CN110955212A - Wisdom agricultural information processing system based on thing networking - Google Patents
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Abstract
The invention relates to an intelligent agricultural information processing system based on the Internet of things, which is characterized by comprising an agricultural environment information acquisition and processing system and a cloud service center. The agricultural environment information acquisition and processing system is used for receiving agricultural data information acquired by the sensor and sending the agricultural data information to the cloud service center for processing and feedback; the cloud service center is used for storing and analyzing each received data and feeding back the data to the agricultural environment information display platform; meanwhile, generating an instruction according to an analysis result and sending the instruction to the agricultural operator monitoring system; the cloud service center collects, analyzes and processes the information collected by the agricultural environment information collecting and processing system to extract effective data of land production parameters; and integrating the parameters of the quantitative biological growth model of the related crops through big data processing and mining technology. The intelligent agricultural environment information detection system provided by the invention can effectively improve the agricultural working efficiency through big data analysis.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an intelligent agricultural information processing system based on the Internet of things.
Background
The rapid development of modern science and technology profoundly influences the change of agricultural production modes. Under the promotion of the rapid development of modern computers and electronic technologies, the traditional extensive agricultural production mode gradually transits to precise agricultural operation. Precision agriculture is a main direction of modern agriculture development, and monitoring of farmland environment information is an important link for implementing and developing precision agriculture. The farmland environment information monitoring can provide guidance for agricultural production of the farmland, provide effective basis for fertilization and irrigation of agricultural producers and decision makers, and have important significance for guaranteeing grain harvest of the farmland.
In the existing farmland soil environment information monitoring technology, the following modes are mainly adopted for collecting soil information: a handheld soil information collection device; taking a soil sample for assay; fixed soil information monitoring station. The handheld soil information collection equipment collects soil information in the field through manual handheld soil information collection equipment, the mode is low in cost and flexible and convenient to use, manpower is consumed, efficiency is low, and the development requirement of modern precision agriculture is hardly met. Fixed farmland information monitoring station is through constructing single soil information monitoring point, form one and acquire the all-weather monitoring system that information is very comprehensive, but fixed soil information monitoring station disposable construction drops into greatly, and the parameter that fixed monitoring station monitoring obtained can only represent the soil information of very small range, set up fixed monitoring station in the field and still often can influence large-scale agricultural machine's field operation, so the soil information parameter of this kind of fixed soil information monitoring station collection is hardly effectual provides accurate guidance for actual agricultural production, hardly popularize on a large scale in practical application.
In addition, the operation mode characteristics of the vast agricultural operators also need to be guided, the guidance work also needs to consume a large amount of manpower and material resources, the monitoring effect is not ideal, and how to effectively make the agricultural operation strategy and improve the operation quality and the work efficiency of the vast operators is also needed to be solved urgently.
Disclosure of Invention
Aiming at the defects in the prior art, the intelligent agricultural information processing system based on the Internet of things can effectively improve the agricultural working efficiency and the agricultural productivity through big data analysis.
In order to achieve the above object, the present invention adopts a technical solution comprising:
an intelligent agricultural information processing system based on the Internet of things is characterized by comprising an agricultural environment information acquisition and processing system, an agricultural environment information display platform, an agricultural worker monitoring system and a cloud service center;
the agricultural environment information acquisition and processing system is used for receiving agricultural data information acquired by the sensor and sending the agricultural data information to the cloud service center for processing and feedback; meanwhile, various data are sent to a display platform and an analysis application platform, and remote control is carried out on agricultural facilities according to the data;
the agricultural environment information display platform is used for receiving and displaying the acquired data information to a user in real time, and the user can send out an alarm prompt in real time when the processing information exceeds preset parameters;
the agricultural worker monitoring system comprises a plurality of wearable devices worn on agricultural workers and used for collecting relevant data of the agricultural workers;
the cloud service center comprises a data analysis server and a historical database server;
the cloud service center is used for storing each received data through the historical database server, analyzing the data through the data analysis server and feeding the data back to the agricultural environment information display platform; meanwhile, generating an instruction according to an analysis result and sending the instruction to the agricultural operator monitoring system; the cloud service center collects the information collected by the agricultural environment information collecting and processing system, analyzes and processes the collected information, and extracts effective data of land production parameters;
the cloud service center comprises knowledge data of an agricultural expert base and an agricultural knowledge base, quantized parameters of a related crop biological growth model including stage growth rules, water irrigation requirements and fertilization rules are integrated through big data processing and mining technologies, and the quantized parameters are stored in a historical database server after data cleaning, statistical modeling, prediction and the like are carried out by calling a data analysis server.
Preferably, the wearable device terminal can be a wrist wearable device terminal or a clip-type wearable device terminal, the wrist wearable device terminal is worn on the wrist of a human body, and the clip-type wearable device terminal is used for being clipped at a collar or a lappet.
Preferably, the sensing device comprises a temperature sensor, an illumination intensity sensor, a gas detector, a humidity sensor, a water body detector and a soil moisture collector; the collected parameters comprise information such as temperature, humidity, illumination, effective radiation, gas concentration, soil N/P/K content, water quality of water, pH value, oxygen content and the like, and working parameters of collecting instruments and equipment and agricultural geographical position information.
Preferably, the cloud service center can predict the growth of the crops or animals based on the improved neural network; the neural network is a BP neural network; the number of the neurons contained in each layer of the neural network can be the same or different; connecting the neurons in different layers according to the weight, wherein the transfer function of the neuron is generally a Sigmoid function; the specific steps of improving and training the BP neural network model are as follows: (1) coding the weights, randomly generating a plurality of groups of codes in a specified range, and using the codes as a plurality of groups of connection weights of the neural network; (2) inputting training samples, calculating the error square sum between the predicted value and the actual value of the neural network under each group of connection weight values, and taking the reciprocal of the error square sum as the fitness of each group of connection weight values; (3) selecting individuals corresponding to the connection weights of 1/3-1/2 with larger group fitness as an evolution group; (4) evolving the evolved population by utilizing cross and mutation operations to generate a new generation population; (5) judging whether the new generation population meets the training target, if so, executing the step (6), if not, replacing the connection weight in the step (2) with the connection weight corresponding to the individual in the evolved population in the previous generation, and repeating the steps (2) to (5); (6) taking a group of connection weights with the maximum fitness as initial weights of the BP neural network; (3) and training the BP neural network until the mean square error of the network is less than the reciprocal of the initial weight, and obtaining a prediction model of the crop or animal yield prediction system.
Preferably, the data analysis server receives farmland data with different attributes;
wherein the farmland data of different attributes may comprise at least one of:
soil temperature, soil humidity, conductivity, atmospheric temperature and humidity, and carbon dioxide concentration;
the data analysis server sends calculation tasks of farmland data with different attributes to different processors of the cloud platform, calculates the influence degree of the farmland data with different attributes on the farmland, reduces intermediate calculation results obtained from the different processors, and obtains calculation results according to the reduced data;
the data analysis server also preprocesses farmland data with different attributes before sending the calculation tasks of the farmland data with different attributes to different processors of the cloud platform; the preprocessing comprises data cleaning, data integration, null value processing and continuous data discretization;
and the historical database server is used for storing the data collected in real time and the data subjected to analysis processing.
Preferably, the further data analysis server receives the collected data of the agricultural operating personnel sent by the wearable device;
the agricultural worker data at least comprises:
location data of agricultural workers;
physiological data of agricultural workers;
job data of agricultural workers;
the data analysis server also preprocesses farmland data with different attributes before sending the calculation tasks of the agricultural worker data with different attributes to different processors of the cloud platform; the preprocessing comprises data cleaning, data integration, null value processing and continuous data discretization;
and the historical database server is used for storing the data collected in real time and the data subjected to analysis processing.
Preferably, the wearable device includes a variety of micro-sensors including, but not limited to: the physiological signal sensor comprises: temperature, electrocardiogram, blood oxygen, blood pressure; sensors for brain electricity, respiration, etc.; the motion sensor has: gyroscopes, acceleration sensors; motion sensors and measuring devices also include: a tension sensor for measuring joint movement, a camera device for monitoring movement; the environment sensor comprises: microphone, light, temperature, biochemistry, global positioning system to measure location; the psychological sensor comprises: skin conductance, microphone.
Preferably, the cloud service center analyzes and compares the real-time data and the historical data of the agricultural workers in a cloud computing mode to obtain an optimal agricultural operation scheme, and feeds back scheme indication information to the agricultural workers.
Preferably, each sensor node constructs a wireless sensor network through self-organization, the constructed wireless sensor network adopts a clustering topological structure, the sensor nodes distributed in a crop planting monitoring area are divided into N clusters according to the geographical positions of the nodes, each cluster selects one sensor node as a cluster head, and the other sensor nodes are member nodes, wherein the cluster head is used for collecting agricultural monitoring data collected by the member nodes in the cluster; each cluster selects one sensor node as a cluster head, and the method specifically comprises the following steps: (1) the base station device 2 broadcasts beacon information to the network with the maximum power, and the sensor nodes in the network record the actual signal intensity of the received beacon information of the base station device 2, wherein the beacon information comprises the theoretical signal intensity of the received beacon information of each sensor node set by the base station device 2; (2) the sensor nodes in the network become candidate cluster heads according to the initially set probability, the candidate cluster heads calculate self preferred values, then broadcast the information that the candidate cluster heads are the cluster heads according to the maximum power, if the information broadcast by another candidate cluster head with a larger preferred value is received within the set time, the competition of the cluster heads is abandoned, otherwise, the sensor nodes become the cluster heads; (3) when the sensor node selected as the cluster head broadcasts the message that the sensor node is the cluster head, other sensor nodes decide to join a certain cluster according to the signal strength, and send the joining message to the cluster head.
Preferably, the system comprises a fault detection mechanism for agricultural monitoring data, the agricultural monitoring data collected by the sensor node and the agricultural monitoring data collected by the neighbor nodes have larger space-time relevance, the mechanism uses the space-time relevance to weight the monitoring data of the bridge dangerous parts of the neighbor nodes based on distance and energy factors, the comparison data is calculated, and whether the agricultural monitoring data to be detected is fault monitoring data or not is judged by calculating whether the difference value between the agricultural monitoring data to be detected and the comparison data is within a certain threshold value range or not.
The invention has the following beneficial effects:
1. the sensor nodes construct a wireless sensor network through self-organization, and the sensor network has a fault detection mechanism of agricultural monitoring data, so that the safe transmission and data fusion of the sensor data are realized.
2. The cloud service center can predict the growth of crops or animals based on the improved neural network, and an optimized planting strategy is given by combining with agricultural monitoring data.
3. Based on the operation mode characteristics of the vast agricultural operators, agricultural operation strategies are formulated, and the operation quality and the work efficiency of the vast operators are improved.
Drawings
FIG. 1 is a diagram of a prior art intelligent agricultural environment information detection system;
fig. 2 is a schematic diagram of an intelligent agricultural information processing system based on the internet of things according to an embodiment of the invention;
FIG. 3 is a schematic diagram of an agricultural environment information collection and processing system according to an embodiment of the present invention;
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Fig. 1 is a schematic diagram of a smart agricultural environment information detection system in the prior art. The invention is further improved on the basis of the system, and an agricultural operator monitoring system is added. Meanwhile, the processing operation process of the cloud service center is improved. Thereby solving the aforementioned problems.
With particular reference to FIG. 2, FIG. 2 shows a schematic diagram of one embodiment of a system; as shown in fig. 2, the architecture adopted by the present embodiment includes: the intelligent agricultural environment information detection system comprises an agricultural environment information acquisition and processing system, an agricultural environment information display platform, an agricultural operator monitoring system and a cloud service center.
The agricultural environment information acquisition and processing system is used for receiving agricultural data information acquired by the sensor and sending the agricultural data information to the cloud service center for processing and feedback; meanwhile, various data are sent to a display platform and an analysis application platform, and remote control is carried out on agricultural facilities according to the data;
the agricultural environment information display platform is used for receiving and displaying the collected data information to a user in real time, and the user can send out an alarm prompt in real time when the processing information exceeds preset parameters.
Agricultural operation personnel monitored control system, including a plurality of wearable equipment of wearing on the agricultural operation personnel body, wearable equipment terminal can be wrist formula wearable equipment terminal or the wearable equipment terminal of clip formula, and the wrist formula wearable equipment terminal is worn in human wrist position, and the wearable equipment terminal of clip formula is used for pressing from both sides collar position or lappet position. The wearable device includes a variety of miniature sensors including, but not limited to: the physiological signal sensor comprises: temperature, electrocardiogram, blood oxygen, blood pressure, etc.; sensors for brain electricity, respiration and the like. The motion sensor has: gyroscopes, acceleration sensors, etc.; motion sensors and measuring devices also include: a tension sensor to measure joint motion, a camera device to monitor motion, etc. The environment sensor comprises: microphone, light, temperature, biochemistry, global positioning system for measuring location, etc.; the psychological sensor comprises: skin conductance, microphone, etc. The induction nodes of the micro-sensor collect important physiological, activity, environmental and psychological signals, and after preprocessing, the signals are further processed, fused, classified and stored. And send the data to the cloud service center.
The wearable equipment also comprises wireless communication equipment for communicating and guiding with agricultural operators.
In one embodiment of the invention, the administrator can still access the cloud service center to master real-time data of the farm when going out. And the manager checks the data of the cloud service center through the mobile phone terminal.
As shown in fig. 3, the agricultural environment information collection and processing system collects land production parameters. The method comprises the steps of collecting information such as temperature, humidity, illumination, effective radiation, gas concentration, soil N/P/K content, water quality, pH value and oxygen content of water, collecting working parameters of instruments and equipment and agricultural geographical position information, and converting marked information and physical information of the real world into digital information for processing by means of a GASS sensor, an infrared sensor, a Hall sensor, an RFID technology, an electromagnetic induction sensor, a spectrum sensor and the like. The information acquisition layer relates to hardware technologies including: the system comprises a two-dimensional code tag, a camera, a sensor, a terminal, a sensor network and the like, and relates to software technology standards such as an agricultural environment energy management technology, an agricultural environment anti-interference technology, an agricultural environment adaptive communication mode, an agricultural professional sensor standard and the like. In addition to the production parameters of these natural lands. The land quality improvement equipment such as irrigation equipment, oxygenation equipment, fertilization equipment and the like is controlled through an agricultural environment intelligent improvement technology to improve the land quality and performance.
The wireless communication module collects agricultural information collected by the agricultural environment information collection and processing system, gathers the agricultural information through various network technologies, and integrates the agricultural information in a large range for processing. The network layer comprises a converged network of communication and the Internet, a network management center, a cloud service center, an intelligent processing center and the like. The information summarizing layer relates to the following technologies: wired network, wireless network, conventional GPRS, and 3G, 4G, 5G, etc.
The cloud service center collects the information, analyzes and processes the collected information, collects and simply calculates effective data (such as real-time monitoring data, environment monitoring data, safety monitoring data and the like) of the extracted land production parameters, and then calls a statistical processing module to store the effective data into a land ecological database after processing.
The cloud service center comprises a large number of knowledge data of agricultural expert bases and agricultural knowledge bases, quantized parameters of relevant crop biological growth models, such as stage growth rules, water irrigation requirements and fertilization rules, are integrated on an agricultural knowledge management platform through big data processing and mining technologies, and the quantized parameters are stored in a crop ecological database required by the invention after data cleaning, statistical modeling, prediction and the like are carried out by calling a statistical submodule.
Compared with the traditional intelligent agricultural management system, the cloud service center provided by the invention is greatly improved, and can be used for storing data uploaded by an agricultural environment information acquisition and processing system and an agricultural operator monitoring system. But also by statistical analysis of historical data. For example, the weather conditions and crop harvest conditions of the past year are compared and analyzed to obtain the weather conditions most suitable for a certain crop, and when the corresponding weather conditions occur, a crop recommendation suitable for planting is provided for a user.
Further, the cloud service center can obtain optimal data from the historical data, optimize various parameters of the main controller based on the optimal data, and send the optimized parameters to the main controller. For example, the cloud service center can obtain historical data of the best growth of crops or animals according to the growth conditions of the crops and the animals, and send parameter settings corresponding to the historical data to the main controller. The main controller is set according to the parameters so as to adjust to the optimal environment for the growth of the plants or animals.
Meanwhile, the cloud service center can predict the growth of crops or animals based on the improved neural network. The neural network is a BP neural network. The number of neurons in each layer of the neural network may be the same or different. Connecting the neurons of different levels according to the weight, wherein the transfer function of the neuron is generally a Sigmoid function. The specific steps of improving and training the BP neural network model are as follows: (1) coding the weights, randomly generating a plurality of groups of codes in a specified range, and using the codes as a plurality of groups of connection weights of the neural network; (2) inputting training samples, calculating the error square sum between the predicted value and the actual value of the neural network under each group of connection weight values, and taking the reciprocal of the error square sum as the fitness of each group of connection weight values; (3) selecting individuals corresponding to the connection weights of 1/3-1/2 with larger group fitness as an evolution group; (4) evolving the evolved population by utilizing cross and mutation operations to generate a new generation population; (5) judging whether the new generation population meets the training target, if so, executing the step (6), if not, replacing the connection weight in the step (2) with the connection weight corresponding to the individual in the evolved population in the previous generation, and repeating the steps (2) to (5); (6) taking a group of connection weights with the maximum fitness as initial weights of the BP neural network; (3) and training the BP neural network until the mean square error of the network is less than the reciprocal of the initial weight, and obtaining a prediction model of the crop or animal yield prediction system.
Furthermore, the cloud service center can also be used for uniformly positioning agricultural operators, analyzing various data collected by a monitoring system of the agricultural operators wearing the wearable equipment, guiding the production operation of the agricultural operators according to an analysis result, and optimizing the working efficiency of the agricultural operators.
The management of the present system is described below with several examples:
further, the cloud service center comprises a data analysis server, a historical database server,
the data analysis server receives farmland data with different attributes;
wherein the farmland data of different attributes may comprise at least one of:
soil temperature, soil humidity, conductivity, atmospheric temperature and humidity, carbon dioxide concentration and the like.
The data analysis server sends the calculation tasks of the farmland data with different attributes to different processors of the cloud platform, calculates the influence degree of the farmland data with different attributes on the farmland, reduces intermediate calculation results obtained on the different processors, and obtains calculation results according to the reduced data.
The data analysis server also preprocesses farmland data with different attributes before sending the calculation tasks of the farmland data with different attributes to different processors of the cloud platform; the preprocessing comprises data cleaning, data integration, null value processing and continuous data discretization.
Furthermore, in order to ensure the accuracy of data, the monitoring precision of the sensor is improved. In one embodiment, each sensor node constructs a wireless sensor network through self-organization, the constructed wireless sensor network adopts a clustering topological structure, the sensor nodes distributed in a crop planting monitoring area are divided into N clusters according to the geographical positions of the nodes, one sensor node is selected as a cluster head in each cluster, and the other sensor nodes are member nodes, wherein the cluster heads are used for collecting agricultural monitoring data collected by the member nodes in the clusters. Each cluster selects one sensor node as a cluster head, and the method specifically comprises the following steps: (1) the base station device 2 broadcasts beacon information to the network with the maximum power, and the sensor nodes in the network record the actual signal intensity of the received beacon information of the base station device 2, wherein the beacon information comprises the theoretical signal intensity of the received beacon information of each sensor node set by the base station device 2; (2) the sensor nodes in the network become candidate cluster heads according to the initially set probability, the candidate cluster heads calculate self preferred values, then broadcast the information that the candidate cluster heads are the cluster heads according to the maximum power, if the information broadcast by another candidate cluster head with a larger preferred value is received within the set time, the competition of the cluster heads is abandoned, otherwise, the sensor nodes become the cluster heads; (3) when the sensor node selected as the cluster head broadcasts the message that the sensor node is the cluster head, other sensor nodes decide to join a certain cluster according to the signal strength, and send the joining message to the cluster head.
In the mechanism, a sensor node in a network becomes a candidate cluster head with an initially set probability, then broadcasts a message that the sensor node is the cluster head with the maximum power, and if the sensor node receives a message broadcasted by another candidate cluster head with a larger preferred value within a set time, the competition of the cluster head is abandoned, otherwise, the sensor node becomes the cluster head.
Furthermore, the embodiment provides a fault detection mechanism for agricultural monitoring data, the agricultural monitoring data acquired by the sensor node and the agricultural monitoring data acquired by the neighbor nodes have larger time-space correlation, the mechanism uses the time-space correlation to weight the monitoring data of the bridge dangerous parts of the neighbor nodes based on distance and energy factors, the comparison data is calculated, whether the agricultural monitoring data to be detected is fault monitoring data is judged by calculating whether the difference value between the agricultural monitoring data to be detected and the comparison data is within a certain threshold value range, and the mechanism has higher detection precision and robustness.
And the historical database server is used for storing the data collected in real time and the data subjected to analysis processing.
According to the technical scheme, the influence degree of farmland data with different attributes on the farmland is calculated in a cloud computing mode, the problem of computing efficiency of massive farmland data is solved, the scale of data input is enlarged, and the mining efficiency of the farmland data is improved.
A further data analysis server receives the collected data of the agricultural workers sent by the wearable equipment;
the agricultural worker data at least comprises:
location data of agricultural workers;
physiological data of agricultural workers;
job data of agricultural workers;
the data analysis server also preprocesses farmland data with different attributes before sending the calculation tasks of the agricultural worker data with different attributes to different processors of the cloud service center; the preprocessing comprises data cleaning, data integration, null value processing and continuous data discretization.
And the historical database server is used for storing the data collected in real time and the data subjected to analysis processing.
According to the scheme, the real-time data and the historical data of the agricultural operating personnel are analyzed and compared in a cloud computing mode, and the optimal agricultural operating scheme is obtained, so that an agricultural operating strategy is effectively formulated, and the operating quality and the working efficiency of the operating personnel are improved.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. An intelligent agricultural information processing system based on the Internet of things is characterized by comprising an agricultural environment information acquisition and processing system, an agricultural environment information display platform, an agricultural worker monitoring system and a cloud service center;
the agricultural environment information acquisition and processing system is used for receiving agricultural data information acquired by the sensor and sending the agricultural data information to the cloud service center for processing and feedback; meanwhile, various data are sent to a display platform and an analysis application platform, and remote control is carried out on agricultural facilities according to the data;
the agricultural environment information display platform is used for receiving and displaying the acquired data information to a user in real time, and the user can send out an alarm prompt in real time when the processing information exceeds preset parameters;
the agricultural worker monitoring system comprises a plurality of wearable devices worn on agricultural workers and used for collecting relevant data of the agricultural workers;
the cloud service center comprises a data analysis server and a historical database server;
the cloud service center is used for storing each received data through the historical database server, analyzing the data through the data analysis server and feeding the data back to the agricultural environment information display platform; meanwhile, generating an instruction according to an analysis result and sending the instruction to the agricultural operator monitoring system; the cloud service center collects the information collected by the agricultural environment information collecting and processing system, analyzes and processes the collected information, and extracts effective data of land production parameters;
the cloud service center comprises knowledge data of an agricultural expert base and an agricultural knowledge base, quantized parameters of a related crop biological growth model including stage growth rules, water irrigation requirements and fertilization rules are integrated through big data processing and mining technologies, and the quantized parameters are stored in a historical database server after data cleaning, statistical modeling, prediction and the like are carried out by calling a data analysis server.
2. The system of claim 1, wherein the wearable device terminal can be a wrist wearable device terminal or a clip-type wearable device terminal, the wrist wearable device terminal is worn on a wrist of a human body, and the clip-type wearable device terminal is used for being clipped at a collar or a placket.
3. The system of claim 1, wherein the sensing device comprises a temperature sensor, a light intensity sensor, a gas detector, a humidity sensor, a water detector and a soil moisture collector; the collected parameters comprise information such as temperature, humidity, illumination, effective radiation, gas concentration, soil N/P/K content, water quality of water, pH value, oxygen content and the like, and working parameters of collecting instruments and equipment and agricultural geographical position information.
4. The system of claim 1, wherein the cloud service center is capable of making growth predictions for the crop or animal based on the improved neural network; the neural network is a BP neural network; the number of the neurons contained in each layer of the neural network can be the same or different; connecting the neurons in different layers according to the weight, wherein the transfer function of the neuron is generally a Sigmoid function; the specific steps of improving and training the BP neural network model are as follows: (1) coding the weights, randomly generating a plurality of groups of codes in a specified range, and using the codes as a plurality of groups of connection weights of the neural network; (2) inputting training samples, calculating the error square sum between the predicted value and the actual value of the neural network under each group of connection weight values, and taking the reciprocal of the error square sum as the fitness of each group of connection weight values; (3) selecting individuals corresponding to the connection weights of 1/3-1/2 with larger group fitness as an evolution group; (4) evolving the evolved population by utilizing cross and mutation operations to generate a new generation population; (5) judging whether the new generation population meets the training target, if so, executing the step (6), if not, replacing the connection weight in the step (2) with the connection weight corresponding to the individual in the evolved population in the previous generation, and repeating the steps (2) to (5); (6) taking a group of connection weights with the maximum fitness as initial weights of the BP neural network; (3) and training the BP neural network until the mean square error of the network is less than the reciprocal of the initial weight, and obtaining a prediction model of the crop or animal yield prediction system.
5. The system of claim 1,
the data analysis server receives farmland data with different attributes;
wherein the farmland data of different attributes may comprise at least one of:
soil temperature, soil humidity, conductivity, atmospheric temperature and humidity, and carbon dioxide concentration;
the data analysis server sends calculation tasks of farmland data with different attributes to different processors of the cloud platform, calculates the influence degree of the farmland data with different attributes on the farmland, reduces intermediate calculation results obtained from the different processors, and obtains calculation results according to the reduced data;
the data analysis server also preprocesses farmland data with different attributes before sending the calculation tasks of the farmland data with different attributes to different processors of the cloud platform; the preprocessing comprises data cleaning, data integration, null value processing and continuous data discretization;
and the historical database server is used for storing the data collected in real time and the data subjected to analysis processing.
6. The system according to claim 1, wherein the further data analysis server receives the collected data of the agricultural worker transmitted by the wearable device;
the agricultural worker data at least comprises:
location data of agricultural workers;
physiological data of agricultural workers;
job data of agricultural workers;
the data analysis server also preprocesses farmland data with different attributes before sending the calculation tasks of the agricultural worker data with different attributes to different processors of the cloud platform; the preprocessing comprises data cleaning, data integration, null value processing and continuous data discretization;
and the historical database server is used for storing the data collected in real time and the data subjected to analysis processing.
7. The system of claim 6, wherein the wearable device comprises a variety of micro sensors including but not limited to: the physiological signal sensor comprises: temperature, electrocardiogram, blood oxygen, blood pressure; sensors for brain electricity, respiration, etc.; the motion sensor has: gyroscopes, acceleration sensors; motion sensors and measuring devices also include: a tension sensor for measuring joint movement, a camera device for monitoring movement; the environment sensor comprises: microphone, light, temperature, biochemistry, global positioning system to measure location; the psychological sensor comprises: skin conductance, microphone.
8. The system according to claim 7, wherein the cloud service center analyzes and compares real-time data of agricultural workers with historical data in a cloud computing mode to obtain an optimal agricultural operation scheme, and feeds back scheme indication information to the agricultural workers.
9. The system according to claim 1, wherein each sensor node constructs a wireless sensor network through self-organization, and the constructed wireless sensor network adopts a clustering topology structure, the sensor nodes distributed in the crop planting monitoring area are divided into N clusters according to the geographical positions of the nodes, each cluster selects one sensor node as a cluster head, and the other sensor nodes are member nodes, wherein the cluster heads are used for collecting agricultural monitoring data collected by the member nodes in the clusters; each cluster selects one sensor node as a cluster head, and the method specifically comprises the following steps: (1) the method comprises the steps that base station equipment broadcasts beacon information into a network at the maximum power, sensor nodes in the network record the actual signal intensity of the received beacon information of the base station equipment, and the beacon information comprises the theoretical signal intensity of the received beacon information of each sensor node set by the base station equipment; (2) the sensor nodes in the network become candidate cluster heads according to the initially set probability, the candidate cluster heads calculate self preferred values, then broadcast the information that the candidate cluster heads are the cluster heads according to the maximum power, if the information broadcast by another candidate cluster head with a larger preferred value is received within the set time, the competition of the cluster heads is abandoned, otherwise, the sensor nodes become the cluster heads; (3) when the sensor node selected as the cluster head broadcasts the message that the sensor node is the cluster head, other sensor nodes decide to join a certain cluster according to the signal strength, and send the joining message to the cluster head.
10. The system according to claim 9, wherein the system comprises a fault detection mechanism for agricultural monitoring data, the agricultural monitoring data collected by the sensor node and the agricultural monitoring data collected by the neighbor nodes have a large space-time correlation, the mechanism uses the space-time correlation to weight the monitoring data of the bridge dangerous parts of the neighbor nodes based on distance and energy factors, calculates comparison data, and judges whether the agricultural monitoring data to be detected is fault monitoring data by calculating whether the difference value between the agricultural monitoring data to be detected and the comparison data is within a certain threshold range.
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112419080A (en) * | 2020-11-26 | 2021-02-26 | 河南黄河小偃麦业科技有限公司 | High-quality wheat flour traceable production management system and method |
| CN112468985A (en) * | 2020-07-08 | 2021-03-09 | 上海悠络客电子科技股份有限公司 | Agricultural environment intelligent system based on big data |
| CN112561367A (en) * | 2020-12-22 | 2021-03-26 | 湖北良顷农业科技有限公司 | Intelligent planting decision control system |
| CN113537425A (en) * | 2021-07-20 | 2021-10-22 | 思藤(深圳)科技咨询有限公司 | Environmental footprint evaluation method and system for agricultural products |
| CN113626627A (en) * | 2021-08-11 | 2021-11-09 | 河北时代电子有限公司 | Method and system for analyzing characteristics of agricultural big data |
| CN113723760A (en) * | 2021-07-30 | 2021-11-30 | 哈尔滨工业大学 | Wisdom agricultural thing networking platform |
| CN114430536A (en) * | 2022-04-06 | 2022-05-03 | 广东邦盛北斗科技股份公司 | Agricultural Internet of things method and system based on Beidou positioning module and cloud platform |
| CN114518143A (en) * | 2022-03-07 | 2022-05-20 | 简智科技(海宁)有限公司 | Intelligent environment sensing system |
| CN114965941A (en) * | 2022-04-15 | 2022-08-30 | 中电建路桥集团有限公司 | Saline-alkali soil physical and chemical characteristic multi-scale intelligent sensing method and system |
| CN115599873A (en) * | 2022-12-06 | 2023-01-13 | 滨州市玉炜网络科技有限公司（Cn） | Data acquisition method and system based on artificial intelligence Internet of things and cloud platform |
| CN116342063A (en) * | 2023-03-29 | 2023-06-27 | 重庆市农业科学院 | Data processing method applied to intelligent agriculture |
| CN116668023A (en) * | 2023-07-25 | 2023-08-29 | 北京建工环境修复股份有限公司 | Soil and groundwater environment big data analysis method and system |
| CN116664333A (en) * | 2023-08-02 | 2023-08-29 | 贵州通利数字科技有限公司 | Information resource system platform based on Internet of things |
| CN116720668A (en) * | 2023-08-11 | 2023-09-08 | 北京佳格天地科技有限公司 | Big data agricultural management method, system and storage medium |
| CN117391613A (en) * | 2023-10-08 | 2024-01-12 | 菏泽单州数字产业发展有限公司 | Agricultural industry garden management system based on Internet of things |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130093592A1 (en) * | 2011-10-14 | 2013-04-18 | Zehua Lan | Internet of Things Based Farm Greenhouse Monitor and Alarm Management System |
| CN106254476A (en) * | 2016-08-09 | 2016-12-21 | 沈阳君农哥科技有限公司 | Agroecological environment information management based on Internet of Things, big data and cloud computing and monitoring method and system |
| CN106651001A (en) * | 2016-11-08 | 2017-05-10 | 浙江理工大学 | Needle mushroom yield prediction method based on improved neural network and implementation system |
| CN107255976A (en) * | 2017-07-24 | 2017-10-17 | 欧思星通科技（北京）有限公司 | The wisdom farm supervisory systems serviced based on " internet+Big Dipper+GIS " |
| CN108171954A (en) * | 2018-02-10 | 2018-06-15 | 深圳森阳环保材料科技有限公司 | Air pollution intelligent real time monitoring system |
| CN108230625A (en) * | 2018-02-10 | 2018-06-29 | 深圳凯达通光电科技有限公司 | It is a kind of that system is monitored based on cloud computing and the mountain flood of wireless sensor network |
| CN108401236A (en) * | 2018-02-10 | 2018-08-14 | 深圳汇创联合自动化控制有限公司 | Freshwater fish culturing ambient intelligence monitoring system |
| CN108401235A (en) * | 2018-02-09 | 2018-08-14 | 深圳市益鑫智能科技有限公司 | A kind of agriculture site environment parameter intelligent acquisition processing system based on big data |
| CN108449730A (en) * | 2018-02-10 | 2018-08-24 | 深圳万智联合科技有限公司 | A kind of sewage network intelligent monitor system based on big data |
| CN108449729A (en) * | 2018-02-10 | 2018-08-24 | 深圳源广安智能科技有限公司 | A kind of orchard environmental monitoring system for reading intelligent agriculture |
| CN108512901A (en) * | 2018-02-10 | 2018-09-07 | 深圳智达机械技术有限公司 | The structural safety monitoring system that builds a bridge based on wireless sensor network |
| CN108594775A (en) * | 2018-04-18 | 2018-09-28 | 河南工业大学 | A kind of agricultural big data information collection and processing system |
| CN108594906A (en) * | 2018-04-18 | 2018-09-28 | 河南工业大学 | A kind of wisdom agricultural environment information detecting system |
| CN109981752A (en) * | 2019-03-06 | 2019-07-05 | 中国科学院合肥物质科学研究院 | Mobile model field agriculture forestry planting intelligent early-warning Internet of things system based on long-distance video |
| CN110187688A (en) * | 2019-06-14 | 2019-08-30 | 青岛农业大学 | Industrialized agriculture intelligence liquid manure integral control system and control method |
| CN110347127A (en) * | 2019-06-26 | 2019-10-18 | 北京农业智能装备技术研究中心 | Crop planting mandatory system and method based on cloud service |
-
2019
- 2019-12-05 CN CN201911237084.XA patent/CN110955212A/en active Pending
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130093592A1 (en) * | 2011-10-14 | 2013-04-18 | Zehua Lan | Internet of Things Based Farm Greenhouse Monitor and Alarm Management System |
| CN106254476A (en) * | 2016-08-09 | 2016-12-21 | 沈阳君农哥科技有限公司 | Agroecological environment information management based on Internet of Things, big data and cloud computing and monitoring method and system |
| CN106651001A (en) * | 2016-11-08 | 2017-05-10 | 浙江理工大学 | Needle mushroom yield prediction method based on improved neural network and implementation system |
| CN107255976A (en) * | 2017-07-24 | 2017-10-17 | 欧思星通科技（北京）有限公司 | The wisdom farm supervisory systems serviced based on " internet+Big Dipper+GIS " |
| CN108401235A (en) * | 2018-02-09 | 2018-08-14 | 深圳市益鑫智能科技有限公司 | A kind of agriculture site environment parameter intelligent acquisition processing system based on big data |
| CN108449730A (en) * | 2018-02-10 | 2018-08-24 | 深圳万智联合科技有限公司 | A kind of sewage network intelligent monitor system based on big data |
| CN108401236A (en) * | 2018-02-10 | 2018-08-14 | 深圳汇创联合自动化控制有限公司 | Freshwater fish culturing ambient intelligence monitoring system |
| CN108230625A (en) * | 2018-02-10 | 2018-06-29 | 深圳凯达通光电科技有限公司 | It is a kind of that system is monitored based on cloud computing and the mountain flood of wireless sensor network |
| CN108171954A (en) * | 2018-02-10 | 2018-06-15 | 深圳森阳环保材料科技有限公司 | Air pollution intelligent real time monitoring system |
| CN108449729A (en) * | 2018-02-10 | 2018-08-24 | 深圳源广安智能科技有限公司 | A kind of orchard environmental monitoring system for reading intelligent agriculture |
| CN108512901A (en) * | 2018-02-10 | 2018-09-07 | 深圳智达机械技术有限公司 | The structural safety monitoring system that builds a bridge based on wireless sensor network |
| CN108594775A (en) * | 2018-04-18 | 2018-09-28 | 河南工业大学 | A kind of agricultural big data information collection and processing system |
| CN108594906A (en) * | 2018-04-18 | 2018-09-28 | 河南工业大学 | A kind of wisdom agricultural environment information detecting system |
| CN109981752A (en) * | 2019-03-06 | 2019-07-05 | 中国科学院合肥物质科学研究院 | Mobile model field agriculture forestry planting intelligent early-warning Internet of things system based on long-distance video |
| CN110187688A (en) * | 2019-06-14 | 2019-08-30 | 青岛农业大学 | Industrialized agriculture intelligence liquid manure integral control system and control method |
| CN110347127A (en) * | 2019-06-26 | 2019-10-18 | 北京农业智能装备技术研究中心 | Crop planting mandatory system and method based on cloud service |
Non-Patent Citations (1)
| Title |
|---|
| 张杰;臧贺藏;杨春英;景丽;王来刚;李国强;冯晓;胡峰;赵晴;郑国清;: "基于物联网的农业环境远程监测系统研究", no. 12 * |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112468985A (en) * | 2020-07-08 | 2021-03-09 | 上海悠络客电子科技股份有限公司 | Agricultural environment intelligent system based on big data |
| CN112419080A (en) * | 2020-11-26 | 2021-02-26 | 河南黄河小偃麦业科技有限公司 | High-quality wheat flour traceable production management system and method |
| CN112561367A (en) * | 2020-12-22 | 2021-03-26 | 湖北良顷农业科技有限公司 | Intelligent planting decision control system |
| CN113537425A (en) * | 2021-07-20 | 2021-10-22 | 思藤(深圳)科技咨询有限公司 | Environmental footprint evaluation method and system for agricultural products |
| CN113723760A (en) * | 2021-07-30 | 2021-11-30 | 哈尔滨工业大学 | Wisdom agricultural thing networking platform |
| CN113626627A (en) * | 2021-08-11 | 2021-11-09 | 河北时代电子有限公司 | Method and system for analyzing characteristics of agricultural big data |
| CN114518143A (en) * | 2022-03-07 | 2022-05-20 | 简智科技(海宁)有限公司 | Intelligent environment sensing system |
| CN114430536A (en) * | 2022-04-06 | 2022-05-03 | 广东邦盛北斗科技股份公司 | Agricultural Internet of things method and system based on Beidou positioning module and cloud platform |
| CN114430536B (en) * | 2022-04-06 | 2022-07-19 | 广东邦盛北斗科技股份公司 | Agricultural Internet of things method and system based on Beidou positioning module and cloud platform |
| CN114965941A (en) * | 2022-04-15 | 2022-08-30 | 中电建路桥集团有限公司 | Saline-alkali soil physical and chemical characteristic multi-scale intelligent sensing method and system |
| CN115599873A (en) * | 2022-12-06 | 2023-01-13 | 滨州市玉炜网络科技有限公司（Cn） | Data acquisition method and system based on artificial intelligence Internet of things and cloud platform |
| CN115599873B (en) * | 2022-12-06 | 2023-08-29 | 广州丰网互联科技有限公司 | Data acquisition method and system based on artificial intelligence Internet of things and cloud platform |
| CN116342063A (en) * | 2023-03-29 | 2023-06-27 | 重庆市农业科学院 | Data processing method applied to intelligent agriculture |
| CN116668023A (en) * | 2023-07-25 | 2023-08-29 | 北京建工环境修复股份有限公司 | Soil and groundwater environment big data analysis method and system |
| CN116668023B (en) * | 2023-07-25 | 2023-09-26 | 北京建工环境修复股份有限公司 | Soil and groundwater environment big data analysis method and system |
| CN116664333A (en) * | 2023-08-02 | 2023-08-29 | 贵州通利数字科技有限公司 | Information resource system platform based on Internet of things |
| CN116664333B (en) * | 2023-08-02 | 2023-11-14 | 贵州通利数字科技有限公司 | Information resource system based on Internet of Things |
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| CN116720668B (en) * | 2023-08-11 | 2023-10-31 | 北京佳格天地科技有限公司 | Big data agricultural management method, system and storage medium |
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| CN117391613B (en) * | 2023-10-08 | 2024-03-15 | 菏泽单州数字产业发展有限公司 | Agricultural industry garden management system based on Internet of things |
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