2104 results about "Information data" patented technology

The present application relates to the technical field of power digital twin, and discloses a digital twin system for a power grid. The system comprises: a data access module, which accesses multi-source heterogeneous data of a power grid; a modeling module, which is used for receiving the data output by the data access module and establishing digital twin models in multiple service scenes; a fusion module, which is used for performing association and fusion of the digital twin models in the multiple service scenes; a computing module, which is used for carrying out multi-scene collaborative simulation and derivation by using the digital twin models; and an optimization module, which is used for optimizing the digital twin models, wherein the multi-source heterogeneous data comprises real-time monitoring data, historical operation data, meteorological environment data, device model data, and geographic information data. The problem in the prior art of low fusion efficiency of multi-source heterogeneous data of a power grid is solved. A standardized data access interface is constructed, and association analysis, knowledge graph technology, etc. are applied, so that seamless integration of the multi-source heterogeneous data of the power grid is achieved, thereby improving the data fusion efficiency.

The invention discloses a geographic information big data-oriented multi-source data processing system, and relates to the technical field of data acquisition and sensors, and the system comprises a data acquisition module which accesses a remote sensing satellite, an unmanned aerial vehicle, an Internet of Things sensor and social media in real time through a multi-source heterogeneous interface, and carries out the adaptive analysis of a data format and metadata marking; the distributed storage module is used for performing partition storage on the geographic information data based on a space-time database and an object storage architecture, and establishing dynamic space-time index and version control; the data fusion module is used for realizing coordinate system conversion, time sequence calibration and semantic knowledge graph matching based on a multi-source data alignment method of dynamic weight distribution; and an intelligent analysis module and a security control module. According to the method, core pain points such as data splitting, low storage efficiency, extensive analysis and compliance risks in the geographic information field are systematically solved, and a full-stack type technical base is provided for scenes such as smart cities, emergency disaster relief and environment monitoring.

The invention relates to the technical field of management and control systems, in particular to a digital intelligence system applied to collaborative management and control of water-power engineering construction participating units, which comprises a project data management module covering water-power engineering construction full life cycle management, extracting multi-source information data from various subsystems, and constructing a project view and a project management information base; the project risk analysis module is used for constructing an intelligent risk analysis model for risk analysis and generating a dynamic evaluation result, a graded early warning notification and an auxiliary decision scheme; the project collaborative management module is used for processing graded early warning notification and auxiliary decision-making schemes by using a multi-layer perceptron model, and generating finalizing service achievements and management process records; and the project document management module is used for performing compliance automatic checking and processing on the electronic documents needing to be archived, and dynamically updating and optimizing the project management information base. Through the closed-loop management and control system, the cooperative management and control efficiency of water-power engineering construction participation units is improved.

The invention discloses a person and post matching method, system and equipment based on Transform and a medium, belongs to the technical field of cross application of natural language processing and human resource management, and aims to solve the technical problem of how to reduce subjective errors in a person and post matching process, improve matching accuracy, fairness and objectivity and improve manual matching efficiency at the same time. According to the technical scheme, the method comprises the following steps: data collection: collecting post information data and job seeker resume data from a plurality of data sources, and cleaning and sorting the collected data to obtain a post description text and a resume text; text preprocessing: preprocessing the acquired and sorted post description text and resume text to obtain the preprocessed post description text and resume text; the method comprises the following steps of: fusing a Transform model and a BERT model; constructing an encoder-decoder structure based on the Transform model; optimizing the model; multi-label text classification: constructing a multi-label classification system; and outputting and evaluating a matching result.

The invention discloses a runoff prediction method and terminal based on a space-time heterogeneous graph neural network, and the method comprises the steps: obtaining hydrological time series data and space station information data collected by each station, and carrying out the preprocessing of the hydrological time series data and the space station information data; performing weighted fusion according to the features of the preprocessed hydrological time series data and the space station information data, generating a feature embedding vector representing a global variable time-space relationship through a graph attention network, and inputting the feature embedding vector into a full connection layer to obtain an output multi-station runoff prediction sequence. Constructing a space-time heterogeneous graph flood forecasting model; performing model parameter optimization on the space-time heterogeneous graph flood forecasting model; and carrying out runoff prediction on the hydrological time sequence data and the space station information data which are received in real time according to the optimized space-time heterogeneous graph flood forecasting model. In this way, hydrological space-time correlation characteristics can be effectively captured in forecasting, so that the accuracy and reliability of forecasting are improved.

The invention relates to the technical field of material verification management, and discloses a government affair material intelligent verification method and system based on machine learning, and the method comprises the steps: obtaining the text information data and verification rules of historical materials; multi-modal feature extraction is carried out; establishing a dynamic verification rule knowledge graph, and verifying the multi-modal features and the target feature points of the text segments to generate a primary verification strategy; performing deep verification processing on the material text fragment to generate a secondary verification strategy; performing third-time verification on the material text fragment subjected to the second-time verification by triggering cross-page feature matching to realize cross-page text fragment association and multi-modal feature fitting so as to generate a third-time verification strategy; performing iterative comparison on the results of the three rounds of verification to generate a credibility score; and the comparison result is adjusted according to the credibility score in combination with the verification risk abnormity assessment, so that a final verification decision strategy can be obtained, and efficient, accurate, dynamic and comprehensive intelligent verification can be realized.

The invention relates to the technical field of unmanned aerial vehicle-unmanned ship data fusion, and discloses an unmanned aerial vehicle-unmanned ship multi-source heterogeneous big data fusion system and method, and the system comprises a task scheduling module which is used for executing the path planning and task distribution of operation tasks; the control and data acquisition module is used for controlling the unmanned aerial vehicle and the unmanned ship to execute a synchronous measurement task; the space-time reference unification module is used for performing dynamic synchronization and consistency alignment processing; the multi-source data fusion module is used for carrying out fusion processing on the aligned heterogeneous sensing information data; the self-adaptive robust enhancement module is used for monitoring the noise level and the change condition of the signal-to-noise ratio of the sensor in the fusion process; and the intelligent analysis and visualization module is used for executing environment modeling, target identification and geographic information expression processing. According to the method, the fusion weight self-adjusting mechanism driven by the maximum entropy is introduced, so that the internal structure can be intelligently adjusted when the data quality fluctuates. According to the technical effect, the target that the weight is dynamically adjusted along with signal noise is achieved.

The invention provides a servo motor real-time adaptive control method and system based on artificial intelligence, and the method comprises the steps: injecting a tiny excitation signal into a servo motor, collecting temperature and electromagnetic response data under different working conditions, and constructing a multi-dimensional information data set; performing real-time reasoning on the relationship between the temperature gradient and the magnetic path characteristics by using deep transfer learning to obtain multi-dimensional parameter deviation vectors such as stator flux linkage unbalance degree, permanent magnet flux weakening degree and torque coefficient change; generating a multi-scale compensation strategy through a physical enhancement reinforcement learning method and the multi-dimensional parameter deviation vector; and carrying out Lie group transformation processing on the control law under a differential geometric framework, and realizing accurate compensation on the temperature gradient effect through a dual-channel-dual-time scale framework. According to the method, heterogeneous sensing and multi-dimensional compensation strategies are adopted, the problem that the control precision of a traditional method is insufficient under the complex temperature gradient condition is solved, and the high-precision motion control performance is maintained under the dynamic working condition that the temperature distribution of the motor is rapidly changed.

The invention provides a lightweight real-time semantic segmentation method for a three-dimensional Gaussian scene, and belongs to the field of computer vision and three-dimensional scene understanding. The process comprises the following steps: S1, data preparation: acquiring multi-modal information data including point cloud, images and poses, and preprocessing the data; s2, carrying out two-dimensional semantic segmentation, and generating a pixel-level semantic segmentation map and a semantic tag for the image by utilizing PP-LiteSeg; s3, performing three-dimensional scene modeling and semantic projection, fusing multi-modal information to complete three-dimensional Gaussian scene reconstruction, and projecting two-dimensional semantics to a Gaussian function to form preliminary Gaussian function semantic features; and S4, semantic optimization and segmentation output: considering image difference, semantic difference and smoothness, establishing a loss function, optimizing semantic features and outputting a segmentation result. According to the method, efficient, real-time and accurate three-dimensional Gaussian semantic segmentation work can be completed, and the method is suitable for scene understanding tasks of light-weight and resource-limited equipment.

The invention relates to the technical field of equipment health assessment, in particular to a power plant equipment health assessment model construction method and system. The method comprises the following steps: acquiring station information data of power plant equipment; site equipment data transmission channel configuration is carried out on the power plant equipment site information data, and a site equipment data transmission channel is generated; carrying out information integration on the power plant equipment station information data by utilizing the station equipment data transmission channel to generate a power plant equipment operation data integration platform; performing power plant equipment operation monitoring on the power plant equipment operation data integration platform to obtain power plant equipment operation monitoring data; and performing equipment multi-working-condition trend fluctuation calculation on the power plant equipment operation monitoring data to obtain equipment working condition trend fluctuation data. Through data integration, refined working condition analysis, unsteady state abnormal state transition analysis and dynamic loss assessment, the precision of the power plant equipment health assessment model is improved.

The invention discloses an intelligent building construction operation management method based on big data, and belongs to the technical field of building construction management. Comprising the following steps: step 1, real-time data acquisition of construction site personnel, equipment and environment is realized, and a dynamic information database is established; 2, preprocessing and feature extraction are carried out on the collected multi-source heterogeneous data, and fusion and intelligent analysis of real-time data are achieved; step 3, accurately identifying risk factors and implementing refined early warning and intelligent intervention aiming at a single risk; 4, construction resource configuration is dynamically optimized, the resource utilization efficiency is improved, and meanwhile the cost and the safety risk are reduced; step 5, constructing a block chain data sharing mechanism, and realizing data tracing and intelligent management of a construction full life cycle; and step 6, forming a self-adaptive closed-loop feedback mechanism, and promoting intelligent transformation of construction management from passive response to active prediction.

The invention provides an intelligent boxing method and system. The intelligent boxing method comprises the steps of obtaining boxing task information and generating a boxing scheme. The boxing task information comprises the size, the weight, the stacking limitation, the number and the priority of each cargo, and the size, the bearing capacity and the occupation state of each loading unit. And under the condition that the size, bearing and stacking constraint conditions are met, a plurality of candidate boxing combinations are generated, the loading utilization rate, the residual space distribution and the shape matching degree are evaluated based on a preset optimization target, and an optimal scheme is selected. The system comprises an input terminal, a task information receiving module, a data storage module, a task and scheme storage module, an operation processing module and a final boxing scheme generation module. According to the method, multi-dimensional constraint matching and weight balance are considered, and space waste and transportation risks are avoided; the maximum greedy algorithm and the adaptive large neighborhood search optimization are combined to realize the combination of automation and experience. The method can be expanded to batch task scheduling and grouping sequencing, so that the calculation complexity is reduced, and the overall boxing and transportation efficiency is improved.

The disclosure relates to systems, methods, and computer-readable media for an artificial intelligence (AI) conversational agent configured for enhanced user interaction in multi-channel contact centers. An example method includes receiving a query regarding a particular property of an asset during a real-time conversation, classifying an intent of the query into a predefined category, triggering, based on the classifying of the intent, a finetuned large language model for the predefined category to answer the query, generating, by the finetuned large language model, a response to the query based on at least one of an information data model and a knowledge graph data store, and outputting the response to the query.

The invention provides a wound surface grading model construction method and a wound surface self-analysis system, and the method comprises the steps: processing an obtained wound surface image, clinical text description data and structured background information data through a specific feature extraction strategy, and obtaining an image feature vector; clinical text description data and structured background information data are subjected to cleaning, word segmentation, entity labeling and other operations, a wound semantic evolution graph is constructed, related vectors are fused to obtain text embedding, wound background context vectors are generated according to the structured background information data, dynamic weighting, splicing and fusion are performed on image and text feature vectors, and the image and the text feature vectors are subjected to image fusion. According to the method, the fusion feature vector is obtained, the corresponding grading model is constructed according to the fusion feature vector, more accurate, comprehensive and explainable grading evaluation can be performed on different types of wounds, and a self-analysis system designed for the constructed model can quickly and accurately judge the wound image condition shot by a user.

The invention relates to the technical field of smart city data visualization, in particular to a visual smart city display method, which comprises the following steps: S1, collecting traffic flow data, energy consumption data, environment monitoring data and city three-dimensional geographic information data in real time; s2, constructing a reference three-dimensional model with a space coordinate system; s3, mapping the data into a dynamic data layer with a timestamp; s4, superposing the dynamic data layer to the reference three-dimensional model according to space-time coordinates to generate a fused dynamic scene; s5, receiving deduction parameters input by a user, and calculating a corresponding evolution path in real time; and S6, converting the evolution path into a dynamic visualization effect, and outputting the dynamic visualization effect to a display terminal. According to the invention, through integrated processing of multi-source dynamic data fusion, space-time path deduction and visual output, continuous expression and dynamic evolution display of the operation state of the smart city are realized.

The invention relates to the technical field of micro-grid energy dispatching, in particular to a micro-grid energy dispatching carbon emission reduction system based on reinforcement learning dual-objective optimization. The data acquisition module is used for acquiring information such as equipment energy consumption data, environmental parameters and carbon emission coefficients in the micro-grid in real time; the data processing module is used for cleaning acquired data, extracting features and performing standardization processing; the intelligent scheduling module is used for extracting information features and a deep reinforcement learning algorithm, optimizing by using a model to obtain an optimization strategy, and forming a scheduling instruction set; the execution feedback module is used for dynamically scheduling the operation state of equipment in the micro-grid according to the scheduling instruction set; the man-machine interaction module provides a user interface and is used for displaying the carbon quota use condition and the system operation state and allowing a user to input an adjustment demand; compared with the prior art, the energy system of the park is used as a unified network, mutual influence of multiple systems is considered, accurate scheduling is achieved, environmental protection and high efficiency are achieved, and the operation cost is reduced.

The invention discloses a ship desulfurization system reliability evaluation method and system based on multi-source information fusion, and the method comprises the steps: collecting multi-source information data of a hybrid desulfurization system, and carrying out the self-adaptive preprocessing; generating a fusion feature vector; constructing a dynamic Bayesian network based on multi-source fusion features, performing real-time reasoning by adopting data-driven transition probability learning and particle filtering, describing transient behaviors of system state evolution and mode switching, and performing dynamic multi-state reliability modeling; a fault mode is automatically extracted, and data-driven systematic risks are identified and quantitatively analyzed; a multi-resolution digital twinborn architecture is constructed, dynamic simulation prediction is carried out, a self-adaptive updating mechanism is adopted to keep the model synchronous with a physical system, and a virtual verification environment for reliability evaluation is provided; according to the method, an intelligent decision optimization system is constructed, self-adaptive generation and dynamic adjustment of a maintenance strategy are realized, closed-loop feedback is carried out, and the accuracy of reliability evaluation of the hybrid desulfurization system is improved.

The invention relates to the technical field of power management, in particular to a multi-source heterogeneous data three-dimensional fusion analysis method based on a power grid unified information model, which comprises the following steps of: performing timestamp alignment and space coordinate normalization processing on SCADA system real-time data, GIS geographic information data, equipment vibration spectrum data and infrared thermal imaging data; and constructing a unified three-dimensional attribute matrix, and fusing the multi-source data based on a power grid unified information model. A three-dimensional space interpolation algorithm is adopted to carry out topological reconstruction on the model, a three-dimensional space-time topological structure containing equipment connection relations, electrical parameters and space coordinates is generated, intelligent prediction is carried out on the equipment fault probability in combination with a depth prediction model, and an augmented reality display interface with prediction and early warning information is generated through a three-dimensional visualization engine. According to the invention, the accuracy and the real-time performance of power grid equipment monitoring are effectively improved, the fault early warning and decision support capability is enhanced, and the operation safety and the management efficiency of a power grid are improved.

The invention discloses a transmission method and device, Internet of Things equipment and electronic equipment, and belongs to the technical field of communication, and the transmission method comprises the steps that target Internet of Things equipment receives a first downlink message sent by first equipment, and the first downlink message is used for indicating at least one piece of Internet of Things equipment to initiate a random access process; wherein the first downlink message comprises at least one of the following items: identification information of the at least one piece of Internet of Things equipment; a cause is paged; accessing process information; transmitting data information; the access resource configuration information of the target Internet of Things device; time control information; the retransmission resource configuration information of the target Internet of Things device; the security information is used for carrying out authentication on the target Internet of Things equipment and carrying out encryption or decryption on transmission data; and a first indication, wherein the first indication is used for indicating the backoff time of data retransmission of the target Internet of Things equipment.

The invention discloses a three-dimensional model intelligent generation method and system based on a natural language, and the method comprises the steps: receiving and describing the natural language inputted by a user in real time, based on the three-dimensional model data structure, the de-noising symbol set and the stop word list, key information data are extracted according to natural language description and mapped into a three-dimensional model parameter set; according to the scene type described by the natural language and the three-dimensional model parameter set, calling a three-dimensional model generation network corresponding to the scene type described by the natural language to generate an initial three-dimensional model; and receiving and analyzing a model optimization instruction input by a user in real time, updating the geometric structure, detail features and physical attributes of the initial three-dimensional model according to an analysis result, generating a dynamic interaction model, and outputting a three-dimensional model file of the dynamic interaction model. By applying the method and the system provided by the invention, the efficiency, the accuracy, the flexibility, the usability and the user experience of three-dimensional model modeling are improved.

The invention discloses a new energy automobile hybrid power system cooperative control device and method.The new energy automobile hybrid power system cooperative control device comprises a multi-source data collection module, a cooperative control center and an execution unit, and the multi-source data collection module transmits collected information data to the cooperative control center; the cooperative control center comprises a data fusion unit, an energy management module, a heat management module and a cooperative decision-making unit, the data fusion unit receives data information of the multi-source data acquisition module, carries out fusion processing, and transmits the data information to the energy management module and the heat management module; the energy management module and the heat management module output a control strategy to the collaborative decision-making unit, and the collaborative decision-making unit judges a correction strategy and then sends a control instruction to the execution unit. The method has the advantages that cooperative control of energy management and heat management is achieved, various dimension data of road conditions, driving behaviors, power states and heat source states are fused, the problem of system unbalance caused by single module optimization is solved, and dynamic cooperation of energy management and heat management is achieved.

The present disclosure describes methods, systems, apparatus, and media for object identification and classification, utilizing multi-feature and multi-modal data. This includes shape, material, brand, price, odor, taste, tactility, and sound. The system integrates a server space for data processing, a querying device for iterative searches, and a data interface module for refining results. It features AI-driven image optimization, feature extraction, and pattern recognition, employing novel techniques for fusing multi-feature and multi-modal embeddings utilizing multi-head attention. Additionally, a linker module powered by two active learning with feedback loops AI models consolidates scattered data into a unified object information database. The system also employs novel AI algorithms for isolating the object of interest through a saliency map and semantic analysis, as well as for enhancing raw images with a GAN-autoencoder.

The invention relates to the technical field of sensors, and discloses a zero drift calibration method and device for a sensor and a computer readable storage medium, and the method comprises the steps: obtaining environment information data of a target sensor; wherein the environment information data comprises gas concentration, environment temperature and humidity and illumination intensity; determining whether to perform zero drift calibration on the target sensor according to the acquired environment information data; and under the condition of determining to perform zero drift calibration on the target sensor, according to the real-time gas concentration of the target sensor, calculating to obtain a zero drift reference value so as to calibrate the measurement value of the calibration target sensor. According to the method, the environment information data is used as a basis for judging calibration, so that the reliability of calibration can be improved. When the reliability of environmental data is high, zero drift calibration is carried out based on the real-time gas concentration, and the calibration precision is ensured. Therefore, the sensor can realize automatic calibration, and the calibration accuracy is ensured.

The invention relates to the technical field of information data processing, particularly provides an active security defense method based on a network traffic time mode and data volume analysis, and solves the problem of poor security and reliability of network security defense. The method comprises the following steps: a network traffic capturing tool is used for acquiring a time mode and a data volume of network traffic corresponding to a network node; obtaining a model data set containing a time pattern and a data volume attack pattern, and segmenting the model data set to obtain a training set, a test set and a verification set; training the active security defense model by using the training set to obtain a trained active security defense model; and laying the active security defense model to a regional position, establishing communication with specific equipment, analyzing network traffic, obtaining an evaluation result of potential network attacks, and if the network attacks exist, adopting security protection measures for protection. According to the invention, the identification and defense efficiency of various network attacks can be improved, and the security and stability of the network are ensured.

The invention relates to the technical field of digital information transmission, in particular to an information security transmission method of a stadium security platform based on the Internet of Things. The method comprises the following steps: integrating a TPM 2.0 security chip to an Internet of Things sensing layer, and applying the TPM 2.0 security chip to gate equipment of a stadium; an electromagnetic locking / control device and a forced synchronization mechanism are arranged on the gate equipment, and physical locking control is carried out on the gate equipment; standardized identity information data are collected through an RFID identity verifier in the gate device; and controlling an electromagnetic locking / control device and a forced synchronization mechanism based on the standardized identity information data to generate a gate control instruction. According to the stadium security and protection platform, an efficient, safe and reliable stadium security and protection platform is constructed by integrating a TPM 2.0 chip, electromagnetic locking control, RFID identity verification, dynamic micro-isolation and a block chain technology.

A forging process optimization system and optimization method based on digital twin, which system and method belong to the technical field of forging. The forging process optimization system comprises a physical entity collection module and a digital twin system, wherein the physical entity collection module comprises a data collection module, and the data collection module is used for collecting information data of the physical entity collection module during operation. A digital twin technique is integrated into a forging process, so as to facilitate the formation of intelligent and networked production, facilitate manufacturing resource configuration and production technique sharing, and facilitate offline operation training and online production guidance. Thus, the yield of parts is increased, resource waste is reduced, and a relatively high economic benefit is achieved; and the blindness and the subjectivity of when a conventional optimization method is used are reduced, and the time for searching for an optimal process parameter combination is shortened, thereby effectively improving the process design efficiency and reducing the sample development cost.

The invention relates to the technical field of geographic information data, in particular to a GIS-based CRM customer ecosphere dynamic visual management system, which comprises the following steps: acquiring geographical location information, transaction data, customer relationship data and time sequence data of customers; constructing a customer ecosphere space model to analyze the geographic position information and the customer relationship data, obtaining space association strength and business association strength among customers, and generating a dynamic evolution matrix; performing real-time spatial clustering analysis on the transaction data, and identifying a client distribution hotspot area; based on the time sequence data and the geographic position information of the customer, constructing a customer space-time trajectory model, analyzing a space-time behavior pattern of the customer, and obtaining a space-time trajectory prediction result including a customer periodic behavior pattern and a space activity trend; and carrying out three-dimensional rendering on the spatial association strength, the business association strength, the customer distribution hotspot area and the spatio-temporal trajectory prediction result to obtain a three-dimensional visual interface.

The invention discloses an intelligent motion capture system and method of a multi-mode sensor, and the method comprises the steps: creating and installing links among an optical module, an inertia module, a touch module, a synchronization core module and a to-be-detected object; sending a synchronous starting pulse signal based on the synchronous core module, setting the synchronous starting pulse signal as a clock reference starting point, and starting the optical module, the inertia module and the touch module based on the clock reference starting point; performing data acquisition on a to-be-measured object based on the optical module, the inertia module and the touch module to obtain an object acquisition information data set; and creating an acquisition original timestamp based on the synchronous start pulse signal, creating a sensor network delay model to dynamically adjust the acquisition original timestamp to obtain a calibration timestamp, and analyzing and correcting the time offset of the object data acquisition information. The method and the device have the effect of improving the accuracy of intelligent motion capture of the multi-mode sensor.

The invention discloses a clock signal control soft PUF (Physical Unclonable Function) based on a coding circuit and an application method thereof, the clock signal control soft PUF comprises the coding circuit, a clock signal generator and a register module, the coding circuit can access externally input image information data, and the image information data is simultaneously used as an input excitation signal of the clock signal control soft PUF; the clock signal generator is used for generating three groups of clock signals of which the frequencies are sequentially increased, the clock cycles of the three groups of clock signals are all smaller than the critical path of the coding circuit, and each group of clock signals is an overclocking clock signal which can enable the coding circuit to be in an abnormal working state; if the external input image information data is accessed, the internal data transmission is abnormal, and at the moment, the coding circuit generates an output signal irrelevant to the input image information data and outputs the output signal to the register module for storage; the method has the advantages of being high in safety and low in resource consumption.

The invention discloses a trusted data space privacy protection method and system based on zero knowledge proof, and belongs to the technical field of data processing.The method comprises the steps that real-time operation data of multiple data sources are collected through the Internet of Things, and a real-time operation data set and a data source information set are obtained; on the basis of the data source information set, zero-knowledge certification is conducted on the real-time operation data set according to real-time certification complexity, a certification data set is generated, and the real-time certification complexity is comprehensively analyzed and determined on the basis of safety perception information, the data privacy degree, the data anomaly degree and the data fluctuation degree; and carrying out encrypted storage on the proof data set by utilizing a block chain technology, and carrying out data sharing and data verification according to the encrypted storage data set. The technical problems that in the prior art, a dynamic privacy protection mechanism for multi-source data in the sharing process is lacked, so that sensitive data is prone to leakage, and credible verification is difficult to achieve are solved.