31results about How to "High sampling frequency" patented technology

The invention provides a high-altitude energy storage monitoring system. The system comprises an environmental data acquisition module, an intelligent temperature control analysis module, an intelligent energy scheduling module, a health monitoring module, a self-adaptive adjustment module and a system optimization module. The invention provides an intelligent and environment-adaptive energy storage system monitoring and maintenance strategy, the temperature control strategy, the energy distribution and the maintenance plan can be dynamically adjusted according to the real-time change of the high-altitude environment, the efficient operation of the energy storage system is realized, and the service life of equipment is prolonged. A sensor technology is used for collecting multi-dimensional environment data, and an intelligent algorithm is combined to monitor the operation state of the equipment in real time, predict the health state of the equipment, optimize energy scheduling and formulate a self-adaptive maintenance strategy, so that the fault risk and the maintenance cost are reduced, and the stable operation of the system in a complex environment is ensured.

The invention discloses a coal mine fireproof early warning method based on intelligent monitoring, and relates to the technical field of coal mine fireproof early warning, and the method comprises the following steps: obtaining coal mine environment information data parameters in real time through a coal mine fireproof early warning system, including humidity dynamic parameter information and air pressure parameter information; the environment is intelligently evaluated through the generated environment safety change index, when the environment is judged to be a high-risk environment, the system dynamically adjusts the sampling frequency based on the preset sampling frequency, fine changes such as methane diffusion or air static pressure fluctuation in the early stage of a fire disaster are captured in time, rapid early warning is ensured, and the safety of the fire disaster is improved. The accuracy and timeliness of signal capture are improved, and early intervention time is won for managers. And in a low-risk environment, the system collects data at a fixed sampling frequency, so that unnecessary calculation tasks and energy consumption are reduced, and long-term monitoring and trend analysis are supported. Through the dual regulation and control mechanism, the system not only ensures the data integrity, but also optimizes the resource utilization and operation efficiency.

The invention discloses an automatic driving vehicle dynamic drift obstacle avoidance control method based on working condition identification, which belongs to the field of automatic driving vehicle control, and comprises the following steps: sensing multi-dimensional dynamic characteristics of a vehicle and a surrounding environment, predicting information entropy of historical multi-dimensional dynamic characteristics by using a graph neural network, and determining the dynamic drift obstacle avoidance of the automatic driving vehicle. The sampling frequency of the sensor is adaptively adjusted according to the prediction error; based on the sensor sampling data after self-adaptive adjustment, a fuzzy logic enhanced time sequence convolutional neural network is adopted to judge whether the vehicle is in a drifting critical zone or not; adaptively adjusting a state weight matrix and a control weight matrix of a model prediction controller according to whether the vehicle is in a drifting critical zone or not, and calculating to obtain a vehicle front wheel steering angle and rear wheel driving force control instruction; and the control instruction is sent to a steering actuator and a driving actuator, and vehicle obstacle avoidance and trajectory tracking are achieved. The invention aims to improve the control capability and safety of the automatic driving vehicle under complex working conditions and improve the overall dynamic performance of the vehicle.

The invention discloses an electric vehicle troubleshooting system and method. The system comprises a pre-charging detection module and a pre-charging detection loop module which are connected to an electric vehicle bus in parallel. The pre-charging detection loop module comprises a plurality of pre-charging detection branch circuits which are connected in parallel, each pre-charging detection branch circuit is connected with an electric vehicle bus, and each pre-charging detection branch circuit comprises a high-voltage component and a high-voltage relay used for controlling the on-off of the current pre-charging detection branch circuit; the detection end of the pre-charging detection module is connected with an electric vehicle bus, the power supply end of the pre-charging detection module is connected with an electric vehicle battery system, and the pre-charging detection module is used for carrying out pre-charging troubleshooting on a high-voltage part corresponding to the high-voltage relay according to the resistance value and / or tube voltage drop value of the electric vehicle bus. According to the electric vehicle troubleshooting system and method provided by the embodiment of the invention, automatic insulation and pre-charging troubleshooting are realized.

The invention is suitable for the technical field of measurement, and provides a time interval measurement method and device, a time-to-digital converter and a medium, and the time interval measurement method comprises the steps: carrying out the delay processing of a received signal, and obtaining a plurality of delay signals; sampling the received signal at a first hopping edge and a second hopping edge of a sampling clock signal to obtain a first sampling moment; sampling the plurality of delay signals at a first hopping edge and a second hopping edge of a sampling clock signal to obtain a second sampling moment; and according to the first sampling moment and the second sampling moment, determining a receiving moment of the received signal, and obtaining a time interval between a transmitting moment of a transmitting signal and the receiving moment. According to the scheme, the measurement accuracy of the time interval is improved, and occupation of logic resources is reduced.

The invention provides a resonance suppression method for a three-phase PWM (Pulse Width Modulation) rectifier with an LCL (Lower Control Limit) filter, which relates to a three-phase PWM rectifier control technology, and is characterized in that on the basis of vector control of power grid voltage orientation, voltage and current sampling is carried out at fixed time intervals, inner loop control is implemented, and on the premise of ensuring volt-second balance in a pulse width modulation process, namely, a PWM process, the voltage and current are sampled at fixed time intervals, so that the resonance suppression efficiency is improved. And the sampling period is minimized. According to the method, the sampling frequency is increased, the system bandwidth is improved, and resonance suppression can be achieved under the low carrier frequency when main circuit parameters are not matched.

The application discloses a kind of leakage point positioning method, equipment and medium of water supply network, belong to water supply network operation and leakage control technical field, for solving in existing leakage point positioning, it is easy to rely on single data, equipment and algorithm complex, and also lack high-precision space linkage and to data adaptability and insufficient refinement, it is difficult to realize the technical problems of accurate positioning of water leakage point of water supply network.Method includes: the operating state of water supply network is processed to multi-source data acquisition, obtains water supply network core data;The standardization processing of feature vector is carried out to the corrected water supply network core data after pre-processing, obtains effective water supply network data;Effective water supply network data is screened under the risk value of leakage point area about leakage point, obtains high suspected leakage point area based on water supply network section;Leakage point spatial positioning calculation is carried out to high suspected leakage point area under local topology, obtains the final leakage point area position in water supply network grid.

The present invention provides a CAN communication auxiliary channel data transmission method, which comprises the following steps: establishing an auxiliary channel DBC file; auxiliary channel mapping; process step flow editing; starting a test; during the test; until the process step flow is executed, the test ends; the beneficial effects of the present invention are: reducing the resource occupation of the intermediate computer, and being able to carry more auxiliary channels; reducing the development cost of the docking protocol between the auxiliary channel lower computer and the intermediate computer; the CAN communication has strong real-time performance and fast transmission speed, and can improve the sampling frequency; the auxiliary channel DBC file is easy to edit, the protocol structure is simple, and the scalability is strong; only a small amount of voltage, temperature, and pressure messages need to be defined to meet the auxiliary channel type and data upload; when an auxiliary channel fails, only the auxiliary channel mapping needs to be modified, the failed auxiliary channel is deleted, and other auxiliary channels are mapped, and there is no need to replace the auxiliary channel lower computer, thereby improving the equipment utilization rate.

The present application relates to the technical field of Pickleball, and specifically relates to an intelligent wearable system for monitoring sports posture. The system synchronously collects multi-axis acceleration and angular velocity original signals before and after the player hits the ball through the inertial measurement unit built in the wearable device, generates time series after time window interception and normalization processing; removes noise and baseline drift by wavelet transform; fuses time domain and frequency domain features to construct a high-dimensional comprehensive feature vector; realizes preliminary classification of actions through a long short-term memory network, refines similar action types by combining a convolutional neural network, and finally outputs accurate classification results through a support vector machine; dynamically optimizes model parameters based on a real-time feedback mechanism, and enhances local feature representation through a channel attention mechanism. The system can accurately identify more than fifteen Pickleball special technical actions, has high robustness and self-adaptive ability, can provide real-time posture specification guidance for athletes, and helps to improve training efficiency and prevent sports injuries.

The invention discloses an event-driven adaptive sampling and hierarchical transmission method based on a data change rate. The event-driven adaptive sampling and hierarchical transmission method specifically comprises the steps that S1, an equipment end collects industrial equipment operation data at a fixed frequency and generates a data frame; s2, the server side calculates the data change rate of adjacent data frames from the same equipment, and generates an abnormal score based on the change rate; s3, the server side divides the data frame into event levels at least including a heartbeat level, a conventional level, an abnormal level and an accident snapshot level according to the abnormal score Score; s4, the server performs adaptive adjustment on an issuing strategy to the client according to the event level; s5, the server triggers an accident snapshot level event, and reliably transmits and issues the accident snapshot level event to the client through a TCP; and S6, the server side enables the data of different event levels to enter different priority queues and dispatches and issues the data according to the priorities. According to the invention, through event grading, sampling self-adaption and grading transmission, priority arrival of key abnormal data and complete reservation of accident data are realized.

The present application relates to the technical field of industrial automation control and facility health management, and particularly relates to a virtual-real mapping digital twin facility fault prediction and health management system, comprising: a state mirror module that obtains target facility operation data and maps to a virtual-real mapping digital twin model to obtain a current facility state mirror; a boundary construction module that determines a critical fault boundary containing a boundary state; a facility topology construction module that constructs a facility control topology graph; a reverse deduction module that reversely deduces a candidate reachable path from the boundary state to the current facility state mirror; a forward rechecking module that generates a virtual disturbance sequence and screens an effective reachable path; a path determination module that determines a minimum fault triggering path; and a health management output module that generates a defense shaping strategy. The present application can improve the reliability of fault path prediction and the pertinence of health management strategies.

The present application relates to the technical field of image generation, and particularly relates to an image generation training method for an AIGC large model, comprising: obtaining standard images by preprocessing collected labeled images, injecting the standard images into an initial model for training to obtain a pre-training diffusion model, inputting random noise into the pre-training diffusion model, and generating initial images by reverse diffusion; optimizing the pre-training diffusion model according to the initial images to obtain an AIGC large model with image generation capability, processing image generation requirements by using the AIGC large model to generate target images; determining whether the number of reverse diffusion sampling steps needs to be increased; determining whether the sampling temperature of the image generation process needs to be increased; and determining the residual connection weight of deep features based on the attack iteration decay rate in the optimization process. The present application improves the generation stability of the target images.

The application relates to the field of power system data acquisition and edge computing, and discloses a multi-source heterogeneous data pre-acquisition and edge intelligent processing method, which comprises the following steps: using a modal division mechanism, combining the operation state of power equipment, and performing condition correction on an adaptive sampling frequency to obtain a corrected sampling frequency, so as to acquire multi-source heterogeneous data; extracting fusion core features from multi-dimensional feature vectors; substituting the fusion core features into a general fault judgment model and a time sequence prediction model to respectively output equipment fault prediction results and time sequence prediction results, and obtaining early warning information; based on the fusion core features, the early warning information and the equipment fault prediction results of each acquisition node, and combined with the Lymerzi complexity theory, the spatio-temporal coupling law is identified; and based on the spatio-temporal coupling law and the time sequence prediction results, edge end decision suggestions are provided for source-grid-load-storage collaborative regulation. The application effectively improves the power system data processing efficiency and the power grid operation and maintenance regulation level.

The present invention proposes a phased array current measurement radar that can take into account both the accuracy and efficiency of current measurement and reduce the equipment cost, including a transmitting antenna, a receiving antenna array, a radar host and a power supply system. By reasonably setting the number and position of the transmitting antenna / receiving antenna, and alternately processing the echo signals received by different receiving antennas, the workload of data processing is reduced while ensuring the accuracy of current measurement, saving hardware costs, reducing the waiting time for data processing, and improving the efficiency of current measurement. In addition, the shore-based phased array current measurement radar can automatically set the radar's transmission power according to the detection distance, protecting the environment and saving electricity.

The invention relates to the field of data processing, in particular to a circuit-oriented data acquisition method, device and system, and the method comprises the steps: carrying out the data acquisition of a target circuit through employing a first sampling frequency, and obtaining first sampling data; calculating a second sampling frequency according to the change degree of the first sampling data and the first sampling frequency; performing data acquisition on the target circuit according to a second sampling frequency in a second time window to obtain second sampling data, the second time window comprising a plurality of sub-time windows, and the second sampling data comprising a plurality of sub-sampling data acquired in the plurality of sub-time windows; calculating a third sampling frequency according to the difference degree between the sub-sampling data and a target sampling frequency, wherein the target sampling frequency is a sampling frequency corresponding to the last sub-time window in the second time window; and performing data acquisition on the target circuit by using the third sampling frequency. And the sampling frequency is adaptively adjusted by analyzing the change degree of real-time data, so that the signal processing precision and efficiency can be better balanced, and the efficient operation of the system is ensured.

The invention belongs to the technical field of additive manufacturing, and discloses a laser energy deposition spectral signal acquisition device and method based on a photomultiplier, a collimating mirror is connected to a laser cladding head of a laser energy deposition forming system, the input end of a first single-channel optical fiber is connected with the output end of the collimating mirror, and the output end of the first single-channel optical fiber is connected with the output end of a second single-channel optical fiber; the output end of the first single-channel optical fiber is connected with the input end of the optical fiber splitter, the optical fiber splitter is provided with at least two output ends, each output end of the optical fiber splitter is connected with a second single-channel optical fiber, the output end of each second single-channel optical fiber is connected with a narrow-band optical filter, a photomultiplier is arranged behind each narrow-band optical filter, and the photomultiplier is connected with the optical fiber splitter. And the output end of each photomultiplier is connected to a digital acquisition card. The invention solves the problems of low sampling frequency, poor sensitivity under weak plasma, weak effective spectral signal and external signal interference of the current spectrograph, and the problem of insufficient refinement capability of a broadband photoelectric sensor.

The application provides a kind of decommissioned battery remaining life prediction method, system and battery management system, the decommissioned battery remaining life prediction method includes: end side equipment carries out feature extraction and correlation screening to the operation data of decommissioned battery, and battery characteristic data after screening is uploaded to side node;Based on the battery characteristic data after screening, the inference is carried out using battery life prediction model, and the remaining life prediction value is obtained;Wherein, the battery life prediction model uploads the parameters of the battery life prediction model by the side node, the cloud server carries out aggregation optimization to the parameters uploaded by each side node and issues the updated parameters, and the side node receives the updated parameters and updates.The method provided by the application can improve the accuracy and real-time performance of the remaining life prediction of decommissioned battery.

The present invention discloses a motor operation fault analysis method based on electric variable analysis, which specifically relates to the technical field of motor fault analysis, including: collecting motor operation data, dividing the collected motor operation data into a plurality of areas according to operation conditions, building a motor virtual simulation model corresponding to each operation condition, obtaining abnormal information by comparing the collected operation data with simulation prediction results, comparing the simulation prediction results with actual dependent variables, and summarizing the abnormal information of each operation condition; inputting the abnormal information of each operation condition into a corresponding fault diagnosis model, outputting a fault type probability matrix and a preliminary fault type set, mutually verifying the output results of each operation condition, and judging whether the preliminary fault type set needs to be corrected, thereby effectively solving the problem that the existing motor operation fault analysis is not fast and accurate enough, and improving the accuracy and reliability of fault diagnosis.

The application discloses a point cloud generation method, a laser radar chip and a laser radar. The point cloud generation method is applied to the laser radar chip. The laser radar chip comprises a receiving array and a plurality of histogram memories. The receiving array comprises a plurality of receiving units. Each receiving unit corresponds to a histogram memory. The method comprises the following steps: determining a receiving unit corresponding to histogram data greater than a target threshold as a valid receiving unit, and determining a receiving unit corresponding to histogram data less than or equal to the target threshold as an invalid receiving unit; assigning the histogram memory corresponding to the invalid receiving unit to the valid receiving unit; improving the sampling frequency of the time of flight of the photons received by the valid receiving unit; and determining the ranging distance and reflectivity corresponding to each valid receiving unit according to the histogram data corresponding to each valid receiving unit to generate a frame of point cloud data. In the foregoing manner, the point cloud measurement precision and the storage space utilization efficiency can be improved simultaneously.

The application discloses a bridge structure self-adaptive sampling monitoring method and device suitable for an extreme road environment, and belongs to the technical field of bridge structure health monitoring.The core target of the application is to solve the problems of repeated arrangement of existing monitoring equipment, road-bridge data fragmentation, and low self-adaptive sampling regulation precision.Through innovative design of a road-bridge multiplexing integrated sensing node, single-node multi-parameter and full-scene monitoring are realized, and the arrangement cost and construction difficulty are reduced; through construction of a self-adaptive sampling strategy based on fuzzy logic control, a feasible algorithm model is given, continuous dynamic regulation of sampling parameters is realized, monitoring precision and low power consumption are taken into account, deep collaborative linkage of road environment monitoring and bridge structure monitoring is realized, and the safe operation of the bridge structure under the extreme road environment is ensured.

The invention provides a fault detection method of an energy storage cabinet, electronic equipment and a system. The method comprises the steps of judging whether an alarm condition is met or not according to a preset fault judgment strategy and electric heat management information acquired by an energy storage module in the energy storage cabinet; when the alarm condition is not met, a second detection module is used for obtaining ventilation and waterproof information of the energy storage cabinet, and the ventilation and waterproof information is used for indicating the pressure difference condition of air flow in front of and behind a filter part and the water accumulation condition of a drainage groove; determining an environmental health degree score based on the ventilation and waterproof information; and when the environmental health degree score is lower than a preset threshold value, adjusting a detection sensitivity parameter of the fault judgment strategy. Compared with a related scheme which only monitors the electrical parameters or the external air temperature environment, the method considers the new detection dimension of the environmental health state of the cabinet body after the waterproof performance is improved, and the characteristic environmental factors corresponding to the ventilation waterproof factors are sensed in advance before the electrical fault is dominated.

The invention relates to a star sensor angle measurement accuracy compensation method, comprising: determining a generation mechanism of a star sensor angle measurement error, and establishing a high-dynamic star sensor angle measurement accuracy error model according to the mechanism; the generation mechanism of the star sensor angle measurement error is due to the mismatch between the star point energy center and the time center; using inertial navigation measurement data to solve carrier angle dynamic information in a flight mission; calculating the star sensor angle measurement error caused by dynamics online by combining the solved carrier angle dynamic information with the established high-dynamic star sensor angle measurement accuracy error model; and using the calculated star sensor angle measurement error caused by dynamics to compensate for the attitude angle output by the star sensor.

The invention discloses a wide-range and high-precision instantaneous rotating speed sensor and a measuring method. The wide-range and high-precision instantaneous rotating speed sensor comprises a broadband light source, an optical fiber coupler, a spectrograph, a signal processing device, a signal detection arm, a signal reference arm and a rotating shaft, the rotating shaft comprises a cylindrical shaft part and a shape processing part, and the radius of the shape processing part changes periodically; the broadband light source, the spectrograph, the signal detection arm and the signal reference arm are respectively connected with the optical fiber coupler through optical fibers; the spectrograph is connected with the signal processing device; the signal detection arm is aligned with the morphology processing part of the rotating shaft; the signal reference arm is aligned with the cylindrical shaft part of the rotating shaft; light emitted by the broadband light source is divided into two beams through the optical fiber coupler; the optical fiber coupler superposes the two returned light beams to generate an interference fringe signal; and the signal processing device obtains rotation angle and rotation speed information of the rotating shaft through processing. The invention provides the wide-range and high-precision instantaneous rotation speed sensor and the measurement method.

The utility model belongs to the technical field of escalator safety protection, and particularly discloses an escalator braking distance and step speed monitoring device which comprises a second chain wheel installed at the end of a main driving shaft on an escalator and a tensioning support installed on a truss of the escalator. According to the utility model, through the cooperation of the rotary encoder, the first chain wheel, the second chain wheel and the transmission chain, when the escalator runs, the second chain wheel drives the first chain wheel to rotate through the transmission chain, and the rotary encoder and the first chain wheel are coaxially arranged, so that the rotation of the first chain wheel is not influenced. The rotary encoder is used for collecting the rotation signal of the first chain wheel, so that the monitoring device can monitor the braking distance of a braking system and can also monitor the running speed of the stairs, the sampling frequency of the rotary encoder is high, and the accuracy of monitoring the braking distance of the escalator and the speed of the stairs is improved.

The invention relates to the technical field of environment monitoring, in particular to a pollution discharge monitoring system and method based on water body detection, and the pollution discharge monitoring method comprises the following steps: S1, setting a plurality of water quality monitoring points on a flowing path of a water body needing to be monitored; s2, judging whether pollution discharge of the current water quality monitoring point exceeds the standard or not; s3, marking the water quality monitoring point with the pollution discharge exceeding the standard as a control monitoring point, extracting a flowing path of the water body flowing out of the control monitoring point, and closing a control valve of the last water quality monitoring point flowing through the flowing path; s4, checking a pollution source in the pollution flow section, calculating the flow rates of the current water quality detection point and the downstream water quality detection points to obtain the last downstream water quality detection point through which the sewage which is discharged in a standard exceeding manner flows, and adjusting the flow rate of the downstream water quality detection point, so that the water which is not discharged in the standard exceeding manner can be discharged to the maximum extent. The water body exceeding discharge is blocked in time, so that the environment pollution is avoided.

The present invention proposes a method and system for distinguishing the fault type of a power transmission line based on a capsule network. By acquiring a large amount of PMU (Phasor Measurement Unit) data, the PMU data is graphically generated into a radar chart; a power transmission line fault type distinction model based on a capsule network is constructed, a training set and a test set are divided, the training set is input into the model for training, and the change characteristics of the graphical PMU data are extracted; the test set is input into the trained model for testing, and the test results are classified and output in the form of a confusion matrix; the fault type of the transmission line is distinguished. The present invention does not require logical reasoning, nor does it require the introduction of a large amount of knowledge describing the behavior of the protection system. It only needs to extract the features corresponding to different fault types from the graphical PMU data. The modeling and model modification process is simple and easy to operate, the PMU data is graphically generated, computing resources are saved, the model training and testing speed is fast, and the diagnostic accuracy is relatively high.

The invention relates to the technical field of contact angle measurement, in particular to a dynamic contact angle measurement method based on surface features. The dynamic contact angle measurement method based on the surface features comprises the following steps: acquiring a contour curve of a target surface, and extracting the surface features of the target surface based on the contour curve; matching a target recording frequency parameter set from a recording frequency parameter matching library based on the surface features of the target surface; adjusting the image acquisition frequency of image acquisition equipment according to the target recording frequency parameter set, and acquiring a side view image of the liquid drop in contact with the target surface by using the image acquisition equipment after frequency adjustment; and obtaining contact angles of the liquid drop at different acquisition time nodes through the side view images at different acquisition time nodes, and obtaining a contact angle change curve based on the contact angles at different acquisition time nodes. According to the invention, redundant data caused by excessive sampling can be avoided, and the sampling frequency can be improved when the contact angle changes drastically.

The application provides a positioning method and device applied to a ship environment. The method provided by the application comprises the following steps: collecting a group of positioning related data when a current sampling period arrives; determining a joint fluctuation index of an IQ sample according to a magnitude variation and a phase variation of the IQ sample, and determining an updated sampling period of next sampling according to the joint fluctuation index, a preset minimum sampling period and a preset standard sampling period; performing next round of data collection when the updated sampling period arrives, and repeating the above steps; screening and preprocessing a plurality of groups of continuously collected positioning related data to obtain a training sample set; training an initial positioning model based on the training sample set to obtain a trained positioning model; inputting a group of to-be-tested data collected in real time into the trained positioning model, so that the trained positioning model outputs a predicted position of a transmitter corresponding to real-time IQ samples in the group of to-be-tested data based on the group of to-be-tested data.

This invention relates to the fields of data processing and industrial internet technology, and discloses information management methods, systems, electronic devices, and storage media. The methods include receiving heterogeneous data from multiple sources such as carbon emissions and energy consumption, mapping them into holographic data packets containing a unified spatiotemporal reference; dynamically constructing a spatiotemporal causal correlation spectrum based on the correlation weights between data nodes; calculating the topological potential energy value of a physical region, and generating hardware control commands when the potential energy value exceeds a warning threshold to adaptively adjust the sampling frequency of the front-end device; monitoring the correlation entropy of data nodes, and when a phase transition condition is met, serializing and migrating a stable subgraph to a second storage layer and generating a hash index; retrieving data based on the index and performing integrity verification. This invention achieves dynamic adjustment of the sampling frequency through topological potential energy feedback and uses correlation entropy to drive storage evolution, optimizing hardware resource configuration while ensuring the spatiotemporal correlation and reliable traceability of data.

The present application relates to a touch positioning method, device, touch interaction system and storage medium. The touch positioning method includes: receiving touch position information transmitted by a stylus; the touch position information includes lamp bead data obtained by collecting and encoding the touch object in the corresponding refresh mode through the stylus; the lamp bead data includes the working time and working status of the lamp bead; identifying the touch position information and determining the current contact point position of the stylus. The present application can realize display touch based on the existing display screen packaging form, and the cost is extremely low, and the full-size touch sensitivity is consistent.