43results about How to "Rich data base" patented technology

The invention relates to the technical field of data analysis and evaluation, in particular to a color fastness evaluation method and system, and the method comprises the following steps: setting test conditions to simulate external factors encountered in the actual use or processing process of textiles; under the simulated test condition, treating the textile sample for a preset time; acquiring a multispectral image of the textile by adopting a multispectral imaging technology, and capturing color change of the textile; developing a self-adaptive image enhancement algorithm, and performing preprocessing; comparing spectral information of the multi-spectral image of the textile before and after processing based on chromatic aberration evaluation analysis so as to quantify the color fastness; analyzing the multispectral image after image enhancement by using a deep learning technology, extracting color attenuation features, and comprehensively evaluating a color fastness score; according to the color fastness evaluation method, basic color difference information is considered, advanced features such as textures, edges and local color modes of the textile are further included, and the accuracy and reliability of color fastness evaluation are remarkably improved.

The invention discloses an automatic conversion analysis method and system based on an implicit common cause fault model, and relates to the technical field of power system fault analysis, and the method comprises the steps: obtaining and marking power equipment, forming a plurality of equipment sets, setting a monitoring period for operation monitoring, and storing data into an operation record library; extracting feature data of the equipment, calculating a correlation index between the equipment according to the category of the equipment, and calculating the correlation between the same category of equipment and different categories of equipment by adopting different strategies; utilizing an implicit common cause fault model to calculate the probability that the reference equipment and other equipment are abnormal at the same time under each abnormal cause, including an independent failure probability, a common cause failure probability and a comprehensive probability; and according to the comparison of the comprehensive probability and a preset threshold value, the monitoring period of the equipment exceeding the threshold value is adjusted, and according to the abnormal reason, the monitoring period of all the associated equipment is also adjusted, so that the effective monitoring and fault prevention of the power system are realized.

The invention discloses a clinical test data analysis system based on a large model, which comprises a data acquisition module, a preprocessing module, a training module, an analysis module and an output module, data acquisition is efficient and supports multiple modes, preprocessing adopts an intelligent algorithm, model training is advanced and has efficient indexes, analysis covers multiple aspects and has evaluation indexes, and result output is diversified and supports customization. The invention further comprises a corresponding data analysis method, related equipment and a medium, and each link is provided with an innovative technology and an evaluation mode to guarantee the performance and the quality; the system realizes efficient data acquisition and integration, intelligent processing and quality improvement, constructs a powerful analysis model, provides various visual result display and interpretation, has real-time updating and personalized service capabilities, can accurately evaluate the curative effect and safety, provides a scientific basis for clinical decisions, assists in improving the efficiency and accuracy of medical data analysis, and has a wide application prospect. Different user requirements are met, and development of the medical industry is promoted.

The invention discloses an intensive culture precise feeding control method based on fuzzy-MPC control, and relates to the technical field of aquaculture intelligent control, and the method comprises the steps: employing a sensing system, collecting multi-source sensing data including fish activity images, healthy growth states and water quality environment data in real time, and forming a comprehensive state data set; and based on the acquired multi-source sensing data, constructing an MTL-LSTM-SAT multi-task learning model. According to the method, the multi-task learning model fusing the LSTM and the soft attention mechanism is constructed, the long-term dependency relationship and dynamic association among the water quality, the feeding and the fish state can be captured, the attention mechanism enables the model to be automatically focused on a key time step, the perception ability of the water quality fluctuation and the feeding influence stage is enhanced, and the accuracy of the water quality fluctuation and feeding influence stage is improved. Meanwhile, double-task collaborative prediction of the growth speed and the disease proportion is achieved through the shared feature layer, a prospective state sequence of the future 7 days is output, and a quantitative and reliable prediction basis is provided for feeding decision making.

The invention relates to the technical field of private domain traffic, in particular to a private domain traffic guiding method and system based on collaborative filtering, and the method comprises the following steps: S1, collecting user behavior data: collecting the behavior data of a user in a private domain platform in real time through client equipment; s2, constructing a user portrait: extracting the interest preference of the user, generating a user interest label, and forming the user portrait; s3, interest extension and cross-domain recommendation: constructing a relation graph between the user and the content, identifying association between the user interest node and the cross-domain content, calculating the edge weight between the user and the content node, and extending the interest boundary of the user in the private domain traffic; and S4, recommended content pushing: pushing cross-domain recommended content to the user, and meanwhile, optimizing the display sequence of the content in combination with the situation information of the user. According to the invention, the diversity and personalization of the recommended content can be enhanced, and the user experience and the diversity of the recommendation effect are further improved.

The present invention discloses a method for intelligently generating threat intelligence aggregation and attack anomaly features based on a large language model, including obtaining network security intelligence of an open source threat intelligence community platform in real time; optimizing the prompt words of the large language model according to predefined multidimensional feature attributes, and extracting multidimensional feature attributes of threat intelligence from network security intelligence; constructing a mesh graph relationship structure database of threat intelligence according to the extracted multidimensional feature attributes of threat intelligence; constructing a threat intelligence knowledge graph according to the mesh graph relationship structure database; querying the threat intelligence knowledge graph, marking attack behavior features, connecting attack behavior feature points, and generating an attack behavior feature subgraph. The method of the present invention enables the security team to quickly respond to emerging threats and improves the ability to respond to network security threats.

The utility model discloses a robot sensor platform with two degrees of freedom and a damping function. The robot sensor platform comprises a damping base, a spherical hinge, a spring, an upper stepping motor, a damping connecting plate, a sensor support, a sensor clamping jaw and a lower stepping motor. The damping device is characterized in that the damping base and the damping connecting plate are connected through a spherical hinge and a spring, and the spherical hinge is a telescopic spherical hinge with damping and is matched with the spring to achieve double damping; the upper stepping motor is fixedly installed on the damping connecting plate, and an output shaft of the upper stepping motor is connected with the sensor support. One side of the sensor support is fixed to a lower stepping motor through a groove, and an output shaft of the lower stepping motor is connected with a sensor clamping jaw. According to the platform, the operation range of the sensor is increased through the two stepping motors, and the precision is ensured while the degree of freedom of the sensor is increased through buffering and damping of the damping assembly.

The present invention relates to the field of data processing technology, and specifically provides a data mining method and deep learning system applied to smart industrial services. The present invention innovatively extracts material flow state characteristics and equipment operation quantitative characteristics by deeply mining the production factors in the automated production line image data, providing a rich data basis for the optimization of production line efficiency, and by retrieving the initial screening production line optimization annotation images that are compatible with these characteristics, two initial screening sets are formed to ensure the pertinence and feasibility of the optimization plan. At the same time, the concept of common weights for efficiency optimization is introduced to comprehensively evaluate the value of each initial screening annotation image for the current production line efficiency optimization, so as to accurately mine the target production line optimization annotation image for direct guidance of the efficiency optimization and upgrade of the industrial production line, which not only improves the accuracy and efficiency of the optimization process, but also brings significant economic benefits and competitiveness improvement to smart manufacturing enterprises.

The invention discloses a strip network railway base station data processing method and system. The method comprises the following steps: obtaining observation data of a plurality of IGS base stations; obtaining observation data of a plurality of standard reference stations in a preset range around the strip network railway reference station, wherein the standard reference stations comprise a national station, a provincial station and a Chinese tectonic environment monitoring network station; and based on the observation data of the IGS reference station, combining the observation data of the standard reference station to perform hierarchical calculation on the plurality of railway reference stations to obtain space coordinate data of each railway reference station. Observation data of an IGS base station and standard base stations such as surrounding national stations and provincial stations are obtained, and a rich data basis is provided for railway base station coordinate measurement and calculation. A hierarchical resolving mode is adopted, the coordinate precision is improved, error accumulation is avoided, and the result reliability is enhanced; the method can adapt to different precision requirements and complex environments, considers local differences, and meets the requirement of a strip network railway for high-precision coordinate data.

This invention discloses a method for detecting the aging state of asphalt pavement by integrating hyperspectral imagery and deep learning, relating to the field of deep learning technology. The method includes: acquiring and preprocessing hyperspectral images of the target road section to obtain surface reflectance images; dynamically identifying and masking vehicles and their shadows in the images using a spectral angle matching method to extract effective pixel spectral datasets; performing hybrid pixel decomposition on the effective pixel spectral datasets using a non-negative matrix factorization algorithm to obtain asphalt endmember spectra and aggregate endmember abundance maps; inputting the asphalt endmember spectra into a pre-trained oxidation degree assessment model to output asphalt oxidation degree values; overlaying the aggregate endmember abundance map with near-infrared band images and inputting it into a pre-trained exposure rate segmentation network to output a binary mask of aggregate exposure areas and calculate the aggregate exposure rate. This method solves the technical problem that existing technologies cannot quickly and non-destructively detect the aging state of asphalt pavement.

The embodiment of the invention provides an insurance recommendation text generation method and device and related equipment, and belongs to the technical field of large language models. The method comprises the following steps: acquiring historical claim settlement data and external event data of a target object, and constructing multi-source fusion data according to the historical claim settlement data and the external event data; inputting the multi-source fusion data into a pre-trained text generation model, so that the text generation model generates an original recommendation text according to the multi-source fusion data; comparing the multi-source fusion data with the original recommended text to obtain deviation data; inputting the deviation data and the original recommended text into a text generation model, so that the text generation model corrects the original recommended text to obtain a target recommended text; and pushing the target recommendation text to the target object. According to the embodiment of the invention, the highly personalized insurance recommendation text can be generated.

The invention discloses an unmanned aerial vehicle water-air dual-medium adaptive communication optimization method and related equipment. The method comprises the following steps: acquiring unmanned aerial vehicle environment sensing information; determining the current communication medium state of the unmanned aerial vehicle according to the unmanned aerial vehicle environment sensing information; the communication medium state comprises an air state, an underwater state and a water-air interface crossing state; generating a target communication strategy according to the current communication medium state of the unmanned aerial vehicle, and controlling the unmanned aerial vehicle to execute the target communication strategy; the communication strategies comprise an air communication strategy, an underwater communication strategy and a water-air dual-medium communication strategy; and dynamically monitoring the communication link performance index of the unmanned aerial vehicle, and dynamically optimizing the target communication strategy in combination with the environment change information and the communication link performance index. The technical problem that the communication performance of the unmanned aerial vehicle on the water-air interface is sharply reduced can be effectively solved, smooth transition and high-reliability maintenance of the communication link in the double-medium environment are achieved, and the method can be widely applied to the technical field of unmanned aerial vehicle communication.

The invention relates to the technical field of karst geological exploration, in particular to an intelligent detection method and system for karst action of a pumped storage power station in a karst area, and the method comprises the steps: firstly constructing a large-range sample database covering multiple types of karst areas; training a Transform basic model based on the database, and performing adaptive fine tuning through a target area lightweight sample to obtain an area model capable of automatically outputting a DBSCAN parameter and a transient electromagnetic low resistance threshold adaptive to the area; meanwhile, mining an earth surface-underground feature association rule based on a sample database to construct an association library, training a GCN model according to the association library, and performing fine tuning through a target region fine tuning data set to obtain a target GCN model; and finally, identifying earth surface and underground karst nodes of the target area by using parameters output by the area model, and inputting the earth surface and underground karst nodes into the target GCN model to output a communication state between the nodes. According to the method, the cross-regional parameter adaptive precision and efficiency of karst detection are improved, and accurate identification of the earth surface-underground karst communication system is realized.

The present application relates to the technical field of fluid measurement, and discloses a rock fracture seepage velocity detection method and system based on optical fiber sensing, which comprises the following steps: constructing a sensing detection network based on optical fibers and auxiliary heat sources of a sensing monitoring network; injecting a thermal pulse into rock fractures based on the sensing detection network to obtain a thermal disturbance field; exciting the rock fractures based on the thermal disturbance field; demodulating sensing optical fiber signals to obtain multi-physical field coupling response data; jointly analyzing the multi-physical field coupling response data based on a multi-physical field coupling forward model to obtain seepage characteristic strain components; performing velocity inversion on the seepage characteristic strain components based on the spatial distribution of the multi-physical field coupling forward model to obtain fracture seepage velocity; and performing difference integration on the fracture seepage velocity to generate seepage velocity distribution monitoring results of the rock fractures.

This invention provides a water level monitoring method, system, electronic device, and storage medium, relating to the field of ship safety monitoring technology. The method includes: acquiring water level status information uploaded by multiple water level monitoring terminals located at the bottom of the ship's fish hold; performing fusion analysis on the water level status information from each monitoring terminal to obtain water level distribution data at the bottom of the fish hold; and performing tiered alarm operations based on the water level distribution data. This invention, through a coherent design of multi-point monitoring, fusion analysis, and tiered response, systematically solves the problems of decreased monitoring accuracy and false alarms caused by ship motion interference, achieving more accurate and reliable monitoring of fish hold water levels.

The invention relates to the field of software performance monitoring, and provides an automatic performance monitoring method and device for a vehicle-mounted multimedia application and a storage medium. The method comprises the following steps: collecting multi-dimensional performance parameters of a target application during operation in real time; based on the multi-dimensional performance parameters of the target application during operation, judging whether a preset triggering condition is met or not; when a preset triggering condition is met, automatically starting a plurality of data acquisition channels in parallel to obtain multi-modal data related to the performance event; performing association analysis based on a unified timestamp on the acquired multi-modal data and the multi-dimensional performance parameters to construct a complete data evidence chain of the performance event; and automatically generating a performance analysis report containing performance bottleneck positioning information and optimization suggestions based on the performance event cause and influence range determined by the correlation analysis. According to the technical scheme, accurate and efficient positioning and automatic diagnosis of the vehicle-mounted application performance problem are achieved.

The invention relates to the technical field of wireless relay station management, and discloses a wireless relay station remote monitoring and management system, which comprises edge sensing units which are deployed at local positions of wireless relay stations and are configured to acquire multi-source operation state data in real time; the state judgment unit is configured to judge the operation state of the wireless relay station according to the radio frequency signal parameter, the equipment temperature and the power supply parameter, and judge the environment state according to the environment sensing data; the state analysis unit is configured to perform periodic stability analysis based on the operation state and the environment state within the preset time length to obtain a stability evaluation value; and the remote monitoring center is connected with the state judgment unit and the state analysis unit, and the remote monitoring center is configured to construct a health portrait of the wireless relay station. According to the invention, the refinement degree of operation monitoring of the wireless relay station and the safety and reliability of system operation are integrally improved.

The invention relates to the technical field of electric power computing power scheduling, and discloses an electric power cross-domain computing power elastic scaling scheduling system supporting a creative GPU (Graphics Processing Unit). The system receives a power cross-domain computing power scheduling request, and searches a matched historical computing power use record set from a historical computing power database according to the request. And carrying out load category division on the historical record set to generate historical computing power record subsets of a plurality of load categories. Computing power feature core calculation is performed on the subsets to obtain a plurality of computing power feature core points and associated time intervals; and using the associated time intervals as the time range of a plurality of scheduling analysis layers, performing multi-scale scheduling feature analysis on the computing power monitoring data sequence of the current power cross-domain within the specified time range through the plurality of set scheduling analysis layers, and outputting a plurality of computing power scheduling feature sets. And carrying out cross-scale fusion calculation on the feature sets to obtain a fusion computing power scheduling feature set.

The present application relates to the technical field of image recognition, and specifically discloses a vehicle part recognition method and system, an electronic device and a storage medium. The technical scheme of the present application generates an enhanced image dataset by acquiring multi-angle vehicle images and performing image enhancement processing, and divides the fine-tuning process of a pre-trained visual model into three stages to progressively unfreeze and update the top, middle and bottom layer parameters in sequence. Performance evaluation and threshold determination are introduced during stage conversion, solving the problems of excessive consumption of computing resources and excessively long training time caused by full parameter fine-tuning relying on large-scale datasets in the prior art, and the problems of insufficient recognition accuracy and generalization ability caused by the difficulty of pre-trained models to adapt to multi-angle complex backgrounds. The present application reduces the model training cost, shortens the model training cycle, and improves the vehicle part recognition accuracy and generalization ability.

The invention provides a digital twinning-based power equipment comprehensive monitoring and predictive maintenance system and method. The system comprises a physical layer, a data acquisition and transmission layer, a digital twinning model layer, a data analysis and intelligent early warning layer and a decision support and visualization layer. The method comprises the steps of data acquisition, model driving, simulation, calculation evaluation, early warning and feedback optimization. According to the method, equipment faults are predicted in advance by means of a digital twinning technology and a machine learning algorithm. The system can accurately analyze the health state of the equipment and predict the remaining service life of key parts by continuously monitoring equipment operation parameters such as vibration, temperature and pressure and combining historical data and an equipment operation mechanism. Once the health index of the equipment is lower than the preset threshold value or the remaining service life is close to the safety margin, the system immediately and automatically gives an early warning, and maintenance personnel can arrange maintenance work in advance, so that unplanned shutdown caused by sudden failure of the equipment is avoided, and the production continuity is ensured.

This invention belongs to the field of forestry information technology, specifically relating to a high-precision segmentation method for individual bamboo trees in moso forests based on the fusion of UAV and ground-based LiDAR. This method addresses the common problems of over-segmentation and under-segmentation in complex bamboo forest scenarios caused by the single data source leading to incomplete canopy information, inaccurate acquisition of understory topography and bamboo culm structure, and the lack of consideration for bamboo growth mechanisms in traditional algorithms. Through collaborative acquisition of LiDAR data from multiple platforms, a registration algorithm constrained by the vertical structure of bamboo culms is used to achieve accurate point cloud fusion, forming an integrated 3D model of "canopy-bamboo culms-topography". An innovative comprehensive clustering algorithm is proposed, integrating canopy geometric attributes and a canopy growth space competition model. This quantifies the bamboo growth competition relationship into dynamic constraints, intelligently defining individual tree boundaries, effectively solving the over-segmentation and under-segmentation problems in the segmentation process, and significantly improving the accuracy and ecological rationality of individual bamboo tree parameter extraction.

The invention relates to the technical field of power fault diagnosis, and discloses a power fault diagnosis method and system based on cross-modal semantic alignment and agent coordination decision, and the method comprises the steps: obtaining multi-modal data needed in the operation and maintenance process of power equipment, and constructing a multi-modal power professional knowledge base; extracting multi-modal features contained in the multi-modal electric power professional knowledge base based on a feature representation technology, calculating dynamic weights of the multi-modal features, and generating an intelligent agent in combination with the electric power fault diagnosis instruction data set; an electric power fault diagnosis task is input, the modal weight of the electric power fault diagnosis task is calculated, and the intelligent agent outputs a fault diagnosis result of the electric power fault diagnosis task and a corresponding processing flow according to the modal weight; and generating a fault diagnosis report based on the fault diagnosis result of the power fault diagnosis task and the corresponding processing flow. According to the invention, intelligence and real-time performance of decision making are greatly improved, and dependence on artificial experience is reduced.

This invention proposes a wearable device interaction method and apparatus, and a computer device based on memory information. The method includes: collecting multimodal raw data through a wearable device; performing event recognition and semantic extraction on the raw data of each modality, and outputting a single-modal semantic description; comprehensively analyzing all single-modal semantic descriptions within the same time period to generate an event chain and event context description; saving an event table, an association information table, and an index table to a database, wherein the event table stores the event chain and event context description, the association information table stores the relationships between events, and the index table establishes a data retrieval channel; automatically generating reminder tasks based on the data in the event table, association information table, and index table, and pushing them to the user when the task triggering conditions are met, wherein the task triggering conditions include at least one of time, location, scenario, and unfinished tasks. This invention achieves efficient and accurate active and passive intelligent interaction, improving user experience and system efficiency.

The invention discloses a number recognition model training and recognition method and device, a storage medium and a product, and the method comprises the steps: building a first sample number set based on a CDR call ticket of a user in a first scene, and enabling the features of a sample in the first sample number set to comprise a first number of call features corresponding to the CDR call ticket; training a preset number recognition model based on the positive samples and the negative samples of the first sample number set to obtain a trained first number recognition model; constructing a second sample number set based on the CDR call ticket of the user in the second scene and the supplementary features of the second scene, wherein the features of samples in the second sample number set comprise a first number of call features corresponding to the CDR call ticket and a second number of supplementary features corresponding to the CDR call ticket; and carrying out secondary training on the first number identification model based on the positive samples and the negative samples of the second sample number set to obtain a trained second number identification model and taking the trained second number identification model as a number identification model in a second scene.

The invention relates to the technical field of livestock feed detection, and provides livestock feed moisture detection equipment and a detection method thereof, which realize efficient and accurate moisture detection. The equipment comprises a shell main body, a sensor device, a control device and other parts, and can automatically detect the moisture of a feed sample, adjust the drying temperature and time according to real-time feedback, optimize the drying process and improve the detection efficiency and accuracy. The drying process is optimized through data analysis, and the energy utilization efficiency is improved. The problems that traditional equipment is large in size, low in detection efficiency, poor in accuracy and the like are solved, an intelligent and efficient moisture detection scheme is provided for animal husbandry, and the livestock feed moisture detection accuracy can be improved.

The invention relates to the technical field of intelligent operation and maintenance of power equipment, and discloses an intelligent distribution transformer operation and maintenance method and system, and the method comprises the steps: fusing the four-dimensional parameters of electricity, state, nameplate and environment; a dynamic attention mechanism anchored by nameplate parameters is adopted to realize adaptive fusion of key features; abnormal detection is based on a transfer learning model, and cross-device knowledge transfer is promoted; residual life prediction is completed through a dynamic method of reference life and personalized aging coefficient correction; a multi-dimensional health assessment system is constructed, quantitative health scores are generated, and a multi-stage intelligent early warning mechanism is established according to the quantitative health scores and the residual life; meanwhile, an intelligent man-machine interaction system based on a BERT model is integrated, and natural language and voice questions and answers are supported; according to the invention, omnibearing intelligent monitoring, refined state evaluation, scientific life prediction and auxiliary decision making of the distribution transformer can be realized.

This invention provides a method for segmenting rock mass structural surfaces in a three-dimensional mesh model of the tunnel face, relating to the fields of three-dimensional laser point cloud processing and tunnel geotechnical engineering. The method involves collecting multimodal sensing data of the tunnel face during excavation, generating a colored three-dimensional mesh model based on this data, and performing tensor quantization on the colored three-dimensional mesh model data. Each vertex of the three-dimensional mesh model is encoded as a normal vector tensor, and the saliency of the vertices is determined based on the tensors to extract the vertex data of the structural surfaces. The structural surface vertices are then clustered, and structural surfaces are extracted from the obtained clustered vertices. Based on the orientation of the structural surfaces, they are grouped, and rock mass parameters are calculated. This invention solves the technical problems in existing technologies where structural surface rock mass segmentation easily leads to over-segmentation or under-segmentation, and where manual geological sketching is inefficient and inaccurate.

The invention relates to the technical field of the Internet of Things, in particular to an electric power facility remote monitoring method based on the Internet of Things. The method comprises the following steps: installing an Internet of Things sensor on the electric power facility for collecting operation data of the electric power facility; the configuration data acquisition module and the wireless communication module are used for receiving data acquired by the Internet of Things sensor and uploading the data to the cloud server; a data analysis algorithm and monitoring report generation module is deployed in the cloud server and is used for processing and analyzing data; and a remote monitoring terminal is configured for receiving data of the cloud server and realizing remote monitoring and management. According to the invention, real-time monitoring, data analysis and remote management of electric power facilities can be realized, the monitoring accuracy and response speed are improved, and the operation and maintenance cost is reduced.

The invention discloses a human resource data visual management method and system based on a semantic analysis technology, and relates to the technical field of human resource management.The method comprises the steps that digital production data and text production data of employees are collected and analyzed, and employee evaluation indexes are determined; performing normalization processing on the digital production data and the text production data, calculating expected output, and determining an employee ability evaluation level; constructing a neural network evaluation model, and training the neural network evaluation model by using the digital production data and the text production data until the model converges; and evaluating the employee ability by using the converged neural network evaluation model, and outputting an employee evaluation level. According to the method, the digital production data and the text production data of the employees are collected at the same time, preprocessing and semantic analysis are carried out, dominant and implicit information in the human resource data can be more comprehensively mined, and the problem that key information is difficult to rapidly and accurately extract in a traditional data processing mode is solved.