A traditional Chinese medicinal material standardization pricing method and system based on grade mapping and price locking mechanism

Abstract

The application discloses a traditional Chinese medicinal material standardization pricing method and system based on grade mapping and price locking mechanism. The method comprises the following steps: automatically acquiring physicochemical detection report data of a third-party detection institution and performing cleaning; performing semantic analysis on unstructured detection reports to extract key indexes, cross-verification mapping the physicochemical indexes and preset grade standards, automatically calculating the standardization grade and generating a digital quality certificate; taking the standardization grade as a decisive weight parameter, combining market dynamic auxiliary parameters, and automatically calculating a benchmark price through a pricing model; writing the benchmark price into a data storage layer protected by an immutable constraint to make the price unchangeable by human intervention, allowing only fixed price matching in the front end, anchoring the price generation associated data to a distributed trusted storage network and constructing a hash tree to realize verifiable transparency; writing the grade and detection standard into an intelligent contract, and automatically triggering a fuse when rechecking is abnormal. The application realizes the full-process automation of traditional Chinese medicinal materials from detection authentication to grading and pricing to transaction closed loop, the price cannot be tampered with, the transparency is verifiable, and the intelligent protection is realized.

Description

Technical Field

[0001] This invention relates to the fields of information technology and e-commerce technology, specifically to a standardized pricing method and system for Chinese medicinal materials based on grade mapping and price locking mechanisms. More specifically, it relates to a technical solution that achieves full automation, immutability, and verifiability of the entire process of Chinese medicinal materials from third-party testing and certification to grade determination, price locking, and transparent disclosure through semantic parsing, knowledge graphs, rule engines, distributed trusted evidence storage, and smart contracts. Background Technology

[0002] Chinese medicinal herbs are the core raw materials of the traditional Chinese medicine (TCM) industry, and their quality and price directly affect the clinical efficacy of TCM and the healthy development of the industry. However, the following technical problems have long existed in the current TCM herb trading sector: 1. Subjectivity and Fragmentation of Grading Standards: The grading of Chinese medicinal materials mainly relies on human experience and subjective judgment. Different producing areas, testing institutions, and trading platforms use inconsistent grading standards, and there is a lack of technical means to automatically cross-validate and map authoritative third-party testing data with the grading standards of trading platforms. Existing Chinese medicinal material price index systems (such as the "Method and System for Generating Price Index of Chinese Medicinal Materials" disclosed in CN104751346A and the "Method for Evaluating Comprehensive Price Index of Chinese Medicinal Materials" disclosed in CN108764637A) only focus on price index calculation based on market transaction data, and do not involve grading determination and grading-pricing linkage mechanisms driven by testing and certification data.

[0003] 2. Price Manipulation and the Black Box of Negotiation: Existing Chinese medicinal herb trading platforms generally allow buyers and sellers to negotiate prices, and price data is stored in modifiable database tables, leaving room for human intervention in pricing. The price generation logic is not public and cannot be verified; buyers cannot confirm whether the prices they see are the true pricing results, nor can they verify whether the prices have been tampered with.

[0004] 3. Lack of technical safeguards in the transaction loop: The acceptance process in the transaction of Chinese medicinal materials lacks a technical mechanism that automatically links with the testing standards. If the quality of medicinal materials changes during storage or circulation, there is a lack of technical means to automatically trigger price adjustments or transaction circuit breakers. The security of the transaction depends on manual judgment and post-event accountability.

[0005] 4. Inconsistent data formats from multiple sources: Physicochemical test reports issued by third-party testing institutions come in various formats, including structured data interface outputs and unstructured PDF / image reports. There is a lack of technical solutions to automatically parse, align, and map test data of different formats to a unified grade standard.

[0006] In summary, existing technologies lack a solution that organically integrates technologies such as automatic parsing of third-party testing and certification data, cross-mapping with platform level standards, algorithm-driven pricing, tamper-proof locking of price data, verifiable and transparent disclosure, and intelligent protection of the transaction closed loop. Summary of the Invention I. Technical problems to be solved

[0007] The technical problems to be solved by this invention are: (1) the determination of the grade of Chinese medicinal materials relies on human experience, and the separation between the test data and the grade standard leads to inconsistent grading; (2) the transaction price of Chinese medicinal materials can be artificially manipulated and altered, and the price generation process cannot be verified, resulting in opaque transactions; (3) the acceptance of Chinese medicinal materials transactions lacks a technical guarantee mechanism that automatically links with the test standard; (4) multi-source heterogeneous test data cannot be automatically parsed and aligned to a unified grade standard. II. Technical Solution

[0008] To address the aforementioned technical problems, this invention provides a standardized pricing method and system for Chinese medicinal materials based on grade mapping and price locking mechanisms.

[0009] In terms of methodology, this invention provides a standardized pricing method for Chinese medicinal materials based on grade mapping and price locking mechanisms, including: S1: Multi-source data access and cleaning - Automatically acquire physicochemical testing report data from authoritative third-party testing institutions, simultaneously collect business attribute data of Chinese medicinal materials, and perform format standardization and noise removal cleaning on the acquired data; S2: Multimodal data alignment and automatic grading - Semantic parsing of unstructured test reports to extract key physicochemical indicators, cross-validation and mapping of the physicochemical indicators with preset grading standards, automatic calculation of the standardized grade of the batch of Chinese medicinal materials, and generation of digital quality certificates with unique identifiers. S3: Algorithm-driven benchmark price generation—using the standardization grade as the decisive weight parameter and combining market dynamic auxiliary parameters, the benchmark price of this batch of Chinese medicinal materials is automatically calculated through a pricing model, wherein the contribution of the standardization grade to the benchmark price is the decisive weight. S4: System-level price locking and transparent disclosure - The benchmark price is written into the data storage layer protected by immutable constraints, so that the written price data cannot be modified by humans, and the front-end trading interface only allows matching at this fixed price; at the same time, the price generation-related data is anchored to the distributed trusted evidence storage network, so that the price data generation process has a verifiable timestamp and tamper-proof evidence storage. S5: Smart Contract Fulfillment and Dynamic Circuit Breaker – The grade of medicinal materials and testing standards are written into the smart contract as acceptance terms. If abnormal testing data occurs during the transaction, the system will automatically execute a price recalculation or a transaction circuit breaker mechanism.

[0010] Furthermore, the automatic acquisition mentioned in step S1 can be achieved through standardized data interface calls or optical character recognition technology; the format standardization and noise reduction cleaning include unified field mapping, unified unit conversion, outlier removal and missing value marking processing.

[0011] Furthermore, the semantic parsing in step S2 includes word segmentation, entity recognition, and semantic analysis; the cross-validation and mapping includes comparing the physicochemical index values ​​with the threshold ranges of the corresponding varieties and grades in the preset grade standards item by item and comprehensively calculating the standardized grade.

[0012] Furthermore, the pricing model described in step S3 can adopt a weighted calculation method, with the basic price level corresponding to the standardization level as the decisive component, and the real-time supply and demand index fluctuation component and the historical average transaction price reference component as auxiliary components, and the level weight is greater than the sum of the auxiliary weights.

[0013] Furthermore, the data storage layer protected by the immutable constraint described in step S4 is implemented by setting a write protection mechanism at the database level, which automatically intercepts and records the audit trail when a modification or deletion request to the written price data is detected.

[0014] Furthermore, step S4 also includes a verifiable and transparent mechanism based on hash trees: a hash tree is constructed for all batches of price data, and the root hash is anchored to a distributed trusted evidence storage network. Transaction participants can independently verify the authenticity of specific batches of price data through the hash path.

[0015] Furthermore, the circuit breaker action described in step S5 includes at least one of suspending trading, recalculating the reclassified price, and marking the item as delisted.

[0016] In terms of the system, this invention provides a standardized pricing system for Chinese medicinal materials based on a grade mapping and price locking mechanism, comprising: a multi-source data access module, an automatic grading engine module, a pricing calculation module, a price immutability constraint module, a verifiable transparency module, and a smart contract circuit breaker module. The specific composition and function of each module are described in claims 8 to 14 and Embodiment 2 of the specification.

[0017] Regarding computer program products, the present invention also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the above-described method. III. Beneficial Effects

[0018] Compared with the prior art, the technical solution of this invention has the following beneficial technical effects: 1. Data-driven automated grading eliminates subjective bias: Semantic parsing technology automatically analyzes third-party testing reports, and combined with knowledge graphs and rule engines, cross-maps physicochemical indicators to grading standards. This achieves a fully automated process from testing data to grading determination, eliminating the subjective bias and inconsistencies of traditional manual grading. A digital quality certificate assigns a unique and traceable grading identifier to each batch.

[0019] 2. Immutable price constraints at the architectural level, eliminating human intervention: Through an immutable data storage layer and write protection mechanism, immutable price constraints are implemented at the underlying data storage layer. Any instruction attempting to modify the price is intercepted and its audit trail is recorded, eliminating the possibility of human intervention in pricing from the system architecture level. The front-end trading interface only allows fixed-price matching, completely blocking negotiation functions and achieving the technical enforcement of a "fixed price."

[0020] 3. A verifiable and transparent mechanism combining hash trees and distributed trusted evidence storage achieves technical-level trust: Price data is constructed into a hash tree and anchored to a distributed trusted evidence storage network. Trading participants can independently verify the authenticity and generation time of price data through the hash path, confirming that the price has not been tampered with without relying on the platform's credit endorsement. The hash value of the detection report is stored synchronously, making the correlation between the price generation logic and the detection data verifiable, achieving true technical-level transparency.

[0021] 4. Smart contract circuit breaker ensures the security of the closed-loop transaction: The testing standards and grade requirements are written into the smart contract as acceptance terms. When an abnormality is found during re-inspection, the circuit breaker mechanism is automatically triggered (suspending trading, re-grading and recalculating the price, or delisting). From a technical point of view, the security of the closed loop from grading to pricing to trading is guaranteed, avoiding the uncertainty of post-event accountability.

[0022] 5. Unified alignment and automated processing of multi-source heterogeneous data: Semantic parsing of unstructured reports, knowledge graph storage of mapping relationships, and rule engine execution of cross-validation work together to solve the technical problems of inconsistent detection report formats and the disconnect between detection data and grading standards, thus achieving unified alignment and automated processing of multi-source heterogeneous data. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the overall process of the standardized pricing method for Chinese medicinal materials based on grade mapping and price locking mechanism of the present invention; Figure 2 This is a detailed flowchart illustrating the multimodal data alignment and automatic classification step (S2) of the present invention; Figure 3 This is a schematic diagram of the price immutability constraint architecture of the present invention; Figure 4 This is a schematic diagram of the verifiable and transparent mechanism based on hash trees in this invention; Figure 5 This is a schematic diagram of the module structure of the standardized pricing system for Chinese medicinal materials based on grade mapping and price locking mechanism of the present invention.

[0024] (Note: The attached drawings must be prepared by the drafting personnel based on the contents of the instruction manual. The above descriptions of the attached drawings are for annotation purposes only, and the specific references to the attached drawings are used in the specific implementation.) Detailed Implementation

[0025] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention. Example 1: Overall Method Flow

[0026] Reference Figure 1 This invention relates to a standardized pricing method for Chinese medicinal materials based on grade mapping and price locking mechanisms. The overall process includes steps S1 to S5:

[0027] The system obtains physicochemical testing report data from authoritative third-party testing institutions through two channels: Channel 1 – Standardized Data Interface: For testing institutions that provide standardized data interfaces (such as the Hong Kong Standards and Testing Centre (STC), the system calls their RESTful API to retrieve the corresponding batch's physicochemical testing indicator dataset using the batch number of the Chinese medicinal materials as an index. The data returned by the interface is structured data in JSON format, containing fields such as test item name, test value, unit, and test conclusion.

[0028] Channel Two – Optical Character Recognition: For non-standard format paper or image-based inspection reports (such as PDF reports or scanned copies), the system uses OCR technology for image preprocessing (denoising, tilt correction, and region segmentation), and then performs character recognition to extract structured fields such as the inspection item name, inspection value, and inspection conclusion.

[0029] In addition, the system synchronously collects business attribute data of medicinal materials, including: place of origin, harvesting period, processing technology (such as raw product / processed product), storage conditions, etc.

[0030] The data cleaning process includes: unifying field mapping for data from different sources (e.g., mapping the names of the same indicator from different institutions to a unified field name), unifying unit conversion (e.g., unifying the conversion of mg / kg to ppm), removing outliers (e.g., marking data whose test values ​​exceed the reasonable physical range as outliers), and handling missing value marking.

[0031] Reference Figure 2 This step consists of three sub-steps: Sub-step 2a – Semantic Analysis: This involves word segmentation, entity recognition, and semantic analysis of the detection report text. Specifically, the word segmenter divides the detection report text into a sequence of words; the entity recognizer identifies physicochemical indicator entities (such as "astragaloside A content", "lead limit", "pesticide residue") and their corresponding numerical entities (such as "0.8mg / g", "5ppm", "not detectable") from the word sequence; and the semantic analyzer determines the semantic relationships between entities (such as which value corresponds to which indicator, whether the limit standard is the upper or lower limit).

[0032] Key physicochemical indicators for extraction include, but are not limited to: content of active ingredients (such as astragaloside A, tanshinone, etc.), limits of heavy metals (lead, cadmium, arsenic, mercury, copper), limits of pesticide residues, limits of microorganisms (total bacterial count, mold, Escherichia coli, etc.), moisture content, and ash content.

[0033] Sub-section 2b – Knowledge Graph Mapping: The knowledge graph stores the mapping relationship between the physicochemical indicators of Chinese medicinal materials and grading standards. This mapping relationship is constructed based on industry standards (such as the Chinese Pharmacopoeia, ISO international standards, and Hong Kong STC testing standards) and the platform's internal grading rules. The knowledge graph adopts an ontology structure and covers: Variety nodes: such as Astragalus membranaceus, Angelica sinensis, and Ligusticum chuanxiong; Level nodes: Special Grade, Grade 1, Grade 2, Grade 3, etc.; Physicochemical index nodes: Names of each test item; Threshold interval edge: Connects the grade node and the physicochemical index node, and marks the threshold interval of each index under each grade (e.g., the astragaloside A content of extra grade astragalus is ≥0.8mg / g).

[0034] The knowledge graph supports dynamic updates. When industry standards are revised or platform rating rules are adjusted, the threshold interval edges can be updated without affecting the overall graph structure.

[0035] Sub-step 2c – Rule Engine Cross-Validation and Mapping: The rule engine receives the physicochemical indicator data extracted from semantic parsing and the level-indicator mapping relationship in the knowledge graph, and executes the following logic: (1) For each physicochemical indicator, determine whether its value falls within the threshold range of the corresponding variety under the target grade; (2) Statistically calculate the compliance status of each indicator (compliant / non-compliant / close to the threshold boundary); (3) Calculate the standardization grade comprehensively: adopt the weighted scoring method, the weight of each indicator is set according to its influence on the quality of medicinal materials, and the compliance scores are weighted and summarized and mapped to the grade range; (4) Output digital quality certificate: including batch unique identifier, standardization grade, value of each physicochemical indicator, details of compliance judgment of each indicator, grading timestamp, and grading engine version identifier.

[0036] The batch unique identifier of the digital quality certificate can be generated using a UUID or a hash value of the batch number and the grading timestamp, ensuring the uniqueness and traceability of the grading results for each batch.

[0037] In this embodiment, the pricing model uses the standardization level as the decisive weight parameter and calculates the benchmark price using the following weighted calculation method: P_base = G_weight × P_grade + S_weight × P_supply + H_weight × P_history in: P_grade: The basic price level corresponding to the standardized grade. Each grade has a preset basic price in the platform's pricing system (e.g., the basic price level for premium Astragalus is 200 yuan / kg, and for Grade 1 it is 150 yuan / kg, etc.). This basic price level is updated regularly by the platform based on industry standards and market research. P_supply: Price component based on the floating coefficient of the real-time supply and demand index. Supply and demand index = Current available supply of Chinese medicinal materials of the same grade and variety / Current demand of buyers. When supply > demand, P_supply is negative (floats downward), when supply < demand, P_supply is positive (floats upward), and when supply and demand are balanced, P_supply is zero. P_history: Historical average transaction price reference component, which is the weighted average of the transaction prices of Chinese medicinal materials of the same grade and variety over the most recent N trading days (e.g., the most recent 30 trading days), with the weight decreasing as the trading days become closer. G_weight, S_weight, and H_weight are the weighting coefficients for the standardization level, supply and demand index, and historical average price, respectively. G_weight + S_weight + H_weight = 1, and G_weight > S_weight + H_weight, ensuring the decisive contribution of the standardization level to the price. In this embodiment, G_weight = 0.65, S_weight = 0.20, and H_weight = 0.15, but the invention is not limited to these specific values; any value satisfying G_weight > S_weight + H_weight is acceptable.

[0038] The pricing model ensures that "standardized grade" is the determining factor for price, with supply and demand fluctuations and historical references making only limited adjustments, thereby realizing the core business logic of "grade determines price, no fixed price".

[0039] It should be noted that the above weighted calculation method is only one specific implementation of the present invention. The pricing model of the present invention is not limited to this specific formula. As long as the technical feature of "standardization level as the decisive weight parameter" is met, other calculation methods can be used to generate the benchmark price.

[0040] Reference Figure 3 and Figure 4 This step consists of three sub-steps: Sub-stage 4a – Price Immutability Constraint Framework: In this embodiment, the data storage layer protected by immutable constraints is implemented as follows: a dedicated price storage table (price_immutable_table) is created at the database level. This table contains the following fields: batch unique identifier, standardization level, benchmark price, pricing timestamp, pricing model version, and test report hash value. A row-level write protection trigger (price_write_guard_trigger) is set for this table, with the following trigger logic: Listen for all UPDATE and DELETE operation requests targeting the price storage table; When such a request is detected, the trigger automatically rejects the request (returning an error code and a blocking message); At the same time, an interception record is written to the audit log table, including: interception time, request source IP, request type (UPDATE / DELETE), target record batch identifier, and original request content summary.

[0041] This architecture ensures that any instruction attempting to modify written prices via SQL statements or application-layer interfaces is intercepted at the database level and its audit trail is recorded, eliminating the possibility of human intervention in prices from the system architecture level.

[0042] It should be noted that the above-mentioned "price storage table + row-level write protection trigger" is only one specific implementation of the data storage layer protected by immutable constraints. This invention is not limited to this specific implementation. Other technical means that can achieve "written price data cannot be modified by humans" (such as read-only mounting of file systems, blockchain native storage, etc.) all fall within the scope of the data storage layer protected by immutable constraints of this invention.

[0043] The design of the front-end transaction interface: When the transaction matching module receives an order request from the buyer, it only reads the fixed price field in the data storage layer protected by immutable constraints as the transaction price. The order request does not contain any price parameter input fields. When the transaction is confirmed, the fixed benchmark price is automatically filled in, realizing the technical enforcement of "fixed price".

[0044] Sub-step 4b – A verifiable and transparent mechanism based on hash trees: In this embodiment, the hash tree is implemented using a Merkle tree. At the end of each pricing period (e.g., after daily or batch pricing is completed), the price storage table records for all batches are used as leaf nodes to construct the Merkle tree. The hash value of the leaf node is calculated as: Hash(Batch Unique Identifier + Base Price + Pricing Timestamp + Inspection Report Hash Value). The hashes of intermediate nodes are calculated layer by layer upwards, ultimately yielding the root hash Root_Hash.

[0045] The Root_Hash is submitted as on-chain transaction data to the consortium blockchain node to obtain on-chain confirmation and timestamp anchoring. The consortium blockchain is a permissioned blockchain, and its nodes include trusted participants such as platform operators, representatives of testing institutions, and representatives of industry associations.

[0046] The buyer's verification process: The buyer obtains the Merkle path of the target batch (the hash values ​​of each sibling node traversed from the leaf node to the root node), and calculates upwards layer by layer starting from the leaf node hash, finally obtaining the calculated root hash Calculated_Root_Hash. If Calculated_Root_Hash matches the Root_Hash anchored on the consortium blockchain, it confirms that the price data for this batch is genuine and has not been tampered with.

[0047] This mechanism allows purchasers to independently verify the authenticity of price data without relying on the platform's credit endorsement, achieving technical-level trust rather than credit-level trust.

[0048] It should be noted that the Merkle tree + consortium blockchain described above is only one specific implementation of the hash tree + distributed trusted evidence storage network. The hash tree can adopt other tree-shaped hash structures, and the distributed trusted evidence storage network can adopt public blockchains, consortium blockchains, or other distributed trusted storage systems, as long as the technical characteristics of "constructing a hash tree, anchoring the root hash to the distributed trusted evidence storage network, and allowing transaction participants to independently verify through the hash path" are met.

[0049] Sub-step 4c – Price generation and related data synchronization and storage: The complete data package for each pricing period (including pricing results for all batches, pricing model parameters, hash values ​​of the corresponding test reports for each batch, grading results, and pricing timestamps) is packaged into on-chain transaction data and submitted to the consortium blockchain. This notarization provides an immutable on-chain proof for the entire logical connection of "testing and certification → grading determination → price generation," and any data tampering at any stage can be detected through on-chain comparison.

[0050] The standardized grade of Chinese medicinal materials, the corresponding threshold range of physicochemical indicators, the identification of testing institutions, the acceptance judgment rules and the circuit breaker triggering conditions are encoded into smart contract terms and deployed to the consortium blockchain.

[0051] The acceptance criteria for smart contracts include: after receiving the goods, the purchaser may entrust a third-party organization to re-inspect the medicinal materials; the comparison results of the physicochemical index values ​​obtained from the re-inspection with the initial test values ​​shall serve as the basis for acceptance criteria.

[0052] Circuit breaker triggering conditions include: Condition A: The physicochemical indicators obtained from the retest exceed the threshold range of the corresponding grade (e.g., the initial test result of astragaloside A is ≥0.8mg / g and it is classified as special grade, but the retest result is 0.5mg / g, which is lower than the special grade threshold of 0.8mg / g). Condition B: The physicochemical indicators obtained from the retest deviate from the initial test value by more than the preset tolerance range (e.g., the tolerance is set to ±10%, and the retest result deviates from the initial test value by more than ±10%).

[0053] The circuit breaker action includes at least one of the following: Action 1: Suspend the transaction status of this batch of Chinese medicinal materials, display the "Under Re-inspection" mark on the front end, and suspend all new order matching; Action 2: Re-execute steps S2 to S4, re-grade and recalculate prices based on the re-inspection data, and generate new benchmark prices and digital quality certificates; Action 3: Mark the batch of Chinese medicinal materials as unavailable and notify the purchaser and supplier to initiate the return or compensation process.

[0054] The circuit breaker mechanism ensures the closed-loop security from rating to pricing to transaction, so that quality anomalies no longer rely on post-event accountability, but are automatically detected and responded to by the system. Example 2: System Module Structure

[0055] Reference Figure 5 This invention relates to a standardized pricing system for Chinese medicinal materials based on a grade mapping and price locking mechanism, comprising the following modules: 1. Multi-source data access module: This module enables standardized data interface calls and optical character recognition (OCR) to acquire physicochemical testing reports and medicinal herb business attribute data from third-party testing institutions, and performs format standardization and cleaning. This module outputs a unified format of testing datasets and business attribute datasets.

[0056] 2. Automatic rating engine module: Contains three sub-modules: The semantic parsing submodule includes a word segmenter, an entity recognizer, and a semantic analyzer, which perform NLP processing on the detection report text and extract key physicochemical indicators. Knowledge Graph Submodule (Grade Mapping Submodule): Stores the ontology of mapping relationships between Chinese medicinal herb varieties, grades, and physicochemical index threshold ranges. It supports querying the index threshold ranges of each grade by variety and supports dynamic updates. The rule engine submodule (rule determination submodule) receives the indicator data extracted from semantic parsing and the mapping relationship with the knowledge graph, executes cross-validation and level calculation logic, and outputs standardized level and digital quality certificate.

[0057] 3. Pricing Calculation Module: Stores pricing model formulas and weight parameters, receives standardized levels and base price tiers from the automatic grading engine, and calculates the benchmark price by combining real-time supply and demand indices and historical average transaction prices. The supply and demand index is obtained by the system periodically calculating the ratio of sales volume to demand on the platform, and the historical average price is obtained by querying the transaction record database.

[0058] 4. Price Immutable Constraint Module: This module implements the data storage layer for immutable constraint protection. In this embodiment, it specifically includes a read-only data view layer and a row-level write protection trigger: a price storage table (price_immutable_table) is created in the database, and a row-level write protection trigger (price_write_guard_trigger) is set to intercept all UPDATE and DELETE operations and write interception records to the audit log table. The front-end trading interface only reads the fixed price field of this view for matching.

[0059] 5. Verifiable Transparent Module: This module includes a hash tree construction submodule and a distributed trusted evidence storage and anchoring submodule. In this embodiment, it specifically comprises a Merkle tree construction submodule and a consortium blockchain anchoring submodule: The Merkle tree construction submodule constructs a Merkle tree from all batches of price data and calculates the root hash in each pricing cycle; the consortium blockchain anchoring submodule submits the root hash and the complete pricing data packet to the consortium blockchain node for confirmation. The purchaser can obtain the verification path through the Merkle path verification interface and verify it independently.

[0060] 6. Smart Contract Circuit Breaker Module: The grade of medicinal materials, threshold values ​​of physicochemical indicators, identification of testing institutions, acceptance rules and circuit breaker conditions are encoded into smart contracts and deployed to the consortium blockchain. The module monitors the re-inspection data and automatically triggers the circuit breaker action when an anomaly occurs.

[0061] The modules mentioned above work together through internal data interfaces and message queues to form a complete technical closed loop from data access → classification → pricing → locking → transparent disclosure → circuit breaker protection. Example 3: Grading and Pricing Examples for Specific Varieties

[0062] Taking Astragalus membranaceus as an example, the specific application of the method of the present invention is illustrated below: Data Access: A batch of Astragalus membranaceus from Longnan, Gansu Province, was submitted to STC in Hong Kong for comprehensive testing. STC issued a physicochemical test report (PDF format). The system used OCR to recognize the report content and semantically analyzed to extract key indicators: Astragaloside A content 0.85 mg / g, total Astragaloside content 1.2 mg / g, lead content 2 ppm, pesticide residues not detected, moisture content 8.5%, and total ash content 3.2%.

[0063] Automatic grading: The knowledge graph maps Astragalus membranaceus grades to indicators as follows: Premium grade Astragaloside A ≥ 0.8 mg / g and lead ≤ 5 ppm with no detected pesticide residues; Grade 1 Astragaloside A ≥ 0.6 mg / g and lead ≤ 5 ppm. The rule engine determines that this batch of Astragaloside A ≥ 0.8 mg / g (compliant), lead ≤ 5 ppm (compliant), and pesticide residues not detected (compliant), and calculates the overall grade as "Premium," generating a digital quality certificate (batch identifier: HQ-2026-LS-001, grade: Premium, detailed indicators: ...).

[0064] Benchmark Price Generation: Pricing model parameters are set as follows: G_weight=0.65, S_weight=0.20, H_weight=0.15. The base price range for premium Astragalus membranaceus is P_grade=200 yuan / kg. Current supply and demand index: 5000kg of premium Astragalus membranaceus for sale / 6000kg of demand → Supply < Demand, P_supply=+15 yuan / kg. The average transaction price of premium Astragalus membranaceus in the last 30 days is P_history=195 yuan / kg. Benchmark price P_base = 0.65×200 + 0.20×15 + 0.15×195 = 130 + 3 + 29.25 = 162.25 yuan / kg.

[0065] Price locking and transparent disclosure: The benchmark price of 162.25 yuan / kg is written to the `price_immutable_table`, and the row-level write protection trigger is activated. The front-end trading interface displays "Astragalus membranaceus, Premium Grade, Longnan, Gansu, 162.25 yuan / kg (fixed price)". When the buyer places an order, the price field is read-only and cannot be modified. The SHA256 hash value of the test report is synchronously anchored to the consortium blockchain along with the pricing result. A Merkle tree is constructed from the price data of all batches on the same day, and the root hash is anchored on the blockchain.

[0066] Smart contract protection: The smart contract encodes the premium-grade Astragalus standard (astragaloside A ≥ 0.8 mg / g, lead ≤ 5 ppm, pesticide residues not detected) as acceptance clauses. If a re-inspection during storage reveals that astragaloside A has dropped to 0.65 mg / g (deviating from the initial detection value of 0.85 mg / g by more than ±10% of the tolerance, and below the premium-grade threshold of 0.8 mg / g), the smart contract will automatically trigger a circuit breaker: suspending trading, re-grading to Grade 1 based on the re-inspection data, recalculating the price to the benchmark price corresponding to the Grade 1 base price tier, or directly removing the product from the market and notifying both parties.

Claims

1. A standardized pricing method for Chinese medicinal materials based on grade mapping and price locking mechanisms, characterized in that, Includes the following steps: S1: Multi-source data access and cleaning - Automatically acquire physicochemical testing report data from authoritative third-party testing institutions, simultaneously collect business attribute data of Chinese medicinal materials, and perform format standardization and noise removal cleaning on the acquired data; S2: Multimodal data alignment and automatic grading - Semantic parsing of unstructured test reports to extract key physicochemical indicators, cross-validation and mapping of the physicochemical indicators with preset grading standards, automatic calculation of the standardized grade of the batch of Chinese medicinal materials, and generation of digital quality certificates with unique identifiers. S3: Algorithm-driven benchmark price generation—using the standardization grade as the decisive weight parameter and combining market dynamic auxiliary parameters, the benchmark price of this batch of Chinese medicinal materials is automatically calculated through a pricing model, wherein the contribution of the standardization grade to the benchmark price is the decisive weight. S4: System-level price locking and transparent disclosure - The benchmark price is written into the data storage layer protected by immutable constraints, so that the written price data cannot be modified by humans, and the front-end trading interface only allows matching at this fixed price; at the same time, the price generation-related data is anchored to the distributed trusted evidence storage network, so that the price data generation process has a verifiable timestamp and tamper-proof evidence storage. S5: Smart Contract Fulfillment and Dynamic Circuit Breaker – The grade of medicinal materials and testing standards are written into the smart contract as acceptance terms. If abnormal testing data occurs during the transaction, the system will automatically execute a price recalculation or a transaction circuit breaker mechanism.

2. The method according to claim 1, characterized in that, The automatic acquisition of physicochemical testing report data from authoritative third-party testing institutions in step S1 includes at least one of the following channels: By calling the data output port of a third-party testing institution through a standardized data interface, the corresponding batch's physicochemical testing indicator dataset can be obtained using the batch number as an index; or Optical character recognition technology is used to identify and extract the structure of non-standard format test reports, obtaining fields such as test item name, test value and test conclusion; The format standardization and noise removal cleaning include unifying field mapping, unifying unit conversion, removing outliers, and marking missing values ​​for data from different sources.

3. The method according to claim 1, characterized in that, In step S2: The semantic parsing of the unstructured test report includes word segmentation, entity recognition and semantic analysis of the test report text, and locating and extracting the values ​​of key physicochemical indicators and their limit standards. The process of cross-validating and mapping physicochemical indicators with preset grade standards includes: comparing the extracted physicochemical indicator values ​​with the threshold ranges of the corresponding varieties and grades in the preset grade standards item by item, determining the compliance status of the batch of Chinese medicinal materials in each indicator dimension, comprehensively calculating the standardization grade, and outputting a digital quality certificate containing details of the compliance determination of each indicator.

4. The method according to claim 1, characterized in that, The pricing model described in step S3 uses a weighted calculation method to generate a benchmark price: P_base = G_weight × P_grade + S_weight × P_supply + H_weight × P_history Wherein, P_grade is the base price level corresponding to the standardization level, P_supply is the floating coefficient price component based on the real-time supply and demand index, P_history is the historical average transaction price reference component, and G_weight, S_weight, and H_weight are the corresponding weight coefficients, and satisfy G_weight + S_weight + H_weight = 1 and G_weight > S_weight + H_weight, so that the contribution of the standardization level to the benchmark price is a decisive weight.

5. The method according to claim 1, characterized in that, The data storage layer protected by immutable constraints in step S4 includes: At the database level, a write protection mechanism for price data is set up. When a request to modify or delete written price data is detected, the mechanism automatically intercepts the request and records the audit trail. The front-end trading interface only reads the fixed price field in the data storage layer protected by immutable constraints as the trading price, and does not provide an entry point for price modification or negotiation parameters.

6. The method according to claim 1 or 5, characterized in that, Step S4 also includes a verifiable transparency mechanism based on a hash tree: Within each pricing cycle, a hash tree is constructed using all batches of price data as leaf nodes, the root hash value is calculated, and the root hash is anchored to a distributed trusted evidence storage network. The transaction participants verify the authenticity of a specific batch of price data through hash paths, including: obtaining the hash path of the target batch of price data and the hash value of its sibling nodes, calculating the hash layer by layer from the leaf node to the root node, and if the calculated root hash is consistent with the root hash anchored on the distributed trusted evidence storage network, then the batch of price data is confirmed to be authentic and has not been tampered with.

7. The method according to claim 1, characterized in that, In step S5: The smart contract performance mechanism includes encoding the standardized grade of Chinese medicinal materials, corresponding physicochemical index thresholds, testing agency identification and acceptance judgment rules into smart contract terms and deploying them to a distributed trusted evidence storage network. The dynamic circuit breaker mechanism includes: when abnormal re-inspection data occurs during the transaction period—that is, the physicochemical indicators obtained from the re-inspection exceed the corresponding grade threshold range or deviate from the initial test value by more than the preset tolerance range—the smart contract automatically triggers a circuit breaker action. The circuit breaker action includes at least one of the following: suspending the transaction status of the batch of Chinese medicinal materials, re-executing the grading and price recalculation, marking the batch of Chinese medicinal materials as delisted and notifying the transaction participants.

8. A standardized pricing system for traditional Chinese medicinal materials based on a grade mapping and price locking mechanism, characterized in that, include: The multi-source data access module is used to automatically acquire physicochemical testing report data and Chinese medicinal material business attribute data from third-party authoritative testing institutions, and to standardize and clean the data. The automatic grading engine module is used to perform semantic parsing on unstructured test reports to extract key physicochemical indicators, cross-validate and map the physicochemical indicators with preset grading standards, automatically calculate the standardized grading level, and output a digital quality certificate with a unique identifier. The pricing calculation module is used to automatically calculate the benchmark price through a pricing model, using the standardization level as the decisive weight parameter and combining market dynamic auxiliary parameters. The price immutability constraint module is used to write the benchmark price into the data storage layer protected by immutability constraints and to intercept modification operations on the price data. The front-end trading interface only allows matching at a fixed price. The verifiable transparent module is used to anchor price generation-related data to a distributed trusted evidence storage network, enabling transaction participants to independently verify the authenticity of price data; The smart contract circuit breaker module is used to write the grade of medicinal materials and testing standards into the smart contract as acceptance terms, and automatically trigger price recalculation or transaction circuit breaker when the testing data is abnormal.

9. The system according to claim 8, characterized in that: The automatic grading engine module includes a semantic parsing submodule, a grading mapping submodule, and a rule determination submodule. The semantic parsing submodule is used to perform word segmentation, entity recognition, and semantic analysis on the detection report text to extract key physicochemical indicators. The grading mapping submodule is used to store the mapping relationship between the physicochemical indicators of Chinese medicinal materials and grading standards. The rule determination submodule is used to cross-validate and map the physicochemical indicators and grading standards and automatically calculate the standardized grading.

10. The system according to claim 8, characterized in that: The mapping relationships stored in the grade mapping submodule cover multiple Chinese medicinal materials, each material corresponds to multiple standardized grades, and each grade corresponds to a set of physicochemical index threshold ranges. The threshold ranges are constructed based on industry standards and platform grading rules and support dynamic updates.

11. The system according to claim 8, characterized in that: The pricing model in the pricing calculation module uses a weighted calculation method: P_base = G_weight × P_grade + S_weight × P_supply + H_weight × P_history Where G_weight is the standardized grade weight and is the decisive weight coefficient, S_weight is the market dynamic weight, H_weight is the historical reference weight, G_weight + S_weight + H_weight = 1, and G_weight > S_weight + H_weight.

12. The system according to claim 8, characterized in that: The price immutability constraint module sets up a write protection mechanism at the data storage level. When a request to modify or delete written price data is detected, it automatically intercepts and records the audit trail. The front-end transaction interface only reads the fixed price field in the data storage layer protected by the immutability constraint as the transaction price.

13. The system according to claim 8, characterized in that: The verifiable transparent module constructs a hash tree using all batches of price data as leaf nodes in each pricing cycle, calculates the root hash to anchor to the distributed trusted evidence storage network, and generates a corresponding hash path and sibling node hash value for each batch of price data for verification by transaction participants.

14. The system according to claim 8, characterized in that: The smart contract deployed by the smart contract circuit breaker module includes the following clause codes: standardization grade identifier of Chinese medicinal materials, corresponding physicochemical index threshold, testing institution identifier, acceptance judgment rules and circuit breaker triggering conditions; the circuit breaker action includes at least one of the following: suspending trading, re-grading and recalculating prices, and delisting mark.

15. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 7.

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