Methods, devices and electronic equipment for identifying abnormal crop planting areas

Abstract

This application discloses a method, apparatus, and electronic device for identifying anomalies in crop planting area, belonging to the field of computer technology. The method includes: acquiring planting area data of a target crop in a target administrative region, a first administrative region, and a second administrative region; the first administrative region is the administrative region one level above the target administrative region, and the second administrative region is an adjacent administrative region at the same level as the target administrative region; based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, obtaining a first anomaly identification result, and based on the planting area data of the target administrative region and the second administrative region, obtaining a second anomaly identification result; if both the first and second anomaly identification results are anomalies, determining that the planting area data of the target administrative region is anomaly data. The crop planting area anomaly identification method disclosed in this application aims to improve the accuracy of crop planting area anomaly identification.

Description

Technical Field

[0001] This application belongs to the field of computer technology, and in particular relates to a method, device and electronic equipment for identifying abnormal crop planting areas. Background Technology

[0002] As a vital staple food crop, the planting area of ​​rice plays a crucial role in ensuring food security. Generally, statistical methods can be used to determine whether the planting area is abnormal by monitoring indicators such as the rate of change between data points.

[0003] However, due to the constant changes in crop planting plans at various administrative levels, the trend of crop planting area is not stable. Statistical detection methods struggle to distinguish whether changes in crop planting area are due to statistical anomalies or simply changes in administrative planting plans, thus resulting in low accuracy in identifying crop planting area anomalies. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method, apparatus, and electronic device for identifying abnormal crop planting areas, in order to improve the accuracy of identifying abnormal crop planting areas.

[0005] Firstly, this application provides a method for identifying abnormal crop planting areas, the method comprising:

[0006] Obtain planting area data for the target crop in the target administrative region, the first administrative region, and the second administrative region respectively; the first administrative region is the administrative region above the target administrative region, and the second administrative region is the adjacent administrative region at the same level as the target administrative region;

[0007] Based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, a first anomaly identification result is obtained, and based on the planting area data of the target administrative region and the second administrative region, a second anomaly identification result is obtained.

[0008] If both the first and second anomaly identification results are abnormal, the planting area data of the target administrative region is determined to be abnormal data.

[0009] According to the crop planting area anomaly identification method of this application, the planting area data of target crops in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region) are obtained respectively. Based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, a first anomaly identification result is obtained, and based on the planting area data of the target administrative region and the second administrative region, a second anomaly identification result is obtained. If both the first and second anomaly identification results are abnormal, the planting area data of the target administrative region is determined to be abnormal data. Based on the planting area data of target crops in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region), anomaly identification is performed on the planting area data of target crops in the target administrative region, taking into account the planting plan of the administrative region, so as to improve the accuracy of crop planting area anomaly identification.

[0010] According to one embodiment of this application, based on planting area data of the target administrative region, the first administrative region, and the second administrative region, a first anomaly identification result is obtained, including:

[0011] Using a vertical hierarchical anomaly detection model, based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, the first random error and random effect of the target administrative region are obtained; the first random error represents the time error of the planting area in the current year, and the random effect represents the objective contribution rate of the target administrative region to the first administrative region in the current year;

[0012] Determine whether there are anomalies in the first random error and random effects of the target administrative region;

[0013] If at least one of the first random error and random effect in the target administrative region is abnormal, obtain the first anomaly identification result indicating the anomaly.

[0014] According to one embodiment of this application, determining whether there are anomalies in the first random error and random effect of the target administrative region includes:

[0015] Based on the first random error of the target administrative region in historical years, a first normal distribution is obtained; the first normal distribution is the distribution of the first random error corresponding to each historical year with respect to the historical year.

[0016] Based on the first normal distribution, determine whether there is an anomaly in the first random error of the target administrative region.

[0017] According to one embodiment of this application, determining whether there are anomalies in the first random error and random effect of the target administrative region includes:

[0018] Based on the random effects of the second administrative region, a second normal distribution is obtained; the second normal distribution is the distribution of each random effect with respect to each administrative region of the second administrative region.

[0019] Determine whether the random effects in the target administrative region are abnormal based on the second normal distribution.

[0020] According to one embodiment of this application, a second anomaly identification result is obtained based on planting area data of the target administrative region and the second administrative region, including:

[0021] Using a horizontal spatiotemporal anomaly discrimination model, based on the planting area data of the target administrative region and the second administrative region, the second random error of the target administrative region is obtained; the second random error represents the spatial error of the planting area in the current year.

[0022] Determine whether there are any anomalies in the second random error of the target administrative region;

[0023] In the event that the second random error is abnormal, obtain the second anomaly identification result that indicates the anomaly.

[0024] According to one embodiment of this application, after determining that the planting area data of the target administrative region is abnormal data when both the first anomaly identification result and the second anomaly identification result are abnormal, the method further includes:

[0025] By using a pre-established posterior distribution model, the planting area data of the target administrative region is corrected based on the planting area data of the target administrative region, fixed effects and random effects, and the planting area data of the first administrative region.

[0026] According to one embodiment of this application, a correction process is performed on the planting area data of the target administrative region based on the planting area data of the target administrative region, fixed effects and random effects, and the planting area data of the first administrative region, using a pre-established posterior distribution model. This includes:

[0027] If the fixed effects of the current year and several adjacent historical years in the target administrative region are all abnormal, the fixed effects of the current year are updated based on the normal fixed effects of the target administrative region; the normal fixed effects are determined based on the normal fixed effects of the historical years of the target administrative region.

[0028] Based on the planting area data of the target administrative region, the updated fixed and random effects, and the planting area data of the first administrative region, the planting area data of the target administrative region is corrected.

[0029] Secondly, this application provides a device for identifying abnormal crop planting areas, comprising:

[0030] The first acquisition module is used to acquire planting area data of target crops in the target administrative region, the first administrative region, and the second administrative region, respectively; the first administrative region is the administrative region above the target administrative region, and the second administrative region is an adjacent administrative region at the same level as the target administrative region;

[0031] The second acquisition module is used to acquire a first anomaly identification result based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, and to acquire a second anomaly identification result based on the planting area data of the target administrative region and the second administrative region.

[0032] The determination module is used to determine that the planting area data of the target administrative region is abnormal data when both the first and second anomaly identification results are abnormal.

[0033] According to the crop planting area anomaly identification device of this application, the planting area data of target crops in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region) are obtained respectively; based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, a first anomaly identification result is obtained, and based on the planting area data of the target administrative region and the second administrative region, a second anomaly identification result is obtained; if both the first and second anomaly identification results are abnormal, the planting area data of the target administrative region is determined to be abnormal data. Based on the planting area data of target crops in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region), anomaly identification is performed on the planting area data of target crops in the target administrative region, taking into account the planting plan of the administrative region, so as to improve the accuracy of crop planting area anomaly identification.

[0034] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described method for identifying abnormal crop planting areas.

[0035] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for identifying abnormal crop planting areas.

[0036] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for identifying abnormal crop planting areas.

[0037] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0038] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0039] Figure 1 This is one of the flowcharts illustrating the method for identifying abnormal crop planting areas provided in this application embodiment;

[0040] Figure 2 This is the second flowchart of the method for identifying abnormal crop planting area provided in the embodiments of this application;

[0041] Figure 3 This is a schematic diagram of the structure of the crop planting area anomaly identification device provided in the embodiments of this application;

[0042] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0043] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0044] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0045] The method, apparatus, and electronic equipment for identifying abnormal crop planting areas provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0046] Among them, the method for identifying abnormal crop planting areas can be applied to the terminal, specifically executed by the hardware or software in the terminal.

[0047] The terminal includes, but is not limited to, portable communication devices such as mobile phones or tablets with touch-sensitive surfaces (e.g., touchscreen displays and / or touchpads). It should also be understood that, in some embodiments, the terminal may not be a portable communication device, but rather a desktop computer with touch-sensitive surfaces (e.g., touchscreen displays and / or touchpads).

[0048] The following embodiments describe a terminal including a display and a touch-sensitive surface. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, mouse, and joystick.

[0049] The crop planting area anomaly identification method provided in this application embodiment can be executed by an electronic device or a functional module or entity in an electronic device that can implement the crop planting area anomaly identification method. The electronic devices mentioned in this application embodiment include, but are not limited to, mobile phones, tablets, computers, cameras and wearable devices. The crop planting area anomaly identification method provided in this application embodiment will be described below using an electronic device as the execution subject as an example.

[0050] like Figure 1 As shown, the method for identifying abnormal crop planting areas includes steps 110, 120, and 130.

[0051] Step 110: Obtain the planting area data of the target crop in the target administrative region, the first administrative region, and the second administrative region respectively; the first administrative region is the administrative region above the target administrative region, and the second administrative region is the adjacent administrative region at the same level as the target administrative region.

[0052] In practice, the target administrative region can be any administrative region, or it can be the administrative region where the data on the planting area of ​​the target crop is to be judged as abnormal.

[0053] In actual implementation, the target crop can be any crop, such as rice, wheat, potatoes, or any theoretically feasible crop.

[0054] In practice, the planting area data of the target crops in the target administrative region, the first administrative region, and the second administrative region can include the planting area data of the target crops in the target administrative region, the first administrative region, and the second administrative region in the current year, or it can include the planting area data of the target crops in the target administrative region, the first administrative region, and the second administrative region in historical years.

[0055] Step 120: Based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, obtain the first anomaly identification result, and based on the planting area data of the target administrative region and the second administrative region, obtain the second anomaly identification result.

[0056] In some embodiments, a first anomaly identification result can be obtained based on the planting area data of the target administrative region, the first administrative region, and the second administrative region using a vertical hierarchical anomaly discrimination model.

[0057] In practice, a vertical hierarchical anomaly detection model can be used to identify the vertical hierarchical dimension of the planting area data of target crops in the target administrative region, from the next higher level administrative region to the current level administrative region.

[0058] In some embodiments, a second anomaly identification result can be obtained based on the planting area data of the target administrative region and the second administrative region using a lateral spatiotemporal anomaly discrimination model.

[0059] In actual implementation, a horizontal spatiotemporal anomaly discrimination model can be used to identify the horizontal dimension of adjacent administrative regions at the same level based on the planting area data of target crops in the target administrative region.

[0060] Step 130: If both the first and second anomaly identification results are abnormal, determine that the planting area data of the target administrative region is abnormal data.

[0061] In some embodiments, if either the first anomaly identification result or the second anomaly identification result is normal, the planting area data of the target crop in the target administrative region is determined to be normal data.

[0062] In actual implementation, if both the first and second anomaly identification results are abnormal, it can be confirmed that the planting area data of the target crop in the target administrative region is abnormal in both the vertical hierarchical dimension and the horizontal dimension. That is, the planting area data of the target crop in the target administrative region is abnormal data.

[0063] In some embodiments, after obtaining the first and second anomaly identification results for the target administrative region, anomaly identification can also be performed on the planting area data of the target crop in the second administrative region to obtain the anomaly type of the planting area data for the target crop in the target administrative region. In some embodiments, if the planting area data of the target crop in the target administrative region is anomalous data and the planting area data of the target crop in the second administrative region is normal data, the anomaly type of the planting area data of the target crop in the target administrative region is a single anomaly. If the planting area data of the target crop in both the target administrative region and the second administrative region is anomalous data, the anomaly type of the planting area data of the target crop in the target administrative region is a regional anomaly.

[0064] In some embodiments, after obtaining the first and second anomaly identification results for the target administrative region, the anomaly type of the target crop's planting area data in the target administrative region can be obtained based on the historical planting area data of the target crop in the target administrative region. For example, if the historical planting area data of the target crop in the target administrative region is abnormal, the anomaly type of the target crop's planting area data in the target administrative region can be determined to be a systematic anomaly.

[0065] In some embodiments, after determining that the planting area data of the target crop in the target administrative region is abnormal data, the planting area data of the target crop in the target administrative region can be corrected.

[0066] According to the crop planting area anomaly identification method of this application, the planting area data of target crops in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region) are obtained respectively. Based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, a first anomaly identification result is obtained, and based on the planting area data of the target administrative region and the second administrative region, a second anomaly identification result is obtained. If both the first and second anomaly identification results are abnormal, the planting area data of the target administrative region is determined to be abnormal data. Based on the planting area data of target crops in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region), anomaly identification is performed on the planting area data of target crops in the target administrative region, taking into account the planting plan of the administrative region, so as to improve the accuracy of crop planting area anomaly identification.

[0067] In some embodiments, a vertical hierarchical anomaly detection model can be used to obtain the first random error and random effect of the target administrative region based on the planting area data of the target administrative region, the first administrative region, and the second administrative region. The first random error represents the time error of the planting area in the current year, and the random effect represents the objective contribution rate of the target administrative region to the first administrative region in the current year. It is determined whether the first random error and random effect of the target administrative region are abnormal. If at least one of the first random error and random effect of the target administrative region is abnormal, a first anomaly identification result indicating anomaly in the planting area data of the target administrative region is obtained.

[0068] In practice, the vertical hierarchical anomaly detection model can be a hierarchical Bayesian model or any other theoretically feasible model. The first random error can represent the error in the planting area of ​​the target administrative region in the current year relative to the planting area in historical years. The random effect represents the contribution rate of the target administrative region to the first administrative region under the influence of objective factors (such as natural disasters, policy impacts, etc.) in the current year.

[0069] In practice, the planting area data of the target administrative region, the first administrative region, and the second administrative region can be input into the vertical hierarchical anomaly discrimination model to obtain the first random error and random effect of the target administrative region, and to determine whether there are any anomalies in the first random error and random effect of the target administrative region.

[0070] In actual implementation, based on the vertical hierarchical anomaly discrimination model, the first random error and random effect of the target administrative region and each second administrative region can be obtained through the planting area data of the target administrative region, the first administrative region and several second administrative regions. Based on the first random error of all second administrative regions, it can be determined whether the first random error of the target administrative region is abnormal, and based on the random effect of all second administrative regions, it can be determined whether the random effect of the target administrative region is abnormal.

[0071] In some embodiments, after obtaining the first random error of the target administrative region and each second administrative region, it can be determined whether the first random error of the target administrative region is abnormal based on the first random errors of all the second administrative regions, using any theoretically feasible mathematical processing method. For example, at least one of the mean, variance, and standard deviation can be obtained based on the first random errors of all the second administrative regions, and it can be determined whether the first random error of the target administrative region is abnormal based on at least one of the mean, variance, and standard deviation.

[0072] In some embodiments, after obtaining the random effects of the target administrative region and each second administrative region, it can be determined whether the random effects of the target administrative region are abnormal based on the random effects of all second administrative regions, using any theoretically feasible mathematical processing method. For example, at least one of the mean, variance, and standard deviation can be obtained based on the random effects of all second administrative regions, and it can be determined whether the random effects of the target administrative region are abnormal based on at least one of the mean, variance, and standard deviation.

[0073] According to the crop planting area anomaly identification method of this application, the planting area data of the target crop in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region) are obtained respectively. Using a vertical hierarchical anomaly discrimination model, based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, the first random error and random effect of the target administrative region are obtained. It is then determined whether the first random error and random effect of the target administrative region are abnormal. If at least one of the first random error and random effect of the target administrative region is abnormal, an indicator of the planting area of ​​the target administrative region is obtained. The system identifies first anomalies in the area data and obtains second anomaly identification results based on the planting area data of the target administrative region and the second administrative region. If both the first and second anomaly identification results are abnormal, the planting area data of the target administrative region is determined to be abnormal data. Based on the planting area data of the target crop in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region), and considering the planting plan of the administrative region, the system identifies anomalies in the planting area data of the target crop in the target administrative region to improve the accuracy of crop planting area anomaly identification.

[0074] In some embodiments, a first normal distribution can be obtained based on the first random error of the target administrative region in historical years; the first normal distribution is the distribution of the first random error corresponding to each historical year with respect to the historical years; based on the first normal distribution, it can be determined whether the first random error of the target administrative region is abnormal.

[0075] In actual implementation, the vertical hierarchical anomaly discrimination model can obtain the first random error and the first normal distribution of the target administrative region in historical years based on the current year's planting area data of the target crop in the target administrative region, the first administrative region, and the second administrative region.

[0076] In some embodiments, the vertical hierarchical anomaly detection model can interpolate the first random error of the target administrative region and the historical year of the target administrative region based on the following formula:

[0077]

[0078] Where α represents the contribution rate of the target administrative region or the i-th second administrative region; y B,t Let x represent the planting area data of the first administrative region in year t. i,t This represents the planting area data for the target administrative region or the i-th second administrative region in year t; β i Represents the solid-state effect of the target administrative region or the i-th second administrative region; u i Let represent the random effect of the target administrative region or the i-th second administrative region, and ; Let represent the first random error of the target administrative region or the i-th second administrative region, and .

[0079] In some embodiments, if the first random error of the target administrative region deviates from the first normal distribution, it can be determined that the first random error of the target administrative region is abnormal.

[0080] In some embodiments, if the absolute value of the first random error of the target administrative region is greater than the first standard deviation, it can be determined that the first random error of the target administrative region deviates from the first normal distribution, and the first random error of the target administrative region is abnormal. The first standard deviation can be three times the standard deviation of the first normal distribution.

[0081] According to the crop planting area anomaly identification method of this application, the planting area data of the target crop in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region) are obtained respectively. Using a vertical hierarchical anomaly discrimination model, based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, the first random error and random effect of the target administrative region are obtained. It is then determined whether the first random error and random effect of the target administrative region are abnormal. If at least one of the first random error and random effect of the target administrative region is abnormal, an indicator of the planting area of ​​the target administrative region is obtained. The system identifies first anomalies in the area data and obtains second anomaly identification results based on the planting area data of the target administrative region and the second administrative region. If both the first and second anomaly identification results are abnormal, the planting area data of the target administrative region is determined to be abnormal data. Based on the planting area data of the target crop in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region), and considering the planting plan of the administrative region, the system identifies anomalies in the planting area data of the target crop in the target administrative region to improve the accuracy of crop planting area anomaly identification.

[0082] In some embodiments, a second normal distribution can be obtained based on the random effects of the second administrative region; the second normal distribution is the distribution of each random effect with respect to each administrative region in the second administrative region; and the random effects of the target administrative region are determined to be abnormal based on the second normal distribution.

[0083] In practice, the vertical hierarchical anomaly detection model can obtain the random effects and second normal distribution of all second administrative regions based on the current year's planting area data of the target crop in the target administrative region, the first administrative region, and the second administrative region.

[0084] In some embodiments, the vertical hierarchical anomaly detection model can interpolate the random effects of the target administrative region and all second administrative regions based on the following formula:

[0085]

[0086] Where α represents the contribution rate of the target administrative region or the i-th second administrative region; y B,t Let x represent the planting area data of the first administrative region in year t. i,t This represents the planting area data for the target administrative region or the i-th second administrative region in year t; β i Represents the solid-state effect of the target administrative region or the i-th second administrative region; u iLet represent the random effect of the target administrative region or the i-th second administrative region, and ; Let represent the first random error of the target administrative region or the i-th second administrative region, and .

[0087] In some embodiments, if the random effects of the target administrative region deviate from the second normal distribution, it can be determined that the random effects of the target administrative region are abnormal.

[0088] In some embodiments, if the absolute value of the random effect in the target administrative region is greater than the second standard deviation, it can be determined that the random effect in the target administrative region deviates from the second normal distribution, indicating an anomaly in the random effect of the target administrative region. The second standard deviation can be three times the standard deviation of the second normal distribution, i.e., ±3. .

[0089] According to the crop planting area anomaly identification method of this application, the planting area data of the target crop in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region) are obtained respectively. Using a vertical hierarchical anomaly discrimination model, based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, the first random error and random effect of the target administrative region are obtained. It is then determined whether the first random error and random effect of the target administrative region are abnormal. If at least one of the first random error and random effect of the target administrative region is abnormal, an indicator of the planting area of ​​the target administrative region is obtained. The system identifies first anomalies in the area data and obtains second anomaly identification results based on the planting area data of the target administrative region and the second administrative region. If both the first and second anomaly identification results are abnormal, the planting area data of the target administrative region is determined to be abnormal data. Based on the planting area data of the target crop in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region), and considering the planting plan of the administrative region, the system identifies anomalies in the planting area data of the target crop in the target administrative region to improve the accuracy of crop planting area anomaly identification.

[0090] In some embodiments, a horizontal spatiotemporal anomaly discrimination model can be used to obtain a second random error of the target administrative region based on the planting area data of the target administrative region and the second administrative region; determine whether the second random error of the target administrative region is abnormal; and if the second random error is abnormal, obtain a second anomaly identification result indicating the anomaly.

[0091] In practice, the horizontal spatiotemporal anomaly discrimination model can be a hierarchical Bayesian model or any other theoretically feasible model. The second random error represents the error in the planting area of ​​the target administrative region in the current year relative to the planting area of ​​the second administrative region.

[0092] In actual implementation, the planting area data of the target administrative region and the second administrative region can be input into the horizontal spatiotemporal anomaly discrimination model to obtain the second random error of the target administrative region and determine whether the second random error of the target administrative region is abnormal.

[0093] In actual implementation, based on the horizontal spatiotemporal anomaly discrimination model, the second random error of the target administrative region and each of the second administrative regions can be obtained through the planting area data of the target administrative region and several second administrative regions. Based on the second random error of all the second administrative regions, it can be determined whether there is an anomaly in the second random error of the target administrative region.

[0094] In some embodiments, after obtaining the second random error of the target administrative region and each second administrative region, it can be determined whether the second random error of the target administrative region is abnormal based on the second random errors of all second administrative regions, using any theoretically feasible mathematical processing method. For example, at least one of the mean, variance, and standard deviation can be obtained based on the second random errors of all second administrative regions, and it can be determined whether the second random error of the target administrative region is abnormal based on at least one of the mean, variance, and standard deviation.

[0095] In some embodiments, a third normal distribution can be obtained based on the second random error of the second administrative region; and the second random error of the target administrative region can be determined as abnormal based on the third normal distribution.

[0096] In practice, the horizontal spatiotemporal anomaly discrimination model can obtain the second random error and the third normal distribution of the target administrative region based on the current year's planting area data of the target crop in the second administrative region and the historical planting area data of the target administrative region.

[0097] In some embodiments, the lateral spatiotemporal anomaly discrimination model can interpolate to obtain the second random error of the target administrative region based on the following formula:

[0098]

[0099] in, Represents the spatial autoregressive coefficient. Reflects the intensity of the influence of the second administrative region; This represents the planting area data for the j-th second administrative region in year t; The spatial weight matrix represents the weight of each second administrative region (each second administrative region takes 1 / n, indicating an equal impact on the target administrative region). This applies when the second administrative region is a non-adjacent region of the target administrative region. (0); The time-regression coefficient represents the historical trend of the target administrative region; Denotes the second random error of the target administrative region in year t. ; Indicates the target administrative region number Data on the planting area of ​​target crops for the year; Indicates the target administrative region number Data on the planting area of ​​the target crops for the year.

[0100] In some embodiments, if the second random error of the target administrative region deviates from the third normal distribution, it can be determined that the second random error of the target administrative region is abnormal.

[0101] In some embodiments, if the absolute value of the random effect of the target administrative region is greater than the third standard deviation, it can be determined that the random difference of the target administrative region deviates from the third normal distribution, and the second random error of the target administrative region is abnormal. The third standard deviation can be three times the standard deviation of the third normal distribution.

[0102] According to the crop planting area anomaly identification method of this application, the planting area data of target crops in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region) are obtained respectively. Based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, a first anomaly identification result is obtained. Using a horizontal spatiotemporal anomaly discrimination model, a second random error of the target administrative region is obtained based on the planting area data of the target administrative region and the second administrative region. It is determined whether the second random error of the target administrative region is abnormal. If the second random error is abnormal, a second anomaly identification result indicating anomaly is obtained. If both the first and second anomaly identification results are abnormal, the planting area data of the target administrative region is determined to be abnormal data. Based on the planting area data of target crops in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region), anomaly identification of the planting area data of target crops in the target administrative region is performed from the perspective of the planting plan of the administrative region, so as to improve the accuracy of crop planting area anomaly identification.

[0103] In some embodiments, when the anomaly identification result indicates that the planting area data of the target administrative region is abnormal, the planting area data of the target crop in the second administrative region can be anomaly identified. If only the planting area data of the target crop in the target administrative region is abnormal, the anomaly type of the target administrative region is determined to be an isolated anomaly. If both the target administrative region and the second administrative region are abnormal, the anomaly type of the target administrative region is determined to be a regional anomaly.

[0104] In some embodiments, the anomaly type of the target administrative region can be determined based on the solid effects of historical years and the current year. The solid effects of each year t and the previous year t-1 within the three consecutive years of the target administrative region satisfy... If the value is greater than 0.15, the anomaly type of the target administrative region is determined to be a systematic shift.

[0105] In some embodiments, after determining the anomaly identification result of the planting area data of the target administrative region as abnormal when both the first anomaly identification result and the second anomaly identification result are abnormal, and when the anomaly identification result indicates that the planting area data of the target administrative region is abnormal, the planting area data of the target administrative region is corrected based on the planting area data of the target administrative region, the solid effect and the random effect, and the planting area data of the first administrative region through a pre-established posterior distribution model.

[0106] In practice, the posterior distribution model can be obtained by training a hierarchical Bayesian model.

[0107] In some embodiments, when the solid effects of the current year and multiple adjacent historical years in the target administrative region are abnormal, the planting area data of the target administrative region can be corrected based on the conditional interpolation strategy of the posterior predicted distribution by using a pre-established posterior distribution model.

[0108] In some embodiments, when the anomaly identification result indicates that the planting area data of the target administrative region is abnormal, the first random error anomaly, random effect anomaly, or second random error anomaly that caused the abnormal planting area data can be corrected, and the planting area data of the target administrative region can be corrected based on the correction result.

[0109] In some embodiments, the planting area data of the target administrative region, the solid effect and the random effect, and the planting area data of the first administrative region can be input into the posterior distribution model. The planting area data of the target administrative region can be corrected by the posterior distribution model to obtain the corrected planting area data of the target administrative region.

[0110] According to the crop planting area anomaly identification method of this application, the planting area data of target crops in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region) are obtained respectively. Based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, a first anomaly identification result is obtained, and based on the planting area data of the target administrative region and the second administrative region, a second anomaly identification result is obtained. If both the first and second anomaly identification results are abnormal, the planting area data of the target administrative region is determined to be abnormal data. Based on the planting area data of target crops in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region), anomaly identification is performed on the planting area data of target crops in the target administrative region, taking into account the planting plan of the administrative region, so as to improve the accuracy of crop planting area anomaly identification.

[0111] In some embodiments, if the solid effects of the current year and multiple adjacent historical years in the target administrative region are abnormal, the solid effect of the current year is updated based on the normal solid effect of the target administrative region; the normal solid effect is determined based on the normal solid effects of historical years in the target administrative region; the planting area data of the target administrative region is corrected based on the planting area data of the target administrative region, the updated solid effect and random effect, and the planting area data of the first administrative region.

[0112] In some embodiments, each year t in the current year and multiple adjacent historical years of the target administrative region satisfies When the value is greater than 0.15, the solid effects of the current year and multiple adjacent historical years of the target administrative region are all abnormal.

[0113] In actual implementation, the normal solid effect can be determined based on the planting area data of the target crop in the target administrative region and the solid effect of the historical years with normal solid effects. For example, it can be the average of the planting area data of the target crop in the target administrative region and the solid effect of the historical years with normal solid effects.

[0114] In some embodiments, the solid effect of the current year can be updated based on the normal solid effect of the target administrative region using the following formula:

[0115] ;

[0116] in, This indicates the solid-state effect of the current year after the update; This indicates a solid-state effect that indicates anomalies in the planting area data of the target crop within the target administrative region; This indicates a normal solid-state effect.

[0117] In some embodiments, the planting area data of the target administrative region can be corrected based on the following formula:

[0118]

[0119] in, This represents the planting area data for the target administrative region after correction. This represents the planting area data for the first administrative region; This represents the planting area data for the target administrative region before correction processing. Indicates the solid-state effect of the target administrative region; This represents the random effect of the target administrative region.

[0120] According to the crop planting area anomaly identification method of this application, the planting area data of target crops in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region) are obtained respectively. Based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, a first anomaly identification result is obtained, and based on the planting area data of the target administrative region and the second administrative region, a second anomaly identification result is obtained. If both the first and second anomaly identification results are abnormal, the planting area data of the target administrative region is determined to be abnormal data. Based on the planting area data of target crops in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region), anomaly identification is performed on the planting area data of target crops in the target administrative region, taking into account the planting plan of the administrative region, so as to improve the accuracy of crop planting area anomaly identification.

[0121] To better understand the method for identifying abnormal crop planting areas provided in the embodiments of this application, further explanation is provided below. It should be understood that the following discussion is merely exemplary.

[0122] This application provides a method for identifying abnormal crop planting areas. The specific steps are as follows: Figure 2 As shown:

[0123] Step 210: Obtain the planting area data of the target crop in the target administrative region, the first administrative region, and the second administrative region respectively; the first administrative region is the administrative region above the target administrative region, and the second administrative region is the adjacent administrative region at the same level as the target administrative region.

[0124] In practice, the target administrative region can be any administrative region, and the target administrative region can be the administrative region where it is necessary to determine whether the planting area of ​​the target crop is abnormal.

[0125] In actual implementation, the target crop can be any crop, such as rice, wheat, potatoes, or any theoretically feasible crop.

[0126] In practice, the planting area data of the target crops in the target administrative region, the first administrative region, and the second administrative region can include the planting area data of the target crops in the target administrative region, the first administrative region, and the second administrative region in the current year, or it can include the planting area data of the target crops in the target administrative region, the first administrative region, and the second administrative region in historical years.

[0127] In some embodiments, after obtaining the planting area data of the target crop in the target administrative region, the first administrative region, and the second administrative region, the planting area data of the target crop in the target administrative region, the first administrative region, and the second administrative region can be standardized, for example, by performing Z-score standardization on the planting area data of the target crop in the target administrative region, the first administrative region, and the second administrative region.

[0128] In some embodiments, the spatial topology of the target administrative region and the second administrative region can be constructed based on the spatial relationship between the target administrative region and each second administrative region.

[0129] Step 220: Using the vertical hierarchical anomaly discrimination model, based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, obtain the first random error and random effect of the target administrative region, and determine whether there are any anomalies in the first random error and random effect of the target administrative region.

[0130] In practice, the vertical hierarchical anomaly detection model can be a hierarchical Bayesian model or any other theoretically feasible model.

[0131] In practice, the planting area data of the target administrative region, the first administrative region, and the second administrative region can be input into the vertical hierarchical anomaly discrimination model to obtain the first random error and random effect of the target administrative region, and to determine whether there are any anomalies in the first random error and random effect of the target administrative region.

[0132] In actual implementation, based on the vertical hierarchical anomaly discrimination model, the first random error and random effect of the target administrative region and each second administrative region can be obtained through the planting area data of the target administrative region, the first administrative region and several second administrative regions. Based on the first random error of all second administrative regions, it can be determined whether the first random error of the target administrative region is abnormal, and based on the random effect of all second administrative regions, it can be determined whether the random effect of the target administrative region is abnormal.

[0133] In some embodiments, after obtaining the first random error of the target administrative region and each second administrative region, it can be determined whether the first random error of the target administrative region is abnormal based on the first random errors of all the second administrative regions, using any theoretically feasible mathematical processing method. For example, at least one of the mean, variance, and standard deviation can be obtained based on the first random errors of all the second administrative regions, and it can be determined whether the first random error of the target administrative region is abnormal based on at least one of the mean, variance, and standard deviation.

[0134] In some embodiments, after obtaining the random effects of the target administrative region and each second administrative region, it can be determined whether the random effects of the target administrative region are abnormal based on the random effects of all second administrative regions, using any theoretically feasible mathematical processing method. For example, at least one of the mean, variance, and standard deviation can be obtained based on the random effects of all second administrative regions, and it can be determined whether the random effects of the target administrative region are abnormal based on at least one of the mean, variance, and standard deviation.

[0135] In some embodiments, a first normal distribution can be obtained based on the first random error of the target administrative region in historical years; the first normal distribution is the distribution of the first random error corresponding to each historical year with respect to the historical years; based on the first normal distribution, it can be determined whether the first random error of the target administrative region is abnormal.

[0136] In actual implementation, the vertical hierarchical anomaly discrimination model can obtain the first random error and the first normal distribution of the target administrative region in historical years based on the planting area data of the target crop in the current year of the target administrative region, the first administrative region and the second administrative region.

[0137] In some embodiments, the vertical hierarchical anomaly detection model can obtain the first random error and the first normal distribution of the historical years of the target administrative region based on the following formula:

[0138]

[0139] Where α represents the contribution rate of the target administrative region or the i-th second administrative region; y B,t Let x represent the planting area data of the first administrative region in year t.i,t This represents the planting area data for the target administrative region or the i-th second administrative region in year t; β i Represents the solid-state effect of the target administrative region or the i-th second administrative region; u i Let represent the random effect of the target administrative region or the i-th second administrative region, and ; Let represent the first random error of the target administrative region or the i-th second administrative region, and .

[0140] In some embodiments, a second normal distribution can be obtained based on the random effects of the second administrative region; the second normal distribution is the distribution of each random effect with respect to each administrative region in the second administrative region; and the random effects of the target administrative region are determined to be abnormal based on the second normal distribution.

[0141] In practice, the vertical hierarchical anomaly detection model can obtain the random effects and second normal distribution of all second administrative regions based on the current year's planting area data of the target crop in the target administrative region, the first administrative region, and the second administrative region.

[0142] In some embodiments, the vertical hierarchical anomaly detection model can obtain the random effects of the target administrative region and all second administrative regions based on the following formula:

[0143]

[0144] Where α represents the contribution rate of the target administrative region or the i-th second administrative region; y B,t Let x represent the planting area data of the first administrative region in year t. i,t This represents the planting area data for the target administrative region or the i-th second administrative region in year t; β i Represents the solid-state effect of the target administrative region or the i-th second administrative region; u i Let represent the random effect of the target administrative region or the i-th second administrative region, and ; Let represent the first random error of the target administrative region or the i-th second administrative region, and .

[0145] Step 230: If at least one of the first random error and random effect in the target administrative region is abnormal, obtain the first anomaly identification result indicating the anomaly.

[0146] In some embodiments, if the first random error of the target administrative region deviates from the first normal distribution, it can be determined that the first random error of the target administrative region is abnormal.

[0147] In some embodiments, if the absolute value of the first random error of the target administrative region is greater than the first standard deviation, it can be determined that the first random error of the target administrative region deviates from the first normal distribution, and the first random error of the target administrative region is abnormal. The first standard deviation can be three times the standard deviation of the first normal distribution.

[0148] In some embodiments, if the random effects of the target administrative region deviate from the second normal distribution, it can be determined that the random effects of the target administrative region are abnormal.

[0149] In some embodiments, if the absolute value of the random effect in the target administrative region is greater than the second standard deviation, it can be determined that the random effect in the target administrative region deviates from the second normal distribution, indicating an anomaly in the random effect of the target administrative region. The second standard deviation can be three times the standard deviation of the second normal distribution, i.e., ±3. .

[0150] Step 240: Using the horizontal spatiotemporal anomaly discrimination model, based on the planting area data of the target administrative region and the second administrative region, obtain the second random error of the target administrative region; determine whether there is an anomaly in the second random error of the target administrative region.

[0151] In practice, the horizontal spatiotemporal anomaly discrimination model can be a hierarchical Bayesian model or any other theoretically feasible model.

[0152] In actual implementation, the planting area data of the target administrative region and the second administrative region can be input into the horizontal spatiotemporal anomaly discrimination model to obtain the second random error of the target administrative region and determine whether the second random error of the target administrative region is abnormal.

[0153] In actual implementation, based on the horizontal spatiotemporal anomaly discrimination model, the second random error of the target administrative region and each of the second administrative regions can be obtained through the planting area data of the target administrative region and several second administrative regions. Based on the second random error of all the second administrative regions, it can be determined whether there is an anomaly in the second random error of the target administrative region.

[0154] In some embodiments, after obtaining the second random error of the target administrative region and each second administrative region, it can be determined whether the second random error of the target administrative region is abnormal based on the second random errors of all second administrative regions, using any theoretically feasible mathematical processing method. For example, at least one of the mean, variance, and standard deviation can be obtained based on the second random errors of all second administrative regions, and it can be determined whether the second random error of the target administrative region is abnormal based on at least one of the mean, variance, and standard deviation.

[0155] In some embodiments, a third normal distribution can be obtained based on the second random error of the second administrative region; and the second random error of the target administrative region can be determined as abnormal based on the third normal distribution.

[0156] In practice, the horizontal spatiotemporal anomaly discrimination model can obtain the second random error and the third normal distribution of the target administrative region based on the current year's planting area data of the target crop in the second administrative region and the historical planting area data of the target administrative region.

[0157] In some embodiments, the lateral spatiotemporal anomaly discrimination model can obtain the second random error of the target administrative region based on the following formula:

[0158]

[0159] in, Represents the spatial autoregressive coefficient. Reflects the intensity of the influence of the second administrative region; This represents the planting area data for the j-th second administrative region in year t; The spatial weight matrix represents the weight of each second administrative region (each second administrative region takes 1 / n, indicating an equal impact on the target administrative region). This applies when the second administrative region is a non-adjacent region of the target administrative region. (0); The time-regression coefficient represents the historical trend of the target administrative region; Denotes the second random error of the target administrative region in year t. ; Indicates the target administrative region number Data on the planting area of ​​target crops for the year; Indicates the target administrative region number Data on the planting area of ​​the target crops for the year.

[0160] Step 250: In the case of an anomaly in the second random error, obtain the second anomaly identification result indicating the anomaly.

[0161] In some embodiments, if the second random error of the target administrative region deviates from the third normal distribution, it can be determined that the second random error of the target administrative region is abnormal.

[0162] In some embodiments, if the absolute value of the random effect of the target administrative region is greater than the third standard deviation, it can be determined that the random difference of the target administrative region deviates from the third normal distribution, and the second random error of the target administrative region is abnormal. The third standard deviation can be three times the standard deviation of the third normal distribution.

[0163] Step 260: If both the first and second anomaly identification results are abnormal, determine that the planting area data of the target administrative region is abnormal data.

[0164] In some embodiments, if either the first anomaly identification result or the second anomaly identification result is normal, the planting area data of the target crop in the target administrative region is determined to be normal data.

[0165] In actual implementation, if both the first and second anomaly identification results are abnormal, it can be confirmed that the planting area data of the target crop in the target administrative region is abnormal in both the vertical hierarchical dimension and the horizontal dimension. That is, the planting area data of the target crop in the target administrative region is abnormal data.

[0166] If the anomaly identification results indicate that the planting area data of the target administrative region is abnormal, anomaly identification is performed on the planting area data of the target crop in the second administrative region. If only the planting area data of the target crop in the target administrative region is abnormal, the anomaly type of the target administrative region is determined to be isolated anomaly. If both the target administrative region and the second administrative region are abnormal, the anomaly type of the target administrative region is determined to be regional anomaly.

[0167] In some embodiments, the anomaly type of the target administrative region can be determined based on the solid effects of historical years and the current year. The solid effects of each year t and the previous year t-1 within the three consecutive years of the target administrative region satisfy... If the value is greater than 0.15, the anomaly type of the target administrative region is determined to be a systematic shift.

[0168] Step 270: Using a pre-established posterior distribution model, the planting area data of the target administrative region is corrected based on the planting area data of the target administrative region, the solid effect and the random effect, as well as the planting area data of the first administrative region.

[0169] In practice, the posterior distribution model can be obtained by training a hierarchical Bayesian model.

[0170] In some embodiments, when the anomaly identification result indicates that the planting area data of the target administrative region is abnormal, the first random error anomaly, random effect anomaly, or second random error anomaly that caused the abnormal planting area data can be corrected, and the planting area data of the target administrative region can be corrected based on the correction result.

[0171] In some embodiments, the planting area data of the target administrative region, the first random error and random effect, and the planting area data of the first administrative region can be input into the posterior distribution model. The planting area data of the target administrative region can be corrected by the posterior distribution model to obtain the corrected planting area data of the target administrative region.

[0172] In some embodiments, if the solid effects of the current year and multiple adjacent historical years in the target administrative region are abnormal, the solid effect of the current year is updated based on the normal solid effect of the target administrative region; the normal solid effect is determined based on the normal solid effects of historical years in the target administrative region; the planting area data of the target administrative region is corrected based on the planting area data of the target administrative region, the updated solid effect and random effect, and the planting area data of the first administrative region.

[0173] In some embodiments, each year t in the current year and multiple adjacent historical years of the target administrative region satisfies When the value is greater than 0.15, the solid effects of the current year and multiple adjacent historical years of the target administrative region are all abnormal.

[0174] In actual implementation, the normal solid effect can be determined based on the planting area data of the target crop in the target administrative region and the solid effect of the historical years with normal solid effects. For example, it can be the average of the planting area data of the target crop in the target administrative region and the solid effect of the historical years with normal solid effects.

[0175] In some embodiments, the solid effect of the current year can be updated based on the normal solid effect of the target administrative region using the following formula:

[0176] ;

[0177] in, This indicates the solid-state effect of the current year after the update; This indicates a solid-state effect that indicates anomalies in the planting area data of the target crop within the target administrative region; This indicates a normal solid-state effect.

[0178] In some embodiments, the planting area data of the target administrative region can be corrected based on the following formula:

[0179]

[0180] in, This represents the planting area data for the target administrative region after correction. This represents the planting area data for the first administrative region; This represents the planting area data for the target administrative region before correction processing. Indicates the solid-state effect of the target administrative region; This represents the random effect of the target administrative region.

[0181] Based on the crop planting area anomaly identification method of this application, a multi-dimensional joint anomaly detection mechanism is adopted, integrating vertical hierarchical association (first administrative region) and horizontal spatiotemporal proximity (second administrative region) to construct a dual detection framework of hierarchical Bayesian model and spatiotemporal autoregressive model. The vertical model analyzes the heterogeneity of the contribution of the target administrative region to the first administrative region, and identifies the first random error shift and random residual anomalies; the horizontal model quantifies the spatial weight of neighboring areas and historical trend inertia, and captures local spatiotemporal deviations. The collaborative decision-making of the two models significantly reduces the risk of misjudgment of traditional single-dimensional analysis, especially showing high sensitivity to hidden regional anomalies (such as synchronous fluctuations in multiple neighboring areas). Furthermore, adaptive anomaly classification and dynamic correction are adopted. Based on posterior confidence intervals, residual distribution shifts, and parameter persistence analysis, the model can autonomously distinguish between isolated anomalies, regional anomalies, and systematic shift types without the need for manual preset thresholds. For systematic shifts, a dynamic parameter adaptive mechanism is introduced: when the interannual change rate of the first random error exceeds 15% for three consecutive years, the historical benchmark parameters and the current posterior mean are automatically merged and reset to avoid correction distortion caused by model lag. Furthermore, a data-driven, lossless local correction method is employed, abandoning the global smoothing filtering algorithm and adopting a conditional interpolation strategy based on the posterior prediction distribution. Correction values ​​are generated only for planting area data deemed abnormal, using the posterior distribution of the integral hierarchical model parameters, while preserving the original information of non-abnormal data. This method both suppresses the excessive smoothing of normal data by traditional filtering methods and reduces the uncertainty of correction results in small sample areas through the Bayesian shrinkage effect.

[0182] This application also provides a device for identifying abnormal crop planting areas.

[0183] like Figure 3 As shown, the crop planting area anomaly identification device 300 includes: a first acquisition module 310, a second acquisition module 320, and a determination module 330.

[0184] The first acquisition module 310 is used to acquire planting area data of target crops in the target administrative region, the first administrative region, and the second administrative region, respectively; the first administrative region is the administrative region above the target administrative region, and the second administrative region is an adjacent administrative region at the same level as the target administrative region;

[0185] The second acquisition module 320 is used to acquire a first anomaly identification result based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, and to acquire a second anomaly identification result based on the planting area data of the target administrative region and the second administrative region.

[0186] The determination module 330 is used to determine the planting area data of the target administrative region as abnormal data when both the first anomaly identification result and the second anomaly identification result are abnormal.

[0187] According to the crop planting area anomaly identification device of this application, the planting area data of target crops in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region) are obtained respectively; based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, a first anomaly identification result is obtained, and based on the planting area data of the target administrative region and the second administrative region, a second anomaly identification result is obtained; if both the first and second anomaly identification results are abnormal, the planting area data of the target administrative region is determined to be abnormal data. Based on the planting area data of target crops in the target administrative region, the administrative region above the target administrative region (i.e., the first administrative region), and the adjacent administrative region at the same level as the target administrative region (i.e., the second administrative region), anomaly identification is performed on the planting area data of target crops in the target administrative region, taking into account the planting plan of the administrative region, so as to improve the accuracy of crop planting area anomaly identification.

[0188] In some embodiments, the second acquisition module 320 includes:

[0189] The first acquisition unit is used to acquire the first random error and random effect of the target administrative region based on the planting area data of the target administrative region, the first administrative region and the second administrative region through a vertical hierarchical anomaly discrimination model; the first random error represents the time error of the planting area in the current year, and the random effect represents the objective contribution rate of the target administrative region to the first administrative region in the current year;

[0190] The first determining unit is used to determine whether there are any anomalies in the first random error and random effect of the target administrative region;

[0191] The second acquisition unit is used to acquire a first anomaly identification result indicating an anomaly when at least one of the first random error and random effect in the target administrative region is abnormal.

[0192] In some embodiments, the first determining unit is used to obtain a first normal distribution based on the first random error of the target administrative region in historical years; the first normal distribution is the distribution of the first random error corresponding to each historical year with respect to the historical year; based on the first normal distribution, it is determined whether the first random error of the target administrative region is abnormal.

[0193] In some embodiments, the first determining unit is further configured to obtain a second normal distribution based on the random effects of the second administrative region; the second normal distribution is the distribution of each random effect with respect to each administrative region in the second administrative region; and to determine whether the random effects of the target administrative region are abnormal based on the second normal distribution.

[0194] In some embodiments, the second acquisition module 320 further includes:

[0195] The third acquisition unit is used to acquire the second random error of the target administrative region based on the planting area data of the target administrative region and the second administrative region through the horizontal spatiotemporal anomaly discrimination model.

[0196] The second determining unit is used to determine whether there is an anomaly in the second random error of the target administrative region;

[0197] The fourth acquisition unit is used to acquire a second anomaly identification result indicating the anomaly when there is an anomaly in the second random error.

[0198] In some embodiments, the crop planting area anomaly identification device 300 further includes:

[0199] The correction module is used to correct the planting area data of the target administrative region when the anomaly identification results indicate that the planting area data of the target administrative region is abnormal. Based on the planting area data of the target administrative region, fixed effects and random effects, and the planting area data of the first administrative region, the module uses a pre-established posterior distribution model to correct the planting area data of the target administrative region.

[0200] In some embodiments, the correction module is used to update the fixed effect of the current year based on the normal fixed effect of the target administrative region when the fixed effects of the current year and multiple adjacent historical years of the target administrative region are abnormal; the normal fixed effect is determined based on the normal fixed effects of the historical years of the target administrative region; and to perform correction processing on the planting area data of the target administrative region based on the planting area data of the target administrative region, the updated fixed effect and random effect, and the planting area data of the first administrative region.

[0201] The crop planting area anomaly identification device in this application embodiment can be an electronic device or a component of an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the device.

[0202] The crop planting area anomaly identification device in this application embodiment can be a device with an operating system. This operating system can be a Microsoft (Windows) operating system, an Android operating system, an iOS operating system, or other possible operating systems; this application embodiment does not specifically limit it.

[0203] The crop planting area anomaly identification device 300 provided in this application embodiment can achieve... Figures 1 to 2 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.

[0204] In some embodiments, such as Figure 4 As shown, this application embodiment also provides an electronic device 400, including a processor 401, a memory 402, and a computer program stored in the memory 402 and executable on the processor 401. When the program is executed by the processor 401, it implements the various processes of the above-described embodiment of the crop planting area anomaly identification method and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0205] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0206] This application also provides a non-transitory computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described method for identifying abnormal crop planting areas and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0207] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0208] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-mentioned method for identifying abnormal crop planting areas.

[0209] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described embodiments of the crop planting area anomaly identification method, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0210] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0211] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0212] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0213] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0214] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0215] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for identifying abnormal crop planting areas, characterized in that, include: Obtain planting area data for the target crop in the target administrative region, the first administrative region, and the second administrative region, respectively; The first administrative region is the administrative region at the next higher level than the target administrative region, and the second administrative region is an adjacent administrative region at the same level as the target administrative region; Using a vertical hierarchical anomaly detection model, based on the planting area data of the target administrative region, the first administrative region, and the second administrative region, a first random error and a random effect of the target administrative region are obtained; the first random error represents the error of the planting area of ​​the target administrative region in the current year relative to the planting area in historical years, and the random effect represents the contribution rate of the target administrative region to the first administrative region under the influence of objective factors in the current year; Determine whether the first random error and the random effect in the target administrative region are abnormal; If at least one of the first random error and the random effect in the target administrative region is abnormal, a first anomaly identification result indicating the anomaly is obtained. Based on the planting area data of the target administrative region and the second administrative region, horizontal dimension identification of adjacent administrative regions at the same level is performed on the planting area data of the target administrative region to obtain a second anomaly identification result; If both the first anomaly identification result and the second anomaly identification result are abnormal, the planting area data of the target administrative region is determined to be abnormal data.

2. The method for identifying abnormal crop planting area according to claim 1, characterized in that, The determination of whether the first random error and the random effect of the target administrative region are abnormal includes: Based on the first random error of the historical years of the target administrative region, a first normal distribution is obtained; the first normal distribution is the distribution of the first random error corresponding to each historical year with respect to the historical year; Based on the first normal distribution, determine whether the first random error of the target administrative region is abnormal.

3. The method for identifying abnormal crop planting area according to claim 1, characterized in that, The determination of whether the first random error and the random effect of the target administrative region are abnormal includes: Based on the random effects of the second administrative region, a second normal distribution is obtained; the second normal distribution is the distribution of each random effect with respect to each administrative region of the second administrative region. Based on the second normal distribution, determine whether the random effects in the target administrative region are abnormal.

4. The method for identifying abnormal crop planting area according to claim 1, characterized in that, Based on the planting area data of the target administrative region and the second administrative region, the method of performing horizontal dimension identification of adjacent administrative regions at the same level for the planting area data of the target administrative region to obtain a second anomaly identification result includes: Using a horizontal spatiotemporal anomaly discrimination model, based on the planting area data of the target administrative region and the second administrative region, a second random error of the target administrative region is obtained; the second random error represents the error of the planting area of ​​the target administrative region in the current year relative to the planting area of ​​the second administrative region. Determine whether the second random error in the target administrative region is abnormal; If the second random error is abnormal, obtain the second anomaly identification result indicating the anomaly.

5. The method for identifying abnormal crop planting area according to any one of claims 1-4, characterized in that, After determining that the planting area data of the target administrative region is abnormal data when both the first anomaly identification result and the second anomaly identification result are abnormal, the method further includes: By using a pre-established posterior distribution model, the planting area data of the target administrative region is corrected based on the planting area data of the target administrative region, fixed effects and random effects, and the planting area data of the first administrative region.

6. The method for identifying abnormal crop planting area according to claim 5, characterized in that, The step of correcting the planting area data of the target administrative region using a pre-established posterior distribution model, based on the planting area data of the target administrative region, fixed effects, random effects, and the planting area data of the first administrative region, includes: If the fixed effects of the current year and multiple adjacent historical years of the target administrative region are all abnormal, the fixed effects of the current year are updated based on the normal fixed effects of the target administrative region; the normal fixed effects are determined based on the normal fixed effects of the historical years of the target administrative region. Based on the planting area data of the target administrative region, the updated fixed and random effects, and the planting area data of the first administrative region, the planting area data of the target administrative region is corrected.

7. A device for identifying abnormal crop planting area, characterized in that, include: The first acquisition module is used to acquire planting area data of target crops in the target administrative region, the first administrative region, and the second administrative region, respectively. The first administrative region is the administrative region at the next higher level than the target administrative region, and the second administrative region is an adjacent administrative region at the same level as the target administrative region; The second acquisition module is used to acquire, through a vertical hierarchical anomaly discrimination model, the first random error and random effect of the target administrative region based on the planting area data of the target administrative region, the first administrative region and the second administrative region; the first random error represents the error of the planting area of ​​the target administrative region in the current year relative to the planting area in historical years, and the random effect represents the contribution rate of the target administrative region to the first administrative region under the influence of objective factors in the current year; Determine whether the first random error and the random effect in the target administrative region are abnormal; If at least one of the first random error and the random effect in the target administrative region is abnormal, a first anomaly identification result indicating the anomaly is obtained. The second acquisition module is further configured to perform horizontal dimension identification of adjacent administrative regions at the same level based on the planting area data of the target administrative region and the second administrative region, and obtain a second anomaly identification result; The determination module is used to determine that the planting area data of the target administrative region is abnormal data when both the first anomaly identification result and the second anomaly identification result are abnormal.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the crop planting area anomaly identification method as described in any one of claims 1-6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the crop planting area anomaly identification method as described in any one of claims 1-6.

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