51results about How to "Fast output" patented technology

The utility model relates to the technical field of raw material mixing equipment, and discloses raw material mixing equipment for producing a wear-resistant shock pad, which comprises a bearing frame, a sealed sealing groove is arranged at the upper part of the bearing frame, a conical mixing barrel for mixing is arranged in the sealing groove, and an inner cavity of the conical mixing barrel is in a conical shape with a wide upper part and a narrow lower part. According to the utility model, the inner cavity is in the shape of the inverted cone, the first driving motor drives the mixing frame to rotate, raw materials move towards the periphery under the action of centrifugal force, the diameter of the conical mixing barrel is gradually reduced from top to bottom, a gradient type constraint space is formed, and the raw materials at the inner bottom of the conical mixing barrel are pushed upwards; circumferential flow driven by centrifugal force is coupled with reaction force of the wall surface, so that a spiral ascending flow state is formed, the mixing efficiency of the raw materials is improved, unmixed raw materials are not left at the bottom in the conical mixing barrel, and the mixing efficiency of the raw materials is improved.

The invention discloses a spatial orientation real-time analysis method based on eye movement, relates to the related technical field of flight training, and aims to overcome the defects of low analysis accuracy and poor adaptability in the prior art. The method comprises the following steps: acquiring pilot eye movement data through eye movement tracking equipment, acquiring head space orientation data through a flight helmet space sensor, and acquiring environment data containing instrument visual complexity and aircraft attitude from a virtual flight simulator; performing time synchronization on the three types of data; extracting features such as coordinates of a fixation point from the synchronous eye movement data; comparing the head orientation data with the reference data to generate deviation data; inputting the eye movement characteristics, the deviation data and the environment data into an evaluation model; the model dynamically adjusts parameters according to environment data, and calculates pilot space orientation capability indexes in combination with eye movement characteristics and deviation data. According to the method, accurate integration of multi-dimensional data can be realized, the analysis comprehensiveness and reliability are improved, a dynamic flight scene is adapted, and real-time accurate evaluation is realized.

The invention discloses a map real-time construction and rescue path dynamic planning system oriented to a fire dense smoke scene, and relates to the technical field of fire rescue. The system comprises a data acquisition and processing module, an environment modeling and path construction module and a post-processing module, based on millimeter wave radar, laser radar and motion sensor data, multi-modal information is obtained, synchronized and converted, a semantic segmentation model is utilized to analyze environment perception data to generate a feasible space, and laser radar point cloud and motion sensor data are fused to realize real-time positioning, map construction and rescue path planning in a fire scene. The path is updated in real time according to fire scene environment change, and a generated result is visually presented through a post-processing module and communicates with a command platform for real-time interaction. The system has the advantages of long-distance sensing of the millimeter wave radar and short-distance high precision of the laser radar, can realize quasi-real-time and high-precision fire scene environment modeling and dynamic path generation, and provides reliable path reference for rapid action decision in fire rescue.

This application discloses a log processing method, apparatus, device, readable storage medium, and vehicle. The log processing method is applied to a log processing system and includes: based on the memory identifier of shared memory corresponding to each application process, searching for the shared memory and a first storage identifier corresponding to each memory identifier, wherein the first storage identifier corresponds to a first target storage space among M storage spaces, the first target storage space is used to store a first target log and a timestamp of the first target log, and the first target log is the log with the smallest timestamp among at least one unprocessed log generated by the application process corresponding to the memory identifier; obtaining the timestamps of the first target log from the N first target storage spaces corresponding to the N first storage identifiers, obtaining N timestamps; and outputting the log corresponding to the smallest timestamp among the N timestamps. According to the embodiments of this application, the processing speed of the log processing system can be improved, and logs generated by different processes can be quickly input in chronological order.

The invention discloses a direct current charging interface identification and positioning method, and belongs to the technical field of automatic charging of electric vehicles. The method comprises a coarse positioning stage and a fine positioning stage. In the coarse positioning stage, the initial position of the charging port is quickly determined through image preprocessing, contour screening and ellipse fitting. In the fine positioning stage, a multi-factor cluster matching algorithm is adopted, and firstly, a matching preselected area of each feature hole is determined according to a preset geometric cluster relationship among a plurality of feature holes in the charging port; secondly, dynamically calculating scaling and rotation coefficients of the template to adapt to different postures; and finally, constructing a matching degree evaluation function in combination with a central gravitation and edge gravitation principle, and performing matching calculation on an original image and a gradient image template so as to realize accurate positioning of the feature hole. The method solves the problem that the recognition precision and efficiency of the charging port are difficult to consider at the same time in a complex light field environment, and has the advantages of high positioning precision, high processing speed, high environmental adaptability and easy engineering deployment.

This invention relates to the field of tunnel engineering, and particularly to a tunnel shotcrete design method, storage medium, and electronic device that considers spatiotemporal effects. The method includes: calculating the final value of vertical deformation pressure and the radius of the plastic zone of the surrounding rock without support based on tunnel foundation parameters and tunnel shotcrete design parameters; calculating the deformation pressure as a function of time using a deformation pressure calculation formula, combining tunnel excavation speed parameters, the radius of the plastic zone of the surrounding rock, and preset shotcrete hardening characteristic parameters; inputting the deformation pressure-time function into a shotcrete load-support structure model, calculating the safety factor of the shotcrete at each time node based on the shotcrete hardening characteristic parameters, and iteratively adjusting the tunnel shotcrete design parameters according to whether the safety factor meets preset requirements, until outputting tunnel shotcrete design parameters that meet the preset requirements. This invention dynamically calculates deformation pressure considering spatiotemporal effects and iteratively adjusts parameters to ensure the load-bearing safety of the shotcrete throughout its entire life cycle and reduces construction risks.

The application discloses a positioning end PPP-RTK real-time precision product monitoring method and device, and belongs to the technical field of satellite navigation and positioning. The method comprises the following steps: acquiring a PPP-RTK positioning result and a post-precise coordinate reference value in real time, calculating an ENU direction positioning deviation, fusing three types of parameters of satellite geometric configuration, product precision and data timeliness to generate a reliability index C, and dynamically adjusting an alarm threshold value according to the index C; establishing a positioning precision database to support rapid query and analysis; formulating a multi-level alarm rule for positioning continuity and precision overrun, and presetting a diagnosis item mapping relationship; automatically executing data recording, root cause diagnosis and information output after triggering the alarm, and realizing full-process automation from abnormal sensing to root cause diagnosis. Through the three-layer intelligent closed-loop system of "evaluation-prediction-diagnosis", the reliability, stability and operation and maintenance efficiency of the PPP-RTK positioning service are significantly improved.

The invention discloses an image data rotation optimization method and device, and the method is applied to a data rotation optimization network, and the method comprises the steps: obtaining packaged image data through an application layer, processing the image data into initial color space data containing brightness data and chrominance data, converting the initial color space data into a pointer type through a bridging layer; the code layer performs block matrix transposition operation on the brightness data and performs block matrix transposition and chrominance calibration processing on the chrominance data at the same time, and rotated brightness data and rotated chrominance data are obtained respectively; and combining the rotated brightness data and the rotated chrominance data into processed color space data in a code layer, returning the processed color space data to an application layer through a bridging layer, and outputting rotated image data. According to the invention, the overall processing duration of image rotation is reduced by adopting the block matrix while the matching between the bright and dark contours of the image and the color is ensured.

The present application relates to a tea bag sorting method based on an improved YOLO model and a parallel robot, which integrates an improved lightweight YOLOv11 model and an optimal suction decision algorithm, cooperates with a double-robot execution end, realizes the full-chain intelligent upgrading of tea bag sorting from "perception" to "decision" to "execution", uses the improved deep learning model, greatly reduces the calculation amount while maintaining high precision, ensures the real-time matching with the high-speed production line, adopts the cooperative work of the double robot based on the optimal suction decision, makes the system throughput increase by more than 70% compared with the single robot system, not only can quickly output the tea bag position, but also innovatively provides the "confidence" quantitative index and direction judgment, realizes the leap from simple "detection" to fine "quality evaluation and classification", and has high economic value.

This invention provides a lithium carbonate price prediction method based on multimodal data collaborative driving, comprising the following steps: multimodal data source identification and data acquisition; multimodal data classification and standardization; personalized noise filtering and outlier correction of multimodal data; extraction of trend and correlation features from structured data; extraction of semantic and visual / speech features from unstructured data; extraction of key information and feature quantification from semi-structured data; weighted collaborative fusion and feature optimization of multimodal features; construction and training of a two-branch collaborative prediction model; model prediction and preliminary verification of prediction results; optimization and dynamic correction of prediction results; interpretability analysis and visualization of prediction results; and method effectiveness verification and continuous improvement. This method, based on multimodal data collaborative driving, addresses the pain points of existing lithium carbonate price prediction technologies, forming a comprehensive, high-precision, and highly practical prediction solution.

The present application belongs to the technical field of material calculation and high-throughput screening, and particularly relates to a kind of ABN2 type silicon nitride sintering aid thermal conductivity prediction screening method. Wherein, the prediction method comprises: obtaining ABN2 type nitride data, screening stable structure according to formation energy threshold value;Adopt "labeled training-unlabeled prediction" phased strategy, utilize graph neural network to fill in the missing B and G;Introduce the improved Slack model based on elastic anisotropy structure correction factor fs Calculation of lattice thermal conductivity κ. The screening method is based on the predicted κ value, and the thermal performance of silicon nitride ceramic is sorted according to the regulation and control requirements of output. The present application designs the correction factor for the strong anisotropic structure characteristics of ABN2 type nitride, significantly improves the prediction accuracy of thermal conductivity, and greatly improves the data coverage by machine learning to fill in the parameters, forming a complete high-throughput screening scheme from parameter filling, thermal conductivity calculation to sintering aid application output.

This invention discloses a physical data-driven method for predicting the dynamic response of vehicles under settlement deformation, belonging to the field of rail transit engineering technology. The method includes the following steps: S1, constructing a coupled dynamics database; S2, data preprocessing; S3, model architecture design; S4, model training; S5, model application. This invention employs the aforementioned physical data-driven method for predicting the dynamic response of vehicles under settlement deformation, enabling rapid prediction of the dynamic response of multiple vehicle components, closely conforming to actual working conditions, ensuring that the prediction results conform to physical laws, improving model interpretability, and increasing prediction accuracy. Furthermore, it extends to complex settlement deformation scenarios, adapting to engineering needs.

The application discloses a kind of for bicuspid valve clamp surgery navigation multi-modal image automatic registration method, comprising the following steps: acquisition patient preoperative heart CTA image, intraoperative CBCT image and intraoperative TEE image / corresponding perspective image, and the CTA, CBCT and TEE image of this acquisition are pretreated;Complete CTA to CBCT three-dimensional automatic coarse registration, establish unified patient coordinate system, and combine with heart and breathing motion compensation, dynamic fine registration is carried out to coarse registration result;Estimate TEE probe pose and imaging plane, identify the common visual target in TEE and CBCT / perspective, complete TEE and CBCT / perspective dynamic automatic registration;Under unified coordinate system, fuse each modality image, generate the multi-modal navigation interface and navigation information of surgery.The method of the application can realize automatic, fast, accurate multi-modal space registration, improve the standardization degree and safety of bicuspid valve clamp surgery navigation.

This invention relates to the field of hydrodynamic prediction technology, specifically to an intelligent prediction method and system for hydrodynamic effects based on a multi-factor, full-level conditional embedding network. First, key influencing factor parameters of hydrodynamic effects are obtained, and standardized conditional input vectors are constructed and preprocessed. Then, an MHCE-Net is built, comprising conditional input, encoding, and mapping modules, as well as encoders, bottleneck layers, decoders, and output modules. The standardized vectors are mapped to conditional feature layers through feature enhancement and dimension matching, and the corresponding layers of the full-level embedding network are fused with convolutional feature maps. Finally, the model is trained by extracting and reconstructing the fused features, and the actual spatial distribution field of physical quantities is obtained through inverse normalization. This invention enhances feature representation capabilities through pre-encoding enhancement of multi-factor conditional vectors, enabling rapid and accurate prediction of spatial fields related to hydrodynamic effects without complex numerical simulations, significantly reducing computational costs and improving prediction efficiency and accuracy. It is suitable for hydrodynamic effect prediction needs in groundwater environments, deformation fields of water-related engineering structures, and eco-hydrological scenarios.

The application relates to the technical field of quality inspection, and discloses a weak-current pipeline laying quality online inspection method and system.The method comprises the following steps: performing structured information analysis on the real-time construction state of target project laying to obtain quality parameter data; performing space-time coordinate mapping on the quality parameter data and the design specification of the target project laying to obtain a dynamic anchoring relationship; based on the dynamic anchoring relationship, continuously performing compliance evaluation on the quality parameter data to obtain a space-time deviation degree; based on the space-time deviation degree, performing key feature extraction on the quality parameter data to obtain a key abnormal mode; based on the design specification, constructing a logical topology graph of the target project laying, based on the logical topology graph, performing influence deduction on the key abnormal mode to obtain an abnormal risk grade; based on the abnormal risk grade, comprehensively integrating the key abnormal mode and the quality parameter data to obtain an inspection report; and the application can improve the efficiency of weak-current pipeline laying quality online inspection.

The application discloses a DRAM and CXL extended memory proportion optimization method and device for a CXL layered memory system, and relates to the technical field of computing systems. The method is to first acquire an offline experience dataset containing a DRAM capacity proportion, a memory access mode feature and a bandwidth-concurrent core number mapping relationship, then determine an effective extended core number as a sample label according to the bandwidth-concurrent core number mapping relationship, and combine the DRAM capacity proportion and the memory access mode feature to form a sample feature vector, train an effective extended core number prediction model, and then, after receiving an effective extended core number target value and a memory access mode feature of a current workload, solve a minimum DRAM capacity proportion that meets a target constraint condition in combination with the prediction model, and finally generate a proportion scheme according to the minimum DRAM capacity proportion and output the proportion scheme for system configuration. In this way, under a given performance target and workload feature, an interpretable and reproducible DRAM and CXL extended memory recommended proportion can be automatically given, and experience-based head-on configuration can be avoided.

The application relates to an equatorial ionospheric irregularity automatic detection method and system, and belongs to the field of artificial intelligence. The method comprises the following steps: downloading GOLD satellite data, pre-processing and labeling, and constructing an original data set; inputting the original data set into a deep learning network model for training to obtain a trained deep learning network model; calculating the performance index of the trained deep learning network model, and outputting an equatorial ionospheric irregularity identification result. The application solves the problem of low accuracy in identifying and detecting equatorial ionospheric irregularities caused by the influence of factors such as slant range, background airglow intensity, instrument characteristics and threshold.

A power supply comprises a power converter, a feedback circuit, a control signal generation circuit and a compensation circuit. The power converter generates an output voltage at an output node. The feedback circuit generates a feedback signal according to the output node voltage. The control signal generating circuit provides a control signal to the power converter according to the feedback signal. The compensation circuit comprises a reference voltage circuit, a voltage division circuit and a switching circuit. When the output node voltage drops from the first voltage level to a level threshold value or drops from the first voltage level to an amplitude threshold value, the switching circuit is conducted to enable the feedback circuit to be coupled to the voltage division circuit, and the feedback circuit sets the control signal generation circuit to increase at least one of the duty ratio of the control signal and the frequency of the control signal. The invention also relates to an operation method of the power supply.

The invention discloses an FPSO mooring system optimization design method, and particularly belongs to the technical field of ocean engineering mooring design. The method comprises the following steps: constructing an initial mooring model; optimization variables are defined, constraint conditions are determined, multiple sets of values of each set of optimization variables are assigned to the initial mooring model, and a multi-objective function is obtained; dynamically analyzing the optimization variables to generate an initial data set; based on the initial data set, establishing a multi-target agent model through an LSTM network; generating a new sample based on the initial data set by adopting an adaptive Latin hypercube sampling method; optimizing by using a genetic algorithm to obtain an optimized solution set; and carrying out real calculation on an optimal solution in the optimal solution set to obtain a simulation result, and judging whether constraint and performance requirements are met or not. According to the method, the problems of high cost and low efficiency of mooring design under complex sea conditions are solved, experience driving of the mooring scheme is changed into intelligent algorithm driving, and reliable technical guarantee is provided for large-scale deployment of the floating platform.

This invention discloses a hybrid prediction method for rapidly acquiring urban wind and thermal environment data. The method includes: constructing a hybrid prediction model using mesoscale numerical modeling and a deep learning model; and rapidly predicting and analyzing the wind and thermal environment of the studied city based on this hybrid model. Firstly, this method addresses the long-standing pain points of slow speed and low accuracy in large-scale urban wind and thermal environment simulation. Secondly, it achieves a simulation speed improvement of more than fifty times compared to traditional single numerical model methods, and this method can be applied to different cities in different climate zones. Compared to traditional methods, the method disclosed in this invention can simultaneously consider the two most important parameters of urban climate—thermal environment and wind environment—and can be extended to different cities in different climate zones through standardized procedures and mature numerical models for further research. This enables rapid evaluation of urban planning schemes and provides solid support for research on low-carbon and energy-saving cities.

The application discloses a quantitative prediction method for atmospheric pollutant corrosion rate of metal under multi-gas coupling effect, relates to the technical field of atmospheric corrosion science and industrial protection, and comprises the following steps: step one, constructing a multi-gas corrosion rate calculation basic framework; taking the rate threshold value data of metal in a graded corrosion environment, the framework provides a unified operation platform for subsequent integration of other gases and adaptation of different metals. The quantitative prediction method for atmospheric pollutant corrosion rate of metal under multi-gas coupling effect realizes full-coverage quantitative analysis of the corrosion effects of three core atmospheric pollutants, namely Cl ‑ , H2S and NH3, effectively solves the technical difficulty that Cl ‑ cannot be independently calculated in the prior art, and realizes precise coupling and linkage calculation of Cl ‑ , H2S and NH3 by converting Cl into a dedicated corrosion contribution item.

The application relates to a multi-center applicable human brain magnetic resonance image brain structure automatic segmentation method, and the segmentation method specifically comprises the following steps: acquiring MRI data of different platforms, and performing N4 bias field correction and image standardization processing on the MRI data; taking U-Net as a basic framework, fusing a self-attention mechanism module, and constructing an MRI brain image structure segmentation network model suitable for multiple platforms; inputting the processed MRI data into the segmentation network model, training the segmentation network model by using transfer learning and domain adaptation technology, estimating the uncertainty of the output result, and further training the model by using high-quality unlabeled data screened out, so that the human brain magnetic resonance image brain structure automatic segmentation is realized. Compared with the prior art, the application can accurately and quickly segment the brain structure of different magnetic resonance imaging platforms, provides a more universal, efficient and reliable tool for the field of brain image analysis, and helps more extensive application and research exploration.

The application provides a glass curtain wall damage identification method and system, the method comprising: S1, a collection stage: applying a hammering excitation to a glass curtain wall, and synchronously collecting vibration response signals under the hammering excitation at multiple signal collection measuring points around the periphery of the glass curtain wall; S2, an analysis stage: processing the vibration response signals to calculate time-frequency energy and information entropy thereof; weighting and fusing the time-frequency energy and information entropy to obtain a damage evaluation index vector; S3, an identification stage: mapping the damage evaluation index vector to each signal collection measuring point to generate a damage distribution cloud map representing damage spatial distribution. The method of the application can evaluate the damage condition of the glass curtain wall by analyzing the vibration characteristics of the vibration response signals without disassembling or damaging the glass curtain wall components, can accurately and quickly locate the damage position, and provides a new way for monitoring the service safety of the glass curtain wall.

The utility model provides a kind of armoured tungsten-rhenium thermocouple, belong to temperature measurement technical field, this armoured tungsten-rhenium thermocouple, including armoured protection pipe, the tungsten-rhenium electrode being arranged in armoured protection pipe inside, insulation material being filled between armoured protection pipe and tungsten-rhenium electrode, terminal box and sealing assembly being arranged in armoured protection pipe one end, pass through double sealing structure, construct full-closed protection system, can effectively resist the invasion of high-temperature molten gas in metallurgical furnace, strong corrosive medium in chemical reaction kettle, make tungsten-rhenium electrode and alumina ceramic insulation layer exempt from corrosion, compared with traditional thermocouple service life improves 2-3 times.Simultaneously, spring-like elastic support piece and ceramic powder insulation layer form double anti-vibration structure.

This invention discloses a rapid detection method and system for the reheating degree of meat dishes based on electrochemical sensing, belonging to the field of food testing technology. Addressing the problem of difficulty in rapidly and accurately evaluating the reheating degree of meat dishes in existing technologies, this invention first places the meat dish to be tested in a space, generating headspace gas at a preset equilibrium temperature and time. An electrochemical sensor unit collects the complete time-response curve of the interaction between the headspace gas and the sensitive layer, extracting dynamic characteristic parameters such as response rise time, peak response value, time to reach peak value, curve integral area, and half-life of the fall phase. Simultaneously, it acquires ingredient attribute parameters and reheating method parameters. These parameters are then input into a pre-trained deep learning prediction model, which outputs the reheating degree level and displays it on a display unit. This method can be used in the catering industry, food processing, and home cooking for rapid detection of the reheating degree of meat dishes, ensuring food quality and food safety.

The application belongs to the technical field of neural network model training, and discloses a neural network model training method and system based on an improved random configuration algorithm, which comprises the following steps: under the condition of reducing the root mean square error of the neural network model output, screening suitable neurons as candidate hidden layer nodes through the inequality constraint condition of the random configuration algorithm; screening K neurons with the fastest training error reduction from the candidate hidden layer nodes, and selecting the neuron least related to the previous L-1 hidden layer nodes from the selected K neurons as the optimal hidden layer node; calculating the output weight through the least square method, and updating the structure of the random configuration network; and judging whether the network structure is completed by using the maximum allowable number of hidden layer nodes and the maximum allowable output error. The application can reduce the overall calculation amount of the algorithm while ensuring the prediction accuracy, and is suitable for application scenarios with high real-time requirements.

This application relates to the field of radar signal processing technology, and discloses a radar adaptive parameter adjustment method and system under complexity constraints. The method first generates an optimal parameter library offline using the Pareto optimization method. Then, in the online state, a second selection is performed from the offline-generated optimal parameter library to obtain a candidate parameter set. This reduces the amount of iterative computation and achieves efficient parameter optimization under resource-constrained conditions. Furthermore, a lightweight machine learning model is used in the online stage to predict the performance of the candidate parameter set, avoiding complex online optimization calculations. This improves the system's adaptability and decision-making speed in dynamic environments, enabling fast and stable online output of parameter combinations that meet real-time requirements and have optimal overall performance.

The present application relates to the technical fields of biomass gasification furnace, and disclose a tower-shaped grate, the present application is arranged with the annular base of receiving ash and slag below the furnace body, so as to ensure that after the discharge of ash and slag, the fresh air outside will be sucked from the output direction of ash and slag, and enter the furnace through ash and slag, the outside air passes through ash and slag again, so that ash and slag are further burned, and the waste heat generated in ash and slag enters the furnace along with air, and the heat can be fully reused, and by means of waterless slagging, the water vapor injection into the furnace is reduced, the temperature in the furnace is prevented from being suddenly reduced, the heat energy is saved, and the purpose of saving heat energy and recycling residual heat energy is achieved.

This invention provides a rapid modeling method and terminal for variable cross-section leaf springs used in light trucks, relating to the field of light truck technology. The method involves calculating the geometric contours of each leaf spring in its extended state, selecting sample points at equal intervals along the length of each leaf spring in its extended state, and calculating the sample point data including coordinates and thickness. The method then calculates the geometric contours of each leaf spring in its free state. Based on the free state of each leaf spring, sample points are reselected, and corresponding sample point data is obtained through interpolation and exported. A leaf spring modeling file and an assembly parameter file are created. The sample point data of each leaf spring in its free state are imported into the leaf spring modeling file, and the leaf spring assembly parameters are imported into the assembly parameter file. The leaf spring toolbox is run to generate the leaf spring model, defining the leaf spring lugs and material properties to create the leaf spring model. This method uses a procedural approach, which can quickly and accurately calculate the sample point data of the variable cross-section leaf spring, improving the efficiency of leaf spring modeling and the accuracy of model creation.

The invention relates to a piping shaft overflow detection method and device, and belongs to the field of urban public safety monitoring. Collecting images covering well lid and water body characteristics; preprocessing and labeling; a YOLO26s target detection model is adopted for recognition training, after a real-time video stream is framed according to the frequency f, obtained images are sequentially input into the trained YOLO26s model, detection results of various features in each frame are output, space-time joint verification is carried out through the feature overlapping degree and multi-frame statistics, and vehicle splashing and real overflow are distinguished; and setting a sliding frame number window, counting the number of frames appearing in each feature in the window, and judging a triggering early warning level according to a priority sequence. According to the invention, the false alarm rate of overflow detection is greatly reduced; and multi-stage early warning in the whole process from occurrence to explosion can be realized, so that the early warning speed is increased.