Government data processing method, system, device and storage medium

Abstract

The present invention discloses a government data processing method, system, device and storage medium, which relate to the field of intelligent government decision-making, including: based on structured policy vectors and government data, generating tamper-proof blockchain evidence triples through a multi-chain collaborative evidence mechanism; based on the blockchain evidence triples, using a cosine similarity algorithm to perform real-time decision matching, calculating the similarity value between the government matter state vector and the structured policy vector, and generating a decision execution record when the similarity value reaches the policy compliance benchmark requirement; based on the decision execution record, starting a multi-chain collaborative audit mechanism, identifying and locating illegal operation nodes and generating an audit report; based on the audit report, optimizing audit data and policy effectiveness weights through a policy analysis engine, and generating a government data optimization proposal; by using a dynamic quantization algorithm to fuse timeliness characteristics and regional parameters to construct a policy knowledge graph and generate a structured policy vector, effectively solving the effectiveness evaluation deviation of the static model.

Description

Technical Field

[0001] The present invention relates to the field of intelligent government decision-making, and in particular to a government data processing method, system, device and storage medium. Background Art

[0002] In recent years, the field of government data processing has gradually integrated knowledge graphs and blockchain technologies to improve decision-making accuracy. At the policy quantification level, traditional methods primarily rely on static rule engines and natural language processing techniques to extract keywords from policy texts. Meanwhile, the application of blockchain technology in government systems focuses on data storage within a single-chain architecture. A typical example is the administrative approval storage system implemented by Ethereum smart contracts, which ensures process traceability through hashing on-chain. Furthermore, vectorized decision-making models have been initially implemented in government scenarios, using algorithms such as Word2Vec to map policy texts into a low-dimensional vector space and combining them with support vector machine (SVM) classifiers for basic policy matching.

[0003] Bottlenecks remain in dynamic policy adaptability and cross-chain collaborative efficiency. First, static quantitative models lead to inaccurate assessments of policy effectiveness: traditional methods ignore the dynamic decay characteristics of policy effectiveness, such as timeliness and regional adaptation coefficients, and calculate policy impact values ​​based solely on fixed weights. Second, single-chain architectures struggle to support the multi-role auditing needs of government affairs: government scenarios must simultaneously meet the triple requirements of data storage, fingerprint anchoring, and compliance auditing. However, existing single-chain solutions, due to their rigid consensus mechanisms, lead to performance and security conflicts. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a government data processing method to solve the problem of inaccurate policy effectiveness evaluation by constructing a dynamic attenuation compensation mechanism.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for processing government data, comprising collecting government data, calculating policy effectiveness weights using a dynamic quantification algorithm, constructing a policy knowledge graph based on the policy effectiveness weights, and obtaining a structured policy vector using a graph node embedding algorithm;

[0008] Based on structured policy vectors and government data, a tamper-proof blockchain evidence triple is generated through a multi-chain collaborative evidence storage mechanism;

[0009] Based on the blockchain evidence triples, the cosine similarity algorithm is used for real-time decision matching. The similarity value between the government affairs state vector and the structured policy vector is calculated. When the similarity value meets the policy compliance benchmark requirements, a decision execution record is generated.

[0010] Based on the decision execution records, a multi-chain collaborative audit mechanism is launched to identify and locate illegal operation nodes and generate audit reports;

[0011] Based on the audit report, the audit data and policy effectiveness weights are optimized through the policy analysis engine, and government data optimization recommendations are generated.

[0012] As a preferred solution of the government data processing method described in the present invention, the government data includes administrative supervision data, public service data, social and economic data, spatial geographic data and policy and regulatory data.

[0013] As a preferred solution of the government data processing method described in the present invention, the steps of calculating the policy effectiveness weight using a dynamic quantization algorithm, constructing a policy knowledge graph based on the policy effectiveness weight, and obtaining a structured policy vector through a graph node embedding algorithm are as follows:

[0014] Based on government data, a dynamic quantitative algorithm is used to calculate the policy effectiveness weight, and graph verification and visualization rendering are performed on the policy data set to generate a knowledge graph storage file;

[0015] The knowledge graph storage file is parsed through the graph node embedding algorithm, and a random walk strategy with weight constraints is used to generate node sequences. The node feature representation is learned based on the contextual relationship in the node sequence, and finally a structured policy vector is generated by aggregation.

[0016] As a preferred solution of the government data processing method described in the present invention, the steps of generating a tamper-proof blockchain evidence triplet based on the structured policy vector and government data through a multi-chain collaborative evidence mechanism are as follows:

[0017] Receive structured policy vectors and government data inputs, build a block data structure through the authority proof consensus mechanism, and splice the blockchain height index and timestamp information in the block data structure to generate the operation chain address identifier and operation chain history record;

[0018] Based on the operation chain address identifier, the structured policy vector and related government data are obtained, and the secure hash algorithm is calculated through the Byzantine fault-tolerant consensus to generate a digest hash value;

[0019] The summary hash value is concatenated with the consensus node digital signature and the current block height encoding through structured metadata assembly, and the Merkle tree root hash aggregation algorithm is used to generate a summary chain block identifier containing the summary chain data fingerprint;

[0020] Based on the government data associated with the summary chain blockchain identifier, a verifiable policy compliance proof is generated through a zero-knowledge concise non-interactive knowledge argumentation algorithm, and an audit chain evidence block containing the audit chain compliance proof is constructed;

[0021] The operation chain address identifier, summary chain block identifier and audit chain evidence block are merged through the cross-chain evidence anchoring method to generate a blockchain evidence triplet.

[0022] As a preferred solution of the government data processing method described in the present invention, wherein: based on the blockchain evidence triples, the cosine similarity algorithm is used to perform real-time decision matching, calculate the similarity value between the government matter state vector and the structured policy vector, and generate a decision execution record when the similarity value meets the policy compliance benchmark requirement. The steps are as follows:

[0023] Based on blockchain evidence triples, the structured policy vector is extracted through the operation chain address identifier and the digest hash integrity is verified, dynamically generating integrity-verified policy vectors and related government data;

[0024] The integrity-verified policy vectors and related government data are spatially aligned using a multi-dimensional feature alignment method, and the cosine similarity algorithm is used to calculate the dot product value and the modulus ratio in steps to generate a similarity quantification indicator.

[0025] Compare similar quantitative indicators to policy compliance benchmark requirements. When it is determined that similar quantitative indicators meet the continuous compliance criteria, dynamically generate approval execution instructions and decision execution records.

[0026] As a preferred solution of the government data processing method described in the present invention, wherein: based on the decision execution record, the multi-chain collaborative audit mechanism is started to identify and locate the illegal operation nodes and generate an audit report. The steps are as follows:

[0027] Through the operation chain address identifier in the decision execution record, the structured policy vector and government data are extracted and verified to generate a policy data set and a decision verification status mark;

[0028] Based on policy data sets and decision verification status marks, the actual execution status of government affairs is compared with the policy compliance benchmark, deviation items are identified, and the operation nodes are located through traceability of the responsibility nodes to generate a set of violation marks;

[0029] Based on the set of violation marks, the operation chain history, summary chain data fingerprint and audit chain compliance proof are integrated to generate an audit report.

[0030] As a preferred solution of the government data processing method of the present invention, wherein: according to the audit report, the audit data and policy effectiveness weights are optimized through the policy analysis engine, and a government data optimization proposal is generated, the steps are as follows:

[0031] Based on the audit report, the policy analysis engine identifies high-frequency violation features and weight conflict points, and generates a structured audit feature vector;

[0032] Based on the structured audit feature vector, locate the weight items that need to be adjusted, calculate the weight compensation amount according to the violation type, and generate a policy effectiveness weight optimization table and compensation explanation document;

[0033] Integrate the policy effectiveness weight optimization table and compensation description document to generate government data optimization proposals.

[0034] In a second aspect, the present invention provides a government data processing system, comprising:

[0035] The policy vector calculation module is used to collect government data, calculate policy effectiveness weights using a dynamic quantification algorithm, construct a policy knowledge graph based on the policy effectiveness weights, and obtain structured policy vectors through a graph node embedding algorithm;

[0036] A blockchain evidence building module, which is used to generate tamper-proof blockchain evidence triples based on structured policy vectors and government data through a multi-chain collaborative evidence mechanism;

[0037] The similarity calculation and comparison module is used to perform real-time decision matching based on blockchain evidence triples using the cosine similarity algorithm. It calculates the similarity between the government affairs state vector and the structured policy vector and generates a decision execution record when the similarity value meets the policy compliance benchmark requirement.

[0038] Illegal operation identification module, which is used to initiate a multi-chain collaborative audit mechanism based on decision execution records, identify and locate illegal operation nodes and generate audit reports;

[0039] The optimization proposal generation module is used to optimize audit data and policy effectiveness weights based on the audit report through the policy analysis engine, and generate government data optimization proposals.

[0040] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the government data processing method described in the first aspect of the present invention is implemented.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the government data processing method as described in the first aspect of the present invention.

[0042] The beneficial effects of the present invention are: by adopting a dynamic quantification algorithm to fuse time characteristics and regional parameters to construct a policy knowledge graph and generate a structured policy vector, it effectively solves the effectiveness evaluation deviation of the static model and improves the accuracy of policy adaptation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 This is a flowchart of the government data processing method.

[0045] Figure 2 Schematic diagram of the government data processing system.

[0046] Figure 3 This is a flowchart of the multi-chain collaborative evidence storage machine.

[0047] Figure 4 Flowchart for real-time decision matching and auditing. DETAILED DESCRIPTION

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0049] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0050] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0051] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides a government data processing method, comprising the following steps:

[0052] S1: Collect government data, use dynamic quantification algorithms to calculate policy effectiveness weights, build a policy knowledge graph based on policy effectiveness weights, and obtain structured policy vectors through graph node embedding algorithms;

[0053] S1.1: Government data includes administrative supervision data, public service data, socio-economic data, spatial geographic data, and policy and regulatory data;

[0054] It should be explained that the collection and integration of government data is specifically manifested in the following: administrative supervision data records the registration changes of market entities, the issuance of administrative penalty decisions, and the archiving of daily supervision and inspection records to form structured forms, such as the corporate credit rating status; public service data is accumulated in the approval forms generated by the handling of people's livelihood matters, such as detailed service records related to education and medical care; social and economic data are aggregated from economic operation monitoring reports issued by statistical departments, such as those covering regional GDP indicators; spatial geographic data is provided through administrative division vector maps provided by surveying and mapping agencies; policy and regulatory data are based on the texts of articles issued by the legislative body, such as the full text of departmental regulations for legal effect verification.

[0055] S1.2: Calculate policy effectiveness weights using a dynamic quantitative algorithm based on government data, perform graph verification and visualization on the policy dataset, and generate a knowledge graph storage file;

[0056] Furthermore, government data is input into a dynamic quantification algorithm to calculate policy effectiveness weights, which are based on time fluctuation factors and department coordination coefficients. Policy effectiveness weights are assigned to policy data set nodes, and graph verification is performed, including entity conflict detection and relationship logic verification. The verified policy data set is visualized through a force-directed layout algorithm, and the visualization rendering outputs graph structure data with topological coordinates. The graph structure data is encapsulated as a knowledge graph storage file.

[0057] The policy effectiveness weight formula is:

[0058]

[0059] in, represents the policy effectiveness weight, with the domain being (0, 1); represents the policy basis weight, with the domain being (0.3, 0.9); represents the time-dependent attenuation coefficient, with the domain being (0, 0.5); Indicates the time when the policy was issued; Indicates the upper limit of synergy gain; Indicates the actual number of collaborative departments; Indicates the number of departments requiring collaboration.

[0060] S1.3: Parse the knowledge graph storage file through the graph node embedding algorithm, use the random walk strategy with weight constraints to generate node sequences, learn the node feature representation based on the contextual relationship in the node sequence, and finally aggregate to generate a structured policy vector.

[0061] Furthermore, the graph node embedding algorithm loads the knowledge graph storage file to parse the graph structure topology. The nodes of the knowledge graph storage file carry the policy effectiveness weight attribute; the random walk strategy with weight constraints uses the policy effectiveness weight as the transition probability; a node sequence is generated to record the entity relationship in the walk path; based on the contextual relationship in the node sequence, the probability distribution of the central node and the neighboring nodes is constructed to learn the node feature representation; the node feature representation generated by multiple rounds of walks is aggregated to perform vector dimension reduction operations to generate a structured policy vector to encapsulate dimensional information.

[0062] It should be noted that the construction process is to extract subsequence groups through a fixed-width sliding window based on the node sequence generated by the random walk strategy with weight constraints. The node at the center of the subsequence group is called the current center node, and the set of nodes arranged continuously before and after the center node is called the neighboring nodes. The goal is defined as: by adjusting the node feature representation, the vector inner product value of the center node feature representation and the neighboring nodes is maximized, while the vector inner product value with the non-neighboring nodes is minimized. The negative sampling method is used to optimize the feature representation. Each time, a non-neighboring node is randomly selected as a negative sample node for the valid node pair of the center node and the neighboring node, and the node feature representation vector coordinates are updated through gradient calculation. Finally, a low-dimensional dense feature representation matrix of all nodes is output, and each node feature representation is encapsulated as a real number vector of fixed dimension.

[0063] S2: Based on structured policy vectors and government data, a tamper-proof blockchain evidence triple is generated through a multi-chain collaborative evidence storage mechanism;

[0064] S2.1: Receive structured policy vectors and government data inputs, construct a block data structure through the authority proof consensus mechanism, and concatenate the blockchain height index and timestamp information within the block data structure to generate an operation chain address identifier and operation chain history record;

[0065] Furthermore, after receiving the structured policy vector and government data input, the following process is executed through the authority proof consensus mechanism: the block data structure contains the blockchain height index and timestamp information; the blockchain height index and timestamp information are spliced ​​to generate an operation chain address identifier hash string; the operation chain address identifier is associated with the block data structure to form the operation chain history record.

[0066] The Proof of Authority consensus mechanism is a blockchain governance model in which the government agency's CA certificate node constructs a block data structure according to legal authority after completing identity authentication. It triggers pre-authorized nodes to perform block verification and signature operations based on legal rules in a fixed time period to form an operation chain history record, and finally generates a blockchain evidence block with legal effect.

[0067] S2.2: Obtain the structured policy vector and associated government data based on the operation chain address identifier, calculate the secure hash algorithm through the Byzantine fault-tolerant consensus, and generate a digest hash value;

[0068] Furthermore, the block data structure is located in the operation chain history record based on the operation chain address identifier, and the encapsulated structured policy vector and related government data are extracted; the extracted content is submitted to the Byzantine fault-tolerant consensus node network, and the node network completes consistency verification through redundant data comparison; after the verification is passed, the Byzantine fault-tolerant consensus node network jointly executes the secure hash algorithm to calculate the structured policy vector and the related government data byte stream to generate a fixed-bit summary hash value.

[0069] S2.3: Concatenate the digest hash value with the consensus node digital signature and the current block height encoding through structured metadata assembly, and use the Merkle tree root hash aggregation algorithm to generate a digest chain block identifier containing the digest chain data fingerprint;

[0070] Furthermore, the following process is executed through structured metadata assembly, taking the byte stream of the summary hash value as the basic block, and continuously appending the consensus node digital signature of the Byzantine fault-tolerant consensus node network to the back end of the basic block; continuing to append the current block height code to the back end of the consensus node digital signature to form a single continuous byte stream. The splicing process adopts a tight connection method without delimiters, and each data block is aligned according to a predefined length: the length of the summary hash value is fixed, the length of the consensus node digital signature is in accordance with the asymmetric encryption algorithm specification, the current block height code is converted into a fixed-length string, and the fixed-length string is input into the Merkle tree root hash aggregation algorithm process, the first hash generates the leaf node hash value, the second hash aggregates the parent node hash value, and iterates to the root node to output a 256-bit summary value containing the summary chain data fingerprint; the summary value encapsulates the generation of the summary chain block identifier and is accompanied by a data fingerprint uniqueness certificate. S2.4: Based on the government data associated with the summary chain blockchain identifier, a verifiable policy compliance certificate is generated through a zero-knowledge concise non-interactive knowledge argumentation algorithm, and an audit chain evidence block containing an audit chain compliance certificate is constructed;

[0071] The government data is indexed based on the summary chain identifier; the zero-knowledge concise non-interactive knowledge argumentation algorithm compiles the policy compliance logic circuit into a verifiable constraint architecture; the associated government data is input into the constraint architecture to perform encryption operations to generate a verifiable policy compliance proof; the verifiable policy compliance proof, summary chain identifier and timestamp are combined to construct the audit chain evidence block data structure; finally, an audit chain evidence block containing the audit chain compliance proof is formed and recorded in the chain structure.

[0072] The audit chain evidence block containing the audit chain compliance proof is a chain storage unit encapsulated by cryptographic structure with three core data types: verifiable policy compliance proof generated by a zero-knowledge concise non-interactive knowledge argumentation algorithm, a summary chain identifier of related government data, and an operation timestamp. Its block header records the audit chain compliance proof type identifier field, and the block body uses a Merkle tree structure to anchor the verifiable policy compliance proof and the summary chain identifier, ultimately forming an unalterable evidence entity.

[0073] S2.5: The operation chain address identifier, summary chain block identifier, and audit chain evidence block are merged through the cross-chain evidence anchoring method to generate a blockchain evidence triplet.

[0074] Furthermore, the cross-chain evidence anchoring method synchronously obtains the three data entities of the operation chain address identifier, the summary chain block identifier and the audit chain evidence block; the operation chain address identifier extracts the timestamp information, the summary chain block identifier parses the Merkle proof path data, and the audit chain evidence block extracts the compliance proof feature code; the three perform time-space alignment operations through the distributed ledger repeater: a time index chain is generated based on the timestamp information sorting, and the Merkle proof path and the compliance proof feature code are cross-hash bound; the anchor point data and the original identifier are combined and encoded to generate a blockchain evidence triplet.

[0075] It should be noted that the blockchain evidence triplet is an indivisible data unit formed by combining the operation chain address identifier, the summary chain block identifier encapsulated operation chain address identifier summary chain data fingerprint, and the audit chain evidence block extracted by the audit chain evidence block through the cross-chain evidence anchoring method. This data unit structuredly encapsulates the timestamp positioning function of the operation chain address identifier, the Merkle tree verification function of the summary chain block identifier, and the compliance audit function of the audit chain evidence block. After the three are bound, the operation chain, summary chain, and audit chain are uniquely associated through the blockchain evidence triplet.

[0076] S3: Based on the blockchain evidence triples, a cosine similarity algorithm is used to perform real-time decision matching. The similarity between the government affairs state vector and the structured policy vector is calculated. When the similarity value meets the policy compliance benchmark requirement, a decision execution record is generated.

[0077] S3.1: Based on blockchain evidence triples, extract the structured policy vector through the operation chain address identifier and verify the integrity of the digest hash, and dynamically generate the integrity-verified policy vector and related government data;

[0078] Specifically, based on the blockchain evidence triplet, an operation chain address identifier positioning query operation is performed, and the operation chain address identifier indexes the distributed ledger to obtain the structured policy vector and related government data; the summary chain block identifier in the blockchain evidence triplet is parsed to obtain the pre-stored summary hash value; the sequential splicing value of the related government data byte stream and the three output results of the secure hash algorithm of the structured policy vector are instantly calculated, and the sequential splicing value secure hash algorithm three output results are verified to be equal to the pre-stored summary hash value through the summary hash value comparison mechanism; the structured policy vector and related government data instances that have been verified in terms of integrity are dynamically generated, including a data set bound to the verified environmental assessment data record and the land use approval policy vector.

[0079] S3.2: Spatially align the integrity-verified policy vectors and associated government data using a multi-dimensional feature alignment method. Quantify the similarity using the cosine similarity algorithm by calculating the dot product and modulus ratio step by step.

[0080] Specifically, the structured policy vector and related government data that have been verified for integrity are placed in a unified mathematical space through a multi-dimensional feature alignment method to perform coordinate transformation: the basis vector direction calibration operation is performed on the characteristic coordinate axis of the structured policy vector and the characteristic coordinate axis of the related government data, and the characteristic scales of different data sources are scaled at the same time; the cosine similarity algorithm is executed in a step-by-step calculation process: first, the sum of the products of the characteristic coordinates of the structured policy vector and the characteristic coordinates of the related government data is calculated element by element to obtain the dot product value; the Euclidean norm of the characteristic coordinates of the structured policy vector and the Euclidean norm of the characteristic coordinates of the related government data are calculated respectively to obtain the vector modulus; the dot product value is divided by the product of the modulus of the structured policy vector and the modulus of the related government data to obtain the modulus ratio; finally, the dot product value is multiplied by the modulus ratio to generate a similarity quantification indicator in the [0,1] interval to output a matching degree value that reflects the degree of consistency between the spatial geographic policy terms and the actual data of enterprise land use.

[0081] The calculation formula of similarity quantification index is:

[0082]

[0083] in, Represents a similarity quantification indicator; Represents the structured policy vector dimensional feature coordinates; Indicates the associated government data dimensional feature coordinates; Indicates the number of feature space dimensions.

[0084] S3.3: Compare similar quantitative indicators to policy compliance benchmark requirements. When it is determined that similar quantitative indicators meet the ongoing compliance criteria, dynamically generate approval execution instructions and decision execution records.

[0085] Furthermore, a multi-dimensional numerical comparison operation is performed on the similarity quantitative indicators and the compliance threshold set defined by the policy compliance benchmark requirements; the change trajectory of the similarity quantitative indicators in the continuous calculation cycle is monitored through the sliding time window mechanism to determine whether the continuous compliance conditions set by the continuous compliance criteria are met; when the similarity quantitative indicator trajectory verifies that the continuous compliance criteria are met, an approval execution instruction with a digital signature and a valid timestamp is dynamically generated, and the approval execution instruction contains the responsible subject specified by the approval execution instruction and the scope of action defined by the approval execution instruction; a non-repudiable decision execution record is created simultaneously, and the record content includes the operation time point, the trigger condition hash value, and the associated policy version identifier, and a causal association chain with the calculation result of the similarity quantitative indicator is established in a cryptographic anchoring manner.

[0086] The continuous compliance criterion is defined as the similarity quantitative indicators must all reach the minimum threshold set by the policy compliance benchmark requirements in the continuous monitoring period, and at the same time, the fluctuation range of the similarity quantitative indicators in adjacent periods must meet the absolute tolerance upper limit of the policy compliance benchmark requirements, and the time window length and tolerance parameters are set differently according to the policy type.

[0087] It should be noted that the policy compliance benchmark requires the establishment of three elements: a minimum compliance threshold, a dynamic offset threshold, and a continuous monitoring cycle value: the minimum compliance threshold limits the compliance boundary of a single cycle of similarity quantitative indicators; the dynamic offset threshold constrains the fluctuation range of similarity quantitative indicators in adjacent cycles; the continuous monitoring cycle value mandates that compliance conditions be met continuously within the time window; the three work together to determine whether the continuous compliance criteria are met.

[0088] S4: Based on the decision execution records, a multi-chain collaborative audit mechanism is initiated to identify and locate illegal operation nodes and generate an audit report;

[0089] S4.1: Extract and verify the structured policy vector and government data through the operation chain address identifier in the decision execution record, and generate the policy data set and decision verification status mark;

[0090] Furthermore, the distributed ledger node is retrieved through the operation chain address identifier stored in the decision execution record to obtain the structured policy vector byte stream and the related government data content, and the pre-stored summary hash value is extracted based on the summary chain block identifier referenced by the decision execution record; the secure hash algorithm calculation of the sequential concatenation value of the structured policy vector byte stream and the related government data content is immediately executed to generate a real-time calculated hash value, and the pre-stored summary hash value and the real-time calculated hash value are compared to verify the data integrity; after the verification is passed, the structured policy vector byte stream is parsed to generate a policy data set; combined with the continuous compliance criteria verification conclusion, a decision verification status mark is attached to the policy data set, and finally an entity pair consisting of a complete policy data set and a decision verification status mark is output.

[0091] S4.2: Based on the policy dataset and decision verification status markers, compare the actual execution status of government affairs with the policy compliance benchmark, identify deviations, and trace the responsible nodes to locate the operation nodes, generating a set of violation markers.

[0092] Furthermore, based on the policy data set and decision verification status mark, the following process is executed: the actual execution status of government affairs is compared with the policy compliance benchmark item by item to identify deviation items; the policy data set associated with the deviation items is bound to record and trace the responsibility node; the operation node is located through the responsibility node to generate a violation mark set consisting of the deviation item identifier, responsibility node address, and operation node identifier.

[0093] S4.3: Based on the violation mark set, integrate the operation chain history, summary chain data fingerprint and audit chain compliance proof to generate an audit report.

[0094] Furthermore, based on the violation mark set, cross-chain data association is performed: matching relevant entries in the operation chain history record through the operation node identifier in the violation mark set; indexing the summary chain data fingerprint through the responsibility node address; extracting the proof fragment mapped to the deviation item identifier in the audit chain compliance certificate; packaging the three types of verification credentials with the violation mark set to generate the audit report that should be described, where the audit report is a statutory audit document generated by encapsulating four types of data through a cross-chain anchoring mechanism, namely, the key-value pair entries in the violation mark set, the operation node trajectory extracted from the operation chain history record, the Merkle tree path proof of the summary chain data fingerprint, and the electronic signature of the audit chain compliance certificate. Its core structure includes the violation evidence chapter and the cross-chain evidence appendix, and finally outputs the evidence traceability closed-loop file.

[0095] S5: Based on the audit report, optimize the audit data and policy effectiveness weights through the policy analysis engine, and generate government data optimization recommendations.

[0096] S5.1: Based on the audit report, the policy analysis engine is used to identify high-frequency violation features and weight conflict points, and generate a structured audit feature vector;

[0097] Specifically, based on the list of violations and evidence chapters recorded in the audit report, a high-frequency violation feature extraction algorithm is executed to scan the violation item fields in all violation tag set key-value pairs; the frequency of occurrence of the same violation items is counted, and the feature weight of each violation item is calculated; the contradictions between the policy compliance benchmark requirement clauses and the audit evidence items are simultaneously detected, and the weight conflict points are identified; the high-frequency violation feature word frequency index and the weight conflict point contradiction score are aggregated to generate a structured audit feature vector whose dimensions match the policy compliance benchmark requirements, representing the "planning permission deviation frequency factor", "cross-departmental collaboration contradiction factor", and "time limit violation accumulation factor" respectively.

[0098] S5.2: Identify weight items that need to be adjusted based on the structured audit feature vector, calculate the weight compensation amount based on the violation type, and generate a policy effectiveness weight optimization table and compensation explanation document;

[0099] Specifically, based on the numerical values ​​of each dimension of the structured audit feature vector, the policy effectiveness weight items that exceed the allowable floating range are located; the weight compensation formula is calculated based on the violation types divided in the audit report; a policy effectiveness weight optimization table is generated to record the adjusted weight values ​​and revision basis index; and a compensation description document is created simultaneously to explain the mapping relationship between the weight adjustment logic and the compensation amount item by item.

[0100] It should be noted that the weight items that need to be adjusted refer to the policy effectiveness weight constituent units in the structured audit feature vector that deviate from the floating range allowed by the policy compliance benchmark requirements, including high-frequency deviation items and conflicting imbalance items. The two are positioned through the structured audit feature vector frequency factor dimension value > threshold or conflict coefficient dimension value > policy compatibility threshold, and finally generate a policy effectiveness weight optimization table.

[0101] S5.3: Integrate the policy effectiveness weight optimization table and compensation description document to generate government data optimization proposals.

[0102] Specifically, based on the policy effectiveness weight optimization table, the weight items that need to be adjusted and the adjusted weight values ​​are extracted, the weight compensation calculation logic is parsed through the compensation description document, and the executable optimization steps are derived; the adjusted weight values ​​are mapped to the corresponding government data structure, and the expected adjustment targets are defined; the optimization steps and the expected adjustment targets are combined to form a structured government data optimization proposal.

[0103] This embodiment also provides a government data processing system, including:

[0104] The policy vector calculation module is used to collect government data, calculate policy effectiveness weights using a dynamic quantification algorithm, construct a policy knowledge graph based on the policy effectiveness weights, and obtain structured policy vectors through a graph node embedding algorithm;

[0105] A blockchain evidence building module, which is used to generate tamper-proof blockchain evidence triples based on structured policy vectors and government data through a multi-chain collaborative evidence mechanism;

[0106] The similarity calculation and comparison module is used to perform real-time decision matching based on blockchain evidence triples using the cosine similarity algorithm. It calculates the similarity between the government affairs state vector and the structured policy vector and generates a decision execution record when the similarity value meets the policy compliance benchmark requirement.

[0107] Illegal operation identification module, which is used to initiate a multi-chain collaborative audit mechanism based on decision execution records, identify and locate illegal operation nodes and generate audit reports;

[0108] The optimization proposal generation module is used to optimize audit data and policy effectiveness weights based on the audit report through the policy analysis engine, and generate government data optimization proposals.

[0109] This embodiment also provides a computer device suitable for the government data processing method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the government data processing method proposed in the above embodiment.

[0110] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0111] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for processing government data proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0112] In summary, the present invention adopts a dynamic quantification algorithm to fuse time-sensitive features and regional parameters to construct a policy knowledge graph and generate a structured policy vector, effectively solving the effectiveness evaluation bias of the static model and improving the accuracy of policy adaptation.

[0113] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for processing government data, characterized by: include, Collect government data, use dynamic quantitative algorithms to calculate policy effectiveness weights, build a policy knowledge graph based on policy effectiveness weights, and obtain structured policy vectors through graph node embedding algorithms. The steps are as follows: Based on government data, a dynamic quantitative algorithm is used to calculate the policy effectiveness weight, and graph verification and visualization rendering are performed on the policy data set to generate a knowledge graph storage file; Furthermore, government data is input into a dynamic quantitative algorithm to calculate policy effectiveness weights, which is based on the time-effect fluctuation factor and departmental coordination coefficient; Assign policy effectiveness weights to policy dataset nodes and perform graph validation including entity conflict detection and relationship logic verification; The policy dataset that has completed verification is visually rendered using a force-directed layout algorithm. The visual rendering outputs graph structure data with topological coordinates, which is then encapsulated as a knowledge graph storage file. The policy effectiveness weight formula is: ； in, represents the policy effectiveness weight, with the domain being (0, 1); represents the policy basis weight, with the domain being (0.3, 0.9); represents the time-dependent attenuation coefficient, with the domain being (0, 0.5); Indicates the time when the policy was issued; Indicates the upper limit of synergy gain; Indicates the actual number of collaborative departments; Indicates the number of departments requiring collaboration; The knowledge graph storage file is parsed through the graph node embedding algorithm, and a random walk strategy with weight constraints is used to generate node sequences. The node feature representation is learned based on the contextual relationship in the node sequence, and finally a structured policy vector is generated by aggregation. Based on the structured policy vector and government data, a tamper-proof blockchain evidence triple is generated through a multi-chain collaborative evidence mechanism. The steps are as follows: Receive structured policy vectors and government data inputs, build a block data structure through the authority proof consensus mechanism, and splice the blockchain height index and timestamp information in the block data structure to generate the operation chain address identifier and operation chain history record; Based on the operation chain address identifier, the structured policy vector and related government data are obtained, and the secure hash algorithm is calculated through the Byzantine fault-tolerant consensus to generate a digest hash value; The summary hash value is concatenated with the consensus node digital signature and the current block height encoding through structured metadata assembly, and the Merkle tree root hash aggregation algorithm is used to generate a summary chain block identifier containing the summary chain data fingerprint; Based on the government data associated with the summary chain blockchain identifier, a verifiable policy compliance proof is generated through a zero-knowledge concise non-interactive knowledge argumentation algorithm, and an audit chain evidence block containing the audit chain compliance proof is constructed; The operation chain address identifier, summary chain block identifier and audit chain evidence block are merged through the cross-chain evidence anchoring method to generate a blockchain evidence triplet; Based on the blockchain evidence triples, the cosine similarity algorithm is used for real-time decision matching. The similarity value between the government affairs state vector and the structured policy vector is calculated. When the similarity value meets the policy compliance benchmark requirements, a decision execution record is generated. Based on the decision execution records, a multi-chain collaborative audit mechanism is initiated to identify and locate illegal operation nodes and generate an audit report. The steps are as follows: Through the operation chain address identifier in the decision execution record, the structured policy vector and government data are extracted and verified to generate a policy data set and a decision verification status mark; Based on policy data sets and decision verification status marks, the actual execution status of government affairs is compared with the policy compliance benchmark, deviation items are identified, and the operation nodes are located through traceability of the responsibility nodes to generate a set of violation marks; Based on the violation mark set, the audit report is generated by integrating the operation chain history, summary chain data fingerprint and audit chain compliance proof; Based on the audit report, the audit data and policy effectiveness weights are optimized through the policy analysis engine, and government data optimization recommendations are generated.

2. The government data processing method according to claim 1, wherein: The government data includes administrative supervision data, public service data, socio-economic data, spatial geographic data and policy and regulatory data.

3. The government data processing method according to claim 1, wherein: According to the blockchain evidence triples, the cosine similarity algorithm is used for real-time decision matching, and the similarity value between the government affairs state vector and the structured policy vector is calculated. When the similarity value reaches the policy compliance benchmark requirement, a decision execution record is generated. The steps are as follows: Based on blockchain evidence triples, the structured policy vector is extracted through the operation chain address identifier and the digest hash integrity is verified, dynamically generating integrity-verified policy vectors and related government data; The integrity-verified policy vectors and related government data are spatially aligned using a multi-dimensional feature alignment method, and the cosine similarity algorithm is used to calculate the dot product value and the modulus ratio in steps to generate a similarity quantification indicator. Compare similar quantitative indicators to policy compliance benchmark requirements. When it is determined that similar quantitative indicators meet the continuous compliance criteria, dynamically generate approval execution instructions and decision execution records.

4. The government data processing method according to claim 1, wherein: According to the audit report, the audit data and policy effectiveness weights are optimized through the policy analysis engine, and a government data optimization proposal is generated. The steps are as follows: Based on the audit report, the policy analysis engine identifies high-frequency violation features and weight conflict points, and generates a structured audit feature vector; Based on the structured audit feature vector, locate the weight items that need to be adjusted, calculate the weight compensation amount according to the violation type, and generate a policy effectiveness weight optimization table and compensation explanation document; Integrate the policy effectiveness weight optimization table and compensation description document to generate government data optimization proposals.

5. A government data processing system, based on the government data processing method according to any one of claims 1 to 4, characterized in that: include, The policy vector calculation module is used to collect government data, calculate policy effectiveness weights using a dynamic quantification algorithm, construct a policy knowledge graph based on the policy effectiveness weights, and obtain structured policy vectors through a graph node embedding algorithm; A blockchain evidence building module, which is used to generate tamper-proof blockchain evidence triples based on structured policy vectors and government data through a multi-chain collaborative evidence mechanism; The similarity calculation and comparison module is used to perform real-time decision matching based on blockchain evidence triples using the cosine similarity algorithm. It calculates the similarity between the government affairs state vector and the structured policy vector and generates a decision execution record when the similarity value meets the policy compliance benchmark requirement. Illegal operation identification module, which is used to initiate a multi-chain collaborative audit mechanism based on decision execution records, identify and locate illegal operation nodes and generate audit reports; The optimization proposal generation module is used to optimize audit data and policy effectiveness weights based on the audit report through the policy analysis engine, and generate government data optimization proposals.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the government data processing method described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the government data processing method described in any one of claims 1 to 4 are implemented.

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