638 results about "Data privacy protection" patented technology

The present disclosure relates to a blockchain-based authentication system for electric vehicles and drones, and a method for authenticating electric vehicles or drones in a smart city. The proposed blockchain-based authentication system enhances security, privacy, and scalability for electric vehicles (EVs) and drones in smart city environments. It employs a consortium blockchain managed by city authorities and stakeholders, utilizing smart contracts for identity registration, credential issuance, and access control. Device nodes store cryptographic keys securely, while an optional authentication server facilitates off-chain integration. The authentication operation of the system includes, ensuring tamper-proof identity verification through on-chain validation. Authentication attempts are immutably recorded for auditing and anomaly detection. By eliminating single points of failure and strengthening data privacy, this decentralized authentication framework offers a scalable and secure solution for integrating EVs and drones into smart city infrastructures.

The invention discloses a collaborative sharing method, system and device on an energy data chain fusing multi-party security calculation and zero knowledge proof and a storage medium, and belongs to the field of energy data security sharing and block chain privacy protection, and the method comprises the steps: executing desensitization processing on original data according to types, and generating a cryptographic commitment value and a data hash identifier; encrypting and fragmenting the desensitized data, registering a task structure body through an intelligent contract, and uploading the task structure body; and each node decrypts the ciphertext, executes calculation and generates a zero-knowledge proof, and submits the zero-knowledge proof to the block chain to be verified by the smart contract. According to the method, data privacy protection and result verifiability unification are realized, cross-mechanism cooperation efficiency is improved, an auditing system on a whole process chain is constructed, and safe sharing of energy data is guaranteed.

The invention discloses an artificial intelligence data privacy protection system based on a block chain and federated learning, and relates to the technical field of block chains and federated learning, and the system comprises a block chain module which employs a main chain-side chain double-chain architecture, a main chain stores a global model hash value and node reputation evaluation data, and a side chain module is used for storing node reputation evaluation data; the side chain stores the encrypted local model parameters through a fragmentation technology; the federated learning module comprises a dynamic difference privacy algorithm and a gradient ternary processing unit, and is used for adding noise to the gradient in a local training stage and converting the gradient into a ternary numerical format; the privacy protection module is used for integrating homomorphic encryption and zero-knowledge proof technologies and realizing ciphertext aggregation and verification of model parameters; and the malicious node detection module is used for identifying abnormal gradient update based on cosine similarity and Multi-Krum algorithm, and is linked with node reputation data in the block chain module. Through a system architecture and a privacy protection mechanism, the efficiency and performance of federal learning are improved while data privacy is ensured, and the method has a wide application prospect.

The invention discloses a financial data privacy protection system based on block chain security multi-party computing, and belongs to the technical field of data protection, and the system specifically comprises the steps that a data preprocessing module segments original financial data, and adds a unique watermark identifier; the distributed account book stores the Hash abstract and watermark mapping relation of the encrypted logic data fragment, and the digital signature and the timestamp are fused to ensure reliability; the computing node cluster receives data through a special interface, dynamically builds a computing group according to a service rule and outputs an encrypted computing result; bidirectional authentication is carried out on the zero-knowledge proof verifier and the computing node cluster to verify the data holding right; the encryption proxy gateway processes an output result of the computing node cluster to complete format conversion and ciphertext superposition; the cross-link routing module constructs a virtual coverage layer, cross-link transmission of encryption results is achieved through protocol conversion and three-way handshake verification, and a heterogeneous chain protocol converter and a verification assembly guarantee transmission safety; the financial data privacy security is comprehensively guaranteed, and the data processing efficiency and reliability are improved.

The invention discloses a landslide prediction and early warning system and method based on a remote sensing technology, and is applied to the technical field of landslide early warning. Comprising a data acquisition module for acquiring multi-source data in a monitoring area; the cloud computing module is used for large-range landslide risk assessment and deformation monitoring; the edge calculation module is used for landslide risk assessment and deformation monitoring of a specific area; the end-cloud cooperative communication module is used for realizing data interaction between the cloud computing module and the edge computing module; the federal learning module is used for realizing cooperative training under data privacy protection of different areas; and the landslide early warning module is used for carrying out early warning based on landslide risk assessment and deformation monitoring results of the cloud computing module and the edge computing module. According to the invention, by using the deep learning model, the end-cloud collaborative architecture and the federal learning driven data fusion framework, efficient, low-cost and high-precision landslide risk prediction is realized.

The invention relates to the technical field of data processing and privacy protection, in particular to a data desensitization and integrity verification method and system based on a differential privacy algorithm, and aims to realize collaborative optimization of data privacy protection and integrity guarantee. Determining a total privacy budget by calculating global sensitivity, a data leakage risk coefficient and independent and joint data privacy information amount, dynamically allocating the privacy budget for a single-task or multi-task scene, and injecting noise based on an allocation result to realize differential privacy desensitization; meanwhile, a verification voucher is generated by using a hash function, a digital signature and an alliance chain, and the data integrity is ensured through signature verification, data consistency verification and block chain verification. The system comprises a data application processing module, a global sensitivity calculation module, a desensitization core processing module, a verification voucher generation module and an integrity verification module, is suitable for government affairs, enterprises and other scenes with high requirements for data privacy and integrity, and effectively improves the safety and reliability of data processing.

The invention discloses a network risk assessment model construction method and system based on an AI large model, and relates to the technical field of artificial intelligence and network security, and the method comprises the following steps: S1, a participating node splits a large model into a plurality of semantic independent fragmentation units through a dynamic fragmentation mechanism based on local multi-source heterogeneous data; according to the network risk assessment model construction method and system based on the AI large model, through a dynamic fragmentation federated distillation learning framework, the inherent contradiction between data privacy protection and AI large model training efficiency in cross-mechanism cooperation is effectively solved. A ten-billion-level parameter model is disassembled into semantic independent units by adopting a self-adaptive parameter fragmentation mechanism, the communication overhead is reduced on the premise of ensuring physical isolation of sensitive data in combination with a local differential privacy and parameter confusion technology, and meanwhile, cross-domain fusion of threat features and attack surface blind spot detection are realized through a layered distillation verification system. And the APT attack detection rate is improved.

The invention provides a cross-platform education data privacy protection analysis system, and relates to the technical field of data privacy protection. The cross-platform education data privacy protection analysis system comprises a multi-modal data acquisition unit, a dynamic privacy analysis engine, a federal learning processing core module, a block chain enhanced access control system, a privacy watermark traceability module and a compliance autonomy unit. Dynamic coupling of scene sensitivity and data protection intensity is realized through a dynamic privacy risk scoring equation and an adaptive differential privacy noise equation; the problems of privacy disclosure, compliance verification and traceability and responsibility investigation in cross-platform education data sharing are solved by combining federal learning, block chain evidence storage and privacy watermarking technologies. According to the method, the privacy risk assessment precision is remarkably improved, the model precision loss is reduced, full-life-cycle data security management and control are supported, and the method is suitable for multiple scenes such as K12 education and college educational administration.

The invention discloses a big data privacy protection modeling method and system based on federated learning and a block chain, and relates to the technical field of privacy protection and joint modeling. According to the method, homomorphic encryption, differential privacy, federated learning, secure multi-party computing and block chain technologies are fused, big data privacy protection and joint modeling are realized, encryption and dimensionality reduction are performed on original data through homomorphic encryption and differential privacy, an encrypted training sample of secure privacy is generated, a local model is trained on an encrypted data set through federated learning, and a big data privacy protection result is obtained. The method comprises the following steps: calculating aggregation parameters by using security multiple parties, constructing a verification network in combination with a block chain, ensuring credibility and integrity of model training, and finally, adding noise optimization performance for a global model by using differential privacy, testing generalization ability through cross validation, and determining a deployable privacy protection joint learning model, thereby breaking traditional data islands, promoting cross-mechanism data cooperation, and improving the privacy protection performance. Big data values are released, and data protection regulations and privacy requirements are met.

The invention discloses a financial privacy security alignment method and system based on federated learning and adversarial training, and the method comprises the steps: enabling a plurality of financial institution clients to obtain local financial data, and carrying out the semantic analysis and sensitivity grading; constructing a federated learning framework, performing homomorphic encryption on a local model gradient by adopting Paillier, uploading the local model gradient to an aggregation server for secure aggregation, and generating global gradient update parameters; dynamically selecting a desensitization strategy based on the business scene weight and the data sensitivity, and executing field-level desensitization processing on the financial data; an adversarial sample is injected in local model training, and a task loss function and an adversarial loss function are jointly optimized; and mapping compliance terms into technical rules, and completing privacy protection and compliance alignment. According to the method, data privacy protection is enhanced, the data leakage risk is effectively reduced, and financial data privacy security is ensured. Safe transmission and effective aggregation of data are ensured, and the global model training efficiency and safety are improved. An adversarial sample is injected, and high accuracy and stability are kept.

The invention discloses a medical data privacy protection method and device, and relates to the field of medical data security. The method comprises the following steps: acquiring medical data feature information of a plurality of medical institutions to form a medical data feature map; constructing a hierarchical federal learning framework based on the medical data characteristic spectrum; performing parameter aggregation optimization through a convolution weighting method of a recursive context guide network; a dynamic differential privacy budget allocation strategy is combined with a multi-first-choice Lambda weighted list DPO technology to optimize a noise injection process, and adaptive differential privacy protection is realized; and in combination with a homomorphic encryption technology, performing multi-party calculation through a security aggregation protocol to obtain a medical data privacy protection analysis system. According to the method, efficient cooperative analysis is carried out while the privacy of the medical data is protected, the problems that in the prior art, simple parameter exchange may cause model inversion attack, special optimization for medical scenes is lacked, and a supervision and auditing mechanism is imperfect are solved, and the utilization efficiency and safety of the medical data are improved.

The invention discloses an adaptive privacy security calculation method and system based on medical data feature perception, and relates to the technical field of data privacy protection. The method comprises the steps that a plurality of participating nodes extract structure and semantic features of local medical data, and data feature description information is generated; operating environment parameters and privacy compliance indexes are collected to form node environment state information; the coordination node generates privacy protection strategy configuration based on the information, and determines disturbance intensity, an encryption mode, a data processing permission and a cooperative computing mode; performing differential privacy processing by the participating nodes, generating an intermediate calculation result and submitting the intermediate calculation result to the coordinating node; and the coordination node executes joint aggregation calculation, outputs a target result, and performs privacy risk assessment and strategy dynamic adjustment. The medical data privacy protection method effectively achieves differentiation, controllability and adaptivity of medical data privacy protection, is suitable for various calculation tasks such as federated learning and statistical modeling, and has high practicability and safety.

The embodiment of the invention discloses a medical data privacy protection method and a computer device. The method comprises the following steps: firstly, acquiring medical industry laws and regulations and target medical institution data asset information; building a logic relation graph containing data classification and security level according to laws and regulations, and importing the laws and regulations information into a first graph database to obtain a laws and regulations graph; extracting a logical relationship in the data asset information, constructing a data logical relationship graph containing a library table column structure, and importing the data logical relationship graph into a second graph database to obtain an asset graph; business process information is collected, and a data flow relation is identified to perfect an asset map; analyzing the name and content of the data assets to obtain a classification and grading analysis result; on the basis of the result, constructing a heterogeneous graph neural network model by using a graph attention network, establishing a mapping relationship between the two maps, and updating the asset map; and finally, carrying out privacy protection on the data assets according to the updated asset atlas. According to the method, the accuracy and efficiency of medical data classification and grading can be improved, and the privacy protection capability is enhanced.

The invention discloses a federated learning driven trusted data space cross-domain cooperative computing method, and relates to the technical field of data processing, and the method comprises the following steps: carrying out access trust verification on a plurality of data providing domains according to a dynamic trust mechanism; after the verification is passed, accessing each data providing domain into a trusted data space for local training, and generating a local model; performing cross-domain cooperative calculation on the plurality of local models, and establishing an initial global model; and carrying out loss generalization evaluation on the initial global model, and triggering a differential feedback learning module to carry out iterative reinforcement to generate a global reinforcement model. According to the method, the technical problems of limited data privacy protection, trust missing among different mechanisms and insufficient model generalization ability of cross-domain data cooperative calculation in the prior art are solved, multi-party cooperation is realized on the premise of no data leakage through a dynamic trust mechanism and differential privacy protection, and the reliability of the cross-domain data cooperative calculation is improved. And the precision and generalization ability of the global model are improved.

The invention discloses an electric power big data privacy protection method and system based on federated learning, and belongs to the technical field of data processing, and the method comprises the steps: obtaining a historical session record set of an electric power user, carrying out the standardization preprocessing of the historical session record set, and obtaining a standardized session data set, extracting a time sequence feature set from the standardized session data set, calling a pre-configured federal learning framework to carry out privacy protection processing on the time sequence feature set, generating a privacy enhancement feature set, generating a power data privacy protection result based on the privacy enhancement feature set, and sending the power data privacy protection result to the server. And feeding back the power data privacy protection result to the power service system. According to the method, the privacy security in the data use process is guaranteed, the utilization value of the power big data in the unstructured session scene is improved, and the problem of adaptability of a traditional power privacy protection technology in unstructured session data processing is effectively solved.

The invention provides a data privacy protection method for a data governance system, and belongs to the technical field of data governance, and the method comprises the steps: carrying out the cross verification of multi-source feature data and the unique identification information of an object collected on site, generating an original data set, carrying out the sensitive information recognition and grading, and generating the preprocessing data with a sensitive grade label; core identification information in the preprocessed data is disassembled to generate standardized desensitized data conforming to privacy protection, a bidirectional encryption mapping relation is established between object unique identification information collected on site and the standardized desensitized data, an encryption index is formed, the encryption index and preset multi-dimensional compliance data are fused, and a data fusion result is obtained; generating standardized fusion data; and based on the access token, generating a differential authorization data set divided according to permission granularity, performing privacy disclosure risk assessment, generating a risk level, performing privacy processing on the risk level, and outputting the risk level to a risk control system. And the data management efficiency is improved.

The invention relates to the technical field of data privacy protection, in particular to a data privacy protection management system based on artificial intelligence, which is characterized in that a resource library building module is used for extracting privacy protection knowledge from multi-source data and building a structured resource library; and the privacy classification module classifies data privacy information and evaluates a sensitive level through intelligent identification and analysis, and provides a basis for a differential protection strategy. The privacy protection module fuses privacy types and sensitive levels, constructs a core policy engine, and comprises a privacy desensitization sub-engine, an access control sub-engine and a privacy risk prediction sub-engine, so as to realize automatic generation and dynamic adaptation of protection policies. And the privacy management module defines an execution sequence and a collaboration mechanism of each sub-engine according to a pre-configuration process, generates a privacy protection method covering the full life cycle of the data, ensures end-to-end collaboration management of links such as data acquisition, storage, use and sharing, and remarkably improves the compliance, efficiency and risk prevention and control capability of data privacy protection.

Disclosed in the present invention are a large language model federated fine-tuning method and apparatus based on gradient compression. The method comprises the following steps: constructing, on the basis of a gradient tensor generated during fine tuning of a large language model, a raw data set having a time series relationship, performing inference by means of an autoencoder to obtain a reconstructed gradient data set, and constructing a reconstruction loss function to optimize the autoencoder; and initializing a base model of the large language model as a global model at a server end, the server end updating the global model to a client, using a pre-trained encoder to obtain a compressed gradient at the client, and at the server end, using a pre-trained decoder to decode and aggregate the compressed gradient, and then updating the global model. The present invention can improve the fine-tuning efficiency of the large language model and reduce computing resource requirements while ensuring data privacy protection, and is suitable for application scenarios such as communication optimization improvement and privacy protection enhancement in the process of scientific computing-oriented large model fine-tuning and training.

The invention discloses a psychological disease pre-diagnosis information processing method and system, and the method comprises the steps: collecting a multi-modal behavior signal and a physiological parameter signal of a user, and carrying out the differential privacy protection processing based on edge calculation and the noise separation processing based on wavelet transform, obtaining a standardized behavior feature sequence and a time sequence physiological feature vector; extracting behavior node features and psychological state markers from the standardized behavior feature sequence, and constructing a three-dimensional incidence matrix in combination with the time sequence physiological feature vector; and calculating a potential risk probability based on a graph neural network model, generating a pre-diagnosis grading result, fusing objective behavior environment data through a progressive protocol, outputting a personalized intervention strategy, and finally generating a comprehensive pre-diagnosis report. According to the method, accurate modeling of psychological-physiological-behavior dynamic association is realized through dynamic knowledge graph construction and cross-modal fusion analysis, meanwhile, data privacy protection is considered, and the accuracy and practicability of psychological health monitoring are effectively improved.

The invention provides a federated learning differential privacy method based on Rayleigh divergence and adaptive noise adjustment, and aims to balance data privacy protection and model training performance and improve model accuracy and convergence speed of federated learning on the premise of protecting user data privacy. And the contradiction between privacy protection and model performance in the existing federated learning is solved. The method comprises the following steps: step 1, constructing a privacy loss quantification model based on Rayleigh divergence; 2, deducing a tight upper bound of a Gaussian noise standard deviation; 3, initializing noise parameters of the federated learning system; 4, the client side executes local model training and noise adding; 5, updating the aggregation model of the central server and evaluating the performance; step 6, implementing a self-adaptive noise adjustment decision based on model performance; and step 7, iterating federal learning training until convergence or completion.

The invention belongs to the technical field of multi-energy micro-grid cooperative regulation and control. The invention discloses a multi-energy micro-grid coordinated regulation and control system based on cross-layer knowledge injection and federated distillation. The system is characterized by comprising a cross-layer knowledge injection network; a federal knowledge distillation module; and the uncertainty map regulation and control module is used for constructing an MEMG uncertainty association map, learning the influence weight of each uncertainty factor on a regulation and control decision through a map attention mechanism, and realizing a dynamic risk avoidance strategy. The invention discloses a multi-energy micro-grid cooperative regulation and control method based on cross-layer knowledge injection and federated distillation. The method is characterized by comprising the following steps: step 1, MEMG topology knowledge coding; 2, cross-layer knowledge injection training is carried out; step 3, federal knowledge distillation optimization; and step 4, performing uncertainty map regulation and control. According to the system and the method, the precision, the robustness and the data privacy protection capability of MEMG regulation and control are improved, and the system and the method are suitable for efficient collaborative optimization of the park-level multi-energy microgrid.

The invention discloses a network security situation awareness system based on federated learning driving, and relates to the technical field of distributed computing. The visual management center is in communication connection with a data acquisition module, a distributed federation learning module, a virtual storage management module, a security policy analysis module and an automatic response processing module, and all the modules are in electric signal connection. Through the federated learning technology, the security situation awareness model is locally trained on the multiple distributed nodes, centralized storage and transmission of data are avoided, the risk problem of data privacy leakage in a traditional network security situation awareness system is effectively solved, each node only processes and analyzes data locally, data uploading is not needed, and the network security situation awareness system is convenient to use. Therefore, on the premise of ensuring data security and privacy, cross-node knowledge sharing and model optimization are realized, and the data privacy protection capability of the system is greatly improved.

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 provides a data privacy protection method and system for a large model knowledge base, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining original privacy data of each participant, carrying out the cleaning and feature extraction of the original privacy data, and recognizing sensitive information fields; performing desensitization processing on the fields containing the sensitive information by utilizing a generative adversarial network to obtain desensitized data; and performing k-anonymization processing on the desensitized data, dividing the data into a plurality of equivalence classes based on the quasi-identifier, and performing generalization processing on the classes with the record number less than k to obtain an anonymized data set. According to the method, the privacy leakage problem of multi-participant data collaboration in large model training is solved.

The invention relates to a data sharing and exchanging method and system based on privacy computing and a big data technology, and belongs to the technical field of data privacy protection and big data analysis. The method comprises the steps of platform initialization and user registration; preprocessing and encrypting data; authority control and task configuration; initiating and scheduling a calculation task; performing privacy calculation; performing result aggregation and decryption; accessing and feeding back a result; auditing and monitoring; the invention further comprises a module for executing the method. According to the platform, through decentralized architecture design and various privacy computing technologies, data availability and invisibility are achieved, the functions of big data cleaning, analysis and modeling are integrated, data privacy safety is protected, and the data value utilization efficiency is improved.

The invention discloses a data security sharing method and device for verifiable bilateral access control based on an attribute threshold, computer equipment and a storable medium, and aims to realize data privacy protection and source authenticity guarantee in an open cloud-edge collaborative environment. According to the method, an attribute threshold matching mechanism is introduced, a bilateral access control strategy is designed, decryption can be performed only when intersection of attribute sets of a data sender and a data receiver and access attribute sets specified by each other meets a preset threshold, and access authorization under fuzzy matching is supported. In order to ensure the authenticity and non-repudiation of the data, the attribute signature of the sender is embedded into the ciphertext structure, and the credibility of the data source is ensured. According to the method, a constant-size ciphertext structure is adopted, and an offline / online double-stage encryption technology is combined, so that the encryption delay and the calculation burden are reduced. At a decryption end, data verification and partial decryption tasks are outsourced to edge nodes, and a result verification mechanism is introduced, so that malicious nodes are effectively prevented from returning wrong results.

The invention relates to the technical field of data privacy protection, in particular to a cross-domain agent knowledge migration and privacy barrier system, which comprises a migration request analysis and sensitive marking unit, an original migration module extraction and identification, authority control and domain exclusive elements marking unit, an operation log containing a domain label and a timestamp generation unit, and a privacy barrier unit. The dynamic privacy barrier processing unit replaces an identity label with a generalization identifier, deletes violation elements according to a target domain sovereignty rule to generate a compliance knowledge module, separates, encrypts and stores a domain label and a timestamp, performs hash processing on a log operation type, and stores the log operation type; the privacy disclosure blocking unit scans residual identity labels and undeleted permission elements through secondary verification, and inputs a compliance block into a migration channel after verification is passed, and the system realizes accurate desensitization and compliance verification of cross-domain knowledge migration, prevents privacy disclosure, and ensures cross-domain collaborative security of agents.

The invention relates to the technical field of artificial intelligence, data privacy protection and compliance management, in particular to a flexible employment compliance decision-making system and method based on a federated learning technology. The system comprises a privacy protection module for performing homomorphic encryption, differential privacy and randomization processing on employee information; the federated learning module is used for carrying out federated learning on the child nodes and generating an employee salary level prediction model; the decision module is used for calculating an individual tax pre-deduction scheme and a risk index; the biological characteristic verification module is used for identifying the identity of the employee and the authenticity of the contract; according to the system, a five-layer progressive privacy protection system is constructed, so that the balance between local data and global intelligence is realized, and the contradiction between data privacy and analysis efficiency is effectively solved; meanwhile, the innovatively designed hierarchical heterogeneous federated learning framework supports different platforms to use different data structures and computing capabilities to participate in training, so that the model adaptability is remarkably improved; on the whole, the method provides efficient, safe and compliant decision support for a flexible employment platform.

The invention discloses a privacy protection constrained passive unsupervised graph domain adaptive mechanical fault diagnosis method and device, and the method comprises the steps: inputting a mechanical vibration signal into a pre-trained mechanical fault diagnosis model, and outputting a diagnosis result; the mechanical fault diagnosis model is obtained by training a passive domain multi-scale image domain adaptive network by adopting training data, and the passive domain multi-scale image domain adaptive network comprises a multi-scale feature extractor module, a topological graph convolution module and a feature classifier module which are connected in sequence; the training process comprises two stages of performing pre-training by using source domain data and performing unsupervised domain adaptive training by using target domain data, and a loss function of the training process comprises a label smooth cross entropy loss function for the pre-training process. And the unsupervised domain adaptive loss function used for the unsupervised domain adaptive training process comprises neighbor clustering loss, regularization loss and label prediction diversity loss. The objective of the invention is to realize complete representation and generalization of fault features by deeply mining topological structure information, and optimize decision boundaries to significantly improve inter-domain alignment precision in a passive domain adaptive process, thereby solving the problem of cross-working condition diagnosis of mechanical equipment under data privacy protection constraints.

The invention relates to the technical field of wires and cables, and particularly discloses a wire and cable test system based on sensor data acquisition, which comprises a data acquisition module, an intelligent analysis module and a function output module. The intelligent analysis module integrates technologies such as multi-modal signal blind source separation, mechanism-data hybrid drive deep learning, federated learning and digital twinborn linkage and self-supervised learning optimization, and forms a layer-by-layer progressive analysis system. Interference components are effectively eliminated through a signal separation technology, meanwhile, the adaptability of the model to different working conditions and different cable types is improved by means of fusion of mechanisms and data, model collaborative optimization under multi-site data privacy protection is realized through federated learning, and a self-supervised learning-driven digital twinborn evolution mechanism is combined, so that the multi-site data privacy protection model is optimized. The problems that a traditional analysis technology is insufficient in generalization ability, simulation precision is prone to drifting, and privacy risks exist in data sharing are solved, and the accuracy of fault recognition and the long-term adaptability of the system are remarkably improved.