Pomegranate pancake whole-process traceability management system based on block chain

Abstract

The invention relates to the technical field of product life cycle management, in particular to a pomegranate pancake full-process traceability management system based on a block chain. The specific implementation process comprises the following steps: collecting native quality data of a product raw material and value-added processing parameters of a processing process, and anchoring the native quality data and the value-added processing parameters to a distributed value account book; triggering a product auditing contract, automatically calculating a theoretical output amount, comparing the theoretical output amount with an actual output amount, and generating a process verification identifier; and performing hash locking on the verification identifier and the whole-process data to form a whole-cycle value resume, and when a quality defect is found, tracing the source of a quality problem and outputting a product whole-life-cycle survey report. According to the method, the native quality of the raw materials is deeply associated with the production process parameters, the automatic auditing contract is utilized to verify the product balance and construct the whole-cycle value resume, accurate attribution and deep traceability of the product quality problem are realized, and the transparency and the quality control management level of the whole process of product production are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of product life cycle management, in particular to a pomegranate pancake whole-process traceability management system based on a block chain. BACKGROUND

[0002] With the development of information technology, distributed ledger technology represented by block chains has gradually become the mainstream of traceability management systems. Companies use the characteristics of decentralization, data difficulty to tamper with and openness and transparency of block chain technology to include pomegranate pancake raw material suppliers, production service providers, brand operators, logistics service providers and terminal retailers into a business collaborative distributed ledger, thereby recording the management status of products at each generation link.

[0003] However, the prior art has inherent defects in practice. There is a lack of fine collection and correlation mechanism for pomegranate pancake quality indicators and quality control processing (for example: pomegranate juice yield and pancake baking temperature) data, resulting in a lack of built-in automated logic verification mechanism, which cannot verify whether the number of pomegranates in the warehouse and the final production of pomegranate pancake batch yield are within a reasonable range, making it difficult to determine the root cause of the problem when quality defects occur.

[0004] Therefore, a pomegranate pancake whole-process traceability management system based on a block chain is proposed. SUMMARY

[0005] The purpose of the present application is to provide a pomegranate pancake whole-process traceability management system based on a block chain, which performs traceability management on the whole production process of pomegranate pancakes.

[0006] To achieve the above purpose, the present application provides the following technical scheme: A pomegranate pancake whole-process traceability management system based on a block chain, comprising: A product data acquisition module acquires original quality data including the total weight of pomegranates and the juice yield in the product raw materials, and obtains value-added processing parameters including the pancake baking temperature curve and the baking duration in the product processing process; A production audit conversion module anchors the original quality data and value-added processing parameters on a distributed value ledger, and triggers a product audit contract based on a balance of goods and production; the product audit contract inputs the original quality data into a production conversion model to automatically calculate the theoretical output corresponding to the product raw materials; The lifecycle traceability module obtains the actual output of product raw materials. The product audit contract compares the actual output with the theoretical output to generate a process verification identifier that characterizes the credibility of the product processing process. The process verification identifier is hash-locked with the original quality data, value-added processing parameters, and actual output to form a full-cycle value history. The full-cycle value history is stored in a distributed value ledger. When product quality defects are found, the original quality data and value-added processing parameters are traced back to output a full product lifecycle investigation report.

[0007] Preferably, the specific implementation process of collecting raw quality data on the total weight and juice yield of pomegranates in the product raw materials, and obtaining value-added processing parameters including the pancake baking temperature curve and baking time during product processing includes: In the raw material processing stage, intelligent weighing and optical sensing equipment are used to collect data on the total weight of pomegranates and the juice yield, and integrate them into raw quality data. In the product processing stage, multi-point temperature probes and timing devices built into the baking equipment are used to record the pancake baking temperature curve and the accumulated baking time information, and integrate them into value-added processing parameters. The raw quality data and value-added processing parameters are uploaded to the data temporary storage area through the data interface.

[0008] Preferably, the specific implementation process of anchoring the original quality data and value-added processing parameters onto the distributed value ledger to trigger a product audit contract based on the balance of production and inventory includes: The programmable interface is invoked to integrate the original quality data and value-added processing parameters into a transaction data structure. The transaction data structure is then digitally signed using an asymmetric encryption private key associated with the production batch, forming a transaction to be uploaded to the blockchain with verifiable identity information and a timestamp. The transaction to be uploaded to the blockchain is broadcast to the consensus network of the distributed value ledger. After verification by multiple network nodes and confirmation by the consensus mechanism, it is encapsulated into an immutable data block and attached to the existing blockchain, completing data anchoring and automatically activating the product audit contract deployed on the distributed value ledger.

[0009] Preferably, the specific implementation process of the product audit contract inputting raw quality data into the production conversion model and automatically calculating the theoretical output corresponding to the product raw materials includes: The product audit contract calls the original quality data to extract the quantitative values ​​of the total weight and juice yield of pomegranates; the production conversion model encapsulates the material conversion coefficient, standard formula parameters, and loss rate adapted to the pomegranate pancake production process; the product audit contract inputs the obtained total weight and juice yield of pomegranates into the production conversion model's production-production balance algorithm to deduce the quantity of finished products that can be produced from the raw materials under ideal working conditions, obtain the theoretical output, and temporarily store it in the internal state variables of the product audit contract.

[0010] Preferably, the specific implementation process of obtaining the actual output of product raw materials, and comparing the actual output with the theoretical output in the product audit contract to generate a process verification identifier characterizing the credibility of the product processing process includes: The actual output is obtained through metering equipment, and an execution request for the product audit contract is submitted to the distributed value ledger. After receiving the execution request, the product audit contract extracts the calculated theoretical output from its internal storage space and compares the actual output with the theoretical output. Deviation is evaluated according to the tolerance range specified in the production process standard, and a process verification mark is generated. When the deviation is within the tolerance range, the process is qualified; otherwise, the process is abnormal.

[0011] Preferably, the specific implementation process of hash-locking the process verification identifier with the original quality data, value-added processing parameters, and actual output to form a full-cycle value history includes: The process verification identifier, original quality data, value-added processing parameters, and actual output are aggregated to form a product dataset containing data information from each stage; the product dataset is input into a deterministic cryptographic hash function to generate a hash digest; the hash digest is used as a digital fingerprint and associated with the product dataset to be encapsulated together into a full-cycle value history.

[0012] Preferably, the entire lifecycle value history is stored in a distributed value ledger. When a product quality defect is discovered, the specific implementation process of tracing the original quality data and value-added processing parameters includes: The entire lifecycle value history is constructed into a transaction request that conforms to the distributed value ledger protocol specification and broadcast to all network nodes. After receiving the transaction request, the network nodes execute the consensus algorithm to verify its validity and store the entire lifecycle value history in the newly generated block. When a product quality defect event triggers a traceability request, a reverse query operation is initiated on the distributed value ledger using the index identifier associated with the defective product batch. By traversing historical blocks, the stored record containing the corresponding entire lifecycle value history is located, and the original quality data and value-added processing parameters locked by hash are extracted.

[0013] Preferably, the specific implementation process for outputting a product lifecycle survey report includes: The system utilizes a product knowledge base based on production process standards to perform multi-dimensional data comparison and logical reasoning, identifying value-added processing deviations and original quality anomalies that are statistically correlated with product quality defects, and integrating and outputting a product lifecycle investigation report.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention addresses the lack of refined data collection and correlation mechanisms for quality indicators and quality control processing of pomegranate pancakes (such as pomegranate juice yield and pancake baking temperature). Through the product data acquisition module, the system can collect raw material quality data (total pomegranate weight and juice yield) and value-added parameters during processing (pancake baking temperature curve and time), providing a precise data foundation for subsequent auditing and traceability.

[0015] 2. This invention improves upon the inability to automatically verify whether the quantity of pomegranates entering the warehouse and the final batch output of pancakes are within a reasonable range. The production audit conversion module compares the actual output with the theoretical output calculated based on the original data through the product audit contract, automatically generating process verification identifiers. This achieves automated assessment of the reliability of the production and processing process, enabling timely detection of process anomalies.

[0016] 3. This invention, through a lifecycle traceability module, hashes and locks all process data, storing it on an immutable blockchain. The system can trace the original quality data and value-added processing parameters associated with the product and output investigation reports. When product quality defects occur, it can determine whether the root cause is a defect in raw materials or an abnormal production process. Attached Figure Description

[0017] Fig. 1 This is a structural diagram of a blockchain-based pomegranate pancake full-process traceability management system proposed in this invention; Fig. 2 This is a schematic diagram of the product audit contract proposed in this invention; Fig. 3 This is a flowchart illustrating the entire process of traceability management for the production of pomegranate pancakes, as proposed in this invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It must be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to constitute any limitation on the scope of protection of this invention. Therefore, all equivalent changes or modifications conceived by those skilled in the art based on the content disclosed in this invention without inventive effort should fall within the scope of protection claimed by this invention.

[0019] Reference Figs. 1 to 3 This invention proposes a blockchain-based full-process traceability management system for pomegranate pancakes, the technical solution of which is as follows: Example 1: This embodiment provides a blockchain-based pomegranate pancake full-process traceability management system for application in the entire process of pomegranate pancake traceability, referring to... Fig. 1The system includes a product data acquisition module, a production audit and transformation module, and a lifecycle traceability module.

[0020] The product data acquisition module collects the original quality data of the total weight and juice yield of pomegranates in the raw materials of the product, and obtains the value-added processing parameters, including the pancake baking temperature curve and baking time, during the product processing. The production audit transformation module anchors the original quality data and value-added processing parameters onto the distributed value ledger, triggering a product audit contract based on the balance of production and materials. The product audit contract inputs the original quality data into the production transformation model and automatically calculates the theoretical output corresponding to the product raw materials. The lifecycle traceability module obtains the actual output of product raw materials. The product audit contract compares the actual output with the theoretical output to generate a process verification identifier that characterizes the credibility of the product processing process. The process verification identifier is hash-locked with the original quality data, value-added processing parameters, and actual output to form a full-cycle value history. The full-cycle value history is stored in a distributed value ledger. When product quality defects are found, the original quality data and value-added processing parameters are traced back to output a full product lifecycle investigation report.

[0021] Furthermore, the specific implementation process of collecting raw quality data on the total weight and juice yield of pomegranates in the product ingredients, and obtaining value-added processing parameters including pancake baking temperature curves and baking time during product processing includes: In the raw material processing stage, intelligent weighing and optical sensing equipment are used to collect data on the total weight of pomegranates and the juice yield, and integrate them into raw quality data. In the product processing stage, multi-point temperature probes and timing devices built into the baking equipment are used to record the pancake baking temperature curve and the accumulated baking time information, and integrate them into value-added processing parameters. The raw quality data and value-added processing parameters are uploaded to the data temporary storage area through the data interface.

[0022] Specifically, in the raw material processing stage, when a batch of purchased pomegranate raw materials weighing approximately 500 kg enters the production workshop, it is transferred to an integrated intelligent weighing and optical sensing device. This intelligent weighing device automatically measures and records the total weight of the batch of pomegranates as 500.2 kg using a high-precision pressure sensor. After preliminary cleaning and sorting, the raw materials enter the juicing process. During this process, the optical sensing device uses near-infrared spectroscopy analysis technology to quickly detect the pomegranate raw materials, determining an average juice yield of 75.3%. The system structurally integrates the two key data points, "total weight 500.2 kg" and "juice yield 75.3%", to form a unique original quality data package associated with the batch number "SP20251024-001".

[0023] In the product processing stage, the pancake batter made from this batch of pomegranate raw materials is fed into automated baking equipment. This equipment is equipped with five highly sensitive multi-point temperature probes and a precision timing device. After baking begins, the temperature probes collect real-time data on the pancake's temperature at different locations once per second, compiling this data into a complete baking temperature curve. This curve precisely records the entire process from initial heating to baking at a constant temperature of 185 degrees Celsius for 3 minutes, and finally cooling down. Simultaneously, the timing device accurately records the cumulative baking time of each pancake as 5 minutes and 15 seconds. This time-series data, containing multi-dimensional temperature change information, along with the "5 minutes and 15 seconds" duration information, is integrated by the system into value-added processing parameters for this batch of products. The collected raw quality data and value-added processing parameters are encapsulated into a unified data message and securely uploaded to the server's data storage area via the factory's internal LAN data interface.

[0024] This embodiment establishes a complete and detailed data archive for the product by accurately capturing and integrating objective quantitative indicators of raw materials (total weight and juice yield) and key process parameters (temperature profile and processing time). This ensures the authenticity and reliability of the source data and improves the accuracy and efficiency of traceability.

[0025] Furthermore, the specific implementation process of anchoring the raw quality data and value-added processing parameters onto the distributed value ledger to trigger a product audit contract based on the balance of production and inventory includes: The programmable interface is invoked to integrate the original quality data and value-added processing parameters into a transaction data structure. The transaction data structure is then digitally signed using an asymmetric encryption private key associated with the production batch, forming a transaction to be uploaded to the blockchain with verifiable identity information and a timestamp. The transaction to be uploaded to the blockchain is broadcast to the consensus network of the distributed value ledger. After verification by multiple network nodes and confirmation by the consensus mechanism, it is encapsulated into an immutable data block and attached to the existing blockchain, completing data anchoring and automatically activating the product audit contract deployed on the distributed value ledger.

[0026] Specifically, the factory server calls the programming interface to integrate the collected raw quality data and value-added processing parameters into a standardized transaction data structure, such as a JSON object. The system retrieves the asymmetric encryption private key uniquely bound to the production batch and held by the production workshop supervisor, and uses an elliptic curve digital signature algorithm to digitally sign the hash value of this JSON object. This signature, along with the supervisor's verifiable identity information (i.e., their public key) and a Unix timestamp accurate to milliseconds, is appended to the JSON object, forming a complete transaction ready to be uploaded to the blockchain.

[0027] Transactions awaiting on-chain recording are broadcast from the factory's blockchain nodes to the entire distributed value ledger consensus network comprised of raw material suppliers, producers, third-party quality inspection agencies, and logistics providers. Each node in the network, upon receiving the transaction, independently verifies it, including checking the validity of the digital signature, the legitimacy of the initiator's identity, and the integrity of the data structure. After confirmation by the network's practical Byzantine fault-tolerant consensus mechanism (i.e., more than two-thirds of the nodes reach agreement), certifying the transaction as genuine and valid, it is encapsulated into a new data block. This block is linked to the previous block via a cryptographic hash and ultimately appended to the existing blockchain. Simultaneously, the successful on-chain recording of this transaction data automatically activates and invokes the product audit smart contract deployed on the distributed value ledger based on the principle of balance of production and resources.

[0028] This embodiment utilizes asymmetric encryption and digital signature technologies to ensure the non-repudiation and integrity of data sources. By broadcasting to a consensus network and undergoing joint verification by multiple nodes, data from a single enterprise is transformed into a credible record of facts recognized by the entire industry chain, thereby improving the transparency and collaborative efficiency of supply chain management.

[0029] Furthermore, the product audit contract inputs raw quality data into the production conversion model, and the specific implementation process of automatically calculating the theoretical output corresponding to the product raw materials includes: The product audit contract calls the original quality data to extract the quantitative values ​​of the total weight and juice yield of pomegranates; the production conversion model encapsulates the material conversion coefficient, standard formula parameters, and loss rate adapted to the pomegranate pancake production process; the product audit contract inputs the obtained total weight and juice yield of pomegranates into the production conversion model's production-production balance algorithm to deduce the quantity of finished products that can be produced from the raw materials under ideal working conditions, obtain the theoretical output, and temporarily store it in the internal state variables of the product audit contract.

[0030] Specifically, refer to Fig. 2The product audit contract calls upon transaction data to extract the native quality data associated with this production batch, namely, the total weight of pomegranates is 500.2 kg and the juice yield is 75.3%. Simultaneously, a pre-set and verified production conversion model embedded in the contract logic is invoked. This model encapsulates a series of key parameters adapted to the factory's pomegranate pancake production process. According to the standard recipe, each standard 80-gram pomegranate pancake requires 40 grams of pure pomegranate juice. The material conversion coefficient defines the conversion relationship from pomegranate raw materials to pomegranate juice. The product audit contract includes a built-in two-dimensional lookup table that sets the standard loss rate corresponding to different juice yield ranges. For example: (juice yield 70%-73%, loss rate 3.3%), (juice yield 73%-76%, loss rate 3.2%), and (juice yield 76%-79%, loss rate 3.1%), etc. When the contract receives a juice yield of 75.3%, it queries the table and matches the range of 73%-76%, thereby automatically calling the 3.2% comprehensive loss rate input into the production conversion model. This loss rate covers the reasonable material loss generated during the processing, mixing, transportation and baking of raw materials.

[0031] The product audit contract inputs two quantitative values—the total weight of pomegranates (500.2 kg) and the juice yield (75.3%)—into the production conversion model's resource balance algorithm. Based on these inputs, the algorithm first derives the total available pomegranate juice, then deducts a 3.2% comprehensive loss rate to calculate the net pomegranate juice available for production. Finally, based on the standard formula parameter that each pancake requires 40 grams of net pomegranate juice, it derives the theoretical output quantity for this batch of raw materials under ideal conditions. After calculation, the system obtains a theoretical output of 9133 standard pomegranate pancakes for this batch. This calculation result, "9133," is written into and temporarily stored as a key benchmark data point within the product audit contract's internal state variables and locked on the blockchain.

[0032] This embodiment embeds a widely accepted production conversion model into a smart contract and uses immutable on-chain raw data for automatic calculations to generate a completely objective and network-wide verifiable theoretical output. This improves the accuracy and credibility of production efficiency assessment and enhances the refinement and intelligence of production management.

[0033] Furthermore, the specific implementation process of obtaining the actual output of product raw materials, and comparing the actual output with the theoretical output in the product audit contract to generate a process verification identifier characterizing the credibility of the product processing process, includes: The actual output is obtained through metering equipment, and an execution request for the product audit contract is submitted to the distributed value ledger. After receiving the execution request, the product audit contract extracts the calculated theoretical output from its internal storage space and compares the actual output with the theoretical output. Deviation is evaluated according to the tolerance range specified in the production process standard, and a process verification mark is generated. When the deviation is within the tolerance range, the process is qualified; otherwise, the process is abnormal.

[0034] Specifically, an automated optical counting and packaging device installed at the end of the production line accurately counts the packaged pomegranate pancakes. After the equipment completes the count, the actual output of this batch is 9050 standard finished products. The production line's central control system then uses this "9050" value as core data to automatically construct an execution request transaction for the previously deployed product audit contract. This request is signed and submitted to the distributed value ledger network. Once the nodes in the network verify the validity of the request, the corresponding audit function of the product audit contract is triggered.

[0035] Upon receiving the execution request, the product audit contract retrieves the calculated and temporarily stored theoretical output (9133 units) from its internal storage. The contract compares the actual output of 9050 (passed as a request parameter) with the internally stored theoretical output of 9133 to calculate the deviation. The process verification flag, based on the quality control clauses set in the contract according to the production process standards, uses a tolerance range of ±2% as the evaluation benchmark. The contract calculates that the deviation rate between the actual and theoretical output is approximately -0.91%, which is within the ±2% tolerance range. Therefore, the contract determines that the material conversion efficiency of this production process meets the standard and immediately assigns the process verification flag value of "process qualified." This flag, as the result of this audit, is recorded as a new state of the contract and permanently stored on the blockchain. Conversely, if the actual output obtained by the metering device is 8900 units, the deviation rate calculated by the contract will exceed the tolerance range, and the generated process verification flag will be "process abnormal," thus issuing a warning to the relevant management nodes.

[0036] This embodiment automatically executes output comparison and deviation assessment through smart contracts, ensuring the absolute objectivity and fairness of the audit results. The entire process of audit requests and results is recorded in a distributed value ledger, making every production verification transparent and traceable. This real-time feedback mechanism allows production managers to identify potential production anomalies immediately, improving the credibility and level of refined management of the production process.

[0037] Furthermore, the specific implementation process of hash-locking the process verification identifier with the original quality data, value-added processing parameters, and actual output to form a full-cycle value history includes: The process verification identifier, original quality data, value-added processing parameters, and actual output are aggregated to form a product dataset containing data information from each stage; the product dataset is input into a deterministic cryptographic hash function to generate a hash digest; the hash digest is used as a digital fingerprint and associated with the product dataset to be encapsulated together into a full-cycle value history.

[0038] Specifically, the system aggregates and processes the core data already generated and recorded on the blockchain. This data includes: process verification identifiers representing the compliance of the production process, i.e., "process qualified"; raw quality data reflecting the basic attributes of the raw materials, i.e., the total weight of pomegranates (500.2 kg) and juice yield (75.3%); value-added processing parameters recording key processing techniques, i.e., baking curve data containing complete temperature change information and baking time (5 minutes and 15 seconds); and the final production result, i.e., actual output of 9050 servings. These four different dimensions of data are integrated into a unified product dataset, such as a complete XML document.

[0039] This product dataset, containing all aggregated information, is input as a whole into a deterministic cryptographic hash function; in this embodiment, the SHA-256 algorithm is used. After processing by this algorithm, regardless of the size of the original dataset, a unique hash digest with a fixed length of 256 bits will be generated. For example, the generated hash digest is a hexadecimal string "0x4A2E...C8F9". This hash digest, due to its high sensitivity to the original data and its unique correspondence, is used as the digital fingerprint of the product dataset. The system tightly associates and binds this generated hash digest with the original product dataset, jointly encapsulating them into a complete digital object and outputting it as a full-cycle value history.

[0040] This embodiment achieves the solidification of the product's full-lifecycle information status by aggregating all key data and generating a unique hash digest. Even minor modifications to the original dataset will result in a recalculated hash digest that differs from the original record, effectively preventing any tampering. This ensures the integrity and immutability of the entire lifecycle value history, establishing a low-cost, high-efficiency trust mechanism.

[0041] Furthermore, the entire lifecycle value history is stored in a distributed value ledger. When product quality defects are discovered, the specific implementation process of tracing the original quality data and value-added processing parameters includes: The entire lifecycle value history is constructed into a transaction request that conforms to the distributed value ledger protocol specification and broadcast to all network nodes. After receiving the transaction request, the network nodes execute the consensus algorithm to verify its validity and store the entire lifecycle value history in the newly generated block. When a product quality defect event triggers a traceability request, a reverse query operation is initiated on the distributed value ledger using the index identifier associated with the defective product batch. By traversing historical blocks, the stored record containing the corresponding entire lifecycle value history is located, and the original quality data and value-added processing parameters locked by hash are extracted.

[0042] Specifically, the system takes the pre-packaged full-cycle value history—including process verification identifiers, original quality data, value-added processing parameters, actual output, and their associated hash digests—and constructs a transaction request conforming to the underlying protocol specifications of this distributed value ledger (e.g., Ethereum's JSON-RPC specification). This request, after being signed by the private key of the production node, is broadcast to all nodes in the network, which are jointly maintained by all parties in the supply chain. Other nodes in the network, such as those of raw material suppliers and quality inspection agencies, upon receiving this transaction request, immediately execute a consensus algorithm (e.g., using Byzantine fault tolerance) to verify its validity, confirming its legitimate origin and complete content.

[0043] After achieving consensus among a majority of network nodes, the entire lifecycle value history is permanently recorded and stored in a newly generated block numbered "1,876,543". Several weeks later, market regulators received a complaint about the batch of products' poor taste, triggering a traceability request for a product quality defect. At this point, quality management personnel can enter the unique index identifier associated with the defective product batch, "SP20251024-001", into the system's traceability interface. Upon receiving the request, the system initiates an efficient reverse lookup operation on the distributed value ledger. By traversing the index data of historical blocks, the system can quickly locate block number "1,876,543" containing the batch information and find the corresponding stored record. The system then parses the record, verifies its hash digest to ensure that the data has not been tampered with, and then accurately extracts the original quality data (total pomegranate weight 500.2 kg, juice yield 75.3%) and value-added processing parameters (baking curve containing complete temperature change information and baking time of 5 minutes and 15 seconds) locked by hash from the verified full-cycle value history, and presents it to the investigators for root cause analysis.

[0044] This embodiment stores the entire lifecycle value history on a distributed value ledger. When a quality issue occurs, the system can trace the entire process of the problematic batch from raw materials to finished product through on-chain queries. This effectively shortens the problem response time, reduces investigation costs, and enhances product quality assurance.

[0045] Furthermore, the specific implementation process for outputting a product lifecycle survey report includes: The system utilizes a product knowledge base based on production process standards to perform multi-dimensional data comparison and logical reasoning, identifying value-added processing deviations and original quality anomalies that are statistically correlated with product quality defects, and integrating and outputting a product lifecycle investigation report.

[0046] Specifically, the system invokes a product knowledge base trained on long-term production process standards and historical quality data. This product knowledge base is a set of rules stored in the form of a document database, which contains fixed logical reasoning rules that associate product quality defects with corresponding original quality data and value-added processing parameters. Based on the triggered quality defect event, the system retrieves and matches the corresponding logical reasoning rules. Following these rules, the system compares the actual original quality data and value-added processing parameters extracted from the entire lifecycle value history with the standard process range defined in the rules. For the current feedback regarding the quality defect of "too dry and hard," the system begins multi-dimensional data comparison and logical reasoning.

[0047] The system compared the extracted raw quality data, namely a juice yield of 75.3%, with the standard juice yield range of "70%-78%" defined in the knowledge base. It deduced that this indicator was within the normal range and, based on a historical statistical model, identified that this raw quality did not have a strong statistical correlation with the "dry and hard" texture of the final product, thus initially ruling out the possibility of abnormal raw materials. On the other hand, the system compared the extracted value-added processing parameter, namely a baking time of 5 minutes and 15 seconds, with the standard process time window of "4 minutes and 45 seconds ± 15 seconds" set in the knowledge base, immediately identifying a significant deviation in value-added processing—the actual baking time exceeded the standard upper limit of 15 seconds.

[0048] The logical reasoning rule base in the knowledge base contains a rule: "If the baking time exceeds the standard upper limit, the product's water loss rate will increase non-linearly, leading to a significantly increased probability of a dry and hard texture." Based on this rule, the system determines that the deviation of this processing parameter has a high statistical correlation with the quality defect reported in this case. The specific implementation process of the logical reasoning is as follows: The system traverses the original quality data and value-added processing parameters extracted from the full-cycle value history, compares the actual value of each parameter with the corresponding standard range stored in the product knowledge base, and generates a deviation list, for example (Deviation_A: Baking time > upper limit, Deviation_B: Baking temperature = normal). The system's reasoning engine (a rule-based forward chain reasoner) uses this deviation list as input facts and matches it in the rule set of the product knowledge base. The reasoning engine first matches a single rule (e.g., IF (Deviation_A) THEN...), and then attempts to match "AND" logical rules that require multiple deviation facts to be combined (e.g., IF (Deviation_A) AND (Deviation_B) THEN...). Because the system matched the rule: "If the baking time exceeds the standard upper limit, the product's water loss rate will increase non-linearly, resulting in a significantly increased probability of a dry and hard texture," the conclusion of this rule was included in the investigation report as a "root cause analysis."

[0049] By integrating the above analysis results, a product lifecycle investigation report was automatically generated, which included a data summary, comparison process, reasoning conclusions, and root cause analysis. The report clearly pointed out that the quality problem of the batch of pomegranate pancakes was most likely caused by the deviation in the production process of baking time exceeding the specified time.

[0050] This embodiment, by introducing a product knowledge base and logical reasoning, can automatically perform in-depth analysis of massive traceability data, pinpoint the causes of quality problems, and distinguish between raw material defects and process deviations. This improves the efficiency of quality incident response and handling, and constructs a closed-loop quality management system from problem discovery and precise traceability to intelligent analysis and continuous improvement.

[0051] This embodiment achieves refined data collection and deep correlation of key nodes in the product lifecycle through a product data acquisition module. It improves upon the shortcomings of only tracking batch numbers or high-level information, which cannot accurately link the intrinsic quality of raw materials (pomegranate juice yield) with specific processing parameters (baking temperature curve).

[0052] The production audit transformation module introduces an automated on-chain audit mechanism based on material balance. This improves upon the problems of lagging output and input accounting, lack of transparency, and susceptibility to errors in production management. By embedding the production transformation model into a smart contract and automatically calculating theoretical output using immutable raw data on the blockchain, it achieves real-time automated verification of production process efficiency and compliance, thereby enhancing the transparency and credibility of the production process.

[0053] The lifecycle traceability module constructs an end-to-end, closed-loop quality traceability and intelligent diagnostic system. By hashing and storing the core data throughout the entire process, including verification identifiers, an immutable full-lifecycle value record is formed. When quality issues occur, the system can not only trace back the data but also perform intelligent analysis by calling the product knowledge base and output investigation reports, improving the response efficiency of quality incidents.

[0054] Example 2: This embodiment deploys the aforementioned blockchain-based pomegranate pancake full-process traceability management system entirely on the pomegranate pancake production line of Factory X, referring to... Fig. 3 This enables full-process traceability management of the pomegranate pancake production process.

[0055] A batch of pomegranate raw materials, numbered "SP20251025-002", entered the pomegranate pancake production line at Factory X. This batch of raw materials was fed into a raw material processing workstation integrated with intelligent weighing and optical sensing equipment. A high-precision pressure sensor automatically weighed the batch of pomegranates, determining the total weight to be 801.5 kg. During the cleaning and sorting process, a near-infrared spectroscopy analyzer performed rapid, non-destructive testing on multiple random samples, determining and calculating the average juice yield of this batch of raw materials to be 73.8%. The system integrated the two core data points, "total weight 801.5 kg" and "juice yield 73.8%", into the batch's original quality data. Subsequently, the pancake batter made from this batch of raw materials was sent to automated baking equipment. Multiple high-sensitivity temperature probes and precision timing devices built into the equipment began working, recording the baking process of each batch of pancakes in real time. The system recorded a complete baking temperature curve, which accurately showed the entire process from initial heating, reaching 175 degrees Celsius and maintaining that temperature for 3 minutes and 5 seconds, to the final cooling. Meanwhile, the timing device recorded the average cumulative baking time of this batch of pancakes as 5 minutes and 25 seconds. This temperature curve data and time information were integrated into the value-added processing parameters for this batch.

[0056] The system encapsulates the collected raw quality data and value-added processing parameters into a standardized, timestamped transaction data structure, such as a JSON object, by calling the program programming interface. The system retrieves the asymmetric encryption private key uniquely linked to the workshop supervisor for that production batch and uses an elliptic curve digital signature algorithm to sign the hash value of the JSON object, forming a transaction with verifiable identity information to be uploaded to the blockchain. This transaction is broadcast to a distributed value ledger consensus network composed of raw material suppliers, producers, logistics providers, and quality inspection agencies. After confirming the legality and integrity of the transaction through a practical Byzantine fault-tolerant consensus mechanism, the nodes in the network encapsulate it into a new, immutable data block and append it to the existing blockchain. Successful confirmation of the on-chain action automatically activates the product audit smart contract deployed on the distributed value ledger. The activated product audit contract automatically calculates the theoretical output, extracting the total weight of pomegranates (801.5 kg) and the average juice yield (73.8%) from the newly uploaded data, while simultaneously searching a two-dimensional lookup table and finding a loss rate of 3.2%.

[0057] The production conversion model, based on a material balance algorithm, multiplies the total weight of pomegranates by the real-time juice yield to obtain the initial total pomegranate juice volume. A 3.2% comprehensive loss is then deducted from this initial total to obtain the net pomegranate juice volume usable for production. According to the factory's standard pomegranate pancake recipe (i.e., each 80-gram pancake requires 40 grams of net pomegranate juice), the total net pomegranate juice volume is divided by the consumption per pancake. Through this series of calculations, the contract derives the theoretical output of this batch of raw materials under ideal operating conditions as 14,450 standard pomegranate pancakes. This theoretical output is temporarily stored as an internal state variable in the smart contract, awaiting subsequent comparison.

[0058] An automated optical counting device installed at the end of the production line counted 14,310 units of the actual output for this batch. This data was submitted to the distributed value ledger, triggering the audit function of the product audit contract. Upon receiving the actual output, the contract compared it with the internally stored theoretical output of 14,450 units, calculating a deviation rate of approximately -0.97%. Based on the ±2% tolerance range set in the contract according to production process standards, the contract determined that this deviation was within acceptable limits, thus generating a process verification identifier with the value "Process Qualified," and permanently recording this result as a new state on the blockchain. The system's backend service aggregated the core data from the entire production process, including the process verification identifier, raw quality data, value-added processing parameters, and actual output, forming a complete product dataset. This dataset was then input into a deterministic SHA-256 cryptographic hash function to generate a unique 256-bit hash digest (e.g., "0x9B1F...A3E7"). The hash digest, acting as a digital fingerprint, is associated with and bound to the product dataset, together encapsulating them into a full-cycle value history. It is then stored as a new transaction in the distributed value ledger, thus completing the hash locking of the data.

[0059] Weeks later, when market regulators received a complaint about the "excessively wet" texture of product batch "SP20251025-002," the production plant needed to conduct quality traceability. Quality management personnel entered the batch's associated index identifier into the system's traceability interface, and the system immediately initiated a reverse lookup operation on the distributed value ledger. By traversing historical blocks, it located the stored record containing the batch's entire lifecycle value history. The system recalculated the hash value of the dataset and compared it with the stored hash digest to confirm that the data had not been tampered with since being uploaded to the chain. After confirmation, the system invoked a product knowledge base based on production process standards for intelligent diagnosis. This product knowledge base is a structured document database that stores a series of rule objects associated with known product quality defects. Each rule object is a JSON document containing fields such as "defect description," "associated process parameters," "parameter standard range," and "logic reasoning script." For the "excessively wet" complaint, the system matched a rule. The logic inference script for this rule was triggered, automatically extracting two key parameters, "baking temperature curve" and "baking time," from the traceability data. The script first determined whether the baking time of 5 minutes and 25 seconds was within the standard range of "4 minutes and 45 seconds ± 15 seconds," identifying that the time exceeded the upper limit. The script further analyzed the temperature curve data, calculating that the average temperature during the constant temperature stage was 175 degrees Celsius, lower than the lower limit of the standard defined in the knowledge base of 180 degrees Celsius. Based on this, the script's built-in reasoning logic—that is, "when the baking temperature is below the lower limit and the baking time exceeds the upper limit, the internal moisture of the product is likely not effectively evaporated, resulting in an overly moist texture"—was satisfied. The system integrated the analysis results and automatically generated a product lifecycle investigation report. The report clearly pointed out that the root cause of this quality problem was most likely due to the baking process being too cold and the baking time being too long, thus accurately attributing the problem to a deviation in the production process rather than a defect in the raw materials, achieving full-process traceability management of the pomegranate pancake production process.

[0060] It should be clarified that the embodiments described above are merely exemplary and are intended to aid in understanding the present invention, not to limit it. Those skilled in the art can make various changes and modifications after grasping the core ideas of the present invention. Therefore, the scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A blockchain-based pomegranate pancake end-to-end traceability management system, characterized in that, include: The product data acquisition module collects the original quality data of the total weight and juice yield of pomegranates in the raw materials of the product, and obtains the value-added processing parameters, including the pancake baking temperature curve and baking time, during the product processing. The production audit transformation module anchors the original quality data and value-added processing parameters onto the distributed value ledger, triggering a product audit contract based on the balance of production and materials. The product audit contract inputs the original quality data into the production transformation model and automatically calculates the theoretical output corresponding to the product raw materials. The lifecycle traceability module obtains the actual output of product raw materials. The product audit contract compares the actual output with the theoretical output and generates a process verification identifier that characterizes the credibility of the product processing process. The process verification identifier is hash-locked with the original quality data, value-added processing parameters, and actual output to form a full-cycle value history. The full-cycle value history is stored in a distributed value ledger. When a product quality defect is found, the original quality data and value-added processing parameters are traced back to output a full product life cycle investigation report.

2. The pomegranate pancake full-process traceability management system based on blockchain according to claim 1, characterized in that, The specific implementation process of collecting raw quality data on the total weight and juice yield of pomegranates in the product ingredients, and obtaining value-added processing parameters including pancake baking temperature curves and baking time during product processing, includes: In the raw material processing stage, intelligent weighing and optical sensing equipment are used to collect data on the total weight of pomegranates and the juice yield, and integrate them into raw quality data. In the product processing stage, multi-point temperature probes and timing devices built into the baking equipment are used to record the pancake baking temperature curve and the accumulated baking time information, and integrate them into value-added processing parameters. The raw quality data and value-added processing parameters are uploaded to the data temporary storage area through the data interface.

3. The pomegranate pancake full-process traceability management system based on blockchain according to claim 1, characterized in that, The specific implementation process of anchoring the raw quality data and value-added processing parameters onto a distributed value ledger to trigger a product audit contract based on the balance of production and resources includes: The programmable interface is invoked to integrate the original quality data and value-added processing parameters into a transaction data structure. The transaction data structure is then digitally signed using an asymmetric encryption private key associated with the production batch, forming a transaction to be uploaded to the blockchain with verifiable identity information and a timestamp. The transaction to be uploaded to the blockchain is broadcast to the consensus network of the distributed value ledger. After verification by multiple network nodes and confirmation by the consensus mechanism, it is encapsulated into an immutable data block and attached to the existing blockchain, completing data anchoring and automatically activating the product audit contract deployed on the distributed value ledger.

4. The pomegranate pancake full-process traceability management system based on blockchain according to claim 1, characterized in that, The product audit contract inputs raw quality data into the production conversion model, and the specific implementation process of automatically calculating the theoretical output corresponding to the product raw materials includes: The product audit contract calls the original quality data to extract the quantitative values ​​of the total weight and juice yield of pomegranates; the production conversion model encapsulates the material conversion coefficient, standard formula parameters, and loss rate adapted to the pomegranate pancake production process; the product audit contract inputs the obtained total weight and juice yield of pomegranates into the production conversion model's production-production balance algorithm to deduce the quantity of finished products that can be produced from the raw materials under ideal working conditions, obtain the theoretical output, and temporarily store it in the internal state variables of the product audit contract.

5. A blockchain-based pomegranate pancake full-process traceability management system according to claim 1, characterized in that, The specific implementation process of obtaining the actual output of product raw materials, comparing the actual output with the theoretical output in the product audit contract, and generating a process verification identifier characterizing the credibility of the product processing process includes: The actual output is obtained through metering equipment, and an execution request for the product audit contract is submitted to the distributed value ledger. After receiving the execution request, the product audit contract extracts the calculated theoretical output from its internal storage space and compares the actual output with the theoretical output. Deviation is evaluated according to the tolerance range specified in the production process standard, and a process verification mark is generated. When the deviation is within the tolerance range, the process is qualified; otherwise, the process is abnormal.

6. The pomegranate pancake full-process traceability management system based on blockchain according to claim 1, characterized in that, The specific implementation process of hash-locking the process verification identifier with the original quality data, value-added processing parameters, and actual output to form a full-cycle value history includes: The process verification identifier, original quality data, value-added processing parameters, and actual output are aggregated to form a product dataset containing data information from each stage; the product dataset is input into a deterministic cryptographic hash function to generate a hash digest; the hash digest is used as a digital fingerprint and associated with the product dataset to be encapsulated together into a full-cycle value history.

7. The pomegranate pancake full-process traceability management system based on blockchain according to claim 1, characterized in that, The entire lifecycle value history is stored in a distributed value ledger. When a product quality defect is discovered, the specific implementation process of tracing the original quality data and value-added processing parameters includes: The entire lifecycle value history is constructed into a transaction request that conforms to the distributed value ledger protocol specification and broadcast to all network nodes. After receiving the transaction request, the network nodes execute the consensus algorithm to verify its validity and store the entire lifecycle value history in the newly generated block. When a product quality defect event triggers a traceability request, a reverse query operation is initiated on the distributed value ledger using the index identifier associated with the defective product batch. By traversing historical blocks, the stored record containing the corresponding entire lifecycle value history is located, and the original quality data and value-added processing parameters locked by hash are extracted.

8. The pomegranate pancake full-process traceability management system based on blockchain according to claim 1, characterized in that, The specific process of generating a product lifecycle survey report includes: The system utilizes a product knowledge base based on production process standards to perform multi-dimensional data comparison and logical reasoning, identifying value-added processing deviations and original quality anomalies that are statistically correlated with product quality defects, and integrating and outputting a product lifecycle investigation report.

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