64 results about "Fuzzy data" patented technology

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 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 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 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 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 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 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 invention relates to the technical field of data processing, in particular to a voice control method for a robot, and the method comprises the steps: extracting an interaction process from an interaction database, carrying out the multi-source analysis of a control instruction, recognizing the instruction fuzzy data in the control instruction, calculating an instruction fuzzy representation value, and dividing the instruction execution tendency of the control instruction. Determining an instruction execution mode, in response to a division result of weak instruction execution tendency, calculating a hidden execution characterization parameter by determining an environment influence characteristic value and a target influence characteristic value and combining the instruction fuzzy characterization value, determining the hidden tendency and the instruction execution mode of the control instruction, obtaining an execution result, and performing data comparison verification. And the execution effective value of the control instruction is calculated to determine the validity of the instruction execution mode, and the interaction step is updated to optimize the robot interaction model. By optimizing the voice interaction model, the number of man-machine interaction times is reduced, the task completion time is shortened, and the execution efficiency is improved.

In a computer-implemented method for providing fuzzy data to a user, firstly, a user request to access data is received; then, an authorization level associated with the received request is identified. Next, fuzzy data corresponding to the received request is accessed within a protected enclave. The accessed data is fuzzified using a fuzzing algorithm that generates a fuzziness level compatible with the identified authorization level. Finally, the accessed fuzzy data is provided to the user from the protected enclave. Related system and computer program products are also disclosed.

The application discloses a control method for switching of an excitation power supply of a superconducting phase modifier. The method includes sampling actual current and reference current of an excitation winding of the superconducting phase modifier, obtaining a current error through difference, determining an output mode through a fuzzy controller, and switching a circuit. The fuzzy controller includes a fuzzy data set and a fuzzy rule set. The fuzzy data set includes a plurality of range intervals, and a partially overlapping interval is arranged between adjacent range intervals. The union of each fuzzy subset is a maximum error limit of the current error. The fuzzy rule set includes a plurality of judgment rules. The judgment rules determine the output mode according to whether the value of the current error falls within the detection value of the corresponding range interval. Through the "soft" decision of the fuzzy logic, the mode is prevented from being frequently switched due to small error fluctuations, the switching loss of the power device is significantly reduced, and the system reliability and service life are improved.

The invention discloses a power network flow monitoring and early warning method based on gray measurement and calculation, and relates to the technical field of network security. Acquiring network communication data of a power grid target area in real time and preprocessing the network communication data to obtain initial network data; performing feature extraction on a data packet in the initial network data to obtain a first feature vector; inputting each first feature vector into a first preset model for detection to obtain a first feature tag, and screening the initial network data according to the first feature tag to obtain fuzzy data; and inputting the fuzzy data into a second preset model for detection to obtain an abnormal detection probability, and performing early warning according to the abnormal detection probability. Power grid network communication data are obtained in real time and preprocessed, data packet features are extracted to obtain a first feature vector, fuzzy data are preliminarily screened out through a lightweight model, then a high-precision model is utilized to carry out accurate detection and early warning on the fuzzy data, and the real-time performance and accuracy of power grid anomaly detection are effectively improved.

The invention relates to the technical field of temperature control, and discloses a friction welding equipment temperature control method which comprises the following steps that an intelligent temperature control module is arranged, the intelligent temperature control module is arranged on friction stir welding equipment, and the intelligent temperature control module comprises a temperature sensor array, an intelligent controller and a cooling medium flow speed regulator. According to the friction welding equipment temperature control method, the control method combining fuzzy control, the neural network and conventional PID is adopted, the fuzzy data technology is used for enhancing the data processing capacity of the neural network so that the neural network can concentrate on fuzzy information, the neural network technology is used for improving fuzzy rules in a fuzzy control system, and the fuzzy control efficiency is improved. The new control method formed by combining the fuzzy theory control and the neural network control is called fuzzy neural network control, the advantages of the fuzzy theory control and the neural network control are integrated, and the data processing capacity and the self-learning capacity are high.

The invention relates to the technical field of information atlas optimization, in particular to an information atlas optimization system based on industrial chain digital processing, and the logic architecture of the system comprises a data layer, an atlas layer and an application layer. Parallel processing of data quality verification and atlas generation is achieved, the fuzzy data influence analysis unit provides dynamic monitoring capacity and helps to identify potential risk points in a supply chain, the intelligent management unit is applied to execute targeted management and control based on quantitative analysis results, and the self-adaptive capacity of the system to industrial chain fluctuation is enhanced.

The invention discloses a food detection sample management method and system based on big data analysis, particularly relates to the technical field of food detection data processing, and is used for solving the problem that in an existing sample management big data platform, unstructured text semantics are fuzzy, and the intelligent retrieval and knowledge mining ability is insufficient due to the lack of deep semantic association among data. The method comprises the following steps of: acquiring sample full-process data containing structured and unstructured data, judging a semantic fuzzy condition of the unstructured data by adopting a text similarity algorithm, and performing sequence analysis and semantic completion on a fuzzy text by utilizing a hidden Markov model in combination with space-time correlation characteristics so as to generate a semantic clarification result; and then a semantic association rule is established and optimized based on the semantic clarification result and the structured data, and finally a food detection sample information knowledge graph is constructed according to the optimization rule, so that intelligent semantic retrieval and deep knowledge mining of the sample information are realized.

The application relates to a facial expression recognition method based on a capsule network and data repair, and comprises the following steps: acquiring facial data, inputting the facial data into a trained recognition model, and obtaining a recognition result, wherein the training process of the recognition model is as follows: acquiring facial training data sets, pre-processing and calibrating images in the facial training data sets, inputting the images into a ResNet18 capsule network to obtain feature vectors of the images, the ResNet18 capsule network comprises a first convolution block Conv1_x, a second convolution block Conv2_x, a third convolution block Conv3_x and an 8D capsule convolution layer; and inputting the feature vectors into a data self-repairing module to perform data repair, wherein the data self-repairing module comprises a self-attention importance weighting module, a rank regularization module and a label repair module. Compared with the prior art, the application has the advantages of accurate recognition of fuzzy data and the like.

The invention relates to the technical field of data processing, in particular to a semantic enhanced storage and distributed retrieval system for unstructured data, which collects target unstructured data through a data collection module, encodes the target unstructured data through a semantic analysis module, and stores the encoded target unstructured data into a database. The method comprises the following steps: acquiring target unstructured data, performing fuzzy data monitoring through a semantic enhancement module, performing data fragmentation storage through a semantic storage module, and acquiring a distributed parallel retrieval scheme through a distributed retrieval module. And semantic analysis, semantic enhancement and data storage are performed according to the target unstructured data, and a distributed parallel retrieval scheme is acquired in real time, so that the target unstructured data can be efficiently stored and retrieved while the semantics of the target unstructured data is enhanced.

The application belongs to the technical field of data processing, and particularly relates to a data processing method and device of a neural network simulator and a terminal, wherein the method comprises the following steps: acquiring instruction data; carrying out data of a source end to a destination end in a transaction level carrying manner according to first carrying parameters carried by a first carrying instruction; carrying data of the destination end to a cache according to second carrying parameters carried by a second carrying instruction; if the cache is not empty, performing the granularity operation based on the data in the cache to obtain a period level data processing result; the period level accurate instruction operation and the transaction level fuzzy data carrying are realized, the instruction of the neural network simulator can be calculated according to the period level, the consistency and accuracy with the hardware are maintained, the period level dependence of the data carrying is optimized, and the complexity of the neural network simulator is reduced.

The invention discloses an intelligent path planning method based on a multi-resolution region segmentation model, and the method comprises the steps: firstly carrying out the obstacle recognition and spatial feature extraction based on a collected environment map image, and constructing an obstacle position coordinate database; secondly, based on the obstacle position coordinate database, a recursive quadtree method is adopted to carry out self-adaptive division on a planning area, and multi-resolution representation of the environment is established; and finally, simultaneously constructing two search trees from a starting point and an ending point, dynamically adjusting a sampling strategy based on leaf area size, obstacle density and depth information, performing path search, and outputting a path planning result. According to the method, the problems that traditional environment modeling information is fuzzy and data support is insufficient are solved, and unification of high efficiency, safety and integrity of path planning is achieved.

The application discloses a food detection sample management method and system based on big data analysis, and particularly relates to the technical field of food detection data processing, and is used for solving the problem of insufficient intelligent search and knowledge mining ability caused by unstructured text semantic ambiguity and lack of deep semantic association between data in the existing sample management big data platform; the problem is solved by the following steps: obtaining sample full-process data containing structured and unstructured data, using a text similarity algorithm to determine the semantic ambiguity of unstructured data, using a hidden Markov model combined with space-time correlation characteristics to perform sequence analysis and semantic completion on the ambiguous text to generate a semantic clarification result, then establishing and optimizing semantic association rules based on the semantic clarification result and the structured data, and finally constructing a food detection sample information knowledge graph according to the optimized rules, so as to realize intelligent semantic search and deep knowledge mining of sample information.