Tracing method and system for food safety and storage medium

Abstract

The invention relates to the technical field of food traceability data processing, and discloses a traceability method and system for food safety and a storage medium. The method comprises the following steps: performing double random selection on food raw material information to generate differential trust identifiers; constructing an asymmetric trust transfer protocol to verify information between nodes, and updating a trust identifier; forming a quality chain and an enhanced trust identifier through three-source cross validation control point information; and in combination with consumer preferences, the scene is reconstructed to generate a food traceability visualization result with multi-level information superposition. The problem that information transmission between traceability chain nodes lacks an effective trust mechanism is solved, and the problems that traceability information verification excessively depends on a single source and a display mode lacks individuation are solved.

Description

Technical Field

[0001] The present application relates to the technical field of food traceability data processing, and in particular to a traceability method, system and storage medium for food safety. Background Art

[0002] Existing food safety traceability technologies primarily focus on establishing information records and connections across the food supply chain. This includes labeling food with physical identification technologies like barcodes and RFID tags, and ensuring data immutability through distributed ledger technologies like blockchain. These technologies can, to a certain extent, trace food from farm to table, helping consumers understand the origin, production process, and quality and safety of their food. At the same time, traditional traceability systems typically rely on centralized databases to store food information, which is then verified and endorsed by regulatory agencies or third-party certification bodies to ensure the reliability of traceability information.

[0003] However, existing food safety traceability technologies face numerous limitations. First, traditional traceability labels are easily copied or tampered with, failing to effectively prevent data falsification. Second, the information transmission between nodes in the traceability system lacks an effective trust mechanism, making it difficult to ensure the integrity and authenticity of data during transmission. Third, the verification of traceability information relies too heavily on a single source, making it difficult to fully guarantee data authenticity. Fourth, the presentation of traceability information is monotonous and lacks personalization, making it impossible to provide differentiated information services tailored to the needs of different consumers, reducing the practicality of traceability information and user experience. Summary of the Invention

[0004] The present application provides a traceability method, system and storage medium for food safety, which are used to solve the problem of lack of effective trust mechanism in information transmission between traceability chain nodes, and the problem that traceability information verification is too dependent on a single source and the display method lacks personalization.

[0005] In the first aspect, the present application provides a traceability method for food safety, which includes: performing double random selection processing on the basic information tuple, physical feature tuple and biometric feature vector of food raw materials to obtain a differentiated trust identifier; constructing an asymmetric trust transfer protocol based on the differentiated trust identifier, performing multi-dimensional verification processing on the information transmission between traceability chain nodes, and obtaining an updated differentiated trust identifier; based on the updated differentiated trust identifier, performing three-source cross-validation processing on the food key control point information, generating a quality chain and combining it with the updated differentiated trust identifier to obtain an enhanced trust identifier; based on the enhanced trust identifier and the consumer preference weight vector, performing scene reconstruction processing on the food quality data to generate a food history visualization result with multi-level information superposition.

[0006] In a second aspect, the present application provides a food safety traceability system, the food safety traceability system comprising: A processing module is used to perform double random selection processing on the basic information tuple, physical feature tuple and biometric feature vector of the food raw materials to obtain a differentiated trust mark; A construction module is used to construct an asymmetric trust transfer protocol based on the differentiated trust identifier, perform multi-dimensional verification processing on the information transmission between the traceability chain nodes, and obtain an updated differentiated trust identifier; A verification module, configured to perform a three-source cross-validation process on the food critical control point information based on the updated differentiated trust identifier, generate a quality chain, and combine it with the updated differentiated trust identifier to obtain an enhanced trust identifier; The reconstruction module is used to perform scene reconstruction processing on the food quality data based on the enhanced trust identifier and the consumer preference weight vector, and generate a food history visualization result with multi-level information superposition.

[0007] In a third aspect, a traceability device for food safety is provided, comprising: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the traceability device for food safety executes the above-mentioned traceability method for food safety.

[0008] In a fourth aspect, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium, which, when executed on a computer, enables the computer to execute the above-mentioned traceability method for food safety.

[0009] In the technical solution provided by the present application, a differentiated trust mark is obtained by performing double random selection processing on the basic information tuple, physical feature tuple and biometric feature vector of the food raw materials, which effectively solves the problem that the traditional traceability mark is easy to be copied or tampered with, and significantly improves the anti-counterfeiting and uniqueness of the food identity mark. At the same time, by collecting multi-dimensional food feature data to form a comprehensive digital fingerprint, each batch of food has a unique identification feature, providing a reliable identity basis for subsequent traceability; an asymmetric trust transfer protocol is constructed based on the differentiated trust mark, and the information transmission between the traceability chain nodes is multi-dimensionally verified to obtain an updated differentiated trust mark, and a dynamic verification mechanism based on node trust is established, which solves the trust deficiency problem existing in the information transmission link of the traditional traceability system, ensures the integrity and authenticity of the data during the transmission process, and at the same time, by distinguishing the trust basis values of different types of nodes, a differentiated trust transfer rule is constructed, which is more in line with the different nodes in the actual food supply chain. Trust characteristics of key control points; based on the updated differentiated trust logo, the information of key control points of food is cross-validated from three sources to generate a quality chain and combine it with the updated differentiated trust logo to obtain an enhanced trust logo. The innovative introduction of a multi-verification source cross-validation mechanism solves the data reliability problem caused by the traditional traceability system's over-reliance on a single information source. Through the mutual verification of independent verification sources, automatic verification sources and third-party verification sources, the credibility of traceability information is greatly improved. At the same time, the verification results of each control point are connected into a quality chain to form a complete food quality control process record; according to the enhanced trust logo and consumer preference weight vector, the food quality data is reconstructed to generate a food process visualization result with multi-level information superposition, realizing the personalized presentation of traceability information, solving the problem of single information display and lack of pertinence in the traditional traceability system. By combining information screening and reconstruction with consumers' personal preferences, the practicality of traceability information and user experience are greatly improved.

[0010] This application adopts an improved trust hash algorithm and dynamic trust adjustment mechanism in the differentiated trust mark generation link, applies similarity calculation and weighted fusion algorithm in information cross-validation, uses association rule mining, cluster analysis and Bayesian fusion algorithm in consumer preference analysis, and introduces a food science-based attenuation model in quality prediction. The precise application of these algorithms fully considers the specific functional requirements and application scenario characteristics of food safety traceability. Through the intelligent processing of food feature data, node trust data, multi-source verification data and consumer behavior data, the traceability process is automated, precise and personalized, which injects new technological vitality into the field of food safety traceability, significantly improves the reliability, security and user-friendliness of the traceability system, and provides consumers with more transparent and credible food safety guarantees. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 This is a schematic diagram of an embodiment of a food safety traceability method in an embodiment of the present application; Figure 2 This is a schematic diagram of an embodiment of a traceability system for food safety in an embodiment of the present application; Figure 3 It is a schematic block diagram of the structure of a traceability device for food safety in an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a traceability method, system and storage medium for food safety. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0014] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In one embodiment of the present application, a method for tracing food safety includes: Step S101: Perform double random selection on the basic information tuple, physical feature tuple, and biometric feature vector of the food raw material to obtain a differentiated trust identifier; Step S102: construct an asymmetric trust transfer protocol based on the differentiated trust identifier, perform multi-dimensional verification processing on the information transmission between the traceability chain nodes, and obtain an updated differentiated trust identifier; Step S103: Based on the updated differentiated trust identifier, perform three-source cross-validation on the food critical control point information, generate a quality chain, and combine it with the updated differentiated trust identifier to obtain an enhanced trust identifier; Step S104: Based on the enhanced trust identifier and the consumer preference weight vector, the food quality data is subjected to scene reconstruction processing to generate a food history visualization result with multi-level information superposition.

[0015] It is understandable that the execution subject of this application can be a traceability system for food safety, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0016] Specifically, food ingredients undergo a double random selection process. This involves collecting basic information tuples (variety, origin coordinates, harvest time, batch number, and responsible person information) and physical characteristic tuples (weight distribution, shape index, color distribution, hardness, and category-specific characteristic parameters). Biometric fingerprint features are then extracted to form a biometric feature vector. This double random selection process uses a first-level random algorithm to select n samples from each batch. A second-level random algorithm then selects m feature points from each sample to form a sample feature matrix. This double randomness ensures representativeness and unpredictability in the sampling, effectively preventing data falsification caused by artificial sample selection. This information is combined with a timestamp and a random salt value to form an initial trust data packet. A differentiated trust identifier is then calculated using an improved trust hash algorithm. Based on this differentiated trust identifier, an asymmetric trust transfer protocol is constructed to provide multi-dimensional verification of information transmission between traceability chain nodes. The traceability chain nodes are first categorized (breeding, primary processing, deep processing, logistics and warehousing, sales, and regulatory nodes), and each node is assigned a triple identity (explicit identifier, implicit identifier, and trust identifier). Node trust coefficients are calculated based on node type and historical behavior. Supervisory nodes have the highest trust base value, followed by breeding, intensive processing, primary processing, logistics and warehousing, and sales nodes. When food is transferred between nodes, the source node combines its differentiated trust identifier with its explicit identifier, a timestamp, and the target node type to form a transfer request, which is signed with its implicit identifier. After identity verification and trust threshold determination, the target node verifies based on its trust acceptance coefficient and submits a receipt confirmation, ultimately generating an updated differentiated trust identifier. Based on the updated differentiated trust identifier, three-source cross-validation of food critical control point information is performed. Customized lists of critical control points are established for different food categories, such as raw material acceptance points, curing points, heating points, cooling points, metal detection points, and packaging points for meat products. An innovative multi-source cross-validation mechanism is introduced, gathering information from self-verification sources recorded by operators, automatic verification sources from automated monitoring equipment, and third-party verification sources from independent third parties to form information triplets. Three similarity values are calculated using pairwise similarity. Weighted summation is then performed based on food type and processing steps to generate an information consistency score. The verification results, consistency scores, quality scores and other information of each control point are serialized to form a quality chain, and combined with the updated differentiated trust mark to generate an enhanced trust mark.

[0017] Based on the enhanced trust mark and consumer preference weight vector, the food quality data is reconstructed and processed in scenarios. A multidimensional quality evaluation system with four dimensions, including basic safety, nutritional value, sensory experience, and functional characteristics, is constructed to form a food quality vector. The quality preferences and historical query behaviors set by consumers are analyzed, and group intelligence optimization is performed based on attributes such as region and age group to obtain the consumer preference weight vector. A personalized quality score is obtained through dot product operation, and the current quality status is predicted based on the quality decay model and environmental parameters. The six key scenarios of food from planting and breeding to consumption are parametrically reconstructed, and then the basic layer, parameter layer, evaluation layer, and knowledge layer information are dynamically superimposed to finally generate a food process visualization result with multi-level information superposition.

[0018] In a specific embodiment, the process of executing step S101 may specifically include the following steps: The food raw material varieties, origin coordinates, harvesting time, batch number and person in charge information are collected and processed to obtain basic information tuples; The weight distribution, shape index, color distribution, hardness and specific category characteristic parameters of food raw materials are collected and processed to obtain physical feature tuples; Extract biometric fingerprint features from food raw materials and perform feature dimensionality reduction and digitization to obtain biometric feature vectors; Select n samples from each batch of food using the first-level random algorithm, and select m feature points from each selected sample using the second-level random algorithm to form a double random guarantee and obtain the sample feature matrix; Combine the basic information tuple, physical feature tuple, biometric feature vector, timestamp and random salt value in a preset format to obtain an initial trusted data packet; The improved trust hash algorithm is applied to the initial trust data packet for calculation, and combined with the dynamic trust adjustment mechanism, a differentiated trust identifier is obtained.

[0019] Specifically, basic information on food ingredients is collected and processed, including variety identification, origin coordinates, harvesting time, batch number assignment, and responsible person registration. Variety identification refers to the identification and classification of food ingredient types and strains. Origin coordinates are obtained through GPS positioning to obtain precise latitude and longitude data. Harvesting time is accurate to the day or even the hour. Batch numbers are generated using unique identification codes based on pre-set rules. Responsible person information includes identity codes and responsibilities. After standardization, this data forms basic information tuples, providing essential attribute data for food traceability. Physical characteristics of food ingredients are collected and processed. Weight distribution is determined through multi-point weighing to obtain weight statistics within a sample. Shape index quantifies shape characteristics by calculating the aspect ratio, roundness, or other morphological parameters. Color distribution utilizes computer vision technology to obtain spatial color distribution data. Hardness is measured using a professional hardness tester. Specific category characteristic parameters are collected for different food types, such as the sugar content and acidity of fruit and the muscle fiber orientation of meat. These physical parameters are standardized and normalized to form physical characteristic tuples, describing the physical properties of food ingredients.

[0020] Extracting biometric fingerprints from food ingredients involves obtaining molecular or cellular features that uniquely characterize the biological properties of the food ingredient. Common techniques include DNA barcoding, protein profiling, or spectral analysis. Due to the high dimensionality and redundancy of raw biometric fingerprint data, feature dimensionality reduction and digitization are necessary. Feature dimensionality reduction uses algorithms such as principal component analysis (PCA) or t-SNE to map high-dimensional data into a low-dimensional space. Digitalization converts the reduced features into fixed-length binary or numerical vectors. Ultimately, the generated biometric feature vector provides a unique molecular identifier for the food. A double random selection process is implemented. The first stage uses a Monte Carlo method to randomly select n samples from each batch of food, ensuring that the samples cover the entire batch distribution. The second stage randomly determines the sampling locations of m feature points on each selected sample to avoid data bias that may be caused by human selection. This double randomness ensures the representativeness of the sampling points while increasing the difficulty of counterfeiting. This two-stage randomization process generates an n×m-dimensional sample feature matrix, which records the typical feature distribution of the batch of food.

[0021] The previously generated basic information tuple, physical feature tuple, and biometric feature vector are then combined with the current timestamp and randomly generated salt value according to a predetermined format to form the initial trust data packet. The timestamp accurately records the time of data generation, and the random salt value increases the uniqueness and unpredictability of the data, preventing collision attacks. The initial trust data packet contains complete characteristic information of the food ingredients, providing the raw data for subsequent trust calculations.

[0022] The initial trust data packet is calculated using an improved trust hash algorithm. This algorithm, based on SHA-256, incorporates a weighting mechanism unique to the food industry, assigning different importance weights to different types of information. A dynamic trust adjustment mechanism automatically adjusts the trust value based on a time decay function to reflect the shelf life of the food; increases the trust enhancement factor based on the number of verifications; and triggers a trust penalty mechanism based on abnormalities. These calculations generate a final, differentiated trust identity, which serves as the unique identifier for the food batch in the traceability system.

[0023] In a specific embodiment, the process of executing step S102 may specifically include the following steps: Classify the nodes on the traceability chain and assign triple identity identification to obtain the explicit identification, implicit identification and trust identification of each node; Calculate the trust base value based on the node type, and calculate the trust dynamic adjustment coefficient based on the node's historical transaction records to obtain the node trust coefficient; The differential trust identifier, the explicit identifier of the source node, the timestamp, and the target node type are combined to form a transfer request, which is then signed using the implicit identifier of the source node to obtain a signed transfer request. Perform source node identity validation and trust threshold judgment on the signed transfer request to obtain a trusted transfer credential; Verify the trust transfer credential based on the trust acceptance coefficient of the target node, and submit the receipt confirmation information to the trust center to obtain the receipt confirmation data; The differentiated trust identifier, the trust transfer credential and the received confirmation data are hashed to obtain an updated differentiated trust identifier.

[0024] Specifically, nodes are divided into six types according to their functions and locations: breeding nodes, primary processing nodes, intensive processing nodes, logistics and warehousing nodes, sales nodes, and regulatory nodes. Each node is assigned a triple identity: the explicit identifier is publicly visible identification information, such as the company name, unified social credit code, etc., which is encoded using standard coding rules; the implicit identifier is a private identifier used within the system, created by combining the node type code with a high-intensity random number generation algorithm to ensure uniqueness and unpredictability; the trust identifier reflects the credibility of the node and is generated by quantifying the node's reputation in the food safety traceability system. When calculating the trust base value based on node type, a hierarchical assignment strategy is adopted, assigning different basic scores according to the importance of different node types in the food safety responsibility chain. The regulatory node receives the highest basic score (e.g., 100 points), followed by the breeding / planting node (90 points), intensive processing node (85 points), primary processing node (80 points), logistics and warehousing node (75 points), and sales node (70 points). At the same time, the trust dynamic adjustment coefficient is calculated based on the node's historical transaction records, and indicators such as the number of transactions, transaction size, anomaly rate and complaint rate are analyzed. The behavioral score is obtained through a weighted calculation method, and then the score is combined with the trust base value to calculate the final node trust coefficient.

[0025] When food is transferred between nodes in the traceability chain, the source node combines the differentiated trust identifier (i.e., the unique food identifier generated in the previous step), the source node's explicit identifier, the current timestamp, and the target node type into a transfer request packet according to a predetermined format. The source node then digitally signs this packet using its implicit identifier (equivalent to its private key), forming a cryptographically secure signed transfer request that cannot be forged or tampered with.

[0026] After receiving the signed transfer request, the trust center performs two verification steps: first, verifying the validity of the source node's identity by decrypting the signature and comparing it with the source node's public information to confirm that the request is indeed from the claimed source node; second, determining the trust threshold by comparing the source node's trust factor with the minimum trust threshold required for this type of transaction. The transaction is only allowed to proceed if the trust factor exceeds the threshold. Once verification is successful, the trust center generates a trust transfer certificate containing the transaction details and verification results.

[0027] The target node verifies the received trust transfer credential based on its own trust acceptance policy, including checking the integrity, validity period, and compatibility with its own business rules. After verification, the target node submits a receipt confirmation message to the trust center, confirming that it has received the batch of food and recognized the relevant trust information. Based on this, the trust center generates receipt confirmation data containing the transaction completion status. The three parts of information, the original differentiated trust identity, the trust transfer credential, and the receipt confirmation data, are hashed and processed. A new hash value is generated using a secure hash algorithm (such as SHA-256). This hash value is combined with the original differentiated trust identity to form an updated differentiated trust identity, recording that the batch of food has completed a valid inter-node transfer.

[0028] In a specific embodiment, the process of executing step S103 may specifically include the following steps: The nodes on the traceability chain are classified into breeding nodes, primary processing nodes, deep processing nodes, logistics and warehousing nodes, sales nodes and supervision nodes, and six key node types are obtained; Encode the public information of each node to obtain the node explicit identification; Perform unique encoding on each node based on the node type and random number generation algorithm to obtain the node implicit identification; Set the trust base score according to the node type, assign the highest base score to the supervision node, and then give it to the planting / breeding node, deep processing node, primary processing node, logistics and warehousing node, and sales node, to obtain the node type trust base table; Perform statistical analysis on the node's historical behavior data, including transaction times, transaction size, anomaly rate, and complaint rate, to obtain a behavior score; The corresponding scores in the node type trust base table are weighted and calculated with the behavior score to obtain the node trust identifier.

[0029] Specifically, nodes in the traceability chain are categorized into six key node types. Planting and breeding nodes refer to entities involved in the production of food raw materials, such as those responsible for crop cultivation or animal husbandry. Primary processing nodes cover the initial processing of raw materials, including cleaning, sorting, and cutting. Advanced processing nodes cover further processing, encompassing key aspects such as formulation and craftsmanship. Logistics and warehousing nodes are responsible for food transportation and storage. Sales nodes refer to entities directly selling food to consumers. Regulatory nodes refer to government regulatory agencies or third-party certification bodies. By analyzing the functional positioning and business scope of each participant, their type is determined and assigned a corresponding type code, such as "01" for the planting and breeding node, "02" for the primary processing node, and so on. Public information for each node is encoded to generate an explicit node identifier. Public information includes publicly visible information such as company name, unified social credit code, business license number, and geographic location. Using standardized encoding rules, the company name is first converted into a pinyin abbreviation, combined with the last six digits of the unified social credit code, and then the first four digits of the administrative region code of the geographic location information are added to form a fixed-length string. This encoding method ensures the uniqueness and readability of the explicit identifier, making it easy for all parties to identify and reference it.

[0030] Each node is uniquely encoded based on its node type and random number generation algorithm to generate a node implicit identifier. The node type code is first prefixed, and a cryptographically secure random number generator is used to create a random string of sufficient length. This random string uses a combination of letters, numbers, and special characters to ensure a sufficiently high entropy value. This combined string is then one-way hashed, and the first 16 bits of the hash value are extracted and concatenated with the node type code to form the node's implicit identifier. This implicit identifier is used only internally within the system as the basis for digital signatures and authentication of nodes. A trust base score is assigned based on node type, and a node trust base table is established for each node type. Supervisory nodes are assigned the highest base score of 100 points due to their authority and impartiality; planting / breeding nodes, as food sources, are assigned 90 points; deep processing nodes are assigned 85 points due to their process complexity and safety risks; primary processing nodes are assigned 80 points; logistics and warehousing nodes are assigned 75 points; and sales nodes are assigned 70 points. This differentiated scoring system reflects the importance and trust weight of different node types in food safety assurance, forming a preliminary node trust base table.

[0031] Statistical analysis is performed on historical node behavior data to calculate a behavior score. First, historical node transaction data is collected, extracting four key metrics: transaction count (reflecting activity), transaction size (reflecting business volume), anomaly rate (reflecting problem frequency), and complaint rate (reflecting consumer satisfaction). Transaction count is logarithmically normalized, converting the raw value to a 0-10 scale. Transaction size is normalized to the industry average, converting the raw value to a 0-10 scale. Anomaly rate and complaint rate are reverse-scored, with lower values receiving higher scores, converting the scale to a 0-10 scale. The four scores are weighted: transaction count with a weight of 0.2, transaction size with a weight of 0.2, anomaly rate with a weight of 0.3, and complaint rate with a weight of 0.3, to calculate a comprehensive behavior score. The corresponding scores in the node type trust base table are weighted with the behavior score to obtain the node trust identifier. A weighted average method is used, with a weight of 0.7 for the base score and a weight of 0.3 for the behavior score, to calculate the comprehensive trust score. This score is converted into a unified trust mark, starting with the letter "T" followed by a numerical value, such as "T92.5" indicating a trust score of 92.5. This trust mark dynamically reflects the trustworthiness of a node and guides the transfer of trust between nodes during the food traceability process.

[0032] In a specific embodiment, the process of executing step S104 may specifically include the following steps: Establish a personalized critical control point list based on food categories, set critical limits and monitoring methods for each critical control point, and obtain a critical control point system; The autonomous verification source information is formed from the data recorded by the operator, the automatic verification source information is formed from the data generated by the automated monitoring equipment, and the third-party verification source information is formed from the verification data provided by an independent third party, thereby obtaining an information triplet; Based on the three types of verification source information in the information triplet, a pairwise similarity function is calculated to obtain three similarity values; The three similarity values are weighted and summed according to the weight coefficients determined by food type and processing stage to obtain the information consistency score; The verification results, consistency scores, quality scores, timestamps and operator identification of each critical control point are serialized to obtain the quality chain; The updated differentiated trust mark and the quality chain are hashed to obtain an enhanced trust mark.

[0033] Specifically, a personalized list of critical control points is established based on the food category. Critical control points refer to operational steps that can control, eliminate or reduce food safety hazards to an acceptable level. For different food categories, their unique hazard factors are identified, such as microbial contamination for meat products, pathogenic bacteria and antibiotic residues for dairy products, and parasites and heavy metal contamination for aquatic products. For each critical control point, clear critical limits and monitoring methods are set. Critical limits are numerical boundaries that distinguish between acceptable and unacceptable, such as the core temperature of the heating control point of meat products must not be lower than 75°C and last for at least 15 minutes; monitoring methods include measuring tools, measurement frequency, measurement location and recording methods, such as using a calibrated thermometer to measure the center temperature of the product for each batch. By systematically organizing this information, a food-specific critical control point system is formed. Critical control point information is collected from three different sources to form information triplets. Self-verified source information comes from data recorded by operators, such as processing parameters and sensory evaluations entered on record sheets. Automatically verified source information comes from data generated by automated monitoring equipment, such as parameters automatically recorded by temperature sensors, humidity monitors, and metal detectors. Third-party verified source information comes from verification data provided by an independent third party, such as test reports from official inspection agencies or third-party laboratories. The three types of information for each critical control point are organized into an information triple structure to ensure the diversity and independence of data sources.

[0034] Based on the three types of verification source information in the information triplet, a pairwise similarity function is calculated to obtain three similarity values. The similarity function is used to quantify the degree of consistency between information from different sources, using methods such as cosine similarity or Euclidean distance. First, the similarity Sim(Is, Ia) between the autonomous verification source and the automatic verification source is calculated to reflect the consistency between manual records and equipment measurements; secondly, the similarity Sim(Is, It) between the autonomous verification source and the third-party verification source is calculated to reflect the consistency between internal records and external verification; finally, the similarity Sim(Ia, It) between the automatic verification source and the third-party verification source is calculated to reflect the consistency between equipment measurement and professional testing. The closer the similarity value is to 1, the more consistent the information is, and the closer it is to 0, the greater the difference. The three similarity values are weighted and summed according to the weight coefficients determined by food type and processing link to obtain the information consistency score. Weight coefficients are dynamically determined based on the characteristics of the food type and processing stage. For example, for cooked food processing, a higher weight is assigned to the similarity between the automated and third-party verified sources at the heating point (CCP3); and a higher weight is assigned to the similarity between the self-verified and third-party verified sources at the packaging point (CCP6). Weights w1, w2, and w3 are set such that w1 + w2 + w3 = 1. The three similarity values are multiplied by their corresponding weights and then summed to obtain the final information consistency score, which reflects the reliability of the CCP information.

[0035] The verification results, consistency score, quality score, timestamp, and operator ID for each critical control point are serialized to form a quality chain. The verification result is a Boolean value indicating whether the control point meets the standard requirements; the consistency score is the information source consistency score calculated in the previous step; the quality score is a quantitative assessment of the actual product quality, calculated based on the deviation between the measured parameters and the ideal value; the timestamp records the exact time the data was generated; and the operator ID is associated with the identity information of the person performing the operation. This information is organized according to a predetermined format to form a data structure containing complete traceability information. This data is then linked in chronological order to the critical control points, forming a quality chain that reflects the quality control status of the entire food process.

[0036] The updated differentiated trust mark and the quality chain are hashed to create an enhanced trust mark. The differentiated trust mark, derived from the previous step, represents the identity of the food batch; the quality chain contains quality control information for each critical control point for that batch. These two are concatenated according to the agreed format and then hashed using the secure hashing algorithm SHA-256 to generate a fixed-length hash value. This hash value is combined with the original differentiated trust mark to form an enhanced trust mark, which retains the unique identity of the food while also verifying the integrity of the quality control process.

[0037] In a specific embodiment, the process of executing step S105 may specifically include the following steps: Construct a multi-dimensional evaluation system for food quality, including basic safety, nutritional value, sensory experience, and functional characteristics, to obtain a food quality vector; Analyze the quality dimension preferences and historical query behaviors set by consumers, and perform group intelligent optimization based on their region and age group to obtain the consumer preference weight vector; Perform dot product operation on the food quality vector and the consumer preference weight vector to obtain a personalized quality score; Based on the quality decay model in the food type library and the real-time collected storage environment parameters, the quality change pattern over time and environment is predicted and calculated to obtain the current quality status; Standardized model adjustment and parameterized reconstruction are performed on the six key scenarios of food production, from breeding, processing, testing, storage and transportation, sales to consumption, to obtain a virtual scenario model; The basic layer information, parameter layer information, evaluation layer information and knowledge layer information are dynamically superimposed on the virtual scene model to obtain a food process visualization result with multi-level information superposition.

[0038] Specifically, after completing three-source cross-validation and obtaining the enhanced trust mark, a multi-dimensional evaluation of food quality is conducted, constructing an evaluation system encompassing four core dimensions. The basic safety dimension focuses on food safety indicators, including regulatory requirements such as microbiological indicators, pesticide residues, and additive use. The nutritional value dimension focuses on the nutritional composition of food, including the content of nutrients such as protein, vitamins, and minerals. The sensory experience dimension evaluates consumer-perceived characteristics such as appearance, smell, and taste. The functional characteristics dimension considers added value such as shelf life, target population, and special benefits. For each dimension, corresponding evaluation indicators and weights are set based on the food type, and the indicator values are standardized to obtain a dimension score. The four dimension scores are combined to form a food quality vector, describing the food's quality status in each dimension. The collection and processing of consumer preference information is a key step in personalized traceability. Through the mobile application interface, consumers' proactive preferences for quality dimensions, such as their emphasis on safety, nutrition, and taste, are collected to form an initial set of preference weights. Simultaneously, the system analyzes consumers' historical query and purchase behavior data to extract implicit preference characteristics, such as frequently queried indicator types and product features of interest, and constructs a behavioral preference weight set. Furthermore, based on consumers' region and age group information, it screens consumer groups with similar attributes from the database, analyzes their shared preference trends, and conducts group intelligence optimization. A Bayesian fusion algorithm integrates explicit preferences, implicit behavioral preferences, and group statistical preferences to generate comprehensive preference weights. After normalization, the sum of the weights of the four dimensions is ensured to be 1, ultimately resulting in a consumer preference weight vector.

[0039] A dot product operation is performed on the food quality vector and the consumer preference weight vector. This involves multiplying the quality score for each dimension by the corresponding preference weight and then summing the results. The specific calculation process is as follows: first, the score for the basic safety dimension is multiplied by the consumer's preference weight for safety; then, the score for the nutritional value dimension is multiplied by the consumer's preference weight for nutrition; then, the score for the sensory experience dimension is multiplied by the consumer's preference weight for sensory experience; and finally, the score for the functional characteristics dimension is multiplied by the consumer's preference weight for functional experience. These four products are summed to obtain the final personalized quality score. This dot product operation accurately matches quality evaluation with individual preferences. The resulting personalized quality score reflects both the objective quality of the food and the subjective preferences of the consumer, providing an evaluation result tailored to individual needs. Food quality is not static but changes dynamically over time and environment. Using a pre-established quality decay model in the food type library and combined with real-time storage environment parameters, this quality change over time and environment is predicted and calculated. This quality decay model, based on food science theory, describes the function of quality parameters of different food types under specific conditions. Real-time environmental parameters, including temperature, humidity, light, and gas composition, are collected through IoT sensors. The current time point, initial quality data, and historical sequences of environmental parameters are input into the decay model to calculate the predicted quality state at the current time point and update the food quality vector, achieving dynamic quality assessment.

[0040] To intuitively display information about the entire life cycle of food, six key scenarios from planting and breeding to consumption are visualized. First, a standardized model library for the six scenarios is established, including planting and breeding scenarios, processing scenarios, testing scenarios, storage and transportation scenarios, sales scenarios, and consumption scenarios. For each specific food, parameterized reconstruction is performed based on its traceability data, and model parameters are adjusted to match the actual situation. For example, the planting and breeding scenario is adjusted based on parameters such as the actual planting environment and growth conditions; the processing scenario is adjusted based on information such as the actual process flow and equipment parameters. This parametric reconstruction ensures the consistency of the scenario model with the actual situation, while establishing logical connections between scenarios, supporting continuous and smooth scene transitions, and ultimately forming a complete virtual scenario model.

[0041] Multi-level information overlay is implemented on the virtual scene model, displaying traceability information in layers. The base layer presents basic scene information, such as location, time, and responsible person; the parameter layer displays key quality parameters and control indicators, such as temperature, humidity, and additive content; the evaluation layer presents professional evaluation results and consumer feedback; and the knowledge layer provides relevant food science knowledge and health information. Using an overlay information display strategy, users can dynamically switch between different information layers based on their needs, achieving personalized and multi-dimensional information acquisition. Ultimately, a multi-level information overlay visualizes the food process, providing consumers with an intuitive and detailed food safety traceability experience.

[0042] In a specific embodiment, the process of executing step S106 may specifically include the following steps: Collect and process the quality dimension preferences set by consumers through mobile applications to obtain an initial preference weight set; Perform association rule mining on consumer historical query and purchase behavior data to extract implicit preference features and obtain a set of behavioral preference weights; Based on the region and age group of consumers, consumer groups with similar attributes are screened from the consumer database to obtain a reference group set; Perform cluster analysis on the preference data of the reference group set to identify the main preference patterns and obtain the group preference model; Based on the initial preference weight set, behavioral preference weight set and group preference model, the Bayesian fusion algorithm is used for integrated calculation to obtain the comprehensive preference weight; The comprehensive preference weights are normalized to ensure that the sum of the basic safety dimension weights, nutritional value dimension weights, sensory experience dimension weights, and functional characteristics dimension weights is 1, and the consumer preference weight vector is obtained.

[0043] Specifically, through the mobile app's interactive interface, a rating slider or questionnaire format is designed to allow consumers to explicitly express their level of concern for different food quality dimensions. Using sliders or numerical input, consumers assign a score of 1-10 to each of the basic safety, nutritional value, sensory experience, and functional characteristics dimensions. These explicitly expressed preference scores are then normalized and converted to weights between 0 and 1, forming an initial set of preference weights. For example, a consumer's rating for safety is 9, for nutrition is 7, for sensory experience is 5, and for functional performance is 3. After normalization, the initial set of preference weights is [0.375, 0.292, 0.208, 0.125]. Association rule mining is performed on consumers' historical query and purchase behavior data to extract implicit preference characteristics. Association rule mining is a data mining technique used to discover associations between different items in a dataset. In this method, consumers' food traceability query and purchase records from the past three months are first collected, and behavioral characteristics such as the most frequently queried indicator types, the information pages with the longest dwell time, and the most frequently purchased food types are extracted. The Apriori algorithm is then used to analyze this behavioral data, calculating the support and confidence between different indicators and behaviors and identifying strong association rules. For example, if a consumer frequently searches for pesticide residue information and spends a long time on it, it can be inferred that they have a high implicit preference for basic safety. They also frequently check nutritional information but rarely review taste, indicating a higher focus on nutritional value than sensory experience. Based on the strength of the strong association rules, weights are assigned to the four dimensions to form a set of behavioral preference weights.

[0044] Based on the demographic characteristics of consumers, a reference group with similar attributes is identified from the consumer database. First, information such as the consumer's region (e.g., northern urban area), age group (e.g., 25-35 years old), gender, and occupation is extracted. Similarity calculation rules are then set: a perfect regional match is assigned 10 points, 1 point for every 10% overlap in age group, 5 points for a similar gender, and 3 points for a similar occupation. Similarity scores are calculated for all consumers in the database, and consumers with scores exceeding a preset threshold (e.g., 20 points) are selected to form the reference group. For example, 1,000 users with similar attributes to the target consumer can be selected as the reference group for subsequent analysis. Cluster analysis is performed on the preference data of the reference group to identify key preference patterns. Using the K-means clustering algorithm, the preference vectors of the reference group members are clustered in four-dimensional space. The optimal number of clusters, K, is determined. The silhouette coefficient (Silhouette Coefficient) is calculated for different K values, and the K value with the highest coefficient is selected. K-means clustering is then performed on the preference data of the reference group, grouping similar preference patterns together. The center of each cluster is calculated as the representative preference pattern. Finally, based on the distance between the target consumer and each cluster center, the preference group to which the target consumer is most likely to belong is determined, and the cluster center of this group is used as the group preference model.

[0045] A Bayesian fusion algorithm is used to integrate the initial preference weight set, the behavioral preference weight set, and the group preference model. Based on Bayesian theory, the Bayesian fusion algorithm integrates information from multiple sources according to their credibility. First, a priori credibility is set for each of the three preference sources: initial preference credibility is 0.5 (consumers' self-perception may differ from their actual behavior), behavioral preference credibility is 0.7 (based on actual behavior but subject to short-term factors), and group preference credibility is 0.4 (highly informative but with significant individual variation). A weighted average of the three sources for each dimension is then calculated and corrected for inter-dimensional correlations to obtain a composite preference weight. The composite preference weight is then normalized to ensure that the sum of the four dimension weights is 1. Specifically, the weight of each dimension is divided by the sum of all dimension weights to obtain a standardized weight ratio. For example, the comprehensive weight obtained by preliminary calculation is [0.62, 0.48, 0.25, 0.15], which becomes [0.413, 0.320, 0.167, 0.100] after normalization. This is the final consumer preference weight vector, which intuitively reflects the distribution of the consumer's attention to the four quality dimensions of food.

[0046] The above describes the traceability method for food safety in the embodiment of the present application. The following describes the traceability system for food safety in the embodiment of the present application. Figure 2In one embodiment of the present application, a traceability system for food safety includes: Processing module 201, for performing double random selection processing on the basic information tuple, physical feature tuple and biometric feature vector of the food raw material to obtain a differentiated trust identifier; A construction module 202 is configured to construct an asymmetric trust transfer protocol based on the differentiated trust identifier, perform multi-dimensional verification processing on the information transfer between the traceability chain nodes, and obtain an updated differentiated trust identifier; Verification module 203, configured to perform three-source cross-validation processing on the food critical control point information based on the updated differentiated trust identifier, generate a quality chain, and combine it with the updated differentiated trust identifier to obtain an enhanced trust identifier; The reconstruction module 204 is used to perform scene reconstruction processing on the food quality data according to the enhanced trust identifier and the consumer preference weight vector, and generate a food history visualization result with multi-level information superposition.

[0047] above Figure 2 The traceability system for food safety in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The traceability device for food safety in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0048] Figure 3 This is a schematic diagram of the structure of a food safety traceability device provided by an embodiment of the present invention. The food safety traceability device 300 may vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 310 (e.g., one or more processors), a memory 320, and one or more storage media 330 (e.g., one or more mass storage devices) storing application programs 333 or data 332. The memory 320 and storage medium 330 may be either transient or persistent storage. The program stored in the storage medium 330 may include one or more modules (not shown), each of which may include a series of instruction operations within the food safety traceability device 300. Furthermore, the processor 310 may be configured to communicate with the storage medium 330, executing the series of instruction operations stored in the storage medium 330 on the food safety traceability device 300 to implement the steps of the aforementioned food safety traceability method.

[0049] The food safety traceability device 300 may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 3 The structure of the food safety traceability device shown does not constitute a limitation on the food safety traceability device provided by the present invention, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0050] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the traceability method for food safety.

[0051] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0052] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or 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 for enabling a food safety traceability device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0053] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A food safety traceability method, characterized in that: The method comprises: The basic information tuple, physical feature tuple and biometric feature vector of food raw materials are double-randomly selected to obtain differentiated trust identification; An asymmetric trust transfer protocol is constructed based on the differentiated trust identifier, and multi-dimensional verification processing is performed on the information transmission between the traceability chain nodes to obtain an updated differentiated trust identifier; Based on the updated differentiated trust mark, three-source cross-validation processing is performed on the food critical control point information to generate a quality chain and combine it with the updated differentiated trust mark to obtain an enhanced trust mark; Based on the enhanced trust identifier and the consumer preference weight vector, the food quality data is subjected to scene reconstruction processing to generate a food history visualization result with multi-level information superposition.

2. The food safety traceability method according to claim 1, characterized in that: The double random selection process of the basic information tuple, physical feature tuple and biometric feature vector of the food raw material is performed to obtain a differentiated trust mark, including: The food raw material varieties, origin coordinates, harvesting time, batch number and person in charge information are collected and processed to obtain basic information tuples; The weight distribution, shape index, color distribution, hardness and specific category characteristic parameters of the food raw materials are collected and processed to obtain physical characteristic tuples; Extracting biometric fingerprint features from the food raw materials and performing feature dimensionality reduction and digitization processing to obtain a biometric feature vector; Select n samples from each batch of food using the first-level random algorithm, and select m feature points from each selected sample using the second-level random algorithm to form a double random guarantee and obtain the sample feature matrix; Combining the basic information tuple, physical feature tuple, biometric feature vector, timestamp and random salt value in a preset format to obtain an initial trusted data packet; The improved trust hash algorithm is applied to the initial trust data packet for calculation, and combined with a dynamic trust adjustment mechanism, to obtain the differentiated trust identifier.

3. The food safety traceability method according to claim 1, characterized in that: The asymmetric trust transfer protocol is constructed based on the differentiated trust identifier, and multi-dimensional verification processing is performed on the information transmission between the traceability chain nodes to obtain an updated differentiated trust identifier, including: Classify the nodes on the traceability chain and assign triple identity identification to obtain the explicit identification, implicit identification and trust identification of each node; Calculate the trust base value based on the node type, and calculate the trust dynamic adjustment coefficient based on the node's historical transaction records to obtain the node trust coefficient; Combining the differentiated trust identifier, the source node explicit identifier, the timestamp, and the target node type to form a transfer request, and signing the request using the source node implicit identifier to obtain a signed transfer request; Perform source node identity validity verification and trust threshold judgment on the signed transfer request to obtain a trusted transfer credential; Verify the trust transfer credential according to the trust acceptance coefficient of the target node, and submit receipt confirmation information to the trust center to obtain receipt confirmation data; The differentiated trust identifier, the trust transfer credential and the receipt confirmation data are hashed to obtain the updated differentiated trust identifier.

4. The food safety traceability method according to claim 3, characterized in that: The nodes on the traceability chain are classified and assigned triple identity identifications to obtain the explicit identification, implicit identification and trust identification of each node, including: The nodes on the traceability chain are classified into breeding nodes, primary processing nodes, deep processing nodes, logistics and warehousing nodes, sales nodes and supervision nodes, and six key node types are obtained; Encode the public information of each node to obtain the node explicit identification; Perform unique encoding on each node based on the node type and random number generation algorithm to obtain the node implicit identification; Set the trust base score according to the node type, assign the highest base score to the supervision node, and then give it to the planting / breeding node, deep processing node, primary processing node, logistics and warehousing node, and sales node, to obtain the node type trust base table; Perform statistical analysis on the node's historical behavior data, including transaction times, transaction size, anomaly rate, and complaint rate, to obtain a behavior score; The corresponding score in the node type trust basic table is weightedly calculated with the behavior score to obtain a node trust identifier.

5. The food safety traceability method according to claim 1, characterized in that: The method of performing three-source cross-validation on the food critical control point information based on the updated differentiated trust mark, generating a quality chain and combining it with the updated differentiated trust mark to obtain an enhanced trust mark includes: Establish a personalized critical control point list based on food categories, set critical limits and monitoring methods for each critical control point, and obtain a critical control point system; The autonomous verification source information is formed from the data recorded by the operator, the automatic verification source information is formed from the data generated by the automated monitoring equipment, and the third-party verification source information is formed from the verification data provided by an independent third party, thereby obtaining an information triplet; Based on the three types of verification source information in the information triplet, a pairwise similarity function is calculated to obtain three similarity values; The three similarity values are weighted and summed according to the weight coefficients determined by food type and processing stage to obtain an information consistency score; Serializing the verification results, consistency scores, quality scores, timestamps, and operator identification of each critical control point to obtain the quality chain; The updated differentiated trust identifier and the quality chain are processed by a hash algorithm to obtain the enhanced trust identifier.

6. The food safety traceability method according to claim 1, characterized in that: The method of performing scene reconstruction processing on the food quality data based on the enhanced trust identifier and the consumer preference weight vector to generate a food history visualization result with multi-level information superposition includes: Construct a multi-dimensional evaluation system for food quality, including basic safety, nutritional value, sensory experience, and functional characteristics, to obtain a food quality vector; Analyze the quality dimension preferences and historical query behaviors set by consumers, and perform group intelligent optimization processing based on their region and age group to obtain the consumer preference weight vector; Performing a dot product operation on the food quality vector and the consumer preference weight vector to obtain a personalized quality score; Based on the quality decay model in the food type library and the real-time collected storage environment parameters, the quality change pattern over time and environment is predicted and calculated to obtain the current quality status; Standardized model adjustment and parameterized reconstruction are performed on the six key scenarios of food production, from breeding, processing, testing, storage and transportation, sales to consumption, to obtain a virtual scenario model; Basic layer information, parameter layer information, evaluation layer information and knowledge layer information are dynamically superimposed on the virtual scene model to obtain a food history visualization result with the multi-level information superposition.

7. The food safety traceability method according to claim 6, characterized in that: The analysis is performed based on the quality dimension preferences and historical query behaviors set by consumers, and group intelligent optimization is performed in combination with the region and age group to obtain the consumer preference weight vector, including: Collect and process the quality dimension preferences set by consumers through mobile applications to obtain an initial preference weight set; Perform association rule mining on consumer historical query and purchase behavior data to extract implicit preference features and obtain a set of behavioral preference weights; Based on the region and age group of consumers, consumer groups with similar attributes are screened from the consumer database to obtain a reference group set; Performing cluster analysis on the preference data of the reference group set to identify the main preference patterns and obtain a group preference model; Based on the initial preference weight set, the behavior preference weight set and the group preference model, a Bayesian fusion algorithm is used to perform integrated calculation to obtain a comprehensive preference weight; The comprehensive preference weight is normalized to ensure that the sum of the basic safety dimension weight, nutritional value dimension weight, sensory experience dimension weight, and functional characteristic dimension weight is 1, and the consumer preference weight vector is obtained.

8. A traceability system for food safety, characterized in that: For implementing the food safety traceability method according to any one of claims 1 to 7, the food safety traceability system comprises: A processing module is used to perform double random selection processing on the basic information tuple, physical feature tuple and biometric feature vector of the food raw materials to obtain a differentiated trust mark; A construction module is used to construct an asymmetric trust transfer protocol based on the differentiated trust identifier, perform multi-dimensional verification processing on the information transmission between the traceability chain nodes, and obtain an updated differentiated trust identifier; A verification module, configured to perform a three-source cross-validation process on the food critical control point information based on the updated differentiated trust identifier, generate a quality chain, and combine it with the updated differentiated trust identifier to obtain an enhanced trust identifier; The reconstruction module is used to perform scene reconstruction processing on the food quality data based on the enhanced trust identifier and the consumer preference weight vector, and generate a food history visualization result with multi-level information superposition.

9. A traceability device for food safety, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the method for food safety traceability according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the processor is caused to execute the food safety traceability method according to any one of claims 1 to 7.

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