Intelligent review method and device for enterprise bidding and tendering

Abstract

The invention provides an intelligent review method and device for enterprise bidding and tendering, and relates to the technical field of blockchains, and the method comprises the steps: automatically deducing adaptive review terms through a review rule mapping engine, and binding identification information to each review term; writing the review terms into a review rule snapshot on a block chain generation chain; generating an irreversible target Hash fingerprint for objective data in response data uploaded by the system account, uploading the irreversible target Hash fingerprint to the block chain, and encrypting the objective data in the response data to generate encrypted data; according to the review rule snapshot, performing judgment processing on each target hash fingerprint to generate a first review result; decrypting the encrypted data, and carrying out identity binding and signature on the expert scoring behavior and the review result to obtain a second review result; and combining the first review result with the second review result, generating a review report, adding an electronic signature on the platform chain, and writing the electronic signature into the block chain. According to the invention, the confidence of the bidding and tendering review result can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of blockchain, and specifically to an intelligent review method and device for enterprise bidding. Background Art

[0002] The evaluation of corporate bidding is a systematic, multi-dimensional and comprehensive judgment process. Usually, the tendering party will set up an evaluation committee with professional background and evaluation qualifications. After the bid deadline, all compliant bid documents will be subject to formal review, qualification review, technical review and commercial review. The bidding units will be scored or ranked based on key factors such as their qualifications, performance capabilities, the advancement and feasibility of their technical solutions, the rationality and competitiveness of their quotations, and the comprehensive score will be calculated based on the scoring weights to determine the best candidate for the bid or directly determine the winning bidder. The entire process must ensure openness, transparency, fairness and impartiality, and comply with relevant laws and regulations and the evaluation standards and procedures stipulated in the bidding documents.

[0003] During the bidding and tendering review process, since the review data mostly relies on manual input or localized system storage, and there is a lack of a verifiable full-process recording mechanism, the review process is easily tampered with, including but not limited to modifying the scoring results, replacing the review opinions, or manually adjusting the sorting order, thereby reducing the confidence in the final review results and making it difficult to trace the responsible party.

[0004] Existing technologies use computers to automate intelligent reviews, reducing human involvement and thereby increasing the confidence of the review process. However, this process lacks effective information consistency verification and encryption protection between nodes. This allows both internal intervention and external attacks to bypass permission controls or log auditing mechanisms, tampering with review content. This still leads to opacity and unfairness in bidding results, seriously undermining the confidence of the review mechanism. Summary of the Invention

[0005] The present application provides an intelligent review method and device for enterprise bidding, which can improve the confidence level of bidding review results.

[0006] In a first aspect of the present application, an intelligent review method for enterprise bidding is provided, the method comprising:

[0007] During the project release phase, based on the bidding project parameters set in the backend, the evaluation rule mapping engine automatically derives the appropriate evaluation terms and binds identification information to each of the evaluation terms;

[0008] Based on the identification information, a smart contract is used to perform hash locking and write the review terms into the blockchain to generate a snapshot of the review rules on the blockchain;

[0009] Perform content recognition on the response file uploaded by the system account and extract the response data, wherein the system account is the account that the supplier has completed real-name binding with a blockchain identity;

[0010] Generate an irreversible target hash fingerprint for the objective data in the response data and upload it to the blockchain, and encrypt the subjective data in the response data to generate encrypted data;

[0011] In the intelligent review stage, each target hash fingerprint is judged and processed according to the review rule snapshot to generate a first review result;

[0012] Decrypting the encrypted data, reviewing the decrypted data according to the subjective scoring terms by the experts, and binding the expert scoring behavior with the review result and signing it to obtain a second review result;

[0013] Combine the first review result with the second review result to generate a review report, affix the electronic signature on the platform chain, and write it into the blockchain.

[0014] Based on the above technical solution, preferably, generating an irreversible target hash fingerprint for the objective data in the response data and uploading it to the blockchain specifically includes:

[0015] Performing field structuring processing on the objective data to obtain a first data block;

[0016] Performing hash digest calculation on the objective data to obtain a second data block;

[0017] generating a target data embedding mode based on the system account, wherein the target data embedding mode is a mode in which the second data block is embedded in the first data block, the target data embedding mode corresponding to the system account and different from a historically generated data embedding mode;

[0018] The second data block is embedded into the first data block through the target data embedding method to obtain the target hash fingerprint.

[0019] Based on the above technical solution, preferably, in the intelligent review stage, each target hash fingerprint is judged and processed according to the review rule snapshot to generate a first review result, specifically including:

[0020] In the intelligent review stage, the target data embedding method is called according to the system account;

[0021] Determining a corresponding data extraction method according to the target data embedding method, and extracting a hash digest from the target hash fingerprint according to the data extraction method to obtain the first data block;

[0022] Determining a field label corresponding to each field value included in the first data block;

[0023] Aligning the field labels with the review terms of the review rule snapshot to establish a logical mapping relationship between the field values and the review terms, ensuring a one-to-one correspondence between the field labels and the review terms;

[0024] Execute a judgment function between the field value and the standard value of the corresponding review clause through analytical logic calculation, and output a Boolean result of the judgment;

[0025] All the Boolean results of the determination are collected and aggregated according to the weights, classifications and determination modes set in the evaluation terms to generate the first evaluation result.

[0026] Based on the above technical solution, preferably, after combining the first review result with the second review result, generating a review report, affixing the report with the electronic signature on the platform chain, and writing the report into the blockchain, the method further includes:

[0027] After receiving an objection request to the review report, locating the review report from the blockchain based on the electronic signature, and then determining the corresponding system account based on the review report;

[0028] Retrieving the review rule snapshot from the blockchain through the system account, and performing version verification and integrity verification on the review rule snapshot;

[0029] After the review rule snapshot is verified, multiple stored hash fingerprints are obtained from the blockchain, each hash fingerprint is parsed and a hash digest is extracted using the data extraction method corresponding to the system account, and the first data block is reconstructed based on the target hash fingerprint among the multiple hash fingerprints;

[0030] Performing field tag recognition on the first data block, parsing the structural relationship between the field tag and the field value, and aligning the field tag with the review clause in the review rule snapshot;

[0031] According to the determination function bound to each review clause recorded in the review rule snapshot, re-execute the parsing logic calculation between the field value and the clause standard value, and output each corresponding verification Boolean result;

[0032] Retrieving the first review result of the review report, performing reverse analysis, and extracting each of the judgment Boolean results;

[0033] Comparing the verification Boolean result with the determination Boolean result, performing an aggregation calculation according to the weights in the original review process, and calculating a verification value for the first review result;

[0034] If it is determined that the verification value is less than or equal to the preset threshold, it is determined that the review report has not been modified.

[0035] Based on the above technical solution, preferably, the step of performing content recognition on the response file uploaded by the system account and extracting the response data specifically includes:

[0036] The supplier uses the on-chain address preset during registration as a unique identity identifier, and registers into the on-chain identity contract by mapping it with the real-name authentication information, binding the actual identity data, and generating a unique system account;

[0037] When the response file is uploaded by the system account, the file format of the response file is automatically identified and the file format is structurally classified;

[0038] According to the content type of the structural classification, different processing paths are adopted for the response file to extract the content of the response file;

[0039] Performing field label mapping on the response file content according to the review clause field specification and the field template library, and semantically classifying the unstructured fields in the response file content and uniformly mapping them into standard response field labels;

[0040] Response field structuring processing is performed on the standard response field label, including unifying the response field data format, standardizing the response field unit, normalizing the response field time expression, verifying the legitimacy of the response field value range, and marking the response field recognition confidence to obtain the response data.

[0041] Based on the above technical solution, preferably, during the project release phase, the evaluation rule mapping engine automatically derives the appropriate evaluation terms based on the bidding project parameters set in the background, and binds identification information to each of the evaluation terms, specifically including:

[0042] Establish parameter fields for bidding projects input by project initiators to form basic metadata for rule matching;

[0043] According to a pre-configured clause adaptation mapping table, condition matching is performed on the basic metadata to automatically derive an applicable review clause set, wherein the review clause set includes multiple review clauses;

[0044] For each of the review clauses, identification information is automatically bound, and the identification information includes one or more of the clause classification, clause application scope, clause version number, and clause status.

[0045] Based on the above technical solution, preferably, the condition matching of the parameter fields is performed according to the pre-configured clause adaptation mapping table to automatically derive the applicable review clause set, specifically including:

[0046] Constructing a structured clause adaptation mapping table, wherein the clause adaptation mapping table is expressed in the form of a nested rule tree, and each rule unit of the clause adaptation mapping table contains a mapping relationship between a set of bidding project parameters and a set of corresponding evaluation clause numbers;

[0047] and converting the basic metadata into a parameter structure object in a standardized key-value pair format;

[0048] The review clause matching engine performs a logical judgment operation on the parameter structure object item by item. The review clause matching engine parses the rule conditions item by item from the clause adaptation mapping table in a preset order and adopts a decision tree path sinking mechanism to complete the rule hit detection operation between the parameter structure object and the rule unit;

[0049] All the evaluation clause numbers that are determined to be satisfied by the rule conditions are summarized as a candidate set of clause numbers applicable to the bidding project;

[0050] Execute clause structure loading for each review clause number in the clause number candidate set, search the review clause metadata table according to the review clause number, obtain complete review clause information, and encapsulate the data corresponding to the review clause information into standardized review clauses to obtain the review clause set.

[0051] In a second aspect of the present application, there is provided an intelligent review device for enterprise bidding, the device being configured to execute any one of the above-described intelligent review methods for enterprise bidding, the device comprising an acquisition module, a processing module, and an output module, wherein:

[0052] The acquisition module is used to automatically derive the adapted review terms based on the bidding project parameters set in the background during the project release phase by the review rule mapping engine, and bind identification information to each of the review terms;

[0053] The processing module is configured to perform hash locking using a smart contract based on the identification information, and write the review terms into a blockchain to generate a snapshot of the review rules on the blockchain;

[0054] The processing module is used to perform content recognition on the response file uploaded by the system account and extract the response data, wherein the system account is the account to which the supplier completes real-name binding using a blockchain identity identifier;

[0055] The processing module is configured to generate an irreversible target hash fingerprint for the objective data in the response data and upload the fingerprint to the blockchain, while encrypting the subjective data in the response data to generate encrypted data;

[0056] The processing module is configured to perform determination processing on each target hash fingerprint according to the review rule snapshot during the intelligent review phase to generate a first review result;

[0057] The processing module is used to decrypt the encrypted data, review the decrypted data according to the subjective scoring terms of the experts, and bind the expert scoring behavior with the review result and sign it to obtain a second review result;

[0058] The output module is used to combine the first review result with the second review result, generate a review report, stamp it with the electronic signature on the platform chain, and write it into the blockchain.

[0059] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0060] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions. When the instructions are executed, any one of the methods described above is executed.

[0061] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0062] 1. This application combines the evaluation clause mapping mechanism driven by bidding project parameters with the on-chain rule snapshot solidification to ensure the uniqueness and non-tamperability of the evaluation basis. At the same time, it performs structured processing and hierarchical notarization on the response data based on the supplier's blockchain identity, generates target hash fingerprints for objective data and stores them on the chain, and encrypts subjective data with expert public keys and binds decryption permissions to ensure the authenticity of data content and the exclusivity of access control; in the intelligent review stage, automatic judgment is performed and the first review result is generated through the precise alignment of on-chain rules and off-chain data. The expert review behavior is also confirmed by identity binding and digital signature. Finally, all review results are uniformly packaged into an evaluation report with the platform chain signature and written into the blockchain. The whole process forms a technical closed loop with verifiable rules and traceable data, which fundamentally improves the confidence of the bidding review results.

[0063] 2. This application constructs a target data embedding method based on system account calls to achieve accurate parsing and field value recovery of the target hash fingerprint in the uploaded response data, and combines the review terms in the review rule snapshot to perform field alignment and logical judgment, automatically output the judgment Boolean result and perform aggregate scoring based on the term weight and classification, thereby forming a structured, standardized and traceable first review result, which significantly improves the degree of automation of the objective review link, the consistency of the judgment logic and the credibility of the review process, and effectively prevents human intervention and result tampering.

[0064] 3. This application constructs a review report positioning mechanism based on on-chain electronic signatures, combines system account-driven rule snapshot verification, hash fingerprint analysis and field-level reconstruction, and supplements with review clause alignment and judgment function recalculation to form a set of reversible, verifiable and comparable first review result verification paths, thereby achieving full-process consistency verification of the review conclusions in terms of structure, content and scoring dimensions, so that after receiving an objection request, the review process can be accurately restored and tampering behavior can be effectively identified, significantly enhancing the verifiability of the review results and the fairness of dispute resolution.

[0065] 4. This application introduces a system account mechanism based on blockchain identity contracts to enable suppliers to use on-chain addresses as unique identities during the registration phase, and to bind real-name authentication data to generate system accounts, thereby ensuring the authenticity and traceability of the source of the response data. Automatically identify the file format of the response file, and determine the processing path based on structural classification to accurately extract the content of the response file. Subsequently, perform field label mapping based on the review terms field specifications and field template library, classify the unstructured field semantics into a unified standard response field label, and further perform structured processing operations on each field to generate response data with complete structure, clear semantics, and standardized format, providing a high-quality data input foundation for subsequent intelligent review.

[0066] 5. This application realizes the automated logical matching of bidding project parameters by constructing a structured clause adaptation mapping table and adopting a nested rule tree and decision path sinking mechanism. It combines the standardized parameter structure object and the review clause metadata table to complete the precise derivation of the review clause number and the dynamic loading of the clause content, and finally generates a set of review clauses that are completely consistent with the project conditions, with complete structure and consistent semantics, thereby significantly improving the intelligence level and configuration efficiency of the review rule adaptation, and ensuring the accuracy, uniqueness and process transparency of the review logic. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a flow chart of an intelligent review method for enterprise bidding disclosed in an embodiment of the present application;

[0068] Figure 2 This is a module diagram of an intelligent review device for enterprise bidding disclosed in an embodiment of the present application;

[0069] Figure 3 This is a structural diagram of an electronic device disclosed in an embodiment of the present application.

[0070] Explanation of the reference numerals: 201, acquisition module; 202, processing module; 203, output module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0071] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0072] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0073] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0074] Enterprise bidding and tendering review is a complex process that relies on an expert committee to conduct multi-dimensional scoring of bid documents and determine the winning bidder accordingly. However, in existing technologies, since review data mainly relies on manual operations and local system management, there is a lack of a fully verifiable mechanism. As a result, the scoring results are easy to tamper with, the review process is difficult to hold accountable, and information interaction lacks encryption protection and consistency verification. The fairness of the review can be easily compromised by internal interference or external attacks, which seriously weakens the credibility of the review system.

[0075] This embodiment discloses an intelligent review method for enterprise bidding, referring to Figure 1 , including the following steps S110-S170:

[0076] S110, in the project release phase, based on the bidding project parameters set in the background, the evaluation rule mapping engine automatically derives the adapted evaluation terms and binds identification information to each of the evaluation terms.

[0077] The embodiments of the present application disclose an intelligent review method for enterprise bidding and tendering, which is applied to a server. The server includes, but is not limited to, electronic devices such as mobile phones, tablet computers, wearable devices, and PCs (Personal Computers), and can also be a backend server running the intelligent review method for enterprise bidding and tendering. The server can be implemented as a standalone server or a server cluster consisting of multiple servers.

[0078] In one possible implementation, during the project release phase, the review rule mapping engine automatically derives applicable review clauses based on the bidding project parameters set in the backend, and binds identification information to each review clause. Specifically, this includes establishing parameter fields specific to the bidding project input by the project initiator to form basic metadata for rule matching. Based on a pre-configured clause adaptation mapping table, conditional matching is performed on the basic metadata to automatically derive the applicable review clause set, which includes multiple review clauses. Identification information is automatically bound to each review clause, including one or more of the clause classification, clause scope of application, clause version number, and clause status.

[0079] Specifically, during the project release phase, the project sponsor enters bidding project parameter fields, including project type, procurement method, budget, bidder qualification type, whether consortium bidding is permitted, and industry classification. These bidding project parameter fields are structured and parsed, and stored in a standardized key-value pair format to form unique basic metadata corresponding to the current bidding project. This basic metadata serves as the input for subsequent review rule matching, ensuring structural consistency, semantic clarity, and field extensibility, driving the execution of mapping logic.

[0080] The review rule mapping engine is called to load the clause adaptation mapping table. This table records the matching rules between different bidding project parameter condition combinations and review clauses in the form of preset conditional expressions. Using basic metadata as input, all matching rules in the mapping table are conditionally matched, and the hit status is determined using logical methods such as row-by-row traversal, Boolean expression evaluation, or path determination. The review clause numbers corresponding to all rules that meet the conditions are extracted and aggregated into a review clause set, forming a complete set of review clauses that should be adapted to the current bidding project, ensuring that the matching process is scalable, explainable, and reproducible.

[0081] Perform identification information binding operations on each review clause in the review clause set. The review clause identification information includes attributes such as review clause classification, review clause scope of application, review clause version number, and review clause status. The review clause classification is used to characterize the position of the review clause in the review process, such as qualification review, responsiveness review, and scoring review; the review clause scope of application defines the set of project parameter conditions under which the review clause is effective; the review clause version number records the revision status of the current content of the review clause; and the review clause status identifies whether the review clause is enabled. The above identification information is written into the review clause structure to form a review clause object with a complete structure, clear status, and controllable version, providing a data basis for subsequent rule snapshot generation and intelligent review execution.

[0082] In one possible implementation, conditional matching is performed on parameter fields according to a pre-configured clause adaptation mapping table, and a set of applicable review clauses is automatically derived, specifically including: constructing a structured clause adaptation mapping table, which is expressed in the form of a nested rule tree, and each rule unit of the clause adaptation mapping table contains a mapping relationship between a set of bidding project parameters and a set of corresponding review clause numbers; and converting basic metadata into a parameter structure object in a standardized key-value pair format; the review clause matching engine performs a logical judgment operation on the parameter structure object item by item, and the review clause matching engine parses the rule conditions one by one from the clause adaptation mapping table in a preset order, and adopts a decision tree path sinking mechanism to complete the rule hit detection operation between the parameter structure object and the rule unit; all review clause numbers that are judged to be true for the rule conditions are uniformly summarized into a candidate set of clause numbers applicable to the bidding project; clause structure loading is performed on each review clause number in the clause number candidate set, and the review clause metadata table is searched according to the review clause number to obtain complete review clause information, and the data corresponding to the review clause information is encapsulated into a standardized review clause to obtain a review clause set.

[0083] Specifically, a structured clause adaptation mapping table is first constructed. The clause adaptation mapping table is expressed in the form of a nested rule tree. Each mapping rule unit is composed of a set of bidding project parameter conditions and a set of evaluation clause numbers. The mapping rules support multi-level condition nesting, logical combination and priority control.

[0084] Assume that the bidding project parameter structure is:

[0085] P={(k1,v1),(k2,v2),...,(k n ,v n )}

[0086] Among them, k i Indicates the name of the i-th parameter field, v i Indicates the specific value corresponding to the i-th field, and P is a standardized parameter structure object.

[0087] The clause adaptation mapping table is a set of mappings between rule conditions and review clause numbers, expressed as:

[0088]

[0089] Among them, C j Represents the j-th rule condition, in the form of a Boolean logic expression C j =f j (P)∈{0,1}, which means that when the parameter structure P satisfies this condition, f j (P) = 1; It represents the subset of review clause numbers corresponding to the condition, and T is the complete set of review clause numbers.

[0090] When designing the clause adaptation mapping table, the parameter space is constructed based on typical fields such as project type, budget amount, procurement method, bidding organization form, industry characteristics, etc., and conditional judgment logic is set at each branch node to implement an extensible configuration model that drives the precise adaptation of review clauses based on parameter changes, ensuring that the rule table has the characteristics of strong expressiveness, high parsing efficiency, and strong configurability.

[0091] All bidding project parameter fields entered by the project initiator are parsed and converted into parameter structure objects in a standardized key-value pair format. The parameter structure object is a standard representation of the structure and semantics of all parameter fields in the current bidding project, including metadata such as field name, field value, field type, and value range enumeration, while maintaining logical independence and combinatorial compatibility between fields. The parameter structure object serves as the input basis for the review clause matching engine to execute rule matching. It can be dynamically loaded, uniformly transmitted, and structured traversed, effectively improving the matching engine's processing performance and the accuracy of rule triggering.

[0092] The review clause matching engine is called, a parameter structure object is input, and a logical judgment operation is performed item by item. The review clause matching engine sequentially reads the rule units in the clause adaptation mapping table in a preset parsing order and uses a decision tree path sinking mechanism to perform conditional judgment on each rule. During the matching process, this mechanism starts from the root node of the rule tree and recursively judges along different logical paths based on the actual field values in the parameter structure object until it matches the terminal leaf node of the rule to determine whether the review clause number is matched. This process has the advantages of short-circuit acceleration, condition clipping, and path positioning, and can efficiently implement rule matching decisions in complex conditional spaces.

[0093] In the process of performing review clause matching based on nested rule trees, the efficiency of rule parsing and matching accuracy are significantly improved by introducing short-circuit acceleration, conditional clipping and path positioning mechanisms. The short-circuit acceleration mechanism means that when a certain node condition does not hold, the evaluation of subsequent conditions of the branch path is immediately terminated to avoid unnecessary deep calculations; the conditional clipping mechanism pre-shields the rule branches that are not related to the current project parameters or cannot be met in the preprocessing stage to reduce the matching scope; the path positioning mechanism uses the mapping relationship between parameter fields and rule nodes to directly locate the path branches that may be hit, thereby skipping the full traversal of irrelevant paths. These three work together to complete rule hit judgment within a constant path depth when facing a large-scale, high-dimensional, and complex parameter space, achieving high-performance, high-controllability, and high-consistency intelligent adaptation of review clauses.

[0094] The corresponding review clause numbers of all rule units with "hit" judgment results are summarized to form a candidate set of clause numbers applicable to the current bidding project. Based on the parameter structure object P, the system executes all rule matching functions f j , perform a set union operation on all the established review clause number subsets to obtain the clause number candidate set:

[0095]

[0096] where [f j (P) = 1] is an indicator function, which takes the value 1 when the condition is met and 0 otherwise. Multiplication is the set value activation symbol, which means that only when the condition is met, the clause number subset T j Counted in the final set.

[0097] The candidate set of clause numbers is a collection of review clause numbers, which is unique, deduplicated, and conditionally sufficient. After the set is subjected to compliance verification and logical integrity verification, it enters the next stage for the loading and structural reconstruction of subsequent clause content.

[0098] The clause structure loading process is executed for each review clause number in the clause number candidate set. The review clause metadata table is accessed according to the review clause number to retrieve all the structural information corresponding to the review clause, including review clause classification, review clause field requirements, review clause judgment logic, review clause material requirements, review clause applicable conditions, review clause scoring method, review clause version number and status, etc. The review clause information is then encapsulated into a standardized review clause data structure, completing semantic binding, logical encapsulation and version mapping operations, and finally generating a complete review clause set applicable to the current bidding project for subsequent rule chaining, intelligent review and expert review. The complete review clause set is:

[0099]

[0100] Final review terms set It is the set of clause numbers T generated by matching the project parameter structure P through the conditional mapping rule R * The corresponding set of structured review clause objects constitutes the core execution benchmark for binding on-chain rule snapshot generation and scoring logic in the intelligent review process.

[0101] S120, based on the identification information, the smart contract is used to perform hash locking and write the review terms into the blockchain to generate a snapshot of the review rules on the chain.

[0102] First, structured encapsulation processing is performed on each review clause object that has completed the binding of identification information. The review clause object contains elements such as the review clause number, review clause classification, review clause scope of application, review clause field definition, review clause judgment logic, review clause required materials, review clause scoring method, review clause version number, review clause status, etc., and is formatted and encoded using JSON or special rule intermediate representation syntax to ensure that the review clause object has semantic integrity and serializability.

[0103] Then, a hash summary calculation is performed on each review clause object after the above structured encoding, and a standard cryptographic hash function such as SHA-256 or Keccak256 is used to perform a summary operation on the review clause structure data to obtain a one-to-one corresponding review clause target hash fingerprint. The target hash fingerprint uniquely maps the current clause content and structural status, and has irreversibility and content fingerprint properties, ensuring its non-tamperability and traceability after being stored on the chain.

[0104] Next, a dedicated review rule snapshot smart contract is deployed. This smart contract features functional modules such as rule registration, version binding, hash logging, clause indexing, and a call interface, and also reserves entry points for state management and access control. The smart contract's registration function is called with the target hash fingerprint of each review clause, along with its review clause number, review clause version number, review clause applicable scope, registration timestamp, and call interface path as parameters. The smart contract then writes this data structure in an immutable form to the blockchain's state storage, forming an on-chain review rule snapshot entry.

[0105] After each review clause is successfully registered, the smart contract generates a rule snapshot index, records it in the on-chain log event, and provides external rule snapshot retrieval and verification interfaces to support rule consistency verification and historical status auditing during the subsequent review process. All review clauses written to the blockchain can use smart contract interfaces to perform hash verification of the current clause content during intelligent review, expert review, and dispute resolution, ensuring that the actual review logic is fully consistent with the on-chain snapshot.

[0106] Finally, the hash value set of all the review terms of the current project is used to generate an overall snapshot summary as the review rule snapshot root node of the project, and the snapshot version number, snapshot generation time, associated project number and status information are recorded. The snapshot index pointer is written to the blockchain to form a unique identifier. The legality, integrity and version status of any single review term can be tracked and verified through the snapshot identifier on the chain.

[0107] Through the above implementation path, the review clause objects with structured logic and legal effect are transformed into data assets with on-chain ownership confirmation. With smart contracts as the operating logic, target hash fingerprints as the solidified credentials, and blockchain as an unalterable carrier, a chain-based governance system for enterprise bidding and tendering review rules with auditability, verifiability and compliance is constructed.

[0108] S130: Perform content recognition on the response file uploaded by the system account and extract the response data.

[0109] The response file uploaded by the system account is identified and the response data is extracted, specifically including: the supplier uses the preset on-chain address during registration as a unique identity identifier, and registers into the on-chain identity contract through mapping with the real-name authentication information, binds the actual identity data, and generates a unique system account; when the response file is uploaded by the system account, the file format of the response file is automatically identified and the file format is structurally classified; according to the content type of the structural classification, different processing paths are adopted for the response file to extract the content of the response file; the response file content is mapped to field labels according to the review clause field specifications and the field template library, and the field label mapping is performed, and the unstructured fields in the response file content are semantically classified and uniformly mapped to standard response field labels; response field structured processing is performed on the standard response field labels, including unifying the response field data format, standardizing the response field unit, normalizing the response field time expression, verifying the legitimacy of the response field value range, and marking the response field recognition confidence to obtain the response data.

[0110] Specifically, before the response process is initialized, the system account's identity must be confirmed. During the platform registration phase, suppliers use a preset on-chain address as their unique identity. This on-chain address is mapped one-to-one with their real-name registration information, which includes the company name, unified social credit code, legal representative's name, registered mobile phone number, and platform account ID. This data is encapsulated and registered with the on-chain identity contract, completing the generation of blockchain identity credentials. This system account serves as the sole identity for all subsequent response actions, linking uploaded data, operation records, and review results to ensure the uniqueness, verifiability, and traceability of the responding entity.

[0111] When a system account submits a response file, it immediately performs a format recognition operation on the response file. The response file may be in PDF format, image format, or structured electronic document format. The specific type is determined by parsing the file header information, determining the extension, and identifying the content signature. Based on the recognition results, the response file is classified as a fixed-layout response file or a structured document response file. This serves as the basis for subsequent path diversion, ensuring that each type of response file enters the corresponding content extraction process.

[0112] Different content processing paths are entered based on the structural classification results of the response file. Fixed-format response files, such as PDF and image-format response files, will use the OCR recognition module to extract text and image content. The recognition module combines a text detection model, a layout analysis model, and a character recognition model to complete the block-by-block extraction of text and structural information. Structured document response files, such as Word and online form files, will directly extract nested fields through the document object model. The extracted content maintains consistency in the hierarchy and attribute mapping, thereby obtaining the complete response file content.

[0113] The extracted response file content is mapped to field labels. Based on the review clause field specifications and field template library, the field label mapping process uses methods such as semantic similarity analysis, keyword tree matching, and named entity recognition to semantically classify and normalize unstructured field information. Field content with various expressions is mapped to a unified standard response field label. For example, expressions such as "Legal Representative Name," "Company Legal Person," and "Legal Person Name" are uniformly mapped to the "Legal Representative" field label to ensure the consistency, structuring, and standardization of subsequent field judgment logic.

[0114] The response file content that has completed the field label mapping is subjected to response field structuring processing. The structuring processing process includes unifying the response field data format, such as retaining decimal places for numerical values; standardizing the response field units, such as converting 10,000 yuan to yuan; normalizing the response field time expression, such as unifying "January 1, 2023" to "2023-01-01"; verifying the legitimacy of the response field value range, such as the company's establishment time must not be later than the bid deadline; marking the response field recognition confidence, such as automatically triggering a review mark when the OCR recognition confidence is lower than the set threshold. The final generated structured response data consists of elements such as field labels, field values, data formats, units, time expressions, and confidence levels, forming response data in a unified format, providing stable and reliable data input for subsequent intelligent reviews.

[0115] S140, generating an irreversible target hash fingerprint for the objective data in the response data and uploading it to the blockchain, while encrypting the subjective data in the response data to generate encrypted data.

[0116] First, after the structured response data is generated, the field classification module is called to classify all response fields according to predefined field classification rules. The field classification rules divide the structured response data into objective data and subjective data based on the field attributes of the review terms. Objective data refers to data that can be directly determined based on the rules, such as the company name, business license number, qualification level, registered capital, project manager certificate number, financial statement indicators, etc., and does not rely on human subjective judgment. Subjective data refers to data that requires comprehensive evaluation by review experts based on experience or judgment, such as technical solutions, performance plans, service commitment statements, personnel organizational structures, etc. Its content is linguistically descriptive, structurally open, and evaluated differently.

[0117] In one possible implementation, an irreversible target hash fingerprint is generated for the objective data in the response data and uploaded to the blockchain, specifically including: performing field structuring processing on the objective data to obtain a first data block; performing hash summary calculation on the objective data to obtain a second data block; generating a target data embedding method based on a system account, the target data embedding method being a method of embedding the second data block into the first data block, the target data embedding method corresponding to the system account and different from the historically generated data embedding method; and embedding the second data block into the first data block through the target data embedding method to obtain a target hash fingerprint.

[0118] Specifically, field structuring is performed on the objective data. Based on the review clause field definitions and field template standards, the objective data in the original response file is parsed and labeled. Elements such as field labels, field values, field units, field formats, and field positions are extracted and encapsulated into a unified data structure as the first data block. This first data block is a field-structured object with stable field boundaries, standard data expression, and clear semantic field labels. It serves as the basic structural unit for the subsequent target hash fingerprint construction.

[0119] A hash digest calculation is performed on the objective data. The field tags and field values are extracted from the first data block and concatenated in a standard serialization order to form a hash input string. The string is then irreversibly encrypted using the SHA-256 or Keccak256 hash algorithm, and a fixed-length hash value is output as the second data block. This second data block is the hash digest of the objective field, uniquely mapping the original field value. It is tamper-proof and deterministic, and is the core summary unit for implementing on-chain evidence storage.

[0120] Based on the target data embedding method generated by the current user, the data embedding method generation module is called with the user's blockchain identity, the relevant field tags, and the historical embedding records as input parameters. Through identity mapping and version control logic, a unique data embedding path rule is assigned to the current user, and the target data embedding method is constructed. This target data embedding method defines the location, hierarchy, tag naming, and structural nesting of the second data block within the first data block, ensuring that each user's embedding method is unique and different from historical methods, preventing target hash fingerprint duplication and embedding path conflicts.

[0121] Execute the target hash fingerprint construction operation, call the target data embedding method, embed the second data block into the specified position of the first data block, and construct an embedded hash structure as the final target hash fingerprint. The target hash fingerprint structure retains both the original field structure information and the hash summary path information, and can verify the integrity of the field value and the embedding mode in subsequent verification. The target hash fingerprint is bound together with the field label, field page position, field extraction confidence and user blockchain identity credentials to form a field evidence object, and the field evidence object is written to the blockchain through the smart contract interface to form an on-chain field-level review data evidence record, ensuring that the authenticity, structure and non-repudiation of the response data are guaranteed throughout the review process.

[0122] Next, all objective data fields are structurally encapsulated, including their target hash fingerprints, field labels, field extraction locations, and the account's blockchain identity credentials. A smart contract interface is then called to upload the encapsulated data to the blockchain. This on-chain evidence operation is handled by a dedicated response data evidence contract, which generates an unalterable record of each field on the chain, including the field identifier, hash value, timestamp, response subject identifier, and project identifier, forming a field-level blockchain non-repudiation evidence track.

[0123] At the same time, for subjective data fields, an independent encryption key pair is assigned to each review expert with scoring authority, and the public key is registered and bound to the corresponding expert account through the expert identity authentication mechanism; then, each subjective field content is identified and distributed according to the field label, and the corresponding expert's encryption public key is called to perform asymmetric encryption operations on the field value, ensuring that the subjective data received by each expert is exclusive encrypted data and can only be decrypted and accessed by their corresponding private key. The generated encrypted data mark field label, encrypted expert ID, encrypted timestamp and access authorization path are uniformly sealed in the off-chain database, and the field encryption summary and public key encryption mapping record are written to the blockchain through smart contracts to ensure that the data transmission process has content confidentiality, reception orientation and decryption controllability, thereby preventing the subjective data from being accessed or tampered by unauthorized entities during transmission, storage and scoring.

[0124] Finally, the encrypted summaries, field labels, and storage paths of all subjective fields are written to the blockchain through smart contracts to generate field index records for subsequent access control verification and encrypted field content consistency verification, ensuring that expert scoring is based on verifiable and unaltered original response content. Through the collaborative mechanism of on-chain storage of objective data and off-chain encryption of subjective data, the response data is made "verifiable," "protectable," "traceable," and "non-repudiable" during the review process, providing structural security and a content credibility baseline for intelligent review.

[0125] S150, in the intelligent review stage, each target hash fingerprint is judged and processed according to the review rule snapshot to generate a first review result.

[0126] In one possible implementation, during the intelligent review stage, each target hash fingerprint is judged and processed based on the review rule snapshot to generate a first review result, specifically including: during the intelligent review stage, calling the target data embedding method according to the system account; determining the corresponding data extraction method through the target data embedding method, and extracting the hash summary from the target hash fingerprint through the data extraction method to obtain a first data block; determining the field labels corresponding to the various field values contained in the first data block; aligning the fields with the review terms of the review rule snapshot through the field labels, establishing a logical mapping relationship between the field values and the review terms, and ensuring a one-to-one correspondence between the field labels and the review terms; executing the judgment function between the field value and the standard value of the corresponding review term through analytical logic calculation, and outputting a judgment Boolean result; collecting all the judgment Boolean results, and aggregating them according to the weights, classifications and judgment modes set by the review terms to generate the first review result.

[0127] Specifically, after entering the intelligent review phase, the target data embedding method corresponding to the account currently participating in the review is first called based on the blockchain identity credentials of the account. The target data embedding method has been generated and registered on-chain for the account during the response data target hash fingerprint construction phase. By parsing the path rules, field level mapping, and embedding parameter structure in the embedding method, the exclusive data extraction path for the target hash fingerprint under the account is obtained, which serves as the unique index method for hash summary extraction.

[0128] Using the aforementioned target data embedding method, a structured parsing operation is performed to extract the embedded hash summary data segment from the target hash fingerprint. This extraction process relies on the embedding path, label matching, and field indexing mechanisms to ensure that the extracted hash summary data accurately corresponds to the original value of the objective field. This hash summary is parsed into a first data block, which is a structured hash data set. Each entry contains a field label and its corresponding hash value, which is used for field identification and data content reconstruction before judgment.

[0129] Each data item in the first data block is analyzed to identify the field label corresponding to the hash digest. Field labels include standard field names such as "Company Name," "Qualification Level," "Registered Capital," and "Project Manager Qualification Certificate Number." Using the field label index structure, each field label is semantically aligned with the field dimensions in the review model, ensuring that the hash digest can be used to execute the decision logic for the corresponding review clause.

[0130] The review rule snapshot is called to perform field alignment on the field labels in the first data block. Based on the field labels bound to each review clause in the review rule snapshot, a logical mapping relationship is established between the field labels and the review clause. Field alignment requires a one-to-one match between the field labels and the review clause field definitions, with consistent field types, formats, and positions. This creates a computable input channel between the field values and the clause's standard values, ensuring stable and consistent input for logical execution.

[0131] Based on the field alignment result, the judgment function between the field value and the standard value of the review clause is executed. The judgment function can include equal value judgment function, greater than or less than interval function, time validity function, regular expression matching function or Boolean logic combination function according to the logical definition type of the review clause. Let the field value of the i-th field be v i , whose corresponding field label is l i , the review clause number matched in the review rule snapshot is t i , the standard value of this review item is s i , and its corresponding judgment function is:

[0132] f i (v i ,s i )

[0133] The logical judgment function between the field value and the standard value of the review clause is expressed as:

[0134] b i =f i (v i ,s i )∈{0,1}

[0135] where b i Indicates a Boolean result. A value of 1 indicates that the field value meets the clause requirements, and a value of 0 indicates that it does not.

[0136] The logical judgment function is set according to the logical type of the review clause, including equal value matching clauses, numerical interval clauses, time validity clauses, and regular expression matching clauses, which are expressed as follows:

[0137]

[0138] in, and Respectively represent the minimum and maximum values required by the review terms. i Finally, the output is Boolean value b i As a result of the judgment of whether the field matches its review terms, the subsequent system will use multiple b i Combine and perform review aggregation operations to form the complete first review result input. Each function f i It is configured in the form of a rule logic module in the intelligent review system. It is scalable, verifiable and composable, and supports dynamic loading and binding with on-chain rule snapshots.

[0139] The field value is input into the judgment function, compared with the standard value set in the review terms, and the Boolean result is output. The Boolean result is true for pass and false for fail. It is the smallest granularity judgment unit.

[0140] All Boolean results from field judgments are aggregated and comprehensively calculated according to the clause weights, clause classification, and aggregation rules defined in the review clause snapshot. Depending on the review model, a logical AND / or aggregation model, a weighted model, or a standardized score mapping using a scoring function can be employed to ultimately generate a structured primary review result. This primary review result includes detailed field judgments, clause-level scoring details, aggregated scores, anomaly alerts, and review conclusions, providing an objective basis and traceability for subsequent subjective scoring consolidation and expert review.

[0141] S160, decrypt the encrypted data, review the decrypted data according to the subjective scoring terms of the experts, and bind the expert scoring behavior and the review result with an identity and sign to obtain a second review result.

[0142] After receiving the encrypted subjective data, first, based on the expert identity information corresponding to each subjective scoring clause, the private key of the corresponding expert account is called from the expert key management module, and an asymmetric decryption operation is performed on the encrypted data. After successful decryption, the original subjective response field content is restored. The decrypted data is loaded into the expert review interface according to the field label, and the current review project number, review clause number and expert blockchain identity certificate are bound. The expert conducts a quantitative or qualitative review of the content based on the scoring dimensions, scoring standards and weight requirements of the subjective scoring clause, records the field content, scoring value, scoring time, scoring device identification and expert blockchain address corresponding to each scoring behavior, and generates an irrefutable digital signature for the scoring behavior based on the identity information. The scoring value and signature are encapsulated together into a structured scoring record to form a second review result, and written into the blockchain as a scoring hash summary to ensure that the results of the subjective review behavior are complete, the identity is authentic, the path is traceable, and the data cannot be tampered with.

[0143] S170, combine the first review result with the second review result, generate a review report, affix the electronic signature on the platform chain, and write it into the blockchain.

[0144] After the first and second review results are generated, the two review results are first structurally integrated. Based on the review clause number, the objective scoring data in the first review result and the subjective scoring data in the second review result are combined and calculated according to the clause classification and weight requirements to generate a review report containing the clause dimension score, scoring source, review path and comprehensive score. The review report is structurally bound with elements such as the project number, the bidder's blockchain identity, the expert's blockchain identity, the scoring time, and the scoring method to generate a unique review data structure. The review data structure is then electronically signed on the chain using the platform's private key to form a signed final review report. The review report smart contract interface is then called to write the signed review report summary, signature value, version identifier and timestamp into the blockchain, achieving full-process ownership confirmation of the review results, non-tamperability of the entire process, and full-node verifiability, ensuring the authenticity, legality and audit traceability of the entire review conclusion.

[0145] In one possible implementation, after combining the first review result with the second review result, generating a review report, affixing the electronic signature on the platform chain, and writing the report into the blockchain, the method further includes: after receiving an objection request to the review report, locating the review report from the blockchain based on the electronic signature, and then determining the corresponding system account based on the review report; retrieving a review rule snapshot from the blockchain through the system account, and performing version verification and integrity verification on the review rule snapshot; after the review rule snapshot is verified, obtaining multiple stored hash fingerprints from the blockchain, parsing each hash fingerprint and extracting the hash summary through the data extraction method corresponding to the system account, and reconstructing the first digital fingerprint based on the target hash fingerprint among the multiple hash fingerprints. Data block; perform field label recognition on the first data block, parse the structural relationship between the field label and the field value, and align the field label with the review clause in the review rule snapshot; according to the judgment function bound to each review clause recorded in the review rule snapshot, re-execute the parsing logic calculation between the field value and the clause standard value, and output each corresponding verification Boolean result; retrieve the first review result of the review report, perform reverse parsing, and extract each judgment Boolean result; compare the verification Boolean result with the judgment Boolean result, perform aggregation calculation according to the weight in the original review process, and calculate the verification value for the first review result; if it is judged that the verification value is less than or equal to the preset threshold, it is determined that the review report has not been modified.

[0146] Specifically, after the review report is written into the blockchain, if an objection request is received against the review report, the review verification mechanism will be immediately activated. The specific implementation process includes eight consecutive steps: review report positioning, account tracing, rule snapshot verification, hash reconstruction and extraction, field alignment verification, Boolean result recalculation, result comparison aggregation, and verification value determination, to ensure that objection handling is auditable, verifiable, and non-repudiation.

[0147] Upon receiving an objection request, the platform uses the review report identifier contained in the objection request to perform a unique hash match using the on-chain electronic signature to locate the target review report stored on the blockchain. Based on the identity credential field in the electronic signature structure, the blockchain identity of the account corresponding to the review report is extracted and used as the primary index for all subsequent data traceability.

[0148] The review configuration item bound to the account is called to retrieve a snapshot of the review rules applicable to the project from the blockchain. The review rule snapshot is frozen and uploaded to the blockchain during the project release phase. It contains information such as the review clause number, field definitions, judgment logic, and scoring weights. A version number and hash digest consistency check is performed on the review rule snapshot to ensure it has not been replaced, tampered with, or redefined.

[0149] Based on the data extraction method bound to the account, all associated hash fingerprint sets are retrieved from the blockchain. For each hash fingerprint, the corresponding target data embedding method is called for structural analysis, and the embedded hash summary content is extracted. The first data block is reconstructed according to the embedding path. The first data block is the original structured value set of the objective response field submitted by the account during the review stage.

[0150] Perform field label identification and semantic confirmation operations on the first data block, parse the correspondence between field labels and field values, and align fields in the review rule snapshot based on the field labels to ensure that each field value corresponds to a unique review clause and that a one-to-one mapping relationship exists between the input data and the rule logic.

[0151] The judgment function is re-executed for the field value and the standard value in the corresponding review clause. The judgment function is strictly operated according to the logical expression recorded in the rule snapshot, including numerical range judgment, string matching, regular expression verification or logical combination function. The execution result is output as the verification Boolean result between each field value and its review criteria.

[0152] Retrieve the first review result recorded in the review report and parse the field-level Boolean results contained therein. Each Boolean result is stored in a structured format within the report, including the clause number, field label, judgment conclusion, and scoring weight. Perform a reverse analysis on this result to extract all the original Boolean judgment outputs as a benchmark for subsequent comparisons.

[0153] Each verification Boolean result is compared with its corresponding judgment Boolean result item by item to determine whether they are completely consistent. Consistent items are included in the aggregate score according to their original weights. Inconsistent items are marked and recorded. All results are then aggregated using the weighting function set in the original review process to calculate the verification value of the first review result.

[0154] The verification value is compared with the preset threshold. If the verification value is less than or equal to the threshold, it means that the first review result recorded in the review report has not been modified at the field, logic and scoring levels, and the review report is judged to be credible; if the verification value is higher than the threshold, the audit process is triggered and the on-chain objection mark is recorded, and the manual review channel is entered to ensure that each objection has a mathematical basis and on-chain evidence support, thereby improving the review credibility and technical consistency of the review mechanism.

[0155] This embodiment also discloses an intelligent review device for enterprise bidding, which is used to execute any of the above-mentioned intelligent review methods for enterprise bidding, referring to Figure 2 The device includes an acquisition module 201, a processing module 202 and an output module 203, wherein:

[0156] The acquisition module 201 is used to automatically derive the adapted review terms based on the bidding project parameters set in the background during the project release phase by the review rule mapping engine, and bind identification information to each review term.

[0157] The processing module 202 is used to use the smart contract to perform hash locking based on the identification information and write the review terms into the blockchain to generate a snapshot of the review rules on the chain.

[0158] Processing module 202 is used to identify the content of the response file uploaded by the system account and extract the response data, wherein the system account is the account to which the supplier completes real-name binding using a blockchain identity identifier.

[0159] The processing module 202 is used to generate an irreversible target hash fingerprint for the objective data in the response data and upload it to the blockchain, and at the same time encrypt the subjective data in the response data to generate encrypted data.

[0160] The processing module 202 is used to perform determination processing on each target hash fingerprint according to the review rule snapshot in the intelligent review stage to generate a first review result.

[0161] The processing module 202 is used to decrypt the encrypted data, review the decrypted data according to the subjective scoring terms of the experts, and bind the expert scoring behavior with the review result and sign it to obtain a second review result.

[0162] The output module 203 is used to combine the first review result with the second review result, generate a review report, stamp it with the electronic signature on the platform chain, and write it into the blockchain.

[0163] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0164] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0165] The communication bus 302 is used to implement the connection and communication between these components.

[0166] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0167] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0168] The processor 301 may include one or more processing cores. The processor 301 utilizes various interfaces and circuits to connect various parts of the entire server. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and calling data stored in the memory 305, the processor 301 performs various server functions and processes data. Optionally, the processor 301 may be implemented using at least one hardware form selected from digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the processor 301 and may be implemented separately on a single chip.

[0169] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be at least one storage device located away from the aforementioned processor 301. As a computer storage medium, the memory 305 may include an operating system, a network communication module, a user interface 303 module and an application program for an intelligent review method for enterprise bidding.

[0170] exist Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call an application program stored in the memory 305 for an intelligent review method for corporate bidding. When executed by one or more processors 301, the electronic device executes one or more methods in the above embodiments.

[0171] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0172] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0173] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0174] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0175] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0176] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory 305 and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory 305 includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disk.

[0177] The present application also discloses a computer-readable storage medium storing instructions, which, when executed by one or more processors 301, enable an electronic device to execute one or more of the methods described in the above embodiments.

[0178] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An intelligent review method for enterprise bidding, characterized by: The method comprises: During the project release phase, based on the bidding project parameters set in the backend, the evaluation rule mapping engine automatically derives the appropriate evaluation terms and binds identification information to each of the evaluation terms; Based on the identification information, a smart contract is used to perform hash locking and write the review terms into the blockchain to generate a snapshot of the review rules on the blockchain; Perform content recognition on the response file uploaded by the system account and extract the response data, wherein the system account is the account that the supplier has completed real-name binding with a blockchain identity; Generate an irreversible target hash fingerprint for the objective data in the response data and upload it to the blockchain, and encrypt the subjective data in the response data to generate encrypted data; In the intelligent review stage, each target hash fingerprint is judged and processed according to the review rule snapshot to generate a first review result; Decrypting the encrypted data, reviewing the decrypted data according to the subjective scoring terms by the experts, and binding the expert scoring behavior with the review result and signing it to obtain a second review result; Combine the first review result with the second review result to generate a review report, affix the electronic signature on the platform chain, and write it into the blockchain.

2. The intelligent evaluation method for enterprise bidding according to claim 1 is characterized in that: Generating an irreversible target hash fingerprint for the objective data in the response data and uploading it to the blockchain specifically includes: Performing field structuring processing on the objective data to obtain a first data block; Performing hash digest calculation on the objective data to obtain a second data block; generating a target data embedding mode based on the system account, wherein the target data embedding mode is a mode in which the second data block is embedded in the first data block, the target data embedding mode corresponding to the system account and different from a historically generated data embedding mode; The second data block is embedded into the first data block through the target data embedding method to obtain the target hash fingerprint.

3. The intelligent evaluation method for enterprise bidding according to claim 2 is characterized in that: In the intelligent review stage, each target hash fingerprint is judged and processed according to the review rule snapshot to generate a first review result, specifically including: In the intelligent review stage, the target data embedding method is called according to the system account; Determining a corresponding data extraction method according to the target data embedding method, and extracting a hash digest from the target hash fingerprint according to the data extraction method to obtain the first data block; Determining a field label corresponding to each field value included in the first data block; Aligning the field labels with the review terms of the review rule snapshot to establish a logical mapping relationship between the field values and the review terms, ensuring a one-to-one correspondence between the field labels and the review terms; Execute a judgment function between the field value and the standard value of the corresponding review clause through analytical logic calculation, and output a Boolean result of the judgment; All the Boolean results of the determination are collected and aggregated according to the weights, classifications and determination modes set in the evaluation terms to generate the first evaluation result.

4. The intelligent evaluation method for enterprise bidding according to claim 3 is characterized in that: After combining the first review result with the second review result to generate a review report, affixing the report with the electronic signature on the platform chain, and writing the report into the blockchain, the method further includes: After receiving an objection request to the review report, locating the review report from the blockchain based on the electronic signature, and then determining the corresponding system account based on the review report; Retrieving the review rule snapshot from the blockchain through the system account, and performing version verification and integrity verification on the review rule snapshot; After the review rule snapshot is verified, multiple stored hash fingerprints are obtained from the blockchain, each hash fingerprint is parsed and a hash digest is extracted using the data extraction method corresponding to the system account, and the first data block is reconstructed based on the target hash fingerprint among the multiple hash fingerprints; Performing field tag recognition on the first data block, parsing the structural relationship between the field tag and the field value, and aligning the field tag with the review clause in the review rule snapshot; According to the determination function bound to each review clause recorded in the review rule snapshot, re-execute the parsing logic calculation between the field value and the clause standard value, and output each corresponding verification Boolean result; Retrieving the first review result of the review report, performing reverse analysis, and extracting each of the judgment Boolean results; Comparing the verification Boolean result with the determination Boolean result, performing an aggregation calculation according to the weights in the original review process, and calculating a verification value for the first review result; If it is determined that the verification value is less than or equal to the preset threshold, it is determined that the review report has not been modified.

5. The intelligent evaluation method for enterprise bidding according to claim 1 is characterized in that: The content identification of the response file uploaded by the system account and the extraction of the response data specifically include: The supplier uses the on-chain address preset during registration as a unique identity identifier, and registers into the on-chain identity contract by mapping it with the real-name authentication information, binding the actual identity data, and generating a unique system account; When the response file is uploaded by the system account, the file format of the response file is automatically identified and the file format is structurally classified; According to the content type of the structural classification, different processing paths are adopted for the response file to extract the content of the response file; Performing field label mapping on the response file content according to the review clause field specification and the field template library, and semantically classifying the unstructured fields in the response file content and uniformly mapping them into standard response field labels; Response field structuring processing is performed on the standard response field label, including unifying the response field data format, standardizing the response field unit, normalizing the response field time expression, verifying the legitimacy of the response field value range, and marking the response field recognition confidence to obtain the response data.

6. The intelligent evaluation method for enterprise bidding according to claim 1, characterized in that: During the project release phase, based on the bidding project parameters set in the background, the evaluation rule mapping engine automatically derives the appropriate evaluation terms and binds identification information to each of the evaluation terms, specifically including: Establish parameter fields for bidding projects input by project initiators to form basic metadata for rule matching; According to a pre-configured clause adaptation mapping table, condition matching is performed on the basic metadata to automatically derive an applicable review clause set, wherein the review clause set includes multiple review clauses; For each of the review clauses, identification information is automatically bound, and the identification information includes one or more of the clause classification, clause application scope, clause version number, and clause status.

7. The intelligent evaluation method for enterprise bidding according to claim 6 is characterized in that: The method of automatically deriving the applicable review clause set by matching the parameter fields according to the pre-configured clause adaptation mapping table specifically includes: Constructing a structured clause adaptation mapping table, wherein the clause adaptation mapping table is expressed in the form of a nested rule tree, and each rule unit of the clause adaptation mapping table contains a mapping relationship between a set of bidding project parameters and a set of corresponding evaluation clause numbers; Convert the basic metadata into a parameter structure object in a standardized key-value pair format; The review clause matching engine performs a logical judgment operation on the parameter structure object item by item. The review clause matching engine parses the rule conditions item by item from the clause adaptation mapping table in a preset order and adopts a decision tree path sinking mechanism to complete the rule hit detection operation between the parameter structure object and the rule unit; All the evaluation clause numbers that are determined to be satisfied by the rule conditions are summarized as a candidate set of clause numbers applicable to the bidding project; Execute clause structure loading for each review clause number in the clause number candidate set, search the review clause metadata table according to the review clause number, obtain complete review clause information, and encapsulate the data corresponding to the review clause information into standardized review clauses to obtain the review clause set.

8. An intelligent evaluation device for enterprise bidding, characterized by: The device is used to execute the intelligent evaluation method for enterprise bidding according to any one of claims 1 to 7, and the device comprises an acquisition module (201), a processing module (202) and an output module (203), wherein: The acquisition module (201) is used to automatically derive the adapted review terms based on the bidding project parameters set in the background during the project release phase by the review rule mapping engine, and bind identification information to each review term; The processing module (202) is used to perform hash locking using a smart contract based on the identification information, and write the review terms into a review rule snapshot on the blockchain generation chain; The processing module (202) is used to perform content recognition on the response file uploaded by the system account and extract the response data, wherein the system account is an account that the supplier completes real-name binding with a blockchain identity identifier; The processing module (202) is used to generate an irreversible target hash fingerprint for the objective data in the response data and upload it to the blockchain, and at the same time encrypt the subjective data in the response data to generate encrypted data; The processing module (202) is used to perform determination processing on each target hash fingerprint according to the review rule snapshot in the intelligent review stage to generate a first review result; The processing module (202) is used to decrypt the encrypted data, review the decrypted data according to the subjective scoring terms of the experts, and bind the expert scoring behavior with the review result and sign it to obtain a second review result; The output module (203) is used to combine the first review result with the second review result, generate a review report, affix an electronic signature on the platform chain, and write it into the blockchain.

9. An electronic device, characterized in that: The electronic device comprises a processor (301), a communication bus (302), a user interface (303), a network interface (304) and a memory (305), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are both used to communicate with other devices, the communication bus (302) is used to realize connection and communication between components in the electronic device, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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