CN113280852B - Point switch parameter detection device and method - Google Patents

Point switch parameter detection device and method Download PDF

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CN113280852B
CN113280852B CN202010103835.5A CN202010103835A CN113280852B CN 113280852 B CN113280852 B CN 113280852B CN 202010103835 A CN202010103835 A CN 202010103835A CN 113280852 B CN113280852 B CN 113280852B
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value
upper computer
acquisition board
resistance
friction force
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CN113280852A (en
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包建东
林涛
刘英舜
刘昭
张晨
司欣格
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Nanjing University of Science and Technology
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Abstract

The invention discloses a device and a method for detecting parameters of a point switch. The device comprises a laser sensor, an action rod, a first acquisition board, an upper computer, a current power sensor, a second acquisition board, a resistance testing device, a female plug and a multi-port concentrator. The method comprises the following steps: after a start measurement button of an upper computer software interactive interface is started, a switch machine action rod starts to move, and the upper computer processes the acquired original displacement data to obtain the variation information of the tension and the maximum friction force; the interactive interface displays a distance change original curve graph and a tension change curve graph, and simultaneously the right interface displays a tension real-time change value; after the point switch moving rod stops moving for a half period, displaying the maximum resistance value of the whole moving process on the right side of the interactive interface, and simultaneously displaying a working current value and a working voltage value; after the resistance tester is inserted into the contact group through the female plug, the static resistance value is displayed on the right side of the interface. The invention has the advantages of simple use, high measurement precision and strong reliability.

Description

Point switch parameter detection device and method
Technical Field
The invention belongs to the technical field of equipment parameter detection, and particularly relates to a device and a method for detecting parameters of a point switch.
Background
A switch is a line connection device for switching a rolling stock from one track to another, is usually laid in large numbers at stations and marshalling stations, and has a function of controlling the turning of the rolling stock. The turnout is important equipment related to the operation safety of rail transit and is also a weak link of a rail transit line, wherein a point switch is a core component for switching the turnout line. The point switch is an important signal basic device for reliably switching the position of the turnout, changing the opening direction of the turnout, locking the switch point and reflecting the position of the turnout, and can well ensure the driving safety, improve the transportation efficiency and improve the labor intensity of driving personnel. The operation state of the point switch is influenced by some core parameters, and the core parameters greatly influence the switching stability and safety of the turnout, so that the detection of the core parameters is very important.
The existing tension and maximum resistance detection adopts a direct measurement method of a sensor, but the pin needs to be frequently replaced during detection, the process consumes time and labor, and the safety and the real-time performance cannot be well met. In addition, the standards adopted for measuring the static resistance of the lines on the terminal block of the existing point switch are different, so that the accuracy is low, and the adjustment cannot be carried out according to the actual environment.
Disclosure of Invention
The invention aims to provide a point switch parameter detection device and method which are high in accuracy and reliability and save material resources and manpower.
The technical solution for realizing the purpose of the invention is as follows: a point switch parameter detection device comprises a laser sensor, an action rod, a first acquisition board, an upper computer, a current power sensor, a second acquisition board, a resistance testing device, a female plug and a multi-port concentrator;
the laser sensor and the action rod are arranged on the same horizontal line, and the laser beam horizontally irradiates the tail part of the action rod; the laser sensor is connected with a first acquisition board through a transmission line, and the first acquisition board is connected with an upper computer through a multi-port concentrator; the current power sensor is connected into a second acquisition board through a transmission line, and the second acquisition board is connected into an upper computer through a multi-port concentrator; one end of the resistance testing device is connected into a contact group in the switch machine box body through the female plug, and the other end of the resistance testing device is connected into the upper computer through the transmission line and the multi-port concentrator.
Furthermore, the laser sensor is arranged on a fixed support, the fixed support is a support with 360-degree-of-freedom adjustable type, and the fixed support comprises a fixed clamping piece, a free arm and a base;
the base comprises a cylinder and a connecting rod which is vertical to the top surface of the cylinder and is fixedly connected with the cylinder, one end of the free arm is nested on the connecting rod of the base and can reciprocate up and down along the connecting rod, and the other end of the free arm clamps the fixed clamping piece; the fixed clamping piece fixes the laser sensor firmly and can drive the laser sensor to rotate 360 degrees around the connecting rod of the base.
Furthermore, a rain shelter is arranged around the laser sensor, and a protective film is arranged on the laser beam display screen.
Further, resistance test device sets up in portable box, and resistance test device is detachable construction, through the fix with screw.
Furthermore, the power supply of the laser sensor is fixed on the fixed support, and the first acquisition board and the second acquisition board are powered by the upper computer.
Furthermore, the resistance testing device comprises a single chip microcomputer, a current stabilizing source, a voltage quantity collecting plate and a liquid crystal display screen;
the current stabilizing source provides adjustable constant direct current, the voltage quantity acquisition board acquires the voltage of the measured line, and the single chip microcomputer performs resistance operation to realize the control of numerical values on the liquid crystal display screen.
Furthermore, the resistance testing device is provided with a plurality of female plug interfaces, so that static resistances on a plurality of lines can be simultaneously measured;
the single chip microcomputer in the resistance testing device realizes the function of wirelessly transmitting data to the upper computer by additionally arranging the Bluetooth communication module.
A method for detecting parameters of a switch machine comprises the following steps:
step 1, after a measurement starting button of an upper computer software interaction interface is started, a switch machine action rod starts to move, and an upper computer processes collected original displacement data to obtain the change information of the pulling force and the maximum friction force;
step 2, displaying a distance change original curve graph and a tension change curve graph on the interactive interface, and displaying a tension real-time change value on the right interface;
step 3, after the movement of the moving rod of the point switch is stopped in a half period, displaying the maximum resistance value of the whole movement process on the right side of the interactive interface, and simultaneously displaying a working current value and a working voltage value;
and 4, after the resistance tester is inserted into the contact group through the female plug, the static resistance value is displayed on the right side of the interface.
Further, the upper computer processes the collected original displacement data, and specifically comprises the following steps:
firstly, converting displacement information into speed information, wherein the formula is as follows:
Figure BDA0002387814260000021
wherein v is the linear velocity of the action rod, s is the displacement, and t is the time;
then, converting the speed information into the change information of the tension force F and the maximum friction force F through a conversion formula, wherein the specific formula is as follows:
n 1 ：n 3 ＝m3：m1 (2-1)
v＝n 3 *d (2-2)
Figure BDA0002387814260000031
T＝F*R (2-4)
obtained from (2-1) to (2-4):
Figure BDA0002387814260000032
in the formula, m1 is the number of first stage gear teeth, m3 is the number of third stage gear teeth, 1 is the motor gear speed, n 3 The rotating speed of the large gear is R, the radius of the gear is R, the distance between the lead screw and the shaft is d, the power of the motor is P, the gear force of the motor is F, and the linear speed of the action rod is v;
in the formula (2-5), m1, m3, d and R are constants, P = mv, m is a constant coefficient, and (2-5) is simplified into the following functional form:
Figure BDA0002387814260000033
in the formula
Figure BDA0002387814260000034
Based on the reciprocal of the tension F and the speed value>
Figure BDA0002387814260000035
The corresponding functional relationship;
the maximum friction force F increases with the increase of the tension force F, a linear correlation exists between the maximum friction force F and the tension force F, and the formula for obtaining the maximum friction force by combining (2-6) is as follows:
Figure BDA0002387814260000036
in the formula, theta is the reciprocal of the maximum friction force f and the speed value
Figure BDA0002387814260000037
The corresponding functional relationship.
Further, the conversion formulas (2-6) and (2-7) from the speed information of the action rod to the pulling force and the maximum friction force adopt a compensation algorithm to realize the solution of the functional relation, and the compensation algorithm is the combined optimization based on the least square method and the BP neural network algorithm, and specifically comprises the following steps:
firstly, dividing the value range of the tensile force into two sections, considering that when F is more than or equal to 2000N,
Figure BDA0002387814260000038
the image relationship of (a) is more linear, so that least squares is used to fit F and->
Figure BDA0002387814260000039
The verification group and the experimental group data are from measured values, the verification group data are substituted into the linear function relational expression to obtain a test value, and the proximity degree of the test value and the actual value is compared, so that the quality of the linear function performance is evaluated;
at F<At the time of 2000N, the system is,
Figure BDA00023878142600000310
the nonlinear relation of the image is obvious, so that the BP neural network algorithm is adopted to solve F and->
Figure BDA0002387814260000041
When a BP neural network algorithm is used, cross validation is added, random values are taken from test data for many times to serve as a training set, and the rest data set serves as a test set for testing, so that an optimal neural network is trained;
the value of the maximum friction force f is found to be 5000-7000N during actual measurement, and
Figure BDA0002387814260000042
the nonlinear relation of the image is obvious, so that the BP neural network algorithm is adopted to solve f and->
Figure BDA0002387814260000043
And when a BP neural network algorithm is used, cross validation is added, random values are taken from test data for many times to serve as a training set, and the rest data group serves as a test set for testing, so that the optimal neural network is trained.
Compared with the prior art, the invention has the following remarkable advantages: (1) The parameters are integrated, and all core parameters can be tested through one set of equipment; (2) The measuring process saves manpower and reduces danger; (3) Based on the combination optimization of the least square method and the BP neural network algorithm, the method improves the measurement accuracy, and has the advantages of strong reliability and simple and convenient use.
Drawings
Fig. 1 is a schematic structural diagram of a switch machine parameter detection device according to the present invention.
Fig. 2 is a schematic structural view of a laser sensor fixing bracket according to the present invention.
FIG. 3 is a schematic structural diagram of the resistance testing apparatus according to the present invention.
FIG. 4 is a software interface diagram of the switch machine parameter detection device of the present invention.
Fig. 5 is a flow chart of the method for detecting the parameters of the switch machine according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the embodiments.
With reference to fig. 1 to 3, the invention relates to a point switch parameter measuring device, which comprises a laser sensor 1, an action rod 2, a first collecting plate 3, an upper computer 4, a current power sensor 5, a second collecting plate 6, a resistance testing device 7, a female plug 8 and a multi-port concentrator 9;
the laser sensor 1 and the action rod 2 are arranged on the same horizontal line, and the laser beam horizontally irradiates the tail part of the action rod 2; the laser sensor 1 is connected with the first acquisition board 3 through a transmission line, and the first acquisition board 3 is connected with the upper computer 4 through a multi-port concentrator 9; the current power sensor 5 is connected to a second acquisition board 6 through a transmission line, and the second acquisition board 6 is connected to the upper computer 4 through a multi-port concentrator 9; one end of the resistance testing device 7 is connected into a contact group in the box body of the switch machine through a female plug 8, and the other end of the resistance testing device 7 is connected into the upper computer 4 through a transmission line and a multi-port concentrator 9.
As a specific example, the laser sensor 1 is disposed on a fixed support 10, the fixed support 10 is a support with 360 degrees of freedom adjustable, and the fixed support 10 includes a fixed clamping piece 11, a free arm 12 and a base 13;
the base comprises a cylinder and a connecting rod which is vertical to the top surface of the cylinder and is fixedly connected with the cylinder, one end of the free arm 12 is nested on the connecting rod of the base 13 and can reciprocate up and down along the connecting rod, and the other end of the free arm clamps the fixed clamping piece 11; the fixed clamping piece 11 fixes the laser sensor 1 firmly and can drive the laser sensor 1 to rotate 360 degrees around the connecting rod of the base 13. The base 13 is used for adjusting the vertical height and the spatial degree of freedom of the laser sensor 1, so that the irradiation direction of the laser beam can accurately reflect the displacement change of the action rod 2.
As a specific example, a rain shelter is arranged around the laser sensor 1, and the laser beam display screen is provided with a protective film.
As a specific example, the resistance testing device 7 is arranged in a portable box body, and the resistance testing device 7 is of a detachable structure and is fixed through screws.
As a specific example, the power supply of the laser sensor 1 is fixed on the fixing support 10, and the first collecting plate 3 and the second collecting plate 6 are powered by the upper computer 4.
As a specific example, the resistance testing device 7 includes a single chip 14, a current stabilizing source 15, a voltage collecting board 16 and a liquid crystal display 17;
the current stabilizing source 15 provides adjustable constant direct current, the voltage quantity acquisition board 16 acquires the voltage of the measured line, and the single chip microcomputer 14 performs resistance operation to realize the control of numerical values on the liquid crystal display screen 17.
As a specific example, the resistance testing device 7 has a plurality of female plugs 8, so as to implement simultaneous measurement of static resistances on a plurality of lines;
the single chip microcomputer 14 in the resistance testing device 7 realizes the function of transmitting data to the upper computer 4 in a wireless manner by additionally arranging a Bluetooth communication module.
With reference to fig. 4 to 5, the method for detecting the parameters of the switch machine of the invention comprises the following steps:
step 1, after a measurement starting button of an upper computer software interaction interface is started, a switch machine action rod starts to move, and an upper computer processes collected original displacement data to obtain the change information of the pulling force and the maximum friction force;
step 2, displaying a distance change original curve graph and a tension change curve graph on the interactive interface, and displaying a tension real-time change value on the right interface;
step 3, after the movement of the moving rod of the point switch is stopped in a half period, displaying the maximum resistance value of the whole movement process on the right side of the interactive interface, and simultaneously displaying a working current value and a working voltage value;
and 4, after the resistance tester is inserted into the contact group through the female plug, the static resistance value is displayed on the right side of the interface.
Further, the upper computer processes the collected original displacement data, and specifically comprises the following steps:
firstly, converting displacement information into speed information, wherein the formula is as follows:
Figure BDA0002387814260000061
wherein v is the linear velocity of the action rod, s is the displacement, and t is the time;
then, converting the speed information into the change information of the tension force F and the maximum friction force F through a conversion formula, wherein the specific formula is as follows:
n 1 ：n 3 ＝m3：m1 (2-1)
v＝n 3 *d (2-2)
Figure BDA0002387814260000062
T＝F*R (2-4)
obtained from (2-1) to (2-4):
Figure BDA0002387814260000063
in the formula, m1 is the number of first stage gear teeth, m3 is the number of third stage gear teeth, 1 is the motor gear speed, n 3 The rotating speed of the large gear is R, the radius of the gear is R, the distance between the lead screw and the shaft is d, the power of the motor is P, the gear force of the motor is F, and the linear speed of the action rod is v;
in the formula (2-5), m1, m3, d and R are constants, P = mv, m is a constant coefficient, and (2-5) is simplified into the following functional form:
Figure BDA0002387814260000064
in the formula
Figure BDA0002387814260000065
Based on the reciprocal of the tension F and the speed value>
Figure BDA0002387814260000066
The corresponding functional relationship;
the maximum friction force F increases along with the increase of the tensile force F, a linear correlation exists between the maximum friction force F and the tensile force F, and the formula for obtaining the maximum friction force by combining (2-6) is as follows:
Figure BDA0002387814260000067
in the formula, theta is the reciprocal of the maximum friction force f and the speed value
Figure BDA0002387814260000068
The corresponding functional relationship.
Furthermore, the conversion formulas (2-6) and (2-7) from the speed information of the action rod to the pulling force and the maximum friction force adopt a compensation algorithm to realize the solution of the functional relationship, and the compensation algorithm is the combined optimization based on a least square method and a BP neural network algorithm, and specifically comprises the following steps:
firstly, dividing the value range of the tensile force into two sections, considering that when F is more than or equal to 2000N,
Figure BDA0002387814260000069
the image relationship of (a) is more linear, so that least squares is used to fit F and->
Figure BDA0002387814260000071
Setting a verification group during fitting, wherein the verification group and experimental group data are from measured values, substituting the verification group data into a linear function relational expression to obtain a test value, and comparing the proximity degree of the test value and the real value so as to evaluate the quality of the linear function performance;
at F<At the time of 2000N, the number of the channels,
Figure BDA0002387814260000072
the nonlinear relation of the images is obvious, so that the BP neural network algorithm is adopted to solve the F and->
Figure BDA0002387814260000073
When a BP neural network algorithm is used, cross validation is added, random values are taken from test data for many times to serve as a training set, and the rest data set serves as a test set for testing, so that an optimal neural network is trained;
the value of the maximum friction force f is found to be 5000-7000N during actual measurement, and
Figure BDA0002387814260000074
the nonlinear relation of the image is obvious, so that a BP neural network algorithm is adopted to solve the f and->
Figure BDA0002387814260000075
And when a BP neural network algorithm is used, cross validation is added, random values are taken from test data for many times to serve as a training set, and the rest data group serves as a test set for testing, so that the optimal neural network is trained.
The invention is further described in detail below with reference to the drawings and specific embodiments.
Examples
The structure of the switch machine parameter detection device of the present embodiment is shown in fig. 1 to 3, and the switch machine parameter detection method of the present embodiment includes the following steps:
step 1, clicking a start measurement button of a software interactive interface (shown in figure 4) of an upper computer 4, after a switch machine action rod 2 moves, displaying a distance change original curve graph and a tension change curve graph compensated by an error compensation algorithm on the interactive interface, and displaying a tension real-time change value on the right interface;
further, the upper computer 4 processes the acquired original displacement data by programming, firstly, according to the data
Figure BDA0002387814260000076
Converting the displacement information into speed change information and then determining whether the information is based on a conversion formula>
Figure BDA0002387814260000077
And f = θ (1/v), converting the speed information into tension and maximum friction variation information.
Further, a conversion formula of the speed of the action rod 2 to the pulling force and the maximum friction force adopts a compensation algorithm to realize error optimization, and the algorithm design is combined optimization based on a least square method and a BP neural network algorithm.
The error compensation method of the switch machine pulling force and the maximum resistance comprises the following steps:
n 1 ：n 3 ＝m3：m1 (2-1)
v＝n 3 *d (2-2)
Figure BDA0002387814260000078
T＝F*R (2-4)
in the formula, m1 is the number of first stage gear teeth, m3 is the number of third stage gear teeth, 1 is the motor gear speed, n 3 The rotating speed of a large gear is adopted, R is the gear radius, d is the shaft distance of a lead screw, P is the power of a motor, F is the gear force of the motor, and v is the linear speed of an action rod;
obtainable from the formulae (2-1) to (2-4):
Figure BDA0002387814260000081
for the S700K switch, m1, m3, d, R are all constants in equation (2-5), and P = mv, m is a constant coefficient, and (2-5) is simplified to the following functional form:
Figure BDA0002387814260000082
in the formula
Figure BDA0002387814260000083
The functional relation corresponding to the reciprocal 1/v of the pulling force F and the speed value;
the maximum friction force F increases with the increase of the tension force F, a linear correlation exists between the maximum friction force F and the tension force F, and the formula for obtaining the maximum friction force by combining (2-6) is as follows:
Figure BDA0002387814260000084
in the formula, theta is a functional relation corresponding to the reciprocal 1/of the speed value and the maximum friction force f.
Through tests, when the pulling force value is [0,2000N ], the result accuracy of the pulling force compensation by adopting the BP neural network algorithm is higher, and when the value is [2000N,6000N ], the pulling force compensation mode by adopting the least square algorithm is more appropriate. The maximum resistance value is [5000,7000], and the value tends to be stable, so the BP neural network compensation mode is more suitable. Therefore, the invention adopts a combined optimization mode based on a least square method and a BP neural network algorithm.
During the test, the tension value is manually adjusted to change the tension value from 2000 to 6000N, and 100 groups of test results are obtained, wherein each group of test results comprises an independent variable v and a dependent variable F. 50 groups of data are randomly taken for multiple times and input into MATLAB 2018B software in a matrix form, and least square estimation is realized through a polyfit function. After obtaining the least square estimation formula, substituting the rest 50 groups of data into the estimation formula, comparing the obtained value with the value measured in the test, and verifying the matching degree of the 50 groups of data and the estimation formula.
During the test, the tension value is manually adjusted to change the tension value from 0 to 2000N, and a sampling book is usedThe quantity is 100, wherein 50 samples are taken as a training set, the remaining 50 samples are taken as a test set, and when sampling, in order to ensure randomness, the samples are randomly generated through a randderm function. V in input neuron selection (2-6), F in output neuron selection (2-7), tangent function tansig as a transfer function, trainline function tranlmm as a training function, leanngdm as a weight threshold learning function, mse as a performance function, and training target error set to 10 -4 And setting the maximum training times to be 1000 times, and adding cross validation in the energy training process. And repeating the process to select the neural network which can ensure that the test sample has the highest precision. And when constructing the BP neural network with the maximum friction force f, the process is similar to the construction of the tension value.
Step 2, after the point switch moving rod 2 stops moving for a half period, displaying the maximum resistance value of the whole moving process on the right side of the interactive interface, and simultaneously displaying a working current value and a working voltage value;
furthermore, a special current power sensor is adopted to obtain working current I and working voltage U, the sensor collects the voltage value and the current value of the motor during action, the power value is calculated, then current and power information is converted into 4-20mA current signals to be output, the 4-20mA current signals are converted into 0-3V voltage signals after passing through a sampling resistor of a collecting plate 150R, the voltage signals are subjected to 24-bit high-precision A/D conversion, and then are transmitted to a USB concentrator through a channel gate and then to an upper computer 4, and the voltage signals are displayed on a main interface through program control by software; the error is limited primarily by the sampling rate of each device.
And 3, after the resistance tester 7 female plug 8 is inserted into the contact group, the right side of the interface can display the static resistance value.
Furthermore, a resistance measuring device is selected for measuring the static resistance, and the device consists of a single chip microcomputer, a voltage acquisition board and a current stabilization source. The current stabilizing module supplies stable current to the circuit, and a 0.5A current stabilizing source is selected in consideration of the resistance value of the static resistor about several ohms; the voltage acquisition board acquires the voltage value in the circuit and then transmits the voltage value to the singlechip which is based on the formula
Figure BDA0002387814260000091
And calculating the resistance value, and when the device is used, only the female plug of the device is inserted into the corresponding male plug, namely the static resistance value in the circuit can be read. />

Claims (7)

1. A point switch parameter detection device is characterized by comprising a laser sensor (1), an action rod (2), a first acquisition board (3), an upper computer (4), a current power sensor (5), a second acquisition board (6), a resistance testing device (7), a female plug (8) and a multi-port concentrator (9);
the laser sensor (1) and the action rod (2) are arranged on the same horizontal line, and the laser beam horizontally irradiates the tail part of the action rod (2); the laser sensor (1) is connected with the first acquisition board (3) through a transmission line, and the first acquisition board (3) is connected with the upper computer (4) through a multi-port concentrator (9); the current power sensor (5) is connected into a second acquisition board (6) through a transmission line, and the second acquisition board (6) is connected into the upper computer (4) through a multi-port concentrator (9); one end of the resistance testing device (7) is connected into a contact group in the box body of the switch machine through a female plug (8), and the other end of the resistance testing device (7) is connected into the upper computer (4) through a multi-port concentrator (9) through a transmission line;
the method also comprises a detection method, and comprises the following steps:
step 1, after a measurement starting button of a software interaction interface of an upper computer (4) is started, a switch machine action rod (2) starts to move, and the upper computer (4) processes collected original displacement data to obtain change information of a pulling force and a maximum friction force;
step 2, displaying a distance change original curve graph and a tension change curve graph on the interactive interface, and displaying a tension real-time change value on the right-side interface;
step 3, after the point switch action rod (2) stops moving for a half period, displaying the maximum resistance value of the whole moving process on the right side of the interactive interface, and simultaneously displaying a working current value and a working voltage value;
step 4, after the resistance tester (7) is inserted into the contact group through the female plug (8), the static resistance value is displayed on the right side of the interface;
the upper computer (4) processes the acquired original displacement data, and specifically comprises the following steps:
firstly, converting displacement information into speed information, wherein the formula is as follows:
Figure FDA0004031498400000011
wherein v is the linear speed of the action rod, s is displacement, and t is time;
then, converting the speed information into the change information of the tension force F and the maximum friction force F through a conversion formula, wherein the specific formula is as follows:
n 1 ：n 3 ＝m3：m1 (2-1)
v＝n 3 *d (2-2)
Figure FDA0004031498400000021
T＝F*R (2-4)
obtained from (2-1) to (2-4):
Figure FDA0004031498400000022
wherein m1 is the number of first-stage gear teeth, m3 is the number of third-stage gear teeth, and n 1 Is the motor gear speed, n 3 The rotating speed of a large gear is adopted, R is the gear radius, d is the shaft distance of a lead screw, P is the power of a motor, F is the gear force of the motor, and v is the linear speed of an action rod;
in the formula (2-5), m1, m3, d and R are constants, P = mv, m is a constant coefficient, and (2-5) is simplified into the following functional form:
Figure FDA0004031498400000023
in the formula
Figure FDA0004031498400000024
Based on the reciprocal of the tension F and the speed value>
Figure FDA0004031498400000025
The corresponding functional relationship;
the maximum friction force F increases along with the increase of the tensile force F, a linear correlation exists between the maximum friction force F and the tensile force F, and the formula for obtaining the maximum friction force by combining (2-6) is as follows:
Figure FDA0004031498400000026
in the formula, theta is the reciprocal of the maximum friction force f and the speed value
Figure FDA0004031498400000027
The corresponding functional relationship;
conversion formulas (2-6) and (2-7) from speed information of the action rod (2) to pulling force and maximum friction force, and the solution of the functional relation is realized by adopting a compensation algorithm, wherein the compensation algorithm is the combined optimization based on a least square method and a BP neural network algorithm, and specifically comprises the following steps:
firstly, dividing the value range of the tensile force into two sections, considering that when F is more than or equal to 2000N,
Figure FDA0004031498400000028
the image relationship of (a) is more linear, so that least squares is used to fit F and->
Figure FDA0004031498400000029
Setting a verification group during fitting, wherein the verification group and experimental group data are from measured values, substituting the verification group data into a linear function relational expression to obtain a test value, and comparing the proximity degree of the test value and the real value so as to evaluate the quality of the linear function performance;
when the F is less than 2000N,
Figure FDA00040314984000000210
the nonlinear relation of the image is obvious, so that the BP neural network algorithm is adopted to solve F and->
Figure FDA00040314984000000211
When a BP neural network algorithm is used, cross validation is added, the test data is randomly taken as a training set for a plurality of times, and the rest data set is taken as a test set for testing, so that the optimal neural network is trained;
the value of the maximum friction force f is found to be 5000-7000N during actual measurement, and
Figure FDA0004031498400000031
the nonlinear relation of the image is obvious, so that the BP neural network algorithm is adopted to solve f and->
Figure FDA0004031498400000032
And when a BP neural network algorithm is used, cross validation is added, random values are taken from test data for many times to serve as a training set, and the rest data group serves as a test set for testing, so that the optimal neural network is trained.
2. A switch machine parameter detection device as claimed in claim 1, characterised in that the laser sensor (1) is arranged on a fixed support (10), the fixed support (10) being a 360 degree of freedom adjustable support, the fixed support (10) comprising a fixed jaw (11), a free arm (12) and a base (13);
the base comprises a cylinder and a connecting rod which is vertical to the top surface of the cylinder and is fixedly connected with the cylinder, one end of the free arm (12) is nested on the connecting rod of the base (13) and can reciprocate up and down along the connecting rod, and the other end of the free arm clamps the fixed clamping piece (11); the fixed clamping piece (11) fixes the laser sensor (1) firmly and can drive the laser sensor (1) to rotate 360 degrees around the connecting rod of the base (13).
3. A switch machine parameter detection device as claimed in claim 2, wherein a rain shelter is provided around the laser sensor (1), and the laser beam display screen is provided with a protective film.
4. A switch machine parameter detection device as claimed in claim 2, characterized in that said resistance testing device (7) is arranged in a portable box, the resistance testing device (7) being of a detachable construction and fixed by screws.
5. A switch machine parameter detection device as claimed in claim 2, characterized in that the power supply of the laser sensor (1) is fixed to the fixed support (10), and the first acquisition board (3) and the second acquisition board (6) are powered by the upper computer (4).
6. The switch machine parameter detection device according to claim 4, characterized in that the resistance test device (7) comprises a single chip microcomputer (14), a current stabilizing source (15), a voltage quantity acquisition board (16) and a liquid crystal display screen (17);
the constant current source (15) provides adjustable constant direct current, the voltage quantity acquisition board (16) acquires the voltage of the measured line, and the single chip microcomputer (14) performs resistance operation to realize the control of numerical values on the liquid crystal display screen (17).
7. A switch machine parameter detection device as claimed in claim 6, characterized in that said resistance test device (7) has a plurality of female plug (8) interfaces for simultaneously measuring static resistances on a plurality of lines;
the single chip microcomputer (14) in the resistance testing device (7) realizes the function of transmitting data to the upper computer (4) in a wireless mode through additionally arranging the Bluetooth communication module.
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