57 results about "Cross organizational" patented technology

A computerized platform enables secure, policy-compliant creation, governance, and monetization of digital artifacts produced by Al-driven “persona” agents in regulated domains (e.g., healthcare, finance). For each agent session, a federated compliance-token generator issues a cryptographically signed token embedding the full delegation chain, credential lineage, real-time policy snapshot, monetization rules, expiration and revocation data. As artifacts or workflows cross organizational or jurisdictional boundaries, a token inheritance module can subdivide, transfer, renew, or revoke the token while appending each event to an immutable, cryptographically linked audit trail. Every agent output is encapsulated as a digitally signed artifact that is indelibly linked to its originating compliance token and enriched with provenance, consent, and service metadata. A permissioned blockchain (or sharded distributed ledger) functions as an asset registry that immutably stores artifacts, tokens, and chronological records of creation, access, transfer, consent change, and policy updates. A smart-contract engine automates revenue allocation and settlement, ensuring continuous traceability, privacy protection, and financial compliance across institutional boundaries.

The invention discloses a cross-organization project collaborative management method and system, a medium and a product, and the method comprises the steps: carrying out the project creation permission verification of a target user in response to a project creation request of the target user; if the target user passes the project creation permission verification, constructing a cross-tenant virtual organization corresponding to the target project in the project creation request; the cross-tenant virtual organization is determined as a project node in a preset project tree, creation event data of the project node is generated, and the project node comprises a plurality of project child nodes; obtaining a task dependency relationship among the plurality of first project child nodes, and constructing a cross-organization collaborative network based on the creation event data and the task dependency relationship; and in response to a state change event of a source project child node in the cross-organization cooperative network, generating a state synchronization instruction, and sending the state synchronization instruction to one or more second project child nodes associated with the source project child node based on the task dependency relationship. According to the invention, the cross-organization project collaborative management efficiency can be improved.

The invention provides a network security risk assessment system and method based on artificial intelligence, relates to the technical field of network information security, and adopts innovative fusion of a dynamic collaborative aggregation concept (DSA) and a trusted game motivation mechanism (TGIM) to realize efficient cross-organization collaboration on the premise of completely protecting data privacy. According to the DSA algorithm, model parameters are dynamically weighted and aggregated by using a data utility factor (DUF) and a threat association degree quantity (TCM), a high-quality global model can be generated without sharing original data, the risk of data leakage is greatly reduced, the TGIM designs a trust score and an exponential return function based on the game theory, high-quality contribution is stimulated, malicious behaviors are effectively prevented, cooperation fairness is ensured, and the method has the advantages of being high in robustness and high in reliability. The decentralized cooperation mode significantly improves the ability of multi-organization cooperation to cope with complex network threats, seamlessly combines privacy protection with efficient cooperation, provides a brand new security assessment normal form for sensitive industries such as finance and medical treatment, and has wide application potential.

The invention relates to the technical field of big data processing and analysis, in particular to a commodity traceability management method and system based on big data. The method comprises the steps of determining a data range and association logic of each type of events by constructing a semantic event system of a commodity life cycle, performing data acquisition, preprocessing and credibility evaluation at edge nodes and generating an evidence header, and further converting data into multilayer verifiable commodity fingerprints of an L1 edge abstract, L2 event level fusion and L3 full link fusion, the method comprises the following steps: realizing feature fusion and dynamic incremental updating, then performing distributed notarization and multi-party verification on fingerprints through a federated anchor point mechanism to ensure that a full link is traceable and cannot be tampered, and finally supporting full link traceability query and anomaly tracking based on a unique commodity identifier. And closed-loop traceable management is realized through visual display and multi-role authority management. According to the invention, the reliability, interpretability and cross-organization trust of commodity traceability are effectively improved.

The invention provides a professional influence evaluation and dynamic deduction method and system based on a knowledge graph and a storage medium. The method comprises the steps of 1, constructing a structured knowledge set representing entities and relationships in a professional field; 2, calculating a global influence absolute score of a target entity based on the structured knowledge set in the step 1; step 3, based on the global influence absolute score in the step 2, performing context-aware context influence analysis; and 4, performing prospective influence deduction based on disturbance simulation of the structured knowledge set in the step 1. The method has the beneficial effects that 1, the holography and objectivity of an evaluation basis are realized, and the credibility and fairness of an evaluation result are fundamentally improved; 2, a uniform and data-driven evaluation scale is established, and cross-organization and cross-region fair benchmarking is realized for the first time; and 3, a context-aware multi-level analysis capability is provided, so that the evaluation has unprecedented depth and fineness.

The invention discloses a real-time circulation monitoring method and platform in a trusted data space, relates to the technical field of data management and security, and solves the problems that a traditional data monitoring scheme depends on batch log analysis, minute-level delay exists, and a verifiable joint auditing mechanism is lacked during cross-organization data circulation, so that the dispute rate is relatively high, and the reliability is high. And the monitoring coverage rate of multi-organization data collaboration is relatively low due to the facts that monitoring data between heterogeneous systems is split and a standardized interface is lacked. The real-time circulation monitoring method and platform in the trusted data space comprises a monitoring system, and the monitoring system comprises a digital fingerprint generation module, a probe deployment module, a dynamic trust evaluation engine, an audit tracking module and a visual console. Through cooperation of all module components, millisecond-level monitoring delay is achieved, compared with a traditional scheme, the response speed is increased by multiple times, real-time analysis of a hundred million-level data event / second is supported, and the risk of data leakage is effectively prevented.

According to the DDoS relieving system and method based on the alliance chain, a decentralized IP address reputation sharing system oriented to multi-organization and multi-node participation is constructed, by introducing the alliance chain and a multi-node consensus mechanism, credible exchange and cooperative use of reputation information under the condition that trust in advance is not needed are achieved, and the reputation sharing efficiency is improved. Single-point fault risks and cross-organization trust barriers existing in a traditional centralized reputation system are effectively eliminated, local real-time flow monitoring and dynamic reputation evaluation results of nodes are combined with the whole-network reputation state shared on the chain, a joint decision-making mechanism is formed, and the reliability of the system is improved. According to the method, the reputation state on the chain is queried through the smart contract, and the protection instruction is generated in combination with the local threshold strategy, so that the protection node can execute the security strategies such as speed limiting and blocking more accurately and timely, and the recognition precision, the response speed and the overall protection effect of distributed denial of service attack mitigation are effectively improved.

The invention discloses a federal multitask learning method and system for hepatocellular carcinoma. The invention belongs to the technical field of distributed machine learning and privacy computing, and is suitable for a scene of cross-device or cross-organization multi-client collaborative training differentiated machine learning models on the premise of data privacy protection. The technical problems to be solved are that in a multi-task federated learning scene, due to large local data difference of different clients, the performance of a client model is poor, and convergence difficulty is possibly caused by the client model which only depends on gradient aggregation to execute different tasks. According to the method, channel selection based on language guidance is adopted, and the client model learns specific domain knowledge by using predefined text prompts; and realizing personalized updating of the client model by adopting parameter aggregation based on language guidance and applying a hierarchical cross attention mechanism. The method is mainly used for tasks of tumor segmentation, early recurrence prediction, survival analysis and the like aiming at hepatocellular carcinoma.

The invention discloses a cross-organization emergency response process model extraction method based on a large language model, belongs to the field of process mining and natural language processing, and constructs a large language model general prompt framework containing emergency response process element definition and judgment criteria. Based on a general prompt framework, a prompt strategy fusing role playing and context learning is adopted to extract explicit entities such as emergency organizations and response tasks and relationships of the explicit entities. Inferring and extracting implicit entities and implicit relation elements on the basis of explicit elements by adopting a thinking chain fused prompt strategy; and finally, making a mapping rule to convert the extracted complete process elements into a logically coherent cross-organization emergency response process model, and presenting a result in a graphical manner. According to the method, domain data training is not needed, accurate flow element extraction can be achieved under the condition of few samples, the problem of implicit flow element missing is solved, and technical support is provided for emergency decision making.

The embodiment of the application provides a cross-organization data interaction method, system and device based on a blockchain smart contract, which can be used in the field of artificial intelligence technology, realizes data security management and control through the blockchain smart contract, reduces the technical capability requirement for a data organization party, guarantees data availability and invisibility, prevents data leakage and abuse, improves data security, does not need to excessively depend on third-party technology, and realizes interaction based on the same simple smart contract development of both parties, realizes equality and mutual trust through the blockchain, does not exist single-party leading and thus generates a security threat to other participating parties, and thus efficiently and intuitively realizes data sharing and circulation.

The invention relates to the technical field of agents, in particular to a cross-domain rule knowledge migration and adaptation method and system of an agent. The method comprises the following steps: generating a preliminary bookkeeping result based on a general accounting rule, and correcting a record according to the preliminary bookkeeping result to obtain rule metadata; vectorizing the rule metadata to obtain a desensitization rule vector set shared across organizations; performing clustering analysis on the desensitization rule vector set to generate a field generality rule template; and in combination with the field generality rule template and new organization correction, obtaining a new organization proprietary rule set. According to the method, the field generality rule template is extracted in advance, the problem that the pain point of the rule is zero when the financial agent is introduced into the new organization is solved, the rule template is directly introduced and rapidly and finely adjusted, the adaptation period is shortened from several months to several days or several weeks, and the time and economic cost of the new organization are remarkably reduced.

This invention relates to the field of network security technology and discloses an incremental dynamic data extraction method based on an expert model. The method includes: generating a local attack attribution subgraph; protecting privacy through homomorphic encryption and differential privacy; using a federated graph neural network to identify cross-organizational attack patterns; reconstructing cross-domain attack chains through Markov chain analysis; verifying the attack path based on zero-knowledge proofs; and outputting a threat level score. This invention resolves the contradiction between data privacy protection and collaborative analysis in cross-organizational APT attack detection, achieving accurate identification and reconstruction of cross-domain attack chains while protecting the sensitive data of each organization.

The invention provides a cross-organization material sharing method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining the idle material data of each member enterprise, and forming a material data set; according to a material allocation request of a demand member enterprise, available state information of a target material in the material data set is read, and a priority weight of the material allocation request is calculated based on an emergency degree, an approval chain length and a historical occupation frequency of the material allocation request; dynamically locking the target material, adjusting the state from an available state to a locked state, and only allowing the request with the highest priority weight to maintain locking; continuously monitoring the allocation approval progress of the locked material, and judging whether the locked state should be released in advance; when the approval process is completely completed and delivery is confirmed through logistics information, the state of the target material is adjusted from a locked state to an allocated state; according to the invention, autonomy and accuracy of cross-organization material sharing are improved.

The invention discloses a cross-subject secure connection method and device and a medium, and relates to the technical field of data processing. The method comprises the following steps: constructing a three-party collaborative architecture taking a connector as a core, taking a service party as a credible intermediary, executing identity adaptation, intelligent contract template matching and digital signature legality verification based on an NDI standard, and after the verification is passed, determining itself as a strategy execution point to start controlled data security connection, therefore, it is ensured that data interaction is strictly constrained by the digital contract at a connection establishment source, the unification of data controllable sharing, efficient mutual recognition and legal credibility is effectively achieved, and the problems that in the existing cross-organization data sharing process, data sovereignty is out of control, and cross-domain mutual recognition is low in efficiency are solved.

This invention provides a real-time synchronization method for cross-organizational approval system personnel based on public and private keys. This method relates to the field of information encryption and synchronization technology. The method involves generating unique public and private key pairs, constructing a multi-level hash tree of active nodes, forming a multidimensional association matrix based on distribution feature maps, performing dynamic programming to obtain optimal propagation paths, and utilizing network slicing for consistency checking and fault location. This method ensures the security, real-time performance, and consistency of cross-organizational personnel information synchronization, improves the overall system response efficiency, and enhances the reliability of data transmission.

The invention discloses a human resource management method and system based on artificial intelligence, and relates to the technical field of human resource management, and the method comprises the following steps: guaranteeing personal data sovereignty through an employee side personal data insurance library and homomorphic encryption; the method comprises the following steps: constructing double agents which belong to employees and organizations and represent respective interests, and generating and signing an automatically executed initial bidirectional contract through anti-fact simulation and game theory negotiation; during the duration of the contract, the employee intelligent agent monitors the multivariate contribution in real time, and adjusts the contract terms according to a dynamic formula; dynamic value measurement and distribution of multi-element contribution are realized by combining causal reasoning interpretable analysis and a Shapley value algorithm through a federated learning cooperative training model integrating differential privacy and homomorphic encryption; by constructing a full-link privacy architecture, a bidirectional agent negotiation system and alliance chain cross-organization ecology, privacy and data utilization and organization and employee benefit balance are realized, talent ecology is constructed, and management intelligence and fairness are improved.

The invention relates to the technical field of engineering management, in particular to an engineering supervision data collaborative management method based on the Internet of Things, which comprises the following steps: acquiring multi-source engineering data in real time through an Internet of Things sensing terminal deployed at a construction site, and generating a multi-source engineering data set; when it is detected that the key parameter combination exceeds the limit, a dynamic permission strategy of three-party operation permission is automatically generated according to the current construction stage feature, and a multi-party cooperation task request is triggered; and distributing the collaborative task request to the corresponding terminal through the message middleware, and synchronously executing collaborative operation. According to the method, event processing time limit grading and operation white list generation are supported, the precision and efficiency of project abnormal response are improved, the problems of fuzzy responsibility and response delay are avoided, the data security, verifiability and supervision compliance of collaborative tasks of all construction parties are improved, and the method is particularly suitable for multi-project concurrency and cross-organization collaborative scenes.

The invention discloses a multi-source data integrated management platform for professional construction, and relates to the technical field of professional construction data processing. The multi-source data integrated management platform for professional construction comprises a data acquisition module which acquires and preprocesses structural, behavior class and interactive response data and constructs a multi-role access behavior data set; the data structure level desensitization module is used for analyzing field display conditions and triggering structure reconstruction; the collaborative scene sensing module is used for evaluating and adjusting field permission layering; the permission drift path identification module comprehensively analyzes the sharing stability and dynamically adjusts the sharing frequency; and the cross-organization data flow supervision module is used for marking cross-organization access behaviors and generating permission reconstruction suggestions. The problems that in a current vocational education management platform, permission granularity and data visibility are difficult to balance, and when the platform faces multiple types of users inside and outside a school, too fine permission distribution is likely to happen, so that information islands are caused, and data cannot be effectively shared and circulated are solved.

The application relates to a digital-analog dual-drive intelligent scheduling method and device across organizational machine workshops, wherein the method comprises the following steps: establishing a scheduling model across organizational machine workshops and performing vector coding on the scheduling model to obtain factory, process and machine vectors; a first and a second parent population are generated through a collaborative initialization strategy, a first child population is obtained by genetic evolution search on the first parent population, and a second child population is obtained by data-driven neighborhood search and recording of search knowledge; new first and second parent populations are generated according to the parent and child populations, and search knowledge of the new first parent population is obtained by multi-operator collaborative neighborhood search; a one-dimensional tensor is obtained by splicing the factory vector and the like, and a corresponding scheduling scheme is obtained in combination with a neighborhood action recommendation neural network model. Therefore, the problems that the existing cross-organizational workshop scheduling method is difficult to realize fast and efficient search while recording historical search experience, and the solving efficiency of the resource grouping complex machining workshop scheduling problem is low and the like are solved.

A system has been created that scores alerts to facilitate alert prioritization by leveraging a classifier trained with cross-organization training data and a classifier trained with single organization training data. When an alert is detected, the system extracts feature values for a cybersecurity feature set from the alert and metadata of a relevant asset to generate a first feature vector that is then fed into an organization agnostic classifier to obtain a first importance classification. The system also extracts feature values for a second feature set from metadata of the relevant asset to generate a second feature vector that is then fed into an organization specific alert classifier to obtain a second importance classification. The ensemble aggregates the importance classifications into a score that can then be used to prioritize the alert relative to other alerts.

The invention relates to a decentralized access control system and method for cross-domain data sharing in the technical field of computers. The system comprises a block chain client, a block chain trust and decision layer and a Vault key management layer. Non-tampering storage of identities and policies is realized based on a distributed trust basis of a block chain, and a fine-grained dynamic authorization capability is provided based on an attribute-based access control (ABAC) model; and the key management layer based on Vault realizes security management and control of the key life cycle. The components jointly construct a decentralized, fine-grained and audible cross-organization data access control system; the hierarchical protection strategy not only ensures the comprehensive coverage of the core technical scheme, but also reserves a proper protection space for subsequent possible technical evolution.

The invention provides a cost calculation restoration method and device supporting a self-defined restoration range, equipment and a medium, belongs to the technical field of cost accounting, configures the cost restoration range, creates an intermediate table for cost restoration calculation, and defines input and output functions. Wherein the cost center selection range of the meter body is constrained by the accounting organization and the range type of the meter head. And receiving a to-be-restored cost calculation single header ID and a restoration range header ID, and initializing the maximum level to be 1. And updating the material unit price and the completion cost of the reset unit price record according to the hierarchy from high to low, summarizing the update header data and transmitting the latest cost price. And calculating a quantity / amount coefficient, and adjusting and inserting the associated table body data. And dumping the intermediate table data to a cost reduction calculation sheet, and filling missing source accounting organization / cost center information. According to the method, cross-organization automatic reduction of cost data is realized, the accuracy and transparency of cost calculation are improved, and multi-organization cost management and decision are effectively supported.

The invention discloses a multi-organization collaborative invoice registration link verifiable access control system, and aims to solve the problems that an existing invoice registration link only records self registration circulation of a single invoice and cannot express a pre-processing relation of a plurality of related invoices and a multi-organization access verification sequence in the link. In order to solve the problems that cross-organization access control depends on out-of-chain log splicing, the process is complex and re-calculation cannot be carried out in the prior art, a related invoice processing chain structure is introduced on the basis of an invoice registration link main structure, and an in-chain service level access verification comprehensive calculation mechanism and an in-chain service level access verification judgment and in-chain write-back mechanism are designed; related invoice processing chains, business processing stages and participation requirements are calculated in the chains, and access judgment conclusions and bases are written in, so that inter-chain independent calculation, sequence verifiable and auditable reproduction of cross-organization access control are realized.

This application provides a business process processing system, method, client, and medium. The system includes a management client, multiple business clients, and a target blockchain platform. Among the multiple business clients, some belong to organizations different from the others. The management client generates processing tasks based on business process processing requests, including business flows, the identity identifiers of the business clients to be processed, and their corresponding sub-processing tasks. It then sends these processing tasks to the multiple business clients to be processed and to the target blockchain platform to write them into the target blockchain. The business clients to be processed approve their corresponding sub-processing tasks according to the business flows, and send the approval results to the other business clients to be processed and the target blockchain platform to write them into the target blockchain. This addresses the problem of low efficiency and security in existing technologies where cross-organizational business process processing requires paper document approval.

The present disclosure describes platforms and systems designed and configured as a collaboration portal that functions as a socialization and interpretation “layer” that is configured to be added to a modern BI or data technology stack to extract its full value. Collaboration portals designed and configured according to the present disclosure may be configured to address the problem of underutilized BI resources by providing technical solutions for aggregating and socializing insights across existing BI tools and across organizational divisions, for example, collaborating, discussing, sharing, socializing, etc. a plurality of insights generated on a plurality of disparate BI tools or other software programs via a central collaboration portal with a plurality of users.

This invention provides an artificial intelligence-based cybersecurity risk assessment system and method, relating to the field of network information security technology. It innovatively integrates the Dynamic Collaborative Aggregation (DSA) concept and the Trust Game Incentive Mechanism (TGIM) to achieve efficient cross-organizational collaboration while fully protecting data privacy. The DSA algorithm dynamically weights and aggregates model parameters using the Data Utility Factor (DUF) and Threat Correlation Metric (TCM), generating a high-quality global model without sharing original data, significantly reducing the risk of data leakage. TGIM, based on game theory, designs trust scores and exponential reward functions to incentivize high-quality contributions and effectively prevent malicious behavior, ensuring fairness in collaboration. This decentralized collaboration model significantly enhances the ability of multiple organizations to collaboratively address complex network threats, seamlessly combining privacy protection with efficient collaboration. It provides a new security assessment paradigm for sensitive industries such as finance and healthcare, possessing broad application potential.

This invention relates to the field of intelligent agent technology, specifically to a method and system for cross-domain rule knowledge transfer and adaptation of intelligent agents. The method includes the following steps: generating preliminary accounting results based on general accounting rules; obtaining rule metadata by correcting records based on the preliminary accounting results; vectorizing the rule metadata to obtain a set of de-identified rule vectors shared across organizations; performing cluster analysis on the de-identified rule vector set to generate domain-common rule templates; and combining the domain-common rule templates with new organization-specific corrections to obtain a new organization-specific rule set. This invention addresses the pain point of building rules from scratch when introducing a financial intelligent agent to a new organization by pre-extracting domain-common rule templates, enabling direct import and rapid fine-tuning of rule templates, shortening the adaptation cycle from months to days or weeks, and significantly reducing the time and economic costs for new organizations.

The application belongs to the technical field of electric power data security and data sharing, and discloses an electric power data security sharing method based on privacy calculation and related equipment. The hybrid storage system solves the problem of limited storage capacity of the blockchain. The on-chain storage metadata guarantees traceability, and the off-chain storage encrypted data improves storage efficiency. The improved BFT consensus algorithm guarantees the consistency of the distributed nodes and solves the trust problem of cross-organization data sharing. The collaborative computing protocol of secret sharing and homomorphic encryption takes into account the privacy protection strength and the computing and communication efficiency. Compared with the traditional single privacy calculation scheme, the computing efficiency is improved, the communication cost is reduced, the proxy re-encryption technology realizes controllable sharing of data, and the compliance requirements are met. The application can effectively support sensitive data sharing scenes such as electric power load prediction and cross-department collaborative analysis, and can guarantee that sensitive information such as power grid topology and user power consumption behavior is not leaked.