672 results about "Data fragment" patented technology

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 provides a data optimization processing method and system for edge calculation in architectural design. According to the method, geometric deviation and attribute change characteristics between building design models are obtained, and a version conflict identification graph for marking high-conflict areas is generated through topological relation comparison; deploying an incremental synchronous controller at an edge computing node of a physical layer, dynamically intercepting a high-conflict area association difference data fragment and compressing the high-conflict area association difference data fragment into an incremental data packet; analyzing a dependency topological relation between the models to predict a conflict propagation path; converting the path into a combined operation instruction conforming to a design rule through a collaborative modeling engine; and executing the instruction to integrate the difference, and outputting a conflict-free multi-building design integrated model after the models are fused. According to the method, second-level dynamic resolution and automatic fusion of building model conflicts are realized, the design iteration efficiency is improved, and the construction rework risk is eliminated.

The invention relates to the technical field of mobile communication transmission, and discloses a heterogeneous network multilink parallel mobile communication transmission method and system. A dynamic link aggregation module of the system collects link state parameters including signal strength, delay jitter and packet loss rate from a plurality of heterogeneous network interfaces in real time; a flow distribution decision module calculates available bandwidth weight of each link according to the data and generates a dynamic flow distribution strategy; the data fragment packaging module splits to-be-transmitted data into a plurality of data fragments according to the strategy, and adds a sequence identifier and a link identifier to each fragment; the multi-channel concurrent transmission module sends corresponding data fragments in parallel through a specified heterogeneous network link; and the receiving recombination verification module sorts the recombination data fragments according to the sequence identifiers, and detects the data integrity through a verification mechanism. According to the system, a set of complete multi-link parallel transmission solution is formed, and the problems existing in a traditional single link and an existing multi-link transmission technology can be effectively solved.

The invention relates to a distributed data security storage and encryption system based on an AI technology. The system comprises a data acquisition and preprocessing layer used for acquiring original data and preprocessing the original data to obtain an identifiable data object; the AI identification and strategy decision layer is used for identifying data features of identifiable data objects based on an interpretable AI model to obtain an encrypted instruction packet; the encryption execution and distributed storage layer is used for performing encryption storage processing on the original data content according to an encryption instruction packet to obtain data access interface information; and the dynamic access control layer is used for obtaining user information in response to a data access request instruction of a user, recombining and decrypting the data by using the data distribution mapping index based on the access control strategy table, and returning a plaintext data fragment. By adopting the system, a high-strength, differentiated and multi-level security mechanism can be realized through the AI, intelligent encryption strategy decision is carried out, and the security requirements of multiple scenes and multiple types of data are met.

The invention discloses a building construction intelligent safety monitoring method based on the Internet of Things, and the method comprises the following steps: obtaining construction environment data, structure state data and operation behavior image data, and carrying out the preprocessing; environment state modeling, local structure strain and behavior recognition and operation scene segmentation are carried out through the edge intelligent processing unit; feature fusion is carried out, and a fusion situation vector is constructed; performing high-frequency anomaly identification and emergency preliminary screening, and generating an edge preliminary early warning result and a high-risk data fragment; constructing a safety evolution trajectory crossing a time window, and fusing historical data to generate a risk semantic map; cloud semantic reasoning operation is executed, and a final risk level judgment result and a corresponding trigger source identifier are generated; and automatically triggering a safety response instruction according to a risk level judgment result. According to the method, the Internet of Things and intelligent semantic analysis are fused, multi-source safety monitoring and self-adaptive response are realized, and the method has the advantages of high real-time performance, global perception and continuous optimization.

The invention discloses a data quality treatment method and system based on an AI Agent, and belongs to the technical field of artificial intelligence and data treatment. An initial data semantic distribution map is constructed, cross-dimension correlation feature factors are extracted, a multi-scale quality anomaly sensitive factor matrix is constructed, time sequence evolution weights are embedded, and a dynamic feature evolution trajectory is formed; performing perception modeling on the evolution trajectory by using an AI Agent, generating a multi-level quality risk thermodynamic diagram, extracting a deviation dense region and constructing an anomaly propagation path set; in combination with upstream and downstream data links and task flow information, calculating a potential impact factor weight, constructing a causal traceability map, and injecting a correction strategy label for a key field node; the AI Agent autonomously selects an adaptive strategy combination according to the target data segment, and performs online intervention on the target data segment; according to the method, closed-loop treatment of the data quality problem from perception and judgment to intervention and feedback is realized, and the method has the advantages of self-adaption, high interpretability and the like.

The invention relates to the technical field of underground coal mine safety, and discloses an underground coal mine early warning system and method. The method comprises the following steps: collecting multidimensional monitoring data of an underground coal mine environment; performing dynamic window division on the multi-dimensional monitoring data to generate a data fragment set; performing abnormal mode recognition based on the data fragment set, and extracting abnormal feature points exceeding a dynamic threshold in each data fragment; inputting the abnormal feature points into a pre-constructed space-time correlation model, and analyzing diffusion paths of the abnormal feature points in a time dimension and a space dimension; according to the diffusion path, generating an abnormal propagation map containing the propagation direction and the intensity attenuation gradient of the abnormal feature points; hierarchical clustering is carried out on the abnormal propagation atlas, and abnormal clusters with similar propagation characteristics are divided; generating an early warning signal containing a priority label by using the spatial and temporal distribution characteristics of the abnormal cluster; performing synchronous verification on the early warning signal and underground equipment state data, and screening out a target early warning signal needing to be responded; and triggering a corresponding emergency control instruction according to the target early warning signal.

The invention provides an unstructured data compression method and device, equipment and a storage medium, and the method comprises the steps: carrying out the feature extraction and clustering processing of a to-be-compressed unstructured data set, and obtaining a data segment clustering group; performing semantic region segmentation processing on each data fragment in the data fragment clustering group to obtain a semantic important region and a semantic non-important region; performing differential coding processing on the semantic non-important regions in the same clustering group to obtain differential coding data; and performing high-quality compression processing on the semantic important regions in the same clustering group, and performing merging processing on the compressed important semantic data and the difference coding data to obtain an unstructured data compression result. According to the method, semantic region segmentation and differential compression processing are performed on the unstructured data, so that the data compression efficiency can be effectively improved on the premise of ensuring the quality of important information, and an efficient solution is provided for storage and transmission of large-scale unstructured data.

The invention belongs to the technical field of machine room power distribution, and provides a machine room power distribution equipment remote monitoring method and system, and the method comprises the steps: collecting operation data in real time through deploying edge equipment, fusing basic operation data, transient feature data and historical abnormal data, and constructing a state scoring model; utilizing a neural network training weight coefficient to realize equipment health degree dynamic evaluation; the sampling frequency is adaptively adjusted based on a scoring result, abnormal data are screened by combining multiple characteristic thresholds such as current sudden change intensity, energy sudden change quantity and temperature gradient, and only high-frequency data fragments in an abnormal time period are reserved; through an edge-cloud collaborative storage mechanism, abnormal data are locally cached after RAID redundancy check, normal data are uploaded to the cloud according to storage duration and access frequency in a grading manner, full-life-cycle traceability of the data is realized through tagging processing, anomaly detection sensitivity is improved, and storage efficiency and fault diagnosis reliability are both considered.

The invention relates to the technical field of battery detection, and discloses an abnormality diagnosis and traceability method and system for battery detection data, and the method comprises the steps: carrying out the normalization processing of the real-time detection data of a battery, and obtaining a preprocessed data set of the real-time detection data; on the basis of pre-stored historical data reference distribution and a historical reference threshold value, comparing the real-time data distribution of the preprocessed data set to obtain an adaptability detection threshold value of the preprocessed data set; performing abnormal state judgment on the preprocessed data set by using the adaptability detection threshold to obtain an abnormal data fragment of the preprocessed data set; tracing change events of associated parameters from historical working states of the battery based on the abnormal data fragments, and constructing an abnormal conduction chain according to a time sequence relation of the change events; according to the invention, the accuracy of a diagnosis result, the depth of traceability analysis and the operability of operation and maintenance guidance can be improved.

The invention discloses a test case generation method and system for security and futures software. The method comprises the following steps: converting data in an original file into an embedded vector through an information analysis agent, and storing the embedded vector in a vector database; generating an initial test case set by using the plurality of test points and the data fragments corresponding to each test point through a case generation agent; constructing a business rule atlas to perform rule similarity scoring on each test case, deleting the test cases with low scores, and forming a high-score test case set by the test cases with high scores; semantic similarity scoring is carried out according to the historical test cases, sorting is carried out according to scores from high to low to form an optimal test case set, and the test case with the highest score is marked as the historical test case to be stored in the vector database. According to the method, the test cases of the security futures software can be automatically generated, redundant cases are reduced by evaluating the test cases, the case quality is improved, the workload of testers is greatly reduced, and the test efficiency is greatly improved.

The invention discloses a customer information storage management method and system based on a block chain, and relates to the technical field of distributed data evidence storage, and the method comprises the steps: collecting terminal customer information, generating an encrypted data packet, carrying out erasure coding, and generating an encrypted data fragment set; carrying out distributed storage based on the encrypted data fragment set, and calculating a data availability commitment at the same time; executing availability sampling according to the data availability commitment, and generating a fragment validity proof; based on the fragment validity proof, performing format and validity verification to obtain a to-be-processed proof set; and generating a recursive aggregation proof according to the to-be-processed proof set, and submitting the recursive aggregation proof to the block chain to verify and update a proof storage state. And cryptographic authentication is carried out on the persistent evidence storage state of the data assets, so that the dual beneficial effects of ensuring data privacy and credible evidence storage are achieved.

The invention discloses a load migration method and device and electronic equipment, and relates to the technical field of cluster resource management and intelligent operation and maintenance, and the load migration method comprises the steps: obtaining historical time sequence data corresponding to each node in a cluster, the historical time sequence data corresponding to the node comprises running state information corresponding to the node at each time point of the historical time sequence; predicting node load information of each node in a future predetermined time period according to the historical time sequence data corresponding to each node; loads of nodes in the cluster comprise data fragments; determining a load migration decision for the nodes in the cluster according to the node load information of each node in a future predetermined time period; migrating partial data fragments of the first node in the cluster to at least one second node according to the load migration decision; wherein the node load of the first node in the future predetermined time period is higher than the node load of the second node in the future predetermined time period.

The invention relates to the technical field of data storage, in particular to a distributed data storage and access processing method. The method comprises the following steps: presetting a guide configuration file in a distributed storage cluster to carry out identity authentication and communication link establishment on each storage node and a cluster management center so as to generate a security data interaction channel; the data sending end equipment sends a data storage request to the cluster management center through the secure data interaction channel and issues data fragments to proper storage nodes; the data receiving end equipment sends a data access request to the cluster management center through the security data interaction channel, generates an access path and feeds back the access path to the data receiving end equipment; the corresponding resource use condition in each storage node is monitored in real time, and when a preset threshold value is reached, the resource is reallocated to other storage nodes according to a preset data migration strategy and a load balancing algorithm based on a resource use insufficiency result. According to the invention, efficient storage and rapid access of data in a distributed environment can be realized.

The invention relates to the technical field of data processing, in particular to an intelligent supervision method and system based on a data visualization platform, and the method comprises the steps: collecting multi-source water conservancy data through dual-channel redundancy check, converting the multi-source water conservancy data into a standardized space-time matrix, carrying out the data cleaning through a containerization adaptation and variational auto-encoder, and marking the confidence; hierarchically storing the data based on a confidence threshold, and generating a routing strategy through reinforcement learning to distribute data fragments; calculating a flood peak evolution prediction result by using space-time diagram convolution, calculating an output risk assessment value by combining a Bayesian network and long and short-term memory, and activating federal learning parameter updating when the confidence coefficient is insufficient; the digital twin model loads physical constraint parameters to deduce a flood control scene, and a scheduling instruction set and a rehearsal animation are generated through multi-objective optimization; and dynamically rendering to generate an interactive visual interface, associating a causal deduction path, and triggering federal model parameter updating by user feedback, thereby realizing dynamic collaboration of the forecasting and early warning rehearsal plan function chain.

The invention provides a data classification method and system for intelligent Internet of Things equipment, and the method comprises the steps: obtaining multi-source heterogeneous data of the Internet of Things equipment, and carrying out the protocol analysis preprocessing of the multi-source heterogeneous data, and obtaining standardized data; fragmenting the standardized data based on the time window to obtain a plurality of data fragments, determining the priority of each data fragment based on the data feature density, and respectively inputting the data fragments with different priorities into a real-time processing channel and a cache channel; performing protocol layer feature extraction on the data fragments transmitted into the real-time processing channel to obtain fragment protocol features; screening the data fragments based on the fragment protocol features to obtain semantic data fragments, and performing semantic feature extraction on the semantic data fragments to obtain fragment semantic features; and inputting the fragment semantic features and the fragment protocol features into a classification identification model to obtain category labels output by the classification identification model. The classification precision and the classification effect are improved.

The invention discloses an Internet of Things main service platform data analysis cooperation method, and relates to the technical field of data analysis, and the method comprises the following steps: obtaining a to-be-analyzed terminal uploading data set, and detecting the real-time transmission time delay and data encryption level of each data segment in the terminal uploading data set; based on the historical data analysis collaborative log, extracting a tolerance loss coefficient between the real-time transmission time delay and the analysis fault-tolerant rate, and obtaining an analysis fault-tolerant deviation value caused by transmission time delay change according to the tolerance loss coefficient and the real-time transmission time delay; if the real-time transmission time delay of the current Internet of Things main service platform does not cause interference on the data encryption analysis algorithm, processing the data encryption grade and the analysis calculation power consumption to obtain a calculation loss coefficient, and performing calculation power compensation on the initial analysis calculation power distribution quantity according to the calculation loss coefficient, the data encryption grade and the analysis fault-tolerant deviation value; obtaining a first actual analytical calculation force; the method has the effect of improving the collaboration efficiency and stability of data analysis of the whole platform.

The invention discloses an archive data security backup method and system based on cloud computing, and the method comprises the steps: obtaining archive data and a cloud backup pool, obtaining an edge verification package based on to-be-backed-up data, carrying out the grouping encryption of the edge verification package through a national password, and enabling the cloud backup pool to carry out the splitting and storage of the backup data according to a regeneration code strategy, the method comprises the following steps: acquiring data fragments and fragment roots, writing Hash of the fragment roots into an alliance chain channel, hosting a decryption key of a confidential envelope according to a storage period, storing fragments of the decryption key into a cloud HSM, and if a cloud backup pool monitors a disaster trigger event, pushing decryption key sub-fragments to a disaster recovery undertaking pool according to a disaster level, the subset of the readable file data is recovered to the edge node, and a recovery log is written back to the alliance chain. According to the method, data backup, safety and reliability are improved through desensitization grading, differential safety protection, regeneration code strategy splitting and multi-node disaster recovery.

The invention discloses a smart city data capitalization-oriented data right confirmation management method and system, and belongs to the technical field of data resource management. The method comprises the following steps: receiving data assets uploaded by a user and carrying out multi-modal preprocessing on data contents to generate a data fingerprint with a unique identifier; counting the proportion of the unconfirmed right data fragments; judging an infringement risk level of the data assets in combination with a risk early warning model; if the unconfirmed right proportion exceeds the dynamically calculated threshold value range or the infringement risk level reaches a preset threshold value, executing an audit interception operation and returning a right confirmation abnormity prompt to the user, and requesting to supplement ownership proof; and embedding a multi-modal fusion watermark into the data assets passing the auditing, and generating a cross-domain verifiable right confirmation voucher. According to the method, the data features are extracted through multi-modal preprocessing to generate the unique data fingerprint, the ownership registration state of the data fingerprint is inquired in combination with the block chain technology, the ownership-confirmed and unconfirmed data fragments are accurately identified, and the problem that the data ownership is difficult to accurately and efficiently identify is solved.

The application discloses a method and device for realizing high-speed operation of an algorithm based on a multi-level field cache, which comprises the following steps: a configuration management CPU sends algorithm parameters to an FPGA chip and saves the algorithm parameters in a RAM; a service processing CPU creates a handle and sends the handle to the configuration management CPU; the configuration management CPU generates a key number according to the handle; the configuration management CPU sends a key library to the FPGA chip; the service processing CPU sends a to-be-operated message to the FPGA chip; an algorithm scheduling module performs fragmentation judgment, and performs subsequent processing according to a sequence judgment result; a password operation module takes the received key, an initialization vector or an intermediate chain variable and the to-be-operated message as input, performs password operation, and outputs a calculation result and an operated intermediate chain variable; whether the to-be-operated message is the last data fragment is judged according to a tail fragment identifier, and subsequent processing is performed according to a tail fragment judgment result. The application can reduce the communication interface and CPU access times, realize high-speed operation of a password algorithm, and improve the overall performance of a password service system.

The present application provides a video segmentation method, a server, a storage medium, and a program product. In the method of the present application, video data to be segmented is segmented into multiple data segments, unimodal features of the data segments, including text features of a text modality and visual features of a visual modality, are respectively extracted by means of a video topics segmentation model, and then the text features and visual features of the data segments are fused, so that the fusion of multimodal information can be performed at the intermediate representation level, the relationship and interaction between different modalities can be better captured, and higher-quality multimodal fusion features of the data segments are obtained. Furthermore, on the basis of the multimodal fusion features of the data segments, whether the data segments are topic boundaries is predicted, so that the topic boundaries of the video data can be accurately predicted, improving the accuracy of topic boundary recognition, thereby improving the accuracy and quality of video topics segmentation results.

The invention discloses a big data measurement asset supply and demand matching and inventory optimization method, and relates to the technical field of big data processing and supply chain management. The method comprises the following steps: segmenting supply chain asset data through a hash function according to multi-dimensional attributes to generate a data fragment set, and constructing a global index tree according to the data fragment set; and monitoring the load state of the leaf nodes of the global index tree in real time, and migrating and verifying data during unbalance. And querying the global index tree, positioning fragment data associated with the query request from the uniformly distributed leaf nodes, generating a preliminary matching asset set, calculating the matching degree of each asset and the query request, and generating a resource allocation result. And updating the global index tree, and generating the latest data view representation. And adjusting the attribute weight coefficient of each dimension in the hash function, and generating optimized storage layout configuration. Irrelevant data are filtered through a neighbor algorithm, and a final matching result set is generated. Balanced storage, efficient query and accurate matching of asset data are realized, and the overall response speed and the resource utilization rate are improved.

The present invention relates to a method for building a knowledge base using a language model. The method for building a knowledge base, performed in a computing device, according to the present invention comprises the steps of: by using a trained language model, generating an anticipated query related to each data fragment obtained by splitting original data into multiple fragments; generating a summary for the generated anticipated query from each data fragment by using the language model; and generating a knowledge base including an index obtained by vectorizing the anticipated queries, and the summaries for the anticipated queries as content data for the index. According to the present invention, search accuracy for information related to a query may be improved by increasing the semantic similarity between the query and a search target in the knowledge base. Further, the language model may generate a more accurate and relevant response to a query of a user through additional information that is retrieved.

The invention relates to a fingerprint lock dynamic encryption authentication method and system and a medium. The method comprises a registration stage and an authentication stage. Wherein in the registration stage, lock body environment real-time physical parameters are collected, and a dynamic environment secret key is generated through a chaotic mapping function; acquiring a user fingerprint, identifying core and non-core feature regions, and encrypting the two types of regions by combining a dynamic environment key and a user key to generate registration encrypted fingerprint data; and dividing the data into two data fragments by using a quantum noise source true random sequence, storing the first data fragment in a local security chip, and encrypting the second data fragment and storing the encrypted second data fragment in a cloud server. In the authentication stage, environment parameters are collected in real time to generate a verification key, and the verification key is compared with a registration key; and when the two fragments are consistent, recombining the two fragments to restore the encrypted fingerprint data, authenticating the fingerprint input by the user, unlocking if the authentication succeeds, and triggering risk grading processing if the continuous failure reaches a threshold value. The overall safety and reliability of the fingerprint lock system are improved.

The application belongs to the technical field of medical signal processing, and discloses a sleep apnea detection method and system, an electronic device and a storage medium. The method comprises the following steps: acquiring a synchronous ECG signal and a breathing signal, and processing the synchronous ECG signal and the breathing signal into a bimodal data segment containing multiple time scales; constructing a parallel neural network architecture, extracting ECG features and breathing signal features from each scale data segment respectively, and forming a cross-modal feature pair; designing a cross-scale dynamic weight correction attention mechanism, introducing a correction factor based on the Euclidean distance between feature vectors, dynamically weighting and information fusion on the cross-modal feature pair between different scales, so as to enhance the relevance of local details and global context; inputting the fused multi-scale features into a classifier, and outputting the probability of a sleep apnea event. Through multi-scale collaborative analysis and a dynamic weight correction attention mechanism, the application effectively fuses physiological information of different time dimensions, and improves the detection accuracy.

The invention discloses a multipath relay transmission method and system for underground confined space construction data, and belongs to the technical field of wireless communication network routing, and the method comprises the steps: obtaining a signal quality parameter of a communication link and a distribution density parameter of an ad hoc network node, and combining the load state of the node and the information of a neighbor node, decision calculation is executed on a plurality of relay nodes in parallel, relay decision vectors corresponding to the relay decision vectors are generated, a plurality of candidate transmission paths are formed through evaluation, construction data blocks to be transmitted are fragmented and distributed to the corresponding optimal candidate transmission paths to be sent in parallel, all received data fragmented sub-blocks are verified and recombined, and the construction data blocks to be transmitted are obtained. And recovering a complete construction data block. A distributed relay decision based on network state feature fusion, data adaptive fragmentation based on path stability and a multipath parallel transmission and feedback optimization mechanism are adopted, so that the data transmission reliability in an underground complex environment can be improved, the transmission delay is reduced, and the dynamic optimization of network performance is realized.

The invention provides a networked automobile risk scene analysis method based on a multi-modal corpus and related equipment, and the method comprises the steps: receiving the multi-modal corpus containing various types of test data, and carrying out the format standardization and time synchronization processing of the multi-modal corpus; performing risk scene analysis on the basis of association information among different types of test data in the multi-modal corpus, identifying abnormal behaviors occurring in the test process, and generating corresponding structured event description information; and extracting key data fragments and image frames associated with the abnormal behaviors, carrying out image-text association on the key data fragments, the image frames and the event description information, and generating a test report based on a preset template. According to the method, a multi-modal corpus analysis mechanism is introduced, risk scene analysis and abnormal behavior recognition are executed, and image-text matching and report generation are automatically completed. The manual arrangement burden is reduced, and the intelligent degree of risk scene identification and the automation level of test report generation are improved.

The invention discloses a target equipment upgrading method and device based on an equipment grading strategy, equipment, a medium and a product, and relates to the field of mobile terminal software upgrading, and the method comprises the steps that a server collects hardware features of target equipment and automatically grades the equipment by using a K-means clustering algorithm; generating a differential OTA upgrade package based on the grading result; segmenting the differential packet into data fragments with fixed sizes, and generating a hash value and a digital signature for each fragment; the equipment end downloads one by one, verifies integrity and signatures in real time, and supports breakpoint resume; the server side predicts the OTA upgrade failure risk through the trained random forest model, and dynamically adjusts a push strategy; the equipment adopts an A / B partition mechanism to realize safe rollback; in a non-secure network environment, the server and the device cooperatively generate a temporary key based on a device ID and a timestamp to encrypt and transmit fragments. The volume of the upgrade package is effectively reduced, the transmission efficiency is improved, the security is enhanced, and the upgrade failure rate is remarkably reduced.

The invention relates to an emotion regression recognition method based on multi-modal signals. The method comprises the steps that original electroencephalogram signals and data fragments of all modals of electrocardiosignals are obtained; inputting the modal data fragments into corresponding shallow layer encoders and fusing the modal data fragments to obtain shallow layer fusion features; inputting the shallow spatio-temporal features of each mode into a corresponding Transform deep encoder to obtain deep features, and performing deep fusion on the deep features and the shallow fusion features to obtain deep fusion features; splicing the deep fusion features and the feature-activated manual features to obtain final features; and outputting an emotion continuous value regression result through the multi-layer perceptron network. According to electroencephalogram and electrocardiosignals, original signals and manual features are combined, step-by-step extraction is carried out in the feature learning process, a multi-modal fusion mechanism of the network is utilized, the performance and generalization ability of the model are improved, meanwhile, continuous prediction of the emotional state is achieved, fine fluctuation of the emotion is more accurately captured, and the application prospect is wide.

The invention relates to the technical field of data distributed storage, in particular to a data distributed secure storage system and method based on computer science and technology. The system comprises a data fragmentation and strategy binding module, a strategy declaration evidence storage and state tracking module, a strategy execution voucher generation module and a voucher verification and data assembly module. The system carries out fragmentation processing on original service data through a dynamic fragmentation algorithm to generate encrypted data fragments and a strategy binding statement; storing the strategy binding declaration to a distributed account book and maintaining a strategy state table; generating a policy execution voucher including an access qualification proof, a digital signature response, a policy hash value and a timestamp based on the system real-time state and the access request; and the distributed storage node implements decentralized data access control by verifying the policy execution credential. According to the method, the single-point fault and strategy tampering risk of centralized authority management are effectively solved, and a reliable data security sharing scheme is provided for a supply chain financial service scene.