A method, device, equipment and medium for detecting the quality of regional agricultural products

Abstract

The embodiment of the specification discloses a quality detection method, device and equipment of regional agricultural products and a medium, the method comprises the following steps: collecting the environmental data of each grid area of the agricultural products to be detected according to the preset Internet of Things device; obtaining the basic information of the agricultural products to be detected and the planting area of each grid area of the agricultural products to be detected; inputting the basic information, the planting area and the environmental data into a preset yield prediction model to obtain the predicted yield value of the agricultural products to be detected in each grid area, and synchronizing the predicted yield value to the corresponding purchase terminal; obtaining the actual yield value of the purchase terminal, comparing the predicted yield value with the actual yield value to determine whether the agricultural products to be detected have quality problems.

Description

Technical Field

[0001] This specification relates to the field of big data technology, and in particular to a method, apparatus, equipment and medium for quality testing of regional agricultural products. Background Technology

[0002] Agriculture, a primary form of production, is one of humanity's most important economic activities. Simply put, it's a system where humans use their wisdom to create environments more conducive to agricultural development, utilizing the domestication of plants and animals to produce goods and ultimately generate economic benefits. Agricultural products are the fruits of agricultural labor, and locally distinctive agricultural products become regional brands. The successful development of these regional brands is crucial not only for the brands themselves but also for the development of the entire local agricultural economy and the future of rural areas. However, in regional agricultural production, some merchants, driven by profit, may adulterate products with inferior goods from other regions, thus affecting the quality and safety of agricultural products and hindering the development of local brands. Therefore, to ensure the quality and safety of agricultural products, protect public health, and promote agricultural and rural economic development, quality testing of agricultural products is a vital part of agricultural production and sales.

[0003] Currently, the quality testing of agricultural products mainly relies on various indicator standards issued by the Ministry of Agriculture and related departments. However, all aspects of agricultural product indicators depend on professional product testing. This requires a lot of work from professional testing personnel to classify agricultural products by quality, obtain the quality indicators of the agricultural products, and confirm that qualified agricultural products can be sold. This process requires professional testing personnel to conduct quality inspections of agricultural products using specialized testing equipment. It relies on a large number of testing devices and professional personnel, which is costly and has low testing efficiency. Moreover, the sampling testing method is generally used, which has low reliability in detecting inferior agricultural products mixed in with other products. Summary of the Invention

[0004] To address the aforementioned technical problems, this specification provides one or more embodiments of a method, apparatus, equipment, and medium for quality testing of regional agricultural products.

[0005] One or more embodiments of this specification employ the following technical solutions:

[0006] This specification provides one or more embodiments of a method for quality testing of regional agricultural products, the method comprising:

[0007] Environmental data of each grid area of ​​the agricultural products to be tested is collected based on pre-installed IoT devices;

[0008] Obtain the basic information of the agricultural product to be tested and the planting area of ​​each grid region of the agricultural product to be tested;

[0009] The basic information, the planting area, and the environmental data are input into a preset yield prediction model to obtain the predicted yield value of the agricultural product to be tested in each grid area, and the predicted yield value is synchronized to the corresponding acquisition terminal.

[0010] The actual output value of the acquisition terminal is obtained, and the predicted output value is compared with the actual output value to determine whether the agricultural product to be tested has quality problems.

[0011] Optionally, in one or more embodiments of this specification, before collecting environmental data of each grid area of ​​the agricultural product to be detected using a pre-set IoT device, the method further includes:

[0012] Obtain the production base area of ​​the agricultural product to be tested, and bind the production base area to the corresponding user;

[0013] Environmental parameters of multiple preset locations within the base area are obtained, and clustering is performed on each of the locations based on the environmental parameters to obtain clustering results;

[0014] The base area is divided according to the clustering results to obtain multiple grid areas for the agricultural products to be tested.

[0015] Optionally, in one or more embodiments of this specification, before collecting environmental data of each grid area of ​​the agricultural product to be detected using a pre-set IoT device, the method further includes:

[0016] The historical production information and corresponding environmental data of the agricultural products to be detected in each grid area are obtained; wherein, the historical production information includes production information from multiple years;

[0017] Based on the historical production information and corresponding environmental data, the environmental factors affecting the agricultural products to be tested in each grid area are determined.

[0018] The basic environmental parameters of the agricultural products to be tested are obtained, and the standard deployment location and quantity of IoT devices in each grid area are determined based on the basic environmental parameters and the standard IoT device deployment table. The IoT devices are then configured based on the standard deployment location and quantity.

[0019] Based on the environmental factors affecting the agricultural products to be tested in each grid area, the types of IoT devices to be added in each grid area are determined, and the IoT devices are configured based on the deployment conditions of each device type.

[0020] In one or more embodiments of this specification, before inputting the basic information, the planting area, and the environmental data into a preset yield prediction model, the method further includes:

[0021] Obtain a crop growth model corresponding to the type of agricultural product to be detected;

[0022] Collect yield data of sample agricultural products corresponding to the types of agricultural products to be tested; wherein, the yield data includes: the planting area of ​​the sample agricultural products, the yield value of the sample agricultural products, and the environmental data of the sample agricultural products;

[0023] An initial yield prediction model for the agricultural product to be tested is established based on the crop production model.

[0024] The initial yield prediction model is trained based on the yield data of the sample agricultural products to obtain a preset yield prediction model that meets the requirements.

[0025] In one or more embodiments of this specification, before synchronizing the predicted output value with the corresponding purchasing terminal, the method further includes:

[0026] Obtain user information and regional brands corresponding to each of the aforementioned base areas;

[0027] Based on the user information and the identifier of the acquisition terminal corresponding to the user information, the authentication request information of the acquisition terminal is determined;

[0028] Obtain a preset acquisition platform corresponding to the regional brand, and send the authentication request information to the preset acquisition platform;

[0029] If the authentication request information passes the review, it will be bound to the acquisition terminal and the preset acquisition platform to summarize and manage the data of each acquisition terminal.

[0030] Optionally, in one or more embodiments of this specification, obtaining the actual output value of the purchasing terminal and comparing the predicted output value with the actual output value to determine whether the agricultural product to be tested has quality problems specifically includes:

[0031] Based on the acquisition terminal, the sales volume of agricultural products to be tested in each grid area is obtained as the actual output value;

[0032] The actual output value is compared with the predicted output value to obtain the difference between the actual output value and the predicted output value;

[0033] If the actual output value is determined to be less than the predicted output value, and the difference is greater than a preset threshold, then the agricultural product to be tested has a quality problem.

[0034] Optionally, in one or more embodiments of this specification, after determining that the actual yield value is less than the predicted yield value and the difference is greater than a preset threshold, the method further includes:

[0035] Based on a preset sampling quantity, samples are taken from the agricultural products to be tested that have quality problems to obtain sampled products;

[0036] The sampling sample is captured using a preset image acquisition device to obtain image information of the sampling sample.

[0037] The image information is input into a pre-trained image recognition model to obtain the adulterants present in the agricultural product to be detected.

[0038] This specification provides one or more embodiments of a quality testing device for regional agricultural products, the device comprising:

[0039] The data acquisition unit is used to collect environmental data of each grid area of ​​the agricultural product to be tested based on the pre-installed Internet of Things (IoT) devices.

[0040] The acquisition unit is used to acquire the basic information of the agricultural product to be tested and the planting area of ​​each grid area of ​​the agricultural product to be tested;

[0041] The prediction unit is used to input the basic information, the planting area and the environmental data into a preset yield prediction model to obtain the predicted yield value of the agricultural product to be detected in each grid area, and to synchronize the predicted yield value to the corresponding acquisition terminal.

[0042] The comparison unit is used to obtain the actual output value of the acquisition terminal, compare the predicted output value with the actual output value, and determine whether the agricultural product to be tested has quality problems.

[0043] This specification provides one or more embodiments of a communication acquisition device for a programmable controller, the device comprising:

[0044] At least one processor; and,

[0045] A memory communicatively connected to the at least one processor; wherein,

[0046] The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to:

[0047] Environmental data of each grid area of ​​the agricultural products to be tested is collected based on pre-installed IoT devices;

[0048] Obtain the basic information of the agricultural product to be tested and the planting area of ​​each grid region of the agricultural product to be tested;

[0049] The basic information, the planting area, and the environmental data are input into a preset yield prediction model to obtain the predicted yield value of the agricultural product to be tested in each grid area, and the predicted yield value is synchronized to the corresponding acquisition terminal.

[0050] The actual output value of the acquisition terminal is obtained, and the predicted output value is compared with the actual output value to determine whether the agricultural product to be tested has quality problems.

[0051] This specification provides one or more embodiments of a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured as follows:

[0052] Environmental data of each grid area of ​​the agricultural products to be tested is collected based on pre-installed IoT devices;

[0053] Obtain the basic information of the agricultural product to be tested and the planting area of ​​each grid region of the agricultural product to be tested;

[0054] The basic information, the planting area, and the environmental data are input into a preset yield prediction model to obtain the predicted yield value of the agricultural product to be tested in each grid area, and the predicted yield value is synchronized to the corresponding acquisition terminal.

[0055] The actual output value of the acquisition terminal is obtained, and the predicted output value is compared with the actual output value to determine whether the agricultural product to be tested has quality problems.

[0056] The above-described at least one technical solution adopted in the embodiments of this specification can achieve the following beneficial effects:

[0057] By dividing the agricultural products to be tested into grid areas, the testing can be based on the analysis of environmental data within each grid area, increasing the accuracy of yield prediction. By comparing actual and predicted yields, it's possible to determine if the tested agricultural products are mixed with those from other regions, avoiding the low efficiency and incomplete testing associated with existing technologies that require extensive surveys by professionals and specialized equipment. Identifying quality issues in the tested agricultural products based on yield comparisons ensures quality control at the source, thus protecting the regional brand of the agricultural products. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0059] Figure 1 This is a schematic flowchart of a method for quality testing of regional agricultural products provided in an embodiment of this specification;

[0060] Figure 2 This is a schematic diagram of the internal structure of a quality testing device for regional agricultural products provided in an embodiment of this specification;

[0061] Figure 3 This is a schematic diagram of the internal structure of a quality testing device for regional agricultural products provided in an embodiment of this specification;

[0062] Figure 4 This is a schematic diagram of the internal structure of a non-volatile storage medium provided in the embodiments of this specification. Detailed Implementation

[0063] This specification provides an embodiment of a method, apparatus, equipment, and medium for quality testing of regional agricultural products.

[0064] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0065] like Figure 1 As shown, this specification provides a flowchart of a method for quality testing of regional agricultural products in one or more embodiments.

[0066] Depend on Figure 1 As can be seen, in one or more embodiments of this specification, a method for quality testing of regional agricultural products includes the following steps:

[0067] S101: Collect environmental data of each grid area of ​​the agricultural product to be tested based on the pre-installed IoT devices.

[0068] Environmental data related to agricultural production, such as temperature, humidity, and light intensity, affect the growth of agricultural products and thus their final yield. To facilitate the detection of environmental data for agricultural products and to make subsequent yield analysis and prediction easier, this embodiment of the specification uses a pre-set Internet of Things (IoT) device to collect environmental data in real time from each grid area of ​​the agricultural product to be detected.

[0069] Furthermore, since different growing environments of agricultural products have different impacts on crop yield, in order to facilitate reliable analysis and prediction of yield, in one or more embodiments of this specification, before collecting environmental data of each grid area of ​​the agricultural product to be detected according to a pre-set Internet of Things device, the method further includes the following steps:

[0070] First, the base area of ​​the agricultural product to be tested is obtained, and the base area is bound to the corresponding user to achieve the purpose of base ownership confirmation. It should be noted that determining the base area is for the standardized management of edible agricultural product planting and breeding bases within the designated production area of ​​the regional brand. The planting and breeding scale of each base within the designated area of ​​the regional brand is determined through measurement, mapping, and other technical means, and the base is bound to the production enterprise or farmer to provide data support for subsequent steps. After binding the base area to the corresponding user, environmental parameters of multiple pre-defined locations within the base area need to be obtained. These environmental parameters are then used to cluster the locations, obtaining clustering results. Based on the clustering results obtained in the above process, the base area is divided into multiple grid areas for the agricultural product to be tested. In a certain application scenario of this specification, the area can be divided into different grids according to the different natural environments in which the edible agricultural product grows. For example, the natural environment on the south side of a mountain differs significantly from that on the north side, so they can be divided into two different grids. Within the same grid, the natural environment in which the edible agricultural product grows can be considered the same; however, the natural environment differs between different grids. In this way, under the conditions of the same crop, the same scale, and the same growth cycle but different growth environments, statistical analysis of yield can be carried out to obtain the impact of the growth environment on crop yield.

[0071] To better detect different environmental data in different grid areas, in one or more embodiments of this specification, before collecting environmental data of each grid area of ​​the agricultural product to be detected using a pre-installed IoT device, the method further includes the following process:

[0072] Historical production information and corresponding environmental data of the agricultural products to be tested are obtained for each grid area. It should be noted that the historical production information includes production information from multiple years. Based on the historical production information and the corresponding environmental data, the environmental factors affecting the agricultural products to be tested in each grid area are determined. In other words, based on the historical production information and environmental data of each year, the environmental parameters affecting agricultural production in each year are compared and analyzed as environmental factors. For example, if the yield of agricultural products in year 1 is 1 ton, and the yield in year 2 is 1.2 tons, comparing the environmental data of year 1 and year 2 reveals that the precipitation in year 1 is lower than that in year 2. Therefore, the environmental parameter affecting agricultural production in each year is precipitation, and precipitation is considered an environmental factor affecting the agricultural products to be tested.

[0073] After acquiring the environmental factors affecting the agricultural products to be tested in each grid area, the basic environmental parameters of the agricultural products to be tested are then obtained. These parameters are those that need to be monitored during the agricultural production process, including but not limited to temperature, humidity, illuminance, atmospheric pressure, carbon dioxide concentration, and solar radiation. Based on these basic environmental parameters and a standard IoT device deployment table, the standard deployment locations and quantities of IoT devices in each grid area are determined. The IoT devices are then configured accordingly based on these standard deployment locations and quantities. Furthermore, to address environmental parameters requiring additional attention due to differences in the environments of each grid area, the types of additional IoT devices to be added in each grid area are determined based on the environmental factors affecting the agricultural products to be tested. The IoT devices are then configured based on the deployment conditions of each device type. For example, if unstable temperature in a certain grid area significantly affects the yield of agricultural products, then additional IoT devices for temperature acquisition are needed to ensure real-time temperature monitoring. This manual describes the deployment of IoT devices within the divided grids based on the above steps. The IoT devices will collect natural environmental data within the grid in real time and upload the collected environmental data to the data platform in real time. In this way, the environmental changes during the growth of the agricultural product over a growth cycle can be recorded, which will greatly affect the yield and quality of the product.

[0074] S102: Obtain the basic information of the agricultural product to be tested and the planting area of ​​each grid area of ​​the agricultural product to be tested.

[0075] To improve the accuracy of subsequent yield forecasts, in one or more embodiments of this specification, it is necessary to obtain the basic information of the agricultural products to be tested and the planting area of ​​each grid area of ​​the agricultural products to be tested, so as to determine the planting and breeding scale of each base within the designated area of ​​the regional brand through measurement, surveying and mapping and other technical means.

[0076] S103: Input the basic information, the planting area and the environmental data into the preset yield prediction model to obtain the predicted yield value of the agricultural product to be tested in each grid area, and synchronize the predicted yield value to the corresponding acquisition terminal.

[0077] After obtaining basic information, planting area, and environmental data according to the above steps S101 and S102, in order to predict the yield of agricultural products in each grid area, the basic information, planting area, and environmental data are input into a pre-set yield prediction model in this embodiment of the specification, so as to obtain the predicted yield value of the agricultural product to be tested in each grid area. Then, in order to facilitate the supervision of the responsible users in each grid area and to facilitate the subsequent traceability and management of the acquisition process, the predicted yield value needs to be synchronized to the corresponding acquisition terminal.

[0078] Furthermore, in one or more embodiments of this specification, before inputting basic information, planting area, and environmental data into the preset yield prediction model, it is necessary to obtain a preset yield prediction model that meets the requirements. Therefore, the method further includes the following process:

[0079] First, a crop growth model corresponding to the type of agricultural product to be tested is obtained. Then, yield data of sample agricultural products corresponding to the type of agricultural product to be tested are collected. It should be noted that the yield data includes: the planting area of ​​the sample agricultural product, the yield value of the sample agricultural product, and environmental data of the sample agricultural product. Then, an initial yield prediction model for the agricultural product to be tested is established based on the crop production model. The initial yield prediction model is trained using the yield data of the aforementioned sample agricultural products to obtain a preset yield prediction model that meets the requirements. It should also be noted that the yield prediction model is a model that takes information such as the variety, scale, season, and environment affecting the yield of edible agricultural products as input, and outputs an estimated yield of edible agricultural products in that region. The data platform has accumulated a large amount of environmental data for one growth cycle of edible agricultural products, which will be a good dataset for training the yield prediction model. Over time, as the dataset accumulates, the estimated yield value calculated by the model will increasingly approach the actual yield.

[0080] Furthermore, in order to trace and record data based on the acquisition terminal, in one or more embodiments of this specification, before synchronizing the predicted output value to the corresponding acquisition terminal, the method further includes the following steps:

[0081] First, obtain the user information and regional brand corresponding to each base area. Based on the user information and the identifier of the acquisition terminal corresponding to the user information, determine the authentication request information of the acquisition terminal. Obtain the preset acquisition platform corresponding to the regional brand and send the authentication request information to the preset acquisition platform. If the authentication request information is approved, bind the acquisition terminal to the preset acquisition platform to summarize and manage the data of each acquisition terminal.

[0082] Furthermore, in one application scenario described in this manual, the acquisition process is digitized by building an acquisition platform. A smart acquisition mini-program can be developed, allowing each farmer and enterprise to open a buying and selling account, enabling sellers to display codes to sell goods, buyers to scan to receive goods, and one-click printing of receipts. This not only simplifies operation and saves significant manpower, material resources, and financial resources, but also ensures that all buying and selling data is recorded in a data platform for unified management, facilitating statistical analysis and application of production figures.

[0083] S104: Obtain the actual output value of the acquisition terminal, and compare the predicted output value with the actual output value to determine whether the agricultural product to be tested has quality problems.

[0084] To ensure the source of agricultural products and guarantee the quality of edible agricultural products in the construction of regional brands, this embodiment of the specification, after obtaining the predicted yield value based on the above steps, first obtains the yield of agricultural products used for sale at the purchasing terminal as the actual yield value, and then compares the predicted yield value and the actual yield value to determine whether the agricultural product to be tested has quality problems.

[0085] Specifically, in one or more embodiments of this specification, the actual output value of the purchasing terminal is obtained, and the predicted output value is compared with the actual output value to determine whether there is a quality problem with the agricultural product to be tested. The specific process includes the following:

[0086] First, based on the purchasing terminals, the sales volume of the agricultural products to be tested in each grid area is obtained as the actual output value. Then, the actual output value is compared with the predicted output value to obtain the difference between the actual and predicted output values. If it is determined that the actual output value is less than the predicted output value, and the difference between the two is greater than a preset threshold, it indicates that the agricultural products to be tested are mixed with agricultural products from outside the designated area, and therefore the agricultural products to be tested have quality problems.

[0087] Furthermore, in one or more embodiments of this specification, if it is determined that the actual yield value is less than the predicted yield value, and the difference is greater than a preset threshold, then after determining that the agricultural product to be detected has a quality problem, the method further includes the following steps:

[0088] For agricultural products with quality problems, samples are taken based on a preset sampling quantity to obtain sampled products. Then, images of the sampled products are acquired using a pre-set image acquisition device to obtain image information of the sampled products. The image information is input into a pre-trained image recognition model to identify adulterants present in the agricultural products under test, facilitating rapid statistical analysis of specific quality problems in the agricultural products under test.

[0089] like Figure 2 As shown, this specification provides a schematic diagram of the internal structure of a quality testing device for regional agricultural products in one or more embodiments. Figure 2 It is known that a quality testing device for regional agricultural products includes:

[0090] The data acquisition unit 201 is used to collect environmental data of each grid area of ​​the agricultural product to be tested based on the pre-installed Internet of Things (IoT) devices.

[0091] The acquisition unit 202 is used to acquire the basic information of the agricultural product to be tested and the planting area of ​​each grid area of ​​the agricultural product to be tested;

[0092] Prediction unit 203 is used to input the basic information, the planting area and the environmental data into a preset yield prediction model to obtain the predicted yield value of the agricultural product to be detected in each grid area, and to synchronize the predicted yield value to the corresponding acquisition terminal.

[0093] The comparison unit 204 is used to obtain the actual output value of the acquisition terminal, compare the predicted output value with the actual output value, and determine whether the agricultural product to be tested has quality problems.

[0094] like Figure 3 As shown, this specification provides a schematic diagram of the internal structure of a quality testing device for regional agricultural products in one or more embodiments. Figure 3 It is known that a quality testing device for regional agricultural products includes:

[0095] At least one processor; and,

[0096] A memory communicatively connected to the at least one processor; wherein,

[0097] The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to:

[0098] Environmental data of each grid area of ​​the agricultural products to be tested is collected based on pre-installed IoT devices;

[0099] Obtain the basic information of the agricultural product to be tested and the planting area of ​​each grid region of the agricultural product to be tested;

[0100] The basic information, the planting area, and the environmental data are input into a preset yield prediction model to obtain the predicted yield value of the agricultural product to be tested in each grid area, and the predicted yield value is synchronized to the corresponding acquisition terminal.

[0101] The actual output value of the acquisition terminal is obtained, and the predicted output value is compared with the actual output value to determine whether the agricultural product to be tested has quality problems.

[0102] like Figure 4 As shown, this specification provides a schematic diagram of the internal structure of a non-volatile storage medium in one or more embodiments. Figure 4 It is known that a non-volatile storage medium stores computer-executable instructions, which are capable of:

[0103] Environmental data of each grid area of ​​the agricultural products to be tested is collected based on pre-installed IoT devices;

[0104] Obtain the basic information of the agricultural product to be tested and the planting area of ​​each grid region of the agricultural product to be tested;

[0105] The basic information, the planting area, and the environmental data are input into a preset yield prediction model to obtain the predicted yield value of the agricultural product to be tested in each grid area, and the predicted yield value is synchronized to the corresponding acquisition terminal.

[0106] The actual output value of the acquisition terminal is obtained, and the predicted output value is compared with the actual output value to determine whether the agricultural product to be tested has quality problems.

[0107] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0108] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed differently than those described herein.

[0109] The order in the embodiments can be followed and the desired result can still be achieved. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments...

[0110] In this approach, multitasking and parallel processing are also possible or potentially advantageous.

[0111] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, various modifications and variations can be made to the one or more embodiments of this specification.

[0112] Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A method for quality testing of regional agricultural products, characterized in that, The method includes: Environmental data of each grid area of ​​the agricultural products to be tested is collected based on pre-installed IoT devices; Obtain the basic information of the agricultural product to be tested and the planting area of ​​each grid region of the agricultural product to be tested; The basic information, the planting area, and the environmental data are input into a preset yield prediction model to obtain the predicted yield value of the agricultural product to be tested in each grid area, and the predicted yield value is synchronized to the corresponding acquisition terminal. The actual output value of the acquisition terminal is obtained, and the predicted output value is compared with the actual output value to determine whether the agricultural product to be tested has quality problems. Before collecting environmental data for each grid area of ​​the agricultural product to be tested using pre-installed IoT devices, the method further includes: Obtain the production base area of ​​the agricultural product to be tested, and bind the production base area to the corresponding user; Environmental parameters of multiple preset locations within the base area are obtained, and clustering is performed on each of the locations based on the environmental parameters to obtain clustering results; The base area is divided according to the clustering results to obtain multiple grid areas for the agricultural products to be tested; Before collecting environmental data for each grid area of ​​the agricultural product to be tested using pre-installed IoT devices, the method further includes: The historical production information and corresponding environmental data of the agricultural products to be detected in each grid area are obtained; wherein, the historical production information includes production information from multiple years; Based on the historical production information and corresponding environmental data, the environmental factors affecting the agricultural products to be tested in each grid area are determined. The basic environmental parameters of the agricultural products to be tested are obtained, and the standard deployment location and quantity of IoT devices in each grid area are determined based on the basic environmental parameters and the standard IoT device deployment table. The IoT devices are then configured based on the standard deployment location and quantity. Based on the environmental factors affecting the agricultural products to be tested in each grid area, the device type for adding IoT devices in each grid area is determined, and the IoT devices are configured based on the deployment conditions of each device type. The method further includes synchronizing the predicted output value with the corresponding purchasing terminal: Obtain user information and regional brands corresponding to each of the aforementioned base areas; Based on the user information and the identifier of the acquisition terminal corresponding to the user information, the authentication request information of the acquisition terminal is determined; Obtain a preset acquisition platform corresponding to the regional brand, and send the authentication request information to the preset acquisition platform; If the authentication request information passes the review, the acquisition terminal will be bound to the preset acquisition platform to aggregate and manage the data of each acquisition terminal.

2. The method for quality testing of regional agricultural products according to claim 1, characterized in that, Before inputting the basic information, the planting area, and the environmental data into the preset yield prediction model, the method further includes: Obtain a crop growth model corresponding to the type of agricultural product to be detected; Collect yield data of sample agricultural products corresponding to the types of agricultural products to be tested; wherein, the yield data includes: the planting area of ​​the sample agricultural products, the yield value of the sample agricultural products, and the environmental data of the sample agricultural products; An initial yield prediction model for the agricultural product to be tested is established based on the crop growth model. The initial yield prediction model is trained based on the yield data of the sample agricultural products to obtain a preset yield prediction model that meets the requirements.

3. The method for quality testing of regional agricultural products according to claim 1, characterized in that, The process of obtaining the actual output value of the purchasing terminal and comparing it with the predicted output value to determine whether the agricultural product to be tested has quality problems specifically includes: Based on the acquisition terminal, the sales volume of agricultural products to be tested in each grid area is obtained as the actual output value; The actual output value is compared with the predicted output value to obtain the difference between the actual output value and the predicted output value; If the actual output value is determined to be less than the predicted output value, and the difference is greater than a preset threshold, then the agricultural product to be tested has a quality problem.

4. The method for quality testing of regional agricultural products according to claim 3, characterized in that, If it is determined that the actual yield value is less than the predicted yield value, and the difference is greater than a preset threshold, then the agricultural product to be tested has a quality problem. The method further includes: Based on a preset sampling quantity, samples are taken from the agricultural products to be tested that have quality problems to obtain sampled products; The sampling sample is captured using a preset image acquisition device to obtain image information of the sampling sample. The image information is input into a pre-trained image recognition model to obtain the adulterants present in the agricultural product to be detected.

5. A quality testing device for regional agricultural products, characterized in that, The device includes: The data acquisition unit is used to collect environmental data of each grid area of ​​the agricultural product to be tested based on the pre-installed Internet of Things (IoT) devices. The acquisition unit is used to acquire the basic information of the agricultural product to be tested and the planting area of ​​each grid area of ​​the agricultural product to be tested; The prediction unit is used to input the basic information, the planting area and the environmental data into a preset yield prediction model to obtain the predicted yield value of the agricultural product to be detected in each grid area, and to synchronize the predicted yield value to the corresponding acquisition terminal. A comparison unit is used to obtain the actual output value of the acquisition terminal, compare the predicted output value with the actual output value, and determine whether the agricultural product to be tested has quality problems; before collecting environmental data of each grid area of ​​the agricultural product to be tested based on the pre-installed IoT device, the unit further includes: Obtain the production base area of ​​the agricultural product to be tested, and bind the production base area to the corresponding user; Environmental parameters of multiple preset locations within the base area are obtained, and clustering is performed on each of the locations based on the environmental parameters to obtain clustering results; The base area is divided according to the clustering results to obtain multiple grid areas for the agricultural products to be tested; Before collecting environmental data from each grid area of ​​the agricultural product to be tested using pre-installed IoT devices, the process also includes: The historical production information and corresponding environmental data of the agricultural products to be detected in each grid area are obtained; wherein, the historical production information includes production information from multiple years; Based on the historical production information and corresponding environmental data, the environmental factors affecting the agricultural products to be tested in each grid area are determined. The basic environmental parameters of the agricultural products to be tested are obtained, and the standard deployment location and quantity of IoT devices in each grid area are determined based on the basic environmental parameters and the standard IoT device deployment table. The IoT devices are then configured based on the standard deployment location and quantity. Based on the environmental factors affecting the agricultural products to be tested in each grid area, the device type for adding IoT devices in each grid area is determined, and the IoT devices are configured based on the deployment conditions of each device type. Synchronizing the predicted output value with the corresponding acquisition terminal also includes: Obtain user information and regional brands corresponding to each of the aforementioned base areas; Based on the user information and the identifier of the acquisition terminal corresponding to the user information, the authentication request information of the acquisition terminal is determined; Obtain a preset acquisition platform corresponding to the regional brand, and send the authentication request information to the preset acquisition platform; If the authentication request information passes the review, the acquisition terminal will be bound to the preset acquisition platform to aggregate and manage the data of each acquisition terminal.

6. A quality testing device for regional agricultural products, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to: Environmental data of each grid area of ​​the agricultural products to be tested is collected based on pre-installed IoT devices; Obtain the basic information of the agricultural product to be tested and the planting area of ​​each grid region of the agricultural product to be tested; The basic information, the planting area, and the environmental data are input into a preset yield prediction model to obtain the predicted yield value of the agricultural product to be tested in each grid area, and the predicted yield value is synchronized to the corresponding acquisition terminal. Obtain the actual output value of the acquisition terminal, compare the predicted output value with the actual output value to determine whether the agricultural product to be tested has quality problems; before collecting environmental data of each grid area of ​​the agricultural product to be tested based on the pre-installed IoT device, the process also includes: Obtain the production base area of ​​the agricultural product to be tested, and bind the production base area to the corresponding user; Environmental parameters of multiple preset locations within the base area are obtained, and clustering is performed on each of the locations based on the environmental parameters to obtain clustering results; The base area is divided according to the clustering results to obtain multiple grid areas for the agricultural products to be tested; Before collecting environmental data from each grid area of ​​the agricultural product to be tested using pre-installed IoT devices, the process also includes: The historical production information and corresponding environmental data of the agricultural products to be detected in each grid area are obtained; wherein, the historical production information includes production information from multiple years; Based on the historical production information and corresponding environmental data, the environmental factors affecting the agricultural products to be tested in each grid area are determined. The basic environmental parameters of the agricultural products to be tested are obtained, and the standard deployment location and quantity of IoT devices in each grid area are determined based on the basic environmental parameters and the standard IoT device deployment table. The IoT devices are then configured based on the standard deployment location and quantity. Based on the environmental factors affecting the agricultural products to be tested in each grid area, the device type for adding IoT devices in each grid area is determined, and the IoT devices are configured based on the deployment conditions of each device type. Synchronizing the predicted output value with the corresponding acquisition terminal also includes: Obtain user information and regional brands corresponding to each of the aforementioned base areas; Based on the user information and the identifier of the acquisition terminal corresponding to the user information, the authentication request information of the acquisition terminal is determined; Obtain a preset acquisition platform corresponding to the regional brand, and send the authentication request information to the preset acquisition platform; If the authentication request information passes the review, the acquisition terminal will be bound to the preset acquisition platform to aggregate and manage the data of each acquisition terminal.

7. A non-volatile storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are capable of: Environmental data of each grid area of ​​the agricultural products to be tested is collected based on pre-installed IoT devices; Obtain the basic information of the agricultural product to be tested and the planting area of ​​each grid region of the agricultural product to be tested; The basic information, the planting area, and the environmental data are input into a preset yield prediction model to obtain the predicted yield value of the agricultural product to be tested in each grid area, and the predicted yield value is synchronized to the corresponding acquisition terminal. The actual output value of the acquisition terminal is obtained, and the predicted output value is compared with the actual output value to determine whether the agricultural product to be tested has quality problems. Before collecting environmental data from each grid area of ​​the agricultural product to be tested using pre-installed IoT devices, the process also includes: Obtain the production base area of ​​the agricultural product to be tested, and bind the production base area to the corresponding user; Environmental parameters of multiple preset locations within the base area are obtained, and clustering is performed on each of the locations based on the environmental parameters to obtain clustering results; The base area is divided according to the clustering results to obtain multiple grid areas for the agricultural products to be tested; Before collecting environmental data from each grid area of ​​the agricultural product to be tested using pre-installed IoT devices, the process also includes: The historical production information and corresponding environmental data of the agricultural products to be detected in each grid area are obtained; wherein, the historical production information includes production information from multiple years; Based on the historical production information and corresponding environmental data, the environmental factors affecting the agricultural products to be tested in each grid area are determined. The basic environmental parameters of the agricultural products to be tested are obtained, and the standard deployment location and quantity of IoT devices in each grid area are determined based on the basic environmental parameters and the standard IoT device deployment table. The IoT devices are then configured based on the standard deployment location and quantity. Based on the environmental factors affecting the agricultural products to be tested in each grid area, the device type for adding IoT devices in each grid area is determined, and the IoT devices are configured based on the deployment conditions of each device type. Synchronizing the predicted output value with the corresponding acquisition terminal also includes: Obtain user information and regional brands corresponding to each of the aforementioned base areas; Based on the user information and the identifier of the acquisition terminal corresponding to the user information, the authentication request information of the acquisition terminal is determined; Obtain a preset acquisition platform corresponding to the regional brand, and send the authentication request information to the preset acquisition platform; If the authentication request information passes the review, the acquisition terminal will be bound to the preset acquisition platform to aggregate and manage the data of each acquisition terminal.

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