CN116686512A - Crop production management method, device, storage medium and electronic equipment - Google Patents
Crop production management method, device, storage medium and electronic equipment Download PDFInfo
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- 238000012272 crop production Methods 0.000 title claims abstract description 163
- 238000007726 management method Methods 0.000 title claims abstract description 73
- 238000003860 storage Methods 0.000 title claims abstract description 23
- 238000004519 manufacturing process Methods 0.000 claims abstract description 166
- 230000004720 fertilization Effects 0.000 claims abstract description 51
- 239000002689 soil Substances 0.000 claims abstract description 49
- 238000012549 training Methods 0.000 claims abstract description 49
- 230000035558 fertility Effects 0.000 claims abstract description 36
- 238000000034 method Methods 0.000 claims abstract description 31
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- 230000001105 regulatory effect Effects 0.000 claims description 3
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- 239000003337 fertilizer Substances 0.000 description 14
- 230000008569 process Effects 0.000 description 12
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- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 6
- 230000007613 environmental effect Effects 0.000 description 6
- 238000003973 irrigation Methods 0.000 description 5
- 230000002262 irrigation Effects 0.000 description 5
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 4
- 241000227653 Lycopersicon Species 0.000 description 4
- 235000007688 Lycopersicon esculentum Nutrition 0.000 description 4
- 230000009471 action Effects 0.000 description 4
- 238000005286 illumination Methods 0.000 description 4
- 229910002092 carbon dioxide Inorganic materials 0.000 description 3
- 239000001569 carbon dioxide Substances 0.000 description 3
- 235000013305 food Nutrition 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 241000196324 Embryophyta Species 0.000 description 2
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 2
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 2
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- 240000007594 Oryza sativa Species 0.000 description 1
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01C—PLANTING; SOWING; FERTILISING
- A01C21/00—Methods of fertilising, sowing or planting
- A01C21/007—Determining fertilization requirements
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Abstract
The invention relates to a crop production management method, a device, a storage medium and electronic equipment, wherein the method comprises the following steps: acquiring production sample data in the production process of the target crops; in a preset initial model of crop production, constructing a constraint equation of growth data, fertilization data, soil fertility data, environment data and yield data in production sample data; training an initial crop production model by using a preset machine learning algorithm and taking yield data in a maximization constraint equation as an objective function to obtain a crop production model; and acquiring growth data and soil fertility data of the current production stage of the crops, using the growth data and the soil fertility data as input data of a crop production model, calculating by using the crop production model to obtain fertilization data and environment data of the current production stage of the crops, and carrying out production management according to the obtained fertilization data and the environment data. The production management efficiency of crops can be improved.
Description
Technical Field
The present invention relates to the field of blockchain technologies, and in particular, to a crop production management method, a device, a storage medium, and an electronic apparatus.
Background
Crop production is one of the global important industries, but current crop production faces some challenges, for example, in the greenhouse crop planting process, fertilization management is mainly performed by relying on experience of crop growers, the yield of crops cannot be effectively improved, and over-application of fertilizers can be caused, so that the benefits of the crop growers are influenced. Meanwhile, food safety and production process tracing are technical problems to be solved for consumers.
Disclosure of Invention
In view of this, the present invention provides a crop production management method, apparatus, storage medium, and electronic device.
Specifically, the invention is realized by the following technical scheme:
according to a first aspect of the present invention, there is provided a crop production management method comprising:
obtaining production sample data in the production process of target crops, wherein the production sample data comprises growth data, fertilization data, soil fertility data, environment data and yield data of the target crops in each growth stage;
constructing a constraint equation of growth data, fertilization data, soil fertility data, environment data and yield data in a preset initial crop production model;
Training the initial crop production model by using a preset machine learning algorithm and taking the output data in the constraint equation as an objective function to obtain a crop production model;
and acquiring growth data and soil fertility data of the current production stage of the crops, using the crop production model as input data of the crop production model to operate, obtaining fertilization data and environment data of the current production stage of the crops, fertilizing according to the obtained fertilization data, and regulating the current growth environment according to the obtained environment data.
According to the crop production management method in the technical scheme, the constraint equation is constructed by utilizing the growth data, the fertilization data, the soil fertility data, the environment data and the yield data in the production sample data, the yield data in the maximized constraint equation is taken as an objective function, the crop production initial model is trained to obtain the crop production model, then the crop production model is solved by collecting the growth data and the soil fertility data of the current production stage of crops, the fertilization data and the environment data of the current production stage are obtained, and the production management is carried out based on the fertilization data and the environment data, so that over-application or less application of fertilizer can be effectively avoided on the basis of guaranteeing the maximum yield of crops, the production management efficiency of the crops is improved, and the income of crop growers is further improved.
According to a second aspect of the present invention, there is provided a crop production management apparatus comprising:
the sample data acquisition module is used for acquiring production sample data in the production process of the target crops, wherein the production sample data comprises growth data, fertilization data, soil fertility data, environment data and yield data of the target crops in each growth stage;
the constraint equation setting module is used for constructing a constraint equation of growth data, fertilization data, soil fertility data, environment data and yield data in a preset crop production initial model;
the model training module is used for training the initial crop production model by using a preset machine learning algorithm and taking the output data in the constraint equation as an objective function to obtain a crop production model;
the production management module is used for collecting growth data and soil fertility data of the current production stage of crops, taking the growth data and the soil fertility data as input data of the crop production model, calculating by utilizing the crop production model to obtain fertilization data and environment data of the current production stage of the crops, fertilizing according to the obtained fertilization data, and adjusting the current growth environment according to the obtained environment data.
According to the crop production management device in the technical scheme, the initial model of crop production is trained according to the production sample data, the precision of the crop production model is obtained, the crop production management scheme when the crop yield reaches the highest can be obtained by obtaining the growth data and the soil fertility data of the current production stage of the crop and solving the crop production model, and the crop is produced and managed according to the crop production management scheme, so that over-application or less-application of fertilizer can be effectively avoided on the basis of ensuring the maximum yield of the crop, the production management efficiency of the crop is improved, and the income of crop growers is further improved.
According to a third aspect of the present invention there is provided a storage medium having stored thereon a computer program which when executed by a processor implements the steps of the crop production management method in any possible implementation of the first aspect.
According to a fourth aspect of the present invention there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the crop production management method in any possible implementation of the first aspect when the program is executed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the related art will be briefly described below, and it will be apparent to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic flow chart of a crop production management method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of step S103 in a crop production management method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a crop production management processing apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The existing method for carrying out fertilization management in the crop planting process according to the experience of a crop planter carries out fertilization management according to the experience, on one hand, insufficient fertilization is easy to cause, so that the crop yield is affected, on the other hand, excessive fertilization is easy to cause, the crop planting cost is increased, the root burning of crops is caused, and the crop yield is affected. For food safety and production process tracing, a tracing method based on a blockchain technology and an intelligent contract technology is currently adopted, that is, for each crop product or food product in a crop production system, a unique digital identifier, such as a two-dimensional code or a bar code, is configured to characterize the production process data of the crop product, including raw material sources, production batches, transportation information and the like. Thus, when the product quality problem is found, the problem can be quickly located through the digital identifier and corresponding measures are taken, so that the health and rights and interests of consumers are ensured. However, according to the method, due to the fact that an encrypted privacy protection strategy is adopted for crop production operation data which is up-linked to the blockchain network, the data are difficult to share, and therefore the reliability of tracing is affected.
In this embodiment, based on the production operation data of the crops, a crop production operation model based on a blockchain technology is constructed, and corresponding decisions, such as a crop species planting proportion decision and a fertilization management decision, are provided for the production operation of the crops. And the share of the crop production operation data is realized by providing a decryption strategy of the crop production operation data.
Referring to fig. 1, an embodiment of the present invention provides a crop production management processing method, which may include the steps of:
s101, obtaining production sample data in the production process of target crops, wherein the production sample data comprises growth data, fertilization data, soil fertility data, environment data and yield data of the target crops in each growth stage;
in this embodiment, for a target crop, such as rice, wheat, vegetables, and fruits, in the case of tomatoes in vegetables, in the annual planting of tomatoes, production data of tomatoes in each growth stage (e.g., seeding stage, seedling stage, growing stage, fruiting stage, harvesting stage) is recorded for each planting land, and the production data recorded in the past year is used as production sample data in the production process of tomatoes.
In this embodiment, as an alternative embodiment, the growth data includes, but is not limited to: growth time, growth stage, plant height, stem diameter, number of leaves, number of branches, size of leaves and the like; fertilization data includes, but is not limited to: fertilizing time, fertilizing fertilizer information and fertilizing mode; soil fertility data includes, but is not limited to: soil moisture, soil nitrogen, phosphorus and potassium content, soil ph value and the like; the environmental data includes, but is not limited to: temperature, illumination, humidity, carbon dioxide concentration, planting area, etc.
In this embodiment, as an alternative embodiment, production data may be collected by means of a sensor, a camera, a handheld device, etc. that are arranged in advance, for example, production data such as environmental data, soil data, crop growth data, or pictures, etc.
In this embodiment, as an alternative embodiment, the collected production data may also be processed by a preprocessing function and then uploaded to the blockchain network.
In this embodiment, as an alternative embodiment, the part of contract codes for performing production data collection are as follows:
pragma solidity ^0.8.0;
contract DataCollection {
struct Data {
uint256 timestamp;
uint256 temperature;
uint256 humidity;
uint256 light;
uint256 soilMoisture;
uint256 nitrogenContent;
}
Data[] public dataList;
function collectData(uint256 _temperature, uint256 _humidity, uint256 _light, uint256 _soilMoisture, uint256 _ nitrogenContent) public {
Data memory newData = Data(block.timestamp, _temperature, _humidity, _light, _soilMoisture, _nitrogenContent);
dataList.push(newData);
}
function preprocessData() public view returns (uint256[] memory) {
uint256[] memory processedData = new uint256[](dataList.length * 5);
for (uint256 i = 0; i<dataList.length; i++) {
processedData[i * 5] = dataList[i].temperature;
processedData[i * 5 + 1] = dataList[i].humidity;
processedData[i * 5 + 2] = dataList[i].light;
processedData[i * 5 + 3] = dataList[i].soilMoisture;
processedData[i * 5 + 4] = dataList[i].nitrogenContent;
}
return processedData;
}
function uploadData() public {
// Upload data to blockchain
}
}
the collectData function is used for collecting Data, storing the collected Data into a Data structure body, and storing the Data structure body into a dataList array; the preprocessData function is used for preprocessing the acquired data, for example, extracting a preset index value from each piece of acquired data, storing the index value into a one-dimensional array and returning the index value; the uploadData function is used to upload processed data into the blockchain network.
In this embodiment, as an alternative embodiment, the collected production data is stored to the blockchain network to ensure non-tamper-resistance and public transparency of the production data.
In this embodiment, various data information (production data) generated during the production and management of crops is stored using a data storage module. As an alternative embodiment, the data storage module adopts a distributed storage technology, and ensures the safety and reliability of data storage by splitting production data into a plurality of data blocks and encrypting the split data blocks for storage.
In this embodiment, in order to ensure the non-tamper property of the production data, the data storage module may further store the hash value obtained by performing the hash operation on the data block onto different nodes of the blockchain network according to the hash algorithm. Thus, the method further comprises:
splitting the production data to obtain a plurality of data blocks, and storing the plurality of data blocks by adopting a distributed storage technology;
and respectively carrying out hash operation on each data block to obtain a hash value, and storing the hash value to different nodes of a block chain network.
In this embodiment, as an alternative embodiment, the partial contract code for data storage is as follows:
pragma solidity ^0.8.0;
contract DataStorage {
bytes32[] public dataHashList;
function splitData(uint256[] memory _data, uint256 _chunkSize) public view returns (uint256[][] memory) {
uint256 numOfChunks = (_data.length + _chunkSize - 1) / _chunkSize;
uint256[][]memory chunkedData = new uint256[][](numOfChunks);
uint256 chunkIndex = 0;
for (uint256 i = 0; i<_data.length; i += _chunkSize) {
uint256[] memory chunk = new uint256[](_chunkSize);
for (uint256 j = 0; j<_chunkSize&&i + j<_data.length; j++) {
chunk[j] = _data[i + j];
}
chunkedData[chunkIndex] = chunk;
chunkIndex++;
}
return chunkedData;
}
function backupData(bytes32 _dataHash) public {
dataHashList.push(_dataHash);
}
}
The split data function is used for carrying out data block partitioning on production data, each data block obtained by splitting comprises a data record of a_chunkSize strip, and a two-dimensional array comprising a plurality of one-dimensional arrays is returned: chunkedData, wherein each one-dimensional array corresponds to a data block; the backup data function is used for storing the hash value of each data block into the dataHashList array so as to realize backup and verification of the data.
S102, constructing a constraint equation of growth data, fertilization data, soil fertility data, environment data and yield data in a preset initial model of crop production;
in this embodiment, as an alternative embodiment, a constraint equation is constructed as follows:
wherein, cropyield is crop yield, i is the quantity of crop growth condition variables, land, water, fertilizer, temperature, light and CO 2 Are all crop growth condition variables respectively representing soil fertility data and growth data, humidity, fertilization data, temperature, illumination and carbon dioxide concentration;
Land i 、Water i 、Fertilizer i 、Temperature i 、Light i and CO 2i Crop growth condition variable values representing the ith growth stage of the crop, respectively.
In this embodiment, based on the constraint equation, an initial crop production model may be constructed, where each parameter of the model is a random value within a corresponding set range.
In this embodiment, as an alternative embodiment, the partial contract code for setting the initial model of crop production is as follows:
pragma solidity ^0.8.0;
contract SmartContract {
DataCollection dataCollection;
DataStorage dataStorage;
DataProcessing dataProcessing;
constructor(address _dataCollection, address _dataStorage, address _dataProcessing) {
dataCollection = DataCollection(_dataCollection);
dataStorage = DataStorage(_dataStorage);
dataProcessing = DataProcessing(_dataProcessing);
}
function collectAndProcessData() public {
uint256[] memory rawdata = dataCollection.preprocessData();
uint256[][]memory data = dataStorage.splitData(rawdata, 5);
uint256[] memory label = new uint256[](data.length);
for (uint256 i = 0; i<data.length; i++) {
uint256 output = dataProcessing.predict(data[i], dataProcessing.trainModel(data[i], label));
if (output>0) {
// Execute management operation
}
}
}
}
the SmartContract function is a constructor and is used for initializing addresses of three subcontracts including data acquisition, data storage and data processing; the collectAndProcessData function is used to block production data, train models and predict outputs, and perform corresponding operations according to the prediction results.
S103, training the initial crop production model by using a preset machine learning algorithm and taking the output data in the constraint equation as an objective function to obtain a crop production model;
in this embodiment, as an alternative embodiment, the machine learning algorithm includes, but is not limited to: neural network algorithms, deep learning algorithms, support vector machine algorithms, and the like.
Fig. 2 is a schematic flow chart of step S103 in a crop production management method according to an embodiment of the present invention, as shown in fig. 2, in this embodiment, as an alternative embodiment, the method includes:
s201, dividing production sample data into a production sample data training set and a production sample data testing set;
s202, extracting a batch of production sample training data from the production sample data training set, inputting growth data, fertilization data, soil fertility data and environment data in the batch of production sample training data into the crop production initial model, and obtaining output data based on the operation of the machine learning algorithm in the crop production initial model;
In this embodiment, as an alternative embodiment, the part of contract codes of the training model are as follows:
pragma solidity ^0.8.0;
contract DataProcessing {
function trainModel(uint256[][]memory _data, uint256[] memory _label) public pure returns (uint256[]memory) {
// Train machine learning model
uint256[] memory modelParams = new uint256[](10);
return modelParams;
}
function predict(uint256[] memory _data, uint256[]memory _modelParams) public pure returns (uint256) {
// Predict output using model
uint256 output = 0;
for (uint256 i = 0; i<_data.length; i++) {
output += _data[i] * _modelParams[i];
}
return output;
}
}
the trainodel function is used for training a machine learning model, input is a two-dimensional array (_data) and a one-dimensional array (_label), labels corresponding to training data and training data respectively, and output is a one-dimensional array (model parameters) representing model parameters obtained by training; the prediction function is used for predicting new production data according to a model obtained through training, and is input into a one-dimensional production data array (_data) and a one-dimensional model parameter array (_model parameters) which respectively represent data to be predicted and model parameters which are trained, and output as a prediction result.
S203, carrying out back propagation operation on the crop production initial model based on the yield output data and the yield data in the batch production sample training data so as to adjust model parameters of the crop production initial model until the error between the adjusted yield output data of the crop production initial model and the corresponding yield data is not greater than a preset single iteration error threshold;
s204, testing the adjusted initial crop production model based on the production sample data test set to obtain a yield predicted value of the adjusted initial crop production model;
S205, calculating a model error of the adjusted crop production initial model according to the yield data in the production sample data test set and the yield predicted value;
s206, determining that the model error is not larger than a preset model error threshold, and taking the adjusted initial crop production model as the crop production model.
In this embodiment, as an alternative embodiment, technologies such as neural network and deep learning are adopted, modeling is performed according to production sample data, and prediction is performed according to the modeling, so as to obtain a crop production and management decision. For example, crop management operations such as irrigation, fertilization, etc. may be automatically performed based on the collected production data, or decision advice may be obtained based on the prediction results, e.g., irrigation amount, fertilization amount, etc. may be adjusted based on the prediction results.
In this embodiment, as an optional embodiment, the method further includes:
determining that the model error is greater than a preset model error threshold, deleting the batch of production sample training data from the production sample data training set, and executing the step of extracting a batch of production sample training data from the production sample data training set.
In this embodiment, the initial model for crop production is trained according to the training set of production sample data, the model parameters of the initial model for crop production are adjusted according to the training word iteration error, and the initial model for crop production with the adjusted model parameters is tested according to the testing set of production sample data, so as to determine whether the initial model for crop production with the adjusted model parameters meets the accuracy requirement, thereby improving the accuracy of the final crop production model by adjusting the accuracy of two rounds of the initial model for crop production.
And S104, acquiring growth data and soil fertility data of the current production stage of the crops, using the crop production model as input data of the crop production model to operate, obtaining fertilization data and environment data of the current production stage of the crops, fertilizing according to the obtained fertilization data, and regulating the current growth environment according to the obtained environment data.
In this embodiment, by acquiring the growth data and the soil fertility data of the current production stage of the crop, solving the crop by using the crop production model, a crop management scheme when the crop yield reaches the optimum (highest) can be obtained, and managing the crop according to the crop management scheme, for example, according to the fertilization data of the current production stage of the crop in the crop management scheme predicted by the crop production model, fertilization is performed according to the fertilization scheme in the fertilization data at a corresponding time point, and for greenhouse planting, according to the environmental data, the parameter values such as illumination, humidity, temperature and the like in the greenhouse are adjusted. Therefore, the crop management is carried out according to the crop management scheme predicted by the crop production model, so that the fertilizer utilization efficiency is optimal when the yield of the fertilizer management reaches the optimal value, the over-application or the less-application of the fertilizer is avoided, and the income of crop growers is improved. For another example, if the current soil humidity is small and the difference between the current soil humidity and the predicted soil humidity is large, the set crop irrigation equipment is automatically adjusted, and the irrigation amount of the crop irrigation equipment is increased until the soil humidity reaches the predicted soil humidity.
In this embodiment, for the same crop grower, multiple crops may be planted, so for the multiple crops planted, there is a technical problem of planting benefit, that is, how to distribute the planting proportion for the multiple crops, so that the benefit of the multiple crops planted can be optimized. As an alternative embodiment, the method further comprises:
a11, constructing a crop production supply chain model based on production cost data and transportation cost data corresponding to different types of crops;
in this embodiment, a crop production supply chain model is used to determine the price per unit of crop. As an alternative embodiment, the price per unit crop is characterized using the crop production supply chain model:
wherein, productioncost is production cost data, transportioncost is transportation cost data, markup is price ratio, markup i For the rate of addition of wholesalers and retailers, i is the number of nodes in the supply chain (wholesalers, retailers). Priceperunit purchases the price that needs to be paid, i.e., the price per unit of crop, for the end consumer.
A12, acquiring the price of each unit crop based on the crop production supply chain model;
In this embodiment, the maximum price solution of unit crop is performed on the crop production supply chain model, and the optimal price per unit crop is obtained.
A13, constructing a crop production profit distribution model based on the price per unit crop, the cost data corresponding to different types of crops and the output data output by different types of crop production models;
in this embodiment, the crop production yield distribution model is constructed as follows:
where Cost is the total Cost data for different types of crops, including but not limited to: land leasing, seed, fertilizer, pesticide, crop management, transportation and other costs. Revenue is the benefit that crop growers receive from the sales of different kinds of crops.
And A14, carrying out maximum benefit solving on the crop production benefit distribution model to obtain the planting proportion of the different types of crops.
In this embodiment, the crop production income distribution model is utilized to maximize the income by coordinating the production proportion of different types of crops planted by the crop grower based on the cost data of different types of crops, the corresponding price per unit of crops and the corresponding yield data, thereby effectively improving the income of the crop grower.
In this embodiment, as an optional embodiment, the method further includes:
setting the sharing authority of the production data;
responding to a data query request, and acquiring the access rights of a query user corresponding to the data query request;
and acquiring the shared production data corresponding to the access rights, and returning to the inquiring user.
In this embodiment, the sharing authority of the production data is set to realize the sharing of the production data, for example, by classifying the production data into sensitive production data and non-sensitive production data, and the non-sensitive production data, for example, environment data, growth data, soil fertility data, etc., is set to be the full sharing authority, that is, all logging users can perform query and acquisition; for sensitive production data, for example, fertilization data is set as limited sharing authority and encrypted through an asymmetrically encrypted private key, only query users meeting set conditions can decrypt the sensitive production data by utilizing an asymmetrically encrypted public key, so that sharing of non-sensitive production data can be realized through setting of sharing authority of production data in different grades, the users send query requests through a pre-provided query interface, the sharing authority grade of the users is determined according to the identification of the users, and corresponding production data is acquired according to the sharing authority grade, thereby inquiring the production process and history of requested crops and improving the reliability of tracing crops.
In this embodiment, as an optional embodiment, after obtaining the shared production data corresponding to the access right, before returning to the querying user, the method further includes:
obtaining a blockchain hash value mapped by the identifier from a blockchain network according to the identifier of the shared production data corresponding to the access right;
calculating a hash value of the shared production data corresponding to the access right;
if the calculated hash value is the same as the blockchain hash value, executing a step of returning to the inquiring user;
and if the calculated hash value is different from the block chain hash value, processing according to a preset exception handling strategy.
In this embodiment, the reliability and the security of the production data can be effectively ensured by verifying the production data obtained by the query through the blockchain technology.
In this embodiment, as another optional embodiment, the method may further provide a visual interface for crop production and management through the management platform module, so that a user may conveniently view and manage various data information in the crop production and operation process through the visual interface.
In this embodiment, as an alternative embodiment, the management platform module may interact with the backend using front-end technology, for example, hypertext markup language (HTML, hyper Text Markup Language), cascading style sheets (CSS, cascading Style Sheets), javaScript, and the like.
In the embodiment, according to the production sample data, training is performed on an initial crop production model to obtain the precision of the crop production model, and the crop production management scheme when the crop yield reaches the highest can be obtained by obtaining the growth data and the soil fertility data of the current production stage of the crop and solving by using the crop production model, and the crop is subjected to production management according to the crop production management scheme, so that over-application or less-application of fertilizer can be effectively avoided, the production management efficiency of the crop is improved, and the income of crop growers is improved; meanwhile, by adopting technical means such as intelligent contracts, distributed storage, internet of things and the like, and adopting a blockchain technology, the data recording and tracing of the whole crop production process are realized, the transparency and publicization of crop production data are realized, and the safety and credibility of agricultural products are improved; further, by adopting an intelligent contract technology, automatic operation management and fund settlement are realized, and the efficiency of crop production and transaction is improved; moreover, by adopting an asymmetric encryption algorithm and classifying production data into sensitive production data and non-sensitive production data, different data are processed differently, and other technical means, the individual privacy and business confidentiality of crop growers and consumers are protected, and the computing resources required by encryption are saved. Has the following beneficial technical effects:
1. Data privacy protection: by adopting the technical means of encryption algorithm, production data classification and the like, the individual privacy and business confidentiality of crop growers and consumers are protected.
2. Realizing the automatic management of intelligent contracts: by adopting the intelligent contract technology, automatic operation management (fertilization management and different kinds of proportioning management of crops) and fund settlement are realized, the efficiency and the safety of crop production and transaction are improved, meanwhile, the fertilizer utilization efficiency is improved, the production management efficiency of crops is further improved, and the income of crop growers is improved.
3. Scalability and innovativeness: by adopting the technical means of distributed storage, intelligent contracts, internet of things, artificial intelligence and the like, the transparency and publicization of the crop production process are realized, the safety and credibility of agricultural products are improved, and meanwhile, technical support is provided for upgrading and transforming the agricultural industry chain.
4. The specific implementation technical scheme of application popularization is provided: by proposing a specific scheme for practice and popularization and verifying the feasibility and the practicability, the requirements of agricultural production and social development are better served.
Based on the same inventive concept, as shown in fig. 4, an embodiment of the present invention further provides a crop production management apparatus 300, including:
A sample data obtaining module 301, configured to obtain production sample data in a production process of a target crop, where the production sample data includes growth data, fertilization data, soil fertility data, environmental data, and yield data of the target crop at each growth stage;
in this embodiment, as an alternative embodiment, the growth data includes, but is not limited to: growth time, growth stage, plant height, stem diameter, number of leaves, number of branches, size of leaves and the like; fertilization data includes, but is not limited to: fertilizing time, fertilizing fertilizer information and fertilizing mode; soil fertility data includes, but is not limited to: soil moisture, soil nitrogen, phosphorus and potassium content, soil ph value and the like; the environmental data includes, but is not limited to: temperature, illumination, humidity, carbon dioxide concentration, planting area, etc.
In this embodiment, as an alternative embodiment, production data may be collected by means of a sensor, a camera, a handheld device, etc. that are arranged in advance, for example, production data such as environmental data, soil data, crop growth data, or pictures, etc.
In this embodiment, as another optional embodiment, a data storage module may be further utilized, and a distributed storage technology is adopted, so that the security and reliability of data storage are ensured by splitting production data into a plurality of data blocks and encrypting the split data blocks for storage, and hash operations are performed on each data block respectively to obtain hash values, and the hash values are stored on different nodes of the blockchain network.
In this embodiment, as a further optional embodiment, the shared production data may be stored by a data sharing unit, and the access right of the querying user corresponding to the data querying request may be obtained by the data querying unit in response to the data querying request; and acquiring the shared production data corresponding to the access rights from the data sharing unit, and returning the shared production data to the inquiring user.
In this embodiment, as yet another optional embodiment, the data verification unit may further obtain, according to an identifier of the shared production data corresponding to the access right, a blockchain hash value mapped by the identifier from a blockchain network; calculating a hash value of the shared production data corresponding to the access right; if the calculated hash value is the same as the blockchain hash value, executing a step of returning to the inquiring user; and if the calculated hash value is different from the block chain hash value, processing according to a preset exception handling strategy.
The constraint equation setting module 302 is configured to construct a constraint equation of growth data, fertilization data, soil fertility data, environment data and yield data in a preset initial model of crop production;
The model training module 303 is configured to train the initial crop production model by using a preset machine learning algorithm and maximizing yield data in the constraint equation as an objective function, so as to obtain a crop production model;
in this embodiment, as an alternative embodiment, the model training module 303 includes:
a classification unit (not shown) for classifying the production sample data into a production sample data training set and a production sample data test set;
the training unit is used for extracting a batch of production sample training data from the production sample data training set, inputting growth data, fertilization data, soil fertility data and environment data in the batch of production sample training data into the crop production initial model, and obtaining output data based on the operation of the machine learning algorithm in the crop production initial model;
the adjusting unit is used for carrying out counter-propagation operation on the crop production initial model based on the output data and the output data in the batch production sample training data so as to adjust model parameters of the crop production initial model until the error between the output data of the adjusted crop production initial model and the corresponding output data is not greater than a preset single iteration error threshold;
The testing unit is used for testing the adjusted initial crop production model based on the production sample data testing set to obtain a yield predicted value of the adjusted initial crop production model;
an error obtaining unit, configured to calculate a model error of the adjusted crop production initial model according to the yield data in the production sample data test set and the yield prediction value;
and the model determining unit is used for determining that the model error is not larger than a preset model error threshold value, and taking the adjusted crop production initial model as the crop production model.
In an embodiment of the present application, as an optional embodiment, the model determining unit is further configured to:
determining that the model error is greater than a preset model error threshold, deleting the batch of production sample training data from the production sample data training set, and notifying a training unit to execute the step of extracting a batch of production sample training data from the production sample data training set.
The production management module 304 is configured to collect growth data and soil fertility data of a current production stage of a crop, use the crop production model as input data of the crop production model, perform operation to obtain fertilization data and environment data of the current production stage of the crop, perform fertilization according to the obtained fertilization data, and adjust a current growth environment according to the obtained environment data.
In this embodiment, as an optional embodiment, the apparatus further includes:
the benefit optimization module (not shown in the figure) is used for constructing a crop production supply chain model based on the production cost data and the transportation cost data corresponding to different types of crops; acquiring a price per unit crop based on the crop production supply chain model; constructing a crop production profit distribution model based on the price per unit crop, the cost data corresponding to different types of crops and the output data output by different types of crop production models; and carrying out maximum benefit solving on the crop production benefit distribution model to obtain the planting proportion of the different types of crops.
In this embodiment, the crop production income distribution model is utilized to maximize the income by coordinating the production proportion of different types of crops planted by the crop grower based on the cost data of different types of crops, the corresponding price per unit of crops and the corresponding yield data, thereby effectively improving the income of the crop grower.
According to the crop production management device, the initial model of crop production is trained according to the production sample data, the precision of the crop production model is obtained, the crop production management scheme when the crop yield reaches the highest can be obtained by obtaining the growth data and the soil fertility data of the current production stage of the crop and solving the crop production model, and the crop is produced and managed according to the crop production management scheme, so that over-application or less application of fertilizer can be effectively avoided on the basis of ensuring the maximum yield of the crop, the production management efficiency of the crop is improved, and the income of crop growers is further improved.
Based on the same inventive concept, the embodiments of the present invention also provide a storage medium having stored thereon a computer program which when executed by a processor realizes the steps of the crop production management method in any of the possible implementations described above.
Alternatively, the storage medium may be a non-transitory computer readable storage medium, for example, a ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.
Based on the same inventive concept, referring to fig. 4, the embodiment of the present invention further provides an electronic device, including a memory 101 (e.g. a nonvolatile memory), a processor 102, and a computer program stored in the memory 101 and capable of running on the processor 102, where the steps of the crop production management method in any of the foregoing possible implementations are implemented by the processor 102 when the program is executed, and may be equivalent to the foregoing crop production management apparatus, and of course, the processor may also be used to process other data or operations. The electronic device may be a PC, server, terminal, etc.
As shown in fig. 4, the electronic device may generally further include: memory 103, network interface 104, and internal bus 105. In addition to these components, other hardware may be included, which is not described in detail.
It should be noted that the crop production management apparatus may be implemented by software, and is a device in a logic sense, and is formed by the processor 102 of the electronic device where the crop production management apparatus is located reading the computer program instructions stored in the nonvolatile memory into the memory 103 for running.
Embodiments of the subject matter and the functional operations described in this specification can be implemented in: digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and structural equivalents thereof, or a combination of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by, or to control the operation of, data processing apparatus. Alternatively or additionally, the program instructions may be encoded on a manually-generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode and transmit information to suitable receiver apparatus for execution by data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.
The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).
Computers suitable for executing computer programs include, for example, general purpose and/or special purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit will receive instructions and data from a read only memory and/or a random access memory. The essential elements of a computer include a central processing unit for carrying out or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks, etc. However, a computer does not have to have such a device. Furthermore, the computer may be embedded in another device, such as a mobile phone, a Personal Digital Assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device such as a Universal Serial Bus (USB) flash drive, to name a few.
Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices including, for example, semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disk or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features of specific embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. On the other hand, the various features described in the individual embodiments may also be implemented separately in the various embodiments or in any suitable subcombination. Furthermore, although features may be acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, although operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous. Moreover, the separation of various system modules and components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. Furthermore, the processes depicted in the accompanying drawings are not necessarily required to be in the particular order shown, or sequential order, to achieve desirable results. In some implementations, multitasking and parallel processing may be advantageous.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, 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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing is merely exemplary of embodiments of the present invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A crop production management method, comprising:
obtaining production sample data in the production process of target crops, wherein the production sample data comprises growth data, fertilization data, soil fertility data, environment data and yield data of the target crops in each growth stage;
constructing a constraint equation of growth data, fertilization data, soil fertility data, environment data and yield data in a preset initial crop production model;
training the initial crop production model by using a preset machine learning algorithm and taking the output data in the constraint equation as an objective function to obtain a crop production model;
And acquiring growth data and soil fertility data of the current production stage of the crops, using the crop production model as input data of the crop production model to operate, obtaining fertilization data and environment data of the current production stage of the crops, fertilizing according to the obtained fertilization data, and regulating the current growth environment according to the obtained environment data.
2. The method of claim 1, wherein training the initial model of crop production with a preset machine learning algorithm to maximize yield data in the constraint equation as an objective function, comprises:
dividing the production sample data into a production sample data training set and a production sample data testing set;
extracting a batch of production sample training data from the production sample data training set, inputting growth data, fertilization data, soil fertility data and environment data in the batch of production sample training data into the crop production initial model, and obtaining output data based on the operation of the machine learning algorithm in the crop production initial model;
Performing counter propagation operation on the crop production initial model based on the yield output data and yield data in the batch production sample training data to adjust model parameters of the crop production initial model until an error between the adjusted yield output data of the crop production initial model and corresponding yield data is not greater than a preset single iteration error threshold;
based on the production sample data test set, testing the adjusted initial crop production model to obtain a yield predicted value of the adjusted initial crop production model;
calculating a model error of the adjusted initial model of crop production according to the yield data in the production sample data test set and the yield predicted value;
and determining that the model error is not larger than a preset model error threshold, and taking the adjusted crop production initial model as the crop production model.
3. The crop production management method of claim 2, further comprising:
determining that the model error is greater than a preset model error threshold, deleting the batch of production sample training data from the production sample data training set, and executing the step of extracting a batch of production sample training data from the production sample data training set.
4. A crop production management method as claimed in claim 2 or 3, characterised in that the method further comprises:
constructing a crop production supply chain model based on production cost data and transportation cost data corresponding to different types of crops;
acquiring a price per unit crop based on the crop production supply chain model;
constructing a crop production profit distribution model based on the price per unit crop, the cost data corresponding to different types of crops and the output data output by different types of crop production models;
and carrying out maximum benefit solving on the crop production benefit distribution model to obtain the planting proportion of the different types of crops.
5. A crop production management method as claimed in any one of claims 1 to 3, characterised in that the method further comprises:
splitting the production data to obtain a plurality of data blocks, and storing the plurality of data blocks by adopting a distributed storage technology;
and respectively carrying out hash operation on each data block to obtain a hash value, and storing the hash value to different nodes of a block chain network.
6. A crop production management method as claimed in any one of claims 1 to 3, characterised in that the method further comprises:
Setting the sharing authority of the production data;
responding to a data query request, and acquiring the access rights of a query user corresponding to the data query request;
and acquiring the shared production data corresponding to the access rights, and returning to the inquiring user.
7. The crop production management method according to claim 6, wherein after the obtaining the shared production data corresponding to the access right, before returning to the querying user, the method further comprises:
obtaining a blockchain hash value mapped by the identifier from a blockchain network according to the identifier of the shared production data corresponding to the access right;
calculating a hash value of the shared production data corresponding to the access right;
if the calculated hash value is the same as the blockchain hash value, executing a step of returning to the inquiring user;
and if the calculated hash value is different from the block chain hash value, processing according to a preset exception handling strategy.
8. A crop production management apparatus, characterized by comprising:
the sample data acquisition module is used for acquiring production sample data in the production process of the target crops, wherein the production sample data comprises growth data, fertilization data, soil fertility data, environment data and yield data of the target crops in each growth stage;
The constraint equation setting module is used for constructing a constraint equation of growth data, fertilization data, soil fertility data, environment data and yield data in a preset crop production initial model;
the model training module is used for training the initial crop production model by using a preset machine learning algorithm and taking the output data in the constraint equation as an objective function to obtain a crop production model;
the production management module is used for collecting growth data and soil fertility data of the current production stage of crops, taking the growth data and the soil fertility data as input data of the crop production model, calculating by utilizing the crop production model to obtain fertilization data and environment data of the current production stage of the crops, fertilizing according to the obtained fertilization data, and adjusting the current growth environment according to the obtained environment data.
9. A storage medium having stored thereon a program or instructions which when executed by a processor perform the steps of the crop production management method of any of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the crop production management method of any one of claims 1 to 7 when the program is executed.
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