Remote digital meeting management system combining block chain and AI large model

Abstract

The invention discloses a remote digital meeting management system combining a block chain and an AI large model, and relates to the technical field of information, and the system comprises a hierarchical dynamic verification chain module which is formed by connecting a real-time layer verification chain and an evidence storage layer auditing chain through a data synchronization interface, the real-time layer verification chain adopts a lightweight alliance chain architecture, and the evidence storage layer audit chain is constructed based on a public chain. According to the remote digital meeting management system combining the block chain and the AI large model, through deep fusion of the block chain and the AI large model, the safety and the intelligent level of remote meeting management are improved. A hierarchical dynamic verification chain is constructed by a block chain technology, so that the non-tampering property and the full-life-cycle traceability of the whole-process data of the conference are ensured, and the defects that a traditional centralized system is easily attacked and the data is easily altered are effectively overcome.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and specifically to a remote digital conference management system that combines blockchain and AI large models. Background Art

[0002] The core challenge faced by traditional remote conference management systems in large international conferences or high-profile events is that it is difficult to coordinate the security and trustworthiness of the entire conference process data with intelligent decision-making capabilities. Existing systems usually adopt a centralized architecture to store conference data, including key content such as participant information, agenda records, resource allocation, and post-meeting analysis results. However, the centralized storage mode is vulnerable to network attacks or internal tampering. Especially in scenarios involving multinational multi-party collaboration, it is difficult to verify the authenticity and integrity of data, resulting in frequent problems such as forged conference registration information, disputes over voting results, and loss of resource allocation records. Although some systems introduce basic encryption technologies or third-party audits, they cannot achieve non-tamperable traceability throughout the data life cycle, and lack an automated trust mechanism when dynamically adjusting the conference process. At the same time, the intelligent level of each link in conference management is limited to rule engines or simple algorithms. For example, resource scheduling such as room allocation and dining seating depends on manually preset parameters and is difficult to handle temporary agenda changes or sudden participant requirements; complex tasks such as cross-language communication and topic hot spot mining still rely on manual processing, resulting in low efficiency and easy errors. Existing technologies have tried to apply blockchain or artificial intelligence separately to the conference scenario, such as using blockchain to store data in a single link, or optimizing local processes through traditional algorithms, but have not solved two key contradictions: First, there is a response delay between the on-chain data verification mechanism of blockchain and the real-time decision-making requirements, making it difficult to support dynamic scheduling during the conference; second, the data quality and source credibility on which the artificial intelligence model training depends lack guarantee, which may lead to algorithmic bias or decision-making distortion. This technological fragmentation makes the conference management system unable to meet the dual requirements of high security standards and high intelligence at the same time, severely restricting the management efficiency and user experience of large-scale digital conferences. Summary of the Invention

[0003] (I) Technical Problems to be Solved Aiming at the deficiencies of the prior art, the present invention provides a remote digital conference management system that combines blockchain and AI large models, and solves the problem of how to construct a secure, trustworthy and self-decision-making full-process conference management architecture through the deep integration of blockchain and artificial intelligence.

[0004] (II) Technical Solutions To achieve the above objectives, the present invention is realized through the following technical solutions: A remote digital conference management system that combines blockchain and AI large models, including: Hierarchical Dynamic Verification Chain Module, which is composed of a real-time layer verification chain and an evidence storage layer audit chain connected through a data synchronization interface. The real-time layer verification chain adopts a lightweight consortium chain architecture, and the evidence storage layer audit chain is built based on a public chain; Federated Multimodal Decision Network Module, whose input end is communicatively connected to the output end of the hierarchical dynamic verification chain module, and is used to generate a conference resource scheduling plan and risk response instructions; Double-chain Heterogeneous Consensus Engine, embedded in the hierarchical dynamic verification chain module, including an asynchronous sharding consensus unit and a global anchoring unit. The asynchronous sharding consensus unit is used for parallel verification of real-time layer data, and the global anchoring unit is used to periodically write the hash fingerprint of real-time layer data into the evidence storage layer audit chain; Dynamic Defense Network Module, whose attack feature library is data-connected to the evidence storage layer audit chain of the hierarchical dynamic verification chain module, and the defense strategy output end is connected to the control end of the federated multimodal decision network module.

[0005] During the operation of the remote digital conference management system, the system realizes the trustworthy storage and real-time verification of the whole-process data of the conference through the hierarchical dynamic verification chain module. When a participant submits registration information, a room allocation request or an agenda adjustment application, the data first enters the AI pre-screening gateway of the real-time layer verification chain for preliminary screening. The anomaly detection model embedded in the AI pre-screening gateway constructs a logical relationship graph based on the historical trustworthy data set, and analyzes potential contradiction points in the input data, such as repeated registration behaviors of the same identity information in different conferences, operations that violate the regional isolation rules in the room allocation request, or abnormal requests for permission applications that exceed the role level.

[0006] For the detected high-risk operations, the system directly intercepts and generates a risk log and stores it in the evidence storage layer audit chain; for the data that passes the preliminary screening, the on-chain smart contract further executes the rule engine verification, including the consistency comparison of the participant's identity certificate and the biometric hash value, the detection of agenda time conflicts, and the real-time verification of resource capacity.

[0007] If the data passes the double verification, it triggers the federated multimodal decision network module to start the dynamic decision-making process; if any link fails, the system returns a specific error type description to the user, and writes the complete verification path and the reason for failure into the blockchain for evidence storage.

[0008] Preferably, the real-time layer verification chain includes an AI pre-screening gateway, and the AI pre-screening gateway is composed of an anomaly detection model and an on-chain smart contract: The anomaly detection model is constructed based on a graph neural network, and the training data comes from the registration information, room allocation records and permission change logs in the historical conference data set; The on-chain smart contract includes participant identity verification rules, agenda compliance verification rules and resource allocation conflict detection rules; Among them, the output end of the anomaly detection model is connected in series with the input end of the on-chain smart contract. Only when the data passes through the screening of the anomaly detection model and the on-chain smart contract verification is passed, the federated multi-modal decision network module is triggered to start.

[0009] Preferably, the federated multi-modal decision network module includes: A resource scheduling agent, whose data input end is connected to the resource allocation interface of the real-time layer verification chain, generates a room allocation or seat allocation plan based on a reinforcement learning model, and dynamically optimizes the plan weight through the Monte Carlo tree search algorithm; A semantic collaboration agent, which has a multilingual large model built-in, and its corpus is stored in the audit chain of the evidence storage layer. The translation result is signed by the private key of the participant's terminal and then transmitted back to the audit chain of the evidence storage layer; A risk prediction agent, which uses a temporal graph convolutional network to analyze the historical meeting data in the audit chain of the evidence storage layer, predicts the probability of agenda conflicts and outputs it to the on-chain smart contract.

[0010] The federated multi-modal decision network module consists of multiple intelligent agents working together. The resource scheduling agent generates an initial resource allocation plan based on a reinforcement learning model and performs dynamic optimization through the Monte Carlo tree search algorithm, including: when a new or cancelled participation request is received, the algorithm combines the real-time venue status, the identity weights of participants and the physical layout constraints, calculates the priorities of different allocation combinations, gives priority to ensuring the adjacent needs of associated participants, and at the same time dynamically reserves emergency buffer resources.

[0011] During the translation process, the semantic collaboration agent calls the cross-cultural corpus in the audit chain of the evidence storage layer to constrain the output of the multilingual large model to ensure the accuracy of terms and cultural adaptability. The translation result needs to be signed by the private key of the participant's terminal, and the validity of the signature is verified by the quick consensus of the nodes in the real-time layer verification chain before it can be written into the multilingual evidence chain partition of the audit chain of the evidence storage layer.

[0012] The risk prediction agent analyzes the historical meeting data stored on the blockchain to identify risk patterns such as agenda conflicts and equipment failures. When the detected risk probability exceeds the threshold, it triggers the on-chain smart contract to automatically execute the emergency plan, including switching to a backup venue, adjusting the agenda time, or diverting participants to a virtual sub-venue. All operation instructions need to be verified by the blockchain and the complete execution path is recorded.

[0013] Preferably, after the resource scheduling agent generates a room allocation or seat allocation plan, it synchronously writes two types of on-chain records to the hierarchical dynamic verification chain module: An execution chain record, which includes a plan identifier, an execution timestamp, and a resource utilization rate indicator; A logic chain record, which encapsulates the parameter update path of the decision model and the fairness constraint verification result in the form of zero-knowledge proof; Among them, the execution chain record and the logic chain record are associated by hash and input into the feedback learning unit of the federated multi-modal decision network module.

[0014] Preferably, the hierarchical dynamic verification chain module implements triple verification on the VIP permission change operation: The first verification generates a preliminary proposal by the risk prediction agent of the federated multi-modal decision network module. The preliminary proposal includes the permission change scope, timeliness, and risk score. The second verification performs rapid consensus among node groups on the proposal by the asynchronous sharding consensus unit of the real-time layer verification chain. The verification content includes user identity hash matching, permission timeliness conflict detection, and role permission upper limit verification. The third verification randomly spot-checks the logical consistency between the proposal and the historical permission records by the final audit node of the deposit layer audit chain, including the compliance of the permission upgrade path and the rationality of the risk score distribution. Only when the results of the triple verification are consistent, the proposal is marked as valid and written into the permission change partition of the deposit layer audit chain.

[0015] For permission change operations with high security requirements, the system implements a triple verification mechanism. The risk prediction agent first generates a change proposal and evaluates its risk level. The node group of the real-time layer verification chain conducts distributed consensus verification on the proposal, checking identity consistency, time compliance, and permission scope legality. The final audit node of the deposit layer audit chain verifies the historical logical consistency through random spot-checking. Only when the results of the triple verification are consistent, the operation instruction party is marked as finally valid. For detected abnormal operations, the system automatically freezes the process and triggers a multi-level response: pushing warning information to the administrator, updating the AI model training parameters, generating a blockchain audit report, and simultaneously activating the collaborative protection of the defense network module.

[0016] Preferably, the dynamic defense network module includes: An attack feature extraction unit that extracts the data pattern of historical attack events from the deposit layer audit chain. An AI adversarial model training unit that generates defense strategies based on the extracted attack features and transmits them back to the federated multi-modal decision network module. Among them, the defense strategies include data poisoning attack interception rules and model parameter encryption rules, and the strategy version numbers are synchronously updated to the deposit layer audit chain.

[0017] The dynamic defense network module continuously analyzes the security event logs stored on the blockchain, constructs an attack feature library, and trains an AI countermeasure model. When a data poisoning attack is detected, the system implants a dynamic filter at the data entry point to reduce the influence weight of abnormal data in the AI model. In the face of a model stealing attack, a parameter sharding encryption storage strategy is adopted, and the key parameters are dynamically assembled only after legitimate verification. The defense strategy is dynamically adjusted according to the attack level, including: traffic restriction and logging for low-risk attacks, and triggering a deep protection protocol for high-risk attacks, including interface access restriction, enhanced multi-factor authentication, and emergency resource switching. All versions of the defense strategy are iteratively recorded on the blockchain to form a traceable knowledge base, ensuring the continuous evolution of the protection system.

[0018] Preferably, after the translation result of the semantic collaboration agent is generated, it needs to be signed with the private key of the participant's terminal and the signature validity is verified by the asynchronous sharding consensus unit of the real-time layer verification chain. After the verification passes, it is written into the multi-language evidence chain partition of the deposit layer audit chain.

[0019] Preferably, when the risk prediction agent detects that the probability of agenda conflict exceeds the preset threshold, it triggers the execution of the following operations by the on-chain smart contract: Call the venue status interface of the real-time layer verification chain to retrieve the availability of the alternate venue; If the alternate venue is available, send a switching instruction to the federated multi-modal decision network module and update the agenda record of the deposit layer audit chain.

[0020] Preferably, the node grouping strategy of the asynchronous sharding consensus unit is dynamically adjusted according to the network load of the real-time layer verification chain. The adjustment parameters take effect after being verified by the global anchoring unit of the deposit layer audit chain, and the historical grouping strategy versions are stored in the deposit layer audit chain.

[0021] Preferably, the agenda compliance verification rules of the on-chain smart contract include: Detecting time conflicts between parallel agendas of the same participant; Verifying the regional isolation between VIPs and ordinary participants in the room allocation plan; Verifying the matching of permission levels for ad-hoc agenda adjustments; After the verification result is consensus among the nodes of the real-time layer verification chain, trigger the resource scheduling agent to regenerate the plan.

[0022] In the specific implementation process, in a multi-time zone scenario across countries, the system introduces a time zone adaptation mechanism. When the agenda time is adjusted, it automatically correlates with the registered time zone data of the participants. When it detects that a large number of participants are in inactive periods, it triggers a hierarchical notification and multi-version agenda generation process. The translation service adds a time zone consistency check during the signature verification phase and initiates biometric secondary authentication for requests with time zone conflicts. When allocating virtual branch venues, the resource scheduling agent gives priority to the geographical location of the participants and the network latency metrics, and generates multilingual guidance information through the semantic collaboration agent to ensure the consistency of the cross-regional participation experience.

[0023] The system achieves full-process auditability through a double-chain recording mechanism. The execution chain records the operation results and real-time metrics of storage, and the logic chain records the decision-making basis and rule matching paths encapsulated with zero-knowledge proofs. The two are associated through hashing to form an inseparable pair of audit evidences. Any data tampering or logical contradiction can be quickly located through two-way verification. When a dispute occurs, the auditor can fully trace the decision-making life cycle based on the blockchain evidence, including the AI model version, contract rule iteration records, and network environment snapshots, to ensure the transparency and credibility of the system behavior.

[0024] Through the deep collaboration between blockchain and AI, a secure, trustworthy, and self-optimizing conference management architecture is constructed. By realizing real-time intelligent decision-making on the data of the entire conference process without being tampered with for the first time, it solves the fundamental contradiction that it is difficult to reconcile the security mechanism and efficiency requirements in traditional systems, and forms a self-reinforcing technical ecosystem through the integrated design of double-chain verification, federated decision-making network, and dynamic defense system.

[0025] (III) Beneficial effects The present invention provides a remote digital conference management system combining blockchain and AI large models. It has the following beneficial effects: (I). The remote digital conference management system combining blockchain and AI large models improves the security and intelligence level of remote conference management through the deep integration of blockchain and AI large models. The blockchain technology constructs a hierarchical dynamic verification chain to ensure the immutability and full-life-cycle traceability of the data in the entire conference process, effectively solving the defects of traditional centralized systems being vulnerable to attacks and data being easily forged; the federated multi-modal decision-making network driven by the AI large model realizes autonomous optimization and real-time response in links such as resource scheduling, risk prediction, and cross-language collaboration, reducing the cost of manual intervention and improving management efficiency.

[0026] (2) The remote digital conference management system combining blockchain and AI large models solves the problem of coordinating security mechanisms and decision-making efficiency, and balances the strong auditing requirements of blockchain and the real-time response capabilities of AI through a dual-chain architecture; the dynamic defense network and self-optimization mechanism further enhance the system's robustness, can actively identify and intercept new types of attacks such as data poisoning and permission tampering, and continuously optimize the AI model and resource allocation strategy based on federated learning. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the overall framework of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0029] Please refer to Figure 1 , the present invention provides a technical solution: a remote digital conference management system combining blockchain and AI large models, including: A hierarchical dynamic verification chain module, which is composed of a real-time layer verification chain and an evidence storage layer audit chain connected through a data synchronization interface. The real-time layer verification chain adopts a lightweight consortium chain architecture, and the evidence storage layer audit chain is built based on a public chain; A federated multi-modal decision network module, whose input end is communicatively connected to the output end of the hierarchical dynamic verification chain module, and is used to generate a conference resource scheduling plan and risk response instructions; A dual-chain heterogeneous consensus engine, embedded in the hierarchical dynamic verification chain module, including an asynchronous sharding consensus unit and a global anchoring unit. The asynchronous sharding consensus unit is used for parallel verification of real-time layer data, and the global anchoring unit is used to periodically write the hash fingerprint of real-time layer data into the evidence storage layer audit chain; A dynamic defense network module, whose attack feature library is data-connected to the evidence storage layer audit chain of the hierarchical dynamic verification chain module, and the defense strategy output end is connected to the control end of the federated multi-modal decision network module.

[0030] The real-time layer verification chain includes an AI pre-screening gateway, which is composed of an anomaly detection model and an on-chain smart contract: The anomaly detection model is built based on a graph neural network, and the training data comes from the registration information, room allocation records, and permission change logs in the historical conference dataset; The on-chain smart contract includes rules for verifying the identity of participants, rules for verifying the compliance of the agenda, and rules for detecting resource allocation conflicts; Among them, the output end of the anomaly detection model is connected in series with the input end of the on-chain smart contract. Only when the data passes the screening of the anomaly detection model and the on-chain smart contract verification is passed, the federated multi-modal decision network module is triggered to start.

[0031] It should be further noted that in the specific implementation process, in the real-time layer verification chain of the conference management system, the AI pre-screening gateway realizes data trusted filtering through the series check mechanism of the anomaly detection model and the on-chain smart contract. When a participant submits a registration information, a room allocation request or a permission change request, the anomaly detection model first analyzes the logical relevance between the input data and the historical conference dataset based on the graph neural network. For example, it detects conflicting registration behaviors of the same identity information in different conferences, abnormal mixing of VIPs and ordinary participants in the room allocation request, or matching contradictions between the permission change request and the user role level.

[0032] If high-risk operations are detected, such as duplicate registration and illegal cross-regional room allocation requests, the model will directly intercept the data and generate a risk log to be stored in the audit chain of the evidence storage layer. For the data that passes the preliminary screening, the on-chain smart contract further performs rule engine verification: for the participant identity verification rule, the contract automatically compares the pre-stored digital identity certificate in the blockchain with the currently submitted biometric hash value; for the agenda compliance rule, the contract verifies whether the agenda time conflicts with other existing agendas; for the resource allocation conflict detection rule, the contract retrieves the real-time occupancy status of the venue and accommodation in real time and rejects requests for over-capacity allocation.

[0033] If the data passes the double verification, the system will trigger the federated multi-modal decision network to generate a dynamic solution; if the verification fails in any link, a specific error code will be returned to the user, such as "identity verification failed - biometric mismatch" or "room allocation request conflict - target area full", and at the same time, the failure record and the verification path hash value will be written into the audit chain of the evidence storage layer for traceability.

[0034] For sudden requirements such as temporary agenda adjustment, the AI pre-screening gateway starts the fast-track mode, preferentially matches the decision mode cache of historical similar scenarios in the anomaly detection stage, shortens the verification response time, and at the same time the on-chain smart contract dynamically relaxes some non-core rules, such as the seating priority of non-VIP participants, to ensure that the system maintains high response efficiency while ensuring security.

[0035] The federated multi-modal decision network module includes: A resource scheduling agent, whose data input end is connected to the resource allocation interface of the real-time layer verification chain, generates a room allocation or seating plan based on the reinforcement learning model, and dynamically optimizes the plan weight through the Monte Carlo tree search algorithm; A semantic collaboration agent, with a multi-language large model built-in, whose corpus is stored in the audit chain of the evidence storage layer, and the translation result is signed by the private key of the participant's terminal and then transmitted back to the audit chain of the evidence storage layer; Risk prediction agent, which uses a temporal graph convolutional network to analyze historical meeting data in the audit chain of the evidence storage layer, predicts the probability of agenda conflicts and outputs it to the on-chain smart contract.

[0036] It should be further noted that in the specific implementation process, during the operation of the federated multi-modal decision-making network module, the resource scheduling agent, based on the verified participant data and venue resource status provided by the real-time layer verification chain, generates an initial room or seat allocation plan through a reinforcement learning model and dynamically optimizes the plan using the Monte Carlo tree search algorithm: when there is a new participant or a request to cancel a participation temporarily, the algorithm calculates the priority scores of different allocation combinations according to the real-time updated seat occupancy rate, participant identity weights, and venue physical layout constraints, and preferentially retains adjacent seats for participants with strong relevance, that is, preferentially retains seats for members of the same institution, and at the same time dynamically adjusts the buffer capacity of unallocated seats to cope with sudden demands; among them, the participant identity weights include VIP levels and historical participation records, and the venue physical layout constraints include epidemic prevention safety distances.

[0037] During the real-time translation process, the semantic collaboration agent extracts a cross-cultural corpus of the target language from the audit chain of the evidence storage layer as context constraints, inputs it into a multilingual large model to generate a preliminary translation, and then performs semantic consistency verification on the translation. Among them, the cross-cultural corpus includes an industry glossary and a cultural taboo word library.

[0038] The process of subsequent semantic consistency verification of the translation includes: if it is detected that the deviation of a key term from the official definition stored on the blockchain exceeds the threshold, such as the translation error rate of technical terms is greater than 5%, an artificial review process is triggered and the write permission of the translation in the evidence storage chain is frozen; if the verification passes, the participant needs to use the terminal private key to digitally sign the translation, and the signed translation is written into an independent partition of the audit chain of the evidence storage layer after consensus by the nodes of the real-time layer verification chain, forming an immutable multi-language communication evidence chain.

[0039] The risk prediction agent analyzes historical meeting conflict events stored in the audit chain of the evidence storage layer through a temporal graph convolutional network, such as the time distribution of equipment failures and the frequency of agenda timeouts, constructs a dynamic risk probability map, and when it detects that the current agenda conflict probability exceeds the preset threshold, such as the interval between two agendas in the same venue is less than 10 minutes and the participant overlap rate is greater than 30%, it sends the risk level and countermeasures to the on-chain smart contract, and the contract automatically retrieves the availability status of alternative venues.

[0040] The process of the contract automatically retrieving the availability status of the alternate venue includes: if there are sufficient resources in the alternate venue, immediately send a switching instruction to the resource scheduling agent and update the agenda record in the evidence storage layer audit chain; if there are insufficient alternate resources, initiate a degradation plan, such as extending the agenda interval, splitting participants into virtual sub-venues, and pushing notifications to relevant participants through the semantic collaboration agent. The decision data flow between agents is deeply coupled with the blockchain verification mechanism to ensure that all decision-making bases are traceable and decision results are auditable.

[0041] After the resource scheduling agent generates a room allocation or seating arrangement plan, it synchronously writes two types of on-chain records to the hierarchical dynamic verification chain module: Execution chain record, including the plan identifier, execution timestamp, and resource utilization rate indicator; Logical chain record, encapsulating the parameter update path of the decision-making model and the fairness constraint verification result in the form of zero-knowledge proof; Among them, the execution chain record and the logical chain record are associated by hash and input into the feedback learning unit of the federated multi-modal decision-making network module.

[0042] It should be further noted that in the specific implementation process, after the resource scheduling agent generates a room allocation or seating arrangement plan, the system synchronously writes two types of data, namely the execution chain record and the logical chain record, to the hierarchical dynamic verification chain module. The execution chain record includes the plan identifier, execution timestamp, and resource utilization rate indicator, and is written to the resource allocation partition of the real-time layer verification chain, triggering the resource scheduling agent to execute the room allocation or seating arrangement plan; at the same time, the system monitors the execution effect of the plan in real time, such as the actual seat occupancy rate and the feedback of participant satisfaction. If it is detected that the resource utilization rate is lower than the preset threshold, such as the vacancy rate in the VIP area exceeds 20% or the general area is overcrowded by 10%, a dynamic adjustment request is sent to the federated multi-modal decision-making network module.

[0043] The logical chain record encapsulates the parameter update path of the decision-making model and the fairness constraint verification result in the form of zero-knowledge proof. The generation process includes: calculating the regional isolation degree score of VIP and general participants based on the identity weight distribution of participants in the current room allocation plan; verifying whether the adjacent rate of associated participants in the seating arrangement plan meets the preset requirements, where associated participants include the same institution or cooperation party; verifying whether the permission level of the temporary adjustment request matches the operation scope. The logical chain record that passes the verification is written to the decision logic partition of the evidence storage layer audit chain after hash operation and is two-way bound with the hash value of the execution chain record to form an inseparable audit evidence pair.

[0044] When the feedback learning unit of the federated multi-modal decision network module receives a new room allocation request, it first retrieves the resource utilization metrics recorded in the historical execution chain and the fairness verification results recorded in the logical chain from the deposit layer audit chain, and analyzes the correlation between the two: If the historical data shows high resource utilization but the fairness score is lower than the threshold, such as the VIP area being over-occupied, resulting in a decline in the experience of ordinary participants, the weight allocation strategy of the reinforcement learning model is adjusted to reduce the identity level weight and increase the spatial balance weight; If an unpassed fairness verification item is detected in the logical chain record, such as insufficient adjacent rate of associated participants, the constraint condition strengthening module of the Monte Carlo tree search algorithm is triggered to limit the low-correlation allocation combinations in the search branches.

[0045] For abnormal scenarios, such as a sudden large-scale cancellation of participants, the system reversely deduces the optimal recovery strategy based on the timestamps and resource status change timings recorded in the execution chain, and verifies the compliance of the strategy through the parameter path recorded in the logical chain. If a conflict between the strategy and the historical fairness rules is detected, such as the recovered seats being preferentially allocated to non-VIP participants violating the existing agreement, the execution of the strategy is frozen and a manual review instruction is pushed to the administrator terminal.

[0046] The interaction mechanism of the above double-chain records extends to the exception handling scenario, including: When a break in the hash correlation between the execution chain record and the logical chain record is detected, such as the deviation between the actual resource utilization rate and the predicted value of the decision model exceeding 30%, the system automatically freezes all subsequent operations of the room allocation plan, extracts the associated AI decision parameters and verification paths from the deposit layer audit chain, and starts the root cause analysis process, including: If the problem stems from the data synchronization delay of the real-time layer verification chain, such as the venue status update not being uploaded to the chain in a timely manner, the room allocation plan is rolled back and the resource scheduling agent is re-triggered; If the problem is caused by a fairness rule vulnerability in the logical chain record, such as the failure to identify a new type of associated participant relationship, the verification rules of the on-chain smart contract are updated and a patch version is generated and synchronized to all nodes. To address the performance bottleneck in high-concurrency scenarios, the system starts a dynamic caching mechanism for the frequently accessed execution chain records, caches the hash values of recently active plans and their mapping relationships with the logical chain at the edge nodes, reduces the cross-chain retrieval latency, and at the same time sets the validity period of the cached data to be synchronized with the global anchoring period of the deposit layer audit chain to ensure strong data consistency.

[0047] The hierarchical dynamic verification chain module implements triple verification for VIP permission change operations: The first verification generates a preliminary proposal by the risk prediction agent of the federated multi-modal decision network module. The preliminary proposal includes the scope, timeliness, and risk score of the permission change. The second verification performs fast consensus among node groups on the proposal by the asynchronous sharding consensus unit of the real-time layer verification chain. The verification content includes user identity hash matching, permission timeliness conflict detection, and role permission upper limit verification. The third verification randomly spot-checks the logical consistency between the proposed changes and historical permission records by the final audit nodes of the audit chain in the evidence storage layer, including the compliance of the permission upgrade path and the rationality of the risk score distribution. Only when the results of the three verifications are consistent, the proposed changes are marked as valid and written to the permission change partition of the audit chain in the evidence storage layer.

[0048] It should be further noted that in the specific implementation process, in the three-verification process of VIP permission change operations, when the system receives a permission change request, first, the risk prediction agent of the federated multi-modal decision network module generates a preliminary proposal. The risk prediction agent analyzes the rationality of the permission change based on the user's historical permission records stored in the evidence storage layer, the current meeting role, and the sensitivity of the associated agenda. If the request involves cross-security level operations, such as an ordinary attendee applying for VIP background access permission, the agent will extract the user's past behavior data and calculate the risk score through a temporal graph convolutional network. If the score exceeds a preset threshold, such as the risk value is greater than 0.7, the request will be directly rejected and a high-risk log will be generated. If the risk score is within an acceptable range, the agent generates a proposal containing the reason for the change, the scope of permission, and the timeliness, and attaches a risk score explanation. Among them, the user's past behavior data includes the historical agenda participation rate and the permission usage frequency.

[0049] Subsequently, the proposal enters the asynchronous sharding consensus unit of the real-time layer verification chain for the second verification. The node group dynamically divides the verification shards according to the type of the proposal, and the verification nodes within each shard independently perform inspections. Among them, the types of proposals include permission level elevation and access scope expansion.

[0050] The process of independent inspection by the verification nodes within each shard includes: comparing whether the user identity hash value in the proposal is consistent with the identity certificate stored on the blockchain; verifying whether the permission change timestamp conflicts with the associated agenda, such as applying for post-meeting permissions but taking effect during the meeting; checking whether the permission scope exceeds the preset upper limit of this role, such as an ordinary attendee applying for speaker-exclusive permissions. If more than 2 / 3 of the node group verifies and passes, the proposal is marked as "temporarily valid" and temporarily stored in the real-time layer buffer. If the number of opposing nodes in any verification shard exceeds 1 / 3, the cross-shard arbitration mechanism is triggered, and the main shard node re-checks the disputed items and generates a final conclusion.

[0051] The third verification is randomly spot-checked by the final audit nodes of the audit chain in the evidence storage layer, including: retrieving all historical permission change records of this user from the evidence storage layer to verify whether the current proposal conforms to the permission upgrade logic, such as progressing step by step from ordinary to VIP to administrator; checking whether the risk score in the proposal is consistent with the score distribution of historical operations of the same type, such as being considered suspicious if it deviates abnormally from the mean by plus or minus 3σ; verifying whether the timestamp is synchronized with the global meeting clock to prevent maliciously tampering with the local time to fabricate the validity period.

[0052] After the spot check passes, the final audit node anchors the proposal and the verification path hash value to the immutable area of the evidence storage layer, and simultaneously releases the temporary mark in the real-time layer cache area as "finally effective".

[0053] If logical inconsistencies are found during the spot check, such as a user skipping the VIP level and directly applying for administrator privileges, the proposal will be immediately frozen and the following processing will be triggered, including: sending a model correction instruction to the federated multi-modal decision network module to request retraining of the permission upgrade logic of the risk scoring model; pushing an alarm message containing the details of the abnormal path to the administrator terminal; generating an audit report with a digital signature in the audit chain of the evidence storage layer for subsequent judicial forensics.

[0054] For high-frequency permission change scenarios, such as batch VIP guest permission granting in a large conference, the system enables a batch proposal optimization channel, including: the risk prediction agent clusters requests of the same type into group proposals and uses group behavior pattern analysis to replace single-user risk assessment; the real-time layer verification node group parallelly verifies the common conditions within the group according to a preset rule set, such as the consistency between the guest list and the evidence storage list of the organizer, and only initiates independent verification for abnormal individuals; the spot check ratio of the final audit node is dynamically reduced to 10%-30%, and the threshold is automatically adjusted according to the historical spot check accuracy rate. If a high-risk individual request is mixed in the group proposal, such as a person not on the list disguising as a guest, the system automatically strips the abnormal item during the second verification stage and generates a sub-case processing queue to ensure the quick effectiveness of compliant requests and the independent interception of risk requests.

[0055] The dynamic defense network module includes: An attack feature extraction unit that extracts the data patterns of historical attack events from the audit chain of the evidence storage layer; An AI countermeasure model training unit that generates defense strategies based on the extracted attack features and sends them back to the federated multi-modal decision network module; Among them, the defense strategies include data poisoning attack interception rules and model parameter encryption rules, and the strategy version numbers are synchronously updated to the audit chain of the evidence storage layer.

[0056] It should be further noted that in the specific implementation process, during the operation of the dynamic defense network module, the attack feature extraction unit continuously scans the security event logs in the audit chain of the evidence storage layer to identify potential attack patterns: when detecting a high-frequency abnormal data write request, such as more than 500 room allocation plan queries initiated by the same IP address within a short period of time, the unit first extracts the context features of this operation, including the request time distribution, the depth of the data access path, and the associated user behavior profile, and performs a similarity match with the historical attack pattern library stored in the blockchain. If a known attack type is matched, the defense strategy generation process is triggered; if it is identified as a new type of attack, such as a timing confusion attack using AI decision delay, the emergency mode is activated, and the original attack data is sliced and encrypted and then broadcast to the blockchain nodes for collaborative analysis.

[0057] The AI countermeasure model training unit generates targeted defense rules based on the attack features: for data poisoning attacks, the model constructs a dynamic filter and implants a feature weight detection mechanism at the data entry of the real-time layer verification chain to verify the distribution rationality of the participant information fields. If it is detected that the concentration of a certain type of feature deviates abnormally from the historical distribution, such as the proportion of registrants from the same institution suddenly increasing to 80%, the sampling weight of this type of data in the AI decision model is automatically reduced, and the manual review process is triggered; among them, the participant information fields include the institution name and the position level; for model stealing attacks, the model generates a parameter sharding encryption strategy, splits the key parameters of the federated multi-modal decision network into multiple ciphertext segments, stores them in different blockchain nodes respectively, and only dynamically assembles the available parameters when the verification requestor has a legitimate identity certificate and the operation context matches.

[0058] In the defense strategy implementation stage, the system dynamically adjusts the protection intensity according to the attack type, including: when detecting a low-risk exploratory attack, such as a single abnormal permission detection, a lightweight interception mode is adopted, only the attack features are recorded and the request frequency of this IP is restricted; for high-risk persistent attacks, such as data pollution by distributed node collaboration, a deep defense protocol is started, including temporarily closing the public write interface of the real-time layer verification chain, forcibly switching to the multi-signature approval mode, and pushing a security verification challenge to the associated participant terminals, such as two-factor authentication based on blockchain certificates. The version number and the effective timestamp of all defense strategies are written into the defense log partition of the evidence storage layer audit chain to form a traceable protection knowledge base.

[0059] For the iterative optimization of defense strategies, the system sets up a dual-channel verification mechanism, including: after a new strategy is deployed, historical attack data is automatically replayed in a sandbox environment to monitor the effectiveness of the strategy and the system performance loss. If it is detected that the deterioration of key indicators exceeds the tolerance threshold, such as the interception rate increase is less than 10% and the latency increase is greater than 200ms, roll back to the previous stable version and mark this strategy as "to be optimized"; among them, monitoring the effectiveness of the strategy includes the poisoning data interception rate, and the system performance loss includes the increase in AI decision latency; for the strategies that pass the verification, the system extracts their core features to generate a simplified version of the knowledge graph, and selects the optimal version through the blockchain node voting mechanism and synchronizes it to the whole network; among them, the core features include the filter rule set and the number of encrypted shards.

[0060] In extreme attack scenarios, such as being simultaneously subjected to multi-type composite attacks, the defense network activates the cross-module cooperation mechanism: calls the risk prediction agent to evaluate the overall risk level of the system. If it reaches a critical state, such as the availability of the core AI model is lower than 70%, then triggers the degradation mode of the federated multi-modal decision network, gives priority to ensuring the basic resource allocation function, and closes non-critical modules, such as multi-language real-time translation, until the defense network restores the system to the security baseline.

[0061] After the translation result of the semantic cooperation agent is generated, it needs to be signed by the private key of the participant's terminal, and the asynchronous sharding consensus unit of the real-time layer verification chain verifies the signature validity. After passing the verification, it is written into the multi-language evidence chain partition of the deposit layer audit chain.

[0062] It should be further noted that in the specific implementation process, after the semantic cooperation agent generates the translation result, the system starts the trusted deposit process: when the participant's terminal receives the real-time translation manuscript, the terminal automatically calls the locally stored private key to digitally sign the translation content, generates a signature data packet containing the translation hash value, timestamp and signer identity identifier, and transmits it to the temporary buffer of the real-time layer verification chain through an encrypted channel. The asynchronous sharding consensus unit of the real-time layer verification chain verifies the signature validity: first, it parses the identity identifier in the signature data packet and matches it with the digital identity certificate pre-stored in the deposit layer audit chain; second, it checks whether the translation hash value is consistent with the original translation text; finally, it verifies whether the timestamp is within the meeting validity period, such as from 1 hour before the agenda starts to 2 hours after it ends. If any verification item fails, such as the identity certificate expires, the hash value is tampered with, or the submission times out, the system immediately freezes this translation and triggers the following processing: sends a re-translation instruction to the semantic cooperation agent, requiring it to regenerate the translation based on the original corpus stored on the blockchain; pushes a signature exception alarm to the participant's terminal, prompting to initiate the signature process again; marks the abnormal event characteristics in the attack feature library of the dynamic defense network module. Among them, the abnormal event characteristics include the same IP address with frequent signature failures.

[0063] For the translated texts that pass the verification, the system writes them into the multilingual evidence chain partition of the deposit and verification layer audit chain: they are stored according to a three-level index structure of "conference number - agenda number - language pair", and each translated text is associated with complete verification path data, including the signature certificate chain, the list of consensus nodes, and the timestamp sequence, forming a traceable translation evidence chain.

[0064] When a participant raises an objection to the accuracy of the translated text, the auditor can trace back to the original text and the signature context in reverse through the hash value in the evidence chain. If it is detected that the content of the translated text is inconsistent with the version at the time of signature, such as the hash value recorded in the deposit and verification chain cannot be reproduced, it is determined that the data in the deposit and verification layer is abnormal, triggering the blockchain node consistency verification and data repair process.

[0065] For the simultaneous interpretation scenario with high real-time requirements, the system enables the fast-track mode: the semantic collaboration agent preloads the high-frequency term library during the translation generation stage, extracts the standard term list of the current conference field from the deposit and verification chain, and gives priority to ensuring the accuracy of key information; in the signature verification link, a streaming processing mechanism is adopted, and the long translation is sliced into paragraph-level data blocks for parallel signature, and the verification results of each data block are fed back to the AI model in real time for context coherence correction.

[0066] If a logical break is detected between paragraphs, such as more than 3 inconsistencies in the terms before and after, the local retranslation mechanism is automatically triggered, and only the problematic paragraphs are replaced while retaining the signature status of the verified parts.

[0067] In the scenario of multinational multi-time zone conferences, the system introduces time zone adaptive verification rules, including: when the timestamp of the translated text signature deviates from the standard time of the main venue by more than the preset threshold, such as when the timestamp of the translated text signature deviates from the standard time of the main venue by more than plus or minus 4 hours, the geographical location data of the associated participants is automatically extracted from the registration information. If the location information conflicts with the signature time zone logically, the deposit and verification process of the translated text is frozen and secondary biometric authentication is required, and it can be written into the blockchain only after passing. For the signature time drift caused by network latency, such as the deviation between the actual generation time and the system reception time is less than 5 minutes, the system starts the time window compensation mechanism, and records the dual timestamps of the original time and the calibrated time in the deposit and verification layer audit chain for cross-verification during subsequent audits.

[0068] When the risk prediction agent detects that the probability of agenda conflict exceeds the preset threshold, it triggers the on-chain smart contract to perform the following operations: Call the venue status interface of the real-time layer verification chain to retrieve the availability of the alternate venue; If the alternate venue is available, send a switching instruction to the federated multi-modal decision network module and update the agenda record in the deposit and verification layer audit chain.

[0069] It should be further noted that in the specific implementation process, when the risk prediction agent detects that the agenda conflict probability exceeds the preset threshold, the system triggers an automated process for on-chain smart contract to execute venue switching and agenda adjustment: the on-chain smart contract first calls the venue status interface of the real-time layer verification chain to retrieve the device status, the number of online participants, and the network load metrics of the current main venue, and at the same time extracts the historical usage records and real-time availability data of the backup venue from the audit chain of the evidence storage layer.

[0070] If the resources of the backup venue are sufficient, such as the online capacity is greater than or equal to 80% of the main venue's demand and the device readiness time is less than or equal to 5 minutes, the contract immediately sends a switching instruction to the resource scheduling agent. The instruction includes the switching time window, the participant diversion ratio, and the access key to the new venue; the resource scheduling agent generates an optimal migration path based on the reinforcement learning model, such as preferentially migrating low-priority participants in overlapping agendas to the backup venue, and pre-writes the hash value of the migration plan to the temporary decision area of the real-time layer verification chain. After the participant terminal confirms receipt, the agenda record of the evidence storage layer audit chain is formally updated.

[0071] If the resources of the backup venue are insufficient or the devices are not ready, the contract starts the dynamic adjustment mode of the agenda time: calculates the available delay time interval according to the urgency of the conflicting agenda and the participant overlap rate, and pushes the time adjustment plan to the associated speaker terminal; among them, the agenda involving the signing ceremony in the urgency of the conflicting agenda is marked as the highest priority; if the speaker accepts the adjustment, the contract locks the new agenda time and updates the evidence storage layer audit chain; if the speaker refuses or fails to respond within the time limit, the participant diversion mechanism is triggered to guide some participants to the virtual branch venue, such as grouping by region or topic preference. After being translated by the semantic collaboration agent, the access link of the virtual branch venue is pushed to the target participant terminal through the encrypted channel verified by the blockchain.

[0072] For high-frequency small-scale conflicts, such as the interval between three consecutive agendas being less than 5 minutes, the system enables a progressive optimization strategy, including: the risk prediction agent no longer triggers a global venue switch, but fine-tunes the parameters of a single agenda through the on-chain smart contract, such as compressing the speech duration by 5% and extending the interval of the Q&A session. Each adjustment requires the private key signatures of the participants and speakers for confirmation. After the signed adjustment record is hashed and associated with the original agenda version, it is written to the evidence storage layer audit chain. For conflicts caused by sudden device failures, such as the network interruption of the main venue, the contract bypasses the regular verification process and directly calls the emergency plan template pre-stored in the evidence storage layer audit chain, such as switching to the dedicated disaster recovery venue, and completes the blockchain verification record within 24 hours after execution, including: the resource scheduling agent traces back all operation logs during the failure period, recalculates the compliance score of the resource allocation plan, and if unauthorized temporary occupation of backup resources is detected, automatically generates a default report and freezes the relevant account permissions.

[0073] In a cross - border multi - time - zone scenario, agenda time adjustment requires additional time - zone synchronization verification: when the contract modifies the agenda timestamp, it automatically associates with the registered time - zone data of the participants. If it is detected that the time modification causes more than 20% of the participants to be in an inactive period, such as local time from 23:00 to 6:00, a hierarchical notification mechanism is triggered, that is: a forced confirmation request is pushed to the core participants, an optional participation time - slot questionnaire is sent to the non - core participants, and multiple agenda copies are dynamically generated according to the feedback results. The participation records of each copy of the agenda are independently archived and finally merged into the main agenda hash tree.

[0074] For the ambiguity of agenda descriptions caused by time - zone conversion, such as "9:00 the next day" without specifying the time zone, the semantic collaboration agent automatically inserts the time - zone identifier and triggers a secondary signature confirmation to avoid subsequent audit disputes.

[0075] Anti - oscillation mechanisms are set for all automated decisions in the above process: if the same agenda is continuously adjusted more than 3 times within 1 hour, the system will freeze the automatic switching function and transfer it to the federated multi - modal decision - making network to initiate multi - factor evaluations, such as participant satisfaction prediction and equipment loss cost models, to generate a final manual review plan, and mark the decision logic chain record as a high - priority audit item to ensure the stability and interpretability of the system in emergency scenarios.

[0076] The node grouping strategy of the asynchronous sharding consensus unit is dynamically adjusted according to the network load of the real - time layer verification chain. The adjustment parameters take effect after being verified by the global anchoring unit of the archival layer audit chain, and the historical grouping strategy versions are stored in the archival layer audit chain.

[0077] It should be further noted that in the specific implementation process, during the dynamic adjustment of the node grouping strategy of the asynchronous sharding consensus unit, the system continuously collects key metrics through the network load monitoring module of the real - time layer verification chain, including: data transmission delay between nodes, transaction verification throughput within the shard, and node computing resource occupancy rate.

[0078] When it is detected that the delay of any shard exceeds the preset threshold, such as the main shard delay is greater than 500ms or the edge shard delay is greater than 1200ms, a grouping strategy optimization algorithm is triggered, including: first, freezing the new transaction verification requests of the shard, and counting the effective verification rate of the nodes within the shard in the past 1 hour. The effective verification rate is the number of successfully verified transactions / the total number of transactions. If the effective verification rate is lower than 80%, it is determined that the node performance is insufficient or there is malicious node interference; if the effective verification rate meets the standard but the delay exceeds the standard, it is determined that the network topology structure is unreasonable.

[0079] For the scenario of insufficient performance, the algorithm retrieves a list of standby nodes from the node reputation database of the audit chain in the evidence storage layer. The reputation score is greater than 90 points and the online rate is greater than 95%. Inefficient nodes are replaced according to the principle of load balancing. For network topology problems, the algorithm re-divides the geographical distribution boundaries of the shards. For example, cross-border nodes are clustered by time zone, and a new shard communication routing table is generated.

[0080] The adjusted grouping policy parameters need to be submitted to the global anchoring unit of the audit chain in the evidence storage layer for compliance verification, including: The verification content includes whether the number of shards exceeds the system's maximum bearing threshold, whether the node allocation complies with the principle of decentralization, and whether the routing table contains high-risk paths. Among them, the grouping policy parameters include the number of shards, the node allocation rule, and the routing table version. System maximum bearing threshold: The lower limit of the number of nodes per shard is 10. Principle of decentralization: The proportion of nodes of a single institution does not exceed 30%. High-risk path: Passing through nodes in known low-reputation areas.

[0081] If the verification passes, the global anchoring unit writes the hash value and the effective timestamp of the new policy into the configuration history area of the audit chain in the evidence storage layer and synchronizes it to all real-time layer nodes for forced execution. If the verification fails, it rolls back to the previous valid policy version, and at the same time marks this adjustment attempt as a "high-risk operation" and triggers the following processing: Sending node anomaly feature data to the dynamic defense network module to update the attack pattern library; Freezing the policy adjustment permission of this shard for 24 hours, during which only manual intervention is allowed.

[0082] For abnormal load fluctuations caused by network attacks, the system enables a dedicated policy for the attack scenario, including: The grouping policy optimization algorithm switches to the conservative mode, suspends the automatic node replacement function, and instead calls the emergency shard template pre-stored in the audit chain in the evidence storage layer, such as the minimum available shard set, and forcibly enables the multi-signature verification mechanism, that is: at least 3 high-reputation nodes are required to jointly sign and confirm the shard operation; At the same time, the traffic scheduling module of the real-time layer verification chain starts to clean the attack traffic, routes the suspected malicious requests to the sandbox shard for behavior analysis, and releases them to the main shard after confirming security.

[0083] During low-load periods, such as during the intermission of a meeting, the system performs preventive policy optimization: Based on the historical load data prediction model in the audit chain in the evidence storage layer, redundant shards are merged in advance. For example, 4 low-active shards are merged into 2, and node resources are released for other high-priority tasks, such as AI model training; During the merging process, the system migrates the uncompleted transactions within the original shard across shards, and the migration path needs to be verified by the global anchoring unit for compatibility with the target shard, such as the same protocol version and the node certificate not expired.

[0084] The backtracking mechanism of historical grouping policy versions is automatically activated when an abnormality is detected in the current policy: when the shard verification error rate rises by more than 50% within three consecutive detection cycles, the system retrieves the five most recent valid policy versions from the audit chain of the evidence storage layer, replays the load data and verification results of the corresponding time period one by one, and selects the version with the lowest error rate as the temporary rollback target; the rollback operation needs to be verified twice by the global anchor unit to ensure that the historical policy parameters are compatible with the current network environment, such as if the validity period of the node certificate has not expired, and evaluates the impact of the rollback on the overall system performance through the federated multimodal decision network, such as predicting the delay change after the rollback. If the evaluation result meets the safety threshold, such as the delay increase does not exceed 15%, the version rollback is executed and a root cause analysis report is generated and stored in the blockchain.

[0085] The above dynamic adjustment mechanism extends to cross-border multi-node collaboration scenarios: when it is detected that nodes in a certain region are collectively offline, such as a regional network failure, the system automatically enables cross-chain mirror sharding and temporarily transfers the verification responsibilities of the affected shards to the backup chain node group in other regions. The transfer process must meet the following conditions: the average node reputation score of the mirror shard is not lower than that of the original shard; the data synchronization delay after the transfer does not exceed 1 second; the complete mirror relationship mapping table is retained in the audit chain of the evidence layer. After the failure is recovered, the system synchronizes the data status of the mirror shard with the original shard through a difference comparison algorithm, and removes the mirror binding after ensuring data consistency.

[0086] The agenda compliance verification rules for on-chain smart contracts include: Time conflict detection of parallel agendas of the same participant; Verification of regional isolation between VIP and ordinary participants in the room allocation plan; The authority level matching check for temporary agenda adjustment; After the verification result passes the node consensus of the real-time layer verification chain, the resource scheduling agent is triggered to regenerate the plan.

[0087] It should be further explained that in the specific implementation process, when the on-chain smart contract performs agenda compliance verification, the system implements differentiated processing procedures for different types of rules. When a participant submits a new agenda registration request, the contract first retrieves the timestamp of the user's agenda that has been stored in the evidence layer audit chain. If it is detected that the time overlap exceeds the preset tolerance value, such as the interval between parallel agendas is less than 15 minutes and the distance between physical venues is greater than 500 meters, a conflict report is automatically generated and the following processing is triggered: a priority assessment request for the conflicting agenda is sent to the federated multimodal decision network module. Based on the agenda type, participant identity weight and historical participation data, if the assessment result is optimizable, such as a low-priority agenda for non-core participants, the resource scheduling agent is called to generate a time fine-tuning plan, such as a 30-minute delay or migration to a virtual branch venue; if the assessment is not adjustable, such as a mandatory agenda by the organizer, the user's new agenda request is frozen and the conflict details are pushed to the administrator terminal.

[0088] For the VIP area isolation verification of the room allocation plan, the contract extracts the distribution of participant identity tags in the current room allocation plan from the real-time layer verification chain. If it is detected that the proportion of ordinary participants in the VIP area exceeds 5% or there is a physical mixing of VIP and ordinary areas, such as sharing an entrance and exit passage, it is determined that the isolation has failed.

[0089] At this time, the contract sends a region reset instruction to the resource scheduling agent, requiring it to regenerate the room allocation plan based on the reinforcement learning model and increase the isolation weight coefficient in the new plan, such as expanding the VIP area boundary buffer zone to 3 meters. The reset plan needs to pass double verification, including: first, the AI pre-screening gateway verifies whether it complies with the isolation rules, and then the on-chain smart contract reviews the historical violation records, such as whether the agent has triggered a reset more than 3 times due to the same type of problem in the past 24 hours. If all pass, the hash value of the new plan is anchored to the proof-of-storage layer audit chain; otherwise, the manual review process is initiated.

[0090] When processing a request for a temporary agenda adjustment, the contract performs a three-level verification of the permission level matching: First, it parses the digital identity certificate of the request initiator and compares it with the pre-stored role permission table in the proof-of-storage chain, such as: only the agenda host has the permission to modify the time; Second, it verifies whether the adjusted agenda parameters exceed the authorization scope of the original agenda, such as the extended duration not exceeding 50% of the initial value; Finally, it checks whether the operation timestamp is within the legal window period, such as only allowing adjustments within 2 hours before the start of the agenda.

[0091] If all three-level verifications pass, the contract marks the adjustment record as "pending execution" status and broadcasts it to the real-time layer verification chain nodes for fast consensus; if any stage fails, it triggers a differentiated response according to the failure type, that is: for the problem of insufficient permissions, it returns an encrypted error code and a permission upgrade guidance link to the requester; for the problem of parameter out-of-bounds, it automatically generates a compliance parameter plan and attaches an AI optimization path description, such as recommended adjustment amplitude and impact prediction.

[0092] In a high-concurrency scenario, the system enables an asynchronous queue mechanism for agenda verification, including: sorting the requests to be processed by priority, where VIP user requests take precedence over ordinary users, and non-urgent operations are temporarily stored in the delayed processing area of the proof-of-storage layer audit chain, and the risk prediction agent evaluates the risk of queue backlog; among them, non-urgent operations include requests for post-meeting data analysis. If it is detected that the backlog exceeds the system's carrying capacity threshold, such as the number of unprocessed requests is greater than 1000, the verification rules are dynamically adjusted: temporarily relax the conflict tolerance value for non-core agendas, such as shortening the parallel interval to 10 minutes, and record the temporary rule version and the effective period in the proof-of-storage chain, and automatically restore the standard rules after the load drops.

[0093] For a multinational multi-regulatory environment, a dynamic compliance rule engine is built into the contract: when a change in the region of the participant's registration information is detected, the data protection regulations corresponding to the region in the evidence chain are automatically associated, and the verification rule set is reconstructed. The rule switch requires cross-chain verification: the compliance node group of the audit chain in the evidence layer verifies the authenticity of the region label, such as the consistency between IP geolocation and the registered address, and generates a multilingual compliance statement through a semantic collaboration agent, which can only take effect after the participant's secondary confirmation.

[0094] The results and process data of all verification operations form a double-chain evidence storage system: the change records of the agenda status at the execution level are written into the operation log partition of the real-time layer verification chain, and the rule matching path and verification basis at the logical level are encapsulated in the form of zero-knowledge proofs and written into the compliance evidence library of the evidence layer audit chain; among them, the verification basis includes the version of the conflict detection algorithm and the source of the isolation weight coefficient. When an audit dispute occurs, based on the hash correlation of the double-chain data, the full life cycle path of the verification decision can be completely traced back, including the AI model version, the iteration record of the contract rule set, and the network load snapshot, ensuring that the system meets the judicial-level audit requirements while guaranteeing efficient operation.

[0095] A remote digital conference management method combining blockchain and AI large models includes the following steps: Step S1: The participant submits registration information, resource allocation requests, or agenda adjustment applications through the terminal. The system receives the data and starts the preprocessing process, encapsulating the original data into a standardized transaction request packet.

[0096] Step S2: The AI pre-screening gateway calls the anomaly detection model to conduct the first logical screening of the request, analyzes potential conflicts based on the historical trusted dataset, intercepts high-risk operations and generates risk logs to be stored in the blockchain; the data that passes the screening is forwarded to the on-chain smart contract; among them, high-risk operations include duplicate registration and over-authorization requests.

[0097] Step S3: The on-chain smart contract performs secondary rule verification, including identity certificate verification, agenda time conflict detection, and resource capacity compliance check. The transactions that pass the verification trigger the real-time layer node consensus verification of the hierarchical dynamic verification chain.

[0098] Step S4: The real-time layer verification chain verifies the transaction in milliseconds through the asynchronous sharding consensus mechanism, and the verification result is temporarily stored in the buffer; at the same time, the global anchoring unit periodically writes the hash fingerprint of the real-time layer data into the evidence layer audit chain to complete the full life cycle evidence storage.

[0099] Step S5: The federated multi-modal decision network starts dynamic decision-making based on the verification result: the resource scheduling agent generates a room or seat allocation plan, the semantic collaboration agent performs multilingual translation and requests signatures, and the risk prediction agent evaluates the conflict probability and generates response instructions.

[0100] Step S6: For permission changes or high-risk operations, including VIP permission upgrades and backup venue switches, the system performs triple verification: AI proposal generation, real-time layer node consensus, and final audit of the deposit layer. It takes effect only after a unanimous vote.

[0101] Step S7: The dynamic defense network continuously monitors the security event logs of the deposit layer, generates defense strategies after identifying attack patterns, dynamically adjusts data filtering rules, encryption policies, and access control permissions, and synchronizes the defense records to the blockchain.

[0102] Step S8: After the multilingual translation results are signed with the private key of the participants, the real-time layer verification chain verifies the signature validity. After passing, it is written to the deposit layer according to the three-level index of "conference - agenda - language" to form a traceable evidence chain.

[0103] Step S9: When agenda conflicts or resource anomalies are detected, the on-chain smart contract automatically triggers an adjustment process: retrieves the status of backup resources, generates migration plans or modifies agenda parameters, and updates the blockchain record after the signature confirmation of the participants or administrators.

[0104] Step S10: The system periodically performs self-optimization operations: dynamically adjusts the node grouping strategy, federated learning updates of the defense knowledge base, and double-chain data consistency verification. All optimization parameters take effect after passing the compliance verification of the audit chain of the deposit layer.

[0105] Through the deep integration of blockchain and AI large models, the security and intelligence level of remote meeting management have been improved. Blockchain technology constructs a hierarchical dynamic verification chain to ensure the immutability and full-life-cycle traceability of the whole-process data of the meeting, effectively solving the defects of traditional centralized systems being vulnerable to attacks and data being easily forged; the federated multi-modal decision-making network driven by AI large models realizes autonomous optimization and real-time response in resource scheduling, risk prediction, cross-language collaboration, etc., reducing the cost of manual intervention and improving management efficiency.

[0106] It solves the coordination problem between the security mechanism and decision-making efficiency, balancing the strong audit requirements of the blockchain and the real-time response ability of AI through a double-chain architecture; the dynamic defense network and self-optimization mechanism further strengthen the system robustness, can actively identify and intercept new types of attacks such as data poisoning and permission tampering, and continuously optimize the AI model and resource allocation strategy based on federated learning.

[0107] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0108] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A remote digital conference management system combining blockchain and AI large models, characterized in that, It includes: A hierarchical dynamic verification chain module, which is composed of a real-time layer verification chain and an evidence storage layer audit chain connected through a data synchronization interface. The real-time layer verification chain adopts a lightweight consortium chain architecture, and the evidence storage layer audit chain is built based on a public chain; A federated multi-modal decision network module, whose input end is communicatively connected to the output end of the hierarchical dynamic verification chain module, and is used to generate a conference resource scheduling plan and a risk response instruction; A dual-chain heterogeneous consensus engine, embedded in the hierarchical dynamic verification chain module, including an asynchronous sharding consensus unit and a global anchoring unit. The asynchronous sharding consensus unit is used for parallel verification of real-time layer data, and the global anchoring unit is used to periodically write the hash fingerprint of real-time layer data into the evidence storage layer audit chain; A dynamic defense network module, whose attack feature library is data-connected to the evidence storage layer audit chain of the hierarchical dynamic verification chain module, and the defense strategy output end is connected to the control end of the federated multi-modal decision network module.

2. The remote digital conference management system combining blockchain and AI large models according to claim 1, wherein: The real-time layer verification chain includes an AI pre-screening gateway, which is composed of an anomaly detection model and an on-chain smart contract: The anomaly detection model is built based on a graph neural network, and the training data comes from the registration information, room allocation records and permission change logs in the historical conference dataset; The on-chain smart contract contains rules for verifying the identity of participants, rules for verifying the compliance of the agenda, and rules for detecting resource allocation conflicts; Among them, the output end of the anomaly detection model is connected in series with the input end of the on-chain smart contract. Only when the data passes the screening of the anomaly detection model and the verification of the on-chain smart contract, the federated multi-modal decision network module is triggered to start.

3. The remote digital conference management system combining blockchain and AI large models according to claim 1, characterized in that: The federated multi-modal decision network module includes: A resource scheduling agent, whose data input end is connected to the resource allocation interface of the real-time layer verification chain, generates a room allocation or seating plan based on a reinforcement learning model, and dynamically optimizes the plan weight through a Monte Carlo tree search algorithm; A semantic collaboration agent, built-in with a multi-language large model, whose corpus is stored in the evidence storage layer audit chain, and the translation result is signed by the private key of the participant's terminal and then sent back to the evidence storage layer audit chain; A risk prediction agent, which uses a temporal graph convolutional network to analyze the historical conference data in the evidence storage layer audit chain, predicts the probability of agenda conflict and outputs it to the on-chain smart contract.

4. The remote digital conference management system combining blockchain and AI large models according to claim 3, characterized in that: After the resource scheduling agent generates a room allocation or seating plan, it synchronously writes two types of on-chain records to the hierarchical dynamic verification chain module: An execution chain record, which includes a plan identifier, an execution timestamp, and a resource utilization rate indicator; A logic chain record, which encapsulates the parameter update path of the decision model and the fairness constraint verification result in the form of zero-knowledge proof; Among them, the execution chain record and the logic chain record are associated by hash and input into the feedback learning unit of the federated multi-modal decision network module.

5. The remote digital conference management system combining blockchain and AI large model according to claim 1, characterized in that: The hierarchical dynamic verification chain module implements triple verification on VIP permission change operations: The first verification generates a preliminary proposal by the risk prediction agent of the federated multi-modal decision network module. The preliminary proposal includes the scope of permission change, timeliness, and risk score; The second verification is that the asynchronous sharding consensus unit of the real-time layer verification chain performs fast consensus on the proposal by node groups. The verification content includes user identity hash matching, permission timeliness conflict detection, and role permission upper limit verification; The third verification randomly spot-checks the logical consistency between the proposed plan and the historical permission records by the final audit nodes of the audit chain in the evidence storage layer, including the compliance of the permission upgrade path and the rationality of the risk score distribution; Only when the results of the triple verification are consistent, the proposed plan is marked as valid and written into the permission change partition of the audit chain in the evidence storage layer.

6. The remote digital conference management system integrating blockchain and AI large model according to claim 1, characterized in that: The dynamic defense network module includes: An attack feature extraction unit that extracts the data patterns of historical attack events from the audit chain in the evidence storage layer; An AI countermeasure model training unit that generates defense strategies based on the extracted attack features and sends them back to the federated multi-modal decision network module; Among them, the defense strategies include data poisoning attack interception rules and model parameter encryption rules, and the strategy version numbers are synchronously updated to the audit chain in the evidence storage layer.

7. The remote digital conference management system combining blockchain and AI large models according to claim 3, characterized in that: After the translation result of the semantic collaboration agent is generated, it needs to be signed by the private key of the participant's terminal, and the signature validity is verified by the asynchronous sharding consensus unit of the real-time layer verification chain. After the verification passes, it is written into the multi-language evidence chain partition of the audit chain in the evidence storage layer.

8. The remote digital conference management system combining blockchain and AI large models according to claim 3, characterized in that: When the risk prediction agent detects that the probability of agenda conflict exceeds the preset threshold, it triggers the on-chain smart contract to perform the following operations: Call the venue status interface of the real-time layer verification chain to retrieve the availability of alternative venues; If an alternative venue is available, send a switching instruction to the federated multi-modal decision network module and update the agenda record in the audit chain of the evidence storage layer.

9. The remote digital conference management system combining blockchain and AI large models according to claim 1, characterized in that: The node grouping strategy of the asynchronous sharding consensus unit is dynamically adjusted according to the network load of the real-time layer verification chain. The adjusted parameters take effect after being verified by the global anchoring unit of the audit chain in the evidence storage layer, and the historical grouping strategy versions are stored in the audit chain in the evidence storage layer.

10. The remote digital conference management system combining blockchain and AI large models according to claim 2, characterized in that: The agenda compliance verification rules of the on-chain smart contract include: Detecting time conflicts between parallel agendas of the same participant; Verifying the regional isolation between VIPs and ordinary participants in the room allocation plan; Verifying the matching of permission levels for ad-hoc agenda adjustments; After the verification results pass the node consensus of the real-time layer verification chain, trigger the resource scheduling agent to regenerate the plan.

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