61 results about "Knowledge validation" patented technology

The invention relates to an engineering document index consistency proofreading method and system based on a multi-modal large model, and the method comprises the steps: Q1. OCR detection and recognition: carrying out the optical character recognition and format analysis of a source document, converting an uploaded PDF document into a processable text message in a Markdown format, and carrying out the format discrimination of a table, a formula and a plain text; and Q2, table and formula processing: adopting a hierarchical processing strategy, intelligently selecting an optimal processing mode according to the complexity of the table, and converting table information into a descriptive long text through a language large model and cue words. According to the method, accurate, reliable and efficient document index checking service can be provided for a user, the quality and efficiency of professional document processing are remarkably improved, the efficiency and quality of knowledge graph construction are remarkably improved, a knowledge verification system capable of being evolved continuously is established, and the method is suitable for popularization and application. And a reliable technical support is provided for knowledge management and professional decision-making in a complex field.

The invention relates to the technical field of data protection, and discloses a privacy protection type data joint modeling method based on federated learning, which comprises the following steps: acquiring local data to perform meta-feature extraction, calculating key statistics to characterize data characteristics, collecting meta-features, grouping the meta-features into similar feature clusters through spectral clusters, and carrying out feature clustering on the similar feature clusters; dynamically allocating and calculating resource weights according to the similar characteristic cluster scale and the equipment computing power; distributing a basic privacy budget according to the client type, calculating a local model accuracy rate and an intra-cluster level difference, dynamically adjusting the privacy budget, adding adaptive Gaussian noise based on the privacy budget, and adjusting gradient sensitivity of gradient calculation; verifying gradient compliance through zero knowledge, carrying out safe aggregation on gradients passing verification, optimizing a meta-model through a knowledge distillation loss function, and generating confrontation sample analysis to obtain a leakage risk value to identify knowledge leakage risks; sensitive neurons in the neuron sensitivity positioning element model are analyzed and calculated, directional noise is injected, and initial parameters are adjusted for initialization training.

The present invention discloses a modular AI-based system for knowledge instantiation and evidence synthesis, unifying five modules: Knowledge Instantiation, ingesting anchor knowledge via retrieval- augmented generation and neuromorphic prompting; Case-Based Agent Generation, orchestrating domain constraints, performance criteria, and sub-task decomposition; Secondary Epistemogenesis, spawning sub-agents with inherited heuristics; Workflow Orchestration and Evidence Synthesis, merging outputs, tracking metadata, and storing final artifacts; and Reinforcement Learning from Human Refinement, capturing feedback and edits. The method for output generation initiates by defining knowledge requirements, indexing public and proprietary knowledge, validating output structure, drafting content, and refining outputs via sub-agent spawning and human oversight. The system ensures domain alignment, concurrency control, and persistent versioning. By combining specialized knowledge sources, multi-agent concurrency, and iterative human feedback loops, it dynamically adapts to evolving knowledge requirements. This invention emphasizes reliability, traceability, and AI-driven outputs in regulated industries, delivering trustworthy outcomes anchored in expert knowledge.

The invention relates to the technical field of block chains, and discloses a block chain and privacy computing collaborative verification system, which comprises a data fragmentation module, a privacy fusion module, a collaborative verification module, a consensus correction module and a fragmentation graph optimization module. The data fragmentation module constructs a multi-stage data collaborative topology through a preprocessing node and a dynamic fragmentation protocol; the privacy fusion module realizes privacy protection by using a ciphertext analysis unit to switch links in a multi-state manner and a zero-knowledge verification unit; a collaborative verification module generates a reference verification strategy through a consensus modeling node multi-dimensional fusion model, and a state optimization unit dynamically aggregates and sorts fragmentation states; the consensus correction module corrects the fragmentation state through an isolation verification unit and a delay compensation unit and calibrates ciphertext parameters; and the fragment atlas optimization module constructs a stability evaluation network monitoring synchronization rate and performs closed-loop adjustment on a verification strategy. The system realizes dynamic fragmentation, privacy fusion and intelligent verification, and improves the efficiency, security and stability of block chain data processing.

The invention discloses a large model generation content traceability technology based on model copyright ID watermark embedding, which comprises the following steps that: firstly, a large model content generator compiles a dynamic watermark embedding process into an arithmetic circuit, and generates a watermark text and a proof by using a zero-knowledge proof algorithm; then the large model content generator publishes the text with the watermark and the proof together, and hides the copyright ID and the secret key; a large model content user verifies the proof and the watermarked text through a smart contract by adopting a zero-knowledge verification algorithm; after verification is passed, an adversarial sample corresponding to the text with the watermark is generated, an adversarial training method is used for optimizing the text with the watermark, and parameters of the Viterbi balance algorithm are dynamically adjusted and improved. According to the method, the concealment and the generation quality are balanced, high-capacity watermark embedding is realized, the tamper resistance and the accurate traceability of multi-model copyright disputes are improved, and the privacy protection intensity is improved.

The invention discloses a data ownership verification and authorization control method based on zero knowledge proof. The method comprises the following steps: constructing a traceable Merk tree structure on a block chain or a distributed account book; a data owner calculates data fingerprints and packages the data fingerprints into declaration nodes to be inserted into the traceable Merk tree; storing the ownership zero knowledge proof and the traceable Merk tree root hash into a block chain together; the verification party executes the zero-knowledge verifier contract on the chain to complete ownership verification without reading original data or identity plaintext; access control is realized after on-chain verification of the service party; triggering local heavy hash to update the traceable Merk tree root hash, and broadcasting a revocation event on a chain; and confirming legality of evolution and authorization change of all nodes. According to the method, the historical traceability and structural consistency of the data ownership and authorization relationship are ensured, and efficient traceability and anomaly detection of the node evolution chain are also realized.

The invention relates to the technical field of computers and information processing, in particular to a block chain-based cross-industry logistics data trusted sharing method and system, and the method comprises the steps: S1, carrying out the cross-industry semantic alignment; s2, performing field-level ciphertext access; s3, zero knowledge permission verification; s4, federal learning and on-chain coordination are carried out; s5, carrying out privacy budget and service linkage; s6, variable structure consensus; step S7, two-way auditing tracing is carried out; and S8, carrying out consistency challenge. Semantic alignment and chaining commitment are achieved through industry ontology, visibility is controlled through attribute-based field-level encryption and zero-knowledge verification, and commitment-driven median anchoring, symmetric truncation, quality * sample size weighted federal aggregation and on-chain differential privacy budget linkage are combined. And an end-to-end trusted sharing closed loop is constructed in cooperation with PBFT scalable consensus of VRF grouping, forward security signature and Merkle bidirectional auditing and challenge-response verification.

The invention discloses a dynamic key threshold fragmentation privacy tracing method based on block chain collaborative verification, and belongs to the field of information security. According to the method, a dynamic key is taken as a hub, decentralized identity, zero-knowledge verification, deep watermark and threshold collaborative decryption are deeply coupled, a temporary key generated by each conference is split into a plurality of fragments, the fragments are stored in a distributed manner by a block chain node and a tracing unit, only a hash fingerprint combined by the fragments is subjected to chaining and evidence storage, and the dynamic key is subjected to zero-knowledge verification and deep watermark and threshold collaborative decryption. A light evidence chain which cannot be tampered but has no privacy exposure is formed; participants are anonymously accessed through a verifiable certificate without leaking specific information, and truth of anonymous identity is ensured through signature of a trusted unit; when data leakage occurs, the tracing unit extracts a watermark, compares a zero knowledge proof, verifies the relevance between a leaked ciphertext and a chain fingerprint, triggers a multi-party threshold decryption recovery key, and finally accurately positions a person in charge in combination with the encrypted and stored DID mapping table without exposing irrelevant information in the whole process.

The invention discloses an industrial equipment fault inference system based on a knowledge graph, and the system comprises a knowledge graph construction module, a fault data collection module, an inference analysis module and a response processing module. The entity extraction unit extracts equipment components, fault types and maintenance record entities from an industrial equipment operation document, equipment manual unstructured data supplementation attributes are integrated, the attributes and association weights are marked, and the relationship construction unit establishes a fault causal relationship between the entities and a component association relationship to form a multi-level knowledge network; a knowledge verification unit verifies entity attribute consistency and relation rationality, a dynamic updating unit receives data updating nodes and relation strength of each unit and receives feedback data optimization weights, and in a fault data acquisition module, a real-time monitoring unit acquires operation parameters and state signals and associates equipment identifiers.

The invention discloses a multi-agent collaborative decision-making method, device and equipment and a computer storage medium, and relates to the technical field of agent decision-making. Detecting whether collaborative decision information conflicting with each other exists in the collaborative decision information of the plurality of agents, and obtaining a conflict detection result; if the conflict detection result is that the collaborative decision information with mutual conflicts exists, performing arbitration processing on the collaborative decision information with mutual conflicts according to a preset arbitration rule, so as to determine the collaborative decision information conforming to the arbitration rule, and taking the collaborative decision information as to-be-verified decision information; according to the method, the preset knowledge base is utilized to perform knowledge verification on the to-be-verified decision information to determine the target decision information, so that the corresponding business can be executed according to the target decision information, and the collaborative decision accuracy of multiple agents is improved.

The invention relates to the technical field of logistics information processing, and particularly discloses a high-quality and high-efficiency logistics order management system. The system comprises a registration and login terminal, an intelligent review terminal, an order processing terminal and a block chain evidence storage terminal, and is modularly deployed based on a distributed architecture. The intelligent review terminal introduces an image recognition and rule engine to realize automatic order splitting and suggestion merging; the order processing terminal combines a scoring model and a path optimization algorithm to realize dynamic payment routing and multi-target path planning; the block chain evidence storage terminal generates a non-tampering credible receipt based on hash and zero knowledge verification, and supports cross-chain auditing. The system supports GeoHash fragmentation, Flink parallel computing, high-availability container scheduling and hot and cold data hierarchical storage, and the order processing efficiency, the system elasticity and the data security are remarkably improved. The logistics order full-life-cycle management method is suitable for logistics order full-life-cycle management in a high-concurrency and high-credibility scene.

The invention discloses a field event element extraction method and system based on prompt learning and knowledge completion, and the method comprises the steps: converting an extraction task into a constrained generation problem through structured prompt, achieving the stable extraction under a small number of labeled samples through the existing knowledge prior of a large language model, reducing the dependence on large-scale labeled data, and improving the extraction efficiency. And the cross-domain adaptive capacity is improved. A vectorization knowledge retrieval mechanism is introduced, related evidences are obtained from an external knowledge source, elements which are not clearly expressed in a text are complemented, the integrity of information is enhanced, and the accuracy and credibility of a result are improved through knowledge verification. And through a self-adaptive fusion mechanism of prompt and knowledge, the model can flexibly balance text context and external knowledge, and the robustness of complex context and fuzzy expression is improved. And finally, standardized and structured element data are output, a convenient interface is provided for downstream event atlas construction and analysis tasks, and the automation degree of domain knowledge processing and the system integration efficiency are improved.

The invention discloses a business processing method and device based on an ant colony algorithm, electronic equipment and a medium. The method comprises the following steps: acquiring a business service request, and analyzing the business service request to obtain a business task; determining each agent matched with the business task; wherein the intelligent agents are used for cooperative processing of business tasks; planning the cooperation path of each agent based on an ant colony algorithm to obtain a target path; and according to the cooperation sequence of the intelligent agents in the target path, executing the business task by using the intelligent agents in sequence. According to the technical scheme, efficient collaboration between agents is achieved through the improved ant colony algorithm. According to the system, a multi-dimensional pheromone matrix is adopted to record and transmit collaborative experience among departments, a knowledge verification mechanism based on swarm intelligence is established, and dynamic optimization and rapid convergence of a cross-department business process are realized.

The invention discloses a port field knowledge graph automatic construction method based on cooperation of multiple intelligent Agents, and the method comprises the steps: processing structured data, semi-structured data and non-structured data of a port field through intelligent Agents with an autonomous decision-making capability, and achieving the entity recognition, relation extraction and knowledge fusion; a plurality of intelligent Agents with the cooperation function are used for executing knowledge discovery, new knowledge verification, conflict detection, knowledge fusion and graph updating tasks respectively, cooperative communication among the intelligent Agents is achieved through a message queue, and dynamic evolution of a knowledge graph is completed; processing the knowledge graph by adopting a customized knowledge graph embedding method and a semantic fusion algorithm designed for professional terms and knowledge structures in the port field; and storing the processed knowledge graph data based on a distributed architecture, wherein the distributed architecture supports high concurrent processing and real-time response. According to the method, the port domain knowledge graph can be efficiently and automatically constructed.

The invention relates to the technical field of information security, and discloses a signature verification system based on a digital certificate. The system comprises a certificate state acquisition module, a distributed verification network module, a local verification agent module and a zero-knowledge verification engine module. According to the invention, most of certificate state verification requests are locally processed by a user by constructing a distributed verification network and cooperating with a local cache and incremental synchronization mechanism, so that the real-time dependence on a centralized online certificate state protocol responder is greatly reduced; the verification performance bottleneck caused by network delay or overhigh load of the center node is effectively overcome, and the processing efficiency of the signature verification service and the service reliability of the whole system are remarkably improved. A zero-knowledge proof verification protocol is introduced, so that when remote verification is necessary, a user does not need to expose a specific serial number of a certificate to be verified to a verification node, the leakage risk of a user service behavior track is fundamentally eradicated, and stronger privacy protection capability is provided.

The invention discloses an event causal relationship identification-oriented energy contrast learning and knowledge verification method, and belongs to the technical field of deep learning and natural language processing. The invention provides a multi-stage knowledge verification system fusing multi-source external knowledge and large language model driven causal reasoning to solve the problems that an existing model is low in quality and prone to introduction of a pseudo causal positive example in comparison sample construction. Comprising a concept-level event heterogeneous graph construction module, a multi-level positive and negative sample pool construction module, a multi-source causal verification module, a lightweight model-driven distillation module, a large model-guided fine screening and sorting module and an energy contrast learning module. By introducing a stochastic gradient Langevin dynamics method, an antagonistic negative sample is dynamically generated, and a causal discrimination boundary between positive and negative samples is modeled through an energy function, so that the recognition capability of the model on the event causal relationship is improved.

The invention belongs to the technical field of computer information processing and data security, and discloses a party construction assessment system based on a block chain technology, and the system comprises a data collection device which is used for obtaining party member attendance, organized life and task completion conditions and binding timestamps; the privacy protection mechanism carries out hierarchical encryption on identity information and assessment indexes and supports zero-knowledge verification; the distributed account book storage structure realizes corresponding storage of an on-chain abstract and an off-chain ciphertext; the intelligent contract execution mechanism calculates an assessment result according to a preset rule and has a rollback function; the permission control component defines a decryption range based on identity, hierarchy, use and time; the verification interface generates integrity proof through the block header and the Merkle path; the cross-organization sharing mechanism only transmits a summarizing result; and the audit tracing structure generates a log in the whole process and writes the log into the block chain. According to the system, credible storage, privacy protection and secure sharing of party building assessment data are realized.

The present application belongs to the field of edge computing, and specifically relates to a carbon lock zero-knowledge verification edge optimization system and method, which comprises an edge data acquisition preprocessing module, a lightweight ZK-SNARK verification module, a dynamic right confirmation module, a blockchain storage module and an intelligent optimization module. By constructing an integrated technical architecture of edge preprocessing, lightweight ZK-SNARK local verification, cross-chain dynamic right confirmation, encrypted distributed storage and reinforcement learning intelligent scheduling, the core defects of traditional schemes, such as cloud dependence, unsuitable computing power, privacy leakage and static right confirmation, are fundamentally solved, and carbon data local private and credible verification, cross-chain NFT dynamic right confirmation and edge resource intelligent optimization distribution are realized, so that the edge adaptability, privacy security, verification efficiency and whole life cycle management capability of carbon traceability are significantly improved.

The invention discloses a battery data query system application method based on cross-chain index and zero knowledge verification, and relates to the field of block chain energy storage, and the method comprises the following steps: a user submits a query request; the aggregation querier and the verification engine query the battery DID from a battery global identity registry, obtain each block chain address list, and send a standardized query request to a cross-chain verification relay of each block chain in the list in parallel; accessing local chain data by each cross-chain verification relay, and generating and returning zero-knowledge proof; and the aggregation querier and the verification engine verify the validity of all certificates, determine unique and credible timestamps and state data by using an intelligent conflict judgment mechanism aiming at possible conflicts, and integrate the verified data and a certification conclusion to generate a battery full-life-cycle credible report. According to the method, a battery data island in energy storage asset digitization is broken, and cross-chain query efficiency is improved while data credibility and privacy security are guaranteed.

The application discloses a kind of based on knowledge verification's trusted question and answer method, including the following steps: identifying and eliminating the reference and omission content in user's asking question, restating user's asking question, and extracting the keyword expressing user's intention.Use the keyword set after restating as query, retrieve relevant articles, calculate the relevance score of the content retrieved and the asking question, and filter out the highest relevance score multi-modal knowledge.Generating multi-modal statements about search results, multi-round, multi-path verification and reasoning are carried out on the multi-modal statements.The authenticity of the iterated multi-modal statements is verified, and the filtered and verified knowledge and evidence are integrated to generate the final trusted user reply.Compared with the prior art, the application can eliminate references and omissions by injecting associated historical context information during the question representation process, and obtain trusted multi-modal knowledge using retrieval enhancement and self-checking mechanisms, thereby more accurately answering the questions asked by the user.

The invention relates to the technical field of information, in particular to a human resource information processing system and method supporting distributed access control, and the system comprises a source human resource information collection module, a dynamic authority fragmentation generation module, a distributed fragment storage engine and a zero-knowledge verification access control module. Wherein the multi-source human resource information acquisition module is used for extracting structured data from the heterogeneous HR subsystem in real time; the dynamic permission fragmentation generation module is used for splitting the employee permission into a plurality of mutually exclusive permission attribute fragments; the distributed fragment storage module is used for generating a fragment position index table; and the zero-knowledge verification access control module verifies the legality of the request through non-interactive zero-knowledge certification. According to the method, the problems of data islands, inaccurate authority management and privacy protection are effectively solved through multi-source data acquisition, authority fragmentation generation, distributed storage and zero-knowledge proof technologies.

The invention provides an encrypted traffic collaborative analysis system based on privacy computing, and relates to the technical field of privacy computing. Metadata features of encrypted traffic are split into a plurality of secret fragments through a fragment splitting module; generating an authorization token through an authority token management module; secret fragments are collected through a security fragment aggregation calculation module, and when the number of the collected secret fragments bound with the same feature identifier reaches a threshold value specified by a secret sharing protocol, a plaintext pointed by the feature identifier is reconstructed for a collaborative analysis task; zero-knowledge verification is carried out on the security fragmentation aggregation calculation module through a zero-knowledge verification module; and outputting a collaborative analysis result through a result output and token destruction module, marking the authorization token as failure, and recording the whole-process metadata record of the collaborative analysis task into an audit log. According to the invention, authority fine-grained management and control under an application scene in an encrypted state can be realized, so that the contradiction between an encrypted traffic collaboration demand and privacy protection is balanced.

The invention belongs to the technical field of data management, and discloses a spatial geographic data increment updating and synchronizing method based on a distributed account book. The method comprises the following steps: capturing incremental data from a data source chain according to a geocoding index in a distributed account book network of spatial geographic data, and generating hash and zero-knowledge proof of the incremental data; according to the incremental data, the corresponding hash and the zero-knowledge proof, generating an encrypted cross-chain transaction by using a multi-level encryption mechanism, and sending the encrypted cross-chain transaction to a plurality of target chains; and according to a hybrid consensus mechanism, performing zero-knowledge verification on the encrypted cross-chain transaction by using a plurality of target chains, and after the zero-knowledge verification is passed, synchronizing incremental data to all spatial geographic data of the target chains. According to the invention, the problems of low data synchronization efficiency, high total transmission cost, insufficient data privacy protection, risk in sharing, lack of a conflict resolution mechanism, insufficient cross-chain communication security and rigid authority management in the prior art are solved.

A processor-implemented method including generating input data based on raw text and a prompt for a predetermined domain, generating, through a large language model (LLM), event candidates corresponding to the raw text, verifying the event candidates based on structured knowledge as target events, normalizing the target events using information related to the predetermined domain, and extracting the normalized target events.

The embodiment of the invention discloses a transaction authentication method and system, a program product and a storage medium, relates to the field of block chains and is also applicable to the field of financial science and technology, and the method comprises the steps: receiving user transaction information sent by a user terminal; wherein the user transaction information comprises user verification information and transaction authentication information; generating user key information based on the user verification information, and storing the user key information to a block chain; performing transaction risk identification on the transaction authentication information to obtain a risk identification result, and if the risk identification result is high risk, performing zero-knowledge verification on the transaction authentication information through a transaction authentication module to obtain transaction verification information; storing the transaction verification information to the block chain, and receiving a transaction authentication state returned by the block chain based on the user key information and the transaction verification information; and sending the transaction authentication state to the user terminal. According to the method provided by the invention, the single-point fault risk of the centralized identity authentication system is solved, and the safety of the system is enhanced.

The invention discloses a verifiable voucher zero-knowledge verification system and method based on an RISC-V trusted execution environment. The method comprises the following steps: 1, carrying out loading and signature preparation on a verifiable voucher VC; 2, after the loading and signature preparation of the VC is completed, the ProverEnclave enters a session starting process, and a JoinRequest message is forwarded to the VerifierEnclave through a Host application program; step 3, the Verifier Enclave is used for checking the received JoinRequest, and the Verifier Enclave is used for checking the JoinRequest; step 4, the message is forwarded to the ProverEnclave through the Host application program; step 5, the Prover Enclave checks the message, and a proof secret key for the VCCircuit is generated; 6, the Prover Enclave starts to execute a zero knowledge proof generation operation, and a Groth16 proof is generated; step 7, the Prover Enclave forwards the generated Groth16 proof to the VerifierEnclave through the Host application program, and the VerifierEnclave is used for generating the Groth16 proof and forwarding the Groth16 proof to the VerifierEnclave; step 8, carrying out zero-knowledge proof verification on the Groth16 proof by using VerifierEnclave, and carrying out zero-knowledge proof verification on the Groth16 proof; and step 9, the VerifierEnclave returns a final response through the Host application program according to a verification result. The method provided by the invention has the characteristics of high security, high privacy and decentralized identity authentication which is efficiently deployed on the lightweight RISC-V platform.

The application discloses a knowledge structure construction method and system based on knowledge verification, an electronic device and a storage medium, and relates to the technical field of artificial intelligence and information retrieval. The method comprises the following steps: a knowledge node verification step, wherein a candidate knowledge node is verified; a sub-question generation step, wherein when the candidate knowledge node passes the verification, at least one fine-grained sub-question is generated based on the theme description of the candidate knowledge node by using a language model; and a knowledge structure updating step, wherein the fine-grained sub-question is added to the knowledge structure as a new knowledge node, and a hierarchical association is established between the new knowledge node and the candidate knowledge node that passes the verification. The application changes information retrieval from passive response to active presetting, reduces information acquisition delay, and can expand the knowledge structure around the confirmed theme while providing verified information and trend prediction for users.

The invention relates to the technical field of artificial intelligence data processing and privacy protection, in particular to a cloud collaborative AI data annotation task real-time segmentation and privacy protection merging system, which comprises a dynamic semantic perception task segmentation module used for receiving a complete AI data sample to be annotated and an AI training target of an annotation task; dynamic semantic perception segmentation and millisecond-level distribution are adopted, task response delay is reduced by 62%, the problem of lag caused by whole sample distribution in a traditional scheme is solved, core semantic blocks (10%-30% of data volume) are distributed to high-cost experts based on a core-edge block differentiation distribution strategy, labeling precision is guaranteed, and the method is suitable for large-scale popularization and application. Edge feature blocks are distributed to low-cost primary annotation personnel to complete basic annotation, the whole personnel do not need to be provided with experts, experimental verification shows that the manpower annotation cost can be saved by 30% or above, a double-layer encryption-zero knowledge verification mechanism achieves no decryption verification, federal learning and differential privacy control information leakage risk, and the GDPR compliance requirement is met.

The invention discloses a transaction method, system and device based on a joint verification mechanism, and a medium, and the method comprises the steps: a transaction initiator and a transaction receiver encrypt balance information through homomorphic encryption, and store the balance information in a block chain account book, the transaction initiator and the transaction receiver generate a zero-knowledge verification key pair and send a corresponding zero-knowledge verification key to the intelligent contract module; acquiring ciphertext balance information of the transaction initiator and the transaction receiver from the blockchain account book by using the zero-knowledge verification key, performing homomorphic encryption calculation, verifying whether the balance and the limit of the transaction initiator meet requirements or not according to a calculation result, and verifying whether the limit of the transaction receiver meets requirements or not; and the balances and transaction records of the two parties in the block chain account book are updated. A joint verification mechanism for verifying the balance sufficiency of the transaction initiator and the limit compliance of the transaction receiver by using zero knowledge and a privacy protection transaction architecture in which the original data in the whole process is not out of the domain are realized, and stronger privacy guarantee is achieved.

This invention discloses a rule-supervised image recognition verification learning method and apparatus, comprising: a symbol recognition stage, in which an image set is input into a neural network model to obtain a set of symbol probabilities predicted by the model, a score index for evaluating the quality of symbol combinations is determined, and a heap structure is established based on the score index according to a dynamic combination ranking algorithm; and a dynamic verification stage, in which a knowledge verification function V is used to verify the symbol combination. KB The heap structure is dynamically maintained by sequentially determining whether the top symbol combination satisfies the knowledge rule based on the order of symbol combinations in the heap structure, until the first verified combination is found, thus finding the combination that satisfies V. KB The optimal symbolic combination solution with "constraint and maximum score index" is found; feedback optimization stage: the model output distribution is adjusted according to the prior distribution information to alleviate the inference shortcut problem, and the found optimal combination solution is used as supervision information to feed back to the model for gradient optimization, thus completing the image recognition training with rules as supervision information.