4658 results about "Data security" patented technology

The invention relates to the technical field of network security and data protection, and discloses a data leakage prevention method and system based on user behavior perception, and the method comprises the steps: obtaining historical operation data, and constructing a personalized behavior reference library; monitoring a current access behavior in real time by sliding a time window, calculating a deviation degree and triggering anomaly detection; performing multi-level feature analysis on the abnormal behavior and calculating a comprehensive abnormal score; dynamically adjusting the access authority according to the score, recording an abnormal behavior and carrying out relevance matching; optimizing the reference model through feedback learning; and evaluating the credibility and the risk level based on an accurate modeling result, and adaptively adjusting permission configuration. According to the method, the user behavior rule can be accurately captured, the data leakage risk can be efficiently identified, the access permission can be dynamically adjusted, the false alarm rate can be reduced, adaptive protection can be realized, and data security and business smoothness can be guaranteed.

The invention relates to the technical field of data encryption and transmission, in particular to a power grid cross-domain operation and maintenance data secure transmission method and system based on encryption, and the method comprises the steps: pre-judging a current risk through a historical network security event, dividing a network environment into different risk intervals based on a risk level evaluation value, encryption strategies with different intensities are correspondingly matched, so that refined security management and control with higher risk and stronger protection are realized; a triple progressive identity authentication system of an equipment layer, a dynamic layer and a biological layer is constructed, a dynamic permission management and control mechanism driven by identity attributes and risk levels is constructed, a basic permission boundary is determined based on inherent identity attributes of an operation and maintenance terminal, and permission strength and validity period are dynamically adjusted in combination with a risk level evaluation value. And finally, generating an access control strategy only adapted to the current terminal, the current risk and the current access request, so as to perform data encryption and transmission through the target encryption strategy and the access control strategy, thereby ensuring the security of data transmission.

The invention discloses an electronic medical record LLM generation method based on animal injury, which realizes dialogue structuring and timestamp synchronization through multistage speech recognition and role affiliation. Using standardized medical term mapping and coding to align the free text to a standardized medical entity, and constructing a high-confidence medical entity network based on a semantic anchor point pool; according to the method, context-sensitive entity relationship extraction is realized by combining a large language model and a semantic enhancement template, a high-accuracy structured relationship chain is generated through clinical logic rule set verification, and finally, an electronic medical record template under diagnosis and treatment specifications is automatically filled and privacy desensitization processing is completed. The semantic consistency, the structural accuracy and the data security of automatic generation of the electronic medical record are improved, and standardization and intelligent circulation of medical information are effectively promoted.

The embodiment of the invention provides a multi-round dialogue security defense method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of data security. According to the method, dialogue semantic graphs capable of representing multi-round dialogue semantic evolution, intention circulation and entity association are constructed by obtaining multi-round dialogue content and a tool calling sequence; the method comprises the following steps of: respectively inputting user input information into two types of maps according to a tool calling graph representing a tool calling time sequence relationship and an interaction relationship to extract double risk feature vectors based on multi-round contexts and tool calling logic, and carrying out collaborative risk judgment in combination with a preset judgment rule; according to the invention, rapid integration of multi-round context information and accurate identification of attack risks are realized, so that a protection strategy can adapt to dynamic interaction requirements of an intelligent dialogue system in time, and the technical problem of low defense efficiency in the prior art is effectively solved.

The invention discloses a network security situation awareness and analysis platform based on AI, and relates to the technical field of network security situation awareness and analysis, and the platform comprises a multi-source data collection module which integrates flow, logs, assets and threat intelligence data, and carries out encryption transmission and standardization; the data preprocessing module purifies and optimizes data, and guarantees data quality and sensitive information security; the AI situation awareness analysis module extracts features through a deep learning model, dynamically evaluates the situation and identifies threats; the threat early warning and decision-making module triggers graded early warning and generates a targeted emergency response scheme; the visual display and interaction module displays information in multiple dimensions and supports query and report generation; and the data storage and tracing module adopts a mixed storage architecture, so that the data security and traceability are ensured. The platform integrates multi-source data and realizes situation accurate perception and intelligent decision by means of an AI technology; the early warning is accurate, the visual interaction is convenient, and the intelligent and efficient level of network security protection is comprehensively improved.

The invention provides an intelligent medical information system based on multi-system integration, a data security management method and device, electronic equipment and a storage medium, and is applied to the technical field of medical data processing. According to the method, three types of information including core security elements, multi-system heterogeneous adaptation (including HIS / LIS / EMR interface specifications and the like) and full-process auditing (including operation logs and the like) are obtained. Normalizing the core security elements by using technologies such as AI multi-mode privacy identification and the like to generate a standardized security baseline file; heterogeneous adaptation information is processed through a lightweight API connector and the like, and a multi-system security access scheme is formed. Associating the baseline file with the access scheme, and generating a full-life-cycle protection strategy in a zero-trust architecture; and processing the audit information to generate a dynamic security audit report. And finally, based on a security-adaptation linkage decision engine and compliance verification, outputting a cross-system data security sharing scheme and a privacy disclosure emergency response instruction, and realizing a medical data security management closed loop.

The invention relates to the field of edge computing data security, in particular to an integrity auditing method and system in an edge computing environment. The problem that an existing cloud auditing model is high in delay and large in bandwidth consumption in an edge scene is mainly solved. A lightweight audit challenge is generated through random sampling, data block aggregation operation is executed at an edge node, Merkel Hash tree path evidence is extracted, and integrity proof of dual verification is formed; and the aggregated evidence and the path evidence are verified by using bilinear mapping, so that efficient and credible auditing is realized. Dynamic data updating and accurate positioning of damaged blocks are supported, communication overhead is remarkably reduced, and real-time credibility of edge data is guaranteed.

The invention discloses a data security risk assessment method based on a big data model, and relates to the technical field of data security, and the method comprises the steps: collecting and preprocessing multi-source data, collecting security-related data from network equipment, a server and an application system, carrying out the preprocessing, carrying out the adaptive feature extraction, and carrying out the data security risk assessment. The feature importance is evaluated by calculating the mutual information amount of features and risk tags, a standardized feature vector set is constructed, multi-model collaborative analysis is performed, feature vectors are input into a cascade collaborative network composed of an anomaly detection model, a threat recognition model, a correlation analysis model and a prediction model, and a risk risk is obtained. Through cross-model feature transmission and a bidirectional information feedback mechanism, deep collaborative analysis and multi-model deep fusion decision making are carried out, a weight is calculated according to historical accuracy of each model, a comprehensive risk score is calculated by adopting dynamic gating deep fusion, and a dynamic threshold value is calculated based on a sliding time window. And the risk is divided into three levels of high risk, medium risk and low risk.

The invention discloses a PLC scheduling algorithm based on a multi-core soft real-time operating system, and relates to the technical field of voltage control, and the algorithm comprises the following four steps: 1, starting a target industrial control system, completing hardware self-inspection, memory allocation and communication module starting, and sending an initialization ready signal after confirming that an assembly is normal; 2, according to PLC task functions and real-time performance, four types of standardized tasks are divided, priority levels are defined, sub-priorities are configured, a dependency relationship is identified, and a task chain is established; 3, counting the number of tasks and task chains, distributing resources by using multiple binding cores, and constructing a global ready queue to adapt to dynamic scheduling; and 4, monitoring an interrupt event through an interrupt manager, realizing high-priority task preemption by semaphores, and allocating tasks to corresponding CPU cores, so that the problems of soft real-time multi-core task preemption and data conflict are solved, the scheduling real-time performance and the data security are improved, the system failure rate is reduced, and the industrial control efficiency and stability are improved.

The invention discloses a data security protection method and system combined with big data analysis, and relates to the field of data security protection, and the method comprises the steps: building a multi-dimensional data collection and fusion data set according to API access records, external threat intelligence and context metadata association data; based on an isolated forest algorithm, calculating a sample path length to evaluate a behavior anomaly probability score; risk indexes of external threats, data sensitivity and permission exceptions are quantified respectively; constructing a data security risk comprehensive assessment model based on a weighted summation algorithm, and updating and optimizing the weight in the model by using a gradient boosting tree algorithm; and according to an output result of the data security risk comprehensive assessment model, setting a security risk level, and according to the security risk level, dynamically responding to a protection measure. The method has the advantages that continuous and dynamic risk assessment and automatic response to third-party data access behaviors are realized by fusing multi-source data and an intelligent algorithm.

The invention discloses a data privacy protection method for an industrial internet platform, and relates to the technical field of data security and privacy protection. According to the method, the sensitive information is accurately identified and deeply analyzed through the sensitive information feature library and the hierarchical matching algorithm, the problem of insufficient accuracy and flexibility during large-scale data processing is solved, the accuracy and reliability of data desensitization are remarkably improved, personal privacy is effectively protected, data availability is maximized, and the method is suitable for large-scale data processing. A hierarchical processing algorithm and a self-adaptive desensitization rule base are utilized, desensitization rules are dynamically adjusted according to dynamic access requirements and sensitivity levels of data, data security and availability are balanced, accurate protection under different scenes is ensured, system adaptability and flexibility are improved, and the data access process is monitored in real time, anomaly detection and rule verification are performed, so that the data access efficiency is improved. And the desensitization rule is dynamically adjusted, so that the security and reliability of the system are enhanced, the user credibility is improved, and the transparency and credibility of the data processing process are ensured.

The invention discloses a data security risk assessment method and system based on a large model, and relates to the field of artificial intelligence large models. The method comprises the following steps: firstly, preprocessing a benchmarking and checking material, and then encoding the preprocessed benchmarking and checking material and a standard specification into a unified semantic space by utilizing a pre-training language model to form a benchmarking and checking vector database; then, on the basis of standard specifications, a data security risk assessment vertical large model is utilized to construct a compliance and security risk analysis assessment item prompt, and a structured assessment problem library is formed after manual verification; and then respectively calling each evaluation item prompt in the structured evaluation problem library, carrying out semantic retrieval in the benchmarking check vector database to generate an enhanced prompt, and then carrying out multi-stage reasoning to obtain a data security risk evaluation result. Through the pre-training language model and the data security risk assessment vertical large model, automatic and intelligent data security risk assessment is realized.

The invention relates to a method and system for encrypting and isolating storage data of a credential mobile terminal, and belongs to the technical field of information. The method comprises the following steps: encrypting and partitioning the whole storage area, and setting an access strategy according to user permission; when a user requests to access, executing multi-factor authentication containing a password and an external hardware certificate, and adding biological characteristic authentication; after the authentication is passed, an encryption key is taken from the domestic security chip; and encrypting and decrypting data by using the secret key, and opening corresponding partition access according to authority. The system comprises a storage partition module, an identity authentication module, a key management module, an encryption and decryption module and an authority control module and is used for executing the method. The method also comprises the steps of equipment startup trusted boot and firmware upgrade verification, is based on a custom curing system, and is compatible with Android 10 +. The problems of poor hardware controllability and positioning security risk of the creative mobile terminal are solved, and full-link data security protection is realized.

The invention relates to the technical field of training data auditing, in particular to a semantic perception black box large language model training data auditing method and system.The method comprises the following steps that content is returned based on a text generation interface, lexical elements and candidate content are analyzed for multi-round sampling distribution, and a convergence index is determined; and calculating semantic paths and weights of the real content and the candidate content, and analyzing differences between the real content and the candidate content and the reference content to obtain an affiliation adaptation judgment result. According to the method, through multi-round sampling behavior fluctuation tracking, section feature dynamic extraction, stability change judgment and sequence-level weight aggregation, fine separation of training data attribution signals is achieved, a multi-level judgment system is constructed for complex expression and diversified output, judgment accuracy is optimized through a tension comparison signal group and an attribution adaptation judgment mechanism, and the judgment accuracy is improved. And a sensitive and adaptive training data member auditing strategy is formed, so that the risk hidden danger omission is effectively prevented, and the model data security boundary control is enhanced.

The invention relates to the technical field of industrial internet-of-things data security, in particular to an edge calculation differential privacy industrial internet-of-things data desensitization verification system and method.According to the system and the method, edge nodes are divided in a three-dimensional mode according to resource capacity, function positioning and privacy requirements, differential differential privacy parameters are formulated in combination with data attributes and leakage influences, and the safety of the industrial internet-of-things data is improved. And a dynamic adaptive grid is matched to realize hierarchical alignment, scene binding and elastic reconstruction, so that a cross-hierarchical privacy risk is avoided, and verification logic is simplified. Noise intensity is dynamically corrected by integrating multi-dimensional factors based on grids, lightweight, medium and deep hierarchical desensitization is designed for three types of nodes, and privacy protection and data availability are balanced. A privacy security and data availability two-dimension, terminal-gateway-area three-level verification chain is constructed, the desensitization effect is comprehensively evaluated, closed-loop iteration is achieved through hierarchical judgment and accurate adjustment, and industrial scene requirements are efficiently met.

The invention provides a data physical node isolation scheduling method and system, and belongs to the technical field of distributed storage and cloud computing. The method comprises the steps of collecting attribute information of tenants and establishing a tenant demand model; monitoring in real time and predicting the resource load of the physical node by using machine learning, and generating a resource evaluation report; formulating differentiated isolation strategies according to the data security levels of the tenants, adopting physical isolation for the tenants with high security levels, and distributing independent physical nodes for the tenants; the scheduling priority is allocated based on the business emergency degree and the resource demand of the tenant, the node load condition is combined, the high-priority tenant is preferentially scheduled to the node with the load lower than the preset threshold value, and meanwhile, the strategy is dynamically adjusted according to real-time information. The corresponding system comprises a tenant management module, a resource monitoring module, an isolation control module and a scheduling decision module. Unification of data security isolation and efficient and dynamic resource scheduling in a multi-tenant environment is achieved, data security and service quality are effectively guaranteed, and the resource utilization rate is increased.

The invention discloses a scene and compliance fused dynamic hierarchical data encryption method and system, and the method comprises the steps: obtaining multi-source data, and carrying out the preprocessing of the multi-source data, and obtaining to-be-encrypted data; key words in the to-be-encrypted data are extracted, and weight coefficients are distributed to the key words according to semantic roles and / or industry risk coefficients; according to the data type, determining a characteristic value corresponding to each keyword; determining a scene coefficient and a compliance coefficient; based on the weight coefficient, the feature value, the scene coefficient and the compliance coefficient, determining a data sensitivity score and a data sensitivity level corresponding to the to-be-encrypted data; and based on the data sensitivity level, encrypting the to-be-encrypted data by adopting the encryption strategy of the corresponding level. According to the method provided by the embodiment of the invention, the security of the high-sensitivity data is ensured, the resource waste caused by excessive encryption is avoided, the whole process is automatic, the labor cost and the overall cost of security protection are greatly reduced, and the comprehensive efficiency of data processing and security protection is improved.

The invention discloses a production line data integration method based on a digital twin model, and belongs to the technical field of electronic data processing. The production line data integration method comprises the following steps: step S0, pre-preparation and standard definition; the method comprises the following steps: S1, data acquisition and preprocessing; step S2, data cleaning and integration; s3, data analysis and mining; 4, constructing a digital twinborn model; and 5, managing system-level data. The method has the following advantages: data islands are cracked, data quality and processing efficiency are improved, internal business logic of data is mined, high-precision feature extraction and pattern recognition are realized, and the method is suitable for popularization and application by means of unifying data standards and cleaning rules, optimizing a merge sorting strategy, building a double-layer association analysis model, adopting a CNN-LSTM hybrid model, perfecting data security and authority management and the like. The method supports the cross-line collaborative decision and precise production optimization, finally solves the problem that the prior art cannot support the system-level digital twinning application, and improves the application efficiency and effect of the digital twinning system.

The invention discloses an oil storage tank oil-water interface measurement system and method and a storage medium, and relates to the technical field of oil-water interface measurement, and the system comprises a data acquisition module, a data processing module, a decision support module and a data security module. According to the oil-water interface measuring system and method for the oil storage tank and the storage medium, a three-dimensional sensing network (distributed sensor network) is constructed by adopting a pressure sensor array and phased array ultrasonic scanning technology and combining dielectric property detection of a microwave dielectric constant sensor; the pressure, the dielectric constant, the liquid level and the vibration data in the tank body are obtained, basic data are provided for subsequent processing, the comprehensiveness of data obtaining can be improved through the sensing network, the pressure data are dynamically calibrated through Takagi-Sugeno fuzzy logic, the influence of temperature drift is effectively eliminated, and the accuracy of data obtaining is improved. The ultrasonic signals are subjected to feature enhancement through the generative adversarial network, and the emulsion layer boundary is accurately recognized.

The invention discloses a toothpaste production raw material tracing method and system based on a block chain, and relates to the field of block chains, and the method comprises the steps: distributing a unique identifier for a raw material, binding basic information to form an initial data packet, taking a supplier as an alliance chain node, uploading the initial data packet, and verifying the qualification through an intelligent contract; the transportation data is encrypted in real time through the Internet of Things and edge calculation and is uploaded, and a detection report deviation value is verified during warehousing; raw materials and finished product batches are associated in a production link, and process parameter compliance is automatically checked and then written into a block; in the warehouse logistics link, environment data and transportation information are integrated, and a tracing sub-block is generated after a goods receiver is verified through code scanning; an improved PBFT consensus mechanism and a cryptographic algorithm are adopted to ensure data security, and transparent tracing of consumers, supervisors and enterprises is realized through hierarchical authority management. The method has the advantages that the raw materials and the finished product full-link data are bound through the unique traceability identifier, non-tampering traceability of the toothpaste raw materials from supply to the terminal is achieved, and multi-party hierarchical query requirements and product safety are met.

The invention discloses a data security transmission method based on an ML-KEM algorithm and a PUF, and the method comprises the steps: in a registration stage, mobile equipment generates an anti-quantum key through employing a PUF technology, and a CA issues an anti-quantum digital certificate based on a Falcon algorithm and a cryptographic accumulator; in an identity authentication stage, a mobile device and an edge gateway complete bidirectional identity authentication by exchanging identity labels, anti-quantum certificates and authentication key parameters in combination with a Falcon algorithm and PUF challenge-response, and negotiate to generate a shared key by using an ML-KEM algorithm, thereby effectively defending threats such as man-in-the-middle attack, replay attack and identity counterfeiting; in a data transmission stage, two communication parties realize data encryption transmission by adopting a symmetric encryption algorithm, and data integrity and source credibility are guaranteed in combination with a Falcon digital signature; the whole scheme has the advantages of quantum security resistance, efficient authentication, dynamic key updating, lightweight encryption and the like.

The invention relates to the technical field of block chain and smart home data management, discloses a smart home data contribution quantification and excitation method and system based on a block chain identifier, and aims to solve the data source problem of vertical large model training in the smart home industry and guarantee data security and personal privacy. The method comprises the steps of equipment identifier registration and binding, home local data processing and desensitization, data contribution degree quantitative evaluation, intelligent contract incentive execution and standardized data delivery and evidence storage. The system comprises a block chain identifier management module, a home edge data center, a data contribution quantitative evaluation module, an intelligent contract incentive engine and a large model training data delivery interface. According to the technical scheme, accurate tracing of data sources and clear definition of contribution subjects can be achieved, the family computing power is activated, the risk of data privacy disclosure is reduced, high-quality and scenarized credible data sources are provided, and marketization circulation and value conversion of smart home data elements are promoted.

The invention discloses a lithium battery SOH and RUL prediction system and method based on multi-scale feature federation, and relates to the technical field of battery detection, the system comprises a data acquisition and feature extraction module, an encryption and uploading module, a model construction module, a recursion updating module and a work order generation module; through the multi-scale feature fusion formula, the time sequence features and the statistical features of the lithium battery are organically combined, the fusion feature vector is generated, degradation information of the lithium battery under different time scales and operation conditions is comprehensively captured, the health state of the lithium battery and the accuracy of prediction of the remaining service life of the lithium battery are improved, and the prediction accuracy of the remaining service life of the lithium battery is improved. And meanwhile, through dynamic Top-K sparse compression, 8-bit linear quantization and AES-256-GCM encryption technologies and in combination with a differential privacy protection mechanism, the data transmission quantity is reduced, the data security is enhanced, sensitive information leakage is prevented, and a reliable guarantee is provided for remote monitoring and predictive maintenance of the lithium battery.

The invention discloses a sandbox type trusted data space construction method and equipment based on a trusted verification architecture, belongs to the technical field of data security circulation and trusted computing, and aims to overcome the defects that a traditional sandbox behavior is unknowable and untrusted and a strategy is rigid. The method comprises the following steps of: starting a sandbox in a hardware trusted execution environment and generating a task unique context identifier; intercepting system call by a monitoring agent; generating a chained operation certificate containing preorder certificate hash; compiling an advanced security policy into a deterministic finite automaton executable policy set; and the auditing unit is used for generating a decision instruction in an illegal scale, storing the chain anchor point to the non-tampering system, forcibly executing decision and generating a Merkel tree evidence. The equipment correspondingly comprises a sandbox management module, a behavior monitoring module, a strategy compiling module, a verification judgment module and an auditing execution module. The full-flow transparent tracing of the operation in the sandbox is realized, the strategy dynamic adaptation is supported, and the problem that a traditional sandbox monitoring log is easy to counterfeit and difficult to verify is solved.

A method for constructing an intelligent bid-invitation purchasing customer service system based on a retrieval enhancement generation technology comprises the following steps: constructing a multi-source knowledge base, and integrating and processing customer service work orders, bid-invitation files and laws, regulations and policies; non-sensitive metadata information is extracted through entity recognition to construct a knowledge graph, and a block chain is used for evidence storage; classified navigation is provided during user consultation, and metadata is complemented through brief interactive questions and answers; performing fusion retrieval through a dual-channel RAG architecture to generate answers; and continuously supplementing data and optimizing training. According to the method, the data security is guaranteed, the semantic comprehension capability is improved, the agile response to information change is realized, and the compliance and controllability of the system are enhanced.

The invention discloses a data security sharing method and system, and relates to the technical field of data sharing. The method comprises the steps that a data provider uploads original data, a privacy budget value is calculated through a differential privacy algorithm, noise is added to obtain noisy data, and the noisy data is processed through an anonymous privacy protection algorithm based on maximum dissimilarity degree clustering to obtain desensitized data; encrypting the desensitized data by adopting a block chain decentralization-based ciphertext policy attribute-based encryption method, obtaining an encrypted data ciphertext, uploading the encrypted data ciphertext to a cloud server, and obtaining a content addressing hash value; encrypting the hash value through an elliptic curve encryption algorithm, and storing the encrypted hash value to a block chain account book; and when an access request of a data requester is received, the cloud server verifies the authority by using a non-interactive zero-knowledge proof protocol and returns an encrypted hash value after passing the verification, and the requester decrypts to obtain the hash value and decrypts the encrypted ciphertext to obtain desensitized data, thereby realizing data sharing.

The invention discloses a multi-agent real-world clinical curative effect evaluation and accurate decision-making system, and relates to the technical field of medical data processing. According to the invention, a research demand analysis agent generates a research scheme after understanding the input content of a user and transmits the research scheme to a data management agent, a statistical analysis modeling agent, a result report generation agent, a data security agent and the data management agent privacy and encrypt data uploaded by the user; meanwhile, additional feature construction is carried out according to the research requirement of a user to form brand new analysis data used by a subsequent statistical analysis modeling agent, and after the statistical analysis modeling agent obtains the data, an analysis result is obtained by calling external software and is transmitted to a result report generation agent; and generating a clinical evaluation report together with the research scheme transmitted by the research demand analysis agent. According to the invention, full-process automatic, specialized and safe research support is realized.

The invention relates to the technical field of big data security analysis and network information security, in particular to a communication data intelligent security supervision system based on big data, which comprises a data acquisition module used for extracting standardized entity behavior feature vectors from multi-source heterogeneous communication data; the baseline modeling module is used for dynamically generating a multi-dimensional behavior baseline portrait based on the historical sequence of the entity behavior feature vectors; the anomaly detection module is used for comparing the real-time entity behavior feature vector with the multi-dimensional behavior baseline portrait so as to calculate a micro anomaly score; the atlas construction module is used for screening the micro-anomaly events according to whether the micro-anomaly score exceeds a preset threshold value or not, and quantifying association confidence among the screened events so as to construct an attack chain atlas; the risk quantification module is used for aggregating the characteristics of the attack chain atlas to determine a systematic risk score; according to the method, the discovery capability and response efficiency of complex attacks such as advanced persistent threats and the like are greatly improved.

The invention discloses a data transaction security assessment method and system, and relates to the technical field of data security and intelligent assessment. The method comprises the steps of performing feature analysis on a data field, determining a sensitivity weight and generating a sensitivity vector; the fields are graded according to the sensitivity vectors, and dynamic adjustment is carried out in combination with transaction related information to obtain a grading result; calculating a data content risk, a field association risk, and a transaction environment risk based on the sensitivity feature set; determining a weight according to a transaction scene, and weighting various risks to obtain a comprehensive score; and judging the risk level according to the comprehensive score and a threshold value dynamically determined based on historical data. According to the invention, through hierarchical sensitivity modeling and dynamic risk calculation, fine-grained security assessment of different types and sensitivity data is realized, and the risk result can be adaptively adjusted along with changes of data contents, transmission paths and transaction environments, so that the accuracy, real-time performance and scene adaptability of assessment are improved, and the risk assessment efficiency is improved. The defects of a static evaluation method in the prior art are overcome.

The invention relates to the technical field of multi-terminal collaborative management, in particular to a multi-terminal collaborative intelligent office internal management system, which comprises a terminal behavior acquisition unit, a user behavior analysis unit, a state synchronization protocol unit and a terminal collaborative unit, a terminal behavior acquisition unit captures an operation behavior to generate an event stream containing an operation type and a sensor state; a user behavior analysis unit identifies a non-editing action sequence, generates an implicit intention abandoning label and triggers a conflict signal when detecting a multi-terminal space-time overlapping conflict; a state synchronization protocol unit dynamically broadcasts an authority instruction according to the label; according to the system, the problems that intention recognition is fuzzy, authority synchronization lags behind and conflict processing is missing are solved, and the office efficiency and the data security are improved.