CN114911174A - Plant bionic method and device - Google Patents
Plant bionic method and device Download PDFInfo
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Abstract
The invention relates to the field of intelligent equipment for agricultural production, and particularly discloses a plant bionic method and a plant bionic device, wherein the plant bionic device comprises a bionic body, and the bionic body comprises an intelligent perception system and is used for intelligently analyzing acquired perception data according to a trained AI recognition algorithm; the model analysis system is used for carrying out simulation analysis on the input perception data according to a designed plant growth analysis model and an environment change analysis model to obtain prediction deduction data such as the current state, the change trend and the like of crops and the environment; the comprehensive analysis system is used for analyzing and determining crop demand information according to the crop planting target and the prediction deduction data; and the external connection system is used for establishing a connection channel of each system. According to the invention, the crops are monitored in real time by an integrated mode of sensor + biological and environmental model + artificial intelligence algorithm + communication operation cooperation and the like, so that fine, systematic and comprehensive crop monitoring data can be provided, and agricultural production management personnel can be assisted to provide a good growth environment for the crops and realize more accurate operation management.
Description
Technical Field
The invention relates to the field of intelligent equipment for agricultural production, in particular to a plant bionic method and a plant bionic device.
Background
With the rapid development and wide application of new-generation information technologies such as internet of things, big data, artificial intelligence, 5G and cloud computing, the agricultural development in China is gradually advancing from mechanization and informatization to intellectualization, namely the intelligent agricultural stage. The production of wisdom agriculture can not leave the crop environmental data and the growth data that various thing networking sensors and image equipment obtained, however the perception to crops themselves and growth environment at present, has the following problem:
1. each sensor device is dispersed and independent, and collects data in a chimney mode. According to the conventional method, one piece of data is sensed, the acquired sensing data or image data is transmitted to a cloud end through an Internet of things platform to be stored on line, and then analysis and feedback processing are performed, so that data of one plant and one environment are divided and discretized, unified analysis cannot be performed in time and space, and the possibility of performing systematic root cause analysis is lost.
2. The front end is not intelligent enough, and all sensors transmit information to the cloud end, so that most of useless junk data occupy a large amount of network transmission bandwidth, cloud storage resources and computing resources. For example, the current agricultural multispectral image camera or the traditional video monitoring equipment only adopts simple and extensive fixed-point image acquisition, so that most of image data is relatively redundant, valuable images are relatively few, and the calculation energy consumption is aggravated while precious storage resources are occupied.
3. When the intelligent equipment works in the field or the greenhouse, the intelligent equipment room can not utilize the capacity of other intelligent equipment, is independent of each other, can not cooperate with each other, and can carry out more real-time and accurate operation. When the agricultural machinery works, the information of the surrounding environment, the crop state and the like can not be obtained in real time unless various professional sensors are configured, and more accurate operation can not be carried out according to specific land parcels, crops, diseases, pests and weeds.
Based on the industry pain points, the intelligent equipment based on multi-sensor integration, built-in multi-crop/environment models and fusion of multi-intelligent algorithms is provided, and the plant bionomics equipment which is a device and equipment for crop growth monitoring and cooperative interaction of comprehensive perception, autonomous decision making and stress early warning is borne, so that agricultural production managers are assisted to provide a good growth environment and intelligent agricultural equipment operation control management for crops. The invention is integrated and integrated, is suitable for implementation and use in greenhouse or field environment, adopts industrial standard manufacturing production, and is suitable for large-area popularization and application in greenhouse or farmland.
Disclosure of Invention
The invention aims to provide a plant bionic method and a plant bionic device so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a device for biomimetic of a plant, the device comprising a biomimetic body, the biomimetic body comprising:
the intelligent sensing system is used for acquiring sensing data, carrying out intelligent analysis on the sensing data by using initial configuration or remote real-time loading and trained AI (artificial intelligence) recognition algorithm, and acquiring crop information and environmental information which can be understood by a machine;
the model analysis system is used for carrying out simulation analysis according to the sensing data of the plant growth analysis model and the environmental change analysis model which are initially configured or remotely loaded in real time to obtain prediction deduction data such as the current state, the change trend and the like of crops and the environment;
the comprehensive analysis system is used for determining crop demand information according to crop planting targets, set index parameters, prediction deduction data and the like;
the external connection system is used for establishing a connection channel of each system; the external connection system at least comprises an intelligent reporting module, wherein the intelligent reporting module is used for uploading the locally cached data to a decision analysis platform for data classification storage and record archiving;
the equipment supporting system comprises an equipment management module, a data management module, a model algorithm management module and an interface management module and is used for realizing the operation management, the resource management and the maintenance management of each system.
As a further scheme of the invention: the intelligent perception system comprises:
the data processing module is used for establishing a unified time and space coordinate system and managing and processing the perception data acquired by each perception device;
the intelligent identification module is used for identifying the perception data according to a preset or real-time loaded identification classification model/algorithm and converting the unstructured/semi-structured perception data into structured perception data which can be understood by a machine according to an identification result;
wherein the perception device comprises:
an RGB view sensor;
a multispectral sensor;
a hyperspectral sensor;
the soil sensor is used for monitoring the temperature, the humidity, the soil pH value, the conductivity, the soil nitrogen-phosphorus-potassium content and the organic matter nutrient of the soil at different depths;
a water level sensor;
a light intensity sensor;
and the meteorological monitoring equipment is used for monitoring wind speed, wind direction, rainfall, solar radiation, air humidity and air temperature.
As a further scheme of the invention: the identifying the classification model comprises:
the crop growth condition recognition method comprises a disease recognition algorithm, a pest recognition algorithm, a weed recognition algorithm and a crop growth condition recognition algorithm, wherein the model algorithms are obtained by training and outputting collected disease image samples, pest image samples, weed images, growth condition spectrum images and other samples of related crops through a deep learning framework such as ResNet or DenseNet;
the recognition classification model and the recognition classification algorithm can be loaded and refreshed in real time, and recognition accuracy and performance are improved.
As a further scheme of the invention: the plant growth analysis model comprises:
a crop growth model, a photosynthesis model, a crop respiration model, a water stress model, a transpiration model, a nutrient absorption model, a dry matter conversion model and various index characteristics; wherein each index characteristic is related to the crop status;
the models are mechanism models of system initial configuration or cloud dynamic loading, each crop growth model is based on sensing data obtained by an intelligent sensing system, model parameters are corrected and optimized according to actual growth conditions through preset step length iterative simulation, the growth conditions of crops are predicted, and management requirements of the crops are determined.
As a further scheme of the invention: the environmental change analysis model includes:
the system comprises various object evolution models, trend prediction models, crop action and influence models and various index characteristics, wherein the various objects comprise weather, soil, moisture, illumination and plant diseases and insect pests;
the model is a mechanism model of system initial configuration or cloud dynamic loading, each environment change analysis model is based on sensing data obtained by an intelligent sensing system, the sensing data comprise soil type, soil temperature, soil humidity, soil moisture, soil nutrients, soil electrolyte, soil pH value, organic matters, CO2 content, air temperature and air humidity, iteration simulation is carried out through preset step length, model parameters are corrected and optimized according to actual evolution conditions, environment change conditions are predicted, and farm management requirements are determined.
As a further scheme of the invention: the integrated analysis system includes:
the first setting module is used for setting parameters of all sensing devices in the intelligent sensing system;
the second setting module is used for selecting and setting a recognition classification algorithm of the sensing data;
the third setting module is used for selecting and setting the selected environment change analysis model, the related submodel and the index feature library depending on the model;
the fourth setting module is used for acquiring real-time data of the intelligent sensing system, selecting and setting simulation and reasoning frequency, and carrying out comprehensive operation analysis according to the crop planting target based on simulation and reasoning results;
the data output module is used for outputting corresponding interaction signals to other systems according to the comprehensive operation analysis result;
and the response module is used for analyzing, judging and responding according to the interactive data and instructions of other systems.
As a further scheme of the invention: the equipment support system includes:
the device management module comprises a maintenance and diagnosis management unit of a single board and a chip of the device management module, a management unit of various sensors which can be plugged in and pulled out of the bionic body, a device resource management unit, a device log management unit, a device alarm management unit and a device maintenance management unit;
the data management module is used for processing all data of the device according to different databases; the processing mode comprises storage, backup, query and conversion;
and the algorithm model management module is used for standardizing and packaging all models and algorithms of the device, and is used for calling each module, remotely upgrading and converting parameters.
As a further scheme of the invention: the equipment management module comprises a power supply management unit and a safety protection unit; the power management unit is an energy subsystem for providing power, and the energy subsystem comprises a solar panel of the equipment and a lithium battery energy storage battery for supplying power, and a 220V external power cable input power safety protection unit which comprises a lightning protection subunit, a waterproof subunit, a damp-proof subunit, a cold-proof subunit, a high-temperature-proof subunit, an anti-theft subunit and an audible and visual alarm subunit.
As a further scheme of the invention: the external connection system comprises a cloud platform interaction module, an agricultural machinery interaction module, an adjacent bionic body interaction module and a heterogeneous sensor interaction module; the external connection system is configured to:
the system comprises a plurality of biont simulating devices, a biont simulating and cloud intelligent agricultural platform, a biont simulating and edge intelligent system, biont simulating and operating intelligent equipment, a control system and a control system, wherein the biont simulating and the operating intelligent equipment are interconnected, communicated and cooperatively processed;
supporting data exchange and signaling control interaction with each external system; the data exchange comprises positioning, environment perception, growth perception, coercion perception, refreshed models and algorithms and data allopatric backup.
The technical scheme of the invention also provides a plant bionic method, which comprises the following steps:
acquiring sensing data, wherein the sensing data comprises crop information and environmental information;
intelligently recognizing the perception data according to a trained intelligent algorithm to obtain the crop and environment states which can be understood by a machine;
according to the designed plant growth analysis model and the environment change analysis model, carrying out simulation analysis on the changes of the crops and the environment state, and giving prediction deduction data of the crops;
determining the demand information of the crops by combining crop planting targets according to the prediction deduction data;
and establishing a connection channel of each system.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the crops are monitored in real time by an integrated mode of sensor + biological and environmental model + artificial intelligence algorithm + communication operation cooperation and the like, so that fine, systematic and comprehensive crop monitoring data can be provided, and agricultural production management personnel can be assisted to provide a good growth environment for the crops and realize more accurate operation management.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
Fig. 1 is a block diagram of the structure of a plant bionic device.
FIG. 2 is an exemplary diagram of a plant growth model in a plant biomimetic apparatus.
FIG. 3 is a block diagram of the external connection system in the method and apparatus for plant simulation.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention discloses a device and equipment for realizing interactive cooperation with intelligent operation equipment such as human, intelligent agricultural platforms or edge intelligent equipment, agricultural machinery and the like, intelligent control equipment between fields and greenhouses and the like through a communication network such as 5G or optical transmission and the like, and finally, analysis results are transmitted through a communication network such as 5G or optical transmission and the like.
The sensing technology related to the invention includes but is not limited to environmental sensors such as air and soil, and visual light (RGB), infrared, multispectral, hyperspectral and other view sensing devices or equipment. The crop growth and environment change model comprises but is not limited to the scientific research achievements in the fields of life sciences, environmental chemistry and the like, such as a crop growth model, a soil evolution model, a photosynthesis model, a nitrogen/phosphorus/potassium and other nutrient absorption model, a starch/protein and other seed grain nutrient conversion and storage model, a pest and disease evolution model and the like; the artificial intelligence technology comprises model algorithms such as various machine learning, deep learning and the like, such as recognition classification, prediction early warning and the like. The communication technology related by the invention comprises but is not limited to 2G/3G/4G/5G, optical fiber, Wifi, Zigbee, Lora, Bluetooth and the like, and the communication and the cooperation with an intelligent agricultural center or edge platform, agricultural equipment, intelligent equipment of farmlands or greenhouses and surrounding plant bionics are realized through communication connection. The device also comprises the technology of utilizing solar energy to store electricity locally and supply power and the like, the reliability design of earthquake resistance, water resistance, moisture resistance, lightning protection, cold resistance, high temperature resistance, corrosion resistance and the like, and the safety design of electric shock resistance, static electricity resistance, theft prevention and the like.
The invention monitors crops in real time by integrated modes of sensor, biological and environmental model, artificial intelligence algorithm, communication operation cooperation and the like, is favorable for providing fine, systematic and comprehensive crop monitoring data, and further is favorable for assisting agricultural production managers to provide good growth environment for crops and realize more accurate operation management.
Example 1
Fig. 1 is a block diagram showing the components of a plant bionic device, in an embodiment of the invention, the plant bionic device comprises a bionic body, and the bionic body comprises:
the intelligent sensing system is used for acquiring sensing data, and the sensing data comprises crop information and environmental information;
the model analysis system is used for carrying out simulation analysis according to the perception data of the trained plant growth analysis model and the trained environment change analysis model to obtain prediction deduction data;
the comprehensive analysis system is used for determining the demand information of the crops according to the prediction deduction data;
the external connection system is used for establishing a connection channel of each system; the external connection system at least comprises an intelligent reporting module, wherein the intelligent reporting module is used for uploading the locally cached data to a decision analysis platform for data classification storage and record archiving;
the equipment supporting system comprises an equipment management module, a data management module, a model algorithm management module and an interface management module and is used for realizing the operation management, the resource management and the maintenance management of each system.
The invention discloses a device and equipment for realizing interactive cooperation with intelligent operation equipment such as human, intelligent agricultural platforms or edge intelligent equipment, agricultural machinery and the like, intelligent control equipment between fields and greenhouses and the like through a communication network such as 5G or optical transmission and the like, and finally, analysis results are transmitted through a communication network such as 5G or optical transmission and the like.
As shown in fig. 1, the plant bionic body includes four main systems and dozens of subsystems, each main system includes an intelligent sensing system (including various peripheral crop sensing modules, environmental sensor modules, and intelligent analysis modules of corresponding sensor sensing parameters), a model analysis system (including a crop growth analysis model, an environmental change analysis model, a comprehensive analysis system, and the like), an external connection system (including a cloud platform interaction system, a near-end bionic body interaction system, an intelligent equipment interaction system, a heterogeneous sensor interaction system, a comprehensive reporting module, a cooperative processing module, and the like), an equipment support system (including an equipment management module, a data management module, an interface management module, a model algorithm management module, and the like).
As a preferred embodiment of the technical solution of the present invention, the intelligent sensing system includes:
the data processing module is used for establishing a unified time and space coordinate system and counting the perception data acquired by each perception device;
the intelligent recognition module is used for recognizing the perception data according to a preset or real-time loaded recognition classification model and converting the unstructured perception data into structured perception data according to a recognition result;
wherein the perception device comprises:
an RGB view sensor;
a multispectral sensor;
a hyperspectral sensor;
the soil sensor is used for monitoring the temperature, the humidity, the soil pH value, the conductivity, the soil nitrogen phosphorus potassium content and the organic matter nutrient of the soil at different depths;
a water level sensor;
a light intensity sensor;
and the meteorological monitoring equipment is used for monitoring wind speed, wind direction, rainfall, solar radiation, air humidity and air temperature.
Further, the identifying the classification model includes:
the crop growth condition recognition method comprises a disease recognition algorithm, a pest recognition algorithm, a weed recognition algorithm and a crop growth condition recognition algorithm, wherein the model algorithms are obtained by training and outputting collected disease image samples, pest image samples, weed images, growth condition spectrum images and other samples of related crops through a deep learning framework such as ResNet or DenseNet;
the intelligent sensing system can comprehensively acquire the growth parameters and environmental parameters of peripheral crops in real time, and the module can be integrated with sensing devices or subsystems including and not limited to an RGB view sensor (high-resolution video monitoring device), a multispectral sensor (multispectral camera), a hyperspectral sensor (hyperspectral spectrometer), a soil sensor (monitoring the temperature, humidity, soil acidity and alkalinity, conductivity, soil nitrogen phosphorus and potassium content or organic matter nutrient content and the like of different depths of soil), a water level sensor, a light illumination sensor, meteorological monitoring equipment (monitoring indexes such as field meteorological wind speed, wind direction, rainfall, solar radiation, air humidity and air temperature), pest and weed sensing or capturing and the like. By means of the integrated multiple types of sensor equipment, a unified time and space coordinate system (by means of manual setting or positioning of sensors) is established, one object is one grade, the other is one grade, and all sensing data and indexes of the crop self-character, the growth environment index, the peripheral stress characteristic and the like are collected and recorded in real time. For example, the multispectral sensor may acquire multispectral image data of plants, water, soil, etc.; the video monitoring equipment can record RGB images of crop growth in a region and simultaneously monitor key change image data of pests, crop diseases, weed growth and other stages around the crop; the illuminance sensor records light intensity and light quality data of crops in all periods of the whole growth period (recorded in half hour), and provides possibility for a comprehensive analysis system to perform root cause analysis according to temperature, stress, growth conditions and the like.
In the intelligent sensing system, for each type of sensing parameter, a corresponding recognition classification model or algorithm obtained through AI or machine algorithms such as deep learning is used for intelligently analyzing and recognizing the sensing data, so that conversion from unstructured data to structured data (which can be understood by a machine and can be used as input parameters of a growth model and the like) is realized. For example, unstructured data imaged by visible light (RGB, the same below) can be used to obtain parameters such as structure, width, density, symmetry, leaf length, leaf width, leaf area, leaf angle, leaf color, leaf spot, insect pest, weed, etc. of a plant through AI; the near-infrared imaging can be used for analyzing the water distribution state, the hydraulics research, the stress physiology research and the like of the plant, and the water distribution condition in the plant root system and the earth pillar can be analyzed through the near-infrared imaging data of the root system; and analyzing the current chlorophyll content by combining a leaf surface multispectral image and a growth recognition algorithm, thereby judging and analyzing whether the current growth is normal.
The identification classification model or algorithm comprises but is not limited to a disease identification algorithm, a pest identification algorithm, a weed identification algorithm, a crop growth recognition algorithm and the like, and the model algorithms are obtained by training and outputting collected disease image samples, pest image samples, weed images, growth spectrum images and other samples of related crops through a deep learning framework such as ResNet or DenseNet. The algorithms can be downloaded or updated and refreshed through a cloud system, and the latest AI algorithm training results are synchronized all the time.
Fig. 2 is a diagram showing an example of a plant growth model in a plant biomimetic apparatus, and as a preferred embodiment of the technical solution of the present invention, the plant generation analysis model includes:
a crop growth model, a photosynthesis model, a crop respiration model, a water stress model, a transpiration model, a nutrient absorption model, a dry matter conversion model and various index characteristics; wherein each index characteristic is related to the crop status;
the models are mechanism models of system initial configuration or cloud dynamic loading, each crop growth model is based on sensing data obtained by an intelligent sensing system, model parameters are corrected and optimized according to actual growth conditions through preset step length iterative simulation, the growth conditions of crops are predicted, and management requirements of the crops are determined.
The model analysis system mainly uses the plant growth analysis model and the environmental change analysis model to perform various model simulation analyses according to the sensing data acquired by the intelligent sensing system, predicts the early warning model, comprehensively analyzes and judges the health condition of the current crop growth and the severity of the surrounding environmental stress, and reports the information of insufficient nutrition, drought and waterlogging in the land, cordyceps sinensis and the like in time.
The plant growth analysis model comprises: the method is not limited to crop growth models, photosynthesis models, crop respiration models, water stress models, transpiration models, nutrient (nitrogen/phosphorus/potassium/calcium/sulfur/trace elements and the like) absorption models, dry matter conversion models and other crop growth sub-models (simultaneously including various index characteristics required by the models, such as leaf area index, plant height, leaf nitrogen content and the like of growth index data during healthy growth of crops), the models are all mechanism models of system initial configuration or cloud dynamic loading, each growth model utilizes real-time parameters provided by an intelligent sensing system, iterative simulation can be carried out on time and by day by taking step length, model parameters can be corrected and optimized according to actual growth conditions, and therefore crop growth conditions can be predicted or judged more accurately and management requirements of crops can be summarized.
Specifically, the environment change analysis model includes:
the system comprises an evolution model, a trend prediction model, a crop action and influence model and various index characteristics of various objects, wherein the various objects comprise weather, soil, water, illumination and plant diseases and insect pests;
the model is a mechanism model of system initial configuration or cloud dynamic loading, and each environment change analysis model is based on sensing data obtained by an intelligent sensing system, including soil type, soil temperature, soil humidity, soil moisture, soil nutrients, soil electrolyte and soil pH value, organic matters, CO2 content, air temperature and air humidity, through preset step length iterative simulation, and according to the actual evolution condition, correcting and optimizing model parameters, predicting the environment change condition and determining the farming management demand.
The environmental change analysis model comprises an evolution model of various objects such as weather, soil, moisture, illumination, diseases, insect pests, weeds and the like, a trend prediction model, a model of crop action and influence (simultaneously comprising various index characteristics required by the model), and the like. The models are mechanism models of system initial configuration or cloud dynamic loading, and each model can perform iterative simulation by taking steps of time, day, week, month and the like as steps according to real-time parameters (including but not limited to soil type, soil temperature, soil humidity, soil moisture, soil nutrients, soil electrolytes, soil pH value, organic matters, CO2 content, air temperature, air humidity and the like) provided by the intelligent sensing system, correct and optimize model parameters according to actual evolution conditions, so that environment change conditions can be predicted or judged more accurately, and the agricultural management requirements of environment utilization can be summarized.
As a preferred embodiment of the technical solution of the present invention, the integrated analysis system includes:
the first setting module is used for setting parameters of all sensing devices in the intelligent sensing system;
the second setting module is used for selecting and setting a recognition classification algorithm of the sensing data;
the third setting module is used for selecting and setting the selected environment change analysis model, the related submodel and the index feature library depending on the model;
the fourth setting module is used for acquiring real-time data of the intelligent sensing system, selecting and setting simulation and reasoning frequency, and carrying out comprehensive operation analysis according to the crop planting target based on simulation and reasoning results;
the data output module is used for outputting corresponding interaction signals to other systems according to the comprehensive operation analysis result;
and the response module is used for analyzing, judging and responding according to the interactive data and instructions of other systems.
The comprehensive analysis system mainly analyzes and judges the result of the plant growth simulation model according to the growth or planting target of the peripheral observed crop and the change analysis and prediction deduction data of the peripheral small environment, provides the demand signals of the current crop planting management, and sends the signals to the intelligent agricultural center platform system, the intelligent edge system, the intelligent agricultural operation machine or the agricultural production manager and the like, and the intelligent agricultural operation machine or the agricultural production manager can perform corresponding management and operation according to the signals. The comprehensive analysis system is used as a main treatment system of the plant bionic body, and has the main functions of:
1. the relevant parameters (such as the picture-taking pixels) or the frequency of the data collected by the intelligent perception system, namely the time and the interval of the data collected by the sensor can be set. If the crop grows in a high-incidence time period suffering from diseases, pests and weeds, the time window and the acquisition precision of the acquired image are self-adaptively and dynamically adjusted;
2. and selecting and setting a recognition classification algorithm of the perception parameters. For example, a simple and efficient recognition algorithm can be adopted during non-key object recognition, so that the occupation of computing resources is reduced or the power consumption is reduced.
3. And selecting and setting the selected environment change analysis model and the related submodel, and the index characteristic library depended on by the model. Under different plots and small weather scenes, the accuracy of different models and the numerical value difference of index features are large;
4. selecting and setting simulation and reasoning frequency according to real-time data of the intelligent sensing system, and carrying out comprehensive operation analysis by utilizing a crop planting target and a simulation or reasoning result;
5. outputting corresponding interactive signals to other systems according to the comprehensive operation analysis result;
6. and analysis, judgment and correspondence can be performed according to other system interaction data and instructions.
The comprehensive analysis system can calculate the mean value and the variance by combining Gaussian probability distribution statistics, judge the index change of the current crop growth stage in the corresponding environment, if the deviation is overlarge, the current crop growth index is abnormal, and send an early warning prompt to the decision analysis platform module through the comprehensive reporting module to inform the current abnormal index, so that a farming disposal basis is provided for farming experts or technicians. Whether the current crop needs irrigation or flood-draining farming operation can also be determined through threshold values of environmental change index conditions under the expected growth, such as soil moisture content, soil temperature and the like.
As a preferred embodiment of the technical solution of the present invention, the apparatus supporting system includes:
the device management module comprises a maintenance and diagnosis management unit of a single board and a chip of the device management module, a management unit of various sensors which can be plugged in and pulled out of the bionic body, a device resource management unit, a device log management unit, a device alarm management unit and a device maintenance management unit;
the data management module is used for processing all data of the device according to different databases; the processing mode comprises storage, backup, query and conversion;
and the algorithm model management module is used for standardizing and packaging all models and algorithms of the device, and is used for calling each module, remotely upgrading and converting parameters.
The equipment supporting system comprises modules of equipment management, data management, model algorithm management, interface management and the like. The method mainly realizes the operation management, resource management, maintenance management and the like of the plant bionic body software system.
The equipment management comprises the maintenance and diagnosis management of a single board and a chip, the management of various pluggable sensors of the bionic body, the equipment resource management, the equipment log management, the equipment alarm management and other equipment maintenance management and the like, and the equipment management also comprises two submodules of power supply management and safety protection management. The power management module is an energy subsystem for managing and providing power for the equipment, and the power subsystem can comprise a solar cell panel or a lithium battery energy storage battery of the equipment for supplying power, and also comprises an external power cable input power source of 110V/220V and the like. The safety protection device mainly comprises various structural designs, circuit designs and software design functions, such as lightning protection, water prevention, moisture prevention, cold prevention, high temperature prevention, theft prevention, damage prevention and the like, and provides an acousto-optic alarm function.
The data management is responsible for storage, backup, query, conversion and the like of all data of the plant bionic body, and different database or data file formats and access methods are used for managing different unstructured sensing data and structured model algorithm data.
The algorithm model management mainly carries out unified standardized encapsulation on all used models and algorithms of the bionic system, and is convenient for calling each module, remote upgrading, parameter conversion and the like. The module is the key that the plant bionic body can be continuously and iteratively upgraded, and can sense the growth of model crops and change the environment more accurately.
Further, the device management module comprises a power management unit and a safety protection unit; the power management unit is an energy subsystem for providing power, and the energy subsystem comprises a solar cell panel of the equipment, a lithium battery energy storage battery for supplying power and an external power cable input power safety protection unit which comprises a lightning protection subunit, a waterproof subunit, a moisture-proof subunit, a cold-proof subunit, a high-temperature-proof subunit, an anti-theft subunit and an audible and visual alarm subunit.
Fig. 3 is a block diagram illustrating a configuration of an external connection system in the plant biomimetic method and apparatus, where the external connection system includes a cloud platform interaction module, an agricultural machinery interaction module, a proximity biomimetic interaction module, and a heterogeneous sensor interaction module; the external connection system is configured to:
the system comprises a plurality of biont simulating devices, a biont simulating and cloud intelligent agricultural platform, a biont simulating and edge intelligent system, biont simulating and operating intelligent equipment, a control system and a control system, wherein the biont simulating and the operating intelligent equipment are interconnected, communicated and cooperatively processed;
supporting data exchange and signaling control interaction with each external system; the data exchange comprises positioning, environment perception, growth perception, coercion perception, refreshed models and algorithms and data allopatric backup.
The external connection system comprises a cloud platform interaction system, an agricultural machine interaction system, an adjacent bionic body interaction system, a heterogeneous sensor interaction system and other modules, and is responsible for interconnection and cooperative processing among a plurality of bionic body devices, a bionic body and cloud intelligent agricultural platform or edge intelligent system, operating intelligent equipment (agricultural machines or agricultural implements, unmanned aerial vehicles and other intelligent operating equipment), other sensors and the like, data exchange and signaling control interaction among the external systems is supported, and data exchange comprises and is not limited to positioning, environment perception, vigor perception, coercion perception, refreshed models and algorithms, data remote backup and the like. The external connection system is provided with a transmission module, the transmission module can adopt a wireless communication or wired communication mode, including but not limited to 2G/3G/4G/5G mobile communication, WLAN/Lora/Zigbee/Bluetooth and other medium-short distance wireless communication, optical fiber or cable wired communication and the like, and the transmission module adopts a low-power consumption communication mode. The external connection system comprises an intelligent reporting module which is responsible for carrying out data classification storage, recording and archiving on the locally cached sensing or image data to a decision analysis platform through a transmission module as required or at regular time (in a mode of half/hour, day, week or month), and provides data support for the subsequent training of an agronomy expert on crop research algorithm model; and the system is responsible for pushing prediction and early warning information in the crop farming production process to a decision analysis platform or terminal equipment.
The plant bionic body is used as an integrated device, can be statically inserted into field or greenhouse soil for planting crops, can be independently deployed in a single point mode or deployed in a group multi-point mode, and is communicated and interconnected through a transmission module and transmitted with data to perform data consistency or normalization processing. The plant bionic body is provided with an external equipment access data interface through a transmission module, and can be compatible with the access of the existing wired or wireless monitoring equipment.
The innovation points of the technical scheme of the invention comprise:
1. providing a scheme and equipment for accurately simulating and sensing the growth requirement of crops by combining various sensors, AI sensing algorithms, growth models, environment evolution models and the like;
2. the integrated equipment comprises an environment sensing sensor and a crop growth observation sensor which are arranged in the equipment, wherein the environment sensor comprises but is not limited to soil, air, illumination, moisture and other sensors, and monitoring parameters comprise but are not limited to soil type, soil temperature, soil humidity, soil moisture, soil nutrients, soil electrolytes, soil pH value, organic matters, CO2 content, air temperature, air humidity and the like; the crop growth sensor comprises but is not limited to visible light sensing, infrared sensing, multispectral sensing and hyperspectral sensing, and monitoring parameters comprise the number, height, leaf area, nitrogen content, chlorophyll content and the like of roots, stems, leaves, ears and the like of crops;
3. an AI recognition and classification algorithm for intelligently analyzing various perception data is arranged in the integrated equipment, wherein the AI recognition and classification algorithm comprises but is not limited to recognition of crop growth, diseases, insect pests, weeds and the like, and comprehensive multi-dimensional perception information is given for more accurate recognition;
4. providing integrated equipment among agricultural fields and in a greenhouse, wherein the equipment is internally provided with crop growth sub-models (simultaneously comprising various index characteristics required by the models, such as growth index data leaf area index, plant height, leaf nitrogen content and the like during healthy growth of crops) including but not limited to a crop growth model, a photosynthesis model, a crop respiration model, a water stress model, a transpiration model, a nutrient (nitrogen/phosphorus/potassium/calcium/sulfur/trace elements and the like) absorption model, a dry matter conversion model and the like;
5. the integrated equipment comprises various object evolution models including but not limited to weather, soil, moisture, illumination, diseases, insect pests, weeds and the like, a trend prediction model, a model for acting and influencing crops (including various index characteristics required by the model), and the like.
6. The integrated equipment is provided with a comprehensive analysis system, and can combine crop planting targets to give current crop growth state and development trend, demand for environmental change and possible risk, and alarm when the demand exceeds a certain threshold.
7. The integrated equipment can exchange data and information with peripheral bionic bodies by using a communication module to perform more accurate analysis and judgment.
8. The integrated equipment between agricultural fields and in the greenhouse can exchange data and information with peripheral intelligent equipment, and the intelligent equipment can perform more accurate operation conveniently.
9. The integrated equipment in agricultural field and greenhouse is used for controlling and managing other sensing equipment or intelligent facilities.
Example 2
In an embodiment of the invention, a plant bionic method comprises the following steps:
acquiring sensing data, wherein the sensing data comprises crop information and environmental information;
intelligently recognizing the perception data according to a trained intelligent algorithm to obtain the crop and environment states which can be understood by a machine;
according to the designed plant growth analysis model and the environment change analysis model, carrying out simulation analysis on the changes of the crops and the environment state, and giving prediction deduction data of the crops;
determining the demand information of the crops by combining crop planting targets according to the prediction deduction data;
and establishing a connection channel of each system.
The functions that can be performed by the plant biomimetic method are performed by a computer device comprising one or more processors and one or more memories, wherein at least one program code is stored in the one or more memories, and loaded and executed by the one or more processors to perform the functions of the plant biomimetic method.
The processor fetches instructions and analyzes the instructions one by one from the memory, then completes corresponding operations according to the instruction requirements, generates a series of control commands, enables all parts of the computer to automatically, continuously and coordinately act to form an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit; the Memory comprises a Read-Only Memory (ROM) for storing a computer program, and a protection device is arranged outside the Memory.
Illustratively, the computer program may be partitioned into one or more modules, stored in memory and executed by a processor, to implement the invention. One or more of the modules may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the terminal device.
Those skilled in the art will appreciate that the above description of the service device is merely exemplary and not limiting of the terminal device, and may include more or less components than those described, or combine certain components, or different components, such as may include input output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal equipment and connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the terminal device by operating or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory mainly comprises a storage program area and a storage data area, wherein the storage program area can store an operating system, application programs (such as an information acquisition template display function, a product information publishing function and the like) required by at least one function and the like; the storage data area may store data created according to the use of the berth-state display system (e.g., product information acquisition templates corresponding to different product types, product information that needs to be issued by different product providers, etc.), and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The terminal device integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the modules/units in the system according to the above embodiment may also be implemented by instructing relevant hardware by a computer program, and the computer program may be stored in a computer-readable storage medium, and when executed by a processor, the computer program may implement the functions of the above embodiments of the system. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunications signal, software distribution medium, etc.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes performed by the present invention or directly or indirectly applied to other related technical fields are also included in the scope of the present invention.
Claims (10)
1. A plant biomimetic apparatus, comprising a biomimetic body, the biomimetic body comprising:
the intelligent sensing system is used for acquiring sensing data, intelligently analyzing the sensing data by using a trained AI algorithm and acquiring crop information and environmental information which can be understood by the machine;
the model analysis system is used for carrying out simulation analysis according to the sensing data of the designed plant growth analysis model and the environment change analysis model to obtain prediction deduction data such as the current state, the change trend and the like of crops and the environment;
the comprehensive analysis system is used for determining crop demand information according to the crop planting target and the prediction deduction data;
the external connection system is used for establishing a connection channel of each system; the external connection system at least comprises an intelligent reporting module, wherein the intelligent reporting module is used for uploading the locally cached data to a decision analysis platform for data classification storage and record archiving;
the equipment supporting system comprises an equipment management module, a data management module, a model algorithm management module and an interface management module and is used for realizing the operation management, the resource management and the maintenance management of each system.
2. The plant biomimetic apparatus of claim 1, wherein the smart perception system comprises:
the data processing module is used for establishing a unified time and space coordinate system and managing and processing the perception data acquired by each perception device;
the recognition module is used for recognizing the perception data according to the trained AI recognition classification model and algorithm and converting the unstructured/semi-structured perception data into the structured perception data which can be understood by the machine according to the recognition result;
wherein the perception device includes but is not limited to:
an RGB view sensor;
a multispectral sensor;
a hyperspectral sensor;
the soil sensor is used for monitoring the temperature, the humidity, the soil pH value, the conductivity, the soil nitrogen phosphorus potassium content and the organic matter nutrient of the soil at different depths;
a water level sensor;
a light intensity sensor;
and the meteorological monitoring equipment is used for monitoring wind speed, wind direction, rainfall, solar radiation, air humidity and air temperature.
3. The apparatus of claim 2, wherein the identifying a classification model comprises:
the crop disease recognition method comprises a disease recognition algorithm, a pest recognition algorithm, a weed recognition algorithm and a crop growth recognition algorithm, wherein the model algorithms are obtained by training and outputting collected disease image samples, pest image samples, weed images, growth images, spectral information and other samples of related crops through a deep learning framework or a machine learning model such as ResNet or DenseNet;
the recognition classification model can be remotely upgraded and loaded, and the recognition precision and performance are gradually improved.
4. The plant biomimetic apparatus of claim 1, wherein the plant growth analysis model comprises:
a crop growth model, a photosynthesis model, a crop respiration model, a water stress model, a transpiration model, a nutrient absorption model, a dry matter conversion model and various index characteristics; wherein each index characteristic is related to the crop status;
the models are mechanism models of system initial configuration or cloud dynamic loading, each crop growth model is based on sensing data obtained by an intelligent sensing system, and through preset step length iterative simulation, model parameters are corrected and optimized according to actual growth conditions, the growth conditions of objects are predicted, and the management requirements of crops are determined.
5. The plant biomimetic apparatus of claim 1, wherein the environmental change analysis model comprises:
the system comprises various object evolution models, trend prediction models, crop action and influence models and various index characteristics, wherein the various objects comprise weather, soil, nutrients, moisture, illumination and insect and weed pests;
the model is a mechanism model of system initial configuration or cloud dynamic loading, each environment change analysis model is based on sensing data obtained by an intelligent sensing system, the sensing data comprise soil type, soil temperature, soil humidity, soil moisture, soil nutrients, soil electrolyte, soil pH value, organic matters, CO2 content, air temperature and air humidity, iteration simulation is carried out through preset step length, model parameters are corrected and optimized according to actual evolution conditions, environment change conditions are predicted, and farm management requirements are determined.
6. The plant biomimetic apparatus of claim 1, wherein the integrated analysis system comprises:
the first setting module is used for setting parameters of all sensing devices in the intelligent sensing system;
the second setting module is used for selecting and setting a recognition classification algorithm of the sensing data;
the third setting module is used for selecting and setting the selected plant growth model, the selected environmental change analysis model, the selected related submodels and the index feature library depended by the models;
the fourth setting module is used for acquiring real-time data of the intelligent sensing system, selecting and setting simulation and reasoning frequency, and carrying out comprehensive operation analysis according to the crop planting target based on simulation and reasoning results;
the data output module is used for outputting corresponding interaction signals to other systems according to the comprehensive operation analysis result;
and the response module is used for analyzing, judging and responding according to the interactive data and instructions of other systems.
7. The plant biomimetic apparatus of claim 1, wherein the device support system comprises:
the device management module comprises a maintenance and diagnosis management unit of a single board and a chip of the device management module, a management unit of various sensors which can be plugged in and pulled out of the bionic body, a device resource management unit, a device log management unit, a device alarm management unit and a device maintenance management unit;
the data management module is used for processing all data of the device according to different databases; the processing mode comprises storage, backup, query and conversion;
and the algorithm model management module is used for standardizing and packaging all models and algorithms of the device, and is used for calling each module, remotely upgrading and converting parameters.
8. The plant bionic device according to claim 7, wherein the equipment management module comprises a power supply management unit and a safety protection unit; the power management unit is an energy subsystem for providing power, and the energy subsystem comprises a solar cell panel of the equipment, a lithium battery energy storage battery for supplying power and an external power cable input power safety protection unit which comprises a lightning protection subunit, a waterproof subunit, a moisture-proof subunit, a cold-proof subunit, a high-temperature-proof subunit, an anti-theft subunit and an audible and visual alarm subunit.
9. The plant biomimetic apparatus according to any one of claims 1-8, wherein the external connection system comprises a cloud platform interaction module, an agricultural machine interaction module, a proximity biomimetic interaction module, and a heterogeneous sensor interaction module; the external connection system is configured to:
the system comprises a plurality of biont simulating devices, a biont simulating and cloud intelligent agricultural platform, a biont simulating and edge intelligent system, biont simulating and operating intelligent equipment, a control system and a control system, wherein the biont simulating and the operating intelligent equipment are interconnected, communicated and cooperatively processed;
supporting data exchange and signaling control interaction with each external system; the data exchange comprises positioning, environment perception, growth perception, coercion perception, refreshed models and algorithms and data allopatric backup.
10. A method of plant biomimetic, comprising:
acquiring sensing data, wherein the sensing data comprises crop information and environmental information;
intelligently recognizing the perception data according to a trained intelligent algorithm to obtain the crop and environment states which can be understood by a machine;
according to the designed plant growth analysis model and the environment change analysis model, carrying out simulation analysis on the changes of the crops and the environment state, and giving prediction deduction data of the crops;
determining the demand information of the crops by combining crop planting targets according to the prediction deduction data;
and establishing a connection channel of each system.
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