27results about How to "Rich data base" patented technology

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 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.

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 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 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 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.

The invention discloses a friction welding drill rod hardness detection device and method, and mainly relates to the technical field of friction welding processes and hardness detection. Comprising the following steps: sampling 20-40cm near a welding seam of a friction welding drill rod, drilling a reference hole in one end of a drill bit, clamping, polishing to remove an oxide skin and a decarburized layer, polishing to be smooth and traceless, marking at least three detection lines at equal angles along the axial direction of a sample, performing multi-point hardness detection along each detection line, and performing encryption detection in a welding seam area and a heat affected area. The method has the beneficial effects that hardness detection planning is conducted on the welding area of the friction welding drill rod, the limitation of short sample detection is broken through, and the influence of heat treatment on the overall performance can be truly reflected.

The invention relates to the technical field of smart home, and provides a cooking decision-making method and device, electronic equipment and a storage medium. The cooking decision-making method comprises the following steps: acquiring multi-mode state data of cooked food in a current time step; wherein the multi-modal state data comprises image feature data and thermodynamic state data of the cooked food; obtaining a joint state embedding vector through a multi-modal encoder according to the multi-modal state data; and based on the joint state embedding vector, determining a cooking decision task of the next time step by using a cooking decision determination model. According to the invention, the cooking process decision of the cooking equipment can be efficiently and accurately made.

The application relates to the technical field of power distribution networks and discloses a hybrid power distribution network architecture matching method suitable for high-proportion new energy access, which comprises the following steps: constructing a probability demand vector set; extracting multi-dimensional capability indexes to construct a dynamic capability vector set and recording dynamic response parameters according to the probability demand vector set; obtaining a cumulative robust matching degree and a dynamic response capability index of a candidate architecture based on the probability demand vector set and the dynamic capability vector set; obtaining a comprehensive score of the candidate architecture according to the cumulative robust matching degree and the dynamic response capability index, and sorting the candidate architecture according to the comprehensive score to obtain an optimal architecture scheme. The comprehensive score of the candidate architecture is calculated by combining the cumulative matching degree of the candidate architecture and the dynamic response capability index constructed according to the dynamic response parameters, so that the optimal architecture has long-term comprehensive adaptability and the ability to respond to high-proportion new energy access and rapid power fluctuation.

The present application belongs to the field of gas storage, and particularly relates to a cavity forming dynamic tracking analysis method, aiming at solving the problem of lacking systematic analysis method when processing complex cavity forming data. The present application comprises: obtaining cavity forming data, calculating salt cavity effective volume based on the cavity forming data; obtaining the underground soluble salt volume and static volume formula based on the salt cavity effective volume; calculating the current cavity forming progress and cavity forming engineering progress based on the underground soluble salt volume; calculating the wellhead water injection pressure and oil cushion pressure based on the blocking solvent interface depth and along-the-way water head loss; calculating the wellhead nitrogen pressure based on the connecting point liquid column pressure balance principle; calculating the gas-water interface depth based on the volume formula; and calculating the pressure at the gas-water interface at the well bottom based on the gas-water interface depth. The present application can accurately reflect the actual state of cavity forming, optimize the salt cavity development process, and improve the resource utilization efficiency.

The application provides a kind of MOCVD process multi-parameter synchronous detection method, system, equipment and storage medium, it is related to semiconductor detection technical field, the application is by being divided into object light and reference light to single mode laser, and using reference light is diffraction to form spherical reference wave, so that object light and spherical reference wave are coincided in the space near semiconductor surface to form stable interference fringe;Again, the phase distribution graph and contrast distribution graph are extracted synchronously for each frame of interference image, and multi-parameter synchronous decoupling and inversion are carried out under unified data source and space reference system, so that in situ, real-time, full-field synchronous acquisition and correlation analysis of semiconductor morphology, temperature and optical reflection characteristics can be realized under the condition of MOCVD high temperature and strong radiation and semiconductor rotation, avoid the time and space asynchronization and information fragmentation problem caused by multiple independent monitoring systems, reduce the complexity of system integration and calibration, while providing unified, accurate and comprehensive data basis for process optimization.

The invention provides a power distribution network line overload early warning method, device and equipment under source load intensity fluctuation, and relates to the technical field of power system analysis. The method comprises the following steps: extracting multi-dimensional features from source-side output data, load-side power consumption data and line operation parameters of a target power distribution network, and generating a multi-dimensional early warning feature vector; based on the multi-dimensional early warning feature vector and a trained load prediction model, calculating an overload risk value of each line in the target power distribution network; in a preset dynamic threshold matrix, selecting a dynamic risk threshold corresponding to the operation state of the target power distribution network; wherein the dynamic threshold matrix comprises dynamic risk thresholds corresponding to different operation states of the target power distribution network; and performing line overload early warning on the target power distribution network based on the overload risk value and the dynamic risk threshold value of each line. According to the invention, the accuracy and reliability of early warning can be greatly improved.

The application relates to the technical field of crane accessory manufacturing, and particularly relates to a crane accessory defect complementary reuse system and method. The method comprises the following steps: performing multi-parameter fusion detection on unqualified accessories, establishing a digital defect model containing geometric deviation, material attribute and working condition associated offset; constructing a reusable accessory resource pool based on a safety access rule library screening model; intelligently matching a complementary combination scheme in the resource pool by using an optimization algorithm, with the minimum system cumulative error as the target; matching a fixed-point compensation process for a scheme that does not meet the error requirement and performing cost-benefit evaluation to generate a composite reuse scheme; and ensuring that the scheme meets the performance safety margin standard through virtual verification based on physical simulation. The application realizes accurate quantification, safe intelligent matching and full-process reliability verification of defective accessories, and significantly improves resource utilization and economy while ensuring reuse safety.

The invention relates to the technical field of power transmission line monitoring, and discloses an infrastructure power transmission line sag dynamic monitoring system in a high-altitude heavy fog environment, and the system comprises a line end high-frequency coupling receiving and transmitting module which is used for carrying out the electromagnetic coupling with a power transmission line; the control and data acquisition module is used for coordinating the transmitting and receiving processes of control signals; the tower footing side edge calculation module is responsible for performing primary processing on the acquired scattering matrix data; the analysis module is responsible for performing depth analysis on the processed scattering matrix data; and the cloud data processing module is used for comprehensively evaluating the three-dimensional form, the physical attribute and the dynamic behavior information. Through the arrangement of the analysis module, a three-dimensional form reconstruction unit and a dynamic behavior analysis unit in the analysis module can invert the three-dimensional geometrical form of the line and identify the dynamic behavior of the line from scattering matrix data in parallel, thereby providing a more comprehensive and three-dimensional line state view. And a richer data basis is provided for accurately judging the health condition of the line.