A control method and system for a whole-vehicle railway tunnel dust removal device

Abstract

The present application provides a control method and system for a whole-vehicle railway tunnel dust removal device. The present application performs acceleration detection on the transmission path of a folding conveyor belt of the railway tunnel dust removal device, and determines a node transmission smoothness sequence based on the transmission acceleration of each path node of the transmission path; performs electrostatic adsorption detection on the current conveyor belt node to obtain the electrostatic adsorption quality characteristics of the current conveyor belt node; performs feature learning based on the electrostatic adsorption quality characteristics to determine the transmission diffusion of the current folding conveyor belt node, determines a first transmission speed based on the transmission diffusion, and performs real-time correction on the first transmission speed according to the node transmission smoothness sequence, and controls the folding conveyor belt to transmit based on the second transmission speed after real-time correction. The transmission speed of the folding conveyor belt can be controlled based on the electrostatic adsorption quality characteristics, thereby reducing the risk of secondary dust emission of the folding conveyor belt during transmission.

Description

Technical Field

[0001] The present application relates to the technical field of conveyor belt control, and more specifically, to a control method and system for a whole-vehicle railway tunnel dust removal device. Background Art

[0002] Railway tunnel dust removal devices are primarily used to address sand damage caused by locomotive sand-scattering operations in long, steep tunnels. The device primarily consists of a power unit, piping system, dust collection box (including a hopper and discharger), horizontal conveyor belt, and folding conveyor belt. During operation, the device collects and transports sand scattered by the locomotive through the piping into the dust collection box. After initial separation in the upper portion of the dust collection box, the sand settles into the hopper below, where it is discharged by the discharger at the bottom of the hopper and onto the horizontal conveyor belt. The material is then transferred via the horizontal conveyor belt to the folding conveyor belt and ultimately transported to a material transport vehicle for storage before being transported out of the tunnel.

[0003] This can effectively control tunnel sand damage, reduce the spread of sand and dust, indirectly achieve dust removal effects inside the tunnel, and improve the tunnel environment.

[0004] As an important component for conveying sand and dust materials, folding conveyor belts are widely used in the construction and maintenance of railway tunnels, especially for transporting residual sand and dust generated by sand spreading operations from the inside of the tunnel to the outside. However, due to the complex tunnel environment and limited space, the folding conveyor belts are often disturbed by the vibration of the equipment body and uneven road surface during operation, causing periodic or sudden bumps and shaking in the conveying path, resulting in loosening, jumping, and sliding of sand on the conveyor belt, thereby causing secondary dust. This not only affects the dust removal effect, but may also cause secondary pollution of the equipment or wear and dust accumulation in key parts, seriously limiting the stable application and promotion of folding conveyor belts in railway tunnel dust removal scenarios. Therefore, how to suppress the dust risk caused by vibration and improve the transmission stability of folding conveyor belts under complex working conditions has become one of the key technical issues that need to be solved urgently. Summary of the Invention

[0005] The present application provides a control method and system for a whole-vehicle railway tunnel dust removal device, which can control the transmission speed of the folding conveyor belt based on the electrostatic adsorption quality characteristics, thereby reducing the risk of secondary dust emission during the transmission process of the folding conveyor belt.

[0006] In a first aspect, the present application provides a control method for a whole-vehicle railway tunnel dust removal device. The method can be executed by a network device, or can also be executed by a chip configured in the network device, and the present application does not limit this.

[0007] Specifically, the method includes:

[0008] Performing acceleration detection on a transmission path of a folding conveyor belt of a railway tunnel dust removal device, and determining a node transmission smoothness sequence based on the transmission acceleration of each path node of the transmission path;

[0009] Performing real-time detection on the current transmission quality of the folding conveyor belt, and judging whether the current conveyor belt node meets the transmission conditions according to the current transmission quality;

[0010] If the transmission belt node meets the transmission condition, perform electrostatic adsorption detection on the current transmission belt node to obtain the electrostatic adsorption quality characteristics of the current transmission belt node;

[0011] Feature learning is performed based on the electrostatic adsorption quality feature to determine the transmission diffusion of the current folding conveyor belt node, a first transmission speed is determined based on the transmission diffusion, and the first transmission speed is corrected in real time according to the node transmission smoothness sequence. The folding conveyor belt is controlled to perform transmission based on the second transmission speed after real-time correction.

[0012] In conjunction with the first aspect, in certain implementations of the first aspect, determining the node transmission smoothness sequence based on the transmission acceleration of each path node of the transmission path specifically includes:

[0013] Acquiring acceleration detection information corresponding to each path node of the transmission path during the transmission process;

[0014] Extract vibration intensity from the acceleration detection information corresponding to any path node to obtain multiple vibration intensity features, and determine the node transmission smoothness corresponding to the path node based on the feature mean of the vibration intensity features;

[0015] According to the label sequence of the path nodes, the node transmission smoothness corresponding to each path node is combined into the node transmission smoothness sequence.

[0016] In combination with the first aspect, in some implementations of the first aspect, in the process of determining whether the current transmission belt node meets the transmission condition based on the current transmission quality, the current transmission quality being higher than a preset transmission quality threshold is used as the transmission condition.

[0017] In conjunction with the first aspect, in certain implementations of the first aspect, performing feature learning based on the electrostatic adsorption quality feature to determine the transmission diffusion degree of the current folding conveyor belt node specifically includes:

[0018] Obtaining a preset feature learning depth, and extracting a plurality of electrostatic adsorption masses corresponding to the electrostatic adsorption mass feature and the vibration intensity features corresponding thereto based on the feature learning depth;

[0019] Based on multiple electrostatic adsorption masses and their corresponding vibration intensity features, correlation feature learning is performed to obtain the vibration impact factor;

[0020] The electrostatic adsorption quality feature is corrected based on the vibration influencing factor, and feature clustering is performed on the corrected electrostatic adsorption quality feature, and the transmission diffusion degree of the current folding conveyor belt node is determined based on the clustering result.

[0021] In combination with the first aspect, in certain implementations of the first aspect, a single hidden layer neural network model is used to perform feature clustering on the corrected electrostatic adsorption quality features.

[0022] In combination with the first aspect, in certain implementations of the first aspect, performing real-time correction of the first transmission speed based on a node transmission smoothness sequence specifically includes: obtaining a current path node, determining the node transmission smoothness corresponding to the current path node based on the node transmission smoothness sequence, and performing proportional correction on the first transmission speed according to the node transmission smoothness to obtain a second transmission speed.

[0023] In combination with the first aspect, in certain implementations of the first aspect, the transmission process of the folding conveyor belt is controlled based on the second transmission speed corrected in real time, and a closed-loop speed controller is used to adjust the motor output of the folding conveyor belt in real time.

[0024] In a second aspect, the present application provides a control system for a whole-vehicle railway tunnel dust removal device, which includes a conveyor belt control unit, the conveyor belt control unit including:

[0025] A conveyor belt detection module is used to detect the acceleration of the transmission path of the folding conveyor belt of the railway tunnel dust removal device, and determine the node transmission smoothness sequence based on the transmission acceleration of each path node of the transmission path;

[0026] A transmission judgment module is used to detect the current transmission quality of the folding conveyor belt in real time and judge whether the current conveyor belt node meets the transmission conditions according to the current transmission quality;

[0027] A transmission belt control module, configured to perform electrostatic adsorption detection on a current transmission belt node when the transmission belt node meets the transmission conditions, and obtain the electrostatic adsorption quality characteristics of the current transmission belt node;

[0028] The conveyor belt control module is also used to perform feature learning on the electrostatic adsorption quality characteristics, determine the transmission diffusion of the current folding conveyor belt node, determine the first transmission speed based on the transmission diffusion, and perform real-time correction on the first transmission speed according to the node transmission smoothness sequence, and control the folding conveyor belt for transmission based on the second transmission speed after real-time correction.

[0029] In a third aspect, the present application provides a computer terminal device, which includes a memory and a processor, wherein the memory stores a code, and the processor is configured to obtain the code and execute the above-mentioned control method for a whole-vehicle railway tunnel dust removal device.

[0030] In a fourth aspect, the present application provides a computer-readable storage medium, which stores at least one computer program, and the computer program is loaded and executed by a processor to implement the operations performed by the above-mentioned control method for a whole-vehicle railway tunnel dust removal device.

[0031] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0032] The present application provides a control method and system for a whole-vehicle railway tunnel dust removal device. First, the acceleration detection is performed on the transmission path of the folding conveyor belt of the railway tunnel dust removal device, and the node transmission smoothness sequence is determined based on the transmission acceleration of each path node of the transmission path; the current transmission quality of the folding conveyor belt is detected in real time, and it is judged whether the current conveyor belt node meets the transmission conditions based on the current transmission quality; if the conveyor belt node meets the transmission conditions, the current conveyor belt node is subjected to electrostatic adsorption detection to obtain the electrostatic adsorption quality characteristics of the current conveyor belt node; feature learning is performed based on the electrostatic adsorption quality characteristics to determine the transmission diffusion of the current folding conveyor belt node, and a first transmission speed is determined based on the transmission diffusion, and the first transmission speed is corrected in real time based on the node transmission smoothness sequence, and the folding conveyor belt is controlled to transmit based on the second transmission speed after real-time correction.

[0033] It can be seen that this application simulates the electrostatic dust removal mechanism, and after the dust particles are adsorbed by the electrode plate at the current conveyor belt node, the adhesion quality of the particles on the electrode surface is detected. The higher the adsorption quality, the easier it is for the dust to be stripped and adsorbed by the electric field, indicating that the looser the particles are and the worse the adhesion is, which means that these particles are more likely to be lifted up during vibration or accelerated transmission. The electrostatic adsorption quality characteristics are used as the characterization basis of the transmission diffusion of ash and sand during the transmission process. The transmission speed of the folding conveyor belt is corrected once by the transmission diffusion, and the transmission smoothness of different path nodes is determined according to the vibration intensity during the transmission process. The transmission speed of the folding conveyor belt is corrected twice based on the transmission smoothness, realizing adaptive control of the conveyor belt speed based on the ash and sand adhesion characteristics and the vibration conditions of the transmission path, reducing the suspension and diffusion of dust particles caused by the transmission process, and improving the dust suppression ability and working stability of the folding conveyor belt in the railway tunnel environment.

[0034] In summary, the present application can control the transmission speed of the folding conveyor belt based on the electrostatic adsorption quality characteristics, thereby reducing the risk of secondary dust generation during the transmission process of the folding conveyor belt. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is an exemplary flow chart of a control method for a whole-vehicle railway tunnel dust removal device according to some embodiments of the present application;

[0036] Figure 2 This is a diagram of the entire structure of the railway tunnel dust removal device provided in this application;

[0037] Figure 3 is a schematic structural diagram of a conveyor belt control unit according to some embodiments of the present application;

[0038] Figure 4 It is a structural schematic diagram of a computer terminal device for implementing a control method for a whole-vehicle railway tunnel dust removal device according to some embodiments of the present application. DETAILED DESCRIPTION

[0039] The present application performs acceleration detection on the transmission path of the folding conveyor belt of the railway tunnel dust removal device, and determines the node transmission smoothness sequence based on the transmission acceleration of each path node of the transmission path; performs real-time detection on the current transmission quality of the folding conveyor belt, and judges whether the current conveyor belt node meets the transmission conditions based on the current transmission quality; if the conveyor belt node meets the transmission conditions, performs electrostatic adsorption detection on the current conveyor belt node to obtain the electrostatic adsorption quality characteristics of the current conveyor belt node; performs feature learning based on the electrostatic adsorption quality characteristics to determine the transmission diffusion of the current folding conveyor belt node, determines the first transmission speed based on the transmission diffusion, and performs real-time correction on the first transmission speed according to the node transmission smoothness sequence, and controls the folding conveyor belt for transmission based on the second transmission speed after real-time correction. The transmission speed of the folding conveyor belt can be controlled based on the electrostatic adsorption quality characteristics, thereby reducing the risk of secondary dust emission during the transmission of the folding conveyor belt.

[0040] In order to better understand the above technical solution, the following will be combined with the accompanying drawings and specific implementation methods to describe the above technical solution in detail. Figure 1 , which is an exemplary flow chart of a control method for a whole-vehicle railway tunnel dust removal device according to some embodiments of the present application. The control method 100 for a whole-vehicle railway tunnel dust removal device mainly includes the following steps:

[0041] In step S101, acceleration detection is performed on a transmission path of a folded conveyor belt of a railway tunnel dust removal device, and a node transmission smoothness sequence is determined based on the transmission acceleration of each path node of the transmission path.

[0042] It should be noted that, in this application, the main components of the railway tunnel dust removal device are as follows: Figure 2 As shown, 1 is a control system, 2 is a folding conveyor belt, 3 is a horizontal conveyor belt, 4 is a dust collecting box, 4-1 is an ash hopper, 4-2 is a discharger, 5 is a pipeline, and 6 is a power unit.

[0043] It should be noted that the transmission path of the folding conveyor belt described in this application is the placement path of the folding conveyor belt, and the path nodes are multiple position intervals separated based on the transmission path of the folding conveyor belt. Optionally, in some embodiments, the transmission path of the folding conveyor belt of the railway tunnel dust removal device can be accelerated by a three-axis acceleration sensor, wherein the three-axis acceleration sensor includes three acceleration detection directions of XYZ. The three-axis acceleration sensor can be installed on any conveyor belt node, and when the horizontal conveyor belt transports materials on the transmission path, it outputs the path node and acceleration detection information of the conveyor belt node until the acceleration detection information corresponding to each path node is output.

[0044] Optionally, in some embodiments, the node transmission smoothness sequence described in the present application includes multiple node transmission smoothness and corresponding path nodes. The node transmission smoothness is used to quantify the degree of transmission smoothness on the path node. In specific implementation, determining the node transmission smoothness sequence based on the transmission acceleration of each path node of the transmission path specifically includes:

[0045] Acquiring acceleration detection information corresponding to each path node of the transmission path during the transmission process;

[0046] Extract vibration intensity from the acceleration detection information corresponding to any path node to obtain multiple vibration intensity features, and determine the node transmission smoothness corresponding to the path node based on the feature mean of the vibration intensity features;

[0047] According to the label sequence of the path nodes, the node transmission smoothness corresponding to each path node is combined into the node transmission smoothness sequence.

[0048] Among them, in the process of extracting vibration intensity from the acceleration detection information corresponding to any path node and obtaining multiple vibration intensity features, the acceleration detection information corresponding to the path node is multiple discrete three-dimensional acceleration detection information, and each three-dimensional acceleration detection information is obtained by performing equal-interval acceleration detection through a three-dimensional acceleration sensor, and each three-dimensional acceleration detection information corresponds to extracting a vibration intensity feature.

[0049] In specific implementation, the vibration intensity characteristic can be determined according to the following formula:

[0050]

[0051] in, To extract the vibration intensity features, 、 and The xyz three-axis acceleration values ​​corresponding to the three-dimensional acceleration detection information, 、 、 are the vibration intensity weights corresponding to the xyz axes respectively, which are calibrated as constants based on experience.

[0052] Preferably, in some embodiments, the node transmission smoothness corresponding to the path node = a preset standard vibration intensity feature / a characteristic mean of multiple vibration intensity features corresponding to the path node.

[0053] In step S102, the current transmission quality of the folding conveyor belt is detected in real time, and it is determined whether the current conveyor belt node meets the transmission conditions based on the current transmission quality.

[0054] It should be noted that the current conveyor belt node is a local area or monitoring point with independent detection and control functions in the transmission path of the horizontal conveyor belt or the folding conveyor belt. The current transmission quality is used to indicate the instant material status of the conveyor belt node located below the discharger. A pressure sensor or weighing module is provided at the node to collect the quality data of the material when it falls into the conveyor belt after being discharged through the discharger; by comprehensively analyzing the load value, stacking thickness, distribution uniformity and other information of the material at this position, the material detection quality index of the corresponding node is formed. The material detection quality is the current transmission quality, which is used to determine whether the node meets the transmission stability requirements. If so, the next step of electrostatic adsorption detection and speed correction control process is entered. Optionally, in some embodiments, a weighing sensor is used to perform real-time detection of the current transmission quality of the folding conveyor belt.

[0055] Optionally, in some embodiments, in the process of judging whether the current transmission belt node meets the transmission conditions based on the current transmission quality, the current transmission quality being higher than a preset transmission quality threshold is used as the transmission condition. In specific implementation, in order to ensure the safe operation of the transmission belt, the effective interval range of the current transmission quality can also be pre-set. When the current transmission quality detected is within the said effective interval range, it is judged that the transmission conditions are met.

[0056] In step S103, if the transmission belt node meets the transmission condition, an electrostatic adsorption detection is performed on the current transmission belt node to obtain the electrostatic adsorption quality characteristics of the current transmission belt node.

[0057] It should be noted that this application simulates the adsorption process of the electrostatic precipitator through electrostatic adsorption detection, so as to evaluate the adhesion and dust risk of materials (mainly dust or sand particles) on the conveyor belt, and specifically quantifies the difference in electrostatic adsorption efficiency of particles under the same transmission quality. For example, a local electrostatic adsorption unit is set up so that the particles are subjected to a high-voltage electric field when passing through the detection area. The dust particles are adsorbed onto the electrode plate in the electric field, and the adhesion and dust trend of the dust are indirectly determined by the proportion of adsorbed particles or the mass difference of the residual particles after adsorption.

[0058] It should be noted that the electrostatic adsorption quality described in this application refers to the ratio of the mass of the adsorbed particles to the total mass of the transmitted dust after the unit mass of dust is adsorbed under the action of an electric field by simulating the electrostatic adsorption method in a specific transmission node area. The larger the electrostatic adsorption mass, the weaker the adhesion between the dust particles, the easier it is to be peeled off by the electric field, and the higher the risk of dust emission; conversely, the particles have strong adhesion and the dust deposition is stable. Optionally, in some embodiments, the current transmission belt node is subjected to electrostatic adsorption detection by a high-voltage electrode group. In specific implementation, an electrostatic adsorption detection unit is set up above or beside the current transmission belt node. The electrostatic adsorption detection unit consists of a high-voltage electrode group and a corresponding capacitive sensor, wherein the high-voltage electrode group includes a corona wire and a micro dust collecting electrode plate. By applying negative polarity high voltage, the electrostatic adsorption field is simulated, and the electrostatic adsorption field is a non-continuous electric field. The detection process is locally triggered for a short time, and the dust adhesion mass on the micro electrode plate is detected by the capacitive sensor as the electrostatic adsorption mass.

[0059] It should be noted that capacitance is determined by the insulating medium (dielectric layer) between two conductors. When dust adheres to the surface of the micro-electrode plate, it is equivalent to adding a layer of medium with a certain dielectric constant between the micro-electrode plate and the counter electrode, which will cause the capacitance between the plates to change. The more dust adheres, the thicker the effective dielectric layer, resulting in regular changes in the measured capacitance value. This can be detected by a highly sensitive capacitance sensor, thereby indirectly judging the quality of the attached dust. In some specific embodiments of the present application, one side of the capacitor is a conductive electrode plate (i.e., a dust-accumulating micro-electrode plate), and an insulating substrate and a lower electrode are provided coplanar with the conductive electrode plate to form a capacitor plate structure; the capacitance sensor is connected between the upper and lower electrodes to form a capacitance measurement circuit, wherein the MEMS micro-capacitance detection chip performs capacitance detection and performs linear mapping based on the capacitance detection value to obtain the corresponding electrostatic adsorption quality.

[0060] Preferably, in some embodiments, the electrostatic adsorption field in the present application is a non-continuous electric field, the detection process is locally triggered for a short time, and after the electrostatic adsorption quality detection is completed, the electrode plate is subjected to timed or quantitative vibration impact through a mechanical vibration device. The vibration action loosens the particles attached to the surface of the electrode plate and slides along the surface of the electrode plate to the bottom ash hopper, thereby realizing the collection and removal of particles and facilitating repeated detection of different transmission states. In specific implementation, the electrostatic adsorption quality corresponding to each path node can be detected separately during the transmission process, and a multi-dimensional feature vector can be composed according to the order of the path nodes as the electrostatic adsorption quality feature.

[0061] In step S104, feature learning is performed based on the electrostatic adsorption quality feature to determine the transmission diffusion of the current folding conveyor belt node, a first transmission speed is determined based on the transmission diffusion, and the first transmission speed is corrected in real time according to the node transmission smoothness sequence. The folding conveyor belt is controlled to perform transmission based on the second transmission speed after real-time correction.

[0062] It should be noted that the transmission diffusion described in this application is used to quantify the degree of spatial diffusion of dust particles during the transmission process under the transmission state of the current conveyor belt node. Feature learning is performed based on the electrostatic adsorption quality characteristics to determine the transmission diffusion of the current folding conveyor belt node. Real-time electrostatic adsorption quality detection also avoids the interference of environmental changes on the risk of secondary dust. Preferably, in some embodiments, feature learning is performed based on the electrostatic adsorption quality characteristics to determine the transmission diffusion of the current folding conveyor belt node specifically including:

[0063] Obtaining a preset feature learning depth, and extracting a plurality of electrostatic adsorption masses corresponding to the electrostatic adsorption mass feature and the vibration intensity features corresponding thereto based on the feature learning depth;

[0064] Based on multiple electrostatic adsorption masses and their corresponding vibration intensity features, correlation feature learning is performed to obtain the vibration impact factor;

[0065] The electrostatic adsorption quality feature is corrected based on the vibration influencing factor, and feature clustering is performed on the corrected electrostatic adsorption quality feature, and the transmission diffusion degree of the current folding conveyor belt node is determined based on the clustering result.

[0066] In specific implementation, the preset feature learning depth is K: it represents the number of samples extracted from nearly K nodes. Based on the feature learning depth, multiple electrostatic adsorption masses that are nearest to the current moment in the electrostatic adsorption mass feature and the vibration intensity feature corresponding to the detection time of the electrostatic adsorption mass are extracted, and the Pearson correlation coefficient between the multiple electrostatic adsorption masses and the corresponding vibration intensity features is used as the vibration influence factor. The larger the vibration influence factor, the greater the degree of interference of vibration on dust adhesion and diffusion. It is necessary to perform proportional correction on the electrostatic adsorption mass feature based on the vibration influence factor, thereby increasing the accuracy of feature clustering.

[0067] In some specific embodiments of the present application, a single hidden layer neural network model is used to perform feature clustering on the corrected electrostatic adsorption quality characteristics, and the transmission diffusion of the current folding conveyor belt node is determined based on the clustering results. In specific implementation, in the process of learning and clustering the corrected electrostatic adsorption quality characteristics using a single hidden layer neural network, the corrected electrostatic adsorption quality characteristics can be input in the form of a data vector, the input layer receives the above data vector, the hidden layer sets multiple activation function nodes, and the output layer maps the learned features to several predefined clustering categories, each category represents a different level of transmission diffusion, and the transmission diffusion data is mapped according to the clustering results corresponding to the corrected electrostatic adsorption quality characteristics, and the corresponding mapping value is obtained as the transmission diffusion, wherein the different dimensions of the data vector respectively include: the electrostatic adsorption mass corresponding to different path nodes.

[0068] In a specific implementation, the system constructs a training set to supervise the single hidden layer neural network model in learning cluster boundaries. This training set consists of multiple known electrostatic adsorption quality feature samples of dust particles with different adhesion properties and their corresponding manual transmission diffusion scores. During training, the samples are input into the single hidden layer neural network model for forward propagation, and the output classification labels are compared with the manual scores. Specifically, multiple corrected electrostatic adsorption quality feature samples are input into the single hidden layer neural network for classification training. The hidden layer of the single hidden layer neural network includes multiple activation function nodes for classifying the input samples. The classification results are output through the output layer of the single hidden layer neural network. The classification results are then compared with the corresponding manual transmission diffusion scores. When the correlation between the clustering results and the manual transmission diffusion scores (which can be measured as mean squared error) falls below a preset threshold, the network parameters are optimized through a backpropagation mechanism. The activation parameters of the activation functions in the hidden layer of the single hidden layer neural network are adjusted until the correlation between the clustering results and the manual transmission diffusion scores reaches a preset standard. The mapping relationship between the clustering results and the manual transmission diffusion scores is then obtained, and the training of the single hidden layer neural network model is determined to be complete.

[0069] Preferably, in some embodiments, the process of determining the first transmission speed based on the transmission diffusion degree may be performed by performing interval mapping according to a preset linear mapping table and a threshold interval corresponding to the transmission diffusion degree to obtain the first transmission speed.

[0070] Optionally, in some other embodiments of the present application, multiple electrostatic adsorption masses corresponding to nearly K path nodes and the corresponding vibration intensity characteristics can be obtained, and the influence weights of the vibration intensity characteristics on the electrostatic adsorption mass can be fitted using multivariate linear regression to form a vibration influence factor. Clustering is performed based on the vibration response factor and the electrostatic adsorption mass characteristics, and the transmission diffusion degree is determined based on the clustering results. This application does not limit this.

[0071] Preferably, in some embodiments, real-time correction of the first transmission speed based on the node transmission smoothness sequence specifically includes: obtaining the current path node, determining the node transmission smoothness corresponding to the current path node based on the node transmission smoothness sequence, and proportionally correcting the first transmission speed according to the node transmission smoothness to obtain the second transmission speed. In specific implementation, the K-means clustering algorithm can be used to cluster the node transmission smoothness to obtain multiple cluster centers and their corresponding proportional correction coefficients, and the proportional correction coefficient is determined based on the cluster center corresponding to the node transmission smoothness to perform proportional correction on the first transmission speed.

[0072] Preferably, in some embodiments, the process of controlling the transmission of the folding conveyor belt based on the second transmission speed after real-time correction can use a closed-loop speed controller to adjust the motor output of the folding conveyor belt in real time based on the second transmission speed. In specific implementation, the error value between the current conveying speed and the second transmission speed is collected as the controller input, and a PID control algorithm is used to output a control signal to drive the motor speed adjustment based on the error. This part of the speed control content can also use other existing speed feedback control algorithms, and this application does not limit this.

[0073] In addition, in another aspect of the present application, in some embodiments, the present application provides a control system for a vehicle-mounted railway tunnel dust removal device, the system comprising a conveyor belt control unit, reference Figure 3 , which is a schematic diagram of exemplary hardware and / or software structure of a conveyor belt control unit according to some embodiments of the present application. The conveyor belt control unit 200 includes: a conveyor belt detection module 201, a transmission judgment module 202, and a conveyor belt control module 203, which are described as follows:

[0074] The conveyor belt detection module 201 is used to detect the acceleration of the transmission path of the folded conveyor belt of the railway tunnel dust removal device, and determine the node transmission smoothness sequence based on the transmission acceleration of each path node of the transmission path;

[0075] The transmission judgment module 202 is used to detect the current transmission quality of the folding conveyor belt in real time and judge whether the current conveyor belt node meets the transmission conditions according to the current transmission quality;

[0076] The transmission belt control module 203 is configured to perform an electrostatic adsorption detection on the current transmission belt node when the transmission belt node meets the transmission condition, and obtain the electrostatic adsorption quality characteristics of the current transmission belt node;

[0077] The conveyor belt control module 203 is also used to perform feature learning on the electrostatic adsorption quality characteristics, determine the transmission diffusion of the current folding conveyor belt node, determine the first transmission speed based on the transmission diffusion, and perform real-time correction on the first transmission speed according to the node transmission smoothness sequence, and control the folding conveyor belt to transmit based on the second transmission speed after real-time correction.

[0078] The above describes in detail an example of a control method and system for a whole-vehicle railway tunnel dust removal device provided in an embodiment of the present application. It can be understood that in order to achieve the above functions, the corresponding device includes a hardware structure and / or software module corresponding to performing each function.

[0079] Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function in the application is executed in hardware or in a computer software-driven hardware manner depends on the specific application and design constraints of the technical solution. Therefore, professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0080] In addition, the present application also provides a computer terminal device, which includes a memory and a processor, the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned control method for a whole-vehicle railway tunnel dust removal device.

[0081] In some embodiments, reference Figure 4 , which is a schematic diagram of the structure of a computer terminal device for implementing a control method for a whole vehicle railway tunnel dust removal device according to some embodiments of the present application. A control method for a whole vehicle railway tunnel dust removal device in the above embodiment can be achieved by Figure 4 The computer terminal device 300 shown in FIG. 1 is implemented as shown in FIG. 1 , and the computer terminal device 300 includes at least one communication bus 301 , a communication interface 302 , a processor 303 and a memory 304 .

[0082] The processor 303 can be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more processors for controlling the execution of a control method for a whole-vehicle railway tunnel dust removal device in this application.

[0083] The communication bus 301 may include a path for transmitting information between the aforementioned components.

[0084] Memory 304 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, an optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer. Memory 304 may be independent and connected to processor 303 via communication bus 301. Memory 304 may also be integrated with processor 303.

[0085] Memory 304 is used to store program code for executing the solution of the present application, and is controlled by processor 303 for execution. Processor 303 is used to execute the program code stored in memory 304. The program code may include one or more software modules. In the above embodiment, the determination of the transmission diffusion degree can be implemented by processor 303 and one or more software modules in the program code in memory 304.

[0086] The communication interface 302 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0087] Optionally, the computer terminal device 300 may further include a power supply 305 for providing power to various devices or circuits in the real-time computer terminal device.

[0088] In a specific implementation, as an example, a computer terminal device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. The processor herein may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0089] The aforementioned computer terminal device can be a general-purpose computer terminal device or a dedicated computer terminal device. In a specific implementation, the computer terminal device can be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of this application do not limit the type of computer terminal device.

[0090] In addition, in other aspects of the present application, a computer-readable storage medium is provided, which stores at least one computer program, and the computer program is loaded and executed by a processor to implement the operations performed by the above-mentioned control method for a whole-vehicle railway tunnel dust removal device.

[0091] In summary, the embodiment of the present application discloses a control method and system for a whole-vehicle railway tunnel dust removal device. By performing acceleration detection on the transmission path of the folding conveyor belt of the railway tunnel dust removal device, a node transmission smoothness sequence is determined based on the transmission acceleration of each path node of the transmission path; the current transmission quality of the folding conveyor belt is detected in real time, and it is judged whether the current conveyor belt node meets the transmission conditions based on the current transmission quality; if the conveyor belt node meets the transmission conditions, an electrostatic adsorption detection is performed on the current conveyor belt node to obtain the electrostatic adsorption quality characteristics of the current conveyor belt node; feature learning is performed based on the electrostatic adsorption quality characteristics to determine the transmission diffusion of the current folding conveyor belt node, a first transmission speed is determined based on the transmission diffusion, and the first transmission speed is corrected in real time according to the node transmission smoothness sequence, and the folding conveyor belt is controlled to transmit based on the second transmission speed after real-time correction. The transmission speed of the folding conveyor belt can be controlled based on the electrostatic adsorption quality characteristics, thereby reducing the risk of secondary dust emission of the folding conveyor belt during the transmission process.

[0092] The above description is merely an embodiment of the present application. Common knowledge such as the specific technical solutions or features of the solutions is not described in detail herein. It should be noted that those skilled in the art may make various modifications and improvements without departing from the technical solution of the present application, and these modifications and improvements should also be considered within the scope of protection of the present application. These modifications and improvements will not affect the effectiveness of the implementation of the present application or the practical application of the patent.

[0093] The scope of protection claimed by this application shall be determined by the content of the claims. The specific embodiments and other descriptions in the specification may be used to interpret the content of the claims. Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of the invention. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application is intended to include such modifications and variations.

Claims

1. A control method for a whole vehicle railway tunnel dust removal device, characterized in that: include: Performing acceleration detection on a transmission path of a folding conveyor belt of a railway tunnel dust removal device, and determining a node transmission smoothness sequence based on the transmission acceleration of each path node of the transmission path; Performing real-time detection on the current transmission quality of the folding conveyor belt, and judging whether the current conveyor belt node meets the transmission conditions according to the current transmission quality; If the transmission belt node meets the transmission condition, perform electrostatic adsorption detection on the current transmission belt node to obtain the electrostatic adsorption quality characteristics of the current transmission belt node; Feature learning is performed based on the electrostatic adsorption quality feature to determine the transmission diffusion of the current folding conveyor belt node, a first transmission speed is determined based on the transmission diffusion, and the first transmission speed is corrected in real time according to the node transmission smoothness sequence. The folding conveyor belt is controlled to perform transmission based on the second transmission speed after real-time correction.

2. The method according to claim 1, wherein Determining the node transmission smoothness sequence based on the transmission acceleration of each path node of the transmission path specifically includes: Acquiring acceleration detection information corresponding to each path node of the transmission path during the transmission process; Extract vibration intensity from the acceleration detection information corresponding to any path node to obtain multiple vibration intensity features, and determine the node transmission smoothness corresponding to the path node based on the feature mean of the vibration intensity features; According to the label sequence of the path nodes, the node transmission smoothness corresponding to each path node is combined into the node transmission smoothness sequence.

3. The method according to claim 1, wherein In the process of judging whether the current transmission belt node meets the transmission condition according to the current transmission quality, the current transmission quality being higher than a preset transmission quality threshold is used as the transmission condition.

4. The method according to claim 1, wherein The feature learning is performed based on the electrostatic adsorption quality feature to determine the transmission diffusion degree of the current folding conveyor belt node, specifically including: Obtaining a preset feature learning depth, and extracting a plurality of electrostatic adsorption masses corresponding to the electrostatic adsorption mass feature and the vibration intensity features corresponding thereto based on the feature learning depth; Based on multiple electrostatic adsorption masses and their corresponding vibration intensity features, correlation feature learning is performed to obtain the vibration impact factor; The electrostatic adsorption quality feature is corrected based on the vibration influencing factor, and feature clustering is performed on the corrected electrostatic adsorption quality feature, and the transmission diffusion degree of the current folding conveyor belt node is determined based on the clustering result.

5. The method according to claim 4, wherein A single hidden layer neural network model is used to perform feature clustering on the corrected electrostatic adsorption quality features.

6. The method according to claim 1, wherein The real-time correction of the first transmission speed according to the node transmission smoothness sequence specifically includes: obtaining a current path node, determining the node transmission smoothness corresponding to the current path node based on the node transmission smoothness sequence, and proportionally correcting the first transmission speed according to the node transmission smoothness to obtain a second transmission speed.

7. The method according to claim 1, wherein The transmission process of the folding conveyor belt is controlled based on the second transmission speed corrected in real time, and the motor output of the folding conveyor belt is adjusted in real time using a closed-loop speed controller.

8. A control system for a whole-vehicle railway tunnel dust removal device, comprising a conveyor belt control unit, wherein the conveyor belt control unit is configured to execute a control method for a whole-vehicle railway tunnel dust removal device according to any one of claims 1 to 7, characterized in that: The conveyor belt control unit comprises: A conveyor belt detection module is used to detect the acceleration of the transmission path of the folding conveyor belt of the railway tunnel dust removal device, and determine the node transmission smoothness sequence based on the transmission acceleration of each path node of the transmission path; A transmission judgment module is used to detect the current transmission quality of the folding conveyor belt in real time and judge whether the current conveyor belt node meets the transmission conditions according to the current transmission quality; A transmission belt control module, configured to perform electrostatic adsorption detection on a current transmission belt node when the transmission belt node meets the transmission conditions, and obtain the electrostatic adsorption quality characteristics of the current transmission belt node; The conveyor belt control module is also used to perform feature learning on the electrostatic adsorption quality characteristics, determine the transmission diffusion of the current folding conveyor belt node, determine the first transmission speed based on the transmission diffusion, and perform real-time correction on the first transmission speed according to the node transmission smoothness sequence, and control the folding conveyor belt for transmission based on the second transmission speed after real-time correction.

9. A computer terminal device, characterized in that: The computer terminal device includes a memory and a processor, the memory stores a code, and the processor is configured to obtain the code and execute a control method for a whole-vehicle railway tunnel dust removal device according to any one of claims 1 to 7.

10. A computer-readable storage medium storing at least one computer program, characterized in that: The computer program is loaded and executed by a processor to implement the operations performed by the control method for a whole-vehicle railway tunnel dust removal device as described in any one of claims 1 to 7.

Images