105 results about "Fuzzy data" patented technology

The invention discloses a big data privacy aggregation encryption method based on polymorphic fuzzy logic. The method comprises the following steps: S1, generating a to-be-encrypted data set containing privacy sensitive information; s2, constructing a fuzzy logic rule base based on polymorphic fuzzy logic; s3, generating a dynamically optimized fuzzy logic rule; s4, forming a fuzzy data set; s5, forming an optimized aggregated data block; s6, generating an encrypted data block, and generating index information corresponding to the encrypted data block; and S7, storing the encrypted data block and the index information to a distributed storage system, and dynamically controlling the access of the distributed storage system based on a data access permission rule, so that access users with different permissions can only obtain data matched with the permissions in the encrypted data block. According to the method, the privacy protection strength and efficiency are effectively improved, and a safer and more reliable solution is provided for a big data privacy protection scene.

The invention relates to the technical field of knowledge graph application, and discloses a knowledge extraction algorithm suitable for a knowledge graph in the intelligent operation and maintenance field, and the algorithm comprises the steps: obtaining historical record data in an operation and maintenance process as original data; carrying out key entity extraction on the original data by adopting an entity recognition model based on BERT-BiGRU-CRF, and carrying out synonym processing to carry out data enhancement to obtain a target entity; connecting the key entities to form triple information by adopting a relationship extraction method based on an artificial rule; establishing an operation and maintenance knowledge graph ontology model by utilizing a Protege ontology construction tool; and carrying out fusion disambiguation on fuzzy data and repeated data in the knowledge graph ontology model to obtain a target knowledge graph model. According to the method, key entities are extracted, synonym processing is carried out, data are enhanced, an ontology model is built, fuzzy and repeated data are fused and disambiguated, solid knowledge support is provided for fault diagnosis and predictive maintenance in intelligent operation and maintenance, and the efficiency and accuracy of intelligent operation and maintenance are improved.

The invention discloses an intelligent risk early warning management method for an information system, and the method comprises the steps: taking data obtained from an intelligent data source of a preset information system within a specified time as to-be-analyzed data; wherein the intelligent data source comprises a reference data source and the to-be-determined data; abnormal data and fuzzy data are obtained according to the consistency proportion, the reference data source and the to-be-analyzed data, the fuzzy data are screened based on the anomaly degree, and a screening result is added into the abnormal data; constructing a knowledge graph by adopting the intelligent data source based on a graph attention network, performing knowledge completion on the knowledge graph, obtaining a matched entity through the knowledge graph after knowledge completion according to the abnormal data, and performing causal tracing according to the matched entity to obtain tracing data; and constructing an intelligent risk early warning management model according to the traceability data, inputting to-be-managed data into the intelligent risk early warning management model, and outputting a management result.

The invention discloses a business district drawing method and device based on geographic space aggregation, and the method comprises the steps: carrying out the spatial density clustering of all network points in a to-be-drawn space, and obtaining a plurality of network point clusters; correspondingly generating a plurality of convex hulls based on the plurality of dot clusters; and performing boundary optimization on the convex hull according to the collected river network data, and determining the optimized convex hull as the business circle in the space to be drawn. According to the method, the problems of fuzzy boundary, data lag, poor strategy adaptability and the like in the traditional technology can be effectively solved, accurate definition of the contour of the business district and standardized output of grade division are realized, and then the efficiency of drawing the business district is improved.

The invention relates to the technical field of inventory order auditing management, in particular to a big data-based non-correspondence auditing system and method.The big data-based non-correspondence auditing system comprises a data preprocessing unit, a data preliminary matching unit and a non-correspondence auditing unit. The data preliminary matching unit receives and processes data by using a message queue, partitions, de-duplicates and sorts the data, puts the data into a to-be-queued queue after preliminary matching by using a hash table structure, the non-corresponding auditing unit extracts features from historical data, establishes a dynamic threshold model through kernel density estimation, weights and fuses a threshold according to a data traffic load, and finally obtains a data stream; the deep learning model processes multi-dimensional features to output a normal delay probability to discriminate fuzzy data, and finally, a postponing area is set to review the fuzzy data according to a dynamic period to determine a final type, so that efficient data processing and accurate auditing are realized, data consistency is greatly improved, and enterprise operation decision scientificity is powerfully guaranteed.

The invention discloses a multi-modal data fusion method and device, equipment and a medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining a multi-modal data fusion demand, and carrying out the type judgment of to-be-fused data after preprocessing in the data fusion demand; if the data type of the to-be-fused data is an accurate data type, performing data fusion processing on the to-be-fused data according to an information theory perspective to obtain a fusion result; and if the data type of the to-be-fused data is a fuzzy data type, cleaning the to-be-fused data into accurate data based on a DS evidence theory to obtain cleaned to-be-fused data, and then skipping to execute data fusion processing on the to-be-fused data according to an information theory perspective to obtain a fusion result. The performance of data fusion is improved in multiple aspects, and more accurate and reliable fusion results can be provided for data fusion application in different fields.

The invention relates to the technical field of artificial intelligence, discloses a multi-modal data-driven classroom teaching quality comprehensive evaluation method and system, and aims to solve the problems of low evaluation precision, high privacy risk and the like caused by difficult multi-modal data fusion standardization, insufficient analysis result interpretability and fuzzy data acquisition compliance in the prior art. The method comprises the following steps: executing privacy-protected multi-modal data acquisition and standardization on an edge device; constructing a cross-modal causal time sequence alignment model to generate a unified teaching event sequence; semantic embedding is generated in combination with the teaching knowledge graph and the graph attention network; fusing causal and semantic information through a causal graph attention network, and outputting a teaching quality comprehensive state vector; when the indexes are abnormal, a causal path is traced back, and an interpretable diagnosis report and a teaching strategy are generated. The system comprises corresponding functional units, and realizes closed-loop evaluation from data acquisition to strategy generation. According to the method, the accuracy, compliance and interpretability of evaluation are effectively improved.

The invention discloses a small and micro enterprise loan availability evaluation method, which belongs to the technical field of data processing, and integrates unstructured operation and semi-structured behavior data, establishes a dynamic data confidence evaluation matrix, and starts artificial recheck to guarantee data quality. And constructing a three-layer fuzzy membership function system, and generating a standardized score by using an improved interval type-2 fuzzy set and an alpha-cut algorithm. Defining an interest rate sensitive dimension, generating an initial interest rate hierarchy through an improved spectral clustering algorithm, and predicting operation fluctuation and adjusting parameters by using a double-layer LSTM (Long Short Term Memory). And through IPC cross validation, carrying out residual analysis on the fuzzy data and on-site collected data, generating a joint confidence interval through D-S evidence fusion, and evaluating loan availability and outputting a suggestion report on the basis of the joint confidence interval. According to the method, the existing evaluation data processing problem is effectively solved, evaluation scientificity, accuracy and comprehensiveness are improved, a reliable decision basis is provided for financial institutions, and accurate customer layering and product adaptation are assisted to be achieved.

The invention discloses a data cleaning method for ship information infrastructure fault diagnosis, and belongs to the field of data processing. The method specifically relates to a preprocessing method of ship information infrastructure monitoring data and a data cleaning method for distinguishing fault data and dirty data. Through data preprocessing, a consistent and complete data set is constructed for subsequent data cleaning. At the moment, abnormal values in the data set are divided into interference data and abnormal equipment states, the abnormal equipment state values have important significance for subsequent fault diagnosis, and the interference data can influence model training. According to the method, a mode of combining FP-Growth and DBSCAN algorithms is adopted, and the characteristics that cabin equipment is complex in coupling and one fault often generates derivative alarms are utilized, so that equipment fault points and interference points are reasonably distinguished. Through the ship information infrastructure fault diagnosis-oriented data cleaning method, the problems of fuzzy data quality and low model precision caused by application of traditional fault diagnosis to ship information infrastructures can be solved.

The invention discloses a power network communication flow hierarchical monitoring method and device, and relates to the technical field of flow monitoring. The method comprises the steps of obtaining a network data packet of a target area; executing the intelligent identification model on the network data packet to obtain an abnormal identification probability, and identifying the network data packet according to the abnormal identification probability to obtain a normal data packet, a fuzzy data packet and an abnormal data packet; performing fuzzy detection model detection on the fuzzy data packet to obtain a first abnormal text; taking data of the abnormal data packet as a second abnormal text, and performing text fusion on the first abnormal text and the second abnormal text to obtain an abnormal score; and performing grade division according to the abnormal score threshold, and generating a grade monitoring scheme. Through identification and anomaly monitoring, the data packet identification accuracy and anomaly detection comprehensiveness are effectively improved, and the network attack risk is reduced.

The invention discloses a multi-device key binding type data encryption storage system based on a national cryptographic algorithm, which comprises a dynamic management module for performing adaptive distribution and national cryptographic algorithm exchange through an edge node key pool based on an optimized initial key, and outputting a dynamic security key through an abnormal triggering update mechanism; the initial encryption module is used for partitioning initial data according to a fixed size, performing filling processing on the last block to obtain partitioned data, extracting data features from the partitioned data, constructing a sensitivity level evaluation model based on the data features to obtain an optimal sensitivity level, and performing encryption on the optimal sensitivity level; the fuzzy data sensitivity degree is converted into a computable quantitative index through a regularity model, so that subjectivity and randomness of manual judgment are avoided.

The embodiment of the invention provides a searchable encryption method, computer equipment, a storage medium and a computer program product, and relates to the technical field of searchable encryption. The method comprises the steps that a cloud server receives a data outsourcing request sent by a data provider; sending the keyword set ciphertext to a trusted execution environment; the trusted execution environment obtains a keyword set plaintext by using the keyword set key; the cloud server forwards the data retrieval request to a trusted execution environment; the trusted execution environment decrypts the combined ciphertext by using a private key in the first key pair to obtain a keyword to be retrieved and a re-encryption key; the trusted execution environment obtains to-be-obtained file data according to the to-be-retrieved keyword; the trusted execution environment obtains re-encrypted file data by using the re-encrypted key, and sends the re-encrypted file data to the data acquirer; and the data acquirer obtains the to-be-acquired file data by using the re-encryption key. According to the method, the data retrieval efficiency is improved, and fuzzy data search is supported.

The invention discloses a mountain railway tunnel green construction decision-making method based on CBR, and relates to the field of construction decision-making, and the method comprises the following steps: 1, integrating scene attribute systems of tunnel portal and tunnel shaft construction parts, obtaining case representations including construction scene attributes, attribute values and green construction measures, and generating a three-dimensional scene through Rhino software parameterization; 2, aiming at a case which is judged to be data fuzzy or missing due to special terrains or weather, based on a standard integrity case index, evaluating ambiguity coefficients of geological conditions, environment sensitive areas, construction machinery types, energy consumption indexes and material transportation path parameters in a case scene attribute system; a fuzzy data virtual filling mechanism is set, and the most similar data is searched from complete cases based on characteristic label matching for virtual replacement, so that data missing and fuzziness in a real scene are effectively dealt with, and the similarity between incomplete characteristic cases is accurately calculated instead of simply discarding the characteristic missing cases.

The invention provides a hospital intelligent operation management data processing method and device, equipment and a storage medium, and the method comprises the steps: obtaining heterogeneous data of hospital operation, carrying out the preprocessing of the heterogeneous data, obtaining an intermediate data set of the heterogeneous data, and storing the intermediate data set in a database; wherein the heterogeneous data comprises structured data and unstructured data of hospital operation and Internet of Things equipment data. The intermediate data set of the heterogeneous data is converted into cross-system fusion data, the fusion data is divided through a preset threshold value, divided data is obtained, and the divided data comprises short-term data, medium-term data and long-term data. And performing fuzzy processing on the divided data to obtain fuzzy data. When data interaction is carried out in different hospital areas, the corresponding data fuzzy rule is implemented according to the permission configuration of each access role to access the fuzzy data, so that the incidence relation and the integration capability of heterogeneous data can be improved, the safety performance of the data can be improved, and the intelligent operation management data of the hospital can be processed.

The application discloses a seat adjustment method and device, a storage medium, an intelligent seat and a vehicle, and relates to the technical field of intelligent cabins. The method comprises the following steps: performing fuzzy processing on environment perception data of the vehicle to obtain a fuzzy data set, wherein the environment perception data is obtained by a sensor array of the vehicle; predicting the fuzzy data set by using a preset random forest regression model to obtain a seat adjustment parameter corresponding to the environment perception data; and adjusting a seat state of the vehicle based on the seat adjustment parameter. Since the environment perception data is obtained in advance by the sensor array, and the fuzzy data set is predicted by using the random forest regression model after the fuzzy processing of the environment perception data, the seat adjustment parameter responding to the external environment change can be obtained. Therefore, the seat state of the vehicle can be automatically adjusted according to the environment perception data, unnecessary user operation is reduced, the distraction risk during driving is reduced, and the comfort and safety of the vehicle are improved.

The invention provides a bimodal image restoration method based on a diffusion model. The bimodal image restoration method comprises the following steps: S1, acquiring a bimodal fuzzy data set; s2, encoding and fusing the multi-modal data; s3, decoding processing is carried out; and S4, submerged space diffusion recovery. The invention aims to solve the problems of low efficiency, poor reconstruction quality and single data source of the existing image reconstruction technology in a complex scene.

Provided are a training method and device of a target detection model, and a target detection method. The training method comprises: obtaining a training sample set, comprising first and second training samples, the first and second training samples respectively comprising clear data and fuzzy data; inputting the first training sample into the target detection model; determining a first loss value of the target detection model according to an output value of the target detection model and the second training sample, using a first loss function; inputting the first loss value into a Gaussian mixture model; constructing a fuzziness matrix using the Gaussian mixture model, the fuzziness matrix being used to represent the fuzziness of the data in the first training sample and the second training sample; constructing a fuzzy loss function according to the fuzziness matrix, the fuzzy loss function being constructed to be able to distinguish between the clear data and the fuzzy data in the first training sample and the second training sample; constructing a second loss function according to the fuzzy loss function; and retraining the target detection model using the first and second training sample sets and the second loss function.

The application discloses a low-altitude slow target threat evaluation method and system based on an LSTM model combined with a KAN network, and the method steps are as follows: S1, obtaining low-altitude slow target pulse data in a set time period, and taking pulse data with a length of N as a sample for preprocessing; S2, preprocessing the pulse data in a fuzzy data mode; S3, constructing a model based on the LSTM model combined with the KAN network, and initializing parameters; S4, processing according to the credibility of data, inputting the processed data into the model constructed in step S3 for network training, and saving the feature extractor and the classifier weight obtained through the training; and S5, inputting the preprocessed low-altitude slow target pulse data to be tested into the model trained in step S4 for evaluation, and obtaining a low-altitude slow target threat evaluation result. The application can improve the threat evaluation accuracy of low-altitude slow small targets in a dynamic environment.

The technical scheme of the invention discloses an NFT-based data trusted space management and utilization method, which is characterized in that corresponding service data trusted spaces are constructed for service data, different types of service data correspond to different service data trusted spaces, and data are isolated among different service data trusted spaces. According to the method, the NFT smart contract is constructed for different business scenes and business data, so that data isolation and independent management of the different business scenes are realized, credible spatialization management of scene data is realized, and the problems of fuzzy data permission boundary, unauthorized access and the like in a traditional centralized management and unified account book mode are effectively avoided. According to the method, the NFT is innovatively introduced as a management unit of the business data, right confirmation, right control and transaction circulation of the business data are unified into the NFT smart contract for management, and right confirmation and dynamic control of the data are better realized.

The invention discloses an MEC task unloading method based on opportunity constraint, and relates to the technical field of cloud computing, and the method comprises the steps: building a time delay energy consumption model, a fuzzy privacy model and an opportunity constraint model through the fuzzy data input quantity and the fuzzy CPU cycle number of a fuzzy to-be-processed task; establishing a joint optimization model through a time delay energy consumption model, a fuzzy privacy model and an opportunity constraint model; solving the joint optimization model to obtain a task unloading strategy and a network selection strategy; distributing the fuzzy to-be-processed task to an unloading position according to a task unloading strategy; and performing resource transmission according to the network selection strategy. According to the method, the joint optimization model is solved through the designed unloading algorithm, and the optimal solution about the task unloading position strategy and the network data transmission selection strategy is obtained. And distributing the tasks to the corresponding unloading positions. And according to the network data transmission selection strategy, distributing task data to different networks according to the proportion so as to transmit the data.

The invention relates to the technical field of corner reflector adaptive adjustment, in particular to a corner reflector adaptive adjustment method, system and device based on multi-source radar remote sensing data. Comprising the following steps: S1, acquiring an installation position of a corner reflector in a terrain lifting complex scene of a power transmission line, extracting terrain parameters according to the installation position, and extracting an interference type according to the terrain parameters; the method accurately adapts to the specific requirements of the terrain uplift complex scene of the power transmission line, achieves the accurate recognition of the terrain uplift complex scene, the comprehensive extraction of terrain parameters and the directional judgment of interference types through the access of power transmission line management end data and a digital elevation model, combines the complementary advantages of multi-source radar data, and achieves the accurate recognition of the terrain uplift complex scene. Quantitative inversion and prediction of interference parameters are completed, the problems of fuzzy interference identification and insufficient data support in complex terrains in the prior art are effectively solved, and the stability of radar echo signals and the accuracy of pole and tower state monitoring data are remarkably improved.

The invention discloses a host vulnerability scanning security protection system under flexible gray measurement and calculation, and relates to the technical field of vulnerability detection. Obtaining the log data packet and importing the log data packet into a preset anomaly detection model to obtain an anomaly detection score; different data sets are obtained through division according to the anomaly detection threshold value; the data set comprises an abnormal data set, a fuzzy data set and a normal data set; performing a preliminary exception division module and a target exception division module on the fuzzy set to obtain a target exception data packet set; merging the target abnormal data packet set into an abnormal data set to extract a data log packet of an abnormal level, querying a preset script library to obtain a corresponding test script, and executing the test script to generate a corresponding test case; and executing the test case to detect vulnerabilities of the target host, and performing corresponding security protection measures on the vulnerabilities. According to the method, the fuzzy level logs are re-divided through the log data packet length, the vulnerability detection speed and efficiency are improved, the security vulnerabilities can be found in time, and corresponding security protection measures can be taken.

The invention discloses a BIM shield monitoring system based on fuzzy data query and a nearest neighbor algorithm. According to the technical scheme, the BIM shield monitoring system is characterized by comprising a data acquisition module, a data preprocessing module, a fuzzy data query module, a nearest neighbor algorithm module, a path prediction module and a BIM platform integration module; the data acquisition module is used for acquiring tunneling data, including tunneling speed, position and direction, of the shield tunneling machine in real time; the data preprocessing module is used for preprocessing the collected tunneling data, including de-noising processing and abnormal value processing, so as to improve the accuracy and reliability of the data; according to the system, through combination of modular design and an advanced algorithm, comprehensive monitoring of the tunneling process of the shield tunneling machine is realized. All the modules are in close cooperation, accurate and efficient monitoring and decision support is provided for shield construction from data acquisition and preprocessing to fuzzy query, prediction and visual display, and the safety, efficiency and intelligent level of shield engineering are improved.

The application discloses a fuzzy decision tree construction method and device, equipment and a computer storage medium. In the case that a construction data set is acquired, a first mapping reduction model is used to perform mapping reduction processing on data in the construction data set, a plurality of fuzzy partitions in the construction data set are obtained, a second mapping reduction model is used to perform mapping reduction processing on data of each fuzzy partition in the plurality of fuzzy partitions, a plurality of fuzzy sub-partitions of each fuzzy partition are obtained, a root node set and a sub-node set are determined according to the obtained fuzzy partitions and fuzzy sub-partitions, and finally, a fuzzy decision tree of the above construction data set is constructed based on the root node set and the sub-node set. In this way, through twice mapping reduction processing, a fuzzy decision tree classifier can be distributed constructed from big data based on distributed parallel computing, data classification processing of a data set with high dimension and large data quantity is realized, and the efficiency of inductive classification of related fuzzy data is improved.

The invention relates to the technical field of telemedicine, and discloses a teleoperation intelligent auxiliary system, which comprises a state monitoring module for monitoring a fuzzy state of a target state parameter in a teleoperation process; the dynamic modeling module is used for dynamically constructing a fuzzy Petri net for describing a remote operation process; the intelligent decision module is used for generating an intelligent emergency decision according to the fuzzy state of the target state parameter and a fuzzy rule base of a fuzzy Petri network under the condition of determining that the emergency event occurs, and the fuzzy rule base describes a logic relationship between the fuzzy state of the target state parameter and an emergency action; and the execution and feedback module is used for generating a first control instruction of the surgical equipment according to the intelligent emergency decision, enabling the surgical equipment to execute an emergency action corresponding to the intelligent emergency decision, and feeding back an execution result of the first control instruction to the dynamic modeling module so as to update the fuzzy Petri network. Fuzzy data in a remote operation can be processed in real time, emergency situations can be quickly responded, the operation process is optimized, and the safety and effectiveness of the operation are improved.

The invention relates to the field of road cavity detection, and discloses a road cavity and underground disease body collapse hidden danger detection device and detection method.The road cavity and underground disease body collapse hidden danger detection device comprises a movable bottom plate, a supporting plate is installed on the movable bottom plate, a rotating disc is movably installed in the middle of the supporting plate and rotates in the supporting plate, and a transmission shaft is installed at the bottom of the rotating disc; a first gear is fixedly installed on the transmission shaft, a rotating mechanism is arranged on one side of the first gear, a hydraulic rod is installed on the rotating disc, a detection arm is installed at the top end of the hydraulic rod, an electromagnetic wave transmitting module is installed at one end of the detection arm, and the electromagnetic wave transmitting module comprises an electromagnetic wave transmitting body and a broadband transmitting antenna. Through the arrangement of the electromagnetic wave transmitting module, the receiving sensor and the data preprocessor, a set of cooperative linkage underground anomaly detection system is constructed, and the problems of large signal interference, fuzzy data analysis, anomaly positioning deviation and the like in the traditional detection technology are effectively solved.

The invention discloses a multi-dimensional risk management maturity model construction method and system, and the method comprises the steps: collecting monitoring data and risk management data of a preset power system, and carrying out the preprocessing of the monitoring data and the risk management data; performing risk identification on the monitoring data to obtain risk data, acquiring a risk type through the risk data by adopting a prior algorithm, and performing power prediction through the risk management data to obtain management and control data; performing dynamic causal matching on the management and control data and the risk type to obtain a matching degree, management data and fuzzy data, performing longitudinal comparison evaluation according to the historical data and the fuzzy data to obtain an optimization degree, and adding the fuzzy data of which the optimization degree is greater than an optimization threshold value into the management data; and constructing a maturity evaluation function according to comprehensive comparison of transverse and longitudinal maturity of the management data, and constructing a power risk management maturity model according to the maturity evaluation function.

The invention discloses a regional crowdsourcing ionospheric scintillation monitoring and early warning method and system based on low-cost equipment, and belongs to the field of ionospheric scintillation monitoring, and the method comprises the steps: recording reference station observation data and ISMR data as an accurate data set; recording observation data acquired by the low-cost equipment as a fuzzy data set; performing flicker intensity assignment of ROTI and GFTI by using ISMR data in the accurate data set; interpolation is carried out on the flicker index of the position where the puncture point coordinates in the fuzzy data set are located by adopting common Kriging interpolation; subtraction is carried out on the interpolation result and the index of the corresponding position in the fuzzy data set, and an abnormal difference value in the subtraction result is calculated; and dividing the obtained ionospheric scintillation detection result according to a scintillation generation range, and analyzing a possible scintillation generation position so as to carry out early warning. A large amount of low-cost equipment observation data in the area is introduced to fill the blank area of professional equipment monitoring data, and the monitoring data density and the resolution of the monitoring result are improved.

The application discloses a data classification and grading method and system based on cooperation of three-level intelligent engines, relates to the technical field of data security, and specifically relates to a technology for classifying and grading data. The application solves the problems of low processing efficiency, insufficient identification accuracy for semantic ambiguous data, incapability of self-evolution of a system, and incapability of simultaneously completing data classification and grading determination in a set of systems in the prior art. The application discloses a data classification and grading method and system based on cooperation of three-level intelligent engines. The method comprises the following steps: a rule engine determines a regular matching fast screening; a semantic engine supplements blind data semantics for undetermined data; and an LLM reasoning engine performs deep reasoning verification on the remaining data. Classification results are output at each level, and grading determination is performed, so that classification and grading integration is realized. After LLM results are confirmed, features are extracted to generate candidate rules, which are fed back to the rule engine, so that system closed-loop evolution is realized. The application is suitable for fields of financial data governance, medical data security, government data management and the like.

The present application discloses a multimodal data fusion method, device, equipment and medium, which relates to the field of data processing technology, including: obtaining multimodal data fusion requirements, and judging the type of the pre-processed data to be fused in the data fusion requirements; if the data type of the data to be fused is an accurate data type, then the fused data is treated from the perspective of information theory to perform data fusion processing to obtain a fusion result; if the data type of the data to be fused is a fuzzy data type, then the data to be fused is cleaned into accurate data based on DS evidence theory to obtain the cleaned data to be fused, and then jump to perform data fusion processing from the perspective of information theory to treat the fused data to obtain a fusion result. The performance of data fusion is improved in many aspects, and more accurate and reliable fusion results can be provided for data fusion applications in different fields.