Method for scoring the nutritional quality index of agricultural products

Abstract

The application provides a method for scoring the nutritional quality index of agricultural products, belonging to the technical field of computer data processing, to generate a reliable, dynamic and highly personalized nutritional quality index, and actively optimize the quality management of the product circulation link. The application improves the reliability and accuracy of the basic data relied on by the scoring by constructing a double-checking and self-learning data verification process, generates a highly customized score for consumers with different health goals through the logical verification of the knowledge graph and the abnormal learning mechanism, and can generate preservation strategy parameters that can guide the operation of the storage link in the reverse direction, converting the information flow into regulation instructions for the physical supply chain.

Description

Technical Field

[0001] This invention relates to the field of computer data processing technology, and in particular to a method for scoring the nutritional quality index of agricultural products. Background Technology

[0002] Agricultural product nutritional quality index scoring is a commercial service method that uses digital means to quantify and assess the nutritional value of agricultural products. With consumers increasingly focusing on healthy eating, the market demand for refined assessments of agricultural product quality is constantly growing. This type of scoring aims to provide consumers with objective and quantifiable data support in their purchasing decisions, and businesses in quality monitoring and marketing. It is an important manifestation of the integration of modern digital agriculture and smart commerce.

[0003] In existing technologies, methods for scoring the nutritional quality of agricultural products typically rely on laboratory testing data of submitted samples. The scoring system obtains the nutrient content from these test reports and performs a weighted calculation according to a fixed weighting model to arrive at a comprehensive score. Some more advanced methods attempt to incorporate information such as the product's origin and brand for simple classification and weighting, or include traceable production batch number information during scoring to increase transparency.

[0004] However, the aforementioned existing technical solutions have obvious limitations in commercial applications. The scoring criteria and weighting settings are often subjective and static, making it difficult to adapt to quality differences caused by different batches and different growth environments. They also fail to reflect the inevitable nutritional degradation that occurs during the storage and distribution process. The diverse data sources lead to inconsistent testing standards, making effective integration difficult. Furthermore, the lack of reliable technical means to ensure the authenticity and immutability of the data affects the credibility of the scoring. The general scoring results fail to consider the differences in individual health needs of consumers, thus limiting their commercial guidance value. Summary of the Invention

[0005] This invention provides a method for scoring the nutritional quality index of agricultural products. Based on knowledge graph data verification, traceability feature-driven dynamic decay prediction, and bidirectional optimization engine-driven personalized assessment and supply chain feedback technology, it can generate a reliable, dynamic, and highly personalized nutritional quality index and proactively optimize the quality management of product circulation.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] Firstly, a method for scoring the nutritional quality index of agricultural products is provided, including the following steps:

[0008] Obtain multi-source nutritional quality parameters and corresponding product traceability information for the target agricultural product batch;

[0009] The multi-source nutritional quality parameters and the product traceability information are input into a knowledge graph containing rules in the field of agricultural product nutritional quality for logical consistency verification, and range verification is performed in combination with historical data to generate verified batch feature data.

[0010] A dynamic nutritional quality vector reflecting the current product status is generated based on the product traceability information.

[0011] Obtain the health concern vector corresponding to the health management modality selected by the user;

[0012] By performing forward mapping and reverse optimization operations on the dynamic nutritional quality vector and the health concern vector, personalized nutritional quality index and preservation strategy parameters are obtained.

[0013] A nutritional quality score report is generated based on the personalized nutritional quality index, the preservation strategy parameters, and the product traceability information obtained from the blockchain node.

[0014] Optionally, the step of inputting the multi-source nutritional quality parameters and the product traceability information into a knowledge graph containing rules in the field of agricultural product nutritional quality for logical consistency verification, and combining historical data for range verification, to generate verified batch feature data specifically includes:

[0015] The multi-source nutritional quality parameters and the product traceability information are correlated and reasoned in the knowledge graph containing rules in the field of agricultural product nutritional quality to obtain logical verification results.

[0016] The range of the multi-source nutritional quality parameters is compared with the range of historical nutritional quality parameters to obtain the range verification results;

[0017] The logical verification result and the range verification result are judged. If the logical verification result indicates that the data is valid but the range verification result indicates that the data is abnormal, the multi-source nutritional quality parameter is marked as data to be learned. After confirming its validity, the data to be learned is used as the verified batch feature data, and the historical nutritional quality parameter range is updated using the data to be learned.

[0018] Optionally, generating a dynamic nutritional quality vector reflecting the current product status based on the product traceability information includes:

[0019] The product traceability information is structured and parsed to extract features including origin attributes and planting methods, and the traceability feature vector is generated.

[0020] The source traceability feature vector is matched with a preset quality decay feature mapping table to retrieve the standard quality decay factor corresponding to the source traceability feature vector.

[0021] Obtain the current storage environment parameters, and use the verified batch feature data and the current storage environment parameters to predict the real-time changes in the nutritional composition of the target agricultural product batch, and output the dynamic nutritional quality vector.

[0022] Optionally, the step of performing forward mapping and inverse optimization operations on the dynamic nutritional quality vector and the health concern vector to obtain personalized nutritional quality index and preservation strategy parameters specifically includes:

[0023] The bidirectional optimization engine analyzes the current storage status of the core nutrient indicators in the health concern vector and the corresponding nutrients in the dynamic nutritional quality vector to generate nutrient decay results.

[0024] Based on the nutrient degradation results, the urgency coefficients of the core nutrient indicators that are about to degrade rapidly in the dynamic nutrient quality vector are corrected, and the corrected results are weighted and fused with the health concern vector to generate the personalized nutrient quality index.

[0025] Based on the nutrient degradation results and the target degradation model, a reverse simulation is performed to calculate the storage environment adjustment scheme required to optimize the degradation rate of the core nutrient indicators, and the preservation strategy parameters are generated.

[0026] Optionally, obtaining the health concern vector corresponding to the health management modality selected by the user specifically includes:

[0027] Provides a health management modality configuration interface with multiple chronic disease management options;

[0028] Receive user selections and customizations on the configuration interface and generate user preference data objects;

[0029] Based on the user preference data object, the corresponding template is retrieved from a modal database that stores basic weight templates and adjusted to generate the health attention vector.

[0030] Optionally, the step of performing a reverse simulation based on the nutrient degradation results and the target degradation model to calculate the storage environment adjustment scheme required to optimize the degradation rate of the core nutrient indicators and generate the preservation strategy parameters specifically includes:

[0031] The optimization objective is to reduce the decay rate of the core nutrient indicators, and the current storage environment parameters are used as initial variables to input the target decay model.

[0032] The storage environment parameters are iteratively simulated and calculated in the target attenuation model to find the parameter combination that satisfies the optimization objective;

[0033] The difference between the parameter combination and the current storage environment parameters is used as the output of the preservation strategy parameters.

[0034] Optionally, obtaining the current warehouse environment parameters specifically includes:

[0035] Environmental data is acquired in real time through a sensor network deployed in the warehouse space, and the acquired environmental data is appended with an acquisition timestamp;

[0036] A hash operation is performed on the data record containing the environmental data and the acquisition timestamp to generate an environmental data hash value;

[0037] The environmental data hash value is anchored to the blockchain, and the environmental data is used as the current storage environment parameter.

[0038] Optionally, after generating the nutritional quality score report, the method further includes:

[0039] The preservation strategy parameters are sent to the management system of the current storage stage of the target agricultural product batch;

[0040] Receive new storage environment parameters from the management system after adjustments based on the preservation strategy parameters;

[0041] Based on the new storage environment parameters and the target decay model, the dynamic nutritional quality vector and the subsequently generated personalized nutritional quality index are updated.

[0042] Optionally, the knowledge graph containing rules in the field of agricultural product nutritional quality is constructed in the following way:

[0043] Collect historical production data and agronomic knowledge related to agricultural product categories to form knowledge units;

[0044] Establish semantic relationships between the knowledge units and logical rules for describing the causal relationship between nutritional parameters and production conditions;

[0045] The actual quality change data of the target agricultural product batch during its historical circulation process is used as a feedback signal to reinforce and update the semantic association and the logical rules.

[0046] In a second aspect, an electronic device is provided, comprising: a processor and a memory; the memory is used to store a computer program, which, when executed by the processor, causes the electronic device to perform the agricultural product nutritional quality index scoring method described in the first aspect.

[0047] In one possible design, the electronic device described in the second aspect may further include a transceiver. This transceiver may be a transceiver circuit or an interface circuit. The transceiver can be used for communication between the electronic device described in the second aspect and other electronic devices.

[0048] In the embodiments of the present invention, the electronic device described in the second aspect may be a terminal, or a chip (system) or other component or assembly disposed in the terminal, or a system containing the terminal.

[0049] Thirdly, a computer-readable storage medium is provided, comprising: a computer program or instructions; when the computer program or instructions are run on a computer, the computer causes the computer to perform the agricultural product nutritional quality index scoring method described in the first aspect.

[0050] In summary, the above methods and systems have the following technical effects:

[0051] This invention enhances the credibility and accuracy of the underlying data upon which the scoring relies by constructing a dual-verification and self-learning data verification process. Through the logical verification and anomaly learning mechanism of knowledge graphs, it intelligently identifies and incorporates reasonable data changes brought about by new varieties and technologies, ensuring that the scoring system keeps pace with agricultural technology development. By introducing a personalized decay model driven by traceability features, it accurately predicts the real-time nutritional status of agricultural products during circulation, reflecting dynamic indicators of the true nutritional value at the time of consumer purchase. This provides consumers with more scientific and timely decision-making basis, greatly enhancing the commercial guidance significance of the scoring. It generates highly customized scores for consumers with different health goals and can even reverse-generate preservation strategy parameters that can guide warehousing operations, transforming information flow into control instructions for the physical supply chain. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating the method for scoring the nutritional quality index of agricultural products provided in an embodiment of the present invention. Detailed Implementation

[0053] The following will be combined with the appendix Figure 1 The technical solutions in this invention will be described below.

[0054] In this embodiment of the invention, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In specific implementation, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a correlation between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. Simultaneously, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0055] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be elaborated upon here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In specific implementation, the required indication method can be selected according to specific needs. This embodiment of the invention does not limit the selected indication method; therefore, the indication methods involved in this embodiment of the invention should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0056] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this embodiment of the invention. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0057] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This embodiment of the invention does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or electronic device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or electronic device. The type of memory can be any form of storage medium, and this embodiment of the invention does not limit this.

[0058] In the embodiments of this invention, "protocol" may refer to a protocol family in the field of communication, a standard protocol with a similar protocol family frame structure, or a related protocol applied to a future agricultural product nutritional quality index scoring method system. The embodiments of this invention do not specifically limit this.

[0059] In this embodiment of the invention, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0060] In the description of the embodiments of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this invention, words such as "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this invention should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0061] The network architecture and business scenarios described in the embodiments of this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0062] Figure 1 This is a flowchart illustrating the method provided in an embodiment of the present invention.

[0063] A method for scoring the nutritional quality index of agricultural products includes the following steps:

[0064] S201. Obtain multi-source nutritional quality parameters and corresponding product traceability information for the target agricultural product batch;

[0065] S202. Input the multi-source nutritional quality parameters and the product traceability information into a knowledge graph containing rules in the field of agricultural product nutritional quality for logical consistency verification, and combine historical data for range verification to generate verified batch feature data.

[0066] S203. Generate a dynamic nutritional quality vector reflecting the current product status based on the product traceability information;

[0067] S204. Obtain the health concern vector corresponding to the health management modality selected by the user;

[0068] S205. By performing forward mapping and reverse optimization operations on the dynamic nutritional quality vector and the health concern vector, personalized nutritional quality index and preservation strategy parameters are obtained.

[0069] S206. Based on the personalized nutritional quality index, the preservation strategy parameters, and the evidence of the product traceability information obtained from the blockchain node, a nutritional quality score report is generated.

[0070] Optionally, the step of inputting the multi-source nutritional quality parameters and the product traceability information into a knowledge graph containing rules in the field of agricultural product nutritional quality for logical consistency verification, and combining historical data for range verification, to generate verified batch feature data specifically includes:

[0071] The multi-source nutritional quality parameters and the product traceability information are correlated and reasoned in the knowledge graph containing rules in the field of agricultural product nutritional quality to obtain logical verification results.

[0072] The range of the multi-source nutritional quality parameters is compared with the range of historical nutritional quality parameters to obtain the range verification results;

[0073] The logical verification result and the range verification result are judged. If the logical verification result indicates that the data is valid but the range verification result indicates that the data is abnormal, the multi-source nutritional quality parameter is marked as data to be learned. After confirming its validity, the data to be learned is used as the verified batch feature data, and the historical nutritional quality parameter range is updated using the data to be learned.

[0074] Specifically: During logical consistency verification, multi-source nutritional quality parameters and traceability information are used as data pairs. Path queries are performed in the semantic network constructed by the graph database. The verification operation is based on the logical rules between nodes (such as the "pesticide residue threshold" attribute associated with the "organic certification" node). If the traceability information contains a specific certification, the system verifies whether its actual detection value is lower than the statutory threshold set in the graph and outputs a Boolean logical verification result.

[0075] When validating historical data ranges, retrieve historical averages based on product category. and standard deviation Calculate the parameter values ​​for the current batch. Standardized score S:

[0076]

[0077] Compare the standardized score S with a preset threshold T (e.g., 2.5-3.5). Then it passes;

[0078] If the logical verification passes but the range verification fails, the data is marked as data to be learned, and an independent verification is initiated (such as confirmation by a third-party interface). After the data is confirmed to be valid, μ and σ are updated with weights to accept reasonable abnormal data.

[0079] Optionally, generating a dynamic nutritional quality vector reflecting the current product status based on the product traceability information includes:

[0080] The product traceability information is structured and parsed to extract features including origin attributes and planting methods, and the traceability feature vector is generated.

[0081] The source traceability feature vector is matched with a preset quality decay feature mapping table to retrieve the standard quality decay factor corresponding to the source traceability feature vector.

[0082] Obtain the current storage environment parameters, and use the verified batch feature data and the current storage environment parameters to predict the real-time changes in the nutritional composition of the target agricultural product batch, and output the dynamic nutritional quality vector.

[0083] Specifically: static traceability information is transformed into input for a dynamic prediction model, and feature vectorization is used to perform structured analysis of the traceability information, extracting features such as origin, planting method, and harvesting time, and encoding them to generate traceability feature vector F;

[0084] For example, "Northeast Black Soil" is encoded as a specific vector component, "organic certification" is marked as 1, and the harvest date is converted to the number of days since a certain baseline date;

[0085] The system matches the source feature vector with the preset quality decay feature mapping table (i.e., the model library index).

[0086] The matching here is specifically achieved by calculating cosine similarity. The retrieved standard quality attenuation factor, in the preferred embodiment, is not just a single numerical value, but refers to a specific attenuation model and its initial parameter set, such as the model number. and its corresponding attenuation coefficient ;

[0087] By obtaining this factor, the model and parameters are determined, and the corresponding target decay model can be called to make predictions in combination with the current environmental parameters:

[0088] Calculate the vector F and compare it with the feature vectors of each model in the preset attenuation model library. cosine similarity C

[0089]

[0090] A quality decay feature mapping table is pre-defined. This table is actually a database that maps specific source features to specific decay rules. The vector F is matched with the feature items in the table. Specifically, vector similarity matching can be used to select the item with the highest similarity and the model with the largest C value as the target decay model.

[0091] Obtain current storage environment parameters anchored by blockchain hash. By combining the validated batch characteristic data V0 and the circulation time t, a dynamic nutritional quality vector is calculated using a time series function. :

[0092]

[0093] vector Output the predicted values ​​of each nutrient at the current moment;

[0094] Optionally, the step of performing forward mapping and inverse optimization operations on the dynamic nutritional quality vector and the health concern vector to obtain personalized nutritional quality index and preservation strategy parameters specifically includes:

[0095] The bidirectional optimization engine analyzes the current storage status of the core nutrient indicators in the health concern vector and the corresponding nutrients in the dynamic nutritional quality vector to generate nutrient decay results.

[0096] Based on the nutrient degradation results, the urgency coefficients of the core nutrient indicators that are about to degrade rapidly in the dynamic nutrient quality vector are corrected, and the corrected results are weighted and fused with the health concern vector to generate the personalized nutrient quality index.

[0097] Based on the nutrient degradation results and the target degradation model, a reverse simulation is performed to calculate the storage environment adjustment scheme required to optimize the degradation rate of the core nutrient indicators, and the preservation strategy parameters are generated.

[0098] Optionally, obtaining the health concern vector corresponding to the health management modality selected by the user specifically includes:

[0099] Provides a health management modality configuration interface with multiple chronic disease management options;

[0100] Receive user selections and customizations on the configuration interface and generate user preference data objects;

[0101] Based on the user preference data object, the corresponding template is retrieved from a modal database that stores basic weight templates and adjusted to generate the health attention vector.

[0102] Specifically: Obtain user health concern vector Engine Analysis decay rate in the short term Identify the core nutrients that accelerate decline. Multiply the corresponding amount of each core nutrient by a urgency factor. (K is positively correlated with the decay rate and takes a value > 1), thus obtaining the correction vector. Final Index To correct the weighted fusion of the vector and the health concern vector.

[0103] Optionally, the step of performing a reverse simulation based on the nutrient degradation results and the target degradation model to calculate the storage environment adjustment scheme required to optimize the degradation rate of the core nutrient indicators and generate the preservation strategy parameters specifically includes:

[0104] The optimization objective is to reduce the decay rate of the core nutrient indicators, and the current storage environment parameters are used as initial variables to input the target decay model.

[0105] The storage environment parameters are iteratively simulated and calculated in the target attenuation model to find the parameter combination that satisfies the optimization objective;

[0106] The difference between the parameter combination and the current storage environment parameters is used as the output of the preservation strategy parameters.

[0107] Optionally, obtaining the current warehouse environment parameters specifically includes:

[0108] Environmental data is acquired in real time through a sensor network deployed in the warehouse space, and the acquired environmental data is appended with an acquisition timestamp;

[0109] A hash operation is performed on the data record containing the environmental data and the acquisition timestamp to generate an environmental data hash value;

[0110] The environmental data hash value is anchored to the blockchain, and the environmental data is used as the current storage environment parameter.

[0111] Specifically: Establish an objective function aimed at minimizing the rate of decline of core nutrients. Gradient descent method was used to analyze the parameters of the storage environment. Perform iterative solution:

[0112]

[0113] in Let ∇J be the learning rate and ∇J be the gradient vector. The optimal parameter combination is obtained when the convergence condition is met. ;

[0114] Output preservation strategy parameters .

[0115] Optionally, after generating the nutritional quality score report, the method further includes:

[0116] The preservation strategy parameters are sent to the management system of the current storage stage of the target agricultural product batch;

[0117] Receive new storage environment parameters from the management system after adjustments based on the preservation strategy parameters;

[0118] Based on the new storage environment parameters and the target decay model, the dynamic nutritional quality vector and the subsequently generated personalized nutritional quality index are updated.

[0119] Optionally, the knowledge graph containing rules in the field of agricultural product nutritional quality is constructed in the following way:

[0120] Collect historical production data and agronomic knowledge related to agricultural product categories to form knowledge units;

[0121] Establish semantic relationships between the knowledge units and logical rules for describing the causal relationship between nutritional parameters and production conditions;

[0122] The actual quality change data of the target agricultural product batch during its historical circulation process is used as a feedback signal to reinforce and update the semantic association and the logical rules.

[0123] The electronic device provided in this embodiment of the invention, exemplarily, can be a network device, or a chip (system) or other component or assembly that can be disposed in a network device. The electronic device may include a processor. Optionally, the electronic device may also include a memory and / or a transceiver. The processor is coupled to the memory and transceiver, for example, by means of a communication bus connection.

[0124] The following is a detailed introduction to the various components of the electronic device:

[0125] In this context, the processor is the control center of the electronic device. It can be a single processor or a collective term for multiple processing elements. For example, a processor can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0126] Alternatively, the processor can perform various functions of the electronic device, such as the methods described above, by running or executing software programs stored in memory and by calling data stored in memory.

[0127] In a specific implementation, as one example, the processor may include one or more CPUs, such as CPU0 and CPU1.

[0128] In a specific implementation, as one example, the electronic device may also include multiple processors. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0129] The memory is used to store the software program that executes the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0130] Optionally, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory can be integrated with the processor or exist independently and coupled to the processor through an interface circuit of an electronic device; the embodiments of the present invention do not specifically limit this.

[0131] A transceiver is used for communication with other electronic devices. For example, if the electronic device is a terminal, the transceiver can be used to communicate with a network device or with another terminal device. Similarly, if the electronic device is a network device, the transceiver can be used to communicate with a terminal or with another network device.

[0132] Optionally, the transceiver may include a receiver and a transmitter. The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0133] Optionally, the transceiver can be integrated with the processor or exist independently and coupled to the processor through the interface circuit of the electronic device. This embodiment of the invention does not specifically limit this.

[0134] It is understandable that the structure of an electronic device does not constitute a limitation on the electronic device. An actual electronic device may include more or fewer components, or combine certain components, or have different component arrangements.

[0135] Furthermore, the technical effects of the electronic device can be referred to the technical effects of the method described in the above method embodiments, and will not be repeated here.

[0136] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0137] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DRRAM).

[0138] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0139] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0140] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0141] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0142] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0143] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0144] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0145] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0146] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0147] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0148] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for scoring the nutritional quality index of agricultural products, characterized in that, Includes the following steps: Obtain multi-source nutritional quality parameters and corresponding product traceability information for the target agricultural product batch; The multi-source nutritional quality parameters and the product traceability information are input into a knowledge graph containing rules in the field of agricultural product nutritional quality for logical consistency verification, and range verification is performed in combination with historical data to generate verified batch feature data. A dynamic nutritional quality vector reflecting the current product status is generated based on the product traceability information. Obtain the health concern vector corresponding to the health management modality selected by the user; By performing forward mapping and reverse optimization operations on the dynamic nutritional quality vector and the health concern vector, personalized nutritional quality index and preservation strategy parameters are obtained. A nutritional quality score report is generated based on the personalized nutritional quality index, the preservation strategy parameters, and the product traceability information obtained from the blockchain node.

2. The method for scoring the nutritional quality index of agricultural products according to claim 1, characterized in that, The process of inputting the multi-source nutritional quality parameters and the product traceability information into a knowledge graph containing rules in the field of agricultural product nutritional quality for logical consistency verification, and combining historical data for range verification, to generate verified batch feature data specifically includes: The multi-source nutritional quality parameters and the product traceability information are correlated and reasoned in the knowledge graph containing rules in the field of agricultural product nutritional quality to obtain logical verification results. The range of the multi-source nutritional quality parameters is compared with the range of historical nutritional quality parameters to obtain the range verification results; The logical verification result and the range verification result are judged. If the logical verification result indicates that the data is valid but the range verification result indicates that the data is abnormal, the multi-source nutritional quality parameter is marked as data to be learned. After confirming its validity, the data to be learned is used as the verified batch feature data, and the historical nutritional quality parameter range is updated using the data to be learned.

3. The method for scoring the nutritional quality index of agricultural products according to claim 2, characterized in that, The generation of a dynamic nutritional quality vector reflecting the current product status based on the product traceability information includes: The product traceability information is structured and parsed to extract features including origin attributes and planting methods, and the traceability feature vector is generated. The traceability feature vector is matched with a preset quality decay feature mapping table to retrieve the standard quality decay factor corresponding to the traceability feature vector. Obtain the current storage environment parameters, and use the verified batch feature data and the current storage environment parameters to predict the real-time changes in the nutritional composition of the target agricultural product batch, and output the dynamic nutritional quality vector.

4. The method for scoring the nutritional quality index of agricultural products according to claim 3, characterized in that, The process of performing forward mapping and inverse optimization operations on the dynamic nutritional quality vector and the health concern vector to obtain personalized nutritional quality index and preservation strategy parameters specifically includes: The bidirectional optimization engine analyzes the current storage status of the core nutrient indicators in the health concern vector and the corresponding nutrients in the dynamic nutritional quality vector to generate nutrient decay results. Based on the nutrient degradation results, the urgency coefficients of the core nutrient indicators that are about to degrade rapidly in the dynamic nutrient quality vector are corrected, and the corrected results are weighted and fused with the health concern vector to generate the personalized nutrient quality index. Based on the nutrient degradation results and the target degradation model, a reverse simulation is performed to calculate the storage environment adjustment scheme required to optimize the degradation rate of the core nutrient indicators, and the preservation strategy parameters are generated.

5. The method for scoring the nutritional quality index of agricultural products according to claim 1, characterized in that, The acquisition of the health concern vector corresponding to the user's selected health management modality specifically includes: Provides a health management modality configuration interface with multiple chronic disease management options; Receive user selections and customizations on the configuration interface and generate user preference data objects; Based on the user preference data object, the corresponding template is retrieved from a modal database that stores basic weight templates and adjusted to generate the health attention vector.

6. The method for scoring the nutritional quality index of agricultural products according to claim 4, characterized in that, The step involves performing a reverse simulation based on the nutrient degradation results and the target degradation model to calculate the storage environment adjustment plan required to optimize the degradation rate of the core nutrient indicators, and generating the preservation strategy parameters, specifically including: The optimization objective is to reduce the decay rate of the core nutrient indicators, and the current storage environment parameters are used as initial variables to input the target decay model. The storage environment parameters are iteratively simulated and calculated in the target attenuation model to find the parameter combination that satisfies the optimization objective; The difference between the parameter combination and the current storage environment parameters is used as the output of the preservation strategy parameters.

7. The method for scoring the nutritional quality index of agricultural products according to claim 3, characterized in that, The acquisition of current warehouse environment parameters specifically includes: Environmental data is acquired in real time through a sensor network deployed in the warehouse space, and the acquired environmental data is appended with an acquisition timestamp; A hash operation is performed on the data record containing the environmental data and the acquisition timestamp to generate an environmental data hash value; The environmental data hash value is anchored to the blockchain, and the environmental data is used as the current storage environment parameter.

8. The method for scoring the nutritional quality index of agricultural products according to claim 7, characterized in that, After generating the nutritional quality score report, the following is also included: The preservation strategy parameters are sent to the management system of the current storage stage of the target agricultural product batch; Receive new storage environment parameters from the management system after adjustments based on the preservation strategy parameters; Based on the new storage environment parameters and the target decay model, the dynamic nutritional quality vector and the subsequently generated personalized nutritional quality index are updated.

9. The method for scoring the nutritional quality index of agricultural products according to claim 1, characterized in that, The knowledge graph containing rules in the field of agricultural product nutritional quality is constructed in the following way: Collect historical production data and agronomic knowledge related to agricultural product categories to form knowledge units; Establish semantic relationships between the knowledge units and logical rules for describing the causal relationship between nutritional parameters and production conditions; The actual quality change data of the target agricultural product batch during its historical circulation process is used as a feedback signal to reinforce and update the semantic association and the logical rules.

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