Agricultural product quality tracing system

Abstract

The invention discloses an agricultural product quality tracing system, and the system comprises a production environment monitoring module which is used for obtaining production environment information; the picking monitoring module is used for checking identity information of a picker or picking equipment; the agricultural product transportation module is used for determining whether to store the current agricultural product based on the production environment information and verification conditions; determining a product type based on the production environment information, and obtaining first box opening verification data; when it is judged that the intelligent storage box meets the storage condition based on the first box opening verification data, the lock module is controlled to be opened; when the lock module is closed, acquiring detection data, and determining whether the agricultural product meets a corresponding quality standard or not based on the detection data and the product type; generating quality inspection information based on the detection data; and the traceability information generation module is used for generating traceability information. The agricultural product transportation module is used for determining whether the agricultural products meet the quality standard or not, and the agricultural products can be directly detected in the logistics transportation link.

Description

Technical Field

[0001] This invention relates to the field of agricultural product quality traceability technology, and specifically to an agricultural product quality traceability system. Background Technology

[0002] With the improvement of residents' living standards, the quality and safety of agricultural products have become a focus of social concern. From the production environment in the production stage, cleaning and packaging in the processing stage, to the storage conditions and transportation time in the logistics and transportation stage, and finally to the display and storage in the terminal sales stage, every link can affect the quality of agricultural products. In the entire chain from farm to table, consumers, regulatory authorities and production and operation entities all have an urgent need for the traceability of agricultural product quality, especially for categories such as fresh fruits and vegetables and livestock and poultry products that are easily perishable and have high safety risks. Effective traceability means are needed to ensure consumer safety and market order.

[0003] To ensure the quality supervision of agricultural products, the industry widely adopts the "one-code traceability" technology solution. This involves using QR codes, RFID tags, and other identification carriers to record key information about agricultural products during production, processing, and distribution. Consumers can scan the code to access this information, and regulatory authorities can use the traceability code to trace the source of problematic agricultural products and determine responsibility. Furthermore, some traceability systems also integrate self-inspection reports from production enterprises and sampling data from third-party testing institutions.

[0004] However, existing "one-code traceability" technology solutions have significant shortcomings. On the one hand, the cost of forging traceability codes is low, making it easy for unscrupulous merchants to copy and forge codes, applying traceability information of qualified products to substandard agricultural products, resulting in "code-product mismatch." On the other hand, even if the traceability code is genuine and valid, consumers only obtain preset static information by scanning the code, and cannot verify the current quality of the agricultural products in their hands in real time. For example, agricultural products may have been illegally adulterated with additives during transportation, but the traceability code still displays the qualified information at the time of manufacture. If consumers want to verify the actual quality of agricultural products, they need to send samples to professional testing institutions, which is not only time-consuming but also incurs testing costs, resulting in extremely high barriers to actual rights protection or quality verification, ultimately failing to ensure the immediate qualification of the agricultural products in their hands. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this application provides an agricultural product quality traceability system. The system uses an agricultural product transportation module to acquire testing data from inside a smart storage box when the lock module is closed. Based on the testing data and product type, it determines whether the agricultural product meets quality standards. When the agricultural product meets the quality standards, it generates quality inspection information based on the testing data. This system enables direct testing of agricultural products during the logistics and transportation process. The quality inspection information directly corresponds to the agricultural product inside the smart storage box, fundamentally avoiding the problem of potential mismatch between traceability codes and agricultural products in single-code traceability methods. Furthermore, the system makes testing more convenient and significantly improves the safety of agricultural products.

[0006] To address the above problems, the present invention provides the following technical solution:

[0007] In a first aspect, embodiments of this application provide an agricultural product quality traceability system, including: a production environment monitoring module, used to acquire production environment information of the current portion of agricultural products generated by a monitoring device set up at the agricultural product production site; The harvesting monitoring module is used to verify the identity information of the harvester or harvesting equipment during the harvesting of agricultural products; The agricultural product transportation module is used to acquire the production environment information and determine whether the smart storage box should store the current portion of agricultural product based on the production environment information and preset verification conditions; when it is determined that the current portion of agricultural product should be stored, the product type of the agricultural product is determined based on the production environment information, and the first opening verification data of the smart storage box is acquired; when it is determined based on the first opening verification data that the smart storage box meets the storage conditions, the locking module of the smart storage box is controlled to open, so that the smart storage box can be opened and used to store the current portion of agricultural product; when the locking module is closed, the detection data inside the smart storage box is acquired, and the agricultural product is determined based on the detection data and the product type to determine whether the agricultural product meets the corresponding quality standard; when it is determined that the agricultural product meets the quality standard, quality inspection information is generated based on the detection data. The traceability information generation module is used to generate traceability information for the current agricultural product based on the production environment information, the identity information, and the quality inspection information. The traceability information is used to trace the growth process, harvesting process, and transportation process of the agricultural product.

[0008] This application provides an agricultural product quality traceability system. The system uses an agricultural product transportation module to acquire testing data from inside a smart storage box when the lock module is closed. Based on the testing data and product type, it determines whether the agricultural product meets quality standards. When the agricultural product meets the quality standards, it generates quality inspection information based on the testing data. This system enables direct testing of agricultural products during the logistics and transportation process. The quality inspection information directly corresponds to the agricultural product inside the smart storage box, fundamentally avoiding the problem of the traceability code not matching the agricultural product in the single-code traceability method. Furthermore, the testing is more convenient and can greatly improve the safety of agricultural products. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the structure of the agricultural product quality traceability system provided in the embodiments of this application.

[0010] Figure 2 A perspective view of the smart storage box provided in an embodiment of this application.

[0011] Figure 3 The intelligent storage box provided in this embodiment of the application provides a three-dimensional view of the storage space.

[0012] Figure 4 An exploded view of the smart storage box provided in an embodiment of this application.

[0013] Figure 5 A perspective view of the first fixing component provided in the embodiments of this application placed on the first plate.

[0014] Figure 6 A perspective view of the second fixing component provided in the embodiments of this application placed on the fourth plate.

[0015] Figure 7 A perspective view of the top plate provided in an embodiment of this application.

[0016] Figure 8 Exploded views of the first fixing component and the third fixing component provided in the embodiments of this application.

[0017] Figure 9 An exploded view of the second fixing component provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0019] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "multiple" means two or more, unless otherwise explicitly specified.

[0020] This application provides an agricultural product quality traceability system. The system uses an agricultural product transportation module to acquire testing data from inside a smart storage box when the lock module is closed. Based on the testing data and product type, it determines whether the agricultural product meets quality standards. When the agricultural product meets the quality standards, it generates quality inspection information based on the testing data. This system enables direct testing of agricultural products during the logistics and transportation process. The quality inspection information directly corresponds to the agricultural product inside the smart storage box, fundamentally avoiding the problem of potential mismatch between traceability codes and agricultural products in one-code traceability methods. Furthermore, the system makes testing more convenient and significantly improves the safety of agricultural products.

[0021] The agricultural product quality traceability system of this application is a system specifically applicable to agricultural supervision purposes, and the classification number of this application is G06Q.

[0022] The agricultural product transportation module of this application is a special equipment for intelligent transportation of agricultural products, and the agricultural product quality traceability system can be applied to the field of intelligent monitoring and early warning warehouses for agricultural products. Therefore, the classification number of this application can be G05B19, for example, G05B19 / 042.

[0023] The production environment monitoring module, harvesting monitoring module, agricultural product transportation module, and traceability information generation module of this application can all be edge computing devices. This application also relates to agricultural technology extension services.

[0024] The agricultural product quality traceability system provided in this application will be described in detail below with reference to the accompanying drawings.

[0025] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of the agricultural product quality traceability system provided in the embodiments of this application. For example... Figure 1 As shown, the agricultural product quality traceability system 100 includes a production environment monitoring module 110, a harvesting monitoring module 120, an agricultural product transportation module 130, and a traceability information generation module 140.

[0026] In some implementations, the production environment monitoring module is used to acquire production environment information of the current batch of agricultural products generated by monitoring devices installed at the agricultural product production site.

[0027] Optionally, the monitoring devices installed at agricultural production sites include positioning devices, temperature sensors, humidity sensors, light sensors, soil testing sensors, and surveillance cameras. Production environment information includes the location information of the agricultural production environment, temperature data, humidity data, historical light data, soil testing data, and surveillance video data.

[0028] In some implementations, the harvest monitoring module is used to verify the identity information of the harvester or harvesting equipment during the harvesting of agricultural products.

[0029] Optionally, the harvesting monitoring module includes a harvesting monitoring camera. The harvesting monitoring module is used to acquire harvesting monitoring video data during agricultural product harvesting through the harvesting monitoring camera, perform image recognition on the harvesting monitoring video data to obtain the facial recognition result of the harvester and / or the equipment recognition result of the harvesting equipment for the current portion of agricultural product, and verify the facial recognition result of the harvester and / or the equipment recognition result of the harvesting equipment against a harvesting verification database.

[0030] Optionally, the harvest monitoring camera equipment captures harvest monitoring video data using a 360-degree wide-angle camera.

[0031] In some implementations, the harvest monitoring module also counts the number of harvesters based on the facial recognition results of the harvesters of the current batch of agricultural products.

[0032] In some implementations, smart storage boxes are used to store agricultural products.

[0033] In some implementations, the agricultural product transportation module is used to perform steps S100 to S600.

[0034] Step S100: Obtain production environment information.

[0035] In some implementations, the agricultural product transportation module is also used to obtain production environment information when the harvesting monitoring module confirms that the identity information of the harvester or harvesting equipment has been verified.

[0036] To prevent substandard agricultural products from entering the logistics and transportation process, a first round of inspection is required before users store agricultural products in smart storage boxes.

[0037] Step S200: Based on production environment information and preset verification conditions, determine whether to use a smart storage box to store the current portion of agricultural products.

[0038] This method allows for the first round of inspection of agricultural products, and unqualified agricultural products cannot enter the logistics and transportation process directly.

[0039] In some implementations, step S200 includes steps S210 to S240.

[0040] Step S210: Determine whether the soil in the production environment is qualified based on the soil testing data in the production environment information and the preset soil testing standards.

[0041] Optionally, soil testing data may include soil parameters such as soil permeability, organic matter content, pH value, content of various nutrients, content of various heavy metals, and pesticide content.

[0042] Optionally, the content of various nutrients includes nitrogen, phosphorus, potassium, etc.

[0043] In some implementations, the soil in the production environment is deemed qualified when each soil testing parameter falls within the corresponding value range of a preset soil testing standard; otherwise, it is deemed unqualified.

[0044] In some implementations, when the production environment standard includes a soil type standard, the soil type is determined based on all soil testing parameters in the soil testing data. If the soil type does not meet the soil type standard, the soil in the production environment is deemed qualified; otherwise, it is deemed unqualified.

[0045] Optionally, a pre-trained artificial intelligence model can be used to determine the soil type based on all soil testing parameters in the soil testing data.

[0046] In some implementations, the product type of agricultural products is first determined based on the monitoring video data in the production environment information. Then, the soil testing standard corresponding to the product type is determined based on the product type and a preset soil testing standard library. Finally, the soil quality of the production environment is determined based on the soil testing data in the production environment information and the soil testing standard corresponding to the product type. The method is described above.

[0047] Optionally, the images of agricultural products in the surveillance video data can be identified to determine the product type of the agricultural products.

[0048] Step S220: When the soil is determined to be qualified, determine whether the production environment meets the production environment standards based on location information, soil test data, temperature data, humidity data, historical light data, and preset production environment standards corresponding to agricultural products.

[0049] The production environment standards corresponding to agricultural products can be preset before step S100, or the product type of agricultural products can be determined first based on the monitoring video data in the production environment information, and then the preset production environment standards corresponding to agricultural products can be obtained based on the product type.

[0050] In some implementations, a type of agricultural product may correspond to multiple standards. For example, rice may be divided into ordinary rice, crab-field rice, and Wuchang rice. In this case, the corresponding place-of-origin environmental standards for agricultural products are obtained and stored in advance through user instructions.

[0051] In some implementations, step S220 includes steps S221 to S225.

[0052] Step S221: When the place of origin environmental standard includes the geographical range of the place of origin corresponding to the agricultural product, determine whether the place of origin is within the specified geographical range of the place of origin in the place of origin environmental standard based on the location information. If not, determine that the production environment does not meet the place of origin environmental standard. If so, proceed to step S223.

[0053] Step S222: When the origin environment standard includes the geographical adaptation coefficient standard range corresponding to the agricultural product, obtain the geographical feature parameters of the origin from the geographical database based on the location information, calculate the geographical adaptation coefficient based on the geographical feature parameters, and determine that the production environment does not meet the origin environment standard when the geographical adaptation coefficient is not within the geographical adaptation coefficient standard range corresponding to the agricultural product; otherwise, proceed to step S223.

[0054] It can access geographic databases via the internet or access pre-stored geographic databases.

[0055] Among them, the geographical adaptation coefficient is used to measure the degree of matching between the production environment and agricultural products.

[0056] In some implementations, the geographical features of the place of origin include altitude, latitude, and landform type coefficient.

[0057] In some implementations, the difference between the geographical feature parameters of each place of origin and the corresponding standard value of the geographical feature parameters in the place of origin environmental standards is normalized, and then each normalized difference is multiplied by the corresponding weight coefficient to obtain the geographical adaptation coefficient.

[0058] Optionally, the weighting coefficients are preset or obtained based on the product type.

[0059] Step S223: Based on soil testing data, temperature data, humidity data, historical light data, and the corresponding production environment standards for agricultural products, continue to determine whether the production environment meets the production environment standards.

[0060] Due to climate change, the climate can vary from year to year. In some years, even if the location of the production area meets the standards, the geographical environment may not be suitable for growing certain types of crops. Therefore, it is necessary to further determine whether the production environment meets the production area environmental standards based on soil testing data, temperature data, humidity data, historical light data, and the corresponding production area environmental standards for agricultural products.

[0061] In some implementations, soil testing data, temperature data, humidity data, and historical light data are input into a pre-trained model for calculating the environmental characteristics of the production area to obtain the environmental characteristics of the current portion of agricultural product. If the environmental characteristics of the current portion of agricultural product are within the standard range of environmental characteristics in the environmental standards, the production environment is determined to meet the environmental standards; otherwise, the production environment is determined to not meet the environmental standards.

[0062] Step S230: When it is determined that the production environment meets the standards for the place of origin, the planting and harvesting stages of agricultural products are qualified based on the monitoring video data.

[0063] In reality, the accuracy of testing instruments is limited. If agricultural products contain trace amounts of illegal additives, the instruments often cannot detect them. However, if people ingest trace amounts of these illegal additives over a long period, it will still cause harm to their health. To fully guarantee the quality of agricultural products, preventative measures must be taken. If no one has added illegal additives, the quality of agricultural products can be guaranteed from the source.

[0064] In some implementations, step S230 includes steps S231 to S235.

[0065] Step S231: When it is determined that the production environment meets the production area environmental standards, image recognition is performed on the monitoring video data to obtain the operational characteristic information of agricultural products and the information of the operators.

[0066] In some implementations, a pre-trained video processing artificial intelligence model is used to perform image recognition on the monitoring video data to obtain operational characteristic information of agricultural products and operator information.

[0067] The operation feature information includes the operation type, operation time, and operation duration for each operation. The operator information includes the operator's facial recognition information.

[0068] Step S232: When it is determined from the operational feature information that the agricultural product has been treated with chemical agents and the amount applied is greater than the corresponding preset dosage, the planting process of the agricultural product is determined to be unqualified.

[0069] Optionally, the chemical agent may be a pesticide or fertilizer.

[0070] In some implementations, if the operation type in the operation feature information is "applying a chemical agent", it is determined that the agricultural product has been treated with a chemical agent; otherwise, it is determined that the agricultural product has not been treated with a chemical agent.

[0071] In some implementations, when it is determined that agricultural products have been treated with chemicals, the monitoring video corresponding to the chemical treatment is further processed to obtain the type and amount of chemical applied.

[0072] If a long period of time has passed since chemicals were applied to crops, the chemicals may become undetectable in the production environment and the agricultural products. However, even trace amounts of illegally added chemicals can still harm the human body. The method described above, compared to testing agricultural products to confirm whether chemicals have been applied and the dosage, avoids the problem of inaccurate trace detection, thus ensuring the quality of agricultural products.

[0073] For example, the type of chemical agent is determined to be a pesticide by identifying the pesticide container in the image and / or the text markings on the pesticide container, and the amount of pesticide to be applied is determined based on the text markings on the pesticide container and / or by calculating the actual size of the pesticide container in the image.

[0074] For example, the type of chemical agent is determined to be fertilizer by identifying the fertilizer bag in the image and / or the text markings on the fertilizer bag, and the amount of fertilizer applied is determined based on the text markings on the fertilizer bag and / or by calculating the actual size of the fertilizer bag in the image.

[0075] Step S233: When it is determined from the operational feature information that the seeds of the agricultural product have been replaced during the planting process, the planting process of the agricultural product is deemed unqualified.

[0076] In some implementations, when the operation type in the operation feature information is seedling digging or grafting, and the crop is identified based on the images before and after the operation, and it is confirmed that the crop type has changed, it is determined that the seeds of the agricultural product have been replaced during the planting process, and the planting process of the agricultural product is deemed unqualified.

[0077] Step S234: When it is determined from the operator information and personnel database that the personnel performing the harvesting operation on agricultural products do not have operating authority, the harvesting process of agricultural products is deemed unqualified.

[0078] Step S235: When it is determined based on the operation feature information that the seeds of the agricultural product have not been replaced during the planting process, the agricultural product has not been treated with chemical agents or the amount of chemical agents applied is not greater than the corresponding preset dose, and it is determined based on the operator information and personnel database that the personnel who perform the harvesting operation on the agricultural product have the operation authority, the planting and harvesting stages of the agricultural product are deemed qualified.

[0079] Step S240: When it is determined that the planting and harvesting stages of agricultural products are qualified, the agricultural products to be stored are determined.

[0080] Step S300: When it is determined that the current portion of agricultural product is to be stored, the product type of the agricultural product is determined based on the production environment information, and the first unpacking verification data of the smart storage box is obtained.

[0081] In some implementations, the first unpacking verification data includes the pressure value of the enclosure, information on whether the enclosure has been damaged, and self-test information of the locking module.

[0082] Step S400: When the smart storage box meets the storage conditions based on the first unpacking verification data, control the lock module of the smart storage box to open so that the smart storage box can be opened and used to store the current portion of agricultural products.

[0083] In some implementations, step S400 includes steps S410 to S440.

[0084] Step S410: When the lock module is determined to be working normally based on the lock module self-test information, the pressure value is less than the preset pressure value, and the intelligent storage box is determined to be undamaged based on the information on whether the box is damaged, the operator's face image is acquired.

[0085] Step S420: Perform face recognition on the face image to obtain the face recognition result.

[0086] Step S430: Query the personnel database based on the face recognition results.

[0087] Step S440: When the face recognition result is in the personnel database and the person corresponding to the face recognition result has the authority to open the box, determine that the smart storage box meets the storage conditions, control the lock module of the smart storage box to open, so that the smart storage box can be opened and used to store the current portion of agricultural products.

[0088] In some implementations, video data of the operator is acquired via camera devices, and an artificial intelligence model is used to perform behavioral recognition based on this video data. When the system detects that the operator is adding additives to the smart storage box, an alarm message is generated. This method can prevent operators from illegally adding additives during the logistics and transportation process.

[0089] To avoid the problem of the traceability code not matching the agricultural product in the one-code traceability method, it is necessary to conduct a second round of inspection on the agricultural products in the smart storage box.

[0090] In some implementations, the method further includes: acquiring the weight measured by a weight sensor, and issuing a closing prompt when the weight is a preset weight.

[0091] Optionally, the closing prompt can be an audio prompt or an image prompt, etc.

[0092] Step S500: When the lock module is closed, acquire the detection data inside the smart storage box, and determine whether the agricultural products meet the corresponding quality standards based on the detection data and product type.

[0093] In some implementations, the detection data includes spectral data, gas sensing data, and image data from inside the smart storage box. Step S500 includes steps S510 to S540.

[0094] Step S510: When the lock module is closed, acquire the spectral data, gas sensing data and image data inside the smart storage box.

[0095] Step S520: Determine the quality standard corresponding to the product type based on the product type and the preset quality standard database.

[0096] Step S530: Calculate the freshness index of agricultural products based on spectral data and image data.

[0097] In some implementations, the moisture content and chlorophyll content in agricultural products are determined based on spectral data and a preset spectral feature database, and the color saturation, surface gloss, and surface defect area ratio of the agricultural product surface are determined based on image data. Then, internal freshness parameters are calculated based on moisture content and chlorophyll content, external freshness parameters are calculated based on color saturation, surface gloss, and surface defect area ratio of the agricultural product surface, and freshness index is calculated based on internal and external freshness parameters.

[0098] In some implementations, the formula for calculating the internal freshness parameter is: , in, Indicates the internal freshness parameter. Indicates moisture content. This indicates the preset minimum moisture content. Indicates chlorophyll content, This indicates the preset maximum chlorophyll content. This indicates the preset minimum chlorophyll content.

[0099] In some implementations, the formula for calculating the external freshness parameter is: , in, Indicates the external freshness parameter. Indicates color saturation. This represents the standard value for preset color saturation. Indicates surface gloss. This indicates the preset minimum surface gloss level. This indicates the preset maximum surface gloss level. This indicates the percentage of surface defects.

[0100] In some implementations, the freshness index is obtained by multiplying the internal freshness parameter by the corresponding calculation coefficient and adding the external freshness parameter by the corresponding calculation coefficient.

[0101] Step S540: Determine whether additives are present in agricultural products based on spectral data, gas sensing data, and image data.

[0102] In some implementations, step S540 includes steps S541 to S546.

[0103] Step S541: Based on spectral data and a preset spectral feature database, determine the types of all solid substances present in agricultural products.

[0104] Step S542: When the types of all solid substances present in the agricultural product include those not listed in the table of solid substance types corresponding to the preset product type, it is determined that an additive is present in the agricultural product.

[0105] Step S543: Determine all types of gases inside the smart storage box based on gas sensor data.

[0106] Step S544: When all gas types include those not found in the preset gas type table corresponding to the product type, it is determined that additives are present in the agricultural product.

[0107] In this way, additives that are not permitted to be added to food can also be detected.

[0108] Step S545: Identify whether there are abnormal surface feature areas in agricultural products based on image data.

[0109] In some implementations, step S545 includes steps S5451 to S5453.

[0110] Step S5451: Identify the color and texture of agricultural product surfaces based on image data.

[0111] Optionally, a pre-trained AI-based image recognition model can be used to identify the color and texture of the agricultural product surface.

[0112] Step S5452: Cluster the surface areas of agricultural products based on their color and texture to obtain multiple feature regions.

[0113] After clustering, information about the color, texture, location, and area of ​​each feature region is obtained.

[0114] Step S5453: Determine whether there are abnormal surface feature areas in agricultural products based on the color, texture, location, and area of ​​all feature areas.

[0115] In some implementations, an artificial intelligence-based abnormal surface feature region determination model is used to calculate the color abnormality, texture abnormality, and edge sharpness of each feature region based on the color, texture, location, and area of ​​all feature regions, and to determine whether agricultural products have abnormal surface feature regions based on the color abnormality, texture abnormality, edge sharpness of each feature region and a preset abnormal feature database.

[0116] Specifically, based on the location and area of ​​the feature region, the abnormal color feature and abnormal texture feature closest to the location and area of ​​the feature region in the abnormal feature database are selected. The difference between the color and texture of the feature region and the abnormal color feature and abnormal texture feature in the abnormal feature database are compared to determine the color abnormality and texture abnormality.

[0117] Optionally, the difference between the feature vector corresponding to the color of the feature region and the feature vector corresponding to the abnormal color feature is calculated to determine the color anomaly. The difference between the feature vector corresponding to the texture of the feature region and the abnormal texture feature is calculated to determine the texture anomaly.

[0118] Among them, the edge sharpness is determined by the color gradient within a preset range of the feature region boundary.

[0119] For example, when jujubes are soaked in ripening agents, they will have a surface feature of being green on one side and red on the other, and the color boundary is very obvious. At this time, the above method can be used to determine that the edge sharpness of the feature area is greater than the abnormal edge sharpness threshold in the preset abnormal feature database, thereby determining that there is an abnormal surface feature area in the agricultural product.

[0120] For example, when agricultural products are soaked in ripening agents, their color becomes brighter and their texture becomes smoother than normal agricultural products. In this case, the above method can be used to determine that the color abnormality and texture abnormality of the feature area are greater than the corresponding preset threshold, thereby determining that there are abnormal surface feature areas in the agricultural product.

[0121] Step S546: When abnormal surface feature areas are identified in agricultural products based on image data, it is determined that additives are present in the agricultural products; otherwise, it is determined that no additives are present in the agricultural products.

[0122] Step S550: When it is determined that additives are present in agricultural products, the types and dosages of all additives are determined based on spectral data, gas sensor data, and image data.

[0123] In some implementations, the types of all solid substances present in agricultural products are determined based on spectral data and a preset spectral feature database; the types of all gases inside the smart storage box are determined based on gas sensor data; abnormal surface feature regions are identified based on image data; and the types of additives corresponding to the abnormal surface feature regions are determined based on the abnormal surface feature regions and a preset abnormal feature database. Finally, the union of all types of solid substances, all types of gases, and all types of additives corresponding to abnormal surface feature regions is determined as the types of all additives.

[0124] Because the intelligent storage box is a closed space, additives can accumulate to a high concentration, especially gaseous additives. By using an intelligent storage box to detect the types of additives, the problem of inaccurate trace detection can be avoided, thus improving the accuracy of additive detection.

[0125] In addition, the smart storage box is closed during the testing process, which can prevent the illegal addition of additives to agricultural products during transportation and ensure the accuracy of quality inspection information.

[0126] In some implementations, a pre-trained multimodal fusion deep learning model is used to determine the types and dosages of all additives based on spectral data, gas sensing data, and image data.

[0127] Optionally, the multimodal fusion deep learning model can be a Transformer model, an OpenFlamingo-based multimodal model, a PaLI-X multimodal model, or a MiniGPT-4 model, etc.

[0128] In some implementations, for each type of solid additive, the concentration of that type of solid additive is calculated based on the characteristic peak intensity of the corresponding additive type in the spectral data, and the dose of that type of solid additive is calculated based on the concentration of that type of solid additive and the area of ​​all abnormal surface feature regions corresponding to that type of additive identified using image data.

[0129] Optionally, the dosage of a type of solid additive can be obtained by multiplying the concentration of the solid additive by the area of ​​all abnormal surface feature regions corresponding to the solid additive.

[0130] In some implementations, for each type of gaseous additive, time-series analysis is performed on the gas sensing data to obtain the concentration-time relationship curve of the additive gas, and the release dose of the gaseous additive within a preset storage time period is calculated.

[0131] Optionally, the formula for calculating the release dose of the gaseous additive is: , in, Indicates the release dose of the gaseous additive. This refers to the closing time of the lock module. The predicted unpacking time is calculated based on the lock module's closing time and a preset storage time period. The function representing the change in the concentration of the additive gas over time. The base of the natural logarithm. This represents the cumulative coefficient of the gas additive. Indicates time.

[0132] Step S560: Determine whether the agricultural product meets the corresponding quality standards based on the freshness index of the agricultural product, the presence of additives, and, when additives are present, the types and dosages of all additives.

[0133] In some implementations, if each of the above-mentioned test data meets the standards specified in the corresponding quality standard for agricultural products, the agricultural product is determined to meet the corresponding quality standard; otherwise, the agricultural product is determined to not meet the corresponding quality standard.

[0134] Step S600: When it is determined that the agricultural products meet the quality standards, quality inspection information is generated based on the test data.

[0135] Optionally, the quality inspection information includes at least one of the following: testing data of agricultural products, a summary of the testing data, and a certificate of conformity for testing.

[0136] In some implementations, the traceability information generation module is used to generate traceability information for the current agricultural product based on production environment information, identity information, and quality inspection information. The traceability information is used to trace the growth, harvesting, and transportation processes of the agricultural product.

[0137] In some implementations, the smart storage box includes a positioning module, and the traceability information generation module also acquires the positioning data of the smart storage box, determines the transportation route trajectory of the smart storage box based on the positioning data, and generates traceability information for the current portion of agricultural products based on the transportation route trajectory, production environment information, identity information, and quality inspection information.

[0138] Optionally, traceability information can be presented to consumers by directly representing or linking to a URL, showing them at least one of the following: the origin of the agricultural product, the production environment, harvesting data, testing data, a certificate of conformity for testing, the transportation route of the smart storage box, and the opening and closing times of the lock module.

[0139] Optionally, a traceability map for the current agricultural product can be generated based on the transportation route, production environment information, identity information, and quality inspection information. The traceability information is displayed to consumers through direct representation or by linking to a URL. This traceability map includes the complete transportation route of the smart storage box and markings for each stage, such as production, harvesting, and transportation.

[0140] Optionally, corresponding visualized data charts are generated based on the detection data from each stage. When consumers click on the stage markers in the traceability map, they can view the corresponding visualized data charts. For example, when consumers click on the stage marker for the transportation stage in the traceability map, they can view the change curves of spectral data, gas sensor data, and image data inside the smart storage box. As another example, when consumers click on the stage marker for the harvesting stage in the traceability map, they can view the harvesting monitoring video data.

[0141] Optionally, data visualization charts include line charts, bar charts, pie charts, etc.

[0142] Optionally, data visualization plots can present data in a dynamic manner.

[0143] In some implementations, traceability information may include barcodes, QR codes, documents, images, and voice information.

[0144] In some implementations, the traceability information generation module is also used to upload traceability information to a database or blockchain network.

[0145] Optionally, the traceability information may also include blockchain-based evidence.

[0146] In some embodiments, the agricultural product quality traceability system also includes a processing environment monitoring module, which includes a camera. The agricultural product transportation module is also used to perform steps S710 to S730.

[0147] Step S710: When the lock module is opened, the control processing environment monitoring module is turned on.

[0148] Step S720: Obtain the storage operation information for the current portion of agricultural products.

[0149] The storage operation information includes storage operation monitoring video data captured by the processing environment monitoring module, the opening time of the smart storage box's lock module, and the weight of the current portion of agricultural products.

[0150] The weight of the current portion of agricultural product is measured by a weight sensor in the smart storage box.

[0151] Optionally, the traceability information may also include storage operation information and the weight of the current portion of agricultural product.

[0152] Step S730: When the lock module is closed, obtain the closing time of the lock module.

[0153] In some implementations, the agricultural product transportation module is also used to perform steps S810 to S840.

[0154] Step S810: When the verification information is received, the verification information is verified.

[0155] Optionally, the verification information may include passwords, facial recognition, fingerprints, or other similar information.

[0156] Step S820: When the verification information passes the verification, obtain the second unpacking verification data of the smart storage box, and determine whether the smart storage box meets the opening conditions based on the second unpacking verification data.

[0157] The second unpacking verification data includes the pressure value of the enclosure, information on whether the enclosure has been damaged, and self-test information of the lock module.

[0158] In some implementations, step S820 includes steps S821 to S824.

[0159] Step S821: When the lock module is determined to be working normally based on the lock module self-test information, the pressure value is less than the preset pressure value, and the intelligent storage box is determined to be undamaged based on the information on whether the box is damaged, the operator's face image is obtained.

[0160] Step S822: Perform face recognition on the face image to obtain the face recognition result.

[0161] Step S823: Query the personnel database based on the face recognition results.

[0162] Step S824: When the face recognition result is in the personnel database and the person corresponding to the face recognition result has the permission to open the box, determine that the smart storage box meets the opening conditions.

[0163] Step S830: When it is determined that the smart storage box meets the opening conditions, the lock module of the smart storage box is opened so that the smart storage box can be opened and the agricultural products can be taken out. The processing environment monitoring module is also opened.

[0164] Step S840: Obtain the extraction operation information for the current portion of agricultural products.

[0165] The retrieval operation information includes video monitoring data of the retrieval operation captured by the processing environment monitoring module, the opening time of the lock module of the smart storage box, the weight of the agricultural product being retrieved, and the type of processing technology to be carried out.

[0166] In order to monitor and ensure the quality of agricultural products throughout the entire process, a third round of inspection is required after the agricultural products have been processed.

[0167] In some implementations, when agricultural products are stored in the smart storage box again, spectral data, gas sensing data, and image data inside the smart storage box are acquired, and the processing technology type previously stored is used to determine whether the agricultural products have undergone the corresponding processing technology.

[0168] In some implementations, a neural network-based artificial intelligence model is used to determine whether agricultural products have undergone the corresponding processing technology based on spectral data, gas sensor data, image data, and previously stored processing technology types.

[0169] In some implementations, the agricultural product quality traceability system also includes a digital asset uploading module. This module, in response to a request from the uploader, uploads the digital asset corresponding to the current agricultural product containing traceability information to the account corresponding to the request; when the current agricultural product is determined to be substandard, the digital asset corresponding to that product is reduced.

[0170] In some implementations, the smart storage box is linked to the manufacturer's account.

[0171] Optionally, the account corresponding to the request can be the manufacturer's account or a third-party escrow account.

[0172] Optionally, accounts can be interconnected with government and financial blockchains to achieve regulatory transparency and financial empowerment of digital assets.

[0173] Alternatively, the digital assets in the account can be digital currencies, such as the digital yuan.

[0174] In some implementations, the digital asset upload module is also used to determine that the current portion of agricultural products is unqualified when the harvest monitoring module determines that the harvester or harvesting equipment has not passed the verification, or when the agricultural product transportation module determines that the smart storage box does not store the current portion of agricultural products or the current portion of agricultural products does not meet the quality standards.

[0175] In summary, the agricultural product quality traceability system provided in this application has the following advantages: 1. The agricultural product transportation module is used to acquire the detection data inside the smart storage box when the lock module is closed. Based on the detection data and product type, it determines whether the agricultural product meets the quality standards. When the agricultural product meets the quality standards, it generates quality inspection information based on the detection data. This allows for direct detection of agricultural products during the logistics and transportation process. The quality inspection information directly corresponds to the agricultural product in the smart storage box, fundamentally avoiding the problem of the traceability code not matching the agricultural product in the one-code traceability method. Furthermore, the detection is more convenient and can greatly improve the safety of agricultural products.

[0176] 2. By determining whether to store the current batch of agricultural products based on production environment information and preset verification conditions, the agricultural products can undergo the first round of inspection. Unqualified agricultural products cannot directly enter the logistics and transportation process.

[0177] 3. By determining that agricultural products have been treated with chemicals based on operational characteristic information, and that the amount applied exceeds the corresponding preset dosage, the method of identifying agricultural products as unqualified in the planting process can avoid the problem of inaccurate trace detection compared to the method of testing agricultural products to confirm whether they have been treated with chemicals and the dosage of chemicals, thereby ensuring the quality of agricultural products.

[0178] 4. By generating an alarm message when it is detected that an operator is putting additives into the smart storage box, it is possible to prevent operators from illegally adding additives during the logistics and transportation process.

[0179] In some embodiments, the agricultural product transportation module 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, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps described above.

[0180] In some implementations, the smart storage box includes a box body, a lock module, a data acquisition module, and a communication module. The communication module is communicatively connected to the agricultural product transportation module. For example, the communication module is wirelessly connected to the agricultural product transportation module. The communication module is also communicatively connected to the data acquisition module. The communication module is used to acquire detection data from the data acquisition module. The box body is used to store agricultural products, the lock module is used to open or close the smart storage box, and the data acquisition module is used to detect the agricultural products inside the smart storage box and obtain detection data.

[0181] Optionally, the agricultural product transportation module is an electronic device independent of the smart storage box.

[0182] Optionally, the agricultural product transportation module is an edge computing module within a smart storage box.

[0183] Optionally, the smart storage box may also include an agricultural product transportation module.

[0184] Alternatively, the container can be made of recyclable plastic, such as polyethylene terephthalate (PET), high-density polyethylene (HDPE), or polypropylene (PP).

[0185] Alternatively, the enclosure may be made of stainless steel.

[0186] Optionally, the lock module includes a mechanical lock and / or an electronic lock.

[0187] The data acquisition module includes a pressure sensor, a fiber optic sensor, a spectral detection device, a gas detection device, and a camera device.

[0188] The pressure sensor is used to monitor the pressure value of the box, the fiber optic sensor surrounds the box with optical fiber and is used to monitor whether the box is damaged, the spectral detection device is used to acquire the spectral data of the agricultural products inside the box, the gas detection device is used to acquire the gas sensing data of the agricultural products inside the box, and the camera device is used to acquire image data.

[0189] In some implementations, the smart storage box also includes a power supply module for supplying power to power-consuming modules such as the communication module and the data acquisition module.

[0190] Optionally, the power supply module may include a battery or be connected to AC power.

[0191] In some implementations, the enclosure includes a deformation mechanism that can be in multiple deformation states, allowing the smart storage box to switch between various usage modes. These modes include storage mode, display mode, and testing platform mode.

[0192] Optionally, the enclosure includes multiple panels, with adjacent panels connected and fixed by a deformation mechanism.

[0193] Optionally, the deformation mechanism includes at least one structure selected from hinges, latches, slide rails, support rods, and connecting rods.

[0194] Optionally, the inner wall of the box includes multiple partition slots for securing partitions. This allows for the placement of partitions inside the box as needed to organize agricultural products in designated areas.

[0195] In some implementations, when the smart storage box is in storage mode, its volume is at its maximum, and the spectral detection device, gas detection device, and camera device in the data acquisition module are all located inside the storage space of the box. This facilitates the spectral detection device, gas detection device, and camera device in detecting the agricultural products inside the box, while preventing external tampering of the detection data.

[0196] In some implementations, when the smart storage box is in display mode, the spectral detection device, gas detection device, and camera device in the data acquisition module are all located outside the storage space of the box.

[0197] Optionally, the position of the box panels can be changed via a deformation mechanism, allowing the top cover panel and / or one or more side wall panels to be flipped outwards, unfolded, or laid flat. For example, one or more panels equipped with a data acquisition module can be flipped outwards, unfolded, or laid flat, so that the spectral detection device, gas detection device, and camera device are all located outside the storage space of the box. In this way, the smart storage box can be directly transformed into a sales shelf, saving manpower and time in transferring agricultural products from the smart storage box to the sales shelf, and avoiding problems such as bumps and damage to agricultural products during the transfer. In addition, since the spectral detection device, gas detection device, and camera device are all located outside the storage space of the box, the smart storage box has a built-in real-time detection function, allowing consumers to test agricultural products on the spot and ensure the immediate quality of the agricultural products in their hands.

[0198] Optionally, when the smart storage box is in display mode, it also acquires detection data through a spectral detection device, a gas detection device, and a camera device, and generates quality inspection information in the manner described above.

[0199] Optionally, the smart storage box also includes a display device or is connected to a display device, and controls the display device to display quality inspection information.

[0200] Optionally, when the smart storage box is in display mode, the volume of the box is the second largest volume or the largest volume.

[0201] In some implementations, when the smart storage box is in the form of a detection platform, the volume of the box is at its minimum, and the spectral detection device, gas detection device, and camera device in the data acquisition module are located outside the storage space of the box.

[0202] Optionally, the multiple panels of the container can be unfolded and assembled into a testing platform with a preset height via a deformation mechanism, with the spectral detection device, gas detection device, and camera device in the data acquisition module located above the testing platform. In this way, the smart storage box can be directly converted into a testing platform, allowing consumers to test agricultural products on-site and ensuring the immediate qualification of the products they receive.

[0203] Figure 2 This is a perspective view of the intelligent storage box provided in an embodiment of this application. Figure 3 The intelligent storage box provided in this embodiment of the application provides a three-dimensional view of the storage space. Figure 4 This is an exploded view of the smart storage box provided in the embodiments of this application. Figure 5 A perspective view of the first fixing component placed on the first plate according to an embodiment of this application. Figure 6 A perspective view of the second fixing component provided in the embodiments of this application placed on the fourth plate. Figure 7 A perspective view of the top plate provided in an embodiment of this application. Figure 8Exploded views of the first and third fixing components provided in the embodiments of this application. Figure 9 An exploded view of the second fixing component provided in an embodiment of this application.

[0204] like Figures 2 to 8 As shown, this embodiment provides an intelligent storage box, including a base 20, a unfolding module 30, a fixing mechanism 40, a locking module 50, and an information module 60. The base 20 is used to place agricultural products; the unfolding module 30 is disposed on the base 20 and can be unfolded and folded, forming a storage space 11 on the base 20 when unfolded; the fixing mechanism 40 is used to fix the unfolded module 30; the locking module 50 is disposed on one side of the unfolding module 30 and is used to open or close the storage space 11. Through the unfolding of the unfolding module 30 and the fixing mechanism 40, the intelligent storage box has a secure storage space 11 for storage. The locking module 50 closes the storage space 11, preventing accidental opening and ensuring the quality of the agricultural products placed in the storage space 11. Furthermore, the folding and unfolding of the module 30 facilitates transportation before loading agricultural products and facilitates later storage and reuse, allowing the box to be recycled and made more environmentally friendly.

[0205] Optionally, the information module 60 includes a data acquisition module and a communication module.

[0206] Optionally, information module 60 may also include an agricultural product transportation module.

[0207] Optionally, the information module 60 also includes a traceability information generation module.

[0208] Optionally, the information module 60 also includes a processing environment monitoring module.

[0209] Optionally, the information module 60 is an edge computing terminal equipped with a lightweight computing engine, with built-in local data caching and processing units.

[0210] like Figures 3 to 4 As shown, optionally, the intelligent storage box also includes a weighing module 70. The weighing module 70 is mounted on the base 20. The data measured by the weighing module 70 is fed back to the information module 60 and recorded by the information module 60. The information module 60 compares the weight data of the agricultural products collected by the weighing module 70 with pre-stored standard weight data. When the weight deviation exceeds a preset threshold, it determines that the agricultural product information is abnormal and triggers an alarm. In this way, based on the weight of the current portion of agricultural product measured by the weighing module 70, it is possible to determine the loss during product operation and whether the agricultural product has been intercepted.

[0211] Optionally, the weighing module 70 is a weight sensor.

[0212] Optionally, the agricultural product transportation module compares the weight data of the agricultural products collected by the weighing module 70 with the pre-stored standard weight data. When the weight deviation exceeds a preset threshold, the module determines that the agricultural product information is abnormal and triggers an alarm.

[0213] like Figure 2 , Figure 4 As shown, optionally, the locking module 50 includes a first locking module 51 and a second locking module 52; the operator's operation information is used to control the opening of the first locking module 51; the operator's verification information is used to control the opening of the second locking module 52; when the first locking module 51 and the second locking module 52 are opened simultaneously, the storage space 11 is opened, and the agricultural products can be taken out. By having the first locking module 51 and the second locking module 52 jointly control the opening of the storage space 11, it is possible to prevent the smart storage box from being opened due to accidental operation or unauthorized operation.

[0214] like Figure 2 , Figure 4 As shown, optionally, the information module 60 is provided with an input panel 61, which is used to input the operator's operation information. After the operation information is input, the operation information on the input panel 61 is fed back to the information module 60. The information module 60 identifies and records the operation information. If the operation information is correct, the first lock module 51 opens. That is to say, if the operation information is incorrect, the first lock module 51 cannot be opened. The correct operation information is used to control the opening of the first lock module 51, and the actual operation information is effectively recorded to prevent unauthorized operation and to remind the operator what operation is required.

[0215] Optionally, the operational information includes operations performed on agricultural products.

[0216] For example, taking tea as an agricultural product, after picking, tea may need to go through processes such as fixation, rolling, fermentation / yellowing, and drying. Therefore, each time the tea is taken out and put back into the storage box, the operator must enter the operation information for this takeout, such as selecting the fixation operation in the input panel.

[0217] Optionally, the information module 60 also includes a counter (not shown in the figure), which is located on the first board 311. After the first lock module 51 is opened, the number on the counter increments by one. By using the counter to record the number of times, it is possible to intuitively know which step of the operation has been performed, and it can also remind the operator whether their operation value has been matched, thus preventing missed operations.

[0218] like Figure 2 , Figure 4As shown, optionally, the smart storage box also includes an identification module 80, which is used to identify the operator. If the operator is correctly identified, the identification module 80 sends a success signal to the information module 60, which then controls the second lock module 52 to open. By identifying the operator, it prevents unauthorized opening, which could lead to the agricultural products being swapped or damaged.

[0219] Optionally, the identification module 80 may include at least one of a password identification module, a face recognition module, and a fingerprint recognition module.

[0220] Optionally, the information module 60 is also used to acquire information about the agricultural products after they are put back into the storage box. If the information indicates that the agricultural products are normal, the module controls the first lock module 51 and the second lock module 52 to close; otherwise, it sends an alarm message to the network server. By identifying abnormalities in the agricultural products, timely warning messages can be issued, and the issue can be traced back to the source, thereby ensuring that the products do not have quality problems.

[0221] For example, it is possible to identify the place of origin information of agricultural products and determine whether it is the place of origin information before it was taken out.

[0222] For example, information can be obtained in real time from some sensors inside the box, such as whether the weight measured by the weight sensor is within a specific range, and then combined with operation information to determine whether someone is performing some inappropriate operation.

[0223] like Figures 2 to 4 As shown, optionally, the retractable module 30 includes a first folding portion 31, a second folding portion 32, and a third folding portion 33. The first folding portion 31 forms one end face; the second folding portion 32 forms two end faces; and the third folding portion forms the remaining end faces. Through the configuration of the three folding portions, the storage space 11 can be quickly opened.

[0224] like Figures 2 to 4 As shown, optionally, the first folding part 31 includes a first plate 311, which carries the lock module 50 and the information module 60. The lower ends of the first plate 311 are rotatably connected to the base 20, and the first plate 311 can rotate around the pivot point to the underside of the base 20. By rotating the first plate 311 to the underside of the base 20, the information module 60, the input panel 61, and the recognition module 80 can be placed between the first plate 311 and the base 20, preventing damage from impacts when not in use.

[0225] like Figures 2 to 4As shown, optionally, the second folding section 32 includes a second plate 321 and a third plate 322. The second plate 321 and the third plate 322 are respectively disposed on both sides of the first plate 311. The lower sides of the second plate 321 and the third plate 322 are rotatably connected to the base 20. The second plate 321 and the third plate 322 can rotate inward toward the storage space 11 and can be laid flat. The ability of the second plate 321 and the third plate 322 to rotate and lay flat effectively reduces the folding space, prevents large-area space occupation, and facilitates recycling and reuse.

[0226] like Figures 2 to 4 As shown, optionally, the third folding section 33 includes a fourth plate 331 and a top plate 332. The top plate 332 is rotatably connected to the fourth plate 331. The top plate 332 can rotate into the storage space 11 to be parallel to the fourth plate 331. The top plate 332 and the fourth plate 331 are located between the second plate 321 and the third plate 322. The fourth plate 331 is rotatably connected to the base 20. The fourth plate 331 can drive the top plate 332 to rotate into the storage space 11 and can be laid flat. By folding the fourth plate 331 and the top plate 332 flat, the folding space can be effectively reduced, preventing large-area space occupation and facilitating recycling and reuse.

[0227] Optionally, when the second plate 321 and the third plate 322 are laid flat, and when the fourth plate 331 is laid flat, the second plate 321 and the third plate 322 are located on top of the fourth plate 331. This effectively ensures the layers of the fold and minimizes the folding space.

[0228] like Figures 3 to 4 As shown, optionally, the corners of the second plate 321, the third plate 322, the fourth plate 331, and the top plate 332 on the side closer to the storage space 11 in the rotation position are rounded; the corners of the second plate 321, the third plate 322, the fourth plate 331, and the top plate 332 on the side farther from the storage space 11 in the rotation position are right angles. The rounded corners on the side closer to the storage space 11 allow the second plate 321, the third plate 322, the fourth plate 331, and the top plate 332 to fold unimpeded, while the right angles on the side farther from the storage space 11 prevent the second plate 321, the third plate 322, the fourth plate 331, and the top plate 332 from opening to the side farther from the storage space 11, preventing easy damage.

[0229] Optionally, the base 20 is provided with a sealing element (not shown in the figure) on the side where the second plate 321, the third plate 322, and the fourth plate 331 are rotatably connected, and the fourth plate 331 is provided with a sealing element on the side where the top plate 332 is rotatably connected. When the unfolding module 30 is unfolded to form the storage space 11, the sealing element seals the rotatable connection. By using the sealing element at the rotatable connection, it is possible to ensure that the storage space 11 is sealed after unfolding, preventing the entry of external air or moisture due to poor sealing, which could lead to the spoilage or dampness of agricultural products.

[0230] like Figures 4 to 9 As shown, optionally, the fixing mechanism 40 includes a first fixing component 41, a second fixing component 42, and a third fixing component 43; the first fixing component 41 is used to fix the first plate 311 to the second plate 321 or the first plate 311 to the third plate 322; the second fixing component 42 is used to fix the fourth plate 331 to the second plate 321 or the fourth plate 331 to the third plate 322; and the third fixing component 43 is used to fix the top plate 332. By fixing the first plate 311, the second plate 321, the third plate 322, the fourth plate 331, and the top plate 332 with the first fixing component 41, the second fixing component 42, and the third fixing component 43, the leakage of internal agricultural products due to insecure fixing during transportation is prevented.

[0231] like Figures 4 to 5 as well as Figure 8 As shown, optionally, the first fixing component 41 has two sets. The first fixing component 41 includes a first slot 411 and a first positioning component 412. The first positioning component 412 is disposed on one side of the first plate 311 located in the storage space 11. The first positioning component 412 is arranged at the corner of the first plate 311 and the second plate 321 and at the corner of the first plate 311 and the third plate 322. The first slot 411 is respectively disposed on the side of the second plate 321 and the third plate 322 close to the first plate 311 and located in the storage space 11. When the first positioning component 412 is placed in the first slot 411, the first plate 311 is fixed to the second plate 321 or the first plate 311 is fixed to the third plate 322. By placing the first card slot 412 in the first card slot 411, and with the first card slot 411 and the first card slot 412 located on one side within the storage space 11, the first plate 311, the second plate 321, and the third plate 322 can be fixed together while preventing them from being folded outside the storage space 11, thus preventing them from being easily damaged by unauthorized personnel.

[0232] like Figure 8 As shown, optionally, the first locking assembly 412 includes a first support member 41a, a first rotating rod 41b, and a first locking member 41c; the first rotating rod 41b is installed inside the first support member 41a, and the first locking member 41c is fixed to the first rotating rod 41b. The first rotating rod 41b drives the first locking member 41c to rotate into the first locking slot 411. By installing the first rotating rod 41b inside the first support member 41a, it is possible to prevent the first rotating rod 41b from being easily touched and to prevent the first rotating rod 41b from being easily rotated due to internal collision contact.

[0233] like Figures 4 to 5 as well as Figure 8As shown, optionally, the cross-section of the first slot 411 is arc-shaped, and the cross-section of the first locking member 41c is arc-shaped. This arc-shaped design ensures that after the first locking member 41c is screwed into the first slot 411, the first locking member 41c and the first slot 411 will not easily separate under pulling force.

[0234] like Figure 4 , Figure 6 as well as Figure 9 As shown, optionally, there are two sets of second fixing components 42. The second fixing components 42 include a second slot 421 and a second positioning component 422. The second positioning component 422 is located on one side of the fourth plate 331 within the storage space 11. The second positioning component 422 is arranged at the corner of the fourth plate 331 and the second plate 321, and at the corner of the fourth plate 331 and the third plate 322. The second slot 421 is respectively located on the side of the second plate 321 and the third plate 322 near the fourth plate 331, and is located within the storage space 11. When the second positioning component 422 is placed in the second slot 421, the fourth plate 331 is fixed to the second plate 321 or the fourth plate 331 is fixed to the third plate 322. By placing the second card slot 422 in the second card slot 421, and with the second card slot 421 and the second card slot 422 located on one side within the storage space 11, the fourth plate 331, the second plate 321, and the third plate 322 can be fixed together while preventing them from being folded outside the storage space 11, thus preventing them from being easily damaged by unauthorized personnel.

[0235] like Figure 4 , Figure 6 as well as Figure 9 As shown, optionally, the second locking assembly 422 includes a second support member 42a, a second rotating rod 42b, and a second locking member 42c; the second rotating rod 42b is installed inside the second support member 42a, and the second locking member 42c is fixed to the second rotating rod 42b. The second rotating rod 42b drives the second locking member 42c to rotate into the second locking slot 421. By installing the second rotating rod 42b inside the second support member 42a, it is possible to prevent the second rotating rod 42b from being easily touched and to prevent the second rotating rod 42b from being easily rotated due to internal collision contact.

[0236] like Figure 4 , Figure 6 as well as Figure 9 As shown, optionally, the cross-section of the second slot 421 is arc-shaped, and the cross-section of the second locking member 42c is arc-shaped. This arc-shaped design ensures that after the second locking member 42c is screwed into the second slot 421, the second locking member 42c and the second slot 421 will not easily separate under pulling force.

[0237] like Figures 4 to 5 as well as Figure 8As shown, optionally, the third fixing component 43 includes a third slot 431 and a third positioning component 432; the third slot 431 is located on the side of the top plate 332 near the storage space 11, and the third positioning component 432 is located on the first plate 311. The third positioning component 432 moves into the third slot 431 to fix the top plate 332. By using the third positioning component 432 located on the first plate 311, that is, fixing the top plate 332 on one side of the first plate 311, the fixing becomes more secure.

[0238] like Figures 4 to 5 , Figures 7 to 8 As shown, optionally, the third locking component includes a push rod 43a and a stop block 43b; the first rotating rod 41b is a hollow rod, and the push rod 43a is located inside the hollow rod. The push rod 43a can push the stop block 43b to move into the third locking slot 431. When the first rotating rod 41b rotates, it causes the stop block 43b to lock into the third locking slot 431. Simply pushing the stop block 43b to move into the third locking slot 431 and then rotating the first rotating rod 41b can fix the top plate 332, making the operation convenient.

[0239] like Figures 4 to 5 , Figures 7 to 8 As shown, optionally, both the first support member 41a and the first rotating rod 41b are provided with a first clearance groove 44, and the top rod 43a is provided with a first push block 45. The first push block 45 can drive the top rod 43a to move axially via the first clearance groove 44, and also drive the first rotating rod 41b and the top rod 43a to rotate. Through the design of the first clearance groove 44 and the first push block 45, while fixing the stop block 43b on the top rod 43a to the top plate 332, the first plate 311 and the second plate 321 or the first plate 311 and the third plate 322 are fixed, making the operation more convenient and reducing the occupation of the storage space 11.

[0240] like Figure 4 , Figure 6 , Figure 9 As shown, optionally, the second support member 42a is provided with a second clearance groove 46, and the second rotating rod 42b is provided with a second push block 47, which drives the second rotating rod 42b to rotate. With the second clearance groove 46 and the second push block 47, the fourth plate 331 can be quickly fixed to the second plate 321 or the fourth plate 331 to the third plate 322, making operation more convenient.

[0241] Optionally, the first plate 311 has rubber layers on both sides and the top, and the fourth plate 331 and the top plate 332 have rubber layers on both sides, which can seal when the fixing mechanism 40 is fixed, further ensuring the sealing effect.

[0242] like Figure 2 , Figure 4As shown, optionally, the first plate 311 includes a frame portion 31a and a baffle 31b; the baffle 31b is hinged to the frame portion 31a, and the locking module 50 is used to control the opening and closing of the baffle 31b and the frame portion 31a. The baffle 31b facilitates the handling of agricultural products.

[0243] Alternatively, the container can be made of recyclable plastic, such as polyethylene terephthalate (PET), high-density polyethylene (HDPE), or polypropylene (PP). Using these materials ensures the container's recyclability and prevents it from being easily damaged, making it more environmentally friendly.

[0244] According to the requirements for agricultural product storage, the container can be made of stainless steel.

[0245] In summary, this application provides an agricultural product quality traceability system, which includes: a production environment monitoring module for acquiring production environment information of the current portion of agricultural product generated by a monitoring device installed at the agricultural product's production site; a harvesting monitoring module for verifying the identity information of the harvester or harvesting equipment during agricultural product harvesting; and an agricultural product transportation module for acquiring production environment information and determining whether to store the current portion of agricultural product in a smart storage box based on the production environment information and preset verification conditions; when it is determined that the current portion of agricultural product should be stored, the product type of the agricultural product is determined based on the production environment information, and a first opening verification number of the smart storage box is acquired. According to the data, when the smart storage box meets the storage conditions based on the first unpacking verification data, the lock module of the smart storage box is opened to allow the smart storage box to be used to store the current portion of agricultural products. When the lock module is closed, the detection data inside the smart storage box is acquired, and the agricultural products are determined to meet the corresponding quality standards based on the detection data and product type. When the agricultural products are determined to meet the quality standards, quality inspection information is generated based on the detection data. The traceability information generation module is used to generate traceability information for the current portion of agricultural products based on production environment information, identity information, and quality inspection information. The traceability information is used to trace the growth process, harvesting process, and transportation process of agricultural products. This application uses an agricultural product transportation module to acquire the detection data inside the smart storage box when the lock module is closed, and to determine whether the agricultural products meet the quality standards based on the detection data and product type. When the agricultural products are determined to meet the quality standards, quality inspection information is generated based on the detection data. This allows for direct testing of agricultural products during the logistics and transportation process. The quality inspection information directly corresponds to the agricultural products in the smart storage box, fundamentally avoiding the problem of the traceability code not matching the agricultural products in the one-code traceability method. Furthermore, the testing is more convenient and can greatly improve the safety of agricultural products.

[0246] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An agricultural product quality traceability system, characterized in that, include: The production environment monitoring module is used to acquire the production environment information of the current batch of agricultural products generated by monitoring devices set up at the agricultural product production site; The harvesting monitoring module is used to verify the identity information of the harvester or harvesting equipment during the harvesting of agricultural products; The agricultural product transportation module is used to acquire the production environment information and determine whether the smart storage box should store the current portion of agricultural product based on the production environment information and preset verification conditions; when it is determined that the current portion of agricultural product should be stored, the product type of the agricultural product is determined based on the production environment information, and the first unpacking verification data of the smart storage box is acquired. When the smart storage box meets the storage conditions based on the first unpacking verification data, the lock module of the smart storage box is opened to allow the smart storage box to be opened and used to store the current portion of the agricultural product; when the lock module is closed, the detection data inside the smart storage box is acquired, and the agricultural product is determined to meet the corresponding quality standard based on the detection data and the product type; when the agricultural product is determined to meet the quality standard, quality inspection information is generated based on the detection data. The traceability information generation module is used to generate traceability information for the current agricultural product based on the production environment information, the identity information, and the quality inspection information. The traceability information is used to trace the growth process, harvesting process, and transportation process of the agricultural product.

2. The agricultural product quality traceability system according to claim 1, characterized in that, The production environment information includes the location information, temperature data, humidity data, historical light data, soil testing data, and monitoring video data of the agricultural product's production environment. The agricultural product transportation module is also used for: Based on the soil testing data in the production environment information and the preset soil testing standards, determine whether the soil in the production environment is qualified; When the soil is determined to be qualified, the production environment is determined to meet the production environment standards based on the location information, soil test data, temperature data, humidity data, historical light data, and preset production environment standards. When it is determined that the production environment meets the production area environmental standards, the planting and harvesting stages of the agricultural products are deemed qualified based on the monitoring video data. Once the planting and harvesting stages of the agricultural product are determined to be qualified, the agricultural product is then stored.

3. The agricultural product quality traceability system according to claim 2, characterized in that, The agricultural product transportation module is also used for: When it is determined that the production environment meets the production site environment standards, image recognition is performed on the monitoring video data to obtain the operational characteristic information of the agricultural products and the operator information; When it is determined based on the operational feature information that the agricultural product has been treated with chemicals and the amount applied is greater than the corresponding preset dosage, it is determined that the planting process of the agricultural product is unqualified. When it is determined, based on the operational feature information, that the seeds of the agricultural product have been replaced during the planting process, the planting stage of the agricultural product is deemed unqualified. When it is determined, based on the operator information and personnel database, that the personnel performing the harvesting operation on the agricultural product do not have operating authority, the harvesting process of the agricultural product is deemed unqualified. When it is determined based on the operational feature information that the seeds of the agricultural product have not been replaced during the planting process, the agricultural product has not been treated with chemicals or the amount of chemicals applied is not greater than the corresponding preset dose, and the personnel who perform the harvesting operation on the agricultural product are determined based on the operator information and personnel database to have the operation authority, the planting and harvesting stages of the agricultural product are determined to be qualified.

4. The agricultural product quality traceability system according to claim 1, characterized in that, The first unpacking verification data includes the pressure value of the container, information on whether the container has been damaged, and self-test information of the lock module. The agricultural product transportation module is also used for: When the lock module is determined to be working normally based on the self-test information of the lock module, the pressure value is less than the preset pressure value, and the smart storage box is determined to be undamaged based on the information on whether the box is damaged, the operator's facial image is acquired. Perform face recognition on the face image to obtain the face recognition result; Based on the facial recognition results, query the personnel database; When the facial recognition result is located in the personnel database and the person corresponding to the facial recognition result has the authority to open the box, it is determined that the smart storage box meets the storage conditions, and the lock module of the smart storage box is controlled to open so that the smart storage box can be opened and used to store the current portion of agricultural products.

5. The agricultural product quality traceability system according to claim 1, characterized in that, The detection data includes spectral data, gas sensing data, and image data from inside the intelligent storage box. The agricultural product transportation module is also used for: The quality standard corresponding to the product type is determined based on the product type and a preset quality standard database. The freshness index of the agricultural product is calculated based on the spectral data and the image data; The presence of additives in the agricultural product is determined based on the spectral data, the gas sensing data, and the image data. When it is determined that additives are present in the agricultural product, the types and dosages of all additives are determined based on the spectral data, the gas sensing data, and the image data. The quality of the agricultural product is determined based on its freshness index, the presence of additives, and, if present, the types and dosages of all additives.

6. The agricultural product quality traceability system according to claim 5, characterized in that, The agricultural product transportation module is also used for: Based on the spectral data and a preset spectral feature database, the types of all solid substances present in the agricultural product are determined; When the types of all solid substances present in the agricultural product include those not listed in the table of solid substance types corresponding to the product type, it is determined that the agricultural product contains additives. Based on the gas sensing data, the types of all gases inside the intelligent storage box are determined; When all the gas types include those that are not in the preset gas type table corresponding to the product type, it is determined that the agricultural product contains additives. Based on the image data, identify whether the agricultural product has any abnormal surface feature areas; When abnormal surface feature areas are identified in the agricultural product based on the image data, it is determined that the agricultural product contains additives; otherwise, it is determined that the agricultural product does not contain additives.

7. The agricultural product quality traceability system according to claim 1, characterized in that, The agricultural product quality traceability system also includes a digital asset uploading module, which is used for: In response to the uploader's request, the digital asset corresponding to the current agricultural product containing the traceability information is uploaded to the account corresponding to the request; When it is determined that the current portion of agricultural product is substandard, the digital assets corresponding to the current portion of agricultural product are reduced.

8. The agricultural product quality traceability system according to claim 1, characterized in that, The agricultural product quality traceability system also includes a processing environment monitoring module, and the agricultural product transportation module is further used for: When the lock module is opened, the processing environment monitoring module is activated. Obtain the storage operation information of the current portion of agricultural product. The storage operation information includes the storage operation monitoring video data captured by the processing environment monitoring module, the opening time of the lock module of the smart storage box, and the weight of the current portion of agricultural product. When the lock module is closed, the closing time of the lock module is obtained.

9. The agricultural product quality traceability system according to claim 8, characterized in that, The agricultural product transportation module is also used for: When verification information is received, the verification information is verified. When the verification information passes the verification, the second unpacking verification data of the smart storage box is obtained, and the smart storage box is judged to meet the opening conditions based on the second unpacking verification data. When it is determined that the smart storage box meets the opening conditions, the lock module of the smart storage box is controlled to open, so that the smart storage box can be opened and the agricultural products can be taken out. The processing environment monitoring module is also controlled to open. Obtain the current portion of agricultural product removal operation information, which includes the removal operation monitoring video data captured by the processing environment monitoring module, the opening time of the lock module of the smart storage box, the weight of the removed agricultural product, and the type of processing technology to be performed.

10. The agricultural product quality traceability system according to claim 1, characterized in that, The intelligent storage box includes a box body, a lock module, a data acquisition module, and a communication module. The communication module is communicatively connected to the agricultural product transportation module and to the data acquisition module. The communication module is used to acquire the detection data from the data acquisition module. The box is used to store agricultural products, the lock module is used to open or close the smart storage box, and the data acquisition module is used to detect the agricultural products inside the smart storage box and obtain detection data. The data acquisition module includes a pressure sensor, a fiber optic sensor, a spectral detection device, a gas detection device, and a camera device; The pressure sensor is used to monitor the pressure value of the box, the optical fiber sensor surrounds the box and is used to monitor whether the box is damaged, the spectral detection device is used to acquire the spectral data of the agricultural products inside the box, the gas detection device is used to acquire the gas sensing data of the agricultural products inside the box, and the camera device is used to acquire image data.

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