Accurate data acquisition optimization method and system for farmland low-carbon production

Abstract

The invention discloses an accurate data acquisition optimization method and system for farmland low-carbon production, and relates to the technical field of data acquisition optimization. Comprising information acquisition: acquiring basic information and feedback address information. Through the set judgment method, the growth condition of one of the sub-crops can be screened and judged, and whether the sub-crops and the normally growing reference crops have differences or not and the factors generating the differences are determined, so that soil information is fed back according to the growth condition of the sub-crops; through a matched adjustment method, the sub-information is adjusted according to the judgment result and the result information to obtain accurate sub-information and an accurate area corresponding to the accurate sub-information, then the soil information is optimized, the effect of accurately collecting the soil information is achieved, a user carries out management operation according to the accurately collected soil information, and the user experience is improved. The environment can be protected; the cost is reduced; and the carbon emission is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data collection optimization, and in particular to a precise data collection optimization method and system for farmland low-carbon production. Background Art

[0002] Low-carbon farmland production refers to an agricultural model that achieves sustainable development by changing agricultural production methods to reduce greenhouse gas emissions and improve resource utilization efficiency. This not only helps to mitigate the impact of climate change, but also improves the economic and environmental benefits of farmland production. Accurate data collection in low-carbon farmland production and production based on the collected data have a good effect on reducing carbon emissions.

[0003] Patent publication number CN113434295A is an edge computing-based farmland monitoring method, device, equipment and storage medium. The method includes: compressing the collected farmland data to obtain compressed data; inputting the compressed data into a pre-trained feature extraction model and extracting features from the compressed data through the feature extraction model to obtain feature information of the compressed data; analyzing the feature information to determine the data type corresponding to each data in the feature information; based on the preset edge computing rules and data types, calling the data processing model corresponding to the data type from the preset model library to detect the feature information; when the detection result reaches the alarm threshold, an abnormal alarm prompt is issued. This solution realizes the monitoring of farmland by storing and uniformly managing farmland data, and performing abnormal detection and early warning on farmland data according to the abnormal detection model.

[0004] Low-carbon production of farmland is closely related to post-management measures such as fertilizing, watering and spraying of pesticides. During the crop planting process, in order to ensure the normal growth of crops, it is necessary to fertilize, water and spray the crops with pesticides. In order to cope with low-carbon production, it is necessary to conduct a comprehensive analysis based on the soil conditions of the farmland to determine the dosage of fertilization, watering and spraying of pesticides required for the crops, so as to reduce carbon emissions while ensuring that the crops grow healthily. When determining the dosage of fertilization, watering and spraying of pesticides required for crops, it is particularly important to accurately obtain soil data. The above-mentioned and similar methods rely too much on sensors when obtaining soil data. If the sensor is damaged, the soil data obtained is less accurate, which may lead to excessive or insufficient fertilization, watering and spraying of pesticides, thereby resulting in higher carbon emissions and environmental damage, which is not suitable for low-carbon production of farmland. Therefore, the present invention is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for optimizing accurate data collection for low-carbon production in farmland, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a precise data collection optimization method for low-carbon production in farmland, the method comprising: Information acquisition: obtaining basic information and feedback address information. The basic information includes farmland area, farmland location and crops. The feedback address information is the address information where the collected data is sent to the user. Information processing: Based on the area and location of farmland, the farmland is divided into several sub-areas through a division method, the soil information of the sub-area is obtained to obtain sub-information, an information database is established to store the sub-information, a regional database is established to store the sub-areas, and the association between the sub-areas and the sub-information is established; Data collection: Divide crops into sub-regions to obtain a number of sub-crops corresponding to the sub-regions, and use a judgment method to determine the growth of individual sub-crops to obtain result information and target crops; Data optimization: The verification method is used to determine whether the growth environment of all sub-crops in the sub-region is in a normal growth state to obtain the judgment result. Based on the judgment result and the result information, the sub-information is adjusted and optimized through the adjustment method to obtain accurate sub-information and accurate region; Data output: adding precise sub-information and precise regions to the information base and the region base regularly through an updating method to obtain an updated information base and an updated region base, and sending the updated information base, the updated region base and the association to the user based on the feedback address information; The judgment method includes: presetting reference crops, obtaining normal growth information of the reference crops to obtain reference information, establishing a correlation relationship between the reference information and the reference crops, screening sub-crops through a screening method to obtain target crops, obtaining growth information of the target crops to obtain target crop information, extracting reference crops consistent with the target crops to obtain comparison crops, extracting reference information of the comparison crops based on the correlation relationship to obtain comparison information, and judging whether there is a difference between the target crop information and the comparison information to obtain result information, wherein the result information includes a first result and a second result, the first result being the existence of a difference, a difference point and a target factor that produces the difference point, and the second result being the absence of a difference.

[0007] Furthermore, the process of determining whether there is a difference between the target crop information and the comparison information is as follows: splitting the comparison information to obtain several information items, obtaining a target cycle based on the growth cycle of the target crop information, selecting information items corresponding to the target cycle from several information items based on the target cycle to obtain a target information item, comparing the target information item with the target crop information to obtain a comparison result, the comparison result includes the existence of a difference or the absence of a difference, when the comparison result is that there is a difference, extracting the difference between the target crop information and the target information item to obtain a difference point, obtaining the cause of the difference point based on the target crop information to obtain the target factor, obtaining result information based on the comparison result, integrating the difference point, the target factor and the existence of the difference to obtain a first result, the absence of a difference is a second result, and the result information is the first result or the second result.

[0008] Furthermore, the screening method includes: determining a sub-region based on a sub-crop, gridding the sub-region, assigning a position of the sub-crop in the grid to obtain a sub-crop position, presetting a random number generation range, the sub-crop position is within the random number generation range, generating a random number based on the random number generation range to obtain a target random number, extracting a sub-crop position that is the same as the target random number to obtain a target crop position, and determining a sub-crop based on the target crop position to obtain a target crop.

[0009] Furthermore, the division method includes: presetting the area size, obtaining the slope of the farmland based on the location of the farmland, presetting the slope threshold, separating the farmland based on the slope threshold to obtain sloping farmland and non-slope farmland, obtaining the grid size based on the area size, and gridding the sloping farmland and non-slope farmland based on the grid size to obtain several sub-areas.

[0010] Furthermore, the verification method includes: obtaining soil information and environmental information of the sub-crops' previous growth to obtain past soil information, obtaining standard growth soil information for planting sub-crops to obtain a standard soil information range, and comparing whether the past soil information is within the standard soil information range; when the past soil information is within the standard soil information range, it is judged that the sub-crops are in a normal growth condition; when the past soil information is not within the standard soil information range, it is judged that the sub-crops are not in a normal growth condition.

[0011] Furthermore, the adjustment method includes: presetting an adjustment unit and a divided area, and when the judgment result feedback is that the sub-crop is in a normal growth condition and the result information feedback is a first result, based on the target factor and the adjustment unit, adjusting the item corresponding to the target factor in the sub-information to obtain precise sub-information of the target crop, and based on the position of the target crop and the divided area to obtain a precise area, the precise sub-information is soil information of the precise area, when the judgment result feedback is that the sub-crop is in a normal growth condition and the result information feedback is a second result, the sub-information is precise sub-information, when the judgment result feedback is that the sub-crop is not in a normal growth condition and the result information feedback is a first result, the sub-information is precise sub-information, and when the judgment result feedback is that the sub-crop is not in a normal growth condition and the result information feedback is a second result, the sub-information is precise sub-information, and when the judgment result feedback is that the sub-crop is not in a normal growth condition and the result information feedback is a second result, extracting the sub-information for error processing and sending it to the user based on the feedback address information.

[0012] Furthermore, the updating method includes: associating precise sub-information with precise areas, presetting an update cycle, obtaining precise sub-information generated by the adjustment method and sub-information adjusted and optimized by the adjustment method in adjacent update cycle time periods, establishing an association relationship between precise sub-information and sub-information, judging whether precise sub-information and sub-information are consistent based on the association relationship, eliminating precise sub-information that is consistent with sub-information, embedding precise sub-information into sub-information in an information library based on the eliminated precise sub-information and the association relationship to obtain an updated information library, and adding the area associated with the precise sub-information to the area library to obtain an updated area library.

[0013] A precise data collection and optimization system for low-carbon production in farmland uses the above-mentioned precise data collection and optimization method for low-carbon production in farmland.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The precise data collection optimization method and system for low-carbon production in farmland can screen and judge the growth condition of one of the sub-crops through a set judgment method, and determine whether there is a difference between the sub-crop and a reference crop with normal growth and the factors that cause the difference, so as to feedback soil information according to the growth condition of the sub-crops. By coordinating with the set adjustment method, the sub-information is adjusted according to the judgment result and the result information to obtain precise sub-information and a precise area corresponding to the precise sub-information, and then the soil information is optimized to achieve the effect of accurately collecting soil information. The user can perform management operations based on the precisely collected soil information, which is beneficial to protecting the environment, reducing costs and reducing carbon emissions.

[0015] At the same time, through the mutual cooperation of the judgment method and the verification method, the verification method optimizes the sub-information according to its feedback results. When the judgment method and the verification method respectively provide feedback that the judgment result is that the sub-crop is not in a normal growth condition and the result information is fed back as the second result, it means that there is an error in the acquisition of the sub-information, and then the sub-information is processed as an error and sent to the user based on the feedback address information. The user checks or maintains the soil sensor for obtaining the sub-information to reduce the excessive reliance on the soil sensor for the acquisition of soil information. For the sake of insurance, multiple inspections can be added. For example, when the judgment result is fed back that the sub-crop is not in a normal growth condition and the result information is fed back as the second result, other sub-crops are selected as target crops for continued judgment and verification. At the same time, a threshold is preset. When the number of times the judgment result is fed back that the sub-crop is not in a normal growth condition and the result information is fed back as the second result reaches the threshold, an error is processed.

[0016] At the same time, through the set screening method, decentralization can be achieved, and a single sub-crop among the sub-crops can be randomly selected as the target crop to ensure the fairness of the selection. At the same time, the screening method can be replaced according to the usage. For example, when more precise data is needed, the screening method can be replaced with a single selection or sampling selection. By managing sloping farmland and non-slope farmland separately, the growth environment and management accuracy of crops can be improved, which also plays a positive role in the sustainable development of farms, the rational use of resources and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the information acquisition, information processing and data collection structure of the present invention; Figure 2 It is a schematic diagram of the data optimization and data output structure of the present invention; Figure 3 It is a structural schematic diagram of the judgment method of the present invention; Figure 4 It is a schematic diagram of the structure of the adjustment method of the present invention; Figure 5 It is a schematic diagram of the structure of the screening method of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] In the process of low-carbon production in farmland, insufficient or excessive fertilization, watering and spraying of pesticides will lead to adverse effects. For example, excessive fertilization will increase the carbon footprint and may also cause water pollution. Overwatering not only wastes water resources, but may also cause soil erosion and carbon dioxide release. Excessive spraying of pesticides will cause damage to the environment. Therefore, obtaining accurate soil information and adjusting the dosage of fertilization, watering and spraying according to the soil information is beneficial to protecting the environment, reducing costs and reducing carbon emissions.

[0020] like Figure 1-Figure 5 As shown, the present invention provides a technical solution: a precise data collection optimization method for low-carbon production in farmland, the method comprising: Information acquisition: obtaining basic information and feedback address information. The basic information includes farmland area, farmland location and crops. The feedback address information is the address information where the collected data is sent to the user. Information processing: Based on the area and location of farmland, the farmland is divided into several sub-areas through a division method, the soil information of the sub-area is obtained to obtain sub-information, an information database is established to store the sub-information, a regional database is established to store the sub-areas, and the association between the sub-areas and the sub-information is established; Data collection: Divide crops into sub-regions to obtain a number of sub-crops corresponding to the sub-regions, and use a judgment method to determine the growth of individual sub-crops to obtain result information and target crops; Data optimization: The verification method is used to determine whether the growth environment of all sub-crops in the sub-region is in a normal growth state to obtain the judgment result. Based on the judgment result and the result information, the sub-information is adjusted and optimized through the adjustment method to obtain accurate sub-information and accurate region; Data output: adding precise sub-information and precise regions to the information base and the region base regularly through an updating method to obtain an updated information base and an updated region base, and sending the updated information base, the updated region base and the association to the user based on the feedback address information; It should be noted that in the information acquisition stage, the farmland area and farmland location can be obtained through GIS or other map systems, or can be measured and marked by the staff themselves. Crops are the crops that need to be planted in the farmland. The types of crops can be one or more, and the actual quantity is determined according to the actual usage. The feedback address information can be directly obtained by the user. In the information processing stage, the farmland is divided into several sub-areas according to the farmland area and farmland location through the set division method, and the soil information of the sub-area is obtained. This process can be obtained by directly installing soil sensors at the center of the area or at a representative location. The farmland is divided into multiple areas through the set division method and then soil information is obtained from a single area. This can make the information data collection more accurate, which is conducive to the separate management of several sub-areas, that is, the dosage of fertilizer, water or spraying agent is determined according to the soil information of each sub-area, so as to reduce carbon emissions, protect the environment, and reduce costs. In order to ensure the effect of thriving growth of crops, in the data collection stage, the crops are divided into several sub-crops corresponding to the sub-regions based on the sub-regions. This process converts the crops in the sub-regions into sub-crops corresponding to the sub-regions. The growth conditions of individual sub-crops in the sub-crops are judged by the set judgment method to obtain the result information and the target crops. How to select a single sub-crop can be screened by a screening method, or all selected in sequence and sampled. The specific situation is determined according to the actual use requirements. In the data optimization stage, the set verification method is used to verify whether the growth environment of all sub-crops in the sub-region is in a normal state to obtain the judgment result. The verification method is used to play a verification role, which is mainly used to judge whether the sub-information is accurate in combination with the result information to improve the accuracy of data collection. The sub-information is adjusted according to the judgment result and the result information through the set adjustment method to obtain accurate sub-information and the accurate area corresponding to the accurate sub-information.

[0021] like Figure 3 As shown, the judgment method includes: presetting a reference crop, obtaining normal growth information of the reference crop to obtain reference information, establishing an association relationship between the reference information and the reference crop, screening sub-crops through a screening method to obtain a target crop, obtaining growth information of the target crop to obtain target crop information, extracting reference crops consistent with the target crop to obtain comparison crops, extracting reference information of the comparison crop based on the association relationship to obtain comparison information, and judging whether there is a difference between the target crop information and the comparison information to obtain result information, wherein the result information includes a first result and a second result, the first result being the existence of a difference, a difference point and a target factor that produces the difference point, and the second result being the absence of a difference.

[0022] It should be noted that the number of reference crops can be multiple, and the reference crops include crops planted in farmland. The process of obtaining the normal growth information of the reference crops to obtain the reference information can be obtained by searching for the normal growth information of the reference crops on the Internet search platform, or by directly cultivating the reference crops artificially and recording the information of the reference crops under normal growth conditions. Through the set screening method, the targets to be judged can be screened from multiple to one, and the specific method can be determined according to the actual use. In order to obtain data more accurately, one by one judgment or sampling judgment can be used for selection. The process of obtaining the growth information of the target crops to obtain the target crop information can directly obtain the actual The image of the target crop can also be used to record the real-time growth characteristics of the target crop. The comparison information of the comparison crop needs to be consistent with the target crop information. For example, when the target crop information is a picture, the comparison information also needs to be a picture. When the target crop information is text information, the comparison information also needs to be text information. The formats of the two data are consistent, so that the subsequent judgment of whether there is a difference between the two can be carried out normally. Through the set judgment method, the growth condition of one of the sub-crops can be screened and judged, and it can be determined whether there is a difference between the sub-crops and the reference crops that grow normally and the factors that cause the difference, so as to feedback the soil information according to the growth condition of the sub-crops, and then optimize the soil information, so as to achieve the effect of accurately collecting soil information.

[0023] The process of judging whether there is a difference between the target crop information and the comparison information is as follows: splitting the comparison information to obtain several information items, obtaining a target cycle based on the growth cycle of the target crop information, selecting information items corresponding to the target cycle from several information items based on the target cycle to obtain a target information item, comparing the target information item with the target crop information to obtain a comparison result, the comparison result includes the existence of a difference or the absence of a difference, when the comparison result is that there is a difference, extracting the difference between the target crop information and the target information item to obtain a difference point, obtaining the cause of the difference point based on the target crop information to obtain the target factor, obtaining result information based on the comparison result, integrating the difference point, the target factor and the existence of the difference to obtain a first result, the absence of a difference is a second result, and the result information is the first result or the second result.

[0024] It should be noted that the comparison information contains several information items, and the information item is the information in a single growth cycle. The target cycle is obtained according to the growth cycle of the target crop information, and the target information item is determined according to the target cycle to obtain the target information item. The specific process of determining whether there is a difference between the two is to use visual recognition technology in conjunction with a special training model to determine the difference in the image or text information. By splitting the comparison information and selecting the corresponding information item as the comparison object in conjunction with the target cycle, it is beneficial to ensure the accuracy of the comparison. The process of obtaining the cause of the difference based on the target crop information to obtain the target factor can be directly obtained by searching on the Internet search platform. For example, the target crop is corn, the difference is the presence of rotten leaves, and the target factor is high soil moisture. By determining whether there is a difference between the target crop information and the comparison information, the growth condition of the target crop can be obtained, which is beneficial to determine the soil information of the target crop at the location according to the growth condition of the target crop.

[0025] like Figure 3 and Figure 5 As shown, the screening method includes: determining a sub-region based on a sub-crop, gridding the sub-region, assigning a position of the sub-crop in the grid to obtain a sub-crop position, presetting a random number generation range, the sub-crop position is within the random number generation range, generating a random number based on the random number generation range to obtain a target random number, extracting a sub-crop position that is the same as the target random number to obtain a target crop position, and determining a sub-crop based on the target crop position to obtain a target crop.

[0026] It should be noted that, through the set screening method, decentralization can be achieved, and a single sub-crop in the sub-crops can be randomly selected as the target crop to ensure the fairness of the selection. During the use of the screening method, the sub-region is determined according to the region where the sub-crop is located, and the sub-region is gridded. Each sub-crop is located in a grid, and each grid is numbered. The number is the position of the grid and also the position of the sub-crop. The random number generation range is determined according to the actual usage, specifically the range of integers, such as 1-50, etc. Random numbers are randomly generated within the random number generation range, and the sub-crop position is determined by the random number. The sub-crop position is selected according to the sub-crop position to obtain the target crop, which is conducive to ensuring the randomness of the screening. At the same time, the screening method can be replaced according to the usage. For example, when more accurate data is required, the screening method can be replaced with single selection or sampling selection. The specific decision is made by the staff according to the actual usage.

[0027] The division method includes: presetting the area size, obtaining the farmland slope based on the farmland location, presetting the slope threshold, separating the farmland based on the slope threshold to obtain sloping farmland and non-slope farmland, obtaining the grid size based on the area size, and gridding the sloping farmland and non-slope farmland based on the grid size to obtain several sub-areas.

[0028] It should be noted that the area size is set by the user, usually based on the performance and cost of the soil sensor. The slope of the farmland can be obtained through the map based on the location of the farmland, and the slope threshold is preset. The farmland is divided into two parts according to the preset slope threshold for subsequent management. The sloping farmland and the non-slope farmland are gridded to obtain several sub-areas. By managing the sloping farmland and the non-slope farmland separately, the growth environment and management accuracy of crops can be improved, which also plays a positive role in the sustainable development of farms, the rational use of resources and environmental protection.

[0029] The verification method includes: obtaining soil information and environmental information of the sub-crops' previous growth to obtain previous soil information, obtaining soil information of the standard growth of the sub-crops to obtain a standard soil information range, and comparing whether the previous soil information is within the standard soil information range. When the previous soil information is within the standard soil information range, it is judged that the sub-crops are in a normal growth condition; when the previous soil information is not within the standard soil information range, it is judged that the sub-crops are not in a normal growth condition.

[0030] It should be noted that the process of obtaining the soil information and environmental information of the sub-crops in the past growth to obtain the past soil information, the past soil information is the soil information from the real time to the start of planting time. By comparing whether the past soil information is within the standard soil information range, it can be determined whether the sub-crops have been growing in a normal growth environment, which is beneficial to the subsequent adjustment method to adjust the sub-information of the target crops. Through the set verification method, it can be determined whether the sub-crops were growing in normal soil information before the current growth cycle, which plays a role in evaluating the sub-information, so as to facilitate the normal operation of the subsequent adjustment method.

[0031] like Figure 4 As shown, the adjustment method includes: presetting an adjustment unit and dividing an area, when the judgment result feedback is that the sub-crop is in a normal growth condition and the result information feedback is a first result, based on the target factor and the adjustment unit, the item corresponding to the target factor in the sub-information is adjusted to obtain precise sub-information of the target crop, based on the position of the target crop and the divided area, the precise area is obtained, and the precise sub-information is soil information of the precise area. When the judgment result feedback is that the sub-crop is in a normal growth condition and the result information feedback is a second result, the sub-information is precise sub-information, when the judgment result feedback is that the sub-crop is not in a normal growth condition and the result information feedback is a first result, the sub-information is precise sub-information, and when the judgment result feedback is that the sub-crop is not in a normal growth condition and the result information feedback is a second result, the sub-information is extracted for error processing and sent to the user based on the feedback address information.

[0032] It should be noted that the adjustment unit is associated with the target factor and is set by the user. The divided area is determined according to the size of the surrounding area that affects the growth of the target crop. The precise area is obtained by adding the divided area to the position of the target crop. The precise sub-information is associated with the precise area. When the judgment result feedback is that the sub-crop is in normal growth and the result information feedback is the first result, it proves that the sub-information of the sub-region cannot fully represent the sub-region. Then, the target factor is coordinated with the adjustment unit to adjust the items corresponding to the target factor in the sub-information to obtain the precise sub-information of the target crop. The specific target factor is coordinated with the adjustment unit. The adjustment size is determined according to the degree of influence of the target factor, that is, the adjustment unit is associated with the target factor. When the judgment result feedback is that the sub-crop is in normal growth and the result information feedback is the second result and when the judgment result feedback is that the sub-crop is not in normal growth When the judgment result feedback is that the sub-crops are not in normal growth conditions and the result information feedback is the second result, it means that there is an error in the acquisition of the sub-information, and then the sub-information is processed as an error and sent to the user based on the feedback address information. The user checks or maintains the soil sensor that obtains the sub-information to reduce the excessive reliance on the soil sensor for the acquisition of soil information. For the sake of insurance, multiple inspections can be added. For example, when the judgment result feedback is that the sub-crops are not in normal growth conditions and the result information feedback is the second result, other sub-crops are selected as target crops for continued judgment and verification. At the same time, a threshold is preset. When the number of times the judgment result feedback is that the sub-crops are not in normal growth conditions and the result information feedback is the second result reaches the threshold, an error is processed.

[0033] like Figure 2 As shown, the updating method includes: associating precise sub-information with precise areas, presetting an update cycle, obtaining precise sub-information generated by the adjustment method and sub-information adjusted and optimized by the adjustment method in adjacent update cycle time periods, establishing an association relationship between precise sub-information and sub-information, judging whether the precise sub-information and sub-information are consistent based on the association relationship, eliminating precise sub-information that is consistent with the sub-information, embedding the precise sub-information into the sub-information in the information library based on the eliminated precise sub-information and the association relationship to obtain an updated information library, and adding the area associated with the precise sub-information to the area library to obtain an updated area library.

[0034] It should be noted that the update cycle is determined according to actual usage. Usually, the update cycle can be set to 7 days. In the process of obtaining the precise sub-information generated by the adjustment method in the adjacent update cycle time period and the sub-information adjusted and optimized by the adjustment method, the adjacent update cycle time period is the time period between the two update cycles. For example, the update cycle is 7 days, and the time period between the two update cycles is the time period between the previous 7 days and the next 7 days. For example, if the update cycle combined with the date is 1.1 and 1.7, then the adjacent update cycle time period is the time between 1.1-1.7. Through the set update method, the acquired precise sub-information and precise sub-area can be regularly updated to the information library and the area library. At the same time, real-time updates can also be performed according to actual usage needs. By eliminating the precise sub-information consistent with the sub-information set in the update method, the system occupancy can be reduced, the efficiency of information updating can be improved, and the efficiency can be improved.

[0035] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments and their equivalents.

Claims

1. A precise data collection and optimization method for low-carbon production in farmland, characterized by: The method comprises: Information acquisition: obtaining basic information and feedback address information. The basic information includes farmland area, farmland location and crops. The feedback address information is the address information where the collected data is sent to the user. Information processing: Based on the area and location of farmland, the farmland is divided into several sub-areas through a division method, the soil information of the sub-area is obtained to obtain sub-information, an information database is established to store the sub-information, a regional database is established to store the sub-areas, and the association between the sub-areas and the sub-information is established; Data collection: Divide crops into sub-regions to obtain a number of sub-crops corresponding to the sub-regions, and use a judgment method to determine the growth of individual sub-crops to obtain result information and target crops; Data optimization: The verification method is used to determine whether the growth environment of all sub-crops in the sub-region is in a normal growth state to obtain the judgment result. Based on the judgment result and the result information, the sub-information is adjusted and optimized through the adjustment method to obtain accurate sub-information and accurate region; Data output: adding precise sub-information and precise regions to the information base and the region base regularly through an updating method to obtain an updated information base and an updated region base, and sending the updated information base, the updated region base and the association to the user based on the feedback address information; The judgment method includes: presetting reference crops, obtaining normal growth information of the reference crops to obtain reference information, establishing a correlation relationship between the reference information and the reference crops, screening sub-crops through a screening method to obtain target crops, obtaining growth information of the target crops to obtain target crop information, extracting reference crops consistent with the target crops to obtain comparison crops, extracting reference information of the comparison crops based on the correlation relationship to obtain comparison information, and judging whether there is a difference between the target crop information and the comparison information to obtain result information, wherein the result information includes a first result and a second result, the first result being the existence of a difference, a difference point and a target factor that produces the difference point, and the second result being the absence of a difference.

2. The method for optimizing accurate data collection for low-carbon production in farmland according to claim 1, characterized in that: The process of judging whether there is a difference between the target crop information and the comparison information is as follows: splitting the comparison information to obtain several information items, obtaining a target cycle based on the growth cycle of the target crop information, selecting information items corresponding to the target cycle from several information items based on the target cycle to obtain a target information item, comparing the target information item with the target crop information to obtain a comparison result, the comparison result includes the existence of a difference or the absence of a difference, when the comparison result is that there is a difference, extracting the difference between the target crop information and the target information item to obtain a difference point, obtaining the cause of the difference point based on the target crop information to obtain the target factor, obtaining result information based on the comparison result, integrating the difference point, the target factor and the existence of the difference to obtain a first result, the absence of a difference is a second result, and the result information is the first result or the second result.

3. The method for optimizing accurate data collection for low-carbon production in farmland according to claim 1, characterized in that: The screening method includes: determining a sub-region based on a sub-crop, gridding the sub-region, assigning a position of the sub-crop in the grid to obtain a sub-crop position, presetting a random number generation range, wherein the sub-crop position is within the random number generation range, generating a random number based on the random number generation range to obtain a target random number, extracting a sub-crop position identical to the target random number to obtain a target crop position, and determining a sub-crop based on the target crop position to obtain a target crop.

4. The method for optimizing accurate data collection for low-carbon production in farmland according to claim 1, characterized in that: The division method includes: presetting the area size, obtaining the farmland slope based on the farmland location, presetting the slope threshold, separating the farmland based on the slope threshold to obtain sloping farmland and non-slope farmland, obtaining the grid size based on the area size, and gridding the sloping farmland and non-slope farmland based on the grid size to obtain a number of sub-areas.

5. The method for optimizing accurate data collection for low-carbon production in farmland according to claim 1, characterized in that: The verification method includes: obtaining soil information and environmental information of the sub-crops during their previous growth to obtain previous soil information, obtaining standard soil information for planting the sub-crops to obtain a standard soil information range, and comparing whether the previous soil information is within the standard soil information range. When the previous soil information is within the standard soil information range, it is judged that the sub-crops are in a normal growth condition, and when the previous soil information is not within the standard soil information range, it is judged that the sub-crops are not in a normal growth condition.

6. The method for optimizing accurate data collection for low-carbon production in farmland according to claim 1, characterized in that: The adjustment method includes: presetting an adjustment unit and dividing an area, when the judgment result feedback is that the sub-crop is in a normal growth condition and the result information feedback is a first result, adjusting the items corresponding to the target factor in the sub-information based on the target factor in coordination with the adjustment unit to obtain precise sub-information of the target crop, and obtaining a precise area based on the position of the target crop in coordination with the divided area, the precise sub-information is soil information of the precise area, when the judgment result feedback is that the sub-crop is in a normal growth condition and the result information feedback is a second result, the sub-information is precise sub-information, when the judgment result feedback is that the sub-crop is not in a normal growth condition and the result information feedback is a first result, the sub-information is precise sub-information, and when the judgment result feedback is that the sub-crop is not in a normal growth condition and the result information feedback is a second result, the sub-information is extracted for error processing and sent to the user based on the feedback address information.

7. The method for optimizing accurate data collection for low-carbon production in farmland according to claim 1, characterized in that: The updating method includes: associating precise sub-information with precise areas, presetting an update cycle, obtaining precise sub-information generated by an adjustment method and sub-information adjusted and optimized by the adjustment method in adjacent update cycle time periods, establishing an association relationship between precise sub-information and sub-information, judging whether precise sub-information and sub-information are consistent based on the association relationship, eliminating precise sub-information that is consistent with sub-information, embedding precise sub-information into sub-information in an information library based on the eliminated precise sub-information and the association relationship to obtain an updated information library, and adding an area associated with the precise sub-information to a regional library to obtain an updated regional library.

8. An accurate data collection and optimization system for low-carbon production in farmland, characterized by: A precise data collection and optimization method for low-carbon production in farmland as described in any one of claims 1 to 7 is used.

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