CN110987267A - Point switch stress signal detection device and method and electronic equipment - Google Patents
Point switch stress signal detection device and method and electronic equipment Download PDFInfo
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- CN110987267A CN110987267A CN201911344662.XA CN201911344662A CN110987267A CN 110987267 A CN110987267 A CN 110987267A CN 201911344662 A CN201911344662 A CN 201911344662A CN 110987267 A CN110987267 A CN 110987267A
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Abstract
The invention provides a switch machine stress signal detection device and method and electronic equipment, comprising the following steps: the stress sheet module and the collecting sub-machine module are connected; the stress sheet module is used for detecting the point switch through the first stress sheet and the second stress sheet to obtain a plurality of stress signals and sending the stress signals to the collecting and distributing module; the acquisition branch module is used for amplifying and converting the plurality of stress signals through the conversion processing unit to obtain a plurality of processed stress signals; and the algorithm processing unit is used for carrying out fusion processing on the processed stress signals to obtain a stress detection value of the point switch, and the protocol conversion unit is used for sending the stress detection value to the terminal platform, so that the detection precision of the internal stress signal of the point switch can be improved, the internal state of the point switch can be accurately monitored in real time, and the safe switching line of the train is ensured.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a device and a method for detecting a switch machine stress signal and electronic equipment.
Background
A switch machine as a switch switching device for reliably switching a switch position, changing a switch opening direction, and locking a switch tongue is an important signal base device for controlling a switch position, and its safety maintenance is important. The common method is to monitor the switch machine through the switch gap monitoring, but the method cannot monitor the switch machine. At present, some schemes are through setting up the internal stress sensor on the goat in order to monitor the internal state of goat, but, because the goat exposes in outside adverse circumstances for a long time, consequently, various environmental factors such as temperature, humidity, sand blown by the wind, sleet, sunshine, the vibration of passing a car can lead to the fact huge influence to goat itself to cause the monitoring accuracy of switch breach to be lower, lead to the real-time supervision of goat to have an error.
Disclosure of Invention
In view of the above, the present invention provides a switch machine stress signal detection device and method, and an electronic device, which can improve the detection accuracy of the switch machine internal stress signal, so as to accurately monitor the switch machine internal state in real time and ensure the train safety switching line.
In a first aspect, an embodiment of the present invention provides a switch machine stress signal detection device, which is disposed on a switch machine, and includes: the stress sheet module and the collecting sub-machine module are connected; the acquisition extension module comprises a conversion processing unit, an algorithm processing unit and a protocol conversion unit which are connected in sequence; the stress sheet module comprises a first stress sheet and a second stress sheet, the first stress sheet is a plurality of stress sheets arranged on the switch machine, and the second stress sheet is a plurality of redundant stress sheets additionally arranged on the basis of the first stress sheet;
the stress sheet module is used for detecting the point switch through the first stress sheet and the second stress sheet to obtain a plurality of stress signals and sending the stress signals to the collecting and distributing module;
the acquisition branch module is used for amplifying and converting the stress signals through the conversion processing unit to obtain a plurality of processed stress signals; and the algorithm processing unit is used for carrying out fusion processing on the processed stress signals to obtain a stress detection value of the switch machine, and the protocol conversion unit is used for sending the stress detection value to a terminal platform.
With reference to the first aspect, an embodiment of the present invention provides a first possible implementation manner of the first aspect, where the algorithm processing unit includes a plurality of local kalman filters and a global kalman filter;
the algorithm processing unit is further configured to perform filtering processing on the plurality of processed stress signals through the local kalman filter, and perform fusion processing on the plurality of processed stress signals after the filtering processing through the global kalman filter, so as to obtain the stress detection value of the point switch.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present invention provides a second possible implementation manner of the first aspect, where the number of local kalman filters is consistent with the number of stress slices in the stress slice module.
With reference to the first aspect, an embodiment of the present invention provides a third possible implementation manner of the first aspect, where the protocol conversion unit includes a CPU processor;
the protocol conversion unit is also used for carrying out protocol conversion on the stress detection value through the CPU, and sending the stress detection value after protocol conversion to the terminal platform.
With reference to the first aspect, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect, where the conversion processing unit includes a plurality of signal amplifying circuits and a/D conversion circuits;
the conversion processing unit is further configured to amplify the stress signals through the signal amplification circuits to obtain a plurality of amplified stress signals; and respectively carrying out conversion processing on the amplified stress signals through the A/D conversion circuits to obtain a plurality of processed stress signals.
With reference to the fourth possible implementation manner of the first aspect, an embodiment of the present invention provides a fifth possible implementation manner of the first aspect, where the number of the signal amplification circuits is consistent with the number of the stress slices in the stress slice module.
With reference to the fourth possible implementation manner of the first aspect, an embodiment of the present invention provides a sixth possible implementation manner of the first aspect, where the number of the a/D conversion circuits is consistent with the number of the stress slices in the stress slice module.
In a second aspect, an embodiment of the present invention further provides a switch machine stress signal detection method, which is applied to the switch machine stress signal detection apparatus in the first aspect, and the method includes:
the stress sheet module detects the point switch through the first stress sheet and the second stress sheet to obtain a plurality of stress signals, and the stress signals are sent to the collecting branch module;
the acquisition branch module amplifies and converts the stress signals through a conversion processing unit to obtain processed stress signals; and carrying out fusion processing on the processed stress signals through an algorithm processing unit to obtain a stress detection value of the point switch, and sending the stress detection value to a terminal platform through a protocol conversion unit.
In a third aspect, an embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor, when executing the computer program, implements the steps of the switch machine stress signal detection method according to the second aspect.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the switch machine stress signal detection method according to the second aspect.
The embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides a switch machine stress signal detection device and method and electronic equipment, wherein the switch machine stress signal detection device comprises the following steps: the stress sheet module and the collecting sub-machine module are connected; the acquisition extension module comprises a conversion processing unit, an algorithm processing unit and a protocol conversion unit which are connected in sequence; the stress sheet module comprises a first stress sheet and a second stress sheet, the first stress sheet is a plurality of stress sheets arranged on the point switch, and the second stress sheet is a plurality of redundant stress sheets additionally arranged on the basis of the first stress sheet; the stress sheet module is used for detecting the point switch through the first stress sheet and the second stress sheet to obtain a plurality of stress signals and sending the stress signals to the collecting and distributing module; the acquisition branch module is used for amplifying and converting the plurality of stress signals through the conversion processing unit to obtain a plurality of processed stress signals; and the algorithm processing unit is used for carrying out fusion processing on the processed stress signals to obtain a stress detection value of the point switch, and the protocol conversion unit is used for sending the stress detection value to the terminal platform, so that the detection precision of the internal stress signal of the point switch can be improved, the internal state of the point switch can be accurately monitored in real time, and the safe switching line of the train is ensured.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic diagram of a switch machine stress signal detection device according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a switch machine according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of another switch machine according to an embodiment of the present invention;
fig. 4 is a schematic view of another switch machine stress signal detection device according to an embodiment of the present invention;
fig. 5 is a schematic view of another switch machine stress signal detection device according to an embodiment of the present invention;
fig. 6 is a schematic view of another switch machine stress signal detection device according to an embodiment of the present invention;
fig. 7 is a flowchart of another method for detecting a switch machine stress signal according to an embodiment of the invention.
Icon:
10-stress slice module; 11-a first stress piece; 12-a second stress patch; 20-collecting the extension module; 21-a conversion processing unit; 22-an arithmetic processing unit; 23-a protocol conversion unit; 31-an action lever; 32-represents a rod; 33-a power unit; 34-a locking device; 35-a bearing seat; 36-a contact base; 37-an electrical circuit; 371 — action circuit; 372-denotes a circuit; 38-switch machine body; 391-square hole sleeve of action rod; 392-denotes a rod square sleeve.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The rail uniqueness is a main characteristic of the rail transit field, that is, a train can only travel along the current rail. In practical application, in order to meet the requirement of capacity, a multi-track parallel mode is inevitably selected, under the condition of multiple tracks, a train needs to be switched under certain necessary scenes, and the turnout switching equipment has the function of flexibly switching tracks at turnout switching points through the pushing and pulling action on the tracks so as to switch the lines of the train.
In particular, a switch-over device is a line connection device for switching a rolling stock from one track to another, usually laid in large numbers at stations, marshalling stations. The turnout conversion equipment mainly comprises two parts, namely an outdoor turnout switch machine and an indoor turnout control circuit part, wherein the outdoor part is an action reaction part, and the indoor part is a control part, namely, the control circuit sends an instruction to request the outdoor turnout switch machine to work, so that the turnout pulling function is realized.
A switch machine as a switch switching device for reliably switching a switch position, changing a switch opening direction, and locking a switch tongue is an important signal base device for controlling a switch position, and its safety maintenance is important. However, the conventional monitoring means only uses a notch monitoring system to monitor the fault, and cannot monitor the stress condition of internal components in the switch machine, such as an action rod, a display rod, a contact seat, a bearing seat and the like of the switch machine, and cannot timely and effectively find out the mechanical fault in the switch machine.
In addition, by arranging various stress sensors including the action rod stress sensor, the indication rod stress sensor, the locking stress sensor, the contact seat stress sensor and the bearing seat stress sensor on the switch machine, the internal parts of the switch machine, such as the action rod, the indication rod, the electric circuit and the like, can be effectively monitored, the internal parts of the switch machine are prevented from being broken down, and the normal work of the switch machine is ensured.
However, the switch machine, as a complex electronic mechanical device, is exposed to a severe external environment for a long time, and various environmental factors such as temperature, humidity, sand and wind, rain and snow, sunlight, and passing vibration have a great influence on electronic circuits and mechanical parts including application sensors. Due to the superposition of the environmental factors, serious noise is caused to stress acquisition inside the switch machine, and the accuracy of stress data acquisition of each measuring point placed in the switch machine equipment is seriously influenced. On one hand, the displacement and the suffered vibration of the sensor have great influence on the data acquired by the stress sheet, and the acquisition precision is influenced; on the other hand, under the influence of conditions such as geography and climate, the stress sensor itself may have measurement errors and measurement failures.
In view of the above situation, the existing method mainly adopts average value preprocessing, median preprocessing, etc. to smooth the acquired data, so as to reduce the influence of noise on the acquired signals. However, the average method and the median method cannot eliminate monitoring errors caused by the self-failure and instability of the sensor, so that the stress signal of the switch machine is not accurately detected, and the monitoring errors of the switch machine are caused.
Based on the above technical problem, embodiments of the present invention provide a switch machine stress signal detection apparatus and method, and an electronic device, which can improve the detection precision of a switch machine internal stress signal, so as to accurately monitor the switch machine internal state in real time, and ensure that a train switches a line safely.
For the convenience of understanding the present embodiment, a switch machine stress signal detection device provided by the present embodiment will be described in detail below.
The first embodiment is as follows:
fig. 1 is a schematic view of a switch machine stress signal detection device according to an embodiment of the present invention. As shown in fig. 1, the apparatus includes: the stress sheet module 10 and the collection extension module 20 are connected; the acquisition extension module comprises a conversion processing unit 21, an algorithm processing unit 22 and a protocol conversion unit 23 which are connected in sequence; the stress sheet module comprises a first stress sheet 11 and a second stress sheet 12, the first stress sheet is a plurality of stress sheets arranged on the switch machine, and the second stress sheet is a plurality of redundant stress sheets additionally arranged on the basis of the first stress sheet.
The stress sheet module is used for detecting the point switch through the first stress sheet and the second stress sheet to obtain a plurality of stress signals and sending the stress signals to the collecting branch module; the acquisition branch module is used for amplifying and converting the plurality of stress signals through the conversion processing unit to obtain a plurality of processed stress signals; and performing fusion processing on the processed stress signals through an algorithm processing unit to obtain a stress detection value of the point switch, and sending the stress detection value to a terminal platform through a protocol conversion unit.
In addition, in the embodiment of the invention, the additionally-installed positions of the stress pieces in the second stress piece are adjacent to the positions of the original stress pieces in the first stress piece and need to keep a certain distance, so that the additionally-installed stress pieces are prevented from influencing the installation and detection of the original stress pieces, and the independence of the detection results of the stress pieces in the first stress piece and the second stress piece is ensured.
This is illustrated here for ease of understanding. Fig. 2 is a schematic structural diagram of a switch machine according to an embodiment of the present invention, as shown in fig. 2, the switch machine includes an actuating rod 31, a display rod 32, a power unit 33, a locking device 34, a bearing seat 35, a contact seat 36, an electrical circuit 37, an actuating circuit 371, a display circuit 372, a switch machine body 38, an actuating rod square hole sleeve 391, and a display rod square hole sleeve 392, wherein the power unit is an electric motor. When a plurality of stress pieces are installed on the square hole sleeve of the indication rod, as shown in fig. 3, the stress pieces of the indication rod are arranged on the outer wall of the square hole sleeve of the indication rod of the switch machine, and the square hole sleeve of the indication rod is fixedly installed on the outer wall of the switch machine body, wherein one stress piece is attached to the inner wall of the square hole sleeve of the indication rod in the four directions, namely the upper direction, the lower direction, the left direction and the right direction. In order to achieve the purpose of improving the accuracy of stress signal detection data and improve the reliability and robustness of stress pieces, the embodiment of the invention needs to additionally install 4 stress pieces, namely a second stress piece, on the basis of 4 existing stress pieces, namely a first stress piece, wherein the additionally installed 4 stress pieces are redundant stress pieces of the original 4 stress pieces, and the additionally installed stress pieces are arranged at different positions from the original stress pieces and are adjacent to the original stress pieces and need to keep a certain distance so as to prevent the additionally installed stress pieces from influencing the installation and detection of the original stress pieces, thereby ensuring the independence of detection results of each stress piece in the first stress piece and the second stress piece.
In addition, on other positions in the switch machine, such as the action rod square hole sleeve, the contact seat and the bearing seat, a second stress piece redundant with the original stress piece can be additionally arranged on the basis of the original stress piece, namely the first stress piece, and the position of the additionally arranged stress piece is adjacent to the position of the original stress piece and needs to keep a certain distance, wherein the installation requirement of the second stress piece is consistent with the installation requirement of the indication rod square hole sleeve for additionally arranging the stress piece, and the embodiment of the invention is not repeated in detail herein.
Therefore, the switch machine is additionally provided with the redundant second stress sheet based on the first stress sheet, so that the influence of external disturbance and internal noise of the stress sheet, namely the stress sensor and the self abnormity of the sensor can be eliminated, the reliability and robustness of a stress measurement system are improved, the data reliability is enhanced, and the detection precision of the switch machine stress signal is improved. In addition, the redundancy of the data of the multiple stress sheets at different spatial positions is fully utilized, and the data processing technology is adopted to implement information complementation on the observation data of the multiple stress sheets obtained according to the time sequence so as to obtain accurate measurement on a measurement object, so that the stress sensing precision of different parts in the switch machine is improved.
The switch machine stress signal detection device provided by the embodiment of the invention comprises: the stress sheet module and the collecting sub-machine module are connected; the acquisition extension module comprises a conversion processing unit, an algorithm processing unit and a protocol conversion unit which are connected in sequence; the stress sheet module comprises a first stress sheet and a second stress sheet, the first stress sheet is a plurality of stress sheets arranged on the point switch, and the second stress sheet is a plurality of redundant stress sheets additionally arranged on the basis of the first stress sheet; the stress sheet module is used for detecting the point switch through the first stress sheet and the second stress sheet to obtain a plurality of stress signals and sending the stress signals to the collecting and distributing module; the acquisition branch module is used for amplifying and converting the plurality of stress signals through the conversion processing unit to obtain a plurality of processed stress signals; and the algorithm processing unit is used for carrying out fusion processing on the processed stress signals to obtain a stress detection value of the point switch, and the protocol conversion unit is used for sending the stress detection value to the terminal platform, so that the detection precision of the internal stress signal of the point switch can be improved, the internal state of the point switch can be accurately monitored in real time, and the safe switching line of the train is ensured.
In practical application, the noise removal problem caused by various complex factors during the stress collection of the switch machine can be modeled as a filtering problem. Typically both the noise and the true signal or state are random processes, and thus the filtering problem is essentially a statistical estimation problem. A commonly used optimal estimation criterion is a linear minimum variance estimation, i.e. it is required that the error variance of the optimal value of the signal or state with the corresponding true value should be minimized, and the filtering implemented based on this concept is called optimal filtering.
The Kalman filter is designed as a filter with the most extensive application and powerful functions, the optimal current estimation state of a signal can be obtained, the principle is that the minimum mean square error is used as the optimal estimation criterion, a state space model of the signal and noise is adopted, the estimation value of a state variable is updated by using the estimation value of the previous moment and the observation value of the current moment, so as to calculate the estimation value of the current moment, and the algorithm realizes the optimal estimation meeting the minimum mean square error for the signal to be processed according to a system equation and an observation equation of Kalman filtering. In addition, the Kalman filter has recursion, is convenient to realize real-time application on a computer, and is suitable for the problem of smooth or non-smooth random process filtering. When filtering calculation is carried out on the Kalman filter in a layer-by-layer recursion mode, the current state can be accurately estimated only by utilizing the estimated value of the previous sampling period and the current measured value, so that the Kalman filter does not occupy too large storage space, and has the advantages of clear and visible calculation steps, good real-time performance, strong anti-interference capability and the like. Therefore, in the embodiment of the invention, the standard Kalman filter is adopted to carry out filtering processing on the stress signals output by each stress sheet in the switch machine.
The standard kalman filter can perform data fusion processing on multi-sensor signals, but has the following problems: (1) under the condition of large redundancy of combined information, the calculated amount is increased sharply by the third power of the dimension of the filter, and the real-time performance cannot be ensured; (2) the increase in sensor subsystems will lead to a corresponding increase in failure rate, and in the event that a subsystem fails without being detected and isolated in time, the failure data will contaminate the entire system, resulting in reduced reliability. Therefore, in order to avoid the above problem, the present invention in an embodiment adopts a method of combining kalman filters, that is, in an algorithm processing unit, a plurality of local kalman filters and a global kalman filter are included.
Specifically, the united Kalman filter is a block estimation method and a two-step cascade data processing technology, and the idea of united filtering is to firstly perform decentralized processing and then perform global fusion. In the combined filtering, the subsystem comprises an external sensor 1 and a sensor 2 … …, the standard Kalman filters respectively correspond to different sensors to form a plurality of local Kalman filters, each local filter works in parallel, after the local filters output local optimal estimation values, the global filters perform information fusion on filtering results generated by all local filter outputs, and global optimal state estimation is given. Under the common condition, the joint filtering can greatly reduce the calculated amount, is flexible and convenient to apply and has high-level multi-sensor fusion effect.
In addition, after outputting the fusion stress estimation value, the kalman filter assigns dynamic information to each local filter, where the information mainly includes two categories: and fusing the estimation value result after fusion and the prediction error variance result of the estimation value. The specific mechanism is as follows: firstly, distributing proper parts of information of the whole system to each local filter; then, each local filter works independently, the distributed information and the measurement information are fused, and the state value and the prediction error variance are corrected to complete the information updating of the local filters; and finally, fusing the corrected local information into a new global state estimation, and performing recursion circulation on the mechanism until a global optimal state estimation is obtained.
Specifically, as shown in fig. 4, n stress pieces are disposed in spatial positions inside the switch machine that are not overlapped, where the number of the first stress piece and the second stress piece in the n stress pieces may be set according to actual situations, and this is not limited to be described in the embodiment of the present invention. In addition, the n stress sheets are collected according to the same collection frequency, and then n stress signals collected by the n stress sheets are amplified and converted by the conversion processing unit and then sent to the algorithm processing unit. As a specific example, when only one stress slice is included in the first stress slice, then (n-1) stress slices from stress slice 2 to stress slice n in fig. 4 constitute the second stress slice. Here, the algorithm processing unit in the embodiment of the present invention includes a plurality of local kalman filters and a global kalman filter; the algorithm processing unit is further used for filtering the processed stress signals through a local Kalman filter respectively, and fusing the processed stress signals through a global Kalman filter to obtain a stress detection value of the point machine. The number of the local kalman filters is consistent with the number of the stress pieces, and it should be noted that the number of the stress pieces is the sum of the number of the stress pieces in the first stress piece and the number of the stress pieces in the second stress piece, so that it is ensured that the stress signal output by each stress piece can be filtered through the local kalman filter. In addition, in practical application, a local kalman filter may be used to perform filtering processing on the stress signals output by the stress slices in the first stress slice, and another or multiple local kalman filters may be used to perform filtering processing on the stress signals output by the stress slices in the second stress slice.
In addition, the state equation of the local kalman filter in the embodiment of the present invention may be expressed by the following formula:
Xi(k+1)＝Φ(k)Xi(k)+G(k)W(k) (1)
wherein, Xi(k +1) represents the state variable of the local Kalman filter i at the moment k +1, phi (k) represents the transformation matrix of the local Kalman filter at the moment k, and Xi(k) Representing the state variable of the local Kalman filter i at the moment k, G (k) representing the process noise matrix of the local Kalman filter at the moment k, and W (k) representing the local Kalman filteringThe process noise at time k, i 1, 2.
Further, in the embodiment of the present invention, the measurement equation of the local kalman filter may be expressed by the following formula:
Zi(k)＝Hi(k)Xi(k)+Vi(k) (2)
wherein Z isi(k) Representing the measurement of the local Kalman filter i at time k, Hi(k) An observation matrix, V, representing the local Kalman filter i at time ki(k) Representing the measurement noise, X, of the local Kalman filter i at time ki(k) Denotes the state variable of the local kalman filter i at time k, i ═ 1, 2.
Therefore, the algorithm of the local kalman filter can be expressed by the following formula:
Xi(k+1,k+1)＝Ki(k+1)·Zi(k+1)+[I-Ki(k+1)Hi(k+1)]·Φ(k)Xi(k,k) (3)
wherein, Xi(K +1) represents the state estimation value of the local Kalman filter i at the time K +1, Ki(k +1) denotes the Kalman filter gain, Z, of the local Kalman filter i at the time k +1i(k +1) represents the system measurement value of the local Kalman filter I at the moment of k +1, I represents an identity matrix, Hi(k +1) represents the observation matrix of the local Kalman filter i at the moment k +1, phi (k) represents the transformation matrix of the local Kalman filter at the moment k, and XiAnd (k, k) represents a state estimation value of the local kalman filter i at the time k, wherein i is 1, 2.
Wherein, the Kalman filter gain of the local Kalman filter is obtained according to the following formula:
Ki(k+1)＝Pi(k+1,k)Hi T(k+1)×[Hi(k+1)Pi(k+1,k)Hi T(k+1)+Ri(k+1)]-1(4)
wherein, Ki(k +1) denotes the Kalman filter gain, P, of the local Kalman filter i at the time k +1i(k +1, k) represents one step of the local Kalman filter i at time kPrediction error variance matrix, Hi(k +1) denotes the observation matrix of the local Kalman filter i at the time k +1, Ri(k +1) denotes a covariance matrix of the observation noise of the local kalman filter i at the time k +1, i ═ 1, 2.
Further, a further prediction error variance matrix of the local kalman filter i at the time k may be obtained according to the following formula:
Pi(k+1,k)＝Φ(k)Pi(k,k)ΦT(k)+G(k)Q(k)GT(k) (5)
wherein, Pi(k +1, k) represents a one-step prediction error variance matrix of the local Kalman filter i at the moment k, phi (k) represents a transformation matrix of the local Kalman filter at the moment k, and Pi(k, k) denotes the error variance matrix of the local kalman filter i at time k, g (k) denotes the process noise matrix of the local kalman filter at time k, q (k) denotes the covariance matrix of the process noise of the local kalman filter at time k, i ═ 1, 2.
And obtaining an error variance matrix of the local Kalman filter i at the moment k +1 according to the following formula:
Pi(k+1,k+1)＝[I-Ki(k+1)Hi(k+1)]Pi(k+1,k) (6)
wherein, Pi(K +1) represents the error variance matrix of the local Kalman filter I at the moment K +1, I represents the identity matrix, Ki(k +1) denotes the Kalman filter gain, H, of the local Kalman filter i at the time k +1i(k +1) denotes the observation matrix of the local Kalman filter i at the time k +1, Pi(k +1, k) denotes a one-step prediction error variance matrix of the local kalman filter i at time k, i ═ 1, 2.
Therefore, through the above equations (1) to (6), the optimal statistics X of the n local kalman filters at the time k +1 can be obtainedi(k +1), wherein, i ═ 1, 2. At this time, the estimated value X is the optimal statistic after the filtering processing of n local Kalman filtersiAnd (k +1) carrying out fusion processing through a global Kalman filter, thereby obtaining the stress detection value of the point machine.
Specifically, the state estimation value of the global kalman filter is obtained by the following formula:
wherein, XmRepresenting the state estimate of the global Kalman filter, P1Error variance matrix, P, representing the first local Kalman filter2Error variance matrix, P, representing a second local Kalman filternError variance matrix, X, representing the nth local Kalman filter1Representing the estimated value, X, of the first local Kalman filter2Representing the estimate of a second local Kalman filter, XnRepresenting the estimate of the nth local kalman filter.
Further, the error variance matrix of the global kalman filter can be obtained by the following formula:
wherein, PmError variance matrix, P, representing a global Kalman filter1Error variance matrix, P, representing the first local Kalman filter2Error variance matrix, P, representing a second local Kalman filternAn error variance matrix of the nth local kalman filter is represented.
Therefore, the global kalman filter implements the fusion process by the above equation (7) and equation (8), and the above fusion process is a method of giving a weight based on the reliability of the estimation result output by each local kalman filter, the higher the reliability of the estimation of one local sensor is, the higher the weight of the estimation in the fusion equation is, and the reliability is represented by the inverse error variance of each local kalman filter, that is, the greater the error variance is, the lower the reliability is, and conversely, the smaller the error variance is, the greater the reliability is. Therefore, the combined filtering algorithm is used for synthesizing the optimal estimation value of the system state and the error variance matrix of the global Kalman filter by using the state error matrix and the estimation result of the local Kalman filter, the calculation amount of the global Kalman filter is small, the synthesis algorithm is simple, and the real-time realization of the global Kalman filter is facilitated.
Further, after the global kalman filter fusion processing is completed, information distribution is performed on the fusion result to each local kalman filter, wherein the information distribution value can be performed according to the following distribution formula:
wherein, Pi(k +1) denotes the error variance matrix of the local Kalman filter i at time k +1, βiRepresenting the distribution factor, P, of the covariance feedback of the global Kalman filter fused to each local Kalman filterm(k +1) denotes an error variance matrix of the global kalman filter at time k +1, i is 1,21+β2+...+βn＝1。
Wherein Q isi(k +1) represents the covariance matrix of the process noise of the local Kalman filter i at time k +1, βiRepresenting the distribution factor Q of the covariance feedback of the global Kalman filter after fusion to each local Kalman filterm(k +1) represents a covariance matrix of process noise of the global kalman filter at the time k +1, i ═ 1,21+β2+...+βn＝1。
Xi(k+1)＝Xm(k+1) (11)
Wherein, Xi(k +1) denotes the estimated value of the local Kalman filter i at the time k +1, Xm(k +1) denotes an estimated value of the global kalman filter at time k +1, i ═ 1, 2.
Here, the distribution method of the global kalman filter mainly includes an average distribution method, wherein the distribution method can be expressed by the following formula:
βi＝1/n (12)
wherein, βiAnd (3) representing an allocation factor of the covariance after the fusion of the global Kalman filter to each local Kalman filter, wherein i is 1, 2.
Optionally, the assignment may be performed by a diagonal matrix method, where the diagonal matrix method may be represented by the following formula:
βi(k)＝diag(a1,a2,...,an) (13)
wherein, βi(k) Representing the distribution factor representation of the covariance feedback of the global Kalman filter fused at the moment k to each local Kalman filter, a1,a2,...,anRepresenting eigenvalues of the diagonal matrix.
Wherein, the eigenvalue of the diagonal matrix can be obtained according to the following formula:
wherein, ajCharacteristic value, P, representing a diagonal matrixi(k,k)jjJj-element, P, representing the error variance matrix of the local Kalman filter i at time k1(k,k)jjJj-elements, P, representing the error variance matrix of the local Kalman filter 1 at time k2(k,k)jjJj-elements, P, representing the error variance matrix of the local Kalman filter 2 at time kn(k,k)jjA jj element, i ═ 1, 2.., n, representing the error variance matrix of the local kalman filter n at time k.
Therefore, in the embodiment of the invention, the algorithm processing unit detects the point switch by additionally arranging the redundant second stress pieces on the point switch based on the arranged first stress pieces to obtain a plurality of stress signals, performs filtering processing by adopting a combined Kalman filter structure, performs filtering on a local Kalman filter by adopting a Kalman state equation and a measurement equation, firstly obtains local optimal estimation, and performs synthesis according to a fusion algorithm in the global Kalman filter on the basis of the local optimal estimation to obtain data with higher filtering precision, so that monitoring errors and the like caused by the self-fault and instability of the stress pieces can be eliminated, and the data quality is improved.
Furthermore, compared with a common centralized Kalman filter, the combined Kalman filter adopted by the embodiment of the invention can effectively resist the problem of output error of the whole system caused by the fault and error condition of a certain stress piece, and improves the integral robustness and fault tolerance of the filter system; in addition, compared with a dispersion filter, the joint Kalman filter can improve the estimation precision of the system by means of the feedback action of the global Kalman filter on the local Kalman filter, and the fault tolerance of the system is ensured. Therefore, when a certain stress piece is isolated due to faults, the combined Kalman filter adopted by the embodiment of the invention has the capability of taking the estimated value of other good local filters as a substitute value, and the system robustness is ensured.
In addition, in practical applications, the algorithm processing unit receives the plurality of processed stress signals sent by the switching conversion unit through a communication Interface such as SPI (Serial Peripheral Interface), IIC (Inter-Integrated Circuit bus), UART (universal asynchronous Receiver/Transmitter), and performs fusion processing on the plurality of processed stress signals, so as to obtain the stress detection value of the switch machine. The arithmetic Processing unit may be an FPGA (Field Programmable Gate Array), a CPLD (Complex Programmable Logic Device), a DSP (Digital Signal Processing), or other arithmetic hardware processors, which are not limited to the above description.
Further, in the embodiment of the present invention, the protocol conversion Unit includes a Central Processing Unit (CPU) processor; at this time, the protocol conversion unit is further configured to perform protocol conversion on the stress detection value through the CPU processor, and send the stress detection value after the protocol conversion to the terminal platform. Specifically, the CPU receives the stress monitoring value of the switch machine output by the algorithm processing unit through the SPI, IIC, UART, or other communication interface, performs protocol conversion on the stress monitoring value, and then sends the stress monitoring value to the terminal platform through the data transmission channel, where the terminal platform is a sensing information integrated access platform, performs data analysis processing according to the stress monitoring value, and outputs the warning information and the like. In addition, the terminal platform may also be a big data platform, which is not limited to be described in the embodiment of the present invention.
Further, the conversion processing unit includes a plurality of signal amplifying circuits and a/D conversion circuits; at this time, the conversion processing unit is further configured to amplify the plurality of stress signals through the plurality of signal amplification circuits to obtain a plurality of amplified stress signals; and the stress signals after being amplified are respectively converted by a plurality of A/D conversion circuits to obtain a plurality of processed stress signals. The number of the signal amplifying circuits and the number of the A/D conversion circuits are consistent with the number of the stress pieces. It should be noted that the number of stress sheets here is the sum of the number of stress sheets in the first stress sheet and the number of stress sheets in the second stress sheet.
For ease of understanding, two stress slices are illustrated here. As shown in fig. 5, the stress sheet 1 is a first stress sheet, the stress sheet 2 is an additional second stress sheet, and the switch machine is detected by the first stress sheet and the second stress sheet to obtain two stress signals, and the two stress signals are sent to the conversion processing unit in the collection extension module, at this time, the two signal amplifying circuits in the conversion processing unit respectively amplify the two stress signals to obtain two amplified stress signals, and the two amplified stress signals are sent to the two a/D conversion circuits, so that the two a/D conversion circuits respectively convert the two amplified stress signals to obtain two processed stress signals, and the two processed stress signals are subjected to fusion processing by the algorithm processing unit to obtain a stress detection value of the switch machine, and finally, the stress detection value is sent to the terminal platform by the protocol conversion unit, i.e. the CPU processor, therefore, the monitoring of the switch machine is realized.
In addition, for the convenience of understanding, the implementation mode of the invention is described by taking the indication rod stress sheet as an example, as shown in fig. 6, ① part in the figure indicates that stress sheets, namely stress sensors, are arranged on the indication rod inside the switch machine, 2 indication rod stress sensors are mounted on the indication rod of the switch machine in order to improve the reliability and the robustness of the stress sensors, and the indication rod stress sensors are arranged on the outer wall of the indication rod square hole sleeve of the switch machine and indicate that the indication rod square hole sleeve is fixedly mounted on the outer wall of the switch machine body, wherein, the indication rod radial stress sensor 1 is a first stress sheet, indicates that the rod radial stress sensor 2 is a second redundant stress sheet of the first stress sheet, and the second stress sheet is mounted at a position different from that of the existing stress sensor, namely the first stress sheet, but should not affect the mounting and monitoring of the original stress sensor, and can be adjacent to the original stress sensor and need to keep a certain distance, thereby ensuring the independence of the monitoring result.
In addition, in the drawing, the ② part is a structure diagram of an acquisition extension module, for simplifying the description, only a processing part for a stress signal output by a rod stress sensor is depicted, and an analog interface is connected between the acquisition extension module and the stress sensor, wherein the ② -1 part is a conversion processing unit and comprises a signal amplification circuit and an A/D conversion circuit, the ② -2 part and the ② -3 part form an algorithm processing unit, the ② -2 part is a local Kalman filter part, the local Kalman filter part can be set through a special DSP chip or an FPGA chip or an advanced RISC (advanced RISC machines) chip, the ② -3 part is a global Kalman filter part, the global Kalman filter part mainly comprises a fusion processing part and an information distribution part, the fusion processing part is used for carrying out fusion processing on the stress signal output by the local Kalman filter to obtain a stress detection value of a point machine output by the global Kalman filter at the current moment, the information distribution part is used for distributing information of the fused stress signal and feeding back the information distribution value to the local Kalman filter part to effectively output a stress detection value of the point machine, and the stress detection value is used for monitoring the internal stress signal of the point machine conversion platform.
Here, the switch machine indicates that 2 stress sensors to which the bars are attached are represented by S1 and S2, respectively, where S1 indicates a first stress sensor, i.e., a first stress piece, and S2 indicates a second stress sensor, i.e., a second stress piece. In addition, local kalman filters corresponding to S1 and S2 are KF1 and KF2, respectively, the two stress sensors have the same acquisition frequency, and acquire stress signals from the two stress sensors with 1 minute as a time frequency, and at this time, for each time point, a stress monitoring value after multi-sensor fusion is output from the global kalman filter MF of the joint kalman filter. For simplification of description, only one type of stress monitoring information is considered here, namely, the stress monitoring information represents the outer wall pressure of the rod square hole sleeve, at this time, the system state is one-dimensional spatial data, the stress state is set to be in white gaussian noise, in addition, the stress sensor has measurement deviation, and the measurement deviation model is also set to be white gaussian noise.
At this time, assuming that the stress signals transmitted from S1 and S2 are 412N and 408N, respectively, at time k, the result of the fusion estimation value of the global kalman filter at time k-1 is 410N. Assuming that the stress signal remains in a constant trend, i.e., Φ (k-1) ═ 1, P is filtered according to the global kalman filter1(k-1) information distribution result, assuming P1If (k-1) ═ 3 and Q (k-1) ═ 4 is set as a constant, then the error variance matrix can be predicted from the one step at time k-1 by the local kalman filter KF1 corresponding to the stress sensor S1:
wherein, P1(k, k-1) represents the one-step prediction error variance matrix of the local Kalman filter KF1 at time k-1.
At this point, it is assumed that the stress measurement can hold the state value at 100%, Hi(k-1) ═ 1, and R is assumed to bei(k)＝4, the local kalman gain of the local kalman filter KF1 at time k can be obtained:
wherein, K1(k) Representing the local kalman gain of the local kalman filter KF1 at time k.
Based on this, the state parameters of the local kalman filter KF1 corresponding to the stress sensor S1 can be updated:
X1(k,k)＝412*0.78+(1-0.78)*410＝411.56N (17)
wherein, X1(k, k) represents the state parameters of the local kalman filter KF1 at time k.
Refer to the above equations (15) to (17), and assume P2(k-1) ═ 4, the error variance matrix can be predicted by obtaining a one-step prediction error of the local kalman filter KF2 corresponding to the stress sensor S2 at the time k-1:
wherein, P2(k, k-1) represents the one-step prediction error variance matrix of the local Kalman filter KF2 at time k-1.
And, the local kalman gain at time k of the local kalman filter KF2 can be obtained:
wherein, K2(k) Representing the local kalman gain of the local kalman filter KF2 at time k.
Further, the state parameters of the local kalman filter KF2 corresponding to the stress sensor S2 may be updated:
X2(k,k)＝408*0.67+(1-0.67)*410＝408.66N (20)
wherein, X2(k, k) denotes the local Kalman filter KF2 at kA status parameter of the time of day.
At this time, the best state estimation value of the global kalman filter MF can be obtained:
wherein, Xm(k, k) represents the best state estimate of the global kalman filter MF.
And obtaining an error variance matrix of the global Kalman filter MF:
wherein, Pm(k, k) denotes the error variance matrix of the global kalman filter MF.
At this time, β is assumed10.57 and β2The allocation information of the global kalman filter MF can be obtained as 0.43:
wherein, P1(k) Representing the error variance matrix of the local kalman filter KF1 at time k.
Wherein, P2(k) Representing the error variance matrix of the local kalman filter KF2 at time k.
And finally obtaining an optimal state estimation value of the global Kalman filter MF:
X1(k)＝X2(k)＝Xm(k)＝410.20N (25)
wherein, X1(k) Represents the best state estimate, X, of the local Kalman filter KF1 at time k2(k) Represents the best state estimate, X, of the local Kalman filter KF2 at time km(k) Representing the optimum of the global Kalman filter MF at time kAnd the state estimation value is sent to a terminal platform, so that the effective monitoring of the point switch is realized.
On the basis of the foregoing embodiments, an embodiment of the present invention further provides a method for detecting a switch machine stress signal, where the method is applied to the apparatus for detecting a switch machine stress signal, and fig. 7 is a flowchart of the method for detecting a switch machine stress signal according to the embodiment of the present invention. As shown in fig. 7, the method comprises the steps of:
step S102, the stress sheet module detects the point switch through the first stress sheet and the second stress sheet to obtain a plurality of stress signals, and the stress signals are sent to the collection branch module;
step S104, the collecting sub-machine module amplifies and converts the stress signals through the conversion processing unit to obtain a plurality of processed stress signals; and performing fusion processing on the processed stress signals through an algorithm processing unit to obtain a stress detection value of the point switch, and sending the stress detection value to a terminal platform through a protocol conversion unit.
The switch machine stress signal detection method provided by the embodiment of the invention is applied to a switch machine stress signal detection device, wherein the switch machine stress signal detection device comprises the following steps: the stress sheet module and the collecting sub-machine module are connected; the acquisition extension module comprises a conversion processing unit, an algorithm processing unit and a protocol conversion unit which are connected in sequence; the stress sheet module comprises a first stress sheet and a second stress sheet, the first stress sheet is a plurality of stress sheets arranged on the point switch, and the second stress sheet is a plurality of redundant stress sheets additionally arranged on the basis of the first stress sheet; the stress sheet module is used for detecting the point switch through the first stress sheet and the second stress sheet to obtain a plurality of stress signals and sending the stress signals to the collecting and distributing module; the acquisition branch module is used for amplifying and converting the plurality of stress signals through the conversion processing unit to obtain a plurality of processed stress signals; and the algorithm processing unit is used for carrying out fusion processing on the processed stress signals to obtain a stress detection value of the point switch, and the protocol conversion unit is used for sending the stress detection value to the terminal platform, so that the detection precision of the internal stress signal of the point switch can be improved, the internal state of the point switch can be accurately monitored in real time, and the safe switching line of the train is ensured.
The embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the steps of the switch machine stress signal detection method provided in the above embodiment are implemented.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the switch machine stress signal detection method of the embodiment are executed.
The computer program product provided in the embodiment of the present invention includes a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, which is not described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A switch machine stress signal detecting device, provided on a switch machine, said device comprising: the stress sheet module and the collecting sub-machine module are connected; the acquisition extension module comprises a conversion processing unit, an algorithm processing unit and a protocol conversion unit which are connected in sequence; the stress sheet module comprises a first stress sheet and a second stress sheet, the first stress sheet is a plurality of stress sheets arranged on the switch machine, and the second stress sheet is a plurality of redundant stress sheets additionally arranged on the basis of the first stress sheet;
the stress sheet module is used for detecting the point switch through the first stress sheet and the second stress sheet to obtain a plurality of stress signals and sending the stress signals to the collecting and distributing module;
the acquisition branch module is used for amplifying and converting the stress signals through the conversion processing unit to obtain a plurality of processed stress signals; and the algorithm processing unit is used for carrying out fusion processing on the processed stress signals to obtain a stress detection value of the switch machine, and the protocol conversion unit is used for sending the stress detection value to a terminal platform.
2. The switch machine stress signal detection device of claim 1, wherein the algorithm processing unit comprises a plurality of local kalman filters and a global kalman filter;
the algorithm processing unit is further configured to perform filtering processing on the plurality of processed stress signals through the local kalman filter, and perform fusion processing on the plurality of processed stress signals after the filtering processing through the global kalman filter, so as to obtain the stress detection value of the point switch.
3. The switch machine stress signal detection device of claim 2, wherein the number of local kalman filters is consistent with the number of stress slices in the stress slice module.
4. A switch machine stress signal detecting device according to claim 1, wherein said protocol conversion unit includes a CPU processor;
the protocol conversion unit is also used for carrying out protocol conversion on the stress detection value through the CPU, and sending the stress detection value after protocol conversion to the terminal platform.
5. The switch machine stress signal detecting device according to claim 1, wherein the conversion processing unit includes a plurality of signal amplifying circuits and a/D conversion circuits;
the conversion processing unit is further configured to amplify the stress signals through the signal amplification circuits to obtain a plurality of amplified stress signals; and respectively carrying out conversion processing on the amplified stress signals through the A/D conversion circuits to obtain a plurality of processed stress signals.
6. The switch machine stress signal detecting device of claim 5, wherein the number of the signal amplifying circuits is consistent with the number of the stress pieces in the stress piece module.
7. The switch machine stress signal detecting device of claim 5, wherein the number of A/D converting circuits is consistent with the number of stress pieces in the stress piece module.
8. A switch machine stress signal detection method applied to the switch machine stress signal detection device according to any one of claims 1 to 7, the method comprising:
the stress sheet module detects the point switch through the first stress sheet and the second stress sheet to obtain a plurality of stress signals, and the stress signals are sent to the collecting branch module;
the acquisition branch module amplifies and converts the stress signals through a conversion processing unit to obtain processed stress signals; and carrying out fusion processing on the processed stress signals through an algorithm processing unit to obtain a stress detection value of the point switch, and sending the stress detection value to a terminal platform through a protocol conversion unit.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the switch machine stress signal detection method as claimed in claim 8 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, performs the steps of the switch machine stress signal detection method according to claim 8.
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