Crop production control method, device, system, electronic device and storage medium

Abstract

Embodiments of the present application provide a crop production control method, device, system, electronic equipment, computer readable storage medium and computer program product. The method comprises: obtaining monitoring information of a current crop production area; determining growth indication information of the crops in the current crop production area based on the monitoring information, the growth indication information being used to indicate the growth of the crops; determining a growth control scheme for the current crop production area based on the growth indication information of the crops; and controlling the working of the growth control equipment corresponding to the current crop production area based on the growth control scheme. Embodiments of the present application aim to realize agricultural automation production with as little labor input as possible, or even without labor input, and are particularly suitable for agricultural production areas with insufficient labor in the future, and help the landing of the national digital rural strategy.

Description

Technical Field

[0001] This application relates to the fields of agricultural automated production and digital twin technology. Specifically, this application relates to a method, apparatus, system, electronic device, computer-readable storage medium, and computer program product for controlling crop production. Background Technology

[0002] With the rapid development of agricultural automation technology, improving agricultural production efficiency has become increasingly important. From a developmental perspective, with the progress of urbanization and the aging population in my country, the rural population, especially the agricultural labor force, is experiencing a significant decline. Given this substantial decrease in the agricultural labor force, conducting research on agricultural automation is of significant practical importance in ensuring normal agricultural production and national food security.

[0003] However, agricultural production in my country is currently mainly carried out manually, and even existing intelligent agricultural production requires the input of manual labor. As the agricultural labor force decreases, the problem of labor shortage in agricultural production will become increasingly prominent.

[0004] Therefore, there is an urgent need for an agricultural automation production solution that minimizes labor input. Summary of the Invention

[0005] The embodiments of this application aim to achieve automated agricultural production with reduced or even no labor input.

[0006] On the one hand, embodiments of this application provide a method for controlling crop production, which performs the following steps for each crop production area:

[0007] Obtain monitoring information on the current crop production area;

[0008] Based on monitoring information, growth indicator information of crops in the current crop production area is determined. The growth indicator information is used to indicate the growth status of crops.

[0009] Based on crop growth indicator information, determine the growth control plan for the current crop production area;

[0010] Based on the growth control scheme, the operation of the growth control equipment corresponding to the current crop production area is controlled.

[0011] In one possible implementation, after controlling the growth control equipment corresponding to the current crop production area to operate, the following steps are included:

[0012] Based on the new monitoring information obtained from the current crop production areas, the pre-established virtual digital twin model is updated. The virtual digital twin model is used to simulate the growth of crops in each crop production area.

[0013] In one possible implementation, the virtual digital twin model includes a simulated production area corresponding to each crop production area, which is used to simulate the growth of crops in the corresponding crop production area.

[0014] Based on new monitoring information acquired about the current crop production area, the pre-established virtual digital twin model is updated, including:

[0015] Based on new monitoring information of the current crop production area, update the crop growth status of the current simulated production area. The current simulated production area is the simulated production area corresponding to the current crop production area.

[0016] In one possible implementation, updating the crop growth status of the current simulated production area includes:

[0017] If the crop growth indication information in the current crop production area includes the first target indication information, then the crop growth status in the current simulated production area is updated at the first update frequency. The first target indication information is used to indicate that the crop growth status is normal.

[0018] If the crop growth indication information in the current crop production area includes the second target indication information, then the crop growth status in the current simulated production area is updated at the second update frequency. The second target indication information is used to indicate that the crop growth status is abnormal.

[0019] The first update frequency is lower than the second update frequency.

[0020] In one possible implementation, the monitoring information includes environmental monitoring information of the current crop production area and / or monitoring image information of the crops in the current crop production area;

[0021] Based on monitoring information, determine the growth indicators of crops in the current crop production area, including:

[0022] Based on environmental monitoring information corresponding to the current crop production area, the first growth indicator information of the crops in the current crop production area is determined. The first growth indicator information is used to indicate the growth status of the crops; and / or,

[0023] Based on the monitoring image information corresponding to the current crop production area, the second growth indication information of the crops in the current crop production area is determined. The second growth indication information is used to indicate whether the crops are growing normally.

[0024] In one possible implementation, the environmental monitoring information includes at least two different growth status assessment index values.

[0025] Based on environmental monitoring information corresponding to the current crop production area, the first growth indicator information of crops in the current crop production area is determined, including:

[0026] Determine the first weight corresponding to at least two growth status assessment index values;

[0027] Based on the first product of each growth status assessment index value and its corresponding first weight, the first growth indication information of crops in the current crop production area is determined.

[0028] In one possible implementation, based on monitoring image information corresponding to the current crop production area, second growth indicator information of crops in the current crop production area is determined, including:

[0029] Based on the monitoring image information corresponding to the current crop production area, determine at least two growth abnormality assessment index values;

[0030] Obtain weight sets and benchmark value sets corresponding to at least two abnormal situations. Each weight set includes at least two second weights, and each benchmark value set includes at least two abnormal benchmark values. The at least two second weights and the at least two abnormal benchmark values ​​correspond one-to-one with at least two growth abnormality assessment index values.

[0031] Determine the abnormal similarity corresponding to at least two abnormal situations. The abnormal similarity corresponding to the abnormal situation is determined based on at least two second product results corresponding to the current crop production area. The second product result is the product between the growth abnormality assessment index value, the reciprocal of the corresponding abnormal benchmark value, and the corresponding second weight.

[0032] If the abnormal similarity of the current crop production area is less than the preset similarity threshold, then the second growth indication information of the crop in the current crop production area is determined to include the first target indication information, which is used to indicate that the growth of the crop is normal.

[0033] If the abnormal similarity corresponding to one of the abnormal conditions in the current crop production area is greater than or equal to the similarity threshold, then the second growth indication information of the crop in the crop production area is determined to include the second target indication information, which is used to indicate that the growth of the crop is abnormal.

[0034] In one possible implementation, the second target indication information is also used to indicate the target anomaly in at least two anomaly situations, wherein the anomaly similarity corresponding to the target anomaly is greater than or equal to a similarity threshold.

[0035] In one possible implementation, a growth control scheme for the current crop production area is determined based on crop growth indicator information, including:

[0036] Based on crop growth indication information and a pre-determined mapping relationship, a growth control scheme for the current crop production area is determined, wherein the mapping relationship is used to represent the mapping relationship between growth indication information and growth control scheme.

[0037] On the other hand, embodiments of this application provide a crop production control device for controlling production in each crop production area. The device includes:

[0038] The information acquisition module is used to acquire monitoring information of the current crop production area;

[0039] The growth status determination module is used to determine the growth indication information of crops in the current crop production area based on monitoring information. The growth indication information is used to indicate the growth status of crops.

[0040] The production control module is used to determine the growth control scheme for the current crop production area based on the crop growth indication information; and to control the operation of the corresponding growth control equipment for the current crop production area based on the growth control scheme.

[0041] On the other hand, embodiments of this application provide a crop production control system, including:

[0042] Growth control equipment is installed in each crop production area;

[0043] The monitoring device is installed in each crop production area to monitor the crop production area and obtain the corresponding monitoring information for each crop production area.

[0044] A crop production control device for performing the following steps for each crop production area:

[0045] Obtain monitoring information on the current crop production area;

[0046] Based on monitoring information, growth indicator information of crops in the current crop production area is determined. The growth indicator information is used to indicate the growth status of crops.

[0047] Based on crop growth indicator information, determine the growth control plan for the current crop production area;

[0048] Based on the growth control scheme, the operation of the growth control equipment corresponding to the current crop production area is controlled.

[0049] On the other hand, embodiments of this application provide 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 any of the above methods.

[0050] On the other hand, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0051] On the other hand, embodiments of this application provide a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the methods described above.

[0052] The beneficial effects of the technical solution provided in this application are as follows: By performing the following steps for each crop production area: obtaining monitoring information of the current crop production area; determining the growth indication information of the crops in the current crop production area based on the monitoring information, the growth indication information is used to indicate the growth status of the crops; determining the growth control scheme for the current crop production area based on the growth indication information of the crops; and controlling the operation of the growth control equipment corresponding to the current crop production area based on the growth control scheme. This can automatically combine the growth status of crops in each crop production area to control the operation of the growth control equipment in each crop production area, which can reduce or even eliminate the need for personnel input in agricultural production. It aims to solve the problem of insufficient agricultural labor in future agricultural production, and is particularly suitable for agricultural production areas with insufficient agricultural labor in the future, thus helping to implement the national digital rural strategy. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below.

[0054] Figure 1 A schematic flowchart of a crop production control method provided in an embodiment of this application;

[0055] Figure 2 A schematic diagram of the framework of a crop production control system based on digital twin provided for an embodiment of this application;

[0056] Figure 3 This is a schematic diagram of the structure of a crop production control device provided in an embodiment of this application;

[0057] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0058] The embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the embodiments described below with reference to the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions of the embodiments of this application.

[0059] Those skilled in the art will understand that, unless otherwise stated, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the terms “comprising” and “including” as used in embodiments of this application mean that the corresponding feature can be implemented as the presented feature, information, data, step, operation, element, and / or component, but do not exclude implementation as other features, information, data, step, operation, element, component, and / or combinations thereof supported by the art. It should be understood that when we say that an element is “connected” or “coupled” to another element, the one element can be directly connected or coupled to the other element, or it can mean that the one element and the other element establish a connection relationship through an intermediate element. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “and / or” as used herein indicates at least one of the items defined by the term; for example, “A and / or B” indicates implementation as “A,” or implementation as “A,” or implementation as “A and B.”

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0061] This solution enables automated agricultural production with minimal human labor input, and the automated system is self-upgradable and self-improving. It is particularly suitable for agricultural areas with future labor shortages, and helps to implement the national digital rural strategy.

[0062] Please see Figure 1 , Figure 1 This is a schematic flowchart illustrating a crop production control method provided in an embodiment of this application. Figure 1 The crop production control method shown is used for production control in each crop production area. The method includes:

[0063] S110. Obtain monitoring information for the current crop production area.

[0064] The current crop production area can refer to the crop production area that requires production control. Optionally, every crop production area can be considered as the current crop production area; alternatively, a portion of the crop production areas can be considered as the current crop production area, without any limitation.

[0065] The monitoring information can refer to information obtained by monitoring the growth of crops in agricultural production areas. Optionally, the monitoring information may include, but is not limited to, at least one of soil information, water quality information, meteorological information, and crop image information, without limitation.

[0066] S120. Based on monitoring information, determine the growth indicator information of crops in the current crop production area. The growth indicator information is used to indicate the growth status of crops.

[0067] The growth status of crops may include, but is not limited to, at least one of the following: the growth pattern of crops and whether crops are growing normally. The growth pattern of crops may include, for example, the height of the crops.

[0068] S130. Based on crop growth indication information, determine the growth control plan for the current crop production area.

[0069] In this embodiment, the corresponding growth control scheme may differ depending on the growth status indicated by the growth indication information. Optionally, the growth control scheme may include at least one control sub-scheme, and each control sub-scheme may include a measure and the corresponding parameters of the measure. Optionally, the parameters corresponding to the measure include, but are not limited to, dosage, control frequency, etc., and are not limited here. For example, the parameters corresponding to irrigation may include, but are not limited to, irrigation flow rate and irrigation area. The parameters corresponding to pesticide spraying may include, but are not limited to, pesticide type and spraying amount.

[0070] In this embodiment, different growth conditions may correspond to different growth control schemes. For example, the agricultural production decision service for "rice bacterial blight" includes two measures and corresponding parameters: fertilization measures, with parameters for phosphorus and potassium fertilizers; and pesticide spraying, with parameters for thiabendazole copper suspension (20% concentration, 100 grams / acre), sprayed twice consecutively with an interval of 7 days.

[0071] S140. Based on the growth control scheme, control the operation of the growth control equipment corresponding to the current crop production area.

[0072] In this embodiment, each crop production area is equipped with at least one growth control device. Optionally, the at least one production control device includes, but is not limited to, at least one of irrigation equipment, pesticide spraying equipment, and fertilizer application equipment, and is not limited thereto.

[0073] The technical solution of this embodiment involves performing the following steps for each crop production area: acquiring monitoring information of the current crop production area; determining growth indicator information of the crops in the current crop production area based on the monitoring information, the growth indicator information being used to indicate the growth status of the crops; determining a growth control scheme for the current crop production area based on the crop growth indicator information; and controlling the operation of the corresponding growth control equipment in the current crop production area based on the growth control scheme. This can automatically combine the growth status of crops in each crop production area to control the operation of the growth control equipment in each crop production area, which can reduce or even eliminate the need for personnel input in agricultural production. It aims to solve the problem of insufficient agricultural labor in future agricultural production, and is particularly suitable for agricultural production areas with insufficient agricultural labor in the future, thus contributing to the implementation of the national digital rural strategy.

[0074] In one possible implementation, there can be at least two crop production areas. Each crop production area grows crops with identical properties; that is, the principle for determining a crop production area includes identical production conditions. For example, a rice-growing area can be considered a crop production area where the rice grown receives the same sunlight, fertilization, irrigation, and other conditions.

[0075] Optionally, crops grown in different crop production areas may have the same or different attributes, and this is not limited here.

[0076] Based on crop growth indicator information, determine the growth control plan for the current crop production area, including:

[0077] Based on crop growth indication information and a pre-determined mapping relationship, a growth control scheme for the current crop production area is determined, wherein the mapping relationship is used to represent the mapping relationship between growth indication information and growth control scheme.

[0078] In this embodiment, the mapping relationship between growth indication information and growth control scheme can be predetermined. Based on the growth indication information of crops and the mapping relationship, the growth control scheme of the current crop production area can be quickly determined, thereby timely regulating the growth of crops and improving the growth quality of crops.

[0079] In one possible implementation, the mapping relationship can be updated based on the expected growth of the growth control scheme and the actual growth of the crop after the growth control scheme is implemented. This makes the determined growth control scheme more suitable for the growth of the crop, thereby further improving the growth quality of the crop.

[0080] In one possible implementation, a virtual digital twin model can be pre-built to simulate the growth of crops in each crop production area.

[0081] A digital twin is a digitized replica that uses digital technology to copy and simulate various objects and systems in the real physical world, updating them in real time. This provides accurate and reliable feedback for practical operations, breaking down the limitations of time and space. In this embodiment, the virtual digital twin model is used to simulate the growth of crops in each agricultural production area; that is, the real-world state of the crops can be reflected in the virtual digital twin model.

[0082] In one possible implementation, after controlling the growth control equipment corresponding to the current crop production area to operate, the following steps are included:

[0083] Based on the new monitoring information acquired about the current crop production area, the pre-established virtual digital twin model is updated.

[0084] In this embodiment, the new monitoring information can be acquired immediately after the growth control equipment corresponding to the current crop production area is activated, or it can be acquired after a certain interval; there is no limitation on this. Correspondingly, the pre-established virtual digital twin model can be updated immediately, or it can be updated after a certain interval; there is no limitation on this either.

[0085] The technical solution of this embodiment updates the pre-established virtual digital twin model based on the new monitoring information of the current crop production area after controlling the growth control equipment corresponding to the current crop production area. This allows the crop growth status to be reflected in real time, enabling relevant personnel to conveniently and intuitively understand the crop growth status, thereby ensuring that the crops can grow well.

[0086] In one possible implementation, the virtual digital twin model includes a simulated production area corresponding to each crop production area, which is used to simulate the growth of crops in the corresponding crop production area.

[0087] Based on new monitoring information acquired about the current crop production area, the pre-established virtual digital twin model is updated, including:

[0088] Based on new monitoring information of the current crop production area, update the crop growth status of the current simulated production area. The current simulated production area is the simulated production area corresponding to the current crop production area.

[0089] In this embodiment, in the virtual digital twin model, each crop production area has a corresponding simulated production area. That is, when there are two or more crop production areas, the virtual digital twin model includes at least two simulated production areas corresponding to each of the two or more crop production areas.

[0090] In this embodiment, each crop production area has a corresponding simulated production area in the virtual digital twin model. Therefore, the corresponding simulated production area can be updated according to the crop growth status of each crop production area. Thus, the different simulated production areas are decoupled from each other. Therefore, when updating the virtual digital twin model, one simulated production area can be updated separately, which can reduce the computing power required for updating the virtual digital twin model and improve the update efficiency of the virtual digital twin model.

[0091] In one possible implementation, updating the crop growth status of the current simulated production area includes:

[0092] If the crop growth indication information in the current crop production area includes the first target indication information, then the crop growth status in the current simulated production area is updated at the first update frequency. The first target indication information is used to indicate that the crop growth status is normal.

[0093] If the crop growth indication information in the current crop production area includes the second target indication information, then the crop growth status in the current simulated production area is updated at the second update frequency. The second target indication information is used to indicate that the crop growth status is abnormal.

[0094] The first update frequency is lower than the second update frequency.

[0095] In this embodiment, specifically, if the crop growth indicator information in the current crop production area includes the first target indicator information, it indicates that the crop growth is normal, and therefore there is no need to pay too much attention to the crop growth in that area. Thus, the crop growth in the current simulated production area can be updated at a lower first update frequency. If the crop growth indicator information in the current crop production area includes the second target indicator information, it indicates that the crop growth is abnormal, and therefore it is necessary to pay close attention to the crop growth in that area. Thus, the crop growth in the current simulated production area can be updated at a higher second update frequency.

[0096] The technical solution of this embodiment updates the growth status of crops in the current simulated production area at a lower first update frequency when the crop growth is normal, and at a higher second update frequency when the crop growth is abnormal. This takes into account both the computing power required to update the virtual digital twin model and the real-time observation of crop growth.

[0097] The following examples, based on any of the above examples, further illustrate how to determine the growth status of crops based on detection information.

[0098] In one possible implementation, the monitoring information includes environmental monitoring information of the current crop production area. Based on the monitoring information, growth indication information of crops in the current crop production area is determined, including:

[0099] Based on the environmental monitoring information corresponding to the current crop production area, the first growth indicator information of the crop in the current crop production area is determined. The first growth indicator information is used to indicate the growth status of the crop.

[0100] The growth status can include crop height, etc., and is not limited here. Environmental monitoring information can include, but is not limited to, soil information, water quality information, and meteorological information, etc. Soil information can include, but is not limited to, soil pH. Water quality information can include, but is not limited to, soil moisture content and biochemical oxygen demand (BOD). Meteorological information can include, but is not limited to, sunshine duration, etc., etc.

[0101] In this embodiment, the growth status of crops in the current crop production area can be determined based on environmental monitoring information.

[0102] In one possible implementation, the environmental monitoring information includes growth status assessment index values, and at least two different growth status assessment index values.

[0103] Based on environmental monitoring information corresponding to the current crop production area, the first growth indicator information of crops in the current crop production area is determined, including:

[0104] Determine the first weight corresponding to at least two growth status assessment index values;

[0105] Based on the first product of each growth status assessment index value and its corresponding first weight, the first growth indication information of crops in the current crop production area is determined.

[0106] Optionally, the first growth indicators of crops can be calculated using the following regression model:

[0107] Ch=β0*X0+β1*X1+β2*X2+...+βn*Xn+u.

[0108] Wherein, Ch represents the first growth indicator information, which in this embodiment can be a height value. β0, β1, and βn are predetermined regression coefficients corresponding to the independent variables; u is a predetermined error term. X0, X1, X2…Xn are the growth status assessment index values ​​in this embodiment.

[0109] It should be noted that at least two growth status assessment indicators are required, including but not limited to growth time and production inputs. Growth time includes, but is not limited to, growth stages and number of days. Growth time can be calculated from the start of planting or from the start of a certain stage of the crop; there is no limitation here.

[0110] In one possible implementation, the height value calculated by the regression model can be compared with the actual height value to update the regression model, such as updating the independent variable coefficients or error terms of the regression model, which is not limited here.

[0111] This embodiment improves the accuracy of height calculation by updating the regression model, which in turn improves the accuracy of determining growth conditions and thus enhances the accuracy of determining growth control schemes, thereby further improving the growth quality of crops.

[0112] In one possible implementation, the monitoring information includes monitoring image information of crops in the current crop production area. Based on the monitoring information, growth indication information of crops in the current crop production area is determined, including:

[0113] Based on the monitoring image information corresponding to the current crop production area, the second growth indication information of the crops in the current crop production area is determined. The second growth indication information is used to indicate whether the crops are growing normally.

[0114] Among them, the monitoring image information can be image information obtained by taking pictures of crops.

[0115] In this embodiment, it is possible to determine whether crops are growing normally based on the monitoring image information corresponding to the current crop production area.

[0116] This embodiment can intuitively reflect whether crops are growing normally by monitoring image information.

[0117] In one possible implementation, based on monitoring image information corresponding to the current crop production area, second growth indicator information of crops in the current crop production area is determined, including:

[0118] Based on the monitoring image information corresponding to the current crop production area, determine at least two growth abnormality assessment index values;

[0119] Obtain weight sets and benchmark value sets corresponding to at least two abnormal situations. Each weight set includes at least two second weights, and each benchmark value set includes at least two abnormal benchmark values. The at least two second weights and the at least two abnormal benchmark values ​​correspond one-to-one with at least two growth abnormality assessment index values.

[0120] Determine the abnormal similarity corresponding to at least two abnormal situations. The abnormal similarity corresponding to the abnormal situation is determined based on at least two second product results corresponding to the current crop production area. The second product result is the product between the growth abnormality assessment index value, the reciprocal of the corresponding abnormal benchmark value, and the corresponding second weight.

[0121] If the abnormal similarity of the current crop production area is less than the preset similarity threshold, then the second growth indication information of the crop in the current crop production area is determined to include the first target indication information, which is used to indicate that the growth of the crop is normal.

[0122] If the abnormal similarity corresponding to one of the abnormal conditions in the current crop production area is greater than or equal to the similarity threshold, then the second growth indication information of the crop in the crop production area is determined to include the second target indication information, which is used to indicate that the growth of the crop is abnormal.

[0123] In this embodiment, the monitoring image information for the same crop may include at least one image taken from at least one shooting angle. After obtaining the monitoring image information for each crop in the current crop production area, feature extraction can be performed on the images to determine at least two growth abnormality assessment index values.

[0124] The following weighted calculation method is adopted:

[0125]

[0126]

[0127] In the formula, y represents the similarity between the crop monitoring model services, which can indicate the similarity between the crop's growth status and abnormal conditions. i f represents the similarity between the i-th growth abnormality assessment index value and the corresponding abnormality baseline value; iThis is the second weight corresponding to the value of the growth abnormality assessment index; y ir This refers to the actual monitored value, which is also the value of the growth abnormality assessment index; y id The theoretical value used to serve the crop monitoring model, also known as the abnormal baseline value.

[0128] One crop monitoring model service corresponds to one type of anomaly. In this embodiment, the similarity value of each crop monitoring model service is calculated. When the similarity exceeds a set threshold, it is determined that there is an anomaly in the crop, and corresponding monitoring and early warning information can be pushed to the system.

[0129] For example, images of the current state of plants can be collected by a camera, and image processing technology can be used for image segmentation and recognition, such as classifying and extracting features from the current state of crops, and comparing the plant color values, spectral values, and abnormal shape ranges with the corresponding indicator feature values ​​in the crop monitoring model service to obtain a quantitative value of similarity.

[0130] In one possible implementation, the second target indication information is also used to indicate the target anomaly in at least two anomaly situations, wherein the anomaly similarity corresponding to the target anomaly is greater than or equal to a similarity threshold.

[0131] The technical solution of this embodiment indicates the abnormal situation of the target through the second target indication information, so as to know the specific abnormality of the crop and improve the accuracy of determining the growth status of the crop.

[0132] In one possible implementation, the abnormal situations analyzed in this embodiment can be compared with the actual abnormal situations, and then the weight set and benchmark value set corresponding to the abnormal situations can be updated based on the comparison results.

[0133] This embodiment compares the abnormal situations analyzed in this embodiment with the actual abnormal situations, and then updates the weight set and benchmark value set corresponding to the abnormal situations based on the comparison results. This can improve the accuracy of determining the growth situation, thereby improving the accuracy of determining the growth control scheme, and thus further improving the growth quality of crops.

[0134] In one possible implementation, the monitoring information includes environmental monitoring information of the current crop production area and monitoring image information of crops in the current crop production area;

[0135] Based on monitoring information, determine the growth indicators of crops in the current crop production area, including:

[0136] Based on the environmental monitoring information corresponding to the current crop production area, the first growth indicator information of the crop in the current crop production area is determined. The first growth indicator information is used to indicate the growth status of the crop.

[0137] Based on the monitoring image information corresponding to the current crop production area, the second growth indication information of the crops in the current crop production area is determined. The second growth indication information is used to indicate whether the crops are growing normally.

[0138] The technical solution of this embodiment determines the growth status of crops through environmental monitoring information and determines whether crops are growing normally through monitoring image information. By monitoring multiple indicators of crops, the comprehensiveness of crop production control is improved and the growth quality of crops is guaranteed.

[0139] Please see Figure 2 , Figure 2 This is a schematic diagram of the framework of a crop production control system based on digital twins, provided for an embodiment of this application.

[0140] like Figure 2 In the technical framework diagram shown, in this embodiment, the crop production control system based on digital twins can include physical entities of crop production areas, virtual simulation, professional services, connection systems, data storage, etc., to achieve integrated and automated "perception-connection-decision-upgrade", realizing automation of the entire agricultural production process and self-upgrading of the production control system.

[0141] The first step is to build a crop production control system based on digital twins, which can include physical entity components, virtual entity components, connection system components, model service library components, and data center components.

[0142] First, let's explain the physical entity part.

[0143] The prerequisite for establishing an accurate production control system is to accurately analyze the physical reality of the crop production area (hereinafter referred to as the crop production area or agricultural production area), and to accurately analyze and interpret the physical entities, production facilities, production conditions and various attributes of the crop production area.

[0144] First, crops are categorized by type, with each crop producing area forming an independent agricultural growth control system. This independent system comprises three parts: unit-level production areas, production area monitoring devices, and growth control equipment. A unit-level production area is the designated planting area for crop cultivation. The principle for determining planting units is the availability of similar production conditions. For example, a rice-growing area can be defined as a single ridge, where rice plants receive the same sunlight, fertilization, and irrigation conditions. Multiple planting units can be constructed in this way.

[0145] Production area monitoring devices are collections of various sensors used to monitor crops, such as devices that monitor meteorological data, soil data, and crop images. Growth control equipment refers to agricultural equipment used in the daily production of crops, such as irrigation systems, lighting systems, and pesticide spraying systems.

[0146] The entire agricultural growth control system consists of unit-level production areas, production area monitoring devices, and growth control equipment, thereby realizing the overall monitoring and production functions of crop production areas.

[0147] Table 1 Physical Entity Structure of Production Control System

[0148]

[0149] Secondly, the virtual entity part will be explained.

[0150] Based on the analysis of the physical entities of the agricultural production area in the previous step, a corresponding virtual digital twin model is constructed. The virtual twin model includes a geometric model and model attributes.

[0151] 1) Constructing a geometric model

[0152] Based on the appearance of the agricultural production area, modeling tools such as 3DMAX are used to construct corresponding modeling models, thereby realizing geometric attribute mapping.

[0153] In the construction of unit-level production area models, the spatial range of each unit production area can be determined by the set of longitude and latitude geographical coordinates of the four boundaries of the production area, and the corresponding crop model can be constructed within the range.

[0154] In the construction of the entire growth control equipment, the monitoring equipment system and the production equipment system can be equipped with corresponding geometric models by collecting the relevant dimensions, materials, and geographical locations (latitude and longitude coordinates). Through unit-level production area models, monitoring equipment system models, and production equipment system models, the geometric model of the entire production control system is constructed.

[0155] 2) Constructing model attributes

[0156] Model attributes are the descriptions of the properties of agricultural growth control equipment at both macroscopic and microscopic scales. Model attributes are attached to the corresponding geometric models and establish a one-to-one correspondence. The focus of model attributes is the model attributes of unit crop production areas.

[0157] Attributes related to crop growth status, such as crop height, color, leaf nitrogen content, fruit quantity, and leaf area index, are constructed. The specific attributes to be constructed can be determined by crop experts. Considering practical feasibility, the acquisition methods for crop growth status attributes can be divided into automatic acquisition and manual reporting. If the attribute can be obtained through existing monitoring equipment, the automatic acquisition mode is adopted; if there are currently no monitoring facilities for the attribute, the manual reporting mode is used.

[0158] The model attributes of the equipment are the parameters that the equipment can control, such as the monitoring indicators that can be obtained by monitoring equipment and the production parameters that can be adjusted by production equipment.

[0159] Table 2 Virtual Entity Structure of Production Control System

[0160]

[0161] Next, we will explain the model service library.

[0162] By utilizing agricultural algorithms and solutions, a model service library for agricultural digital twins is constructed. This library enables daily monitoring and production decision-making for agricultural growth control equipment. The model service library includes production status services, crop production monitoring model services, and crop production decision-making model services.

[0163] 1) Production Status Service

[0164] Based on relevant theoretical models or experience, a crop growth status model service is constructed. The growth status index system is selected and combined from the model attributes of the virtual model. Corresponding independent variable factors for each index are constructed, including growth time (stage, number of days), production inputs, etc. For example, to construct a regression model for the crop height growth status index, one approach is to use a regression equation.

[0165] Ch=β0*X0+β1*X1+β2*X2+...+βn*Xn+u;

[0166] In the formula, Ch represents the crop height value; X0, X1, X n β0, β1, and βn are the regression coefficients of the corresponding independent variables, such as input factors like the number of growing days and the amount of fertilizer applied; u is the error term.

[0167] 2) Production monitoring model service

[0168] The production monitoring model service is used to analyze and draw conclusions about abnormal crop conditions, and may include various disease models and abnormal production factor models for the crop. Each crop monitoring model service represents the state and characteristics of crop attributes under a certain abnormal condition.

[0169] A typical monitoring model service, such as "rice bacterial blight," identifies the following graphic characteristics of the crop: yellowish-green spots appearing on the tips and edges of diseased leaves; diseased plants are prone to lodging; and soil moisture levels are excessively high. During the crop's growth, the monitoring system compares these characteristics with various conditions found in other agricultural monitoring services to analyze and determine the monitoring model service to which the crop's anomaly belongs.

[0170] 3) Production Decision Model Services

[0171] Agricultural production services are targeted production plans adopted based on crop growth status and crop monitoring conclusions. Each production decision service includes implementation measures and parameters. For example, the agricultural production decision service for "rice bacterial blight" includes two measures and corresponding parameters: fertilization measures, with parameters for phosphorus and potassium fertilizers; and pesticide spraying, with parameters for thiabendazole copper suspension (20% concentration, 100 grams / acre), sprayed twice consecutively with an interval of 7 days.

[0172] In addition, the connection system will be explained.

[0173] The connectivity system serves as the link between physical and virtual agricultural entities. On one hand, real-time monitoring data collected by physical devices is uploaded to the agricultural digital twin system and assigned attributes to the corresponding virtual entities. On the other hand, the agricultural digital twin system controls the physical devices through command feedback. Agricultural production is a complex and dynamic process, and a reliable, long-term connectivity system can be established using reliable equipment. For example, establishing 5G macro base stations and deploying image, soil, humidity, and weather sensors can enable data transmission from physical to virtual entities. Software interfaces such as WebSockets can be used to establish long-lived connections, allowing virtual entities to issue commands to physical entities and control them.

[0174] In addition, the data center is also explained.

[0175] The data center stores data on crop growth status, recording actual values ​​collected by the connection system in the server database. The recording method is "individual—time—attribute—value," thus achieving the recording of various attributes throughout the entire lifecycle of the physical entity. Based on the recorded data, the service plan is continuously corrected and optimized. For example, the actual growth status of the crop is compared with the theoretical status to optimize the calculation parameters of the theoretical status, enabling dynamic maintenance and upgrades of the system.

[0176] Once a crop production control system based on digital twins is established, production control can be carried out, including crop production status, production monitoring, and agricultural production decision-making.

[0177] The current state of crop production can be summarized as follows:

[0178] Daily crop status monitoring is performed based on a crop production status model. Real-time collected agricultural production data is transmitted to the corresponding attributes of the virtual model via a connection system. The collected values ​​are recorded by the data system. The target values ​​calculated by the crop production status model are compared with the actual collected values. If the target value is less than the error threshold, the daily decision-making service provided by the crop production status model is executed. The system then issues production instructions (measures, parameters) to the production equipment via the connection system, and the production equipment performs the corresponding agricultural production activities.

[0179] Production monitoring can include the following:

[0180] Using each unit-level production area as the monitoring object, daily monitoring of crop production is performed based on crop monitoring model services. Crop status information is acquired through image sensors and specialized pest and disease monitoring sensors, input into the digital twin system, and compared with the feature values ​​of each crop monitoring model service. The following weighted calculation method can be used:

[0181]

[0182]

[0183] In the formula, y represents the similarity of the crop monitoring model services. i f is the similarity value of the i-th monitoring indicator. i Calculate the weight for this indicator, y ir y represents the actual monitored value of this indicator. id The theoretical values ​​served by the crop monitoring model. By calculating the similarity values ​​served by each crop monitoring model, when the similarity exceeds a set threshold, corresponding monitoring and early warning information is pushed to the system.

[0184] A typical implementation method involves collecting image information of the current state of plants through a camera, and using image processing technology to perform image segmentation and recognition, such as classifying and extracting features from the current state of crops, and comparing the plant color values, spectral values, and abnormal shape ranges with the corresponding indicator feature values ​​in the crop monitoring model service to obtain a quantitative value of similarity.

[0185] Agricultural production decisions can be made as follows:

[0186] When the crop production situation is judged to be normal, the daily decision-making service of the crop production situation model is executed. According to the corresponding plan of the model service on the time axis, the system sends production instructions (measures, parameters) to the production equipment through the connection system, and the production equipment executes the corresponding agricultural production activities.

[0187] When an abnormality is detected in crop production, the system executes corresponding plans based on monitoring and early warning information provided by production monitoring services. The system sends production instructions (measures, parameters) for this plan to the production equipment via a connection to the main system, and the equipment then performs the corresponding agricultural production activities. After execution, crop production monitoring continues until normalcy is restored.

[0188] After a period of time, the production control system based on digital twins can be used to control crop production, and the production control system can be corrected based on real data.

[0189] The production control system can be modified based on real data in the following ways:

[0190] Based on the actual data values ​​of crop growth processes stored in the data center, and taking unit-level production areas as the unit, regression analysis, principal component analysis and other model training methods are used to correct and optimize various model services in the model service library, thereby achieving system self-adaptation and self-upgrading.

[0191] For example, to revise the growth trend model, the service model is modified based on the actual input and output data of each production unit. The model can take the form of a multinomial model, logarithmic model, power function model, exponential model, or a comprehensive model. One approach is a linear regression analysis model of "unit-growth value-time-influencing factors," which optimizes and adjusts the analysis model year by year using actual production input and growth result data for each crop unit. Based on the stored values ​​in the data center, a growth value-indicator value data table is constructed.

[0192] Table 3. Actual data for the growth status model.

[0193]

[0194] Regression analysis can be performed using the least squares (OLS) estimation method, and the correlation coefficient R can be solved. 2 When R 2 If the value is less than the set threshold (e.g., 0.85), it indicates that the model's factor explanatory power is insufficient. In this case, the influencing factors should be reconstructed and recalculated until the correlation test requirements are met. When R... 2 When the value exceeds the set threshold, it indicates that the overall explanatory effect of the model is significant, and the joint linear effect of the explanatory variables is significant. The calculation process can use professional statistical analysis software such as SPSS to perform ordinary least squares estimation (OLS) on the model expression, ultimately constructing the growth trend model:

[0195] Y i =β0*t+β1*X1+β2*X2+β3*X3+...+β n X n +u.

[0196] Among them, Y i t represents the crop growth status index value, t represents the crop growth time, X represents various factors of production input, and u represents the random disturbance term.

[0197] In this way, the service models are trained based on the actual data values ​​stored in the data center, thereby correcting and optimizing the various model services, enabling the long-term self-adaptation and self-upgrading of the production control system, and thus achieving long-term agricultural automated production.

[0198] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a crop production control device provided in an embodiment of this application. Figure 3 The crop production control device 30 shown includes an information acquisition module 310, a growth status determination module 320, and a production control module 330, wherein:

[0199] Information acquisition module 310 is used to acquire monitoring information of the current crop production area;

[0200] The growth status determination module 320 is used to determine the growth indication information of crops in the current crop production area based on monitoring information. The growth indication information is used to indicate the growth status of crops.

[0201] The production control module 330 is used to determine the growth control scheme for the current crop production area based on the crop growth indication information; and to control the operation of the growth control equipment corresponding to the current crop production area based on the growth control scheme.

[0202] In one possible implementation, the device 30 further includes:

[0203] The model update module is used to update the pre-established virtual digital twin model based on the new monitoring information acquired for the current crop production area. The virtual digital twin model is used to simulate the growth of crops in each crop production area.

[0204] In one possible implementation, the virtual digital twin model includes a simulated production area corresponding to each crop production area, which is used to simulate the growth of crops in the corresponding crop production area; the update module is used to update the growth of crops in the current simulated production area based on new monitoring information of the current crop production area, where the current simulated production area is the simulated production area corresponding to the current crop production area.

[0205] In one possible implementation, the model update module is used to update the growth status of crops in the current simulated production area at a first update frequency if the growth indication information of crops in the current crop production area includes first target indication information. The first target indication information is used to indicate that the growth status of crops is normal.

[0206] If the crop growth indication information in the current crop production area includes the second target indication information, then the crop growth status in the current simulated production area is updated at the second update frequency. The second target indication information is used to indicate that the crop growth status is abnormal.

[0207] The first update frequency is lower than the second update frequency.

[0208] In one possible implementation, the monitoring information includes environmental monitoring information of the current crop production area and / or monitoring image information of the crops in the current crop production area;

[0209] The growth status determination module 320 is used to determine first growth indication information of crops in the current crop production area based on environmental monitoring information corresponding to the current crop production area. The first growth indication information is used to indicate the growth status of the crops; and / or,

[0210] Based on the monitoring image information corresponding to the current crop production area, the second growth indication information of the crops in the current crop production area is determined. The second growth indication information is used to indicate whether the crops are growing normally.

[0211] In one possible implementation, the environmental monitoring information includes at least two different growth status assessment index values.

[0212] The growth status determination module 320 determines the first weights corresponding to at least two growth status assessment index values;

[0213] Based on the first product of each growth status assessment index value and its corresponding first weight, the first growth indication information of crops in the current crop production area is determined.

[0214] In one possible implementation, the growth condition determination module 320 is used to determine at least two growth abnormality assessment index values ​​based on monitoring image information corresponding to the current crop production area.

[0215] Obtain weight sets and benchmark value sets corresponding to at least two abnormal situations. Each weight set includes at least two second weights, and each benchmark value set includes at least two abnormal benchmark values. The at least two second weights and the at least two abnormal benchmark values ​​correspond one-to-one with at least two growth abnormality assessment index values.

[0216] Determine the abnormal similarity corresponding to at least two abnormal situations. The abnormal similarity corresponding to the abnormal situation is determined based on at least two second product results corresponding to the current crop production area. The second product result is the product between the growth abnormality assessment index value, the reciprocal of the corresponding abnormal benchmark value, and the corresponding second weight.

[0217] If the abnormal similarity of the current crop production area is less than the preset similarity threshold, then the second growth indication information of the crop in the current crop production area is determined to include the first target indication information, which is used to indicate that the growth of the crop is normal.

[0218] If the abnormal similarity corresponding to one of the abnormal conditions in the current crop production area is greater than or equal to the similarity threshold, then the second growth indication information of the crop in the crop production area is determined to include the second target indication information, which is used to indicate that the growth of the crop is abnormal.

[0219] In one possible implementation, the second target indication information is also used to indicate the target anomaly in at least two anomaly situations, wherein the anomaly similarity corresponding to the target anomaly is greater than or equal to a similarity threshold.

[0220] In one possible implementation, the production control module 330 is used to determine the growth control scheme for the current crop production area based on crop growth indication information and a predetermined mapping relationship, wherein the mapping relationship is used to represent the mapping relationship between the growth indication information and the growth control scheme.

[0221] The crop production control device of this embodiment can execute the steps of the crop production control method shown in the foregoing embodiments of this application. The implementation principle is similar and will not be repeated here.

[0222] This application provides a crop production control system, including growth control equipment, a monitoring device, and a production control device. The growth control equipment is installed in each crop production area; the monitoring device is installed in each crop production area to monitor the crop production areas and obtain monitoring information corresponding to each crop production area; the crop production control device is used to perform the following steps for each crop production area: acquiring monitoring information of the current crop production area; determining growth indication information of the crop in the current crop production area based on the monitoring information, the growth indication information indicating the growth status of the crop; determining a growth control scheme for the current crop production area based on the crop growth indication information; and controlling the growth control equipment corresponding to the current crop production area to operate based on the growth control scheme.

[0223] This application provides an electronic device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of a crop production control method.

[0224] In one alternative embodiment, an electronic device is provided, such as Figure 4 As shown, Figure 4 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of this application.

[0225] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0226] Bus 4002 may include a pathway for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0227] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium capable of carrying or storing computer programs and capable of being read by a computer, without limitation herein.

[0228] The memory 4003 stores computer programs that execute embodiments of this application, and its execution is controlled by the processor 4001. The processor 4001 executes the computer programs stored in the memory 4003 to implement the steps shown in the foregoing method embodiments.

[0229] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement the steps and corresponding content of the aforementioned method embodiments.

[0230] This application also provides a computer program product, including a computer program that, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments.

[0231] It should be understood that although arrows indicate various operation steps in the flowcharts of this application's embodiments, the order in which these steps are implemented is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of this application's embodiments, the implementation steps in each flowchart can be executed in other orders as required. Furthermore, some or all steps in each flowchart, based on the actual implementation scenario, may include multiple sub-steps or multiple stages. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage can also be executed at different times. In scenarios where execution times differ, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and this application's embodiments do not limit this.

[0232] The above are only optional implementation methods for some implementation scenarios of this application. It should be noted that for those skilled in the art, other similar implementation methods based on the technical concept of this application, without departing from the technical concept of this application, also fall within the protection scope of the embodiments of this application.

Claims

1. A method of crop production control, characterized by, For each crop production area, the following steps should be performed: Obtain monitoring information on the current crop production area; Based on the monitoring information, growth indication information of crops in the current crop production area is determined, and the growth indication information is used to indicate the growth status of crops; the monitoring information includes monitoring image information of crops in the current crop production area; Based on the crop growth indication information, a growth control scheme for the current crop production area is determined; Based on the growth control scheme, control the operation of the growth control equipment corresponding to the current crop production area; Based on the new monitoring information obtained for the current crop production area, the pre-established virtual digital twin model is updated. The virtual digital twin model is used to simulate the growth of crops in each crop production area. The step of determining the crop growth indication information of the current crop production area based on the monitoring information includes: Based on the monitoring image information corresponding to the current crop production area, a second growth indication information for the crops in the current crop production area is determined. The second growth indication information is used to indicate whether the crops are growing normally. The step of determining the second growth indication information of crops in the current crop production area based on the monitoring image information corresponding to the current crop production area includes: Based on the monitoring image information corresponding to the current crop production area, determine at least two growth abnormality assessment index values; Obtain weight sets and benchmark value sets corresponding to at least two abnormal situations. Each weight set includes at least two second weights, and each benchmark value set includes at least two abnormal benchmark values. The at least two second weights and the at least two abnormal benchmark values ​​correspond one-to-one with the at least two growth abnormality assessment index values. Determine the abnormal similarity corresponding to the at least two abnormal situations respectively, wherein the abnormal similarity corresponding to the abnormal situation is determined based on at least two second product results corresponding to the current crop production area, and the second product result is the product between the growth abnormality assessment index value, the reciprocal of the corresponding abnormal benchmark value, and the corresponding second weight; If the abnormal similarity of the current crop production area is less than the preset similarity threshold, then the second growth indication information of the crop in the current crop production area is determined to include the first target indication information, which is used to indicate that the growth of the crop is normal. If the abnormal similarity corresponding to one of the abnormal conditions in the current crop production area is greater than or equal to the similarity threshold, then the second growth indication information of the crop in the crop production area is determined to include second target indication information, which is used to indicate that the growth of the crop is abnormal.

2. The method of claim 1, wherein, The virtual digital twin model includes a simulated production area corresponding to each crop production area, and the simulated production area is used to simulate the growth of crops in the corresponding crop production area. The process of updating the pre-established virtual digital twin model based on the acquired new monitoring information of the current crop production area includes: Based on the new monitoring information of the current crop production area, the growth status of crops in the current simulated production area is updated, where the current simulated production area is the simulated production area corresponding to the current crop production area.

3. The method of claim 2, wherein, The update of crop growth status in the current simulated production area includes: If the crop growth indication information in the current crop production area includes the first target indication information, then the crop growth status in the current simulated production area is updated at the first update frequency. The first target indication information is used to indicate that the crop growth status is normal. If the crop growth indication information in the current crop production area includes the second target indication information, then the crop growth status in the current simulated production area is updated at the second update frequency. The second target indication information is used to indicate that the crop growth status is abnormal. The first update frequency is lower than the second update frequency.

4. The method according to claim 1, characterized in that, The monitoring information also includes environmental monitoring information of the current crop production area; The step of determining the crop growth indication information of the current crop production area based on the monitoring information further includes: Based on the environmental monitoring information corresponding to the current crop production area, a first growth indication information for the crops in the current crop production area is determined. The first growth indication information is used to indicate the growth status of the crops.

5. The method according to claim 4, characterized in that, The environmental monitoring information includes at least two different growth status assessment index values. The step of determining the first growth indication information of crops in the current crop production area based on environmental monitoring information corresponding to the current crop production area includes: Determine the first weights corresponding to the values ​​of the at least two growth status assessment indicators; Based on the first product of each growth status assessment index value and its corresponding first weight, the first growth indication information of the crop in the current crop production area is determined.

6. The method according to claim 1, characterized in that, The second target indication information is also used to indicate a target anomaly among the at least two anomaly situations, wherein the anomaly similarity corresponding to the target anomaly is greater than or equal to the similarity threshold.

7. The method according to any one of claims 1-6, characterized in that, The step of determining a growth control scheme for the current crop production area based on the crop's growth indication information includes: Based on the crop growth indication information and a pre-determined mapping relationship, a growth control scheme for the current crop production area is determined, wherein the mapping relationship is used to represent the mapping relationship between the growth indication information and the growth control scheme.

8. A crop production control device, characterized in that, The apparatus, used for production control in each crop production area, includes: The information acquisition module is used to acquire monitoring information of the current crop production area; The growth status determination module is used to determine the growth indication information of crops in the current crop production area based on the monitoring information. The growth indication information is used to indicate the growth status of crops. The monitoring information includes monitoring image information of crops in the current crop production area. The production control module is used to determine the growth control scheme for the current crop production area based on the crop growth indication information; and to control the operation of the growth control equipment corresponding to the current crop production area based on the growth control scheme. The model update module is used to update the pre-established virtual digital twin model based on the new monitoring information obtained for the current crop production area. The virtual digital twin model is used to simulate the growth of crops in each crop production area. The production control module is used to determine the second growth indication information of the crops in the current crop production area based on the monitoring image information corresponding to the current crop production area. The second growth indication information is used to indicate whether the crops are growing normally. The production control module is used to determine at least two growth abnormality assessment index values ​​based on monitoring image information corresponding to the current crop production area; obtain weight sets and benchmark value sets corresponding to at least two abnormal situations, each weight set including at least two second weights, and each benchmark value set including at least two abnormal benchmark values, wherein the at least two second weights and the at least two abnormal benchmark values ​​correspond one-to-one with the at least two growth abnormality assessment index values; and determine the abnormality similarity corresponding to the at least two abnormal situations, wherein the abnormality similarity corresponding to the abnormal situation is determined based on the at least two second product results corresponding to the current crop production area, and the second product result is the growth abnormality assessment index value. The product of the estimated index value, the reciprocal of the corresponding abnormal benchmark value, and the corresponding second weight is used. If the abnormal similarity of the current crop production area is less than a preset similarity threshold, then the second growth indication information of the crop in the current crop production area is determined to include the first target indication information, which is used to indicate that the crop growth is normal. If the abnormal similarity corresponding to one of the abnormal conditions in the current crop production area is greater than or equal to the similarity threshold, then the second growth indication information of the crop in the current crop production area is determined to include the second target indication information, which is used to indicate that the crop growth is abnormal.

9. A crop production control system, characterized in that, include: Growth control equipment is installed in each crop production area; The monitoring device is installed in each crop production area to monitor the crop production area and obtain the monitoring information corresponding to each crop production area; the monitoring information includes monitoring image information of crops in the current crop production area; A crop production control device for performing the following steps for each crop production area: Obtain monitoring information on the current crop production area; Based on the monitoring information, growth indication information of crops in the current crop production area is determined, and the growth indication information is used to indicate the growth status of crops; Based on the crop growth indication information, a growth control scheme for the current crop production area is determined; Based on the growth control scheme, control the operation of the growth control equipment corresponding to the current crop production area; Based on the new monitoring information obtained for the current crop production area, the pre-established virtual digital twin model is updated. The virtual digital twin model is used to simulate the growth of crops in each crop production area. The step of determining the crop growth indication information of the current crop production area based on the monitoring information includes: Based on the monitoring image information corresponding to the current crop production area, a second growth indication information for the crops in the current crop production area is determined. The second growth indication information is used to indicate whether the crops are growing normally. The step of determining the second growth indication information of crops in the current crop production area based on the monitoring image information corresponding to the current crop production area includes: Based on the monitoring image information corresponding to the current crop production area, determine at least two growth abnormality assessment index values; Obtain weight sets and benchmark value sets corresponding to at least two abnormal situations. Each weight set includes at least two second weights, and each benchmark value set includes at least two abnormal benchmark values. The at least two second weights and the at least two abnormal benchmark values ​​correspond one-to-one with the at least two growth abnormality assessment index values. Determine the abnormal similarity corresponding to the at least two abnormal situations respectively, wherein the abnormal similarity corresponding to the abnormal situation is determined based on at least two second product results corresponding to the current crop production area, and the second product result is the product between the growth abnormality assessment index value, the reciprocal of the corresponding abnormal benchmark value, and the corresponding second weight; If the abnormal similarity of the current crop production area is less than the preset similarity threshold, then the second growth indication information of the crop in the current crop production area is determined to include the first target indication information, which is used to indicate that the growth of the crop is normal. If the abnormal similarity corresponding to one of the abnormal conditions in the current crop production area is greater than or equal to the similarity threshold, then the second growth indication information of the crop in the crop production area is determined to include second target indication information, which is used to indicate that the growth of the crop is abnormal.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

Images