A method, system and medium for intelligent identification of pesticide packaging bags based on big data

Abstract

The present invention discloses a method, system and medium for intelligent identification of pesticide packaging bags based on big data, which accurately identifies the category of pesticide packaging bags based on big data retrieval and image recognition algorithms. According to a preset time interval, the pesticide packaging bag information database is updated in real time to improve the accuracy of pesticide packaging bag image recognition. In addition, after identifying the pesticide packaging bags to be recycled, the recycling cruise vehicle automatically drives to the location of the pesticide packaging bags to be recycled for recycling, thereby improving the recycling efficiency of the pesticide packaging bags and reducing labor costs.

Description

Technical Field

[0001] The present invention relates to the field of computer image recognition, and more specifically, to a method, system and medium for intelligently identifying pesticide packaging bags based on big data. Background Art

[0002] As the application scope of science and technology in agriculture continues to expand, pesticides are increasingly used in agricultural production, and the amount of pesticide spraying is also increasing. On the one hand, this is because many pests have developed antibodies, and on the other hand, it is because there are not enough types of pesticides or the technical level is low.

[0003] In addition, in some large farmlands, a large amount of pesticides are needed. Pesticides are generally packaged in specific pesticide bags. After the pesticides are used, a large number of pesticide bags are thrown into the farmland, causing serious pollution to the environment. In many large farmlands, there are a large number of scattered pesticide bags, and the farmland environment is complex. Some pesticide bags are often damaged, incomplete, and have blurred identification information, making it difficult for agricultural personnel to distinguish which type of pesticide the original pesticide bag belongs to. However, some pesticide bags need to be recycled and disposed of in time after use, otherwise it will further pollute the environment. If they are recycled manually, sorting pesticide bags requires a lot of human labor resources, which is both time-consuming and laborious.

[0004] In view of the above technical problems, there is an urgent need for a method that can accurately identify the information of pesticide packaging bags and quickly recycle pesticide packaging bags to improve recycling efficiency, reduce labor costs, and protect the agricultural environment. Summary of the invention

[0005] In order to solve at least one of the above technical problems, the present invention proposes a method, system and medium for intelligent identification of pesticide packaging bags based on big data.

[0006] The first aspect of the present invention provides a method for intelligently identifying pesticide packaging bags based on big data, comprising:

[0007] Obtain image information of the pesticide packaging bag to be identified;

[0008] Dynamically select a preset image recognition algorithm based on the acquired image information of the pesticide packaging bag to be identified, perform image recognition, and identify the specific pesticide category;

[0009] Obtain information on the types of pesticide packaging bags that need to be recycled;

[0010] According to the type information of the pesticide packaging bags to be recycled, the corresponding location information is obtained, and the location information of the pesticide packaging bags is used as navigation information of the recycling cruise vehicle, so that the recycling cruise vehicle navigates to the location to recycle the pesticide packaging bags to be recycled.

[0011] In this solution, the acquisition of the image information of the pesticide packaging bag to be identified is specifically as follows:

[0012] Obtaining a grayscale image of the pesticide packaging bag to be identified;

[0013] Performing image preprocessing on the grayscale image of the pesticide packaging bag to obtain an image matrix of the pesticide packaging bag;

[0014] Based on a machine learning algorithm, the image information of the pesticide packaging bag to be identified is identified from the image matrix of the pesticide packaging bag, wherein the image information includes the appearance information of the pesticide packaging bag and the QR code, barcode, and pesticide text identifier information on the surface of the pesticide packaging bag.

[0015] In this solution, the method of dynamically selecting a preset image recognition algorithm based on the acquired image information of the pesticide packaging bag to be identified, performing image recognition, and identifying the specific pesticide category includes:

[0016] Retrieving image information of each pesticide packaging bag from the big data, the image information including appearance information of the pesticide packaging bag and QR code, bar code, and pesticide text identifier information on the surface of the pesticide packaging bag;

[0017] Constructing a pesticide packaging bag information database, converting the image information of each pesticide packaging bag in the image information into a set of image feature values, and importing the image feature values ​​into the pesticide packaging bag information database;

[0018] After a preset time period, new pesticide packaging bag image information is retrieved from the big data, the image information is converted into a set of image feature values, and the image feature values ​​are imported into the pesticide packaging bag information database until all new pesticide packaging bag image information is imported into the pesticide packaging bag information database, so as to realize the regular update of the pesticide packaging bag information database.

[0019] In this solution, the method of dynamically selecting a preset image recognition algorithm based on the acquired image information of the pesticide packaging bag to be identified, performing image recognition, and identifying the specific pesticide category also includes:

[0020] Establishing an image recognition algorithm model, preprocessing the pesticide packaging bag information feature values ​​in the pesticide packaging bag information database to obtain a pesticide packaging bag image training data set, importing the pesticide packaging bag image training data set into the image recognition algorithm model, and performing image recognition algorithm training;

[0021] According to the acquired image information of the pesticide packaging bag to be identified, the integrity and efficiency of each image information of the pesticide packaging bag are calculated. The image information includes the appearance information of the pesticide packaging bag and the QR code, barcode, and pesticide text identifier information on the surface of the pesticide packaging bag. The image information integrity and image information efficiency calculation formula is specifically as follows:

[0022]

[0023] R1 is the image information completeness, where n is the total number of image information area blocks of the pesticide packaging bag, a i is a complete pixel of a region block of image information, and U is all pixels in the entire region;

[0024]

[0025] R2 is the image information efficiency, where n is the total number of image information area blocks of the pesticide packaging bag, and b i is the effective pixel of a region block of image information, and W is all the pixels in the whole region;

[0026] The completeness of each image information and the efficiency of each image information are calculated, and the completeness of each image information and the efficiency of each image information are sorted to obtain the sorting result, and the sorting result is imported into the image recognition algorithm model. The image recognition algorithm model will dynamically select an image recognition algorithm suitable for the current image information according to the sorting result to identify the specific pesticide category.

[0027] In this solution, the information on the types of pesticide packaging bags that need to be recycled is obtained as follows:

[0028] The user inputs the category information of the pesticide packaging bags to be recycled from the input device;

[0029] The category information of the pesticide packaging bags that need to be recycled is obtained from the input device, and the information of the pesticide packaging bags that the user needs to recycle is a combination of one or more than two.

[0030] In this solution, the type information of the pesticide packaging bags to be recycled is used to obtain the corresponding location information, and the location information of the pesticide packaging bags is used as navigation information for the recycling cruise vehicle, so that the recycling cruise vehicle navigates to the location to recycle the pesticide packaging bags that need to be recycled, specifically:

[0031] The specific location information of the pesticide packaging bag to be identified in the three-dimensional map model is obtained, and the location information is sent as navigation information to the navigation and positioning system of each recycling cruise vehicle. According to the current position of each recycling cruise vehicle, the navigation and positioning system calculates and selects the recycling cruise vehicle closest to the location of the pesticide packaging bag to be identified, and navigates to the location for recycling processing.

[0032] Each recovery cruise vehicle has a built-in navigation and positioning system and an automatic driving system, and each recovery cruise vehicle can communicate with each other;

[0033] The built-in navigation and positioning system and automatic driving system of the recovery cruise vehicle have the function of communicating with the image recognition module and the video acquisition module;

[0034] The recovery cruise vehicle has at least one or more in a specific area;

[0035] The second aspect of the present invention further provides a pesticide packaging bag intelligent identification system based on big data, the system comprising: a memory, a processor, the memory comprising a pesticide packaging bag intelligent identification method program based on big data, the pesticide packaging bag intelligent identification method program based on big data when executed by the processor implements the following steps:

[0036] Obtain image information of the pesticide packaging bag to be identified;

[0037] Dynamically select a preset image recognition algorithm based on the acquired image information of the pesticide packaging bag to be identified, perform image recognition, and identify the specific pesticide category;

[0038] Obtain information on the types of pesticide packaging bags that need to be recycled;

[0039] According to the type information of the pesticide packaging bags to be recycled, the corresponding location information is obtained, and the location information of the pesticide packaging bags is used as navigation information of the recycling cruise vehicle, so that the recycling cruise vehicle navigates to the location to recycle the pesticide packaging bags that need to be recycled.

[0040] The third aspect of the present invention also provides a computer-readable storage medium for intelligent identification of pesticide packaging bags based on big data, wherein the computer-readable storage medium includes a program for an intelligent identification method for pesticide packaging bags based on big data. When the program for intelligent identification method for pesticide packaging bags based on big data is executed by a processor, the steps of the intelligent identification method for pesticide packaging bags based on big data as described in any one of the above items are implemented.

[0041] The present invention discloses a method, system and medium for intelligent identification of pesticide packaging bags based on big data, which accurately identifies the category of pesticide packaging bags based on big data retrieval and image recognition algorithms. According to a preset time interval, the pesticide packaging bag information database is updated in real time to improve the accuracy of pesticide packaging image recognition. In addition, after identifying the pesticide packaging bags to be recycled, the recycling cruise vehicle automatically drives to the location of the pesticide packaging bags to be recycled for recycling, thereby improving the recycling efficiency of the pesticide packaging bags and reducing labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1A flowchart of a method for intelligently identifying pesticide packaging bags based on big data is shown in the present invention;

[0043] Figure 2 A flow chart showing construction of a pesticide packaging bag information database in the present invention is shown;

[0044] Figure 3 A flow chart showing the recycling process of the cruise vehicle in the present invention is shown;

[0045] Figure 4 A block diagram of a pesticide packaging bag intelligent identification system based on big data is shown in the present invention; DETAILED DESCRIPTION

[0046] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0048] Figure 1 A flow chart of a method for intelligently identifying pesticide packaging bags based on big data is shown in the present invention.

[0049] like Figure 1 As shown, the first aspect of the present invention provides a method for intelligent identification of pesticide packaging bags based on big data, comprising:

[0050] S102, obtaining image information of the pesticide packaging bag to be identified;

[0051] S104, dynamically selecting a preset image recognition algorithm according to the acquired image information of the pesticide packaging bag to be identified, performing image recognition, and identifying a specific pesticide category;

[0052] S106, obtaining information on the category of pesticide packaging bags that need to be recycled;

[0053] S108, obtaining corresponding location information according to the type information of the pesticide packaging bags to be recycled, and using the location information of the pesticide packaging bags as navigation information for the recycling cruise vehicle, so that the recycling cruise vehicle navigates to a location to recycle the pesticide packaging bags to be recycled;

[0054] It should be noted that the acquisition of the image information of the pesticide packaging bag to be identified is specifically as follows:

[0055] Obtaining a grayscale image of the pesticide packaging bag to be identified;

[0056] Performing image preprocessing on the grayscale image of the pesticide packaging bag to obtain an image matrix of the pesticide packaging bag;

[0057] Based on a machine learning algorithm, the image information of the pesticide packaging bag to be identified is identified from the image matrix of the pesticide packaging bag, wherein the image information includes the appearance information of the pesticide packaging bag and the QR code, barcode, and pesticide text identifier information on the surface of the pesticide packaging bag.

[0058] It should be noted that the image information of the pesticide packaging bag to be identified is obtained by using an image recognition module, and the image recognition module is composed of a plurality of optical acquisition devices and an image recognition system, and the optical acquisition devices are fixedly placed at a specific position.

[0059] Figure 2 A flow chart showing construction of a pesticide packaging bag information database in the present invention is shown;

[0060] According to an embodiment of the present invention, constructing a pesticide packaging bag information database specifically includes:

[0061] S202, retrieving image information of each pesticide packaging bag from the big data, wherein the image information includes appearance information of the pesticide packaging bag and a QR code, a bar code, and pesticide text identifier information on the surface of the pesticide packaging bag;

[0062] S204, constructing a pesticide packaging bag information database, converting the image information of each pesticide packaging bag in the image information into a set of image feature values, and importing the image feature values ​​into the pesticide packaging bag information database;

[0063] S206, after a preset time period, retrieve new pesticide packaging bag image information from the big data, convert the image information into a set of image feature values, and import the image feature values ​​into the pesticide packaging bag information database until all new pesticide packaging bag image information is imported into the pesticide packaging bag information database, so as to realize the regular update of the pesticide packaging bag information database.

[0064] It should be noted that the method of dynamically selecting a preset image recognition algorithm based on the acquired image information of the pesticide packaging bag to be identified, performing image recognition, and identifying the specific pesticide category also includes:

[0065] Establishing an image recognition algorithm model, preprocessing the pesticide packaging bag information feature values ​​in the pesticide packaging bag information database to obtain a pesticide packaging bag image training data set, importing the pesticide packaging bag image training data set into the image recognition algorithm model, and performing image recognition algorithm training;

[0066] According to the acquired image information of the pesticide packaging bag to be identified, the integrity and efficiency of each image information of the pesticide packaging bag are calculated. The image information includes the appearance information of the pesticide packaging bag and the QR code, barcode, and pesticide text identifier information on the surface of the pesticide packaging bag. The image information integrity and image information efficiency calculation formula is specifically as follows:

[0067]

[0068] R1 is the image information completeness, where n is the total number of image information area blocks of the pesticide packaging bag, a i is a complete pixel of a region block of image information, and U is all pixels in the entire region;

[0069]

[0070] R2 is the image information efficiency, where n is the total number of image information area blocks of the pesticide packaging bag, and b i is the effective pixel of a region block of image information, and W is all the pixels in the whole region;

[0071] The completeness and efficiency of each image information are calculated, and the completeness and efficiency of each image information are sorted to obtain the sorting results. The sorting results are imported into the image recognition algorithm model. The image recognition algorithm model will dynamically select an image recognition algorithm suitable for the current image information according to the sorting results to identify the specific pesticide category. In particular, the above-mentioned R1 image information completeness and R2 image information efficiency calculation formulas are applicable to various image information of the pesticide packaging bag, and the various image information includes the appearance information of the pesticide packaging bag and the QR code, bar code, and pesticide text identifier information on the surface of the pesticide packaging bag.

[0072] In addition, the dynamic selection is suitable for the image recognition algorithm of the pesticide packaging bag to be identified, and the specific pesticide category is identified. The image recognition algorithm includes but is not limited to convolutional neural network algorithm, HOG feature extraction algorithm, ORB algorithm, SIFT algorithm, and SURF algorithm.

[0073] It should be noted that the information on the types of pesticide packaging bags that need to be recycled is obtained as follows:

[0074] The user inputs the category information of the pesticide packaging bags to be recycled from the input device;

[0075] The category information of the pesticide packaging bags that need to be recycled is obtained from the input device, and the information of the pesticide packaging bags that the user needs to recycle is a combination of one or more. In particular, the input device includes but is not limited to a user mobile device and a computer terminal device.

[0076] Figure 3 A flow chart showing the recycling process of the cruise vehicle in the present invention is shown;

[0077] According to an embodiment of the present invention, the recycling process of the cruise vehicle specifically includes:

[0078] S302, collecting video information in a specific area, extracting target terrain feature points from the collected video information through image frames, and reconstructing a three-dimensional map model to obtain a three-dimensional map model of the specific area;

[0079] S304, synchronously importing the three-dimensional map model into the navigation and positioning system of the recovery cruise vehicle;

[0080] S306, obtaining specific location information of the pesticide packaging bag to be identified in the three-dimensional map model, and sending the location information as navigation information to the navigation and positioning system of each recycling cruise vehicle;

[0081] S308, according to the current position of each recycling cruise vehicle, the navigation and positioning system of the recycling cruise vehicle calculates and selects the recycling cruise vehicle closest to the position of the pesticide packaging bag to be identified, and navigates to the position for recycling;

[0082] It should be noted that each recovery cruise vehicle has a built-in navigation and positioning system and an automatic driving system, and each recovery cruise vehicle can communicate with each other; the built-in navigation and positioning system and the automatic driving system of the recovery cruise vehicle have the function of communicating with the image recognition module and the video acquisition module; the recovery cruise vehicle has at least one or more in a specific area. In addition, the recovery cruise vehicle recovery process uses a video acquisition module and an image recognition module. The video acquisition module includes multiple camera devices, and the camera devices are placed in specific locations.

[0083] Figure 4 A block diagram of a pesticide packaging bag intelligent identification system based on big data of the present invention is shown.

[0084] The second aspect of the present invention further provides a pesticide packaging bag intelligent identification system 4 based on big data, the system comprising: a memory 41, a processor 42, the memory 41 comprising a pesticide packaging bag intelligent identification method program based on big data, the pesticide packaging bag intelligent identification method program based on big data when executed by the processor 42 implements the following steps:

[0085] Obtain image information of the pesticide packaging bag to be identified;

[0086] Dynamically select a preset image recognition algorithm based on the acquired image information of the pesticide packaging bag to be identified, perform image recognition, and identify the specific pesticide category;

[0087] Obtain information on the types of pesticide packaging bags that need to be recycled;

[0088] According to the type information of the pesticide packaging bags to be recycled, the corresponding location information is obtained, and the location information of the pesticide packaging bags is used as navigation information of the recycling cruise vehicle, so that the recycling cruise vehicle navigates to the location to recycle the pesticide packaging bags to be recycled.

[0089] It should be noted that the acquisition of the image information of the pesticide packaging bag to be identified is specifically as follows:

[0090] Obtaining a grayscale image of the pesticide packaging bag to be identified;

[0091] Performing image preprocessing on the grayscale image of the pesticide packaging bag to obtain an image matrix of the pesticide packaging bag;

[0092] Based on a machine learning algorithm, the image information of the pesticide packaging bag to be identified is identified from the image matrix of the pesticide packaging bag, wherein the image information includes the appearance information of the pesticide packaging bag and the QR code, barcode, and pesticide text identifier information on the surface of the pesticide packaging bag.

[0093] It should be noted that the image information of the pesticide packaging bag to be identified is obtained by using an image recognition module, and the image recognition module is composed of a plurality of optical acquisition devices and an image recognition system, and the optical acquisition devices are fixedly placed at a specific position.

[0094] It should be noted that the method of dynamically selecting a preset image recognition algorithm based on the acquired image information of the pesticide packaging bag to be identified, performing image recognition, and identifying the specific pesticide category includes:

[0095] Retrieving image information of each pesticide packaging bag from the big data, the image information including appearance information of the pesticide packaging bag and QR code, bar code, and pesticide text identifier information on the surface of the pesticide packaging bag;

[0096] Constructing a pesticide packaging bag information database, converting the image information of each pesticide packaging bag in the image information into a set of image feature values, and importing the image feature values ​​into the pesticide packaging bag information database;

[0097] After a preset time period, new pesticide packaging bag image information is retrieved from the big data, the image information is converted into a set of image feature values, and the image feature values ​​are imported into the pesticide packaging bag information database until all new pesticide packaging bag image information is imported into the pesticide packaging bag information database, so as to realize the regular update of the pesticide packaging bag information database.

[0098] It should be noted that the method of dynamically selecting a preset image recognition algorithm based on the acquired image information of the pesticide packaging bag to be identified, performing image recognition, and identifying the specific pesticide category also includes:

[0099] Establishing an image recognition algorithm model, preprocessing the pesticide packaging bag information feature values ​​in the pesticide packaging bag information database to obtain a pesticide packaging bag image training data set, importing the pesticide packaging bag image training data set into the image recognition algorithm model, and performing image recognition algorithm training;

[0100] According to the acquired image information of the pesticide packaging bag to be identified, the integrity and efficiency of each image information of the pesticide packaging bag are calculated. The image information includes the appearance information of the pesticide packaging bag and the QR code, barcode, and pesticide text identifier information on the surface of the pesticide packaging bag. The image information integrity and image information efficiency calculation formula is specifically as follows:

[0101]

[0102] R1 is the image information completeness, where n is the total number of image information area blocks of the pesticide packaging bag, a i is a complete pixel of a region block of image information, and U is all pixels in the entire region;

[0103]

[0104] R2 is the image information efficiency, where n is the total number of image information area blocks of the pesticide packaging bag, and b i is the effective pixel of a region block of image information, and W is all the pixels in the whole region;

[0105] The completeness and efficiency of each image information are calculated, and the completeness and efficiency of each image information are sorted to obtain the sorting results. The sorting results are imported into the image recognition algorithm model. The image recognition algorithm model will dynamically select an image recognition algorithm suitable for the current image information according to the sorting results to identify the specific pesticide category. In particular, the above-mentioned R1 image information completeness and R2 image information efficiency calculation formulas are applicable to various image information of the pesticide packaging bag, and the various image information includes the appearance information of the pesticide packaging bag and the QR code, bar code, and pesticide text identifier information on the surface of the pesticide packaging bag.

[0106] In addition, the dynamic selection is suitable for the image recognition algorithm of the pesticide packaging bag to be identified, and the specific pesticide category is identified. The image recognition algorithm includes but is not limited to convolutional neural network algorithm, HOG feature extraction algorithm, ORB algorithm, SIFT algorithm, and SURF algorithm.

[0107] It should be noted that the information on the types of pesticide packaging bags that need to be recycled is obtained as follows:

[0108] The user inputs the category information of the pesticide packaging bags to be recycled from the input device;

[0109] The category information of the pesticide packaging bags that need to be recycled is obtained from the input device, and the information of the pesticide packaging bags that the user needs to recycle is a combination of one or more. In particular, the input device includes but is not limited to a user mobile device and a computer terminal device.

[0110] It should be noted that, according to the category information of the pesticide packaging bags to be recycled, the corresponding location information is obtained, and the location information of the pesticide packaging bags is used as navigation information of the recycling cruise vehicle, so that the recycling cruise vehicle navigates to the location to recycle the pesticide packaging bags to be recycled, specifically:

[0111] Collect video information in a specific area, extract target terrain feature points from the collected video information through image frames, reconstruct a three-dimensional map model, obtain a three-dimensional map model of the specific area, and synchronously import the three-dimensional map model into the navigation and positioning system of the recovery cruise vehicle;

[0112] Each recovery cruise vehicle has a built-in navigation and positioning system and an automatic driving system, and each recovery cruise vehicle can communicate with each other;

[0113] The built-in navigation and positioning system and automatic driving system of the recovery cruise vehicle have the function of communicating with the image recognition module and the video acquisition module;

[0114] The recovery cruise vehicle has at least one or more in a specific area;

[0115] The specific location information of the pesticide packaging bag to be identified in the three-dimensional map model is obtained, and the location information is sent as navigation information to the navigation and positioning system of each recycling cruise vehicle. According to the current position of each recycling cruise vehicle, the navigation and positioning system calculates and selects the recycling cruise vehicle closest to the location of the pesticide packaging bag to be identified, and navigates to the location for recycling processing.

[0116] The third aspect of the present invention also provides a computer-readable storage medium for intelligent identification of pesticide packaging bags based on big data, wherein the computer-readable storage medium includes a program for an intelligent identification method for pesticide packaging bags based on big data. When the program for intelligent identification method for pesticide packaging bags based on big data is executed by a processor, the steps of the intelligent identification method for pesticide packaging bags based on big data as described in any one of the above items are implemented.

[0117] The present invention discloses a method, system and medium for intelligent identification of pesticide packaging bags based on big data, which accurately identifies the category of pesticide packaging bags based on big data retrieval and image recognition algorithms. According to a preset time interval, the pesticide packaging bag information database is updated in real time to improve the accuracy of pesticide packaging image recognition. In addition, after identifying the pesticide packaging bags to be recycled, the recycling cruise vehicle automatically drives to the location of the pesticide packaging bags to be recycled for recycling, thereby improving the recycling efficiency of the pesticide packaging bags and reducing labor costs.

[0118] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0119] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0120] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0121] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0122] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0123] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for intelligent identification of pesticide packaging bags based on big data, characterized in that: include: Obtain image information of the pesticide packaging bag to be identified; Establishing an image recognition algorithm model, preprocessing the pesticide packaging bag information feature values ​​in the pesticide packaging bag information database to obtain a pesticide packaging bag image training data set, importing the pesticide packaging bag image training data set into the image recognition algorithm model, and performing image recognition algorithm training; According to the image information of the pesticide packaging bag to be identified, the integrity and efficiency of each image information of the pesticide packaging bag are calculated. The image information includes the appearance information of the pesticide packaging bag and the QR code, barcode, and pesticide text identifier information on the surface of the pesticide packaging bag. The image information integrity and image information efficiency calculation formula is specifically as follows: ， is the image information completeness, where n is the total number of image information area blocks of the pesticide packaging bag, is a complete pixel of a region block of image information, and U is all pixels in the entire region; ， is the image information efficiency, where n is the total number of image information area blocks of the pesticide packaging bag, is the effective pixel of a region block of image information, and W is all the pixels in the whole region; Calculate the completeness of each image information and the efficiency of each image information, sort the completeness of each image information and the efficiency of each image information to obtain a sorting result, import the sorting result into the image recognition algorithm model, and the image recognition algorithm model will dynamically select an image recognition algorithm suitable for the current image information according to the sorting result to identify a specific pesticide category; Obtain information on the types of pesticide packaging bags that need to be recycled; According to the type information of the pesticide packaging bags to be recycled, the corresponding location information is obtained, and the location information of the pesticide packaging bags is used as navigation information of the recycling cruise vehicle, so that the recycling cruise vehicle navigates to the location to recycle the pesticide packaging bags that need to be recycled.

2. According to the big data-based intelligent identification method for pesticide packaging bags according to claim 1, it is characterized in that: The obtaining of the image information of the pesticide packaging bag to be identified is specifically as follows: Obtaining a grayscale image of the pesticide packaging bag to be identified; Performing image preprocessing on the grayscale image of the pesticide packaging bag to obtain an image matrix of the pesticide packaging bag; Based on a machine learning algorithm, the image information of the pesticide packaging bag to be identified is identified from the image matrix of the pesticide packaging bag, wherein the image information includes the appearance information of the pesticide packaging bag and the QR code, barcode, and pesticide text identifier information on the surface of the pesticide packaging bag.

3. The method for intelligent identification of pesticide packaging bags based on big data according to claim 1 is characterized in that: The method of dynamically selecting a preset image recognition algorithm based on the acquired image information of the pesticide packaging bag to be identified, performing image recognition, and identifying the specific pesticide category includes: Retrieving image information of each pesticide packaging bag from the big data, the image information including appearance information of the pesticide packaging bag and QR code, bar code, and pesticide text identifier information on the surface of the pesticide packaging bag; Constructing a pesticide packaging bag information database, converting the image information of each pesticide packaging bag in the image information into a set of image feature values, and importing the image feature values ​​into the pesticide packaging bag information database; After a preset time period, new pesticide packaging bag image information is retrieved from the big data, the image information is converted into a set of image feature values, and the image feature values ​​are imported into the pesticide packaging bag information database until all new pesticide packaging bag image information is imported into the pesticide packaging bag information database, so as to realize the regular update of the pesticide packaging bag information database.

4. The method for intelligent identification of pesticide packaging bags based on big data according to claim 1 is characterized in that: The information on the category of pesticide packaging bags that need to be recycled is obtained as follows: The user inputs the category information of the pesticide packaging bags to be recycled from the input device; The category information of the pesticide packaging bags that need to be recycled is obtained from the input device, and the information of the pesticide packaging bags that the user needs to recycle is a combination of one or more than two.

5. The method for intelligent identification of pesticide packaging bags based on big data according to claim 1 is characterized in that: The above-mentioned information on the category of pesticide packaging bags to be recycled is used to obtain the corresponding location information, and the location information of the pesticide packaging bags is used as navigation information of the recycling cruise vehicle, so that the recycling cruise vehicle navigates to the location to recycle the pesticide packaging bags to be recycled, specifically: Collect video information in a specific area, extract target terrain feature points from the collected video information through image frames, reconstruct a three-dimensional map model, obtain a three-dimensional map model of the specific area, and synchronously import the three-dimensional map model into the navigation and positioning system of the recovery cruise vehicle; Acquire specific location information of the pesticide packaging bag to be identified in the three-dimensional map model, and send the location information as navigation information to the navigation and positioning system of each recycling cruise vehicle. According to the current location of each recycling cruise vehicle, the navigation and positioning system calculates and selects the recycling cruise vehicle closest to the location of the pesticide packaging bag to be identified, and navigates to the location for recycling; Each recovery cruise vehicle has a built-in navigation and positioning system and an automatic driving system, and each recovery cruise vehicle can communicate with each other; The built-in navigation and positioning system and automatic driving system of the recovery cruise vehicle have the function of communicating with the image recognition module and the video acquisition module; The recovery cruise vehicle has at least one or more in a specific area.

6. A pesticide packaging bag intelligent identification system based on big data, characterized in that: The system includes: a memory and a processor, wherein the memory includes a pesticide packaging bag intelligent identification method program based on big data, and when the pesticide packaging bag intelligent identification method program based on big data is executed by the processor, the following steps are implemented: Obtain image information of the pesticide packaging bag to be identified; Establishing an image recognition algorithm model, preprocessing the pesticide packaging bag information feature values ​​in the pesticide packaging bag information database to obtain a pesticide packaging bag image training data set, importing the pesticide packaging bag image training data set into the image recognition algorithm model, and performing image recognition algorithm training; According to the image information of the pesticide packaging bag to be identified, the integrity and efficiency of each image information of the pesticide packaging bag are calculated. The image information includes the appearance information of the pesticide packaging bag and the QR code, barcode, and pesticide text identifier information on the surface of the pesticide packaging bag. The image information integrity and image information efficiency calculation formula is specifically as follows: ， is the image information completeness, where n is the total number of image information area blocks of the pesticide packaging bag, is a complete pixel of a region block of image information, and U is all pixels in the entire region; ， is the image information efficiency, where n is the total number of image information area blocks of the pesticide packaging bag, is the effective pixel of a region block of image information, and W is all the pixels in the whole region; Calculate the completeness of each image information and the efficiency of each image information, sort the completeness of each image information and the efficiency of each image information to obtain a sorting result, import the sorting result into the image recognition algorithm model, and the image recognition algorithm model will dynamically select an image recognition algorithm suitable for the current image information according to the sorting result to identify a specific pesticide category; Obtain information on the types of pesticide packaging bags that need to be recycled; According to the type information of the pesticide packaging bags to be recycled, the corresponding location information is obtained, and the location information of the pesticide packaging bags is used as navigation information of the recycling cruise vehicle, so that the recycling cruise vehicle navigates to the location to recycle the pesticide packaging bags that need to be recycled.

7. The pesticide packaging bag intelligent identification system based on big data according to claim 6 is characterized in that: The above-mentioned information on the category of pesticide packaging bags to be recycled is used to obtain the corresponding location information, and the location information of the pesticide packaging bags is used as navigation information of the recycling cruise vehicle, so that the recycling cruise vehicle navigates to the location to recycle the pesticide packaging bags to be recycled, specifically: Collect video information in a specific area, extract target terrain feature points from the collected video information through image frames, reconstruct a three-dimensional map model, obtain a three-dimensional map model of the specific area, and synchronously import the three-dimensional map model into the navigation and positioning system of the recovery cruise vehicle; Acquire specific location information of the pesticide packaging bag to be identified in the three-dimensional map model, and send the location information as navigation information to the navigation and positioning system of each recycling cruise vehicle. According to the current location of each recycling cruise vehicle, the navigation and positioning system calculates and selects the recycling cruise vehicle closest to the location of the pesticide packaging bag to be identified, and navigates to the location for recycling; Each recovery cruise vehicle has a built-in navigation and positioning system and an automatic driving system, and each recovery cruise vehicle can communicate with each other; The built-in navigation and positioning system and automatic driving system of the recovery cruise vehicle have the function of communicating with the image recognition module and the video acquisition module; The recovery cruise vehicle has at least one or more in a specific area.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a pesticide packaging bag intelligent identification method program based on big data. When the pesticide packaging bag intelligent identification method program based on big data is executed by a processor, the steps of the pesticide packaging bag intelligent identification method based on big data as described in any one of claims 1 to 5 are implemented.

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