A positioning visualization system, device and method for a battery-powered vehicle in a shield tunnel

Abstract

This application relates to the field of wireless communication technologies, and particularly to a positioning visualization system, device and method for battery-powered vehicles in shield tunnels. The method includes: collecting in real time the received signal strengths of each WIFI router in the shield tunnel; constructing a logarithmic transmission loss model to obtain the dynamic weights of each WIFI router at any acquisition moment; combining the dynamic weights with all path loss exponents of each WIFI router before the any acquisition moment, and using the Kalman filtering algorithm to estimate the path loss exponent of each WIFI router at the any acquisition moment; calculating the metric distance between each WIFI router and the battery-powered vehicle at the any acquisition moment by using the logarithmic transmission loss model through the path loss exponent and received signal strength of each WIFI router at the any acquisition moment; constructing an objective function for positioning the battery-powered vehicle to position the battery-powered vehicle. This application aims to improve the positioning accuracy of the battery-powered vehicle.

Description

Technical Field

[0001] This application relates to the field of wireless communication technologies, and particularly to a positioning visualization system, device, and method for battery-powered vehicles in shield tunnels. Background Art

[0002] The battery-powered vehicle in a shield tunnel is one of the important auxiliary equipment for a tunnel boring machine, and undertakes tasks such as transporting sand, soil, materials, and equipment during tunnel boring construction. Accurately positioning the battery-powered vehicle in a shield tunnel can not only reasonably schedule the operation of vehicles and improve engineering efficiency, but also combine the working condition video of the location where the battery-powered vehicle in the shield tunnel is located to achieve positioning visualization, help discover potential dangers in advance, and thus take measures to avoid accidents.

[0003] Patent application CN107124701A discloses a positioning method and device for a WIFI terminal. The WIFI terminal is communicatively connected to multiple communication devices, the wireless signals emitted by the WIFI terminal received by the multiple communication devices are analyzed to obtain different coordinate information, and the positioning of the WIFI terminal is completed through coordinate information comparison. However, a tunnel is a long and narrow enclosed environment, and there are also features such as "large slopes" and "small radii" in the tunnel under construction. In the above environment, the propagation of wireless signals will be affected by factors such as attenuation and multipath fading. If the above disclosed positioning method is adopted, it will lead to the inability to fully consider the differences in the attenuation of wireless signals received by different communication devices, thereby reducing the positioning accuracy of the battery-powered vehicle in the shield tunnel. Summary of the Invention

[0004] In view of the above, it is necessary to provide a positioning visualization system, device, and method for a battery-powered vehicle in a shield tunnel, which can improve the positioning accuracy of underground targets compared with traditional target positioning based on ground penetrating radar data:

[0005] In a first aspect, an embodiment of the present application provides a positioning visualization method for a battery-powered vehicle in a shield tunnel, the method including the following steps:

[0006] Real-time collect the received signal strength of each WIFI router in the shield tunnel, the signal being emitted by the battery-powered vehicle;

[0007] Based on the relationship between the received signal strength and the transmission distance, construct a logarithmic transmission loss model, and obtain the dynamic weights of each WIFI router at any collection moment through the distribution of all the received signal strengths of each WIFI router before any collection moment, and the differences in the received signal strengths of each WIFI router compared with its respective neighboring WIFI routers before the any collection moment;

[0008] Combining the dynamic weights with all the path loss exponents of each WIFI router before the any collection moment, use the Kalman filter algorithm to estimate the path loss exponent of each WIFI router at the any collection moment; wherein, the initial value of the path loss exponent is a preset value;

[0009] Based on the path loss exponent and the received signal strength of each WIFI router at the any collection moment, use the logarithmic transmission loss model to calculate the metric distance between each WIFI router and the battery vehicle at the any collection moment;

[0010] Select each positioning router from all the WIFI routers;

[0011] At the any collection moment, based on the difference between the distance between each positioning router and the estimated coordinates of the battery vehicle and the metric distance, and combining the estimation error of the path loss exponent by the Kalman filter algorithm, construct an objective function for positioning the battery vehicle, and position the battery vehicle by solving the estimated coordinates.

[0012] In one embodiment, the expression of the logarithmic transmission loss model is:

[0013] ; in the formula, represents the received signal strength at a position with a distance d from the transmitter; A represents the preset received signal strength at a position with a preset distance from the transmitter; γ represents the real-time path loss exponent; lg( ) represents the logarithmic function with base 10; d represents the distance from the transmitter.

[0014] In one embodiment, the expression of the dynamic weight is:

[0015] ; in the formula, represents the dynamic weight of the i-th WIFI router at the j-th collection moment; norm( ) represents the normalization function; represents the mean value of all the received signal strengths of the i-th WIFI router before the j-th collection moment; represents the mean value of the differences between the received signal strengths of the i-th WIFI router and all its neighboring WIFI routers at the t-th collection moment; T represents the total number of collection moments before the j-th collection moment; α and β both represent preset weights greater than 0, and the sum of α and β is 1.

[0016] In one embodiment, the using the Kalman filter algorithm to estimate the path loss exponent of each WIFI router at the any collection moment includes:

[0017] Adjust the Kalman gain when using the Kalman filter algorithm to estimate the path loss exponent through the dynamic weight;

[0018] Arrange all the path loss indices of each WIFI router before the any collection moment in chronological order to form a path loss index vector;

[0019] Take the path loss index vector as the state vector of the Kalman filtering algorithm, and through the adjusted Kalman gain, combined with the Kalman filtering algorithm, obtain the path loss index of each WIFI router at the any collection moment.

[0020] In one embodiment, the method for the Kalman gain when estimating the path loss index by the Kalman filtering algorithm adjusted by the dynamic weight is as follows:

[0021] Take the product of the Kalman gain before adjustment of the Kalman filtering algorithm and the dynamic weight as the Kalman gain after adjustment of the Kalman filtering algorithm.

[0022] In one embodiment, the method for selecting the positioning router is as follows:

[0023] At the any collection moment, arrange all the distances calculated by the logarithmic transmission loss model in ascending order, and take the WIFI routers corresponding to the first preset number of distances as each positioning router.

[0024] In one embodiment, the expression of the objective function is:

[0025] ; represents the objective function for positioning the battery car at the j-th collection moment; M represents the number of positioning routers; D( ) represents the distance function; represents the coordinates of the m-th positioning router at the j-th collection moment; represents the estimated coordinates of the battery car at the j-th collection moment; represents the distance between the m-th positioning router and the battery car calculated by the logarithmic transmission loss model at the j-th collection moment; norm[ ] represents the normalization function; represents the trace of the error covariance matrix when the Kalman filtering algorithm estimates the path loss index of the m-th positioning router at the j-th collection moment; ε represents a preset positive number.

[0026] In one embodiment, the positioning of the battery car by solving the estimated coordinates includes: at the any collection moment, when the value of the objective function is the smallest, the estimated coordinates of the battery car are the actual position of the battery car.

[0027] In a second aspect, an embodiment of the present application further provides a positioning visualization device for a shield tunnel battery car, the device including: a WIFI router, a working condition camera, a visualization module, a signal receiving module, a distance calculation module, and a positioning module;

[0028] The WIFI router is used to receive the wireless signal emitted by the battery car in the shield tunnel;

[0029] The working condition camera is used to obtain the working condition video of the tunnel position where the battery car is located and access the visualization module;

[0030] The visualization module is used to display the working condition video;

[0031] The signal receiving module is used to collect the received signal strength of each WIFI router in the shield tunnel in real time, and the signal is emitted by the battery car;

[0032] The distance calculation module is used to construct a logarithmic transmission loss model based on the relationship between the received signal strength and the transmission distance, and obtain the dynamic weight of each WIFI router at any acquisition moment through the distribution of all received signal strengths of each WIFI router before any acquisition moment, and the difference in the received signal strength of each WIFI router compared with its respective neighboring WIFI routers before the any acquisition moment;

[0033] Combining the dynamic weight with all path loss exponents of each WIFI router before the any acquisition moment, using the Kalman filter algorithm to estimate the path loss exponent of each WIFI router at the any acquisition moment; wherein, the initial value of the path loss exponent is a preset value;

[0034] Using the logarithmic transmission loss model through the path loss exponent and the received signal strength of each WIFI router at the any acquisition moment, calculate the metric distance between each WIFI router and the battery car at the any acquisition moment;

[0035] The positioning module is used to select each positioning router from all WIFI routers;

[0036] At the any acquisition moment, through the difference between the distance between each positioning router and the estimated coordinates of the battery car compared with the metric distance, combined with the estimation error of the path loss exponent by the Kalman filter algorithm, construct an objective function for positioning the battery car, and position the battery car by solving the estimated coordinates.

[0037] In a third aspect, an embodiment of the present application further provides a positioning visualization system for a shield tunnel battery car, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the positioning visualization method for a shield tunnel battery car described in any one of the above are implemented.

[0038] The present application has at least the following beneficial effects:

[0039] By calculating the dynamic weight, the present application assigns a smaller weight to the WIFI router with more severe signal attenuation, and a larger weight to the WIFI router with a greater difference in received signal strength between adjacent WIFI routers, fully considering the differences in signal attenuation at different positions, which is convenient for improving the accuracy of subsequent estimation of signal transmission loss; furthermore, through the dynamic weight, combined with the Kalman filter algorithm, the signal transmission loss is dynamically estimated, which can reduce the error accumulation of signal transmission loss and can adapt to the changes in the shield tunnel environment in real time; using the dynamically estimated signal transmission loss, the distance between the WIFI router and the battery car is accurately calculated through the logarithmic transmission loss model; further, by selecting the positioning router and constructing the objective function, the battery car is positioned, which can reduce unnecessary calculations, improve the operation efficiency, and optimize the positioning result of the battery car. The present application can effectively cope with complex environments such as "large slopes" and "small radii" in shield tunnels, reduce the influence of multipath fading and signal attenuation on the positioning of battery cars, and improve the positioning accuracy of battery cars. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0041] Figure 1 It is a flowchart of the steps of a positioning visualization method for a shield tunnel battery car provided by an embodiment of the present application;

[0042] Figure 2 It is a schematic installation diagram of a WIFI router in a shield tunnel;

[0043] Figure 3 It is a schematic flowchart of positioning a battery car. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example", etc. are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or more advantageous than other embodiments or design solutions. Rather, the use of words such as "exemplary", "or", "for example", etc. is intended to present relevant concepts in a specific manner.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. It should be understood that unless otherwise stated in this application, " / " means "or".

[0046] In addition, it should be noted that the terms "first" and "second" in this application are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0047] The following specifically describes the specific solutions of a positioning visualization system, device and method for a shield tunnel battery car provided by this application in conjunction with the accompanying drawings.

[0048] Please refer to Figure 1 , which shows a flowchart of the steps of a positioning visualization method for a shield tunnel battery car provided by an embodiment of this application. The method includes the following steps:

[0049] Step S1, collect the received signal strength of each WIFI router in the shield tunnel in real time, and the signal is emitted by the battery car.

[0050] Arrange a plurality of WIFI routers in the shield tunnel to receive the signal strength of the wireless signal emitted by the battery car in real time, and obtain the received signal strength of each WIFI router.

[0051] In this embodiment, the method of arranging WIFI routers in the shield tunnel is as follows: Install the WIFI routers crosswise and at equal intervals on the tunnel walls on both sides of the shield tunnel. Among them, the distance between two adjacent WIFI routers on the same side is 50m. The installation schematic diagram of the WIFI router in the shield tunnel is as Figure 2 shown, Figure 2 where 1 is the WIFI router and 2 is the tunnel wall. It should be noted that: 50 is only an embodiment of this application, and the implementer can limit it according to the actual situation, and this application does not make special restrictions.

[0052] Step S2: Construct a logarithmic transmission loss model, and use the Kalman filtering algorithm to estimate the path loss exponent of each WIFI router at any acquisition moment; calculate the distance between each WIFI router and the battery car at the any acquisition moment by using the logarithmic transmission loss model through the path loss exponent and the received signal strength of each WIFI router at the any acquisition moment.

[0053] This application adopts a positioning method based on a transmission loss model, and estimates the distance between a point to be measured and a reference point by using the relationship that the signal strength of a wireless signal decreases as the transmission distance increases. Specifically: taking the received signal strength as an eigenvalue, the received signal strength weakens as the distance increases, and the received signal strength can indicate the energy size of the wireless signal in the medium. Furthermore, the received signal strength of WIFI router 1 is converted into the distance between WIFI router 1 and the battery car.

[0054] Step S2.1: Based on the relationship between the received signal strength and the transmission distance, construct a logarithmic transmission loss model.

[0055] According to the transmission attenuation characteristics of wireless signals in free space, this application adopts a logarithmic transmission loss model, and the expression is:

[0056] ; In the formula, represents the received signal strength at a position with a distance d from the transmitter; A represents the preset received signal strength at a position with a preset value of the distance from the transmitter; γ represents the real-time path loss exponent, which reflects the influence degree of the received signal strength of WIFI router 1 by the current communication environment. Taking the jth acquisition moment as an example, when calculating the distance between WIFI router 1 and the battery car at the jth acquisition moment, γ is the path loss exponent at the jth acquisition moment; lg( ) represents the logarithmic function with base 10; d represents the distance from the transmitter.

[0057] In this embodiment, the value of the preset value is 1m, the received signal strength at 1m is 40dB, the value range of the path loss exponent is [2, 4], the value of the preset value and the value range of the path loss exponent are preset manually, and the implementer can adjust the value of the preset value and the value range of the path loss exponent according to the specific tunnel construction environment. This application does not make special restrictions.

[0058] Step S2.2: Obtain the dynamic weight of each WIFI router at the any acquisition moment through the distribution of all received signal strengths of each WIFI router before the any acquisition moment, and the difference in the received signal strength of each WIFI router compared with its respective neighboring WIFI routers before the any acquisition moment.

[0059] Furthermore, considering that the tunnel is a long and narrow enclosed environment, and there are features such as "large slope" and "small radius" in the tunnel under construction, the propagation of wireless signals will experience phenomena such as attenuation and multipath fading. In the above environment, as the position of the battery car changes in the tunnel, the loss suffered by its transmitted signal will also change accordingly.

[0060] When the battery car passes through different areas in the tunnel during operation, the fluctuation difference in the received signal strength of the adjacent WIFI router 1 can reflect the change in the loss degree of wireless signals at different positions in the tunnel. The greater the fluctuation difference in the received signal strength of the adjacent WIFI router 1, the greater the change in the transmission loss between the battery car and the WIFI router 1 caused by the movement of the battery car position.

[0061] To reduce the error generated when obtaining the distance between the WIFI router 1 and the battery car and improve the subsequent positioning accuracy of the battery car, it is necessary to dynamically adjust the logarithmic transmission loss model.

[0062] First of all, considering that under normal circumstances, the signal loss differences caused by position changes of the relatively close WIFI router 1 are similar, but when the battery car is in the "large slope" and "small radius" positions in the shield tunnel, the signal loss differences generated by the position changes of the adjacent WIFI router 1 will be significantly enlarged.

[0063] Secondly, considering that the Kalman filter algorithm, due to its strong dynamic modeling ability and good performance in dealing with noise, can gradually accumulate historical information and quickly respond to environmental changes according to the state vector of the input data, so as to achieve an accurate estimate of the input data. Therefore, this application adopts the Kalman filter algorithm, uses the change trend of the received signal strength to adjust the estimation process, and recursively estimates the path loss index of each WIFI router at each acquisition moment according to the change law of the path loss index of each WIFI router 1 before each acquisition moment, so as to achieve an accurate estimate of the path loss index and reduce the error generated when obtaining the distance between the battery car and the WIFI router 1.

[0064] Taking the jth acquisition moment as an example, on the one hand, if the difference in the received signal strength between each WIFI router 1 and its respective neighboring WIFI router 1 before the jth acquisition moment is greater, it reflects that the change in the battery car position may cause a greater signal loss between each WIFI router 1 and the battery car, the communication environment between each WIFI router 1 and the battery car changes more, and the impact on the path loss index of each WIFI router 1 estimated by the Kalman filter algorithm is greater. A larger dynamic weight is set for each WIFI router 1 at the jth acquisition moment to improve the role of the input data in the Kalman filter algorithm, so as to more quickly adjust the estimated value of the path loss index to adapt to environmental changes.

[0065] In this embodiment, the number of neighboring WIFI routers 1 of each WIFI router 1 is 6. The implementer can set the number of neighboring WIFI routers 1 by himself / herself, and this application does not have special restrictions. The method for obtaining the neighboring WIFI routers 1 of any WIFI router 1 is as follows: sort the distances between the any WIFI router 1 and the other WIFI routers 1 in ascending order, and take the WIFI routers 1 corresponding to the first 6 distances as the neighboring WIFI routers 1 of the any WIFI router 1.

[0066] On the other hand, the smaller the received signal strength of each WIFI router 1, the farther the distance between each WIFI router 1 and the battery car may be, the more serious the wireless signal loss is, and the lower the estimation accuracy of the distance between the two, which further affects the estimation of the path loss exponent. Therefore, at the j-th acquisition moment, a smaller dynamic weight is set for each WIFI router 1 to avoid the increase of estimation error caused by the dependence of the Kalman filtering algorithm on the input data.

[0067] Finally, based on the above analysis, the dynamic weight of each WIFI router 1 at the j-th acquisition moment is obtained through the distribution of all the received signal strengths of each WIFI router 1 before the j-th acquisition moment, and the difference in the received signal strengths of each WIFI router 1 compared with its neighboring WIFI routers 1 before the j-th acquisition moment. The expression is:

[0068] ; in the formula, represents the dynamic weight of the i-th WIFI router at the j-th acquisition moment; norm( ) represents the normalization function; represents the mean value of all the received signal strengths of the i-th WIFI router before the j-th acquisition moment; represents the mean value of the differences between the received signal strengths of the i-th WIFI router and all its neighboring WIFI routers at the t-th acquisition moment; T represents the total number of acquisition moments before the j-th acquisition moment; α and β both represent preset weights greater than 0, and the sum of α and β is 1.

[0069] In this embodiment, the difference between the received signal strengths is the absolute value of the difference. As other implementation manners, on the basis of being able to measure the difference between the received signal strengths, the implementer can adopt other calculation methods, such as the square of the difference, the ratio, etc., and this application does not have special restrictions.

[0070] In this embodiment, the arctangent function is used to normalize .

[0071] In this embodiment, the values of α and β are both 0.5. The values of α and β are preset manually, and the implementer can limit them according to the actual situation. This application does not have special restrictions.

[0072] Step S2.3: Combine the dynamic weight and all path loss exponents of each WIFI router before the any collection moment, and use the Kalman filtering algorithm to estimate the path loss exponent of each WIFI router at the any collection moment; wherein, the initial value of the path loss exponent is a preset value; through the path loss exponent and received signal strength of each WIFI router at the any collection moment, use the logarithmic transmission loss model to calculate the metric distance between each WIFI router and the battery car at the any collection moment.

[0073] Furthermore, through the dynamic weight, adjust the Kalman gain when the Kalman filtering algorithm estimates the path loss exponent of each WIFI router 1 at each collection moment. The expression is:

[0074] ; in the formula, represents the adjusted Kalman gain when the Kalman filtering algorithm estimates the path loss exponent of the i-th WIFI router at the j-th collection moment, reflecting the degree of dependence of the Kalman filtering algorithm on the input data when estimating the path loss exponent; represents the dynamic weight of the i-th WIFI router at the j-th collection moment; represents the Kalman gain before adjustment when the Kalman filtering algorithm estimates the path loss exponent of the i-th WIFI router at the j-th collection moment. Among them, the calculation of the Kalman gain before adjustment of the Kalman filtering algorithm is a well-known technology, and this application will not elaborate.

[0075] It should be noted that: the smaller the mean value of all received signal strengths of the i-th WIFI router 1 before the j-th collection moment, the farther the distance between the i-th WIFI router 1 and the battery car may be, the smaller the dynamic weight, and the smaller the adjusted Kalman gain, avoiding the increase of estimation error caused by the dependence of the Kalman filtering algorithm on the input data;

[0076] If the difference in received signal strength between the i-th WIFI router 1 and its neighboring WIFI routers 1 is greater, it indicates that the probability of the change of the wireless communication channel transmission characteristics between the i-th WIFI router 1 and the battery car is greater, the dynamic weight is greater, and the adjusted Kalman gain is greater, so that the Kalman filtering algorithm can quickly adjust the estimated value of the path loss exponent according to the input data and adapt to the change of the communication environment.

[0077] In this embodiment, when using the Kalman filtering algorithm to estimate the path loss exponent of the i-th WIFI router 1, the specific input settings are:

[0078] The initial error covariance matrix is set to a 6-level diagonal matrix with equal diagonal elements and a magnitude set to 0.01, reflecting the estimation error of the path loss exponent in the initial calculation process. Implementers can adjust it according to specific circumstances.

[0079] The initial state transition matrix is set to a 20-level identity matrix, reflecting the initial change relationship of the state vector. Implementers can adjust it according to specific circumstances.

[0080] Arrange all the path loss exponents of the i-th WIFI router 1 before the j-th collection moment in chronological order to form the path loss exponent vector of the i-th WIFI router 1 at the j-th collection moment; use the path loss exponent vector of the i-th WIFI router 1 at the j-th collection moment as the state vector of the Kalman filter algorithm, and the adjusted Kalman gain when estimating the path loss exponent of the i-th WIFI router at the j-th collection moment through the Kalman filter algorithm , combined with the Kalman filter algorithm, to obtain the path loss exponent of the i-th WIFI router 1 at the j-th collection moment.

[0081] Based on the dynamic weight and received signal strength of the i-th WIFI router 1 at the j-th collection moment, use the logarithmic transmission loss model to calculate the distance between the i-th WIFI router 1 and the battery car at the j-th collection moment.

[0082] According to the calculation method of the distance between the i-th WIFI router 1 and the battery car at the j-th collection moment, calculate the distances between each WIFI router 1 and the battery car at the j-th collection moment.

[0083] It should be noted that: in the first preset number of collection moments, due to insufficient data volume, the logarithmic transmission loss model cannot be accurately updated dynamically. Therefore, in the first preset number of collection moments, a preset logarithmic transmission loss model needs to be used for distance calculation. Specifically:

[0084] In the first preset number of collection moments, the path loss exponent of each WIFI router 1 at each collection moment is 3. Based on the received signal strength and path loss exponent of each WIFI router 1 at each collection moment, use the logarithmic transmission loss model to calculate the distances between each WIFI router 1 and the battery car at each collection moment. Among them, 3 is just an embodiment of this application, and implementers can adjust its specific value according to the specific construction environment.

[0085] In this embodiment, the value of the preset number is 20, and the value of the preset number is preset artificially. Implementers can set it by themselves, and this application does not make special restrictions.

[0086] Step 3, at any of the acquisition moments, based on the difference between the distances between each positioning router and the estimated coordinates of the battery car compared to the metric distance, and combined with the estimation error of the path loss exponent by the Kalman filtering algorithm, construct an objective function for positioning the battery car, and position the battery car by solving the estimated coordinates.

[0087] This application adopts the maximum likelihood estimation method to estimate the position of the battery car at the j-th acquisition moment according to the distances between each WIFI router 1 and the battery car at the j-th acquisition moment. The specific process is as follows:

[0088] Since the closer the distance between the WIFI router 1 and the battery car, the greater the accuracy of estimating the position of the battery car. Therefore, sort the distances between all WIFI routers 1 and the battery car calculated by the logarithmic transmission loss model in ascending order at the j-th acquisition moment, and use the WIFI routers 1 corresponding to the first preset number of distances as each positioning router.

[0089] In this embodiment, the value of the preset number is 6. The value of the preset number is preset manually, and the implementer can set it by himself / herself. This application does not make special restrictions.

[0090] Furthermore, considering that the trace of the error covariance matrix of the Kalman filtering algorithm can reflect the overall error of all path loss exponents, and thus characterize the estimation error of the path loss exponent at the j-th acquisition moment, the role of the WIFI router 1 corresponding to the path loss exponent with a larger estimation error can be reduced when estimating the position of the battery car subsequently, so as to improve the positioning accuracy of the battery car.

[0091] At the j-th acquisition moment, based on the difference between the distances between each positioning router and the estimated coordinates of the battery car compared to the distances calculated by the logarithmic transmission loss model, and combined with the estimation error of the path loss exponent by the Kalman filtering algorithm, obtain the objective function for positioning the battery car, and the expression is:

[0092] ; represents the objective function for positioning the battery car at the j-th acquisition moment; M represents the number of positioning routers; D( ) represents the distance function; represents the coordinates of the m-th positioning router at the j-th acquisition moment; represents the estimated coordinates of the battery car at the j-th acquisition moment; represents the distance between the m-th positioning router and the battery car calculated by the logarithmic transmission loss model at the j-th acquisition moment; norm[ ] represents the normalization function; denotes the trace of the error covariance matrix when estimating the path loss exponent of the m-th positioning router by the Kalman filter algorithm at the j-th acquisition moment; ε represents a preset positive number used to avoid a zero denominator. The value of ε is preset manually and can be set by the implementer. In this embodiment, the value of ε is 0.01.

[0093] In this embodiment, the arctangent function is used to normalize it.

[0094] In this embodiment, the distance between the coordinates of the positioning router and the estimated coordinates of the battery car is the Euclidean distance.

[0095] At the j-th acquisition moment, when the value of the objective function is minimized, the estimated coordinates of the battery car are the actual position of the battery car. The schematic flow chart of the battery car positioning is as Figure 3 shown.

[0096] In this embodiment, the genetic algorithm is used to solve the minimum value of the objective function. The implementer can select other existing feasible optimization algorithms by himself / herself, and this application does not make special restrictions.

[0097] Based on the same inventive concept as the above method, the embodiment of the present application also provides a positioning visualization device for a shield tunnel battery car, including: a plurality of WIFI routers, two working condition cameras, a visualization module, a signal receiving module, a distance calculation module, and a positioning module;

[0098] The plurality of WIFI routers are installed on both sides of the shield tunnel in a crossed and equally spaced manner for receiving the wireless signals emitted by the battery car in the shield tunnel;

[0099] The two working condition cameras are respectively installed at the head and tail of the battery car for acquiring the working condition videos of the tunnel position where the battery car is located and accessing the visualization module for display to realize the positioning visualization of the battery car;

[0100] The signal receiving module is used to collect the received signal strength of each WIFI router in the shield tunnel in real time, and the signal is emitted by the battery car;

[0101] The distance calculation module is used to construct a logarithmic transmission loss model based on the relationship between the received signal strength and the transmission distance, and obtain the dynamic weights of each WIFI router at any acquisition moment through the distribution of all the received signal strengths of each WIFI router before any acquisition moment and the difference in the received signal strengths of each WIFI router compared with its respective neighboring WIFI routers before the any acquisition moment;

[0102] Combining the dynamic weights with all the path loss exponents of each WIFI router before the any collection moment, use the Kalman filter algorithm to estimate the path loss exponent of each WIFI router at the any collection moment; wherein, the initial value of the path loss exponent is a preset value;

[0103] Based on the path loss exponent and the received signal strength of each WIFI router at the any collection moment, use the logarithmic transmission loss model to calculate the metric distance between each WIFI router and the battery car at the any collection moment;

[0104] A positioning module, configured to select each positioning router from all the WIFI routers;

[0105] At the any collection moment, based on the difference between the distance between each positioning router and the estimated coordinates of the battery car compared with the metric distance, and combining the estimation error of the path loss exponent by the Kalman filter algorithm, construct an objective function for positioning the battery car, and position the battery car by solving the estimated coordinates.

[0106] Based on the same inventive concept as the above method, an embodiment of the present application further provides a positioning visualization system for a shield tunnel battery car, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above methods for positioning a shield tunnel battery car are implemented.

[0107] In summary, the present application calculates the dynamic weights, assigns smaller weights to the WIFI routers with more serious signal attenuation, and assigns larger weights to the WIFI routers with larger differences in received signal strength between adjacent WIFI routers, fully considering the differences in signal attenuation at different positions, which is convenient for improving the accuracy of subsequent estimation of signal transmission loss; furthermore, through the dynamic weights, combined with the Kalman filter algorithm to dynamically estimate the signal transmission loss, the error accumulation of the signal transmission loss can be reduced, and the changes in the shield tunnel environment can be adapted in real time; using the dynamically estimated signal transmission loss, accurately calculate the distance between the WIFI router and the battery car through the logarithmic transmission loss model; further, by selecting the positioning router and constructing the objective function to position the battery car, unnecessary calculations can be reduced, the operation efficiency can be improved, and the positioning result of the battery car can be optimized. The present application can effectively cope with complex environments such as "large slopes" and "small radii" in shield tunnels, reduce the influence of multipath fading and signal attenuation on the positioning of battery cars, and improve the positioning accuracy of battery cars.

[0108] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of code, or a portion thereof that contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. In the description corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0109] For those skilled in the art, it is obvious that this application is not limited to the details of the above-described exemplary embodiments, and that the application can be implemented in other specific forms without departing from the basic characteristics of the application. Therefore, from any point of view, the above-described embodiments of the application should be regarded as exemplary and non-limiting.

Claims

1. A method for visualizing the positioning of a battery vehicle in a shield tunnel, characterized in that: The method comprises the following steps: Real-time collection of the received signal strength of each WIFI router in the shield tunnel, the signal is transmitted by the battery vehicle; Based on the relationship between the received signal strength and the transmission distance, a logarithmic transmission loss model is constructed, and the dynamic weight of each WIFI router at any collection time is obtained through the distribution of all received signal strengths of each WIFI router before any collection time, and the difference in received signal strength of each WIFI router compared with its neighboring WIFI routers before any collection time; Combining the dynamic weight with all path loss indexes of each WIFI router before any collection time, using a Kalman filter algorithm to estimate the path loss index of each WIFI router at any collection time; wherein the initial value of the path loss index is a preset value; The metric distance between each WIFI router and the battery vehicle at any collection time is calculated by using the path loss index and the received signal strength of each WIFI router at any collection time and a logarithmic transmission loss model; Select each positioning router from all WIFI routers; At any of the acquisition moments, the distance between each positioning router and the estimated coordinates of the battery vehicle is compared with the difference in the measured distance, combined with the estimation error of the path loss exponent by the Kalman filter algorithm, to construct an objective function for positioning the battery vehicle, and the battery vehicle is positioned by solving the estimated coordinates.

2. A method for visualizing the positioning of a battery vehicle in a shield tunnel as claimed in claim 1, characterized in that: The expression of the logarithmic transmission loss model is: ; In the formula, represents the received signal strength at a position with a distance of d from the transmitting end; A represents the preset received signal strength at a position with a preset distance from the transmitting end; γ represents the real-time path loss index; lg( ) represents a logarithmic function with a base of 10; d represents the distance from the transmitting end.

3. A method for visualizing the positioning of a battery vehicle in a shield tunnel as claimed in claim 1, characterized in that: The expression of the dynamic weight is: ; In the formula, represents the dynamic weight of the i-th WIFI router at the j-th acquisition time; norm() represents the normalization function; represents the mean value of all received signal strengths of the i-th WIFI router before the j-th acquisition time; represents the mean difference between the received signal strengths of the i-th WiFi router and all its neighboring WiFi routers at the t-th collection time; T represents the total number of collection times before the j-th collection time; α and β both represent weights preset to be greater than 0, and the sum of α and β is 1.

4. A method for visualizing the positioning of a battery vehicle in a shield tunnel as claimed in claim 1, characterized in that: The using of the Kalman filter algorithm to estimate the path loss index of each WIFI router at any collection time comprises: The Kalman gain when the Kalman filter algorithm estimates the path loss index is adjusted by the dynamic weight; Arrange all path loss indexes of each WIFI router before any of the collection moments in time sequence to form a path loss index vector; The path loss index vector is used as the state vector of the Kalman filter algorithm, and the path loss index of each WIFI router at any collection time is obtained by combining the adjusted Kalman gain with the Kalman filter algorithm.

5. A method for visualizing the positioning of a battery vehicle in a shield tunnel as claimed in claim 4, characterized in that: The Kalman gain when the Kalman filter algorithm estimates the path loss index by adjusting the dynamic weight is as follows: The product of the Kalman gain of the Kalman filter algorithm before adjustment and the dynamic weight is used as the Kalman gain of the Kalman filter algorithm after adjustment.

6. A method for visualizing the positioning of a battery vehicle in a shield tunnel as claimed in claim 1, characterized in that: The method for selecting the positioning router is: At any of the acquisition moments, all distances calculated by the logarithmic transmission loss model are arranged in ascending order, and the WIFI routers corresponding to the first preset number of distances are used as the positioning routers.

7. A method for visualizing the positioning of a battery vehicle in a shield tunnel as claimed in claim 1, characterized in that: The expression of the objective function is: ; represents the objective function of battery vehicle positioning at the jth acquisition time; M represents the number of positioning routers; D( ) represents the distance function; represents the coordinates of the mth positioning router at the jth collection time; represents the estimated coordinates of the battery car at the jth acquisition time; represents the distance between the mth positioning router and the battery vehicle calculated by the logarithmic transmission loss model at the jth acquisition time; norm[ ] represents the normalization function; It represents the trace of the error covariance matrix when the Kalman filter algorithm estimates the path loss index of the mth positioning router at the jth acquisition time; ε represents a preset positive number.

8. A method for visualizing the positioning of a battery vehicle in a shield tunnel as claimed in claim 1, characterized in that: The positioning of the battery vehicle by solving the estimated coordinates includes: at any of the acquisition moments, when the value of the objective function is the minimum, the estimated coordinates of the battery vehicle are the actual position of the battery vehicle.

9. A device for visualizing the positioning of a battery vehicle in a shield tunnel, using a method for visualizing the positioning of a battery vehicle in a shield tunnel according to claim 1, characterized in that: The device comprises: a WIFI router, a working condition camera, a visualization module, a signal receiving module, a distance calculation module and a positioning module; The WIFI router is used to receive the wireless signal transmitted by the battery vehicle in the shield tunnel; The working condition camera is used to obtain the working condition video of the tunnel where the battery vehicle is located and connect to the visualization module; The visualization module is used to display the working condition video; A signal receiving module is used to collect the received signal strength of each WIFI router in the shield tunnel in real time, and the signal is transmitted by the battery car; A distance calculation module, configured to construct a logarithmic transmission loss model based on the relationship between the received signal strength and the transmission distance, and obtain the dynamic weight of each WIFI router at any collection time through the distribution of all received signal strengths of each WIFI router before any collection time, and the difference in received signal strength of each WIFI router compared with its neighboring WIFI routers before any collection time; Combining the dynamic weight with all path loss indexes of each WIFI router before any collection time, using a Kalman filter algorithm to estimate the path loss index of each WIFI router at any collection time; wherein the initial value of the path loss index is a preset value; The metric distance between each WIFI router and the battery vehicle at any collection time is calculated by using the path loss index and the received signal strength of each WIFI router at any collection time and a logarithmic transmission loss model; A positioning module is used to select each positioning router from all WIFI routers; At any of the acquisition moments, the distance between each positioning router and the estimated coordinates of the battery vehicle is compared with the difference in the measured distance, combined with the estimation error of the path loss exponent by the Kalman filter algorithm, to construct an objective function for positioning the battery vehicle, and the battery vehicle is positioned by solving the estimated coordinates.

10. A positioning visualization system for a battery vehicle in a shield tunnel, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method for visualizing the positioning of a shield tunnel battery vehicle as described in any one of claims 1-8 are implemented.

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