth intervals to acquire detection growth information;
constructing a biomass prediction model based on historical data and a machine learning algorithm, wherein the biomass prediction model predicts crop biomass in the next stage according to current detection growth information;
feeding back the monitoring data in real time according to the prediction result of the crop biomass and the actually acquired detection growth information, updating a biomass prediction model, and adjusting a crop management strategy;
And establishing a relation model between the biomass and the yield of the predicted crop and the crop yield, thereby obtaining a yield predicted value.
As a preferred technical solution of the first aspect of the present invention, the update logic of the phase growth information:
Analyzing a large amount of collected planting data based on priori knowledge and artificial intelligence technology, so as to obtain a preferable section of planting characteristic data, comparing the detected growth information with the stage growth information, analyzing the preferable section, and if the planting characteristic data in the detected growth information is superior to the planting characteristic data in the stage growth information, replacing the planting characteristic data in the detected growth information with the planting characteristic data in the stage growth information of the current stage growth section, and updating the stage growth information of the stage growth section.
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 the agricultural environment comprehensive monitoring support system based on multi-source data fusion in the first aspect, comprising the following steps:
Constructing an environment monitoring blockchain, carrying out vegetation region deployment on the environment monitoring blockchain to divide a plurality of planting management regions, and deploying each planting management region as an independent node on the environment monitoring blockchain;
Collecting plant planting information with a time stamp in a planting management area, preprocessing the plant planting information to obtain first target data, dividing the first target data into a plurality of plant species fields based on a fuzzy function, and respectively setting storage nodes based on the plant species fields;
Layering data in the plant species field based on the storage nodes to obtain first layer data and second layer data, determining basic information of the current plant species based on the first layer data, and acquiring monitoring information of the current plant species based on the second layer data in real time, wherein the monitoring information extracts planting characteristic data according to a time stamp;
Predicting crop biomass in the next stage based on monitoring information in the plant species field, and acquiring a yield estimated value corresponding to the crop in a node period in a preset space range based on the crop biomass;
And evaluating the growth condition of the current crop based on the yield predicted value, writing an intelligent contract according to the growth condition of the crop, and establishing a crop management strategy based on the intelligent contract.
In a third aspect, the invention provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program for the processor to call;
The processor executes the agricultural environment comprehensive monitoring support system based on multi-source data fusion according to the first aspect by calling a computer program stored in the memory.
In a fourth aspect, the present invention provides a computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the integrated agricultural environment monitoring support system based on multi-source data fusion of the first aspect.
Compared with the prior art, the invention has the following beneficial effects:
The invention realizes the transparency and the safety of data through a blockchain technology and an intelligent contract, improves the efficiency of agricultural production and management, ensures more accurate resource utilization, optimizes crop management, reduces risks, provides accurate yield estimation, is beneficial to market planning, promotes agricultural modernization, provides data support for policy formulation, improves the toughness of an agricultural system, provides powerful technical guarantee for agricultural sustainable development, improves the trust of consumers in the aspect of increasing food traceability, simultaneously standardizes the agricultural environment, and better supports the real-time adjustment of agriculture.
Drawings
FIG. 1 is a schematic diagram of an agricultural environment integrated monitoring support system framework of the present invention;
FIG. 2 is a schematic diagram of a division mode of a planting management area in an agricultural environment according to the present invention;
FIG. 3 is a schematic diagram of an agricultural environment integrated monitoring support method of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
1, As shown in fig. 1-2, the invention provides an agricultural environment comprehensive monitoring support system based on multi-source data fusion, which comprises a block chain construction module 100, a storage division module 200, a data processing module 300, a data analysis module 400 and a control support module 500, wherein the modules are connected through wires or wireless;
The blockchain construction module 100 constructs an environment monitoring blockchain, deploys vegetation areas of the environment monitoring blockchain to divide a plurality of planting management areas, deploys each planting management area on the environment monitoring blockchain as an independent node, and sends plant planting information in the planting management areas to the storage division module 200;
It should be noted that, the distributed account book technology based on the blockchain ensures that all monitoring data cannot be tampered once being uplink, and ensures the authenticity and reliability of the data. A shared environment monitoring platform is established based on the blockchain, intelligent contracts on the blockchain can automatically record and monitor agricultural activities such as crop planting, fertilization, harvesting and the like, real-time monitoring and data analysis can be also carried out, and the blockchain system can predict and early warn risks in agricultural production such as plant diseases and insect pests, climate change and the like, timely take measures and realize rapid data transmission and interoperation. Recording each link from planting, processing and selling of agricultural products, realizing whole-course tracing, enhancing trust of consumers on quality of the agricultural products, promoting information sharing and cooperation among various main bodies of an agricultural industry chain, and improving operation efficiency and treatment level of the whole industry chain.
Specifically, the application logic of the planting management area includes:
dividing a vegetation area into a plurality of planting management areas based on plant species, wherein the area geographic information comprises geographic positions, climate conditions and soil information;
It should be noted that, to a certain extent, the geographical location, the climate condition and the soil information are not greatly changed, but in practical application, especially in the modern mechanized technological era, the requirements of the topography required for planting different crops are different, and the topography of the farmland location can be changed, so that the regional geographical information represents the geographical information of the divided regions within the preset time interval.
Each planting management area independently records and stores plant information, and deploys the corresponding plant information on a block chain as an independent node, and is connected with nodes of other areas to form a distributed network;
The method comprises the steps that an Internet of things sensor is deployed in each planting management area, the Internet of things sensor comprises a sensor network and remote sensing detection equipment, plant growth environment data are collected in real time based on the sensor network, plant growth image data are collected based on the remote sensing detection equipment, the Internet of things sensor is used for monitoring crop growth and pest and disease conditions, and the accuracy of analysis and evaluation of the sensor network by a system is verified.
The storage dividing module 200 collects plant planting information with a time stamp in a planting management area, performs preprocessing on the plant planting information to obtain first target data, divides the first target data into a plurality of plant species fields based on a fuzzy function, sets storage nodes based on the plant species fields respectively, and sends different plant species fields of the storage nodes to the data processing module 300;
in practice, plant scientists often combine several aspects to distinguish and identify plant species, and as technology advances, image recognition technology is used to identify plant species.
Specifically, the acquisition logic in the plant species field is:
analyzing a large number of plant species images through a deep learning algorithm to obtain species distinguishing features;
Constructing a fuzzy function, taking a species distinguishing feature as a fuzzy input variable, acquiring the membership degrees of a determined variable and an uncertain variable relative to the plant species field through the fuzzy degree function by the species distinguishing feature, and determining the relevance between state variables based on the membership degrees;
and carrying out statistics mapping on membership degrees of distinguishing features of all the current species to obtain fuzzy numerical intervals corresponding to the plant species field, thereby determining the plant species field.
The data processing module 300 performs layering arrangement on the data in the plant species field based on the storage nodes to obtain first-layer data and second-layer data, determines the basic information of the current plant species based on the first-layer data, and acquires the monitoring information of the current plant species based on the second-layer data in real time, wherein the monitoring information extracts planting characteristic data according to the time stamp and sends the planting characteristic data to the data analysis module 400;
The basic information comprises social and economic data, agricultural production management data and crop growth data, the basic information is displayed on an introduction page corresponding to the storage node, the contact point is triggered to view monitoring information after the basic information of the storage node is known, and the monitoring information comprises space node data, plant growth environment data, weather station data and remote sensing data, wherein:
the space node data of a plurality of spatially continuous nodes are combined together to form regional space data, and the regional space data is marked as a geographic position based on the regional space data;
plant growth environment data comprising soil information within a preset time interval, wherein the soil information comprises soil type, soil humidity, soil temperature, soil pH value, soil salinity and soil nutrient content;
Weather station data including illumination intensity, ambient temperature, precipitation, ambient humidity, carbon dioxide concentration, wind speed and solar radiation data, and taking an average value of the weather station data within a preset time interval as a weather condition of the preset time interval, wherein the preset time interval comprises a month, a quarter or a year duration.
The remote sensing device comprises a satellite or an unmanned aerial vehicle, plant growth image data are collected based on the remote sensing device, crop coverage, growth conditions and pest and disease conditions are obtained based on the plant growth image data, and the plant growth image data are high-resolution images.
Specifically, the acquisition logic of planting feature data:
Acquiring space node data, plant growth environment data and weather station data according to a preset time interval, correlating the space node data, the plant growth environment data and the weather station data to form data monitoring information, and predicting the data monitoring information through a deep learning algorithm to acquire first growth information;
Acquiring remote sensing data in a preset time interval, extracting image detection information based on the remote sensing data, and carrying out feature matching on the image detection information and plant species features in a plant species feature library;
distributing corresponding characteristic labels based on the plant species characteristics corresponding to the second growth information, and carrying out fusion calculation on the plant species characteristics with the same characteristic label in the first growth information to update plant species characteristics;
and aiming at plant species characteristics of each plant species with the same characteristic tag, acquiring corresponding characteristic similarity, weighted averaging all characteristic tag characteristic similarity corresponding to the same plant species to obtain fused plant species characteristics, and marking the fused plant species characteristics as planting characteristic data.
The data analysis module 400 predicts the biomass of the crop in the next stage based on the monitoring information of the plant species field, acquires the yield predicted value corresponding to the crop in the node period within the preset space range based on the biomass of the crop, and sends the yield predicted value to the control support module 500;
Specifically, the yield-predicted-value obtaining logic is:
Acquiring the growth progress of crops corresponding to the plant species field based on a plant species characteristic library, dividing the growth progress to acquire a plurality of stage growth intervals, setting and determining stage growth information for each stage growth interval, and tracking the progress of the crops based on the stage growth intervals to acquire detection growth information;
constructing a biomass prediction model based on historical data and a machine learning algorithm, wherein the biomass prediction model predicts crop biomass in the next stage according to current detection growth information;
according to the prediction result of the crop biomass and the actually acquired detection growth information, the monitoring data are fed back in real time, a biomass prediction model is updated, a crop management strategy is adjusted, and the prediction accuracy is improved;
And establishing a relation model between the biomass and the yield of the predicted crop and the crop yield, thereby obtaining a yield predicted value.
Update logic of the phase growth information:
Comparing and analyzing the detected growth information with the stage growth information, and if the planting characteristic data in the detected growth information is better than the planting characteristic data in the stage growth information, replacing the planting characteristic data in the detected growth information with the planting characteristic data in the stage growth information of the current stage growth section, and updating the stage growth information of the stage growth section;
The method is characterized in that a large amount of collected planting data is analyzed based on priori knowledge and artificial intelligence technology, so that a preferable interval of planting characteristic data is obtained, and the quality of the current planting characteristic data is judged based on the preferable interval.
The control support module 500 evaluates the current crop growth status based on the yield forecast values, writes intelligent contracts according to the crop growth status, and establishes crop management policies based on the intelligent contracts.
It should be noted that the crop management strategies include seeding management, fertilization management, disease removal management and harvesting management, the activities are recorded on the blockchain, the operations can be automatically controlled and executed based on intelligent contracts, all modules are integrated into one system, and a data access and management interface is provided for users, so that manual intervention is reduced, and efficiency is improved.
Embodiment 2 referring to fig. 3, the detailed description of this embodiment is not described in detail in embodiment 1, and this embodiment provides an agricultural environment comprehensive monitoring support method based on multi-source data fusion, which includes the following steps:
Constructing an environment monitoring blockchain, carrying out vegetation region deployment on the environment monitoring blockchain to divide a plurality of planting management regions, and deploying each planting management region as an independent node on the environment monitoring blockchain;
Collecting plant planting information with a time stamp in a planting management area, preprocessing the plant planting information to obtain first target data, dividing the first target data into a plurality of plant species fields based on a fuzzy function, and respectively setting storage nodes based on the plant species fields;
Layering data in the plant species field based on the storage nodes to obtain first layer data and second layer data, determining basic information of the current plant species based on the first layer data, and acquiring monitoring information of the current plant species based on the second layer data in real time, wherein the monitoring information extracts planting characteristic data according to a time stamp;
Predicting crop biomass in the next stage based on monitoring information in the plant species field, and acquiring a yield estimated value corresponding to the crop in a node period in a preset space range based on the crop biomass;
And evaluating the growth condition of the current crop based on the yield predicted value, writing an intelligent contract according to the growth condition of the crop, and establishing a crop management strategy based on the intelligent contract.
The application logic of the planting management area comprises:
dividing a vegetation area into a plurality of planting management areas based on plant species, wherein the area geographic information comprises geographic positions, climate conditions and soil information;
Each planting management area independently records and stores plant information, and deploys the corresponding plant information on a block chain as an independent node, and is connected with nodes of other areas to form a distributed network;
The method comprises the steps that an Internet of things sensor is deployed in each planting management area, the Internet of things sensor comprises a sensor network and remote sensing detection equipment, plant growth environment data are collected in real time based on the sensor network, plant growth image data are collected based on the remote sensing detection equipment, the Internet of things sensor is used for monitoring crop growth and pest and disease conditions, and the accuracy of analysis and evaluation of the sensor network by a system is verified.
The acquisition logic in the plant species field is:
analyzing a large number of plant species images through a deep learning algorithm to obtain species distinguishing features;
Constructing a fuzzy function, taking a species distinguishing feature as a fuzzy input variable, acquiring the membership degrees of a determined variable and an uncertain variable relative to the plant species field through the fuzzy degree function by the species distinguishing feature, and determining the relevance between state variables based on the membership degrees;
and carrying out statistics mapping on membership degrees of distinguishing features of all the current species to obtain fuzzy numerical intervals corresponding to the plant species field, thereby determining the plant species field.
The basic information comprises socioeconomic data, agricultural production management data and crop growth data, the basic information is displayed on an introduction page corresponding to the storage node, the contact point is triggered to view monitoring information after the basic information of the storage node is known, and the monitoring information comprises space node data, plant growth environment data, weather station data and remote sensing data, wherein:
Spatial node data, namely combining spatial node data of a plurality of spatially continuous nodes together to form regional spatial data through farmland position, area and terrain information acquired by a GIS positioning system, and marking the regional spatial data as geographic positions;
the plant growth environment data comprises crop biomass corresponding to different soil information, wherein the soil information comprises soil type, soil humidity, soil temperature, soil pH value, soil salinity and soil nutrient content;
Weather station data including illumination intensity, ambient temperature, precipitation, ambient humidity, carbon dioxide concentration, wind speed and solar radiation data, taking an average value of the weather station data in a preset time interval as a weather condition of the preset time interval, wherein the preset time interval comprises a month, a quarter or a year duration;
Remote sensing data, plant growth image data collected based on remote sensing monitoring equipment, and crop coverage, growth conditions and pest and disease damage conditions are obtained based on the plant growth image data.
Acquiring logic of planting characteristic data:
Acquiring space node data, plant growth environment data and weather station data according to a preset time interval, correlating the space node data, the plant growth environment data and the weather station data to form data monitoring information, and predicting the data monitoring information through a deep learning algorithm to acquire first growth information;
Acquiring remote sensing data in a preset time interval, extracting image detection information based on the remote sensing data, and carrying out feature matching on the image detection information and plant species features in a plant species feature library;
distributing corresponding characteristic labels based on the plant species characteristics corresponding to the second growth information, and carrying out fusion calculation on the plant species characteristics with the same characteristic label in the first growth information to update plant species characteristics;
and aiming at plant species characteristics of each plant species with the same characteristic tag, acquiring corresponding characteristic similarity, weighted averaging all characteristic tag characteristic similarity corresponding to the same plant species to obtain fused plant species characteristics, and marking the fused plant species characteristics as planting characteristic data.
The acquisition logic of the yield estimated value is as follows:
Acquiring the growth progress of crops corresponding to the plant species field based on a plant species characteristic library, dividing the growth progress to acquire a plurality of stage growth intervals, setting and determining stage growth information for each stage growth interval, and tracking the progress of the crops based on the stage growth intervals to acquire detection growth information;
constructing a biomass prediction model based on historical data and a machine learning algorithm, wherein the biomass prediction model predicts crop biomass in the next stage according to current detection growth information;
feeding back the monitoring data in real time according to the prediction result of the crop biomass and the actually acquired detection growth information, updating a biomass prediction model, and adjusting a crop management strategy;
And establishing a relation model between the biomass and the yield of the predicted crop and the crop yield, thereby obtaining a yield predicted value.
Update logic of the phase growth information:
Analyzing a large amount of collected planting data based on priori knowledge and artificial intelligence technology, so as to obtain a preferable section of planting characteristic data, comparing the detected growth information with the stage growth information, analyzing the preferable section, and if the planting characteristic data in the detected growth information is superior to the planting characteristic data in the stage growth information, replacing the planting characteristic data in the detected growth information with the planting characteristic data in the stage growth information of the current stage growth section, and updating the stage growth information of the stage growth section.
Example 3
An electronic device according to an exemplary embodiment includes a processor and a memory, wherein the memory stores a computer program that is callable by the processor;
The processor executes the agricultural environment comprehensive monitoring support system based on multi-source data fusion by calling the computer program stored in the memory.
Fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present application, where the electronic device may have a relatively large difference due to different configurations or performances, and may include one or more processors (Central Processing Units, CPUs) and one or more memories, where at least one computer program is stored in the memories, and the at least one computer program is loaded and executed by the processors to implement an agricultural environment integrated monitoring support system based on multi-source data fusion provided in the foregoing method embodiments. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have a wired or wireless network interface, an input-output interface, and the like, for input-output. The embodiments of the present application are not described herein.
Example 4
A computer readable storage medium having stored thereon a computer program that is erasable according to an exemplary embodiment is shown;
When the computer program runs on the computer equipment, the computer equipment is caused to execute the agricultural environment comprehensive monitoring support system based on multi-source data fusion.
In an exemplary embodiment, a computer readable storage medium, such as a memory, comprising at least one computer program executable by a processor to perform one of the above embodiments of an agricultural environment integrated monitoring support system based on multi-source data fusion is also provided. For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, and the like.
In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program comprising one or more program codes, the one or more program codes being stored in a computer readable storage medium. One or more processors of the electronic device are capable of reading the one or more program codes from the computer-readable storage medium, the one or more processors executing the one or more program codes, so that the electronic device is capable of executing the above-mentioned integrated agricultural environment monitoring support system based on multi-source data fusion.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
Those of ordinary skill in the art will appreciate that all or a portion of the steps implementing the above-described embodiments can be implemented by hardware, or can be implemented by a program instructing the relevant hardware, and the program can be stored in a computer readable storage medium, and the above-mentioned storage medium can be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only of alternative embodiments of the application and is not intended to limit the application, but any modifications, equivalents, improvements, etc. which fall within the spirit and principles of the application are intended to be included in the scope of the application.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of 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.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. The agricultural environment comprehensive monitoring support system based on multi-source data fusion is characterized by comprising a block chain construction module (100), a storage division module (200), a data processing module (300), a data analysis module (400) and a control support module (500), wherein the modules are connected through wires or wirelessly;
The system comprises a blockchain construction module (100) for constructing an environment monitoring blockchain, carrying out vegetation region deployment on the environment monitoring blockchain to divide a plurality of planting management regions, and deploying each planting management region on the environment monitoring blockchain as an independent node;
The storage dividing module (200) is used for collecting plant planting information with a time stamp in a planting management area, preprocessing the plant planting information to obtain first target data, dividing the first target data into a plurality of plant species fields based on a fuzzy function, and respectively setting storage nodes based on the plant species fields;
The data processing module (300) is used for carrying out layering arrangement on the data in the plant species field based on the storage nodes, obtaining first-layer data and second-layer data, determining the basic information of the current plant species based on the first-layer data, and acquiring the monitoring information of the current plant species based on the second-layer data in real time, wherein the monitoring information extracts planting characteristic data according to the time stamp;
Acquiring logic of planting characteristic data:
Acquiring space node data, plant growth environment data and weather station data according to a preset time interval, correlating the space node data, the plant growth environment data and the weather station data to form data monitoring information, and predicting the data monitoring information through a deep learning algorithm to acquire first growth information;
Acquiring remote sensing data in a preset time interval, extracting image detection information based on the remote sensing data, and carrying out feature matching on the image detection information and plant species features in a plant species feature library;
distributing corresponding characteristic labels based on the plant species characteristics corresponding to the second growth information, and carrying out fusion calculation on the plant species characteristics with the same characteristic label in the first growth information to update plant species characteristics;
Aiming at plant species characteristics of each plant species with the same characteristic tag, obtaining corresponding characteristic similarity, weighting and averaging all characteristic tag characteristic similarities corresponding to the same plant species to obtain fused plant species characteristics, and marking the fused plant species characteristics as planting characteristic data;
the data analysis module (400) predicts crop biomass in the next stage based on monitoring information in the plant species field, and acquires a yield predicted value corresponding to the crop in a node period in a preset space range based on the crop biomass;
And a control support module (500) for evaluating the growth condition of the current crop based on the yield pre-estimated value, writing an intelligent contract according to the growth condition of the crop, and establishing a crop management strategy based on the intelligent contract.
2. The agricultural environment comprehensive monitoring support system based on multi-source data fusion according to claim 1, wherein the application logic of the planting management area comprises:
dividing a vegetation area into a plurality of planting management areas based on plant species, wherein the area geographic information comprises geographic positions, climate conditions and soil information;
Each planting management area independently records and stores plant information, and deploys the corresponding plant information on a block chain as an independent node, and is connected with nodes of other areas to form a distributed network;
The method comprises the steps that an Internet of things sensor is deployed in each planting management area, the Internet of things sensor comprises a sensor network and remote sensing detection equipment, plant growth environment data are collected in real time based on the sensor network, plant growth image data are collected based on the remote sensing detection equipment, the Internet of things sensor is used for monitoring crop growth and pest and disease conditions, and the accuracy of analysis and evaluation of the sensor network by a system is verified.
3. The agricultural environment comprehensive monitoring support system based on multi-source data fusion according to claim 2, wherein the acquisition logic of the plant species field is:
analyzing a large number of plant species images through a deep learning algorithm to obtain species distinguishing features;
Constructing a fuzzy function, taking a species distinguishing feature as a fuzzy input variable, acquiring the membership degrees of a determined variable and an uncertain variable relative to the plant species field through the fuzzy degree function by the species distinguishing feature, and determining the relevance between state variables based on the membership degrees;
and carrying out statistics mapping on membership degrees of distinguishing features of all the current species to obtain fuzzy numerical intervals corresponding to the plant species field, thereby determining the plant species field.
4. The agricultural environment comprehensive monitoring support system based on multi-source data fusion according to claim 3, wherein the basic information comprises socioeconomic data, agricultural production management data and crop growth data, the basic information is displayed on an introduction page corresponding to the storage node, the contact point is triggered to view the monitoring information after the basic information of the storage node is known, the monitoring information comprises space node data, plant growth environment data, weather station data and remote sensing data, and the monitoring information comprises the following components:
Spatial node data, namely combining spatial node data of a plurality of spatially continuous nodes together to form regional spatial data through farmland position, area and terrain information acquired by a GIS positioning system, and marking the regional spatial data as geographic positions;
the plant growth environment data comprises crop biomass corresponding to different soil information, wherein the soil information comprises soil type, soil humidity, soil temperature, soil pH value, soil salinity and soil nutrient content;
Weather station data including illumination intensity, ambient temperature, precipitation, ambient humidity, carbon dioxide concentration, wind speed and solar radiation data, taking an average value of the weather station data in a preset time interval as a weather condition of the preset time interval, wherein the preset time interval comprises a month, a quarter or a year duration;
Remote sensing data, plant growth image data collected based on remote sensing monitoring equipment, and crop coverage, growth conditions and pest and disease damage conditions are obtained based on the plant growth image data.
5. The integrated agricultural environment monitoring support system based on multi-source data fusion according to claim 4, wherein the obtaining logic of the yield predicted value is as follows:
Acquiring the growth progress of crops corresponding to the plant species field based on a plant species characteristic library, dividing the growth progress to acquire a plurality of stage growth intervals, setting and determining stage growth information for each stage growth interval, and tracking the progress of the crops based on the stage growth intervals to acquire detection growth information;
constructing a biomass prediction model based on historical data and a machine learning algorithm, wherein the biomass prediction model predicts crop biomass in the next stage according to current detection growth information;
feeding back the monitoring data in real time according to the prediction result of the crop biomass and the actually acquired detection growth information, updating a biomass prediction model, and adjusting a crop management strategy;
And establishing a relation model between the biomass and the yield of the predicted crop and the crop yield, thereby obtaining a yield predicted value.
6. The agricultural environment comprehensive monitoring support system based on multi-source data fusion according to claim 5, wherein the stage growth information updating logic is characterized in that:
Analyzing a large amount of collected planting data based on priori knowledge and artificial intelligence technology, so as to obtain a preferable section of planting characteristic data, comparing the detected growth information with the stage growth information, analyzing the preferable section, and if the planting characteristic data in the detected growth information is superior to the planting characteristic data in the stage growth information, replacing the planting characteristic data in the detected growth information with the planting characteristic data in the stage growth information of the current stage growth section, and updating the stage growth information of the stage growth section.
7. An agricultural environment comprehensive monitoring support method based on multi-source data fusion is based on the realization of the agricultural environment comprehensive monitoring support system based on multi-source data fusion as set forth in any one of claims 1 to 6, and is characterized by comprising the following steps:
Constructing an environment monitoring blockchain, carrying out vegetation region deployment on the environment monitoring blockchain to divide a plurality of planting management regions, and deploying each planting management region as an independent node on the environment monitoring blockchain;
Collecting plant planting information with a time stamp in a planting management area, preprocessing the plant planting information to obtain first target data, dividing the first target data into a plurality of plant species fields based on a fuzzy function, and respectively setting storage nodes based on the plant species fields;
Layering data in the plant species field based on the storage nodes to obtain first layer data and second layer data, determining basic information of the current plant species based on the first layer data, and acquiring monitoring information of the current plant species based on the second layer data in real time, wherein the monitoring information extracts planting characteristic data according to a time stamp;
Predicting crop biomass in the next stage based on monitoring information in the plant species field, and acquiring a yield estimated value corresponding to the crop in a node period in a preset space range based on the crop biomass;
And evaluating the growth condition of the current crop based on the yield predicted value, writing an intelligent contract according to the growth condition of the crop, and establishing a crop management strategy based on the intelligent contract.
8. An electronic device is characterized by comprising a processor and a memory, wherein the memory stores a computer program which can be called by the processor;
The processor executes an agricultural environment integrated monitoring support system based on multi-source data fusion according to any one of claims 1 to 6 by calling a computer program stored in the memory.
9. A computer readable storage medium having instructions stored thereon which, when executed on a computer, cause the computer to perform an agricultural environment integrated monitoring support system based on multi-source data fusion according to any one of claims 1-6.
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| CN117455062A (en) * | 2023-11-09 | 2024-01-26 | 贵州航天智慧农业有限公司 | Crop yield prediction algorithm based on multi-source heterogeneous agricultural data |
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