Train operation parameter-based optimal sanding decision-making simulation method

Abstract

The present invention relates to the technical field of mathematical modeling of optimal value functions, and specifically to a train operation parameter-based optimal sanding decision-making simulation method. The method comprises the following steps: step (1): constructing a hierarchical structure model for sanding logical judgment, and introducing an AHP algorithm to calculate a weight vector of an objective, so as to implement autonomous sanding logical judgment; step (2): using an adhesion coefficient as an evaluation criterion for an sanding effect to establish a basic database of a plurality of train operation parameters and corresponding optimal sanding quantities; and step (3): using multivariate fitting to establish a sanding quantity optimal value function, calculating the optimal sanding quantity under the overall effect of the train operation parameters, and adjusting an air pressure so as to output an optimal sanding decision to a lower computer. The present invention provides a feasible simulation calculation method for autonomous sanding logical judgment and optimal sanding quantity decision-making, so as to greatly improve particle ejection velocity, sanding accuracy and adhesion-increasing efficiency.

Description

Simulation method for optimal sand spreading decision-making based on train operation parameters Technical Field

[0001] The present invention relates to the technical field of mathematical modeling of optimal value functions, and in particular to an optimal sand spreading decision simulation method based on train operation parameters. Background Art

[0002] To address the low adhesion problem often encountered in railway systems, sanding is often used to increase wheel-rail adhesion. The principle is to install a sanding device in front of the locomotive's traction wheelset to spray adhesion-enhancing particles onto the wheel-rail interface, thereby increasing its shear resistance and removing contaminants from the rail surface, thereby achieving adhesion.

[0003] Currently, there are three prominent issues with sand spreading in its current application. First, the logical judgment of sand spreading is highly subjective, primarily based on the locomotive driver's own experience, which can easily lead to problems such as delayed sand spreading. Second, the sand spreading process uses a fixed flow rate, which cannot be adjusted. If the amount of sand spread is too small, it will not meet the locomotive's adhesion coefficient requirements. If the amount of sand spread is too large, it will not only waste particles, but also aggravate wheel-rail damage and even cause circuit insulation failures. Third, the particle injection speed of the current sand spreading device is too low. Under the influence of external factors, there is a significant difference between the amount of particles that actually enter the wheel-rail interface to increase adhesion and the amount of sand spread. To address these issues, domestic and foreign experts have proposed adjusting the sand spreading flow rate based on the train's operating speed or acceleration. However, this method still has drawbacks: first, the flow rate adjustment is a hierarchical control, resulting in low control accuracy; second, there are many factors that affect the amount of sand spread, and making sand spreading decisions based on only a single factor is one-sided.

[0004] As an important part of the sand spreading device, the sand spreading control system has an urgent problem to be solved by technicians in this field: how to provide an optimal sand spreading decision simulation method based on train operation parameters that comprehensively analyzes the factors affecting the amount of sand spreading, automates the sand spreading logic judgment and the decision of the optimal sand spreading amount, and greatly improves the sand spreading accuracy and viscosity increasing efficiency.

[0005] Summary of the Invention

[0006] This paper proposes a simulation method for optimal sand-spreading decisions based on train operating parameters. This method comprehensively analyzes the impact of these parameters on sand-spreading quantity. Using the AHP (Analysis of the Hierarchy) algorithm and modeling the optimal sand-spreading quantity function, it provides a novel simulation method for autonomous sand-spreading logic and optimal sand-spreading quantity decisions. This simulation method was tested for feasibility and effectiveness on a high-speed sand-spreading device, significantly improving particle injection velocity, sand-spreading accuracy, and viscosity-increasing efficiency.

[0007] The automatic control method for sand spreading based on train operation parameters according to the present invention comprises the following steps:

[0008] Step (1): Construct a hierarchical structure model of sand spreading logic judgment based on train operation parameters, introduce the AHP algorithm to calculate the weight vector of the target, and realize the autonomous judgment of sand spreading logic;

[0009] Step (2): Using the adhesion coefficient as the evaluation criterion for the sand spreading effect, a basic database of various train operation parameters and the corresponding optimal sand spreading amount is established;

[0010] Step (3): Use multivariate fitting to establish the optimal value function of sand spreading amount, calculate the optimal sand spreading amount under the comprehensive influence of train operation parameters, and output the optimal sand spreading decision to the lower computer by adjusting the air pressure.

[0011] As a preference, in step (1), a hierarchical model of sand spreading logic judgment is established, specifically:

[0012] The two decision-making options of sanding or not sanding are used as the option layer of the hierarchical model;

[0013] The train operation parameters including locomotive acceleration, train speed, creep rate, braking level and traction level are used as the criterion layer of the hierarchical structure model;

[0014] The weight vector representing the sand spreading logic is used as the target layer of the hierarchical model.

[0015] Preferably, in step (1), the process of obtaining the weight vector is as follows:

[0016] According to the nine-scale method and weight function, the comparison matrices from the criterion layer to the target layer and from the solution layer to the criterion layer are constructed respectively;

[0017] The consistency ratio method was used to test the consistency of the comparison matrix between each level;

[0018] When the comparison matrix satisfies the consistency test, the geometric mean method in the AHP algorithm is introduced to solve the sand spreading weight vector, specifically:

[0019] Among them, W i is the weight vector of the i-th factor affecting decision-making in the criterion layer, a ij is the ratio between the scale corresponding to the i-th factor and the scale corresponding to the j-th factor in the comparison matrix, a kj is the ratio between the scale corresponding to the kth factor and the scale corresponding to the jth factor in the comparison matrix, and n is the number of factors affecting the amount of sand spreading.

[0020] As a preference, in step (1), the weight vector of the sand spreading logic is calculated as follows: W = W A T ×W B ;

[0021] Among them, W is the weight vector of the sand spreading logic, W A is the weight vector from the criterion layer to the target layer, W B is the weight vector from the solution layer to the criterion layer; when the value representing sand spreading in the target layer weight vector is greater than 0.5, it means that the demand for sand spreading is strong and the sand spreading action is executed.

[0022] As a preference, in step (2), a basic database of various train operation parameters and corresponding optimal sand spreading amounts is established, specifically:

[0023] Based on a rolling simulation test machine, a test scheme was developed to determine the recovery criteria for the wheel-rail adhesion coefficient after sanding under low-adhesion conditions and to determine the effect of train operating parameters on the optimal sanding amount.

[0024] The operating parameters of each train were designed as a multi-level orthogonal test table, and the amount of sand spread in each test was adjusted. The adhesion coefficient was used as the evaluation criterion for the sand spreading effect. The maximum adhesion coefficient corresponds to the optimal sand spreading amount.

[0025] Determine the optimal sand spreading amount under each set of tests and establish a basic database of various train operating parameters and the corresponding optimal sand spreading amount.

[0026] Preferably, in step (3), a multivariate fitting is used to establish an optimal value function for the amount of sand spreading, and the optimal amount of sand spreading under the comprehensive influence of train operation parameters is calculated, specifically:

[0027] Based on the basic database of optimal sand spreading amount, the optimal value function type is selected, and the fitting formula between train operating parameters and optimal sand spreading amount is obtained by the least squares method;

[0028] The optimal sand spreading amount under the current train operation parameters is calculated based on the optimal value function relationship.

[0029] As a preference, in step (3), after logical judgment and optimal value calculation, the optimal sand spreading decision is output to the lower computer by adjusting the air pressure, specifically:

[0030] Conduct linear regression analysis on the effect of air pressure on sand spreading amount and establish a functional relationship between the two.

[0031] Calculate the air pressure value corresponding to the optimal sand spreading amount according to the functional relationship between air pressure and sand spreading amount;

[0032] The electrical components such as solenoid valves and air pressure controllers are used to execute the optimal sand spreading decision, realize the sand spreading logic judgment and adjust the optimal sand spreading amount.

[0033] Preferably, an optimal sand spreading decision simulation system based on train operation parameters is adopted, and the optimal sand spreading decision simulation system based on train operation parameters includes a train operation parameter detection system, an optimal sand spreading decision model and a sand spreading device;

[0034] The optimal sand spreading decision simulation method is written into a laptop computer through a program. The upper computer is the train operation parameter detection system, and the lower computer is the air pressure controller. Both the upper and lower computers share data with the computer through a data cable.

[0035] The sand spreading device includes an air pressure generator, an air pressure controller, a sand box, a sand spreading valve, a sand spreading pipe, a high-speed spray gun and multiple air supply pipes; the air pressure generator is located at the front end of the sand spreading device, and its air outlet is connected to the air pressure controller through an air supply pipe. After adjustment, the compressed air flows through a diverter and is respectively input into the sand spreading valve and the high-speed spray gun; the sand spreading valve is installed at the bottom of the sand box and is used to suck in the viscosity-increasing particles stored in the sand box; the sand spreading valve and the high-speed spray gun are connected through the sand spreading pipe.

[0036] Preferably, the high-speed sand spreading device adopts a two-stage accelerated sand spreading mode, the sand spreading valve uses the suction effect to accelerate the viscosity-increasing particles once, and the high-speed spray gun uses a contraction nozzle to perform a second acceleration.

[0037] Preferably, the train operation parameter detection system is used to collect train operation parameters, including an encoder and an acceleration sensor.

[0038] The beneficial effects of the present invention are:

[0039] (1) This paper introduces the AHP algorithm to establish a hierarchical model for sand-spreading logic judgment based on train operating parameters. The model consists of a solution layer, a criterion layer, and a target layer. This model transforms the sand-spreading logic judgment problem into the problem of determining a sand-spreading weight vector. Based on the vector size, the model can autonomously determine whether the current train requires sand-spreading. This simulation method effectively avoids subjective human intervention and improves the accuracy of sand-spreading logic judgment.

[0040] (2) The present invention is based on a rolling simulation test machine, which simulates the impact of train operating parameters on the optimal sand spreading amount, establishes a basic database of many operating parameters and corresponding optimal sand spreading amounts, and uses multivariate fitting to establish the optimal value function of sand spreading amount. Linear regression analysis is performed on the test of the impact of air pressure on sand spreading amount, and a linear relationship between the two is established. The optimal sand spreading decision is executed through electrical components such as solenoid valves and air pressure controllers, realizing the logical judgment of sand spreading and the stepless regulation of the optimal value of sand spreading amount. Compared with traditional sand spreading amount adjustment methods such as manual experience and graded control of sand spreading amount, the optimal sand spreading decision simulation method proposed in the present invention avoids the subjectivity and lag caused by manual experience intervention, and the control accuracy is as high as 0.1kg / min.

[0041] (3) The high-speed sand-spreading device used in the present invention to test the optimal sand-spreading simulation method consists of key components such as a sand-spreading valve and a high-speed spray gun. Compressed air is passed through the sand-spreading valve to generate a suction effect that accelerates the particles once, while the converging nozzle of the high-speed spray gun accelerates the particles twice. This sand-spreading method avoids the energy loss caused by particles passing through the sand-spreading tube. The two-stage acceleration sand-spreading mode is used to increase the particle injection speed to approximately 50 m / s, thereby improving the accuracy of sand-spreading and the viscosity-increasing efficiency.

[0042] (4) The sand-spreading automatic control device based on train operation parameters uses new hard particles with increased viscosity. Compared with ordinary quartz sand particles, the new hard particles with increased viscosity can improve the viscosity-increasing effect by 30% and reduce the particle usage by 87.5%. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] FIG1 is a schematic diagram of an automatic sand spreading control method based on train operation parameters in an embodiment.

[0044] FIG2 is a flow chart of a method for calculating a sand spreading logic weight vector in an embodiment.

[0045] FIG3 is a schematic diagram of a sand spreading logic judgment hierarchical structure model based on train operation parameters established by the AHP algorithm in an embodiment.

[0046] FIG4 is a schematic diagram of the functional relationship between air pressure and sand spreading amount established after performing regression analysis on the test of the influence of air pressure on sand spreading amount in the embodiment.

[0047] FIG5 is a schematic structural diagram of a high-speed sand spreading device for detecting the effectiveness of the optimal sand spreading decision simulation method in an embodiment.

[0048] FIG6 is a schematic diagram of the assembly structure of the sand spreading valve and the sand box in the embodiment.

[0049] FIG7 is a schematic diagram of the high-speed spray gun structure in an embodiment.

[0050] FIG8 is a schematic diagram of the detection results of the particle injection velocity of the sand spreading device in the embodiment.

[0051] Markings in the accompanying drawings: 1-air pressure generator, 2-air outlet, 3-air pressure input interface, 4-air pressure output interface, 5-air pressure controller, 6-diverter, 7-sand spreading valve air pressure input pipe, 8-sand spreading valve, 9-sand box, 10-sand spreading pipe, 11-high-speed spray gun, 12-data cable, 13-laptop computer, 14-train operation parameter detection system, 15-high-speed spray gun air pressure input pipe, 16-sand spreading valve sand suction port, 17-sand spreading valve air inlet, 18-sand spreading valve throat, 19-sand spreading valve sand outlet, 20-high-speed spray gun sand inlet, 21-high-speed spray gun nozzle, 22-high-speed spray gun mixing chamber, 23-high-speed spray gun outlet. DETAILED DESCRIPTION

[0052] In order to further understand the content of the present invention, the present invention is described in detail with reference to the accompanying drawings and embodiments. It should be understood that the embodiments are merely for explaining the present invention and are not intended to limit the present invention.

[0053] Example

[0054] As shown in FIG1 , this embodiment provides a simulation method for optimal sand spreading decision-making based on train operation parameters, which includes the following steps:

[0055] Step (1): Construct a hierarchical structure model of sand spreading logic judgment based on train operation parameters, calculate the weight vector of sand spreading logic through the AHP algorithm, realize the autonomous judgment of sand spreading logic, and decide whether the train needs to spread sand according to the size of the weight vector, as shown in Figure 2. Specifically:

[0056] First, as shown in Figure 3, a hierarchical model of sand-spreading logic judgment based on train operating parameters is constructed. The two decision-making options of sand-spreading or not sand-spreading are taken as the solution layer A of the hierarchical model. The criteria or factors affecting the sand-spreading amount decision, including locomotive acceleration, train speed, creep rate, braking level, and traction level, are taken as the criterion layer B of the hierarchical model. The vector representing the sand-spreading logic weight is taken as the target layer C of the hierarchical model.

[0057] Secondly, based on the nine-scale method and weight function, comparison matrices from the solution layer to the criterion layer and from the criterion layer to the target layer are established respectively.

[0058] (1) From the scheme layer to the criterion layer: Since the factors influencing the acceleration, creep rate, speed, braking level and traction level in the criterion layer are always changing during the locomotive movement, the weight function of each factor on the sand spreading is established according to industry experts. The comparison matrix B from the scheme layer to the criterion layer is: N ,as follows:

[0059] Where N = 1, 2, 3, 4, 5 represent the five influencing factors of acceleration, creep rate, speed, braking level and traction level respectively. N ) is the weight function of the influence of each influencing factor on sand spreading in the criterion layer.

[0060] (2) From the criterion layer to the target layer: Industry experts in the railway system established a comparison matrix from each factor in the criterion layer to the target layer based on the nine-scale method. Specifically, the experts used their own experience to score the influence of each factor in the criterion layer that affects sand spreading, with 1-9 indicating that the factor is not important or absolutely important to the target, and the scores of the factors from 1 to 9 indicate that the degree of influence on the target is gradually important. Each influencing factor is then compared pairwise. Table 1 shows the comparison scale values ​​of each parameter in the criterion layer. A comparison matrix A from the criterion layer to the target layer is established. The criterion layer to target layer scale table (Table 1) and the comparison matrix are as follows:

[0061] Table 1 Comparison scale of parameters in the criterion layer

[0062] Then, before the hierarchical sorting, the consistency ratio method is used to perform consistency tests on the comparison matrices from the criterion layer to the target layer and from the solution layer to the criterion layer, respectively, to determine whether the consistency ratio CR is less than 0.1. The calculation is as follows:

[0063] Where CI and RI are the consistency index and random consistency index, respectively, and λ is the maximum eigenvalue of the matrix. The calculation found that although the comparison matrices A and B are not consistent matrices, they meet the matrix consistency test, indicating that the matrix construction is reasonable.

[0064] Finally, after the consistency check is satisfied, the geometric mean method in the AHP algorithm is used to perform hierarchical ranking calculations, including hierarchical single ranking calculations and hierarchical total ranking calculations. Hierarchical single ranking calculations are to solve the weight vectors of the matrix A from the criterion layer to the target layer and the matrix B from the solution layer to the criterion layer, respectively, while the hierarchical total ranking calculation solves the weight vector of the sand spreading logic, which is calculated as follows: W=W A T ×W B ;

[0065] Among them, W i is the weight vector of the i-th factor affecting decision-making in the criterion layer, a ij is the ratio between the scale corresponding to the i-th factor and the scale corresponding to the j-th factor in the comparison matrix, a kj is the ratio between the scale corresponding to the kth factor and the scale corresponding to the jth factor in the comparison matrix, n is the number of factors affecting the amount of sand spreading. W is the weight vector of the sand spreading logic, W A is the weight vector from the criterion layer to the target layer, W B is the weight vector from the solution layer to the criterion layer.

[0066] The sand spreading logic involves calculating the target layer weight vector. When the weight vector representing sand spreading is greater than 0.5, there is a strong demand for sand spreading. The optimal sand spreading decision simulation method adjusts the air pressure to control the amount of sand spread. When W > 0.5, the optimal sand spreading decision is output to the lower-level computer, the air pressure controller, and the sand spreading action is executed.

[0067] Step (2): Based on a rolling simulation test machine, conduct a test on the influence of train operating parameters on the amount of sand spreading. Using the adhesion coefficient as the evaluation criterion for the sand spreading effect, establish a basic database of various train operating parameters and the corresponding optimal sand spreading amount as the basis for the optimal sand spreading decision model. Specifically:

[0068] First, a test plan was developed to examine the impact of train operating parameters on sand spreading. Using the laboratory's independently developed dual-wheel rolling simulation tester, the test specimen speed was set to simulate the train's operating speed, and a speed curve function was set to simulate the train's acceleration during operation. Different acceleration and deceleration rates were input to simulate the train's traction level during startup and braking level during braking. The creep rate was calculated by simulating the speed difference between the upper and lower specimens of the wheelset. This is as follows:

[0069] Wherein, V1 and V2 are the linear velocities of the wheel and rail specimens, respectively; ω1 and ω2 are the rotational speeds of the wheel and rail specimens, respectively (r / min); d1 and d2 are the diameters of the wheel and rail specimens, respectively (mm);

[0070] Secondly, various low-adhesion conditions encountered during actual train operation were simulated, and the standard for recovering the wheel-rail adhesion coefficient after sanding under low-adhesion conditions was selected. Generally, a maximum usable adhesion coefficient at the wheel-rail interface greater than 0.2 can meet the train's traction and braking requirements. Each train operating parameter was designed as a multi-level orthogonal test table, and the sanding amount under each test was adjusted. The adhesion coefficient was used as the evaluation criterion for sanding effectiveness, with the maximum adhesion coefficient corresponding to the optimal sanding amount.

[0071] Finally, the optimal sand spreading amount corresponding to each set of train operating parameters was determined, and a basic database of various train operating parameters and corresponding optimal sand spreading amounts was established. Table 2 shows the optimal values ​​of sand spreading amounts:

[0072] Table 2 Basic database of optimal sand spreading amount

[0073] Step (3): Based on the basic database of train operating parameters and the corresponding optimal sand spreading amount, multivariate fitting is used to establish the optimal value function of sand spreading amount, and the optimal sand spreading amount under the comprehensive influence of train operating parameters is calculated. By performing regression analysis on the effect of air pressure on sand spreading amount, a functional relationship between air pressure and sand spreading amount is established, the corresponding air pressure value is calculated, and the optimal sand spreading decision is output to the lower computer by adjusting the air pressure. Specifically:

[0074] First, multivariate fitting is used to establish the optimal value function for sand spreading. The optimal value function type is selected, and the fitting formula between train operating parameters and the optimal sand spreading amount is obtained through the least squares method. The optimal sand spreading amount is calculated based on the functional relationship. The principle is as follows:

[0075] in, is a set of linearly independent functions of train operation parameters, a k is the corresponding unknown coefficient (k=1,2,3,4,5), and the fitting criterion is to make the test result y i (i=1,2,…,n) and f(x i ) is minimized. With accuracy consistent with engineering applications, the relationship between actual train operating parameters and sand spreading amount is approximated. By analyzing and observing the changing patterns, the optimal sand spreading amount is calculated for train operating parameters under a specific low-adhesion operating condition.

[0076] Secondly, a linear regression analysis was performed on the test of the effect of air pressure on the amount of sand being spread, and a functional relationship between the two was established. The process of establishing the functional relationship between air pressure and the amount of sand being spread is the same as described above and will not be repeated here. As shown in Figure 4, the functional relationship between air pressure and the amount of sand being spread was established after the regression analysis of the test of the effect of air pressure on the amount of sand being spread. The results show that the air pressure and the amount of sand being spread have a linear relationship, as follows: Q = 1.5898P + 0.56233;

[0077] Where Q is the amount of sand spread, in kg / min, and P is the compressed air pressure, in MPa. 2 The value of is 0.88. The closer it is to 1, the better the linear regression curve fits the observed value, and the closer it is to the actual functional relationship between the amount of sand spread and the air pressure.

[0078] Finally, the air pressure corresponding to the optimal sand spreading amount is calculated based on the functional relationship between air pressure and sand spreading amount. Electrical components such as solenoid valves and air pressure controllers are used to implement the optimal sand spreading decision, achieving logical sand spreading judgment and adjusting the optimal sand spreading amount. The air pressure controller automatically outputs compressed air at the precise pressure required (0-2.0 MPa) based on the optimal sand spreading decision, eliminating the need for manual pressure adjustment during this simulation.

[0079] This embodiment adopts an optimal sand spreading decision simulation system based on train operation parameters, including a train operation parameter detection system 14, an optimal sand spreading decision model and a sand spreading device (as shown in FIG5 );

[0080] The optimal sand-spreading decision-making simulation method is programmed into a laptop computer 13. The computer's host computer is the train operating parameter detection system 14, and the slave computer is the air pressure controller 5. Both computers share data with the computer via a data cable. The optimal sand-spreading decision-making simulation method calculates the air pressure corresponding to the optimal sand-spreading amount required for train operating parameters under low-adhesion conditions. Specifically, the method operates as follows: First, the train operating parameters are input into the sand-spreading logic judgment hierarchy model to determine whether sand-spreading should be performed. Second, the optimal sand-spreading amount is calculated based on the train operating parameters using the optimal sand-spreading amount function, and the optimal sand-spreading decision is output.

[0081] The train operation parameter detection system 14 is used to collect train operation parameters, including encoders and acceleration sensors.

[0082] The sand-spreading device includes an air pressure generator 1, an air pressure controller 5, a sand box 9, a sand-spreading valve 8, a sand-spreading pipe 10, a high-speed spray gun 11, and multiple air pipes. The air pressure generator 1 is located at the front end of the sand-spreading device, and its air outlet 2 is connected to the air pressure controller 5 via an air pipe. The compressed air pressure is automatically adjusted by the air pressure control mechanism, and then flows through a diverter 6 and is respectively input into the sand-spreading valve air pressure input pipe 7 and the high-speed spray gun air pressure input pipe 15 at the rear end of the sand-spreading device. The sand box 9 is used to store the thickening particles used for sand-spreading and is fixed to the sand box bracket. The sand-spreading valve 8 is mounted at the bottom of the sand box 9 and is used to suck in the thickening particles stored in the sand box 9. The sand-spreading pipe 10 is connected to the sand-spreading valve 8 and the high-speed spray gun 11, respectively, to transport a mixed jet of thickening particles and compressed air. The high-speed spray gun 11 is used to spray the thickening particles. The air pipes connect the various units to transmit compressed air and are all connected using quick-connect connectors. The threads on the other end of the quick-connect connector are wrapped with raw tape. The following describes the installation, connection methods, detailed functions and features of the key components of the sand spreading device.

[0083] Air pressure generator (air compressor) 1: used to provide clean and dry compressed air for the entire sand spreading device.

[0084] Air pressure controller 5: Used to automatically output the required air pressure, it includes an air pressure input interface 3, an air pressure output interface 4, and a control signal input interface. Air pressure input interface 3 is connected to the air compressor outlet via an air pipe. Air pressure output interface 5, after passing through a flow divider 6, is connected via air pipes to the sanding valve and the air inlet of the high-speed spray gun, respectively. The air pipes are connected using quick-connect connectors to ensure good airtightness. The control signal input interface is connected to the computer and software via a data cable 12.

[0085] Sand box 9: used to store sand-spreading and viscosity-increasing particles, including a sand box cover and a sand viewing port, so that the position of particles inside the sand box can be observed at any time and replenished in time.

[0086] Sand spreading valve 8: When compressed air passes through the sand spreading valve, it entrains the viscosity-enhancing particles in the sand box. Figure 6 shows the structural assembly diagram of the sand spreading valve and the sand box, including: a sand spreading valve sand suction port 16, a sand spreading valve air inlet 17 (connected to the sand spreading valve air pressure input pipe 7), a sand spreading valve throat 18, and a sand spreading valve sand outlet 19. Compressed air enters the sand spreading valve from the air inlet. When passing through the throat of the sand spreading valve (the throat is the smallest diameter of the pipe), the air flow velocity increases and the pressure decreases, forming a certain negative pressure at the throat, resulting in entrainment flow. Under the action of entrainment, the air at the sand suction port causes the viscosity-enhancing particles in the sand box to be continuously sucked into the internal cavity of the sand spreading valve. After being accelerated once by the compressed air, they enter the sand spreading pipe through the sand outlet.

[0087] Sanding hose 10: Used to transmit a mixed jet of compressed air and thickening particles. One end of the sanding hose is connected to the sand outlet of the sanding valve, and the other end is connected to the sand inlet of the high-speed spray gun. Both ends are fastened with stainless steel clamps to ensure good airtightness.

[0088] High-speed spray gun 11: Used to perform secondary acceleration on the particle jet to increase the particle injection speed. Figure 7 shows a schematic diagram of the high-speed spray gun structure, including a high-speed spray gun sand inlet 20, a high-speed spray gun nozzle 21, a high-speed spray gun mixing chamber 22, and a high-speed spray gun outlet 23. Particles and compressed air enter the mixing chamber from the spray gun's sand inlet. At this point, another stream of compressed air (input from the high-speed spray gun air pressure input pipe 15) enters the spray gun from the nozzle, performing a secondary acceleration on the particles before they are finally ejected from the spray gun outlet. Figure 8 shows the particle injection speed test results of the sand spreading device. After speed testing, it was found that under an air pressure of 0.5-0.7 MPa, the injection speed of thickening particles with particle sizes of 0.125-0.3 mm and 0.3-0.6 mm could both reach approximately 50 m / s, achieving the design goal of high-speed injection.

[0089] This embodiment discloses a simulation method for optimal sand-spreading decision-making based on train operating parameters. First, a hierarchical model of sand-spreading logic based on train operating parameters is established using the AHP algorithm, consisting of a scenario layer, a criterion layer, and a target layer. This transforms the sand-spreading logic judgment problem into the problem of determining a sand-spreading weight vector. After the solution, the sand-spreading weight vector is determined based on the magnitude of the vector. This simulation method effectively avoids subjective human intervention and improves the accuracy of the sand-spreading logic judgment. Second, a rolling simulation test machine is used to simulate the impact of train operating parameters on the optimal sand-spreading amount. A basic database of various operating parameters and corresponding optimal sand-spreading amounts is established, and a multivariate fitting function is used to establish the optimal sand-spreading amount function. Linear regression analysis is performed on the impact of air pressure on sand-spreading amount, establishing a linear relationship between the two. The optimal sand-spreading decision is then executed using electrical components such as solenoid valves and air pressure controllers, achieving stepless control of the sand-spreading logic judgment and the optimal sand-spreading amount. Compared to traditional sand-spreading amount control methods, such as manual experience and graded sand-spreading amount control, the optimal sand-spreading decision-making simulation method proposed in this invention avoids the subjectivity and lag caused by manual experience intervention, achieving a control accuracy of up to 0.1 kg / min. Finally, the high-speed sand-spreading device used in this invention to test the optimal sand-spreading simulation method boasts efficient spraying performance. It consists of key components, including a sand-spreading valve and a high-speed spray gun. Compressed air flows through the sand-spreading valve to create a primary acceleration of particles through a suction effect, while a converging nozzle at the high-speed spray gun provides a secondary acceleration. This sand-spreading method avoids energy loss as particles pass through the sand-spreading tube. By employing a two-stage acceleration sand-spreading mode, the particle ejection velocity is increased to approximately 50 m / s, improving both sand-spreading accuracy and viscosity-enhancing efficiency.

[0090] The above is a schematic description of the present invention and its embodiments, which is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs a structure and embodiment similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. The optimal sand spreading decision simulation method based on train operation parameters is characterized by: The following steps are involved: Step (1): construct a hierarchical structure model of sand spreading logic judgment based on train operation parameters, introduce the AHP algorithm to calculate the weight vector of the target, and realize autonomous judgment of sand spreading logic; In step (1), a hierarchical model of sand-spreading logic judgment is established, specifically: The two decision-making options of sanding or not sanding are used as the option layers of the hierarchical model; The locomotive acceleration, train speed, creep rate, braking level and traction level are used as the criterion layers of the hierarchical model; The weight vector representing the sand spreading logic is used as the target layer of the hierarchical model; Step (2): Using the adhesion coefficient as the evaluation criterion for the sand spreading effect, a basic database of various train operation parameters and the corresponding optimal sand spreading amount is established; In step (2), a basic database of various train operation parameters and corresponding optimal sand spreading amounts is established, specifically: Based on the rolling simulation test machine, the recovery standard of the wheel-rail adhesion coefficient after sanding under low adhesion conditions is selected, and the test scheme for the influence of train operation parameters on the optimal sanding amount is determined; The operating parameters of each train are designed as a multi-level orthogonal test table, and the amount of sand spread in each test is adjusted. The adhesion coefficient is used as the evaluation criterion for the sand spreading effect. The maximum adhesion coefficient corresponds to the optimal sand spreading amount. Determine the optimal sand spreading amount under each test and establish a basic database of various train operation parameters and the corresponding optimal sand spreading amount; Step (3): Use multivariate fitting to establish the optimal value function of sand spreading amount, calculate the optimal sand spreading amount under the comprehensive influence of train operation parameters, and output the optimal sand spreading decision to the lower computer by adjusting the air pressure.

2. The optimal sand spreading decision simulation method based on train operation parameters according to claim 1 is characterized in that: In step (1), the specific process of obtaining the weight vector is as follows: According to the nine-scale method and weight function, the comparison matrices from the criterion layer to the target layer and from the solution layer to the criterion layer are constructed respectively; The consistency ratio method was used to test the consistency of the comparison matrix between each level; When the comparison matrix satisfies the consistency test, the geometric mean method in the AHP algorithm is introduced to solve the sand spreading weight vector, which is: Among them, W i is the weight vector of the i-th factor affecting decision making in the criterion layer, a ij is the ratio between the scale corresponding to the i-th factor and the scale corresponding to the j-th factor in the comparison matrix, a kj is the ratio between the scale corresponding to the kth factor and the scale corresponding to the jth factor in the comparison matrix, and n is the number of factors affecting the amount of sand spreading.

3. The optimal sand spreading decision simulation method based on train operation parameters according to claim 2 is characterized in that: In step (1), the weight vector calculation of the sand spreading logic is as follows: W=W A T ×W B ; Among them, W is the weight vector of the sand spreading logic, W A is the weight vector from the criterion layer to the target layer, W B is the weight vector from the scheme layer to the criterion layer; after calculation, if the value representing sand spreading in the target weight vector is greater than 0.5, it means that sand spreading is required under the current train operation parameters.

4. The optimal sand spreading decision simulation method based on train operation parameters according to claim 3 is characterized in that: Step (3): Use multivariate fitting to establish the optimal value function of sand spreading amount, and calculate the optimal sand spreading amount under the comprehensive influence of train operation parameters, specifically: Based on the basic database of optimal sand spreading amount, the optimal value function type is selected, and the fitting formula between train operation parameters and optimal sand spreading amount is obtained by the least square method; The optimal sand spreading amount under the current train operation parameters is calculated according to the optimal value function relationship.

5. The optimal sand spreading decision simulation method based on train operation parameters according to claim 4 is characterized in that: In step (3), after logical judgment and optimal value calculation, the optimal sand spreading decision is output to the lower computer by adjusting the air pressure, specifically: Linear regression analysis was conducted on the test of the effect of air pressure on the amount of sand spread, and the functional relationship between the two was established; The air pressure value corresponding to the optimal sand spreading amount is calculated according to the functional relationship between air pressure and sand spreading amount; Solenoid valves and air pressure controller APC are used to execute optimal sand spreading decisions, realize sand spreading logic judgment and adjust the optimal sand spreading amount.

6. The optimal sand spreading decision simulation method based on train operation parameters according to claim 5 is characterized in that: An optimal sand spreading decision simulation system based on train operation parameters is adopted, and the optimal sand spreading decision simulation system based on train operation parameters includes a train operation parameter detection system (14), an optimal sand spreading decision model and a sand spreading device; The optimal sand spreading decision simulation method is written into a computer (13) through a program, the upper computer of the computer (13) is a train operation parameter detection system (14), the lower computer of the computer (13) is an air pressure controller (5), and the computer (13), the train operation parameter detection system (14) and the air pressure controller (5) share data through a data line; The sand spreading device comprises an air pressure generator (1), an air pressure controller (5), a sand box (9), a sand spreading valve (8), a sand spreading pipe (10), a high-speed spray gun (11) and a plurality of air supply pipes; the air pressure generator (1) is located at the front end of the sand spreading device, and its air outlet (2) is connected to the air pressure controller (5) through the air supply pipe, and the compressed air is adjusted and flows through a diverter (6) and is respectively input to the sand spreading valve (8) and the high-speed spray gun (11); the sand spreading valve (8) is installed at the bottom of the sand box (9) and is used to suck the viscosity-increasing particles stored in the sand box (9); the sand spreading valve (8) and the high-speed spray gun (11) are connected through the sand spreading pipe (10).

7. The optimal sand spreading decision simulation method based on train operation parameters according to claim 6 is characterized in that: The high-speed sand spreading device adopts a two-stage accelerated sand spreading mode, the sand spreading valve (8) uses the entrainment effect to accelerate the viscosity-increasing particles once, and the high-speed spray gun (11) uses a contraction nozzle to perform a second acceleration.

8. The optimal sand spreading decision simulation method based on train operation parameters according to claim 6 is characterized in that: The train operation parameter detection system (14) is used to collect train operation parameters, and comprises an encoder and an acceleration sensor.

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