134results about How to "Increase independence" patented technology

The invention relates to the technical field of optical signal processing, in particular to an optical signal processing algorithm based on biological agent fluorescence detection, which comprises the following steps: S1, collecting fluorescence signals with different wavelengths emitted by each probe, and generating a multi-channel fluorescence spectrum data set; s2, carrying out spectral de-mixing on the fluorescence signals in the fluorescence spectrum data set, separating a plurality of overlapped fluorescence signals, and decoupling the fluorescence signals with different wavelengths; s3, for highly overlapped fluorescence signals, carrying out secondary overlapping separation treatment on the fluorescence signals by using a nonlinear optimization method; s4, extracting the intensity, wavelength and time change characteristics of each fluorescent probe signal, and constructing a multi-dimensional fluorescent signal characteristic vector; and S5, by amplifying the characteristic peak value of the weak fluorescence signal, it is ensured that the signal is not covered by strong signals of other probes in multi-probe detection. According to the invention, the resolution of fluorescence detection is improved, so that the high-precision detection effect can be maintained under complex experimental conditions.

The invention belongs to the technical field of computer vision and artificial intelligence, and discloses a combined zero sample image classification method based on hierarchical feature fusion. According to the method, a solution based on hierarchical feature fusion is innovatively provided for the technical difficulty of combined zero sample image classification. According to the method, features of different depths in a CLIP model visual encoder are selected and subjected to hierarchical fusion, multi-level and multi-scale visual information is effectively extracted, the fused features are further used for visual and semantic cross-modal interaction, and the understanding ability of the model for complex combinations is remarkably improved. In addition, by introducing a specific loss item, attributes and object features are effectively separated, and the feature aliasing problem is avoided. In a combined zero sample image classification task, the method shows excellent generalization ability and recognition effect, and can significantly improve the classification performance of the model for unknown complex combined images.

The invention relates to the technical field of industrial automation and intelligent detection, and discloses a multi-mode industrial line inspection robot and system.The robot comprises a crawler saddle, a machine body is installed at the top of the crawler saddle, and mechanical arms are arranged at the driving ends of the two sides of the machine body and used for controlling a detection assembly to make contact with the outer wall of a rail and the outer wall of a pipeline; a cleaning assembly is arranged on the front side of the detection assembly, the cleaning assembly is used for removing dirt on the outer wall of a to-be-detected part before detection, and the system comprises a main control and navigation module used for robot path monitoring, environment perception and dynamic obstacle avoidance and achieving autonomous navigation and path adjustment in a complex environment in combination with GPS and IMU data. Through ground and high-altitude division cooperation and multi-modal detection, all-directional inspection in a complex scene is realized, the applicability, the detection precision and the inspection efficiency are improved, the problem of insufficient applicability of a single scene in the prior art is solved, and an efficient scheme is provided for intelligent maintenance of industrial lines.

The invention discloses a page rendering method, a page rendering device, electronic equipment and a readable storage medium, the method comprises the steps that a document object model tree is constructed by analyzing a source code of a target page, and each HTML tag in the source code corresponds to an object node in the document object model tree; a document structure tree is constructed by analyzing the document object model tree, the document structure tree comprises document nodes and partition nodes, each partition node comprises at least one paragraph node, each paragraph node comprises at least one content node, and each content node comprises at least one content node; the document node, the partition node, the paragraph node and the content node all comprise node control information; and performing page rendering according to the document structure tree.

The invention discloses a power cable heating state monitoring and fault prediction method based on artificial intelligence, and relates to the technical field of cables, and the method comprises the steps: collecting power cable data in an operation state, and marking the collected power cable data; a training framework of the state monitoring and fault prediction model is a meta learning and transfer learning fusion framework; a neural network based on feature dynamic influence is adopted; and an auto-encoder based on feature path decoupling is adopted. According to the method, the feature independence is optimized through a feature path decoupling mechanism, the independence among the power cable data features is evaluated and optimized through a regularization loss function, in addition, linear dependence among the power cable data features is calculated by adopting covariance, the expression ability of the independent power cable data features is enhanced through a penalty mechanism, and the power cable data features are more accurately extracted. The processing capability of the model to the power cable data is improved, and the model can still keep accurate expression of the power cable data structure even under the condition that the power cable data features are missing.

The invention discloses a water area pollutant emergency treatment system and equipment in the technical field of water pollution treatment, and the system comprises a plurality of water quality monitoring modules which are used for collecting water quality factors of a covered area and generating real-time water quality data; the data collection module is used for acquiring, storing and forwarding real-time water quality data; the prediction module is used for analyzing and predicting the pollutant component evolution condition based on the real-time water quality data by using a big data model, and generating a pollutant component evolution condition graph of the water area; the emergency preprocessing scheme generation module is used for generating a corresponding preprocessing scheme and a preprocessing position based on the pollutant diffusion range diagram and the pollutant component evolution situation diagram; the biological filtration assemblies are used for being deployed at the pretreatment position and filtering and cleaning the water area. Plants aiming at specific pollutants are planted on the biological filtration assembly, and long-term ecological restoration can be achieved while water area pollutants are emergently treated.

The invention discloses a grape wine production place identification model training method and system based on a BP neural network, and relates to the technical field of grape wine production place identification, and the method comprises the following steps: collecting a large amount of sample data, including various chemical and physical indexes capable of reflecting the characteristics of different production places of grape wine, carrying out the preliminary cleaning and preprocessing of the data, and carrying out the training of a grape wine production place identification model; and obvious errors and abnormal values are removed. According to the method, the multi-collinearity problem caused by the high correlation between the features is solved through correlation evaluation and classification processing, and the feature data set is reasonably divided by using the Pearson's correlation coefficient matrix and the overall correlation coefficient. Feature selection and dimension reduction processing are carried out on a high-correlation data set, the stability of the training process is ensured, numerical instability and weight updating fluctuation risks are reduced, the stability and convergence of the model are improved, meanwhile, the training efficiency and resource utilization are improved, feature independence is enhanced, and finally the precision and generalization ability of the model are improved. And rapid and accurate wine producing area identification is realized.

The invention provides a test scene generalization generation method and system for automatic driving defect test, and belongs to the technical field of automatic driving test. Specifically, scene elements of a scene to be tested are extracted, and the extracted scene elements are divided into a training set and a test set; constructing a defect test scene model; training the defect test scene model based on the training set, and performing reinforcement learning by using a reward function, an attention mechanism and a DDQN algorithm to obtain a trained defect test scene model; and inputting the scene elements of the test set into the trained defect test scene model to generate a test scene. The test scene around the automatic driving vehicle is extracted and trained, and the automatic driving vehicle judges the influence degree of the surrounding environment on the predicted path, so that the training purpose is achieved, the independence between the test scene generation process and the tested system vehicle is enhanced, and the test efficiency is improved. And the generated scene is not influenced by the motion model adopted by the tested system.

The invention provides a flexible resource parameter identification method based on a KAN neural network, and relates to the technical field of power grid resource parameter identification, and the method comprises the following steps: obtaining historical operation data of power grid resources, and identifying flexible resources in the power grid resources through an analytic hierarchy process; according to the identified flexible resources, constructing a flexible resource digital model through a digital twinning technology, and extracting a sample data set according to the flexible resource digital model; based on the extracted sample data set, constructing and training a resource parameter identification model through a KAN neural network; and acquiring operation data of the flexible resources in real time, and identifying flexible resource parameters by using the trained resource parameter identification model. According to the method, the accuracy of identifying the flexible resource parameters by the model can be improved, more accurate decision support can be provided for power grid operators by identifying the flexible resource parameters, and more reasonable decisions can be made in the aspects of resource scheduling, energy distribution and the like.

The invention provides an asymmetric self-supervised molten pool defect identification method based on feature redundancy loss and a related system, and belongs to the field of industrial quality monitoring and defect identification. By performing a view enhancement strategy on a label-free molten pool data set, the learning invariance and feature distinguishing capabilities of the model are enhanced; a self-supervised learning network architecture is introduced, and low-dimensional representation is extracted from the enhanced view of the molten pool image; feature redundancy loss is introduced to serve as a target loss function, redundancy between components of the vectors is reduced to the maximum extent, independence of component representation is improved, coupling between component representation is relieved, and correct convergence of network learning is guaranteed. According to the method, invariant feature knowledge of various label-free molten pool images can be learned, all useful information is fully utilized, identification based on the molten pool defect state is realized, and single molten pool image health state evaluation with strong indication and high precision can be output.

The invention provides a method and device for detecting a melting layer in cloud, electronic equipment and a storage medium, and belongs to the technical field of remote sensing image processing, and the method comprises the steps: obtaining a melting layer probability value, judged in each distance library, between each radar observation profile of a to-be-observed region and a profile template; and calculating the melting layer probability values corresponding to all the distance libraries in each radar observation profile, determining whether the melting layer in the cloud exists in the to-be-observed area, and outputting the height of the melting layer in the cloud. According to the method and the device for detecting the melting layer in the cloud, the electronic equipment and the storage medium provided by the invention, the non-dimensionalized radar observation profile including the reflectivity factor template and the Doppler velocity template is used for matching the observed radar observation profile, so that the probability that the melting layer appears in each distance library is calculated; and empirical parameters are not needed, and a temperature profile or a 0 DEG C layer height is not needed, so that the independence of radar identification of a melting layer and the reliability of actual operation are enhanced. The calculation mode is simple and the result is visual.

The invention discloses a label printing platform system based on image conversion, and belongs to the technical field of label printing platforms. The system comprises an image conversion processing module, an interaction control module, a printing platform management module, a printing event recording module, a consumable state monitoring module and an intelligent analysis module. Different image features are determined by providing a man-machine interaction platform, so that an operator can edit a label template by himself / herself, various label printing requirements are met, and the independence of products in the industrial production process is improved; by establishing the consumable analysis model, analyzing the influence of different printing templates on the quantity of printing consumables during label printing and calculating the total quantity of printing consumables required by a printer for completing a label printing task, whether the current printing consumables are sufficient or not is warned in advance, the printing consumables are effectively supervised, and the printing efficiency is improved. And the label printing quality and the industrial production efficiency are improved.

The present application discloses a single-frequency GNSS unmanned aerial vehicle positioning method, device, medium and product, which relates to the field of unmanned aerial vehicle positioning. At any current moment: the distance and ionospheric delay of each satellite are determined based on the position coordinates of each satellite and the position coordinates of the unmanned aerial vehicle at the previous moment; the tropospheric delay at the current moment is determined based on environmental parameters; three satellites are determined as first target satellites, and two satellites are determined as second target satellites; pseudo-range observation values ​​are calculated based on the distance, corrected clock error data, ionospheric delay and tropospheric delay of each first target satellite; phase observation values ​​are calculated based on the distance, corrected clock error data, ionospheric delay, carrier phase integer ambiguity and tropospheric delay of each second target satellite; the position coordinates of the unmanned aerial vehicle are determined based on the pseudo-range observation values ​​and position coordinates of each first target satellite, and the phase observation values ​​and position coordinates of each second target satellite. The present application improves the accuracy of unmanned aerial vehicle positioning and reduces costs.

The invention discloses a tactile stick and a safe path navigation method thereof, electronic equipment and a storage medium, and is applied to the technical field of blind navigation. The method comprises the steps of planning a path according to a current position and a target position of a user, and generating an initial path; guiding a user to advance along the initial path; establishing a path width model, and obtaining a safe path width through the path width model; the position of the user is monitored in real time in the advancing process, and it is ensured that the user is within the safe path width range all the time. Through organic combination of the tactile stick and the visual camera, a cooperative working system is formed, and the position relation between a user and the safety path width is monitored in real time; through vibration feedback of different levels, the user is reminded to adjust the advancing direction in real time so as to ensure that the user always advances within the safe path width range of the path. And the safety and independence of visually impaired people are greatly improved.

The invention relates to the technical field of transformer testing, and discloses a transformer parameter comprehensive tester which comprises an anti-interference shell, a power socket, a grounding pole, a USB interface, an RS232 communication interface, a power switch, an internal and external power supply change-over switch, a display touch screen, a micro printer, a wiring terminal panel, a Bluetooth communication module, a communication bus and an internal power supply. By integrating the anti-interference shell, the power socket, the grounding pole, the USB interface, the RS232 communication interface, the power switch, the internal and external power supply change-over switch, the display touch screen, the miniprinter, the wiring terminal panel, the Bluetooth communication module, the communication bus and the internal power supply, all test items can be completed through one-time wiring, and the workload of field test personnel is reduced; the testing accuracy and reliability are improved, the influence of external electromagnetic interference on the testing result is effectively reduced through the anti-interference shell and the shielding measure, the requirements of different occasions and different transformer parameter testing can be met, and the testing device has wide application prospects.

The invention relates to a saline-alkali soil water collection irrigation and drainage dual-purpose ditch which comprises a plurality of water collection ditches. The bracket is detachably connected to the top of the water collecting ditch and is formed by splicing a plurality of sections; the rainwater collecting irrigation mechanism comprises a rainwater collecting assembly, a first-stage irrigation assembly and a second-stage irrigation assembly, and the rainwater collecting irrigation mechanism has a rainwater collecting mode, a natural irrigation mode and a strong irrigation mode; in rainy days, the rainwater collecting irrigation mechanism is in a rainwater collecting mode, and the rainwater collecting assembly is started to collect rainwater into the water collecting ditch; when the water in the water collecting ditch is sufficient, the rainwater collecting irrigation mechanism can be in a natural irrigation mode, and the first-stage irrigation assembly is started; when the rainwater collecting irrigation mechanism is in a strong irrigation mode, the secondary irrigation assembly is started, anti-seepage cloth is laid on the inner wall of the water collecting ditch, concealed pipes are buried below the bottom wall of the water collecting ditch, and the anti-seepage cloth is located above the concealed pipes. The system can realize collection, storage and utilization of rainwater, provides necessary fresh water resources for improvement of the saline-alkali soil, is combined with the concealed pipes, does not affect drainage, and has double effects of water collection irrigation and salt drainage.

The invention provides a server hardware system evaluation method and device, equipment and a medium, and the method comprises the steps: preparing n to-be-evaluated servers with the same brand and model and the same operation duration, collecting the historical fault data, calculating the fault occurrence frequency and probability, evaluating the severity of the influence of the fault on the system reliability, and calculating the reliability of the system according to the severity. Calculating the uncertainty and the weighted information amount of the weighted information entropy for representing the hardware fault, and finally converting the weighted information entropy into an evaluation score based on a mapping function; according to the invention, reliability evaluation and comparison of universal hardware systems suitable for various different brands and models of servers can be realized.

The invention discloses a continuous detection system for a gas alarm in the technical field of gas detection, the system can be carried on an intelligent computer with a processor for use, a control end needs to be provided with a plurality of gas sensors, and the continuous detection system comprises a sensor unit and a control unit, the system is composed of high-sensitivity electrochemical and semiconductor sensors, and the multiple sensors can detect gas concentration changes as low as ppm level and are responsible for collecting and processing gas samples in real time so as to ensure that the system can accurately monitor and give an alarm; and a data processing unit. Compared with the prior art, the continuous detection system provided by the invention provides higher reliability and real-time performance for the gas alarm. Through an advanced sensor technology and intelligent data processing, quick response to harmful gas and accurate alarm are ensured. In addition, in combination with a renewable energy power supply design, the independence and the application range of the equipment are greatly improved, and the device is particularly suitable for being used in remote areas or areas with unstable electric power.

The invention discloses a portable agricultural unmanned aerial vehicle shelter which comprises a shelter body, a limiting seat is arranged in the shelter body, a supporting plate is installed on the limiting seat, a motor is installed at the side end of the limiting seat, and the output end of the motor is connected with a first sleeve through a first driving part; the interior of the first sleeve is connected to the bottom of a supporting plate through a first screw rod, and the side end of the supporting plate is connected with a clamping plate through a rotating assembly. According to the portable agricultural unmanned aerial vehicle shelter, an unmanned aerial vehicle is placed on a supporting plate, a lifting plate arranged at the bottom of the supporting plate drives a second sleeve to descend, a ball in the second sleeve drives a second screw to rotate, and a first rotating rod on the second screw drives a clamping plate to rotate; the unmanned aerial vehicle base can be conveniently clamped and fixed through the clamping plates, the overall mechanical structure has high stability and durability, and the problem that the stability is reduced due to the fact that springs are adopted for limiting is solved.

The invention discloses a robust front feedback hybrid active noise control system and method, and belongs to the technical field of active noise control. According to the system provided by the invention, by utilizing the linear prediction filtering subsystem, the broadband auxiliary filtering subsystem and the narrowband auxiliary filtering subsystem, the independence among the feedforward broadband active noise control subsystem, the feedforward narrowband active noise control subsystem and the feedback active noise control subsystem can be improved, and the convergence performance of the whole system is improved; and the separation performance of the broadband auxiliary filtering subsystem and the narrowband auxiliary filtering subsystem on the residual noise is respectively improved by using an efficient self-adaptive variable step size minimum mean square algorithm. The system provided by the invention not only can suppress the wide-narrow band mixed noise component related to the reference signal in the target noise, but also can suppress the narrow-band noise component from other noise sources in the target noise, thereby effectively overcoming the coupling influence among multiple noise sources, improving the noise reduction performance of the system, and widening the practical application range of the system.

The utility model provides an air quality detector, including main casing and the top cover of being connected in the top of main casing, be equipped with a plurality of independent chambers for installing sensor in main casing, and the part of main casing is directly opposite sensor is evenly distributed with a plurality of air inlet hole, be equipped with a plurality of interval setting ridge between top cover and main casing to make the clearance between the periphery of top cover and main casing form the flow guide groove. The utility model discloses a flow guide groove is arranged in the periphery of top cover, and the air inlet hole is used for air inlet in the position of corresponding sensor in lower part, not only makes the air even flow, avoids the airflow dead angle and retention, improves the response time and measurement accuracy of sensor, also ensures internal gas flow unobstructed, reduces the internal and external pressure difference, promotes the detection accuracy, through setting up independent detection chamber, different sensor module is isolated, and each sensor is equipped with independent detection chamber, prevents the cross interference between sensor, ensures each sensor independent work, improves the independence and reliability of measurement data.

The invention provides a robot autonomous getting-off method, a robot and a system, and relates to the technical field of robot control. The method comprises the steps that in response to a getting-off instruction, a drivable area from a current position to a vehicle door position is determined; controlling a robot body to move towards a vehicle door in the driving area; collecting a first depth image in the direction of a vehicle door, and performing vehicle door edge detection on the first depth image to obtain the position of the lower edge of the vehicle door; and controlling the robot body to get off according to the detected position of the lower edge of the vehicle door. The invention provides a perfect solution for autonomous getting-off of the robot, the adaptability and autonomy of the robot under various terrains are enhanced, and therefore higher operation reliability and safety are achieved.

This invention relates to the field of natural language processing technology, and more particularly to a method and system for language transfer recognition based on cultural semantic embedding. The method includes the following steps: acquiring source language data and target language data; performing semantic isomorphic centering on the source and target language data to obtain semantically equivalent data; decoupling pragmatic features based on the semantically equivalent data to obtain pragmatic feature data; performing contrastive cultural latent space embedding based on the pragmatic feature data to obtain bilateral cultural representation data; performing cultural modality affine transformation based on the bilateral cultural representation data to obtain cultural difference data; performing implicit distortion pattern discrimination based on the cultural difference data to obtain cultural semantic distortion data; and generating transfer results based on the cultural semantic distortion data to obtain language transfer result data. This invention decouples pragmatic features under semantic equivalence constraints and achieves language transfer recognition through cultural semantic embedding and modality affine modeling.

The utility model discloses a parallel tail end multi-degree-of-freedom mechanical arm which comprises two groups of branch chains which are symmetrically arranged and fixed at two ends of a platform, each group of branch chains comprises a base, a first motor and a second motor are respectively arranged on two sides of the base, the first motor is connected with a first short connecting rod through a rocker arm, and the second motor is connected with a middle supporting piece. One end of the supporting piece is hung below the base through a support, the other end of the supporting piece is movably connected with the middle of the first long connecting rod, one end of the first long connecting rod is connected with the first short connecting rod, and the other end of the first long connecting rod is movably connected with the tail end platform. The other end of the second long connecting rod is movably connected with the tail end platform. According to the mechanical arm, the multi-degree-of-freedom parallel mechanical arm of the end effector can be achieved, and the problems that an existing parallel mechanical arm is complex in structure and high in control coupling degree, the degree of freedom of the end effector is limited, and installation and maintenance are difficult are solved.

The present invention relates to the field of smart factory control technology, and discloses a remote real-time monitoring and control method for smart factories based on the industrial Internet. First, an ant colony algorithm is used to establish a shortest path table between various devices in a factory. Based on the shortest path table, when the factory receives an order, according to the equipment resources required in the order, when there are equipment resources in the factory that meet the order requirements, the ant colony algorithm will be used to arrange appropriate equipment to process the order. At the same time, the smart factory will establish a TDMA time allocation table in real time according to the number of equipment in the current workshop, and communicate with the equipment in the workshop at different times through the TDMA time allocation table; in the process of arranging equipment to process the order, the smart factory will regularly access the equipment in the order based on the TDMA time allocation table to ensure remote real-time monitoring and control of order production.

The present invention discloses a health analysis open service system and method. The system includes: a labeling subsystem, which is configured to label sample images and generate multiple data sets based on preset labeling rules, and convert the data sets into data sets that can be understood by deep learning algorithms; an algorithm subsystem, which is configured to provide an online programming environment, and generate one or more health deep learning models based on the converted data sets and the online programming environment; a health business subsystem, which is configured to use one or more of the health deep learning models to analyze the received images to be analyzed, and generate health analysis results. The present invention solves the technical problem that the health analysis efficiency and quality provided by the existing health analysis system are not high enough.

The utility model relates to the technical field of hydrometeorological monitoring, in particular to a hydrometeorological monitoring device which comprises a supporting plate, fixing frames, an air cushion, an air valve, a meteorological detector, a limiting plate, a liquid wheel, a fixing plate and the like, a plurality of fixing frames are evenly distributed at the bottom of the supporting plate, the air cushion is connected between the fixing frames, the air valve is installed on the air cushion, and the meteorological detector is connected with the limiting plate. A meteorological detector is installed in the middle of the top of the supporting plate, limiting plates are symmetrically installed on the left side and the right side of the lower portion of the supporting plate front and back, a liquid wheel is installed on the outer side of each limiting plate, and fixing plates are installed on the left side and the right side of the middle of the top of the supporting plate. The two double-shaft motors are matched with the bevel gear sets to independently drive the left hydraulic wheel, the right hydraulic wheel, the front hydraulic wheel and the rear hydraulic wheel respectively, multi-direction propelling power is provided for the device, and the problems that a traditional floating type monitoring device is poor in maneuverability and cannot actively respond and cover monitoring blind areas are solved.

The invention discloses a system and a method for realizing user-defined arrangement and micro-component rendering of a portal homepage, and the system comprises a micro-component registration and loading module which is used for defining each micro-component in the portal homepage, registering each micro-component into a unified component library through a micro-front-end architecture, and allowing each micro-component to be loaded as required during operation; the micro-component attribute management module is used for providing an independent attribute management interface for each micro-component, allowing a user to modify personalized configuration of the micro-component, taking effect in real time in a data binding mode, and reflecting the attribute management interface during component rendering; and the micro-component asynchronous rendering module is used for ensuring the loading speed and performance of the page by adopting an asynchronous loading and rendering technology. The method has the advantages of high customizability, micro-component independence, responsive design, high-performance rendering and the like by realizing customized arrangement and micro-component rendering of the home page of the portal.

The application discloses a kind of online model parameter identification methods of servo driver based on AI algorithm, steps are as follows: S1. the parameter identification network of being served object is constructed, and current state and target value are input, and the parameter and the predicted state of next time of identification are output;S2. sampling strategy and parameter identification network iterative training are carried out;S3. parameter identification is carried out after the data of true machine sampling is optimized to network.The identification method uses AI algorithm for the system identification of servo motor can solve the problems such as low identification precision and low efficiency, to promote the application in industrial field.