Information processing method, prescription chart generation method, job control method, and related devices

Abstract

Embodiments of the present application provide an information processing method, a prescription map generation method, a job control method and related devices, wherein the method comprises: obtaining to-be-analyzed information of a target crop area and user information, determining the type of a target crop corresponding to the to-be-analyzed information based on the user information, and determining an analysis result of the to-be-analyzed information based on the type of the target crop, the to-be-analyzed information and the user information.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, in particular to an information processing method, a prescription chart generation method, a job control method and related devices. BACKGROUND

[0002] In the current crop Q&A system, the common practice is to decompose the crop health problem raised by the user, extract the keywords, then search for the associated answers from the answer base based on the keywords, and finally push to the user.

[0003] Since the above-mentioned answer acquisition method mainly depends on the user's problem description, but there are many descriptions of the same problem, therefore, if the user's problem description is inappropriate, it is easy to lead to the inaccuracy of the final answer obtained, or even unable to obtain the answer. SUMMARY

[0004] The purposes of the present application include, for example, providing an information processing method, a prescription chart generation method, a job control method and related devices, which can improve the accuracy of crop health information output by the crop Q&A system.

[0005] In order to achieve the above-mentioned purposes, the technical solutions of the embodiments of the present application are as follows:

[0006] In a first aspect, the embodiments of the present application provide an information processing method, which comprises:

[0007] Obtaining the to-be-analyzed information of the target crop area and the user information; the to-be-analyzed information is used to represent the growth state of the crops in the target crop area;

[0008] Determining the type of the target crop corresponding to the to-be-analyzed information based on the user information, wherein the user information comprises at least one of the geographical position information and the crop type information of the target crop area;

[0009] Determining the analysis result of the to-be-analyzed information based on the type of the target crop, the to-be-analyzed information and the user information.

[0010] In a second aspect, the embodiments of the present application provide a prescription chart generation method, which comprises:

[0011] Generating the disease prescription chart and / or the nutrition prescription chart corresponding to the target crop area based on the analysis result of the crops in the target crop area, wherein the analysis result is determined by the information processing method.

[0012] In a third aspect, the embodiments of the present application provide a job control method, which comprises:

[0013] Based on the disease prescription chart and / or the nutrition prescription chart obtained by the prescription chart generation method, a work device is controlled to work on crops in a target crop area.

[0014] In a fourth aspect, an embodiment of the present application provides an information processing device, the device comprising:

[0015] An acquisition module is configured to acquire user information and to-be-analyzed information of a target crop area, wherein the to-be-analyzed information is used to represent a growth state of crops in the target crop area.

[0016] A first determination module is configured to determine a type of target crops corresponding to the to-be-analyzed information based on the user information, wherein the user information comprises at least one of geographical position information and crop type information of the target crop area.

[0017] A second determination module is configured to determine an analysis result of the to-be-analyzed information based on the type of the target crops, the to-be-analyzed information, and the user information.

[0018] In a fifth aspect, an embodiment of the present application further provides an electronic device comprising a processor and a memory, wherein the memory is configured to store a computer program, and the processor is configured to implement the information processing method, the prescription chart generation method, and the work control method when the computer program is executed.

[0019] In a sixth aspect, an embodiment of the present application further provides a work device comprising a device body, a material unit installed on the device body, a spraying mechanism installed on the device body, and the electronic device, wherein the material unit is configured to store work materials, the spraying mechanism is configured to output the work materials to target crops, and the electronic device is configured to control work states of the material unit and the spraying mechanism, and the material unit is internally provided with multiple storage spaces that are independent of each other.

[0020] In a seventh aspect, an embodiment of the present application further provides a storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the information processing method, the prescription chart generation method, and the work control method.

[0021] The application obtains target crop region to-be-analyzed information and user information, determines the type of the target crop corresponding to the to-be-analyzed information based on the user information, and determines the analysis result of the to-be-analyzed information based on the type of the target crop, the to-be-analyzed information, and the user information. Based on the geographic location information and / or crop type information in the user information, the type of the target crop corresponding to the to-be-analyzed information can be automatically and accurately determined according to the geographic location characteristics and / or the crop type, and then the to-be-analyzed information can be processed in combination with the crop type and / or the geographic characteristics, so that the analysis result (such as at least one of the health condition of the target crop, the cause analysis result, and the corresponding farming measure) is more targeted and can better meet the actual needs of the user. Thus, the problem that the system cannot accurately identify the object to which the problem is directed only by relying on the problem information can be avoided, so that the user needs to input other information again to assist in confirmation or the answer information is inaccurate. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be considered as limiting the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0023] Figure 1 One of the step flowcharts of the information processing method provided by the embodiments of the present application;

[0024] Figure 2 The second step flowchart of the information processing method provided by the embodiments of the present application;

[0025] Figure 3 The third step flowchart of the information processing method provided by the embodiments of the present application;

[0026] Figure 4 The fourth step flowchart of the information processing method provided by the embodiments of the present application;

[0027] Figure 5 The fifth step flowchart of the information processing method provided by the embodiments of the present application;

[0028] Figure 6 The sixth step flowchart of the information processing method provided by the embodiments of the present application;

[0029] Figure 7 The seventh step flowchart of the information processing method provided by the embodiments of the present application;

[0030] Figure 8 The eighth step flowchart of the information processing method provided by the embodiments of the present application;

[0031] Figure 9 A structural block diagram of an information processing device provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0032] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0033] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative labor fall within the scope of protection of the present application.

[0034] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0035] In the description of the present application, it should be noted that if the terms such as “upper”, “lower”, “inner”, “outer” and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the present application is usually placed, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the indicated device or element must have a particular orientation, be constructed and operated in a particular orientation, therefore, it cannot be understood as a limitation on the present application.

[0036] In addition, if the terms “first”, “second” and the like appear, they are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0037] It should be noted that the features in the embodiments of the present application can be combined with each other without conflict.

[0038] Please refer to Figure 1 , Figure 1 A step flowchart of an information processing method provided by an embodiment of the present application, the method comprises:

[0039] Step 101: obtaining the to-be-analyzed information of the target crop area and user information.

[0040] The to-be-analyzed information is used to represent the growth state of the crops in the target crop area.

[0041] Step 102: determining the type of the target crop corresponding to the to-be-analyzed information based on the user information.

[0042] The user information includes at least one of the geographical location information of the target crop area and the crop type information.

[0043] Step 103: determining the analysis result of the to-be-analyzed information based on the type of the target crop, the to-be-analyzed information, and the user information.

[0044] It should be noted that the to-be-analyzed information can include at least one of to-be-analyzed text input by the user and to-be-analyzed images, the to-be-analyzed text can be "Why do the leaves turn yellow?", and the to-be-analyzed images can be crop images and / or soil images. In order to ensure that the crop Q&A system can meet the needs of different users, it is usually recommended that the user register before entering the crop Q&A system when the user uses the crop Q&A system for the first time. The registration information of the user of the crop Q&A system can include the geographical location information of the crop area and / or the crop type information, etc. The crop type information can include wheat, corn, rice, cotton, etc., but is not limited thereto.

[0045] There are various ways to determine the type of the target crop in the to-be-analyzed image based on the user information.

[0046] In an embodiment, the to-be-analyzed information can include at least one of to-be-analyzed images and to-be-analyzed text, but is not limited thereto.

[0047] The following is an example of the to-be-analyzed information only containing to-be-analyzed images, and the to-be-analyzed images being crop images:

[0048] Method one: in the case where the user information includes crop type information:

[0049] When the user information contains one crop type information, the crop type information in the user information is taken as the type of the target crop.

[0050] Since the crop type information in the user information is set by the user, the crop type information in the user information can be one crop type information or multiple crop type information. When the user information only contains one crop type, it can be determined as the type of the target crop, thereby eliminating the need to process the to-be-analyzed images through an image recognition algorithm to obtain the type of the target crop, which can improve the efficiency of obtaining the type of the target crop and reduce the computational cost.

[0051] When the crop type information in the user information is multiple, the to-be-analyzed images are matched with the images corresponding to the multiple crop type information in the user information, and the crop type that matches successfully is taken as the type of the target crop.

[0052] Although multiple crop categories are included in the user information, the image recognition step can be simplified by matching the to-be-analyzed image with the images corresponding to the multiple crop categories, and the crop category can be directly identified in the to-be-analyzed image without starting from zero, which is beneficial to reduce the identification difficulty and improve the identification accuracy.

[0053] Method two: when the user information includes geographical information of the target crop area:

[0054] When the user information includes geographical location information of the target crop area, the first crop category information suitable for planting in the target crop area is determined based on the geographical location information of the target crop area, and the first crop category information includes at least one crop category. From the first crop category information, the category of the target crop matching the crop represented by the to-be-analyzed image is determined.

[0055] Since different geographical location information is suitable for planting different crop categories, for example, when the geographical location information is south, the suitable crop categories are A crop category, B crop category, and C crop category, and when the geographical location information is north, the suitable crop categories are D crop category, E crop category, and F crop category. Therefore, based on the geographical location information of the target crop area in the user information, the first crop category information suitable for planting in the current geographical location information can be determined, and the to-be-analyzed image is matched with the images corresponding to each crop category information in the first crop category information to determine the category of the target crop in the target crop area.

[0056] This kind of target crop category determination method can simplify the image recognition step, and does not need to start from zero to identify the to-be-analyzed image to obtain the category of the target crop. The category of the target crop is determined based on the geographical location of the target crop area, and the calculation method is simple, which can quickly determine the category of the target crop in the to-be-analyzed image.

[0057] It should be noted that the present application can determine the analysis result of the to-be-analyzed information based on the category of the target crop, the to-be-analyzed information, and the user information. The analysis result of the to-be-analyzed information can include: health information of the target crop, which can include but is not limited to: growth stage of the target crop, nutrition condition of the target crop, and health condition of the target crop. The health condition can include whether the target crop has a disease, the cause of the disease, and the agricultural measures to solve the disease.

[0058] The application determines the type of the target crop corresponding to the to-be-analyzed information based on the user information, and determines the analysis result of the to-be-analyzed information based on the type of the target crop, the to-be-analyzed information and the user information. Based on the geographic location information and / or the crop type information in the user information, the type of the target crop corresponding to the to-be-analyzed information can be accurately determined according to the geographic location characteristics and / or the crop type, and then the to-be-analyzed information can be processed in combination with the crop type and / or the geographic characteristics, so that the analysis result is more targeted and can better meet the actual needs of the user. Thus, the problem that the system cannot accurately identify the object to which the question information is directed can be avoided, so that the user needs to input other information again to assist in confirmation or the answer information is inaccurate.

[0059] In order to accurately determine the type of the target crop corresponding to the to-be-analyzed information, in an embodiment of the application, as shown in Figure 2 an information processing method is provided, which specifically includes the following steps:

[0060] Step 102-1: determining the candidate crop types that can be planted in the target crop area based on the geographic location information.

[0061] Step 102-2: when the number of the candidate crop types is 1, determining that the candidate crop type is the type of the target crop corresponding to the to-be-analyzed information.

[0062] Step 102-3: when the number of the candidate crop types is greater than 1, processing the to-be-analyzed image to determine the type that matches the target crop in the to-be-analyzed image from the candidate crop types.

[0063] Different geographic locations can plant different crop types. When the geographic location is the south, the crop types that can be planted include sugarcane, wheat, soybean, cotton and the like. When the geographic location is the north, the crop types that can be planted include corn, sorghum, millet and the like. Therefore, the crop types that can be planted are different in different geographic locations, and thus the candidate crop types that can be planted in the current target crop area can be determined based on the geographic location information in the user information.

[0064] The candidate crop types that can be planted can be one or more. When the number of the candidate crop types is different, the processing manner for determining the type of the target crop corresponding to the to-be-analyzed information is different.

[0065] Manner one: when the number of the candidate crop types is 1, determining that the candidate crop type is the type of the target crop corresponding to the to-be-analyzed information.

[0066] In a case where the number of candidate crop species is greater than 1, the target crop species of the to-be-analyzed information needs to be determined from the plurality of candidate crop species. The target crop species of the to-be-analyzed information can be determined from the candidate crop species by processing the to-be-analyzed image.

[0067] In a case where the number of candidate crop species is greater than 1, in order to ensure the accuracy of the determined target crop species, the candidate crop species and the to-be-analyzed image can be input into the trained recognition model, and a matched crop species can be selected from the candidate crop species based on the recognition result of the to-be-analyzed image by the recognition model.

[0068] In the embodiments provided in the present application, in addition to determining the target crop species of the to-be-analyzed information based on the candidate crop species, in order to further improve the accuracy of determining the target crop species, the collection time of the to-be-analyzed image can be determined, and then the planting period corresponding to the collection time can be determined, and then the plantable crop species corresponding to the planting period can be determined, and finally the target crop species of the to-be-analyzed information can be determined from the plantable crop species corresponding to the planting period.

[0069] Since the target crop species is affected by the geographical location, the target crop species planted in different planting periods is also different. For example, when the geographical location information is the south, the planting period of sugarcane is from February to April, the planting period of soybean is November, and the planting period of cotton is from April to May. Therefore, the crop species planted in different planting periods is also different.

[0070] In a case where the number of plantable crop species is 1, the crop species is determined as the target crop species of the to-be-analyzed information. In a case where the number of plantable crop species is greater than 1, the to-be-analyzed image is processed to determine a crop species matched with the target crop in the to-be-analyzed image from the plurality of plantable crop species as the target crop species corresponding to the to-be-analyzed information.

[0071] In a case where the user information includes crop species information, how to determine the target crop species is described below with reference to step 102. In another embodiment of the present application, as shown in Figure 3 An information processing method is provided, and specifically includes the following steps:

[0072] Step 102-4: In a case where the number of crop species contained in the crop species information is 1, the target crop species corresponding to the to-be-analyzed information is determined as the crop species in the crop species information.

[0073] Step 102-5: In a case where the number of crop species contained in the crop species information is greater than 1, the to-be-analyzed image is processed to determine a crop species matched with the target crop in the to-be-analyzed image from the crop species information.

[0074] When the crop category information is included in the user information, the crop category information in the user information is taken as the category of the target crop of the to-be-analyzed information.

[0075] When the number of crop category information in the user information is multiple, the to-be-analyzed image is matched with images corresponding to the multiple crop category information in the user information, and the crop category that is successfully matched is taken as the category of the target crop of the to-be-analyzed information.

[0076] For example, the to-be-analyzed image can be input into a trained recognition model, and through the recognition model, a matched category is selected from the multiple crop categories based on the recognition result of the to-be-analyzed image, and is taken as the category of the target crop of the to-be-analyzed information.

[0077] In the embodiments provided in the present application, when the number of crop categories included in the crop category information is greater than 1, the category of the target crop of the to-be-analyzed information can also be determined in the following manner:

[0078] The collection time of the to-be-analyzed image can be determined, the planting period corresponding to the collection time can be determined, the plantable crop categories corresponding to the planting period can be determined, and finally based on the plantable crop categories corresponding to the planting period and the crop category information in the user information, the category of the target crop corresponding to the to-be-analyzed information is finally determined.

[0079] When the number of plantable crop categories is greater than 1, the plantable crop categories are matched with the crop categories in the user information, so as to determine the crops repeated between them as the matching crop categories, and the to-be-analyzed image is processed to determine the category matching the target crop in the to-be-analyzed image from the multiple matching crop categories as the category of the target crop corresponding to the to-be-analyzed information.

[0080] The to-be-analyzed image can be input into a trained recognition model, and through the recognition model, a matched category is selected from the multiple matching crop categories based on the recognition result of the to-be-analyzed image, and is taken as the category of the target crop of the to-be-analyzed information.

[0081] When the user information includes the geographic location information of the target crop region and the to-be-analyzed information includes to-be-analyzed text, how to determine the category of the target crop, for the above step 102, in another embodiment of the present application, as shown in Figure 4 An information processing method is provided, which specifically includes the following steps:

[0082] Step 102-6: determining the candidate crop categories plantable in the target crop region based on the geographic location information.

[0083] Step 102-7: When the number of candidate crop categories is 1, the candidate crop category is determined as the category of the target crop corresponding to the information to be analyzed.

[0084] Step 102-8: When the number of candidate crop categories is greater than 1, based on the acquisition time of the text to be analyzed, the crop category corresponding to the planting period and the acquisition time is determined as the category of the target crop from the candidate crop categories.

[0085] Different geographical location information can plant different crop categories. When the geographical location is south, the planted crop categories can be sugarcane, wheat, soybean, cotton, etc. When the geographical location is north, the planted crop categories can be corn, sorghum, millet, etc. Therefore, the crop categories usually planted in different geographical locations are different, and therefore, it is necessary to determine the candidate crop categories that can be planted in the current target crop area based on the geographical location information in the user information.

[0086] Based on the number of candidate crop categories, the way to determine the category of the target crop in the candidate crop categories is different:

[0087] When the number of candidate crop categories is 1, the candidate crop category is determined as the category of the target crop corresponding to the information to be analyzed.

[0088] When the number of candidate crop categories is greater than 1, based on the acquisition time of the text to be analyzed, the crop category corresponding to the planting period and the acquisition time is determined as the category of the target crop from the candidate crop categories.

[0089] When the number of candidate crop categories is greater than 1, the category of the target crop of the text to be analyzed needs to be determined from the multiple candidate crop categories. The acquisition time of the analysis text can be obtained to determine the planting period, so that from the multiple candidate crop categories, the first target crop category corresponding to the planting period corresponding to the acquisition time is determined, that is, the category of the target crop.

[0090] When there are multiple first target crop categories corresponding to the planting period corresponding to the acquisition time, the keywords of the crop category are extracted from the text to be analyzed, the keywords of the crop category in the text to be analyzed are matched with the keywords of each first target crop category, and finally the final target crop category is determined based on the matching result.

[0091] When the keywords of the crop category in the text to be analyzed and the keywords of each first target crop category are not matched, a prompt information is output in the current interface to prompt the user to input the crop category information, and the input crop category information is taken as the category of the target crop.

[0092] For the above step 103, in another embodiment of the present application, as Figure 5As shown, an information processing method is provided, which specifically comprises the following steps:

[0093] Step 103-1: determining disease information and / or nutrition information associated with the type of target crop and the geographical location information of the target crop area.

[0094] Step 103-2: processing the image to be analyzed based on the disease information and / or nutrition information to determine the health information of the target crop.

[0095] Based on the geographical location information of the target crop area in the user information registered by the user in the crop Q&A system, the common disease conditions and nutrition conditions of the type of target crop under the geographical location information are determined.

[0096] For example, the disease conditions of the same type of target crop in the northern region and the southern region are different, and because the content of trace elements in the soil in the northern region and the southern region is different, the nutrition conditions of the type of target crop in the northern region and the southern region are different. The soil in some places may have more nitrogen elements, while the soil in some places may have less nitrogen elements, resulting in different nutrition conditions of the type of target crop under different geographical location information.

[0097] In an embodiment, a first image set of the disease conditions of the target crop under the geographical location information is determined, the first image set including at least one disease condition of the target crop, a second image set of the nutrition conditions of the target crop under the geographical location information is determined, the second image set including at least one nutrition condition of the target crop, the first image set being a collection of images of all common disease conditions of the target crop under the geographical location information, and the second image set being a collection of images of all common nutrition conditions of the target crop under the geographical location information.

[0098] The nutrition conditions include but are not limited to nitrogen element deficiency or excess, phosphorus element deficiency or excess, magnesium element deficiency or excess, potassium element deficiency or excess, calcium element deficiency or excess, etc.

[0099] The disease conditions include but are not limited to insect pests, bacterial diseases, viral diseases, and drug diseases.

[0100] From the first image set, the disease condition matching the target crop represented by the image to be analyzed is determined.

[0101] From the second image set, the nutrition condition matching the target crop represented by the image to be analyzed is determined.

[0102] Finally, the disease condition and / or nutrition condition matching the target crop represented by the image to be analyzed are taken as the health information of the target crop.

[0103] Determine the health information of the target crop in combination with the geographical location information of the target crop area, which can reduce the computational burden of image recognition to some extent, and also improve the recognition efficiency and accuracy.

[0104] In order to further improve the identification accuracy of the health information of the target crop, when processing the to-be-analyzed image based on the disease information and / or the nutrition information to determine the health information of the target crop, the disease identification model corresponding to the disease information associated with the type of the target crop and the geographical location of the target crop area and / or the nutrition identification model corresponding to the nutrition information associated with the type of the target crop and the geographical location of the target crop area can be determined, and the to-be-analyzed image is processed by the disease identification model and / or the nutrition identification model to obtain the health information of the target crop.

[0105] Determine the disease identification model corresponding to the disease information associated with the type of the target crop and the geographical location of the target crop area, and the nutrition identification model corresponding to the nutrition information associated with the type of the target crop and the geographical location of the target crop area, input the to-be-analyzed image into the disease identification model and / or the nutrition health model, the disease identification model outputs the disease information of the target crop, and the nutrition health model outputs the nutrition information of the target crop, and finally the output disease information and / or nutrition information of the target crop are used as the health information of the target crop.

[0106] Before using the disease identification model and the nutrition identification model, the disease identification model and the nutrition identification model need to be trained in advance, different disease identification models are trained based on different disease conditions of different crop types corresponding to different geographical locations, that is, any disease identification model corresponds to specific geographical location information, before using the disease identification model, the geographical location information corresponding to the to-be-analyzed image can be determined first, and then the target disease identification model is determined based on the geographical location information corresponding to the to-be-analyzed image, so as to identify the disease information of the target crop in the to-be-analyzed image based on the target disease identification model.

[0107] Different nutrition identification models are trained based on different nutrition conditions of different crop types corresponding to different geographical locations, that is, any nutrition identification model corresponds to specific geographical location information, before using the nutrition identification model, the geographical location information of the to-be-analyzed image can be determined first, and then the target nutrition identification model is determined based on the geographical location information corresponding to the to-be-analyzed image, so as to identify the nutrition information of the target crop in the to-be-analyzed image based on the target nutrition identification model.

[0108] Based on the disease identification model and / or the nutrition identification model, the health condition of the target crop can be accurately identified by combining the geographical area and the species characteristics, and by distinguishing the nutrition and the disease based on the geographical location information and the crop type in advance, the disease or the nutrition problem that cannot occur in the geographical location information or the type is excluded, and the identification efficiency of the health information of the target crop is improved.

[0109] It should be noted that the disease identification model and the nutrition identification model of the above embodiment can also be trained for different disease conditions and nutrition conditions of different target crops of different geographical location information, that is, the disease identification model and the nutrition identification model can identify the disease conditions and nutrition conditions of the target crops under multiple geographical location information.

[0110] As shown in Figure 6 An information processing method is provided, specifically comprising the following steps:

[0111] Step 201: Obtain a remote sensing image of a target land.

[0112] The target land includes a target crop area and other crop areas.

[0113] Step 202: Determine a target pixel area corresponding to an image to be analyzed from the remote sensing image.

[0114] Wherein, the remote sensing image and the image to be analyzed have the same shooting angle.

[0115] Step 203: Determine the health information of the target crop in the other crop areas according to the pixel features of the target pixel area and the pixel features of the other crop areas in the remote sensing image.

[0116] The remote sensing image can be obtained by a remote sensing unmanned aerial vehicle. The image to be analyzed can be obtained by surveying and mapping, can be obtained based on a field camera, or can be obtained by a camera of an electronic device held by a user. The present application does not make specific limitations on the acquisition method of the image to be analyzed. In the remote sensing image, the target pixel area corresponding to the image to be analyzed is determined, and based on the target pixel area, the health information of the target crop in the other areas in the remote sensing image is determined.

[0117] Determine the health information of the target crop in the other crop areas according to the pixel features of the target pixel area and the pixel features of the other crop areas in the remote sensing image.

[0118] How to determine the health information of the target crop in the other crop areas according to the pixel features of the target pixel area and the pixel features of the other crop areas in the remote sensing image, in an embodiment:

[0119] Divide the other areas in the remote sensing image except the target pixel area into several sub-areas; for each sub-area, determine the difference value between the pixel features of the sub-area and the target pixel area, wherein the pixel features can be pixel values, and determine the pixel difference value between the sub-area and the target pixel area; when the pixel difference value is less than a preset difference value, the health information of the target crop in the sub-area is determined as the health information of the target crop in the target crop area.

[0120] When the pixel difference value is greater than the preset difference value, it is determined that the health information of the target crop in the target pixel region is inconsistent with the health information of the target crop in the sub-region. For the sub-region whose health information is inconsistent with the health information of the target crop, the health information of the sub-region whose health information is inconsistent with the health information of the target crop can be determined according to the acquisition process of determining the health information of the target crop in the target crop region. The specific determination manner is not described here.

[0121] In the above, the pixel difference value can refer to the difference between the pixel average value of all pixel points in one of the two regions and the pixel average value of all pixel points in the other region, or the difference between the pixel weighted average value, or the difference between the similarity of the pixel normal distribution, but is not limited thereto, as long as the similarity of the pixel characteristics of the two regions can be determined.

[0122] The preset difference value is not specifically limited in the embodiment of the application, and can be obtained according to experiments or set by the user according to user needs.

[0123] In other embodiments, the pixel difference value can also be obtained by using a similarity determination model to process the images of the sub-region and the target pixel region. The training method of the similarity determination model is the same as that of the neural network model in related technologies, and is not described here.

[0124] When the user information includes the geographical location information of the target crop region and the to-be-analyzed information includes to-be-analyzed text, as shown in Figure 7 An information processing method is provided, which specifically includes the following steps:

[0125] Step 301: determining a crop database corresponding to the type and geographical location information of the target crop from an agricultural knowledge graph.

[0126] Step 302: processing the to-be-analyzed text to extract a problem keyword.

[0127] Step 303: searching for information corresponding to the problem keyword from the crop database to obtain an analysis result of the to-be-analyzed text.

[0128] Based on the type and geographical location information of the target crop, a crop database is determined from an agricultural knowledge graph. The crop database contains all data corresponding to the type of the target crop in the geographical location information, including health information of different target crops, cause analysis information corresponding to different health information, and agricultural measures information corresponding to different health information, etc.

[0129] The to-be-analyzed text is subjected to character recognition to extract a problem keyword from the to-be-analyzed text. The problem keyword can be a keyword representing the growth of crops, such as a problem keyword representing the growth of crops, such as crop yellowing, crop drying, etc.

[0130] In an example, a problem keyword in the to-be-analyzed text is determined, and the problem keyword in the to-be-analyzed text is "yellowing" when the to-be-analyzed text is "how to turn yellow". Based on the problem keyword in the to-be-analyzed text, the growth condition of the target crop can be determined, and information corresponding to the problem keyword "yellowing" is searched from the crop database corresponding to the category and geographical location information of the target crop, that is, the analysis result of the to-be-analyzed text is obtained, wherein the information corresponding to the problem keyword "yellowing" searched from the crop database can be at least one of the following information: lack of trace elements, need to supplement trace elements, and how to supplement trace elements, as the analysis result corresponding to the to-be-analyzed text.

[0131] When the user information includes the geographical location information of the target crop area, and the to-be-analyzed information includes a to-be-analyzed image and a to-be-analyzed text, as shown in Figure 8 An information processing method is provided, which specifically includes the following steps:

[0132] Step 401: determining the crop characteristics of the target crop in the to-be-analyzed image and the problem keyword in the to-be-analyzed text.

[0133] Step 402: determining the crop database corresponding to the category and geographical location information of the target crop from the agricultural knowledge graph.

[0134] Step 403: determining the health analysis result of the target crop from the crop database based on the crop characteristics and the keyword.

[0135] For example, based on image recognition technology, the crop characteristics of the crop in the to-be-analyzed image are recognized, wherein the crop characteristics can be the leaf characteristics, root and stem characteristics, flower and fruit characteristics, etc. of the crop. Based on text recognition technology, the problem keyword in the to-be-analyzed text is determined, wherein the problem keyword represents the growth condition of the crop. Based on the category and geographical location information of the target crop, the crop database is determined from the agricultural knowledge graph, and the crop database contains all data corresponding to the category of the target crop under the geographical location information, including the health information of different target crops, the cause analysis information corresponding to different health information, and the agricultural measure information corresponding to different health information, etc. After the crop database is determined, the disease condition and / or the nutrition condition corresponding to the crop characteristics and the problem keyword, the cause analysis information of the disease condition and / or the nutrition condition, or the agricultural measure information corresponding to the disease condition and / or the nutrition condition, etc. can be determined from the crop database based on the crop characteristics and the keyword, as the health analysis result of the target crop.

[0136] A prescription graph is generated according to the analysis result of the target crop:

[0137] Based on the analysis result of the crops in the target crop area, a disease prescription map and / or a nutrition prescription map corresponding to the target crop area is generated.

[0138] The analysis result can be determined by the information processing method in any of the above embodiments, and the disease prescription map and / or the nutrition prescription map are used to indicate the material applied to the target crop and the application amount of the material.

[0139] Based on the disease prescription map and / or the nutrition prescription map corresponding to the target crop area, the farming measures for the target crop are determined to improve the disease and / or nutrition condition of the target crop.

[0140] In an example, the farming measures can be reflected by the prescription map. For example, for the disease prescription map, since it can be used to indicate the disease prescription of the target crop area or even each area in the entire field, such as recording which area needs to apply which drug for the disease and the dosage of the drug, and which area does not need to apply the drug, the reflected farming measures are: planning the operation route based on the disease prescription map, and spraying or scattering the drug at the position where the drug needs to be applied according to the operation path and the disease prescription map. Similarly, for the nutrition prescription map, since it can be used to indicate the nutrition prescription of the target crop area or even each area in the entire field, such as recording which area needs to supplement which nutrient and the amount of the nutrient, which area does not need to supplement the nutrient, and which area needs to dilute the nutrient, the reflected farming measures are: planning the operation path based on the nutrition prescription map, and performing the corresponding operation at the corresponding position according to the operation path and the nutrition prescription map, such as spraying water or other diluents in the area where the nutrient needs to be diluted, supplementing the amount of the nutrient in the area where the nutrient needs to be supplemented, and not passing through the area where the nutrient does not need to be supplemented or stopping outputting the nutrient or the diluent when passing through the area where the nutrient does not need to be supplemented.

[0141] When the disease condition of the target crop includes multiple types, multiple disease prescription maps of the target crop are generated, or multiple disease conditions of the target crop are recorded in one disease prescription map. When the nutrition condition of the target crop includes multiple types, multiple nutrition prescription maps of the target crop are generated, or multiple nutrition conditions of the target crop are recorded in one nutrition prescription map. Thus, when the corresponding farming measures are subsequently performed, multiple farming measures can be performed in one operation based on the prescription map, and multiple disease problems and nutrition problems can be solved at one time. Of course, multiple disease problems and nutrition problems can also be solved in multiple times.

[0142] Therefore, the present application also provides a work control method, which comprises: controlling the work equipment to work on the crops in the target crop area based on the disease prescription map and / or the nutrition prescription map obtained by the prescription map generation method in any of the above embodiments. The specific work mode can refer to the above description, and will not be repeated here.

[0143] It should be noted that the working device can be a drone aircraft or an unmanned vehicle.

[0144] In order to realize that the working device can perform multiple agricultural operations on the target land in one operation process, and to improve the efficiency of multiple agricultural operations, in one embodiment, the working device can include a device body, a material unit mounted on the device body, a spraying mechanism mounted on the device body, and an electronic device; the material unit is used to store working materials, the spraying mechanism is used to output working materials to the target crops, and the electronic device is used to control the working state of the material unit and the spraying mechanism; wherein the material unit is internally provided with multiple storage spaces independent of each other.

[0145] The working state of the material unit can include but is not limited to: outputting materials, stopping outputting materials; the working state of the spraying mechanism can include but is not limited to: performing spraying operation, stopping spraying operation.

[0146] Therefore, different materials can be stored through multiple storage spaces, so that different materials can be applied at the same location or at different locations in one operation process according to actual needs, which is beneficial to improve the application efficiency of different materials. For example, it is assumed that in the disease prescription map of the target land, it is recorded that some areas exist a certain disease, other areas exist another disease, and some areas exist multiple diseases.

[0147] Therefore, before working on the disease, multiple drugs for eliminating the above multiple diseases can be placed in the multiple storage spaces respectively, that is, in one operation process, the electronic device can control the output of the drug in the corresponding storage space according to the disease situation of the area where the current location is located, and the spraying mechanism can be used to apply the corresponding dose of drug to the crops in the area where the current location is located.

[0148] In the above, the multiple storage spaces can have multiple implementation manners according to the changes of the material unit, for example, when the material unit is a tank, the multiple storage spaces can be mutually independent spaces formed by multiple layers inside the tank; for another example, when the material unit includes multiple tanks, the multiple storage spaces can be directly formed by the storage spaces of the multiple tanks respectively.

[0149] Please refer to Figure 9 The embodiment also provides an information processing device 700, and the device comprises:

[0150] The acquisition module 501 is configured to acquire the to-be-analyzed information of the target crop area and user information; the to-be-analyzed information is used to represent the growth state of the crops in the target crop area.

[0151] The first determining module 502 is configured to determine a type of a target crop corresponding to the to-be-analyzed information based on the user information, wherein the user information comprises at least one of geographical position information of a target crop region and crop type information.

[0152] The second determining module 503 is configured to determine an analysis result of the to-be-analyzed information based on the type of the target crop, the to-be-analyzed information and the user information.

[0153] Optionally, the first determining module 502 is further configured to:

[0154] determine candidate crop types that can be planted in the target crop region based on the geographical position information;

[0155] when the number of the candidate crop types is 1, determine that the candidate crop type is the type of the target crop corresponding to the to-be-analyzed information;

[0156] when the number of the candidate crop types is greater than 1, process the to-be-analyzed image to determine, from the candidate crop types, a type that matches the target crop in the to-be-analyzed image.

[0157] Optionally, the first determining module 502 is further configured to:

[0158] when the number of crop types contained in the crop type information is 1, determine that the type of the target crop corresponding to the to-be-analyzed information is the crop type in the crop type information;

[0159] when the number of crop types contained in the crop type information is greater than 1, process the to-be-analyzed image to determine, from the crop type information, a type that matches the target crop in the to-be-analyzed image.

[0160] Optionally, the second determining module 503 is further configured to:

[0161] determine disease information and / or nutrition information associated with the type of the target crop and the geographical position information of the target crop region;

[0162] process the to-be-analyzed image based on the disease information and / or the nutrition information to determine health information of the target crop.

[0163] Optionally, the second determining module 503 is further configured to:

[0164] determine a disease recognition model of the disease information and / or a nutrition recognition model of the nutrition information;

[0165] process the to-be-analyzed image by using the disease recognition model and / or the nutrition recognition model to obtain the health information of the target crop.

[0166] Optionally, the information to be analyzed comprises an image to be analyzed; and the device further comprises:

[0167] the processing module 504 is configured to acquire a remote sensing image of a target land plot, the target land plot comprising a target crop area and other crop areas;

[0168] determine, from the remote sensing image, a target pixel area corresponding to the image to be analyzed;

[0169] determine, according to a pixel feature of the target pixel area and a pixel feature of the other crop areas in the remote sensing image, health information of the target crop in the other crop areas.

[0170] Optionally, the processing module 504 is further configured to:

[0171] divide other areas in the remote sensing image except the target pixel area into a plurality of sub-areas;

[0172] for each of the sub-areas, determine a pixel difference value between the sub-area and the target pixel area;

[0173] when the pixel difference value is less than a preset difference value, determine that the health information of the target crop in the sub-area is the health information of the target crop in the target pixel area.

[0174] Optionally, the user information comprises geographic location information of the target crop area, and the information to be analyzed comprises text to be analyzed; and the first determination module 502 is further configured to:

[0175] determine, based on the geographic location information, candidate crop species that can be planted in the target crop area;

[0176] when the number of the candidate crop species is 1, determine that the candidate crop species is the species of the target crop corresponding to the information to be analyzed;

[0177] when the number of the candidate crop species is greater than 1, determine, based on an acquisition time of the text to be analyzed, a crop species planted in a growth period corresponding to the acquisition time from the candidate crop species as the species of the target crop.

[0178] Optionally, the user information comprises geographic location information of the target crop area, and the information to be analyzed comprises text to be analyzed; and the second determination module 503 is further configured to:

[0179] determine, from an agricultural knowledge graph, a crop database corresponding to the species of the target crop and the geographic location information;

[0180] processing the to-be-analyzed text to extract a problem keyword;

[0181] finding information corresponding to the problem keyword from the crop database to obtain an analysis result of the to-be-analyzed text.

[0182] Optionally, the user information includes geographical location information of a target crop area, and the to-be-analyzed information includes a to-be-analyzed image and a to-be-analyzed text; the second determination module 503 is further configured to:

[0183] determining a crop feature of a target crop in the to-be-analyzed image and a problem keyword in the to-be-analyzed text;

[0184] determining a crop database corresponding to the type of the target crop and the geographical location information from an agricultural knowledge graph;

[0185] determining a health analysis result of the target crop from the crop database based on the crop feature and the keyword.

[0186] In summary, the present application obtains to-be-analyzed information and user information of a target crop area, determines the type of the target crop corresponding to the to-be-analyzed information based on the user information, and determines an analysis result of the to-be-analyzed information based on the type of the target crop, the to-be-analyzed information, and the user information. Based on the geographical location information and / or the crop type information in the user information, the type of the target crop corresponding to the to-be-analyzed information can be accurately determined according to geographical location characteristics and / or crop types, and then the to-be-analyzed information can be processed in combination with the crop type and / or geographical characteristics, so that the analysis result (such as at least one of the health condition of the target crop, the cause analysis result, and the corresponding farming measure) is more targeted and can better meet the actual needs of the user. Thus, the problem that the system cannot accurately identify the object to which the problem is directed only by relying on the problem information can be avoided, so that the user needs to input other information again to assist in confirmation, or the answer information is inaccurate.

[0187] The present application also provides an electronic device including a processor and a memory, the memory being configured to store a computer program, and the processor being configured to implement the information processing method, the prescription map generation method, or the job control method when executing the computer program.

[0188] The present application also provides a job device including a device body, a material unit mounted on the device body, a spraying mechanism mounted on the device body, and an electronic device; the material unit is configured to store a job material, the spraying mechanism is configured to output the job material to a target crop, and the electronic device is configured to control the job state of the material unit and the spraying mechanism; wherein the material unit is internally provided with a plurality of storage spaces that are independent of each other.

[0189] The embodiments of the present application further provide a storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the information processing method, the prescription chart generation method or the job control method.

[0190] In the embodiments of the present application, it should be understood that the disclosed apparatus and method can also be implemented by other manners. The apparatus embodiments described above are only schematic, for example, the flow charts and block diagrams in the drawings show the possible implementation architectures, functions and operations of the apparatus, method and computer program product according to the embodiments of the present application. In this regard, each block in the flow charts or block diagrams can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logic function. It should also be noted that, in some alternative implementations, the functions noted in the blocks can occur in different orders from those described in the drawings. For example, two consecutive blocks can actually be executed in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flow charts, and the combination of blocks in the block diagrams and / or flow charts, can be implemented by a dedicated hardware-based system for implementing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0191] In addition, the functional modules in each of the embodiments of the present application can be integrated together to form a separate part, or each module can exist independently, or two or more modules can be integrated to form a separate part. When the functions are realized in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0192] It is to be noted that, as used in this document, the term "indicia" is intended to cover any and all indications of the presence of a substance, including but not limited to visible indications, such as color, and invisible indications, such as a change in the refractive index of a material. It is to be understood that the phraseology or terminology employed herein, such as "first" and "second", etc., are for the purpose of differentiating one entity or operation from another entity or operation only and is not intended to require or imply that there is any such actual relationship or order between or among these entities or operations. Also, the use of the term "including" or "comprising" or any other variant thereof is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a... " does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0193] The above description is only various embodiments of the present application, and the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, and all such changes or replacements should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An information processing method characterized by comprising: The method comprises: obtaining to-be-analyzed information of a target crop area and user information; the to-be-analyzed information is used to represent the growth state of crops in the target crop area; based on the user information, determining the type of the target crop corresponding to the to-be-analyzed information, wherein the user information includes geographical location information and crop type information of the target crop area; based on the type of the target crop, the to-be-analyzed information and the user information, determining the analysis result of the to-be-analyzed information; the user information includes geographical location information of the target crop area, and the to-be-analyzed information includes a to-be-analyzed image; the determination of the type of the target crop corresponding to the to-be-analyzed information based on the user information comprises: determining the candidate crop types that can be planted in the target crop area based on the geographical location information, wherein the geographical location information is used to determine the candidate crop types that can be planted in the target crop area; when the number of candidate crop types is 1, determining the candidate crop type as the type of the target crop corresponding to the to-be-analyzed information; when the number of candidate crop types is greater than 1, processing the to-be-analyzed image to determine the type that matches the target crop in the to-be-analyzed image from the candidate crop types.

2. The method of claim 1, wherein, The determination of the type of the target crop corresponding to the to-be-analyzed information based on the user information comprises: when the number of crop types contained in the crop type information is 1, determining the type of the target crop corresponding to the to-be-analyzed information as the crop type in the crop type information; when the number of crop types contained in the crop type information is greater than 1, processing the to-be-analyzed image to determine the type that matches the target crop in the to-be-analyzed image from the crop type information.

3. The method of claim 1, wherein, The determination of the analysis result of the to-be-analyzed information based on the type of the target crop, the to-be-analyzed information and the user information comprises: determining disease information and / or nutrition information associated with the type of the target crop and the geographical location information of the target crop area; processing the to-be-analyzed image based on the disease information and / or the nutrition information to determine the health information of the target crop.

4. The method of claim 3, wherein, The processing of the to-be-analyzed image based on the disease information and / or the nutrition information to determine the health information of the target crop comprises: determining a disease recognition model of the disease information and / or a nutrition recognition model of the nutrition information; processing the to-be-analyzed image through the disease recognition model and / or the nutrition recognition model to obtain the health information of the target crop.

5. The method of claim 1, wherein, The method further comprises: obtaining a remote sensing image of a target plot, the target plot comprising a target crop area and other crop areas; from the remote sensing image, determining a target pixel area corresponding to the to-be-analyzed image; determining the health information of the target crop in the other crop areas according to the pixel features of the target pixel area and the pixel features of the other crop areas in the remote sensing image.

6. The method of claim 5, wherein, The method comprises: dividing the other pixel regions in the remote sensing image except the target pixel region into a plurality of sub-regions; for each of the sub-regions, determining a pixel difference value between the sub-region and the target pixel region; when the pixel difference value is less than a preset difference value, determining the health information of the target crop in the sub-region as the health information of the target crop in the target pixel region.

7. The method of claim 1, wherein, The information to be analyzed includes text to be analyzed; and the method comprises: determining a candidate crop species that can be planted in the target crop region based on the geographic location information; when the number of the candidate crop species is 1, determining the candidate crop species as the species of the target crop corresponding to the information to be analyzed; when the number of the candidate crop species is greater than 1, determining, based on the acquisition time of the text to be analyzed, a crop species planted in the same period as the acquisition time as the species of the target crop from the candidate crop species.

8. The method of claim 1, wherein, The information to be analyzed includes text to be analyzed; and the method comprises: determining a crop database corresponding to the species of the target crop and the geographic location information from an agricultural knowledge graph; processing the text to be analyzed to extract problem keywords; finding information corresponding to the problem keywords from the crop database to obtain an analysis result of the text to be analyzed.

9. The method of claim 1, wherein, The information to be analyzed includes text to be analyzed; and the method comprises: determining crop features of the target crop in the information to be analyzed and problem keywords in the text to be analyzed; determining a crop database corresponding to the species of the target crop and the geographic location information from an agricultural knowledge graph; determining a health analysis result of the target crop from the crop database based on the crop features and the problem keywords.

10. A method of generating a prescription map, characterized by, The method comprises: generating a disease prescription map and / or a nutrition prescription map corresponding to the target crop region based on an analysis result of crops in the target crop region, wherein the analysis result is determined by the information processing method of any one of claims 1-9.

11. A job control method characterized by comprising: The method comprises: controlling a work device to work on crops in a target crop region based on a disease prescription map and / or a nutrition prescription map obtained by the prescription map generation method of claim 10.

12. An information processing apparatus comprising: The device comprises: an acquisition module configured to acquire information to be analyzed of a target crop region and user information; the information to be analyzed is used to represent a growth state of crops in the target crop region; a first determination module configured to determine a species of a target crop corresponding to the information to be analyzed based on the user information, wherein the user information includes geographic location information and crop species information of the target crop region; A second determining module is configured to determine an analysis result of the to-be-analyzed information based on the category of the target crop, the to-be-analyzed information, and the user information. The user information includes geographical position information of a target crop area, and the to-be-analyzed information includes a to-be-analyzed image. The first determining module is specifically configured to: determine candidate crop categories that can be planted in the target crop area based on the geographical position information, wherein the geographical position information is used to determine the candidate crop categories that can be planted in the target crop area; when the number of the candidate crop categories is 1, determine that the candidate crop category is the category of the target crop corresponding to the to-be-analyzed information; when the number of the candidate crop categories is greater than 1, process the to-be-analyzed image to determine, from the candidate crop categories, a category that matches the target crop in the to-be-analyzed image.

13. An electronic device, comprising: A processor and a memory are included, the memory is configured to store a computer program, and the processor is configured to implement the method of any one of claims 1-11 when executing the computer program.

14. A work apparatus characterized by comprising: The work equipment includes an equipment body, a material unit mounted on the equipment body, a spraying mechanism mounted on the equipment body, and the electronic device of claim 13; the material unit is used to store work materials, the spraying mechanism is used to output the work materials to target crops, and the electronic device is used to control the work states of the material unit and the spraying mechanism; wherein the material unit is internally provided with a plurality of storage spaces that are independent of each other.

15. Storage medium, characterized in that A computer program is stored thereon, and the computer program is executed by a processor to implement the method of any one of claims 1-11.

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