An agricultural data sharing system based on big data

Abstract

The present application relates to the field of data analysis, and more particularly to an agricultural data sharing system based on big data, comprising: a correlation sharing module configured to determine correlation sharing datasets; a correlation evaluation module configured to determine whether to perform sharing risk analysis and sharing quality analysis on the corresponding correlation sharing dataset based on the set correlation state of each correlation sharing dataset; a risk analysis module configured to determine whether to perform regional risk analysis or crop risk analysis on the correlation sharing dataset according to regional distribution correlation parameters, and determine whether to perform sharing protection processing on each sharing dataset based on a distribution correlation proportion index; and a quality analysis module configured to determine whether to perform quality optimization processing on each correlation sharing dataset according to set response correlation parameters. The present application ensures the quality of shared data and the safety of experimental planting schemes.

Description

Technical Field

[0001] This invention relates to the field of data analysis, and more particularly to an agricultural data sharing system based on big data. Background Technology

[0002] In the current agricultural production and development process, the reliance on agricultural data is gradually increasing. This necessitates effective analysis and storage of acquired agricultural data, as well as its sharing. During agricultural data sharing, it is crucial to ensure both the accuracy of the shared data and the correlation between data points to avoid data silos. In particular, agricultural experimental planting processes often require encrypted protection. Without targeted processing of the shared data, there is a risk of leakage of experimental planting plans. Therefore, how to prevent the leakage of experimental planting deployment plans while ensuring the effective sharing of experimental planting data is a problem that urgently needs to be solved by those skilled in the art.

[0003] Chinese Patent Publication No. CN115037760A discloses an agricultural shared experimental platform based on the Internet of Things (IoT), comprising a platform system with four technical architecture layers: a sensing layer, a transmission and access layer, a service layer, and an application layer. By integrating with universities, experts, and farms, it enables rapid matching of experimental fields for research, automatic data collection using IoT devices, thereby simplifying the experimental process and rationally and quickly allocating experimental reference groups. However, the above solution has the following drawback: it fails to provide targeted protection for the shared data, leading to the risk of leakage of experimental planting deployment plans during the sharing of experimental planting data. Summary of the Invention

[0004] To address this issue, the present invention provides an agricultural data sharing system based on big data, which overcomes the problem in existing technologies that fail to provide targeted protection for the shared data, leading to the risk of leakage of experimental planting deployment plans during the sharing of experimental planting data.

[0005] To achieve the above objectives, the present invention provides an agricultural data sharing system based on big data, comprising:

[0006] The association sharing module is used to determine the association sharing dataset, which is determined based on the set analysis association parameters obtained by performing association analysis on the stage shared dataset;

[0007] The association assessment module, which is connected to the association sharing module, is used to determine whether to perform sharing risk analysis and sharing quality analysis on the corresponding association sharing dataset based on the set association status of each association sharing dataset. The set association status is determined according to the set area association parameters and the set experiment association parameters.

[0008] The risk analysis module, which is connected to the correlation assessment module, is used to determine whether to perform regional risk analysis or crop risk analysis on the correlated shared dataset based on the regional distribution correlation parameters, and to determine whether to perform shared protection treatment on each shared data based on the distribution correlation ratio index. The regional distribution correlation parameters are determined based on the number of distribution correlation areas existing in the experimental planting area corresponding to each shared data.

[0009] The quality analysis module, which is connected to the association assessment module and the risk analysis module respectively, is used to determine whether to perform quality optimization processing on each associated shared dataset based on the set response association parameters.

[0010] Furthermore, the set association state includes a first type of set association state and a second type of set association state;

[0011] The association evaluation module records the associated shared datasets that have a set region association parameter greater than a preset set region association parameter or a set experiment association parameter greater than a preset set experiment parameter as associated shared datasets in a first-class set association state.

[0012] The association evaluation module records the associated shared datasets that have set region association parameters less than or equal to preset set region association parameters and set experiment association parameters less than or equal to preset set experiment association parameters as associated shared datasets in the second-class set association state.

[0013] Furthermore, the risk analysis module performs shared risk analysis on associated shared datasets that are in a state of set association, wherein,

[0014] The regional distribution association parameter is determined based on the distribution association ratio index corresponding to each shared data item in each associated shared dataset. The distribution association ratio index is based on the number of distribution association areas existing in the experimental planting area corresponding to each shared data item.

[0015] Furthermore, the risk analysis module performs regional risk analysis on shared datasets where the regional distribution correlation parameter is greater than a preset regional distribution correlation parameter, wherein...

[0016] The determination of whether to implement sharing protection measures for the corresponding shared data is based on the distribution correlation ratio index of each type of shared data.

[0017] Furthermore, the risk analysis module performs shared protection processing on shared data where any distribution correlation ratio index is greater than a preset distribution correlation ratio index, and determines the data protection index of the shared data based on the distribution correlation ratio index.

[0018] The data protection index and the distribution correlation ratio index are positively correlated.

[0019] Furthermore, the risk analysis module performs crop risk analysis on shared datasets where the regional distribution correlation parameters are less than or equal to preset regional distribution correlation parameters or where regional risk analysis has been completed.

[0020] The risk analysis module detects the cross-capture parameters of the data for each shared data item within the associated shared dataset, and determines whether to perform sharing protection processing on the corresponding shared data based on the cross-capture parameters of each shared data item.

[0021] Furthermore, the risk analysis module performs shared protection processing on any shared data whose data cross-capture parameter is greater than the preset data cross-capture parameter, and determines the data protection index of the shared data based on the data cross-capture parameter;

[0022] The data protection index is positively correlated with the data cross-capture parameter.

[0023] Furthermore, the quality analysis module performs shared quality analysis on associated shared datasets that are in a binary set association state or have completed shared risk analysis;

[0024] The set response association parameters are determined based on the response proportion index of each shared data item.

[0025] Furthermore, the quality analysis module determines that associated shared datasets whose set response correlation parameters are less than or equal to preset set response correlation parameters undergo quality optimization processing, wherein...

[0026] Obtain response deviation data from the associated shared dataset that needs quality optimization, and issue data quality warnings based on the response deviation data.

[0027] The response deviation data refers to response analysis data where the response proportion index is less than or equal to the preset set of response correlation parameters.

[0028] Furthermore, the association and sharing module periodically performs association analysis on the shared dataset for each stage;

[0029] The set analysis correlation parameter of any of the associated shared datasets is greater than the preset set analysis correlation parameter, which is determined based on the correlation effect index of each shared dataset.

[0030] The stage-shared dataset is a collection of shared data acquired within the current stage evaluation period.

[0031] Compared with the prior art, the beneficial effects of the present invention are that the technical solution of the present invention divides the associated shared datasets by the correlation between various shared data in the analysis process, and determines the specific data processing process according to the association parameters of the set region and the set experiment association parameters of the associated shared datasets. This makes the actual processing process of the associated shared datasets more consistent with the exposure risks and data quality anomalies of the actual experimental planting schemes of the shared data. In this way, the processing quality of the data that needs to be shared is improved. The present invention improves the actual data processing efficiency while ensuring the quality of the shared data and the safety of the experimental planting scheme.

[0032] Furthermore, in this invention, the associated shared dataset is determined by the set analysis correlation parameters obtained through association analysis of the stage shared dataset. The set analysis correlation parameters characterize the correlation between various shared data in the associated dataset during the analysis process, thereby ensuring the correlation degree of various data in the determined associated shared dataset. This narrows the analysis scope for subsequent data analysis, avoids the risk of excessive data fragmentation during data sharing, and improves the data analysis efficiency of the preprocessing process of shared data.

[0033] Furthermore, in this invention, the set association state of each associated shared dataset is determined based on the set region association parameters and the set experiment association parameters. The set region association parameters and the set experiment association parameters characterize the degree of overlap between the experimental planting areas corresponding to the shared data contained in the associated shared dataset and the degree of association between the corresponding experimental crops in the experimental planting scheme. This further characterizes the strength of the directional influence of the data in the associated shared dataset on the deployment scheme of the experimental planting. Based on the determined set association state, a targeted data processing method is determined, making the data processing process more in line with the actual situation. While ensuring the quality of the shared data and the security of the experimental planting scheme, the processing effect of the shared data is improved, and the data analysis efficiency is increased.

[0034] Furthermore, the risk analysis module in this invention performs shared risk analysis on shared datasets in a type of set association state. It judges the degree of concentrated risk in the experimental area by regional distribution association parameters. When the regional distribution association parameters are large, it indicates that the experimental area corresponding to the shared data is densely distributed. At this time, the environmental dataset is blurred to replace the precise regional information, blocking the possibility of inferring the layout of the scheme through regional location. The exposure risk of the control group deployment scheme during the experimental planting process is judged by data cross-capture parameters. When the data cross-capture parameters are large, it indicates that there are a large number of crop control relationships in the shared data. At this time, the numerical range is replaced according to the protection index or only the increase ratio is shared to avoid inferring the experimental purpose through the control data. This invention ensures the quality of shared data and the safety of experimental planting schemes while avoiding excessive data fragmentation caused by excessive protection. Attached Figure Description

[0035] Figure 1 This is a module connection diagram of the big data-based agricultural data sharing system of the present invention;

[0036] Figure 2 This is a flowchart illustrating the present invention's method for determining whether to perform sharing risk analysis or sharing quality analysis based on the association status of the set it is in, thereby performing sharing risk analysis or sharing quality analysis.

[0037] Figure 3 This is a flowchart illustrating the present invention's method for determining whether to perform regional risk analysis or crop risk analysis on a shared dataset based on regional distribution correlation parameters.

[0038] Figure 4 This is a flowchart illustrating the process of determining whether to perform quality optimization on a shared dataset based on set response association parameters, as described in this invention. Detailed Implementation

[0039] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0040] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0041] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0042] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0043] Please see Figures 1 to 4 As shown, the present invention provides an agricultural data sharing system based on big data, comprising:

[0044] The association sharing module is used to determine the association sharing dataset, which is determined based on the set analysis association parameters obtained by performing association analysis on the stage shared dataset;

[0045] The association assessment module, which is connected to the association sharing module, is used to determine whether to perform sharing risk analysis and sharing quality analysis on the corresponding association sharing dataset based on the set association status of each association sharing dataset. The set association status is determined according to the set area association parameters and the set experiment association parameters.

[0046] The risk analysis module, which is connected to the correlation assessment module, is used to determine whether to perform regional risk analysis or crop risk analysis on the correlated shared dataset based on the regional distribution correlation parameters, and to determine whether to perform shared protection treatment on each shared data based on the distribution correlation ratio index. The regional distribution correlation parameters are determined based on the number of distribution correlation areas existing in the experimental planting area corresponding to each shared data.

[0047] The quality analysis module, which is connected to the association assessment module and the risk analysis module respectively, is used to determine whether to perform quality optimization processing on each associated shared dataset based on the set response association parameters.

[0048] This invention is used for the preprocessing of agricultural planting data in the data sharing process during the experimental planting stage. While ensuring the effectiveness of data sharing, it reduces the risk of leakage of specific plans in the experimental planting process. The experimental planting range that needs to be shared is recorded as the shared analysis range. In this invention, there are several experimental planting areas for crop experimental planting within the shared analysis range. Each experimental planting area corresponds to planting a single, arbitrary type of experimental crop. The agricultural planting data of each category corresponding to the experimental planting area is continuously monitored. The detection results of agricultural planting data of each category obtained from each experimental planting area are recorded as shared data that needs to be uploaded. Each piece of shared data is marked with the experimental planting area, acquisition time, and category of agricultural planting data. The categories of agricultural planting data include, but are not limited to: meteorological data, basic soil data, hydrological data, key nodes of crop growth period (dates of emergence, jointing, and maturity), phenotypic observation results (average plant height, leaf area index), types of pests and diseases (such as wheat aphids and rice blast), and the occurrence time of pests and diseases. The gene sequences of different categories of experimental crops are different.

[0049] This invention utilizes several shared analysis records. Each shared analysis record contains at least one set of parameters during the data processing phase of the data sharing stage within the shared analysis scope, including set regional correlation parameters, set experimental correlation parameters, distribution evaluation index, regional distribution correlation parameters, distribution correlation ratio index, data cross-capture parameters, set response correlation parameters, and set analysis correlation parameters. Each shared analysis record also has a corresponding qualification mark, which indicates whether the sharing quality and security level of the agricultural planting data within the shared analysis scope meet the user's needs. It is understood that the user can determine whether the sharing quality and security level of the agricultural planting data within the shared analysis scope meet their needs based on self-defined indicators. These self-defined indicators include, but are not limited to, sharing utilization efficiency, which is the reciprocal of the average time consumed in each fusion analysis process based on the shared data.

[0050] Specifically, the set association states include a first type of set association state and a second type of set association state;

[0051] The association evaluation module records the associated shared datasets that have a set region association parameter greater than a preset set region association parameter or a set experiment association parameter greater than a preset set experiment parameter as associated shared datasets in a first-class set association state.

[0052] The association evaluation module records the associated shared datasets that have set region association parameters less than or equal to preset set region association parameters and set experiment association parameters less than or equal to preset set experiment association parameters as associated shared datasets in the second-class set association state.

[0053] Specifically, for a single associated shared dataset, the set region association parameters To share the number of experimental planting areas within the scope of the analysis, The set of experimental association parameters represents the number of different experimental planting areas corresponding to each shared data item within the associated shared dataset. This represents the number of categories of experimentally planted crops corresponding to each shared data item within the associated shared dataset. To share the number of categories of experimentally planted crops within the scope of the analysis;

[0054] The values ​​of the preset set region association parameter and the preset set experiment association parameter can be determined by the user according to the actual working scenario. For example, the user can set them according to the shared analysis records. The higher the user's requirements for the sharing quality and security of agricultural planting data within the shared analysis scope, the larger the value of the preset set region association parameter and the preset set experiment association parameter. A method for determining the value of the preset set region association parameter is provided, which records the maximum value of the set region association parameter of the associated shared dataset in the second-class set association state in the shared analysis records that meet the user's requirements for the sharing quality and security of agricultural planting data within the shared analysis scope as the preset set region association parameter. A method for determining the value of the preset set experiment association parameter is provided, which records the maximum value of the set experiment association parameter of the associated shared dataset in the second-class set association state in the shared analysis records that meet the user's requirements for the sharing quality and security of agricultural planting data within the shared analysis scope as the preset set experiment association parameter.

[0055] Specifically, the risk analysis module performs shared risk analysis on associated shared datasets that are in a state of set association, wherein,

[0056] The regional distribution association parameter is determined based on the distribution association ratio index corresponding to each shared data item in each associated shared dataset. The distribution association ratio index is based on the number of distribution association areas existing in the experimental planting area corresponding to each shared data item.

[0057] Specifically, for a single associated shared dataset, when the associated shared dataset is in a set association state, it indicates that the experimental planting areas corresponding to the shared data contained in the associated shared dataset have a strong correlation or the experimental planting crops corresponding to the shared data have a high degree of correlation in the experimental planting scheme. Therefore, it is necessary to conduct a sharing risk analysis for the associated shared dataset to prevent the risk of leakage of the scheme due to the strong orientation of the associated shared dataset to the deployment scheme of the experimental planting. The regional distribution association parameter is used to characterize whether there is a positional correlation in the distribution of the experimental planting areas corresponding to the shared data in the associated shared dataset.

[0058] For a single associated shared dataset in a set-association state, the regional distribution association parameter is the average of the distribution association proportion index of the experimental planting areas corresponding to each shared data item within the associated shared dataset. For a single experimental planting area, the distribution association proportion index is the proportion of the number of distributed associated areas corresponding to the shared data item within the associated shared dataset to the total number of different experimental planting areas corresponding to each shared data item within the associated shared dataset. The distributed associated areas are experimental planting areas whose distribution evaluation index is less than or equal to a preset distribution evaluation index. This refers to the number of distributed associated regions that exist within the experimental planting area corresponding to each shared data item in the associated shared dataset. The number of different experimental planting areas corresponding to each shared data item in the associated shared dataset; for any two experimental planting areas, the distribution evaluation index between the two experimental planting areas is the distance between the centroids of the corresponding areas in the graph.

[0059] The value of the preset distribution evaluation index can be determined by the user based on the actual working scenario. For example, the user can set it based on the shared analysis records. The higher the user's requirements for the sharing quality and security of agricultural planting data within the shared analysis scope, the smaller the value of the preset distribution evaluation index. A method for determining the value of the preset distribution evaluation index is provided, which is the average value of the distribution evaluation index corresponding to each distribution-related region in the shared analysis records that meets the user's requirements for the sharing quality and security of agricultural planting data within the shared analysis scope.

[0060] Specifically, the risk analysis module performs regional risk analysis on shared datasets with regional distribution correlation parameters greater than preset regional distribution correlation parameters.

[0061] The determination of whether to implement sharing protection measures for the corresponding shared data is based on the distribution correlation ratio index of each type of shared data.

[0062] Specifically, the risk analysis module performs shared protection processing on shared data where any distribution correlation ratio index is greater than a preset distribution correlation ratio index, and determines the data protection index of the shared data based on the distribution correlation ratio index.

[0063] The data protection index and the distribution correlation ratio index are positively correlated.

[0064] Specifically, for a single associated shared dataset in a type-one set association state, if the regional distribution association parameter is greater than the preset regional distribution association parameter, it indicates that the experimental planting areas corresponding to the shared data included in the associated shared dataset not only overlap but also have a relatively concentrated distribution. This indicates that the degree of association between the planting areas included in the currently determined associated shared dataset is relatively strong. Therefore, regional risk analysis is performed on the associated shared dataset to optimize the actual deployment of the experimental planting areas included in the current associated shared dataset and the degree of disclosure of the deployment association, ensuring the degree of protection of the shared data for the actual scheme during the experimental planting process.

[0065] For any shared data item within a shared dataset that is in a state of association of a set, if the distribution association ratio index is greater than the preset distribution association ratio index, it indicates that the shared data currently contains a large amount of data carrying deployment information of the experimental planting area within the shared dataset. To ensure the security of the deployment plan during the actual experimental planting process, the shared data item is protected by sharing protection processing. The environmental dataset of the corresponding experimental planting area during the current evaluation period is used to replace the referential information of the experimental planting area. The environmental dataset is a set of data ranges of values ​​obtained for each category of planting environmental data during the current evaluation period. The value range includes the corresponding value. The ambiguity of the data range is positively correlated with the determined data protection index. The categories of planting environmental data include, but are not limited to: meteorological data, soil basic data, and hydrological data.

[0066] The values ​​of the preset regional distribution association parameter and the preset distribution association ratio index can be determined by the user according to the actual working scenario. For example, the user can set them according to the shared analysis records. A method for determining the value of the preset regional distribution association parameter is provided, in which the shared analysis record for regional risk analysis of the associated shared dataset is recorded as the distribution reference record, and the minimum value of the regional distribution association ratio index in the distribution reference record that meets the user's requirements for the sharing quality and sharing security of agricultural planting data within the shared analysis scope is recorded as the preset regional distribution association parameter. A method for determining the value of the preset distribution association ratio index is provided, in which the minimum value of the distribution association ratio index of the shared data that has undergone sharing protection processing in the distribution reference record that meets the user's requirements for the sharing quality and sharing security of agricultural planting data within the shared analysis scope is recorded as the preset distribution association ratio index.

[0067] Specifically, the risk analysis module performs crop risk analysis on shared datasets where the regional distribution correlation parameter is less than or equal to a preset regional distribution correlation parameter or where regional risk analysis has been completed.

[0068] The risk analysis module detects the cross-capture parameters of the data for each shared data item within the associated shared dataset, and determines whether to perform sharing protection processing on the corresponding shared data based on the cross-capture parameters of each shared data item.

[0069] Specifically, the risk analysis module performs shared protection processing on any shared data whose cross-capture parameter is greater than the preset cross-capture parameter, and determines the data protection index of the shared data based on the cross-capture parameter.

[0070] The data protection index is positively correlated with the data cross-capture parameter.

[0071] Specifically, for a single associated shared dataset in a type-one set association state, if the regional distribution association parameter is less than or equal to the preset regional distribution association parameter, it indicates that although the experimental planting areas corresponding to the shared data included in the associated shared dataset are relatively overlapping, the actual distribution is relatively dispersed. Based on the relevant data, it can be inferred that the actual risk of the associated shared dataset is low. Therefore, crop risk analysis is performed on associated shared datasets whose regional distribution association parameter is less than or equal to the preset regional distribution association parameter or whose regional risk analysis has been completed, so as to reduce the risk of leakage of the shared data to the experimental purpose and experimental comparison scheme during the experimental planting process.

[0072] For any shared data item within a single associated shared dataset used for crop risk analysis, the data cross-capture parameter is the proportion of the number of cross-captured data items of different items within the associated shared dataset to the total number of shared data items of different items within the associated shared dataset. This refers to the number of cross-captured data points of different items within the associated shared dataset for that particular shared data item. The number of shared data items for different items in the associated shared dataset; the cross-capture data is shared data in which the experimental crops in the corresponding experimental planting area have a control relationship with the experimental crops in the experimental planting area corresponding to the shared data item.

[0073] For any shared data item within a shared dataset that is in a state of association with a single set, if the data cross-capture parameter of the shared data is greater than the preset data cross-capture parameter, it indicates that the shared data currently contains a large amount of data information with control group attributes within the associated shared dataset. To ensure the safety of the experimental purpose and deployment plan during the actual experimental planting process, the shared data is subjected to sharing protection processing. The larger the value of the data protection index determined for the shared data, the greater the degree of fuzziness processing for the shared data. If the data protection index is greater than the first preset data protection index but less than or equal to the second preset data protection index, then the value of the shared data is replaced by a value range, and a value range containing the value is determined to replace the value of the shared data. The fuzziness of the data range is positively correlated with the data protection index. The fuzziness is the ratio of the absolute value of the difference between the maximum and minimum values ​​of the corresponding data range to the value of the shared data. If the data protection index is greater than the second preset data protection index, then only the increase ratio of the value of the shared data compared to the value obtained in the previous evaluation period is shared. How to determine the increase ratio of each shared data item is a content that is already known to those skilled in the art and will not be elaborated here.

[0074] The values ​​of the preset data cross-capture parameters, the first preset data protection index, and the second preset data protection index can be determined by the user according to the actual working scenario. For example, the user can set them based on shared analysis records. The higher the user's requirements for the sharing quality of agricultural planting data within the shared analysis scope, the smaller the value of the preset data cross-capture parameters. A method for determining the value of the preset data cross-capture parameters is provided, where the shared analysis record for which the data protection index of the shared data is determined based on the data cross-capture parameters is recorded as a cross-reference record. The minimum value of the data cross-capture parameters in the cross-reference records that meet the user's requirements for the sharing quality and security of agricultural planting data within the shared analysis scope is recorded as the preset data cross-capture parameter. A method for determining the values ​​of the first preset data protection index and the second preset data protection index is provided, where the minimum value of the data protection index of the shared data in the cross-reference records that meet the user's requirements for the sharing quality and security of agricultural planting data within the shared analysis scope, after numerical range replacement, is recorded as the first preset data protection index. The minimum value of the data protection index of the shared data in the cross-reference records that meet the user's requirements for the sharing quality and security of agricultural planting data within the shared analysis scope, after the shared increase ratio, is recorded as the second preset data protection index.

[0075] Specifically, the quality analysis module performs shared quality analysis on associated shared datasets that are in a state of association of two types of sets or have completed shared risk analysis;

[0076] The set response association parameters are determined based on the response proportion index of each shared data item.

[0077] Specifically, the quality analysis module determines that associated shared datasets whose set response correlation parameters are less than or equal to preset set response correlation parameters undergo quality optimization processing, wherein...

[0078] Obtain response deviation data from the associated shared dataset that needs quality optimization, and issue data quality warnings based on the response deviation data.

[0079] The response deviation data refers to response analysis data where the response proportion index is less than or equal to the preset set of response correlation parameters.

[0080] Specifically, for a single associated shared dataset, when the dataset is in a type II set association state, it indicates that the overlap between the experimental planting areas corresponding to the shared data contained in the dataset and the correlation between the corresponding experimental crops in the experimental planting scheme are both low. This suggests that the associated shared dataset has weak directionality for the deployment scheme of the experimental planting. Therefore, for associated shared datasets in a type II set association state or those that have completed sharing risk analysis, a sharing quality analysis is performed to avoid low data sharing quality in the experimental planting process due to poor reliability and effectiveness of the acquired shared data. The set response association parameter characterizes whether there are other data responses when the data in the associated shared dataset changes, thus characterizing the quality of the acquired data. This determines whether optimization of the shared data in the associated shared dataset is needed. For data with response deviations, a data quality warning is sent to the user. The set response association parameter is the average of the response proportion index of each response analysis data contained in the associated shared dataset. If there is no response analysis data, the set response association parameter is recorded as 0. For a single response analysis data, the response proportion index is... , where m is the number of shared data within the associated shared dataset that has an agricultural logical association with the data in this response analysis. This refers to the number of response analysis data within the associated shared dataset that have an agricultural logical relationship with the response analysis data. If there is no shared data within the associated shared dataset that has an agricultural logical relationship with the response analysis data, the response proportion index of that response analysis data is recorded as 0. For a single shared data item, if the difference between the average value of each acquisition of that shared data item in the current evaluation period and the average value of each acquisition of that shared data item in the previous evaluation period is greater than a preset difference level, then that shared data item is recorded as response analysis data. y is the absolute value of the difference between the average value of all values ​​obtained for this shared data in the current evaluation period and the average value of all values ​​obtained for this shared data in the previous evaluation period. This is the average of the values ​​obtained from each instance of this shared data within the previous evaluation period. If the difference is 0, then the degree of difference is recorded as 0. How to determine whether there is an agricultural logical relationship between the shared data is a topic that is already known to those skilled in the art and will not be elaborated here. For example, changes in soil moisture cause changes in crop transpiration rate, and there is an agricultural logical relationship between the two.

[0081] The user can determine the values ​​of the preset set response association parameter and the preset difference degree according to the actual working scenario. For example, the user can set them according to the shared analysis records. The higher the user's requirements for the sharing quality of agricultural planting data within the shared analysis scope, the larger the value of the preset set response association parameter. A method for determining the value of the preset set response association parameter is provided, which is the maximum value of the set response association parameter of the associated shared dataset that has undergone quality optimization processing in the shared analysis records that meet the user's sharing quality requirements for agricultural planting data within the shared analysis scope. A preset difference degree value is also provided, which is 0.1.

[0082] Specifically, the association and sharing module periodically performs association analysis on the shared datasets of each stage;

[0083] The set analysis correlation parameter of any of the associated shared datasets is greater than the preset set analysis correlation parameter, which is determined based on the correlation effect index of each shared dataset.

[0084] The stage-shared dataset is a collection of shared data acquired within the current stage evaluation period.

[0085] In this invention, a cyclical phase evaluation cycle is applied. The duration of the phase evaluation cycle can be determined by the user. The higher the user's requirements for the sharing quality of agricultural planting data within the shared analysis scope, the shorter the duration of the phase evaluation cycle. One phase evaluation cycle duration is provided, which is 10 days. At the end of each phase evaluation cycle, correlation analysis is performed on the corresponding phase shared dataset to determine the associated shared dataset.

[0086] If the current time is the end of a phase evaluation period, the set of shared data obtained within the shared analysis scope during that phase evaluation period is denoted as the phase shared dataset. Association analysis is performed on the phase shared datasets. Each associated shared dataset contains several shared data items from the phase shared dataset. For a single associated shared dataset, the set analysis association parameter is the average of the association effect indices of each shared data item within that associated shared dataset. For a single shared data item within that associated shared dataset, the association effect index is the proportion of the number of analytical data items present in that shared data item within the associated shared dataset to the total number of shared data items within the associated shared dataset. This refers to the number of analytical data points that exist within the associated shared dataset for this shared data item. The number of shared data existing in the associated shared dataset; the data used for analysis is the shared data of agricultural planting data that is used together with the category of agricultural planting data corresponding to the shared data to analyze the agricultural planting situation.

[0087] The value of the preset set analysis association parameter can be determined by the user according to the actual working scenario. For example, the user can set it according to the shared analysis records. The higher the user's requirements for the sharing quality of agricultural planting data within the shared analysis scope, the larger the value of the preset set analysis association parameter. A method for determining the value of the preset set analysis association parameter is provided, which is the minimum value of the set analysis association parameter of the shared analysis records that meet the user's sharing quality requirements for agricultural planting data within the shared analysis scope.

[0088] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An agricultural data sharing system based on big data, characterized in that, include: The association sharing module is used to determine the association sharing dataset, which is determined based on the set analysis association parameters obtained by performing association analysis on the stage shared dataset; The association assessment module, which is connected to the association sharing module, is used to determine whether to perform sharing risk analysis and sharing quality analysis on the corresponding association sharing dataset based on the set association status of each association sharing dataset. The set association status is determined according to the set area association parameters and the set experiment association parameters. The risk analysis module, which is connected to the correlation assessment module, is used to determine whether to perform regional risk analysis or crop risk analysis on the correlated shared dataset based on the regional distribution correlation parameters, and to determine whether to perform shared protection treatment on each shared data based on the distribution correlation ratio index. The regional distribution correlation parameters are determined based on the number of distribution correlation areas existing in the experimental planting area corresponding to each shared data. The quality analysis module is connected to the association assessment module and the risk analysis module respectively, and is used to determine whether to perform quality optimization processing on each associated shared dataset based on the set response association parameters. The set association state includes a first type of set association state and a second type of set association state; The association evaluation module records the associated shared datasets that have a set region association parameter greater than a preset set region association parameter or a set experiment association parameter greater than a preset set experiment parameter as associated shared datasets in a first-class set association state. The association evaluation module records the associated shared datasets that have a set region association parameter less than or equal to a preset set region association parameter and a set experiment association parameter less than or equal to a preset set experiment association parameter as associated shared datasets in the second type of set association state. For a single associated shared dataset, the set region association parameters , To share the number of experimental planting areas within the scope of the analysis, The set of experimental association parameters represents the number of different experimental planting areas corresponding to each shared data item within the associated shared dataset. , This represents the number of categories of experimentally planted crops corresponding to each shared data item within the associated shared dataset. This is to determine the number of categories of experimentally planted crops that exist within the scope of the shared analysis.

2. The agricultural data sharing system based on big data according to claim 1, characterized in that, The risk analysis module performs shared risk analysis on associated shared datasets that are in a state of set association, wherein... The regional distribution association parameter is determined based on the distribution association ratio index corresponding to each shared data item in each associated shared dataset. The distribution association ratio index is based on the number of distribution association areas existing in the experimental planting area corresponding to each shared data item.

3. The agricultural data sharing system based on big data according to claim 2, characterized in that, The risk analysis module performs regional risk analysis on shared datasets where the regional distribution correlation parameter is greater than the preset regional distribution correlation parameter. The determination of whether to implement sharing protection measures for the corresponding shared data is based on the distribution correlation ratio index of each type of shared data.

4. The agricultural data sharing system based on big data according to claim 3, characterized in that, The risk analysis module performs shared protection processing on shared data whose distribution correlation ratio index is greater than the preset distribution correlation ratio index, and determines the data protection index of the shared data based on the distribution correlation ratio index. The data protection index and the distribution correlation ratio index are positively correlated.

5. The agricultural data sharing system based on big data according to claim 4, characterized in that, The risk analysis module performs crop risk analysis on shared datasets where the regional distribution correlation parameter is less than or equal to a preset regional distribution correlation parameter or where regional risk analysis has been completed. The risk analysis module detects the cross-capture parameters of the data for each shared data item within the associated shared dataset, and determines whether to perform sharing protection processing on the corresponding shared data based on the cross-capture parameters of each shared data item.

6. The agricultural data sharing system based on big data according to claim 5, characterized in that, The risk analysis module performs shared protection processing on any shared data whose data cross-capture parameter is greater than the preset data cross-capture parameter, and determines the data protection index of the shared data based on the data cross-capture parameter. The data protection index is positively correlated with the data cross-capture parameter.

7. The agricultural data sharing system based on big data according to claim 6, characterized in that, The quality analysis module performs shared quality analysis on associated shared datasets that are in a state of association of two types of sets or have completed shared risk analysis. The set response association parameters are determined based on the response proportion index of each shared data item.

8. The agricultural data sharing system based on big data according to claim 7, characterized in that, The quality analysis module determines the quality optimization process for associated shared datasets whose set response correlation parameters are less than or equal to preset set response correlation parameters. Obtain response deviation data from the associated shared dataset that needs quality optimization, and issue data quality warnings based on the response deviation data. The response deviation data refers to response analysis data where the response proportion index is less than or equal to the preset set of response correlation parameters.

9. The agricultural data sharing system based on big data according to claim 1, characterized in that, The association and sharing module periodically performs association analysis on the shared datasets of each stage; The set analysis correlation parameter of any of the associated shared datasets is greater than the preset set analysis correlation parameter, which is determined based on the correlation effect index of each shared dataset. The stage-shared dataset is a collection of shared data acquired within the current stage evaluation period.

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