Positioning visualization system, device and method for shield tunnel battery car

Abstract

The invention relates to the technical field of wireless communication, in particular to a positioning visualization system, device and method for a shield tunnel battery car, and the method comprises the steps: collecting the received signal strength of each WIFI router in a shield tunnel in real time; constructing a logarithmic transmission loss model, and acquiring the dynamic weight of each WIFI router at any acquisition moment; estimating the path loss index of each WIFI router at any acquisition moment by using a Kalman filtering algorithm in combination with the dynamic weight and all path loss indexes of each WIFI router before any acquisition moment; according to the path loss index and the received signal strength of each WIFI router at any acquisition moment, calculating the measurement distance between each WIFI router and the battery car at any acquisition moment by using a logarithm transmission loss model; and constructing a target function for positioning the battery car, and positioning the battery car. The invention aims to improve the positioning precision of the battery car.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a positioning visualization system, device and method for a battery vehicle in a shield tunnel. Background Art

[0002] The shield tunnel battery car is one of the important supporting equipment of the tunnel boring machine. It is responsible for transporting sand, stone, earth, materials, equipment, etc. in tunnel excavation construction. Accurately locating the position of the shield tunnel battery car can not only reasonably dispatch the vehicle operation and improve the project efficiency, but also realize the positioning visualization by combining the working condition video of the shield tunnel battery car, helping to discover potential dangers in advance and take measures to avoid accidents.

[0003] Patent application CN107124701A discloses a positioning method and a positioning device for a WIFI terminal, which establishes a communication connection between the WIFI terminal and multiple communication devices, analyzes the wireless signals sent by the WIFI terminal received by the multiple communication devices, obtains different coordinate information, and completes the positioning of the WIFI terminal by comparing the coordinate information. However, a tunnel is a narrow and long closed environment, and the tunnel under construction also has characteristics such as "large slope" and "small radius". In the above environment, the propagation of wireless signals will have attenuation, multipath fading and other factors. If the above public positioning method is used, it will not be possible to fully consider the differences in the attenuation of wireless signals received by different communication devices, thereby reducing the positioning accuracy of the shield tunnel battery vehicle. Summary of the invention

[0004] In view of the above, it is necessary to provide a positioning visualization system, device and method for a shield tunnel battery vehicle, which can improve the positioning accuracy of underground targets compared with traditional target positioning based on ground penetrating radar data: In a first aspect, an embodiment of the present application provides a method for visualizing the positioning of a battery vehicle in a shield tunnel, the method comprising 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.

[0005] In one embodiment, 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.

[0006] In one embodiment, 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.

[0007] In one embodiment, the estimating the path loss index of each WIFI router at any collection time by using a Kalman filter algorithm includes: 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.

[0008] In one embodiment, the Kalman gain when estimating the path loss index by the Kalman filter algorithm through the dynamic weight adjustment 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.

[0009] In one embodiment, 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.

[0010] In one embodiment, the objective function is expressed as: ; 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.

[0011] In one embodiment, the positioning of the electric vehicle by solving the estimated coordinates includes: at any of the acquisition moments, when the value of the objective function is minimum, the estimated coordinates of the electric vehicle are the actual position of the electric vehicle.

[0012] In a second aspect, the embodiment of the present application further provides a device for visualizing the positioning of a battery vehicle in a shield tunnel, the device comprising: 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.

[0013] In the third aspect, an embodiment of the present application also provides a positioning visualization system for a shield tunnel electric vehicle, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned methods for positioning visualization of a shield tunnel electric vehicle are implemented.

[0014] This application has at least the following beneficial effects: This application calculates dynamic weights, assigns smaller weights to WIFI routers with more serious signal attenuation, and assigns larger weights to WIFI routers with greater differences in received signal strength between adjacent WIFI routers, fully considering the differences in signal attenuation at different locations, and facilitating the improvement of the accuracy of subsequent estimation of signal transmission loss; and then through dynamic weights, 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 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 a positioning router and constructing an objective function to locate the battery car, it can reduce unnecessary calculations, improve operating efficiency, and optimize the positioning results of the battery car. This application can effectively cope with complex environments such as "large slopes" and "small radii" in shield tunnels, reduce the impact 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

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A flowchart of the steps of a method for visualizing the positioning of a battery vehicle in a shield tunnel provided in one embodiment of the present application; Figure 2 This is a schematic diagram of the installation of a WIFI router in a shield tunnel; Figure 3 The figure is a flow chart of positioning of electric vehicle. DETAILED DESCRIPTION

[0017] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" and the like are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "or", "for example" and the like is intended to present related concepts in a concrete manner.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. It should be understood that, unless otherwise specified, " / " means or.

[0019] It should also be noted that the terms "first" and "second" in the present application are used to distinguish similar objects rather than to describe a specific order or sequence.

[0020] The following is a detailed description of the specific scheme of the positioning visualization system, device and method for a shield tunnel electric vehicle provided by the present application in conjunction with the accompanying drawings.

[0021] See also Figure 1 , which shows a flowchart of a method for visualizing the positioning of a battery vehicle in a shield tunnel provided by an embodiment of the present application, the method comprising the following steps: Step S1, real-time collection of the received signal strength of each WIFI router in the shield tunnel, the signal is transmitted by the battery vehicle.

[0022] Multiple WIFI routers are arranged in the shield tunnel to receive the signal strength of the wireless signal transmitted by the battery vehicle in real time and obtain the receiving signal strength of each WIFI router.

[0023] In this embodiment, the method for arranging WIFI routers in the shield tunnel is: the WIFI routers are installed crosswise and evenly spaced on the tunnel walls on both sides of the shield tunnel, wherein the distance between two adjacent WIFI routers on the same side is 50m. The installation diagram of the WIFI router in the shield tunnel is as shown in FIG. Figure 2 As shown, Figure 2 1 is a WIFI router, and 2 is a tunnel wall. It should be noted that 50 is only an embodiment of the present application, and the implementer may define it according to the actual situation, and the present application does not impose any special limitation.

[0024] Step S2, constructing a logarithmic transmission loss model, using the Kalman filter algorithm to estimate the path loss index of each WIFI router at any collection time; using the path loss index and received signal strength of each WIFI router at any collection time, using the logarithmic transmission loss model, calculate the distance between each WIFI router and the battery vehicle at any collection time.

[0025] This application adopts a positioning method based on a transmission loss model, and uses the relationship that the signal strength of the wireless signal during the transmission process decreases with the increase of the transmission distance to estimate the distance between the test point and the reference point. Specifically: the received signal strength is used as the characteristic value, and the received signal strength weakens with the increase of the distance. The received signal strength can indicate the energy size of the wireless signal in the medium, and then the received signal strength of the WIFI router 1 is converted into the distance between the WIFI router 1 and the electric vehicle.

[0026] Step S2.1, constructing a logarithmic transmission loss model based on the relationship between the received signal strength and the transmission distance.

[0027] According to the transmission attenuation characteristics of wireless signals in free space, this application adopts a logarithmic transmission loss model, which is expressed as: ; In the formula, represents the received signal strength at a position d away 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 index, which reflects the degree to which the received signal strength of the WIFI router 1 is affected by the current communication environment. Taking the jth acquisition moment as an example, when the distance between the WIFI router 1 and the battery vehicle is calculated at the jth acquisition moment, γ is the path loss index at the jth acquisition moment; lg( ) represents a logarithmic function with base 10; d represents the distance from the transmitter.

[0028] In this embodiment, the preset value is 1m, the received signal strength at 1m is 40dB, the value range of the path loss index is [2,4], the value of the preset value and the value range of the path loss index are preset manually, and the implementer can adjust the value of the preset value and the value range of the path loss index according to the specific tunnel construction environment. This application does not impose any special restrictions.

[0029] Step S2.2, obtaining 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.

[0030] Furthermore, considering that a tunnel is a long and narrow closed environment, and that tunnels under construction also have characteristics such as "large slope" and "small radius", the propagation of wireless signals will experience attenuation and multipath fading. In the above environment, as the position of the electric vehicle in the tunnel changes, the loss of its transmitted signal will also change accordingly.

[0031] When the battery car passes through different areas in the tunnel during operation, the fluctuation difference of the received signal strength of adjacent WIFI routers 1 can reflect the change in the degree of wireless signal loss at different locations in the tunnel. The greater the fluctuation difference of the received signal strength of adjacent WIFI routers 1, the greater the change in transmission loss between the battery car and the WIFI router 1 caused by the movement of the battery car.

[0032] In order to reduce the error generated when obtaining the distance between the WIFI router 1 and the battery vehicle and improve the subsequent positioning accuracy of the battery vehicle, it is necessary to dynamically adjust the logarithmic transmission loss model.

[0033] First, considering that under normal circumstances, the signal loss differences caused by position changes of WIFI routers 1 that are closer are similar, but when the electric vehicle is in a "large slope" and "small radius" position in the shield tunnel, the signal loss differences caused by position changes of adjacent WIFI routers 1 will be significantly enlarged.

[0034] Secondly, considering that the Kalman filter algorithm can gradually accumulate historical information and quickly respond to environmental changes based on the state vector of the input data due to its strong dynamic modeling ability and good noise processing performance, it can achieve accurate estimation of the input data. Therefore, the present application adopts the Kalman filter algorithm, uses the changing trend of the received signal strength to adjust the estimation process, and recursively estimates the path loss index of each WIFI router at each collection time according to the changing law of the path loss index of each WIFI router 1 before each collection time, thereby achieving accurate estimation of the path loss index and reducing the error generated when obtaining the distance between the battery vehicle and the WIFI router 1.

[0035] Taking the j-th collection moment as an example, on the one hand, if the difference in received signal strength between each WIFI router 1 and its neighboring WIFI routers 1 before the j-th collection moment is greater, it reflects that the signal loss between each WIFI router 1 and the electric vehicle caused by the change in the position of the electric vehicle may be greater, and the greater the change in the communication environment between each WIFI router 1 and the electric vehicle, the greater the impact on the path loss index of each WIFI router 1 estimated by the Kalman filter algorithm. At the j-th collection moment, a larger dynamic weight is set for each WIFI router 1, which improves the role of the input data in the Kalman filter algorithm, thereby adjusting the estimated value of the path loss index more quickly to adapt to environmental changes.

[0036] 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, and this application does not impose any special restrictions. The method for obtaining the neighboring WIFI routers 1 of any WIFI router 1 is: arrange the distances between any WIFI router 1 and the other WIFI routers 1 in ascending order, and use the WIFI routers 1 corresponding to the first 6 distances as the neighboring WIFI routers 1 of any WIFI router 1.

[0037] 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 electric vehicle may be, the more serious the wireless signal loss is, and the lower the accuracy of the estimated distance between the two, which in turn affects the estimation of the path loss index. Therefore, a smaller dynamic weight is set for each WIFI router 1 at the j-th collection moment to avoid the Kalman filter algorithm's dependence on input data and cause an increase in estimation error.

[0038] Finally, based on the above analysis, the dynamic weight of each WIFI router 1 at the jth collection time is obtained through the distribution of all received signal strengths of each WIFI router 1 before the jth collection time, and the difference in received signal strength of each WIFI router 1 compared with its neighboring WIFI routers 1 before the jth collection time. The expression 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.

[0039] In this embodiment, the difference between the received signal strengths is the absolute value of the difference. As other implementation methods, on the basis of being able to measure the difference between the received signal strengths, the implementer may adopt other calculation methods, such as the square of the difference, the ratio, etc., and this application does not impose any special restrictions.

[0040] In this embodiment, the inverse tangent function is used to Normalize.

[0041] In this embodiment, the values ​​of α and β are both 0.5. The values ​​of α and β are preset manually and can be limited by the implementer according to the actual situation. This application does not impose any special restrictions.

[0042] Step S2.3, combining the dynamic weight with all path loss indexes of each WIFI router before any collection time, using the 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; through the path loss index and received signal strength of each WIFI router at any collection time, using the logarithmic transmission loss model, calculate the metric distance between each WIFI router and the battery vehicle at any collection time.

[0043] Furthermore, the Kalman gain of the Kalman filter algorithm when estimating the path loss index of each WIFI router 1 at each collection time is adjusted through dynamic weights, and the expression is: ; In the formula, It represents the adjusted Kalman gain when the Kalman filter algorithm estimates the path loss index of the i-th WiFi router at the j-th acquisition time, reflecting the degree of dependence of the Kalman filter algorithm on the input data when estimating the path loss index; represents the dynamic weight of the i-th WIFI router at the j-th collection moment; It represents the Kalman gain before adjustment when the Kalman filter algorithm estimates the path loss index of the i-th WIFI router at the j-th collection time. The calculation of the Kalman gain before adjustment of the Kalman filter algorithm is a well-known technology and will not be described in detail in this application.

[0044] 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 acquisition time, 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, so as to avoid the increase of estimation error caused by the dependence of the Kalman filter algorithm on the input data; 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 change in the transmission characteristics of the wireless communication channel between the i-th WIFI router 1 and the battery vehicle is greater, the dynamic weight is greater, and the adjusted Kalman gain is greater, so that the Kalman filter algorithm can quickly adjust the estimated value of the path loss index according to the input data to adapt to changes in the communication environment.

[0045] In this embodiment, when the Kalman filter algorithm is used to estimate the path loss index of the i-th WIFI router 1, the specific input setting is: The initial error covariance matrix is ​​set to a 6-level diagonal matrix with equal diagonal elements and a size of 0.01, reflecting the estimated error of the path loss exponent during the initial calculation process. The implementer can adjust it according to the specific situation.

[0046] The initial state transfer matrix is ​​set to a 20-level identity matrix to reflect the initial change relationship of the state vector. The implementer can adjust it according to the specific situation.

[0047] Arrange all path loss indexes of the i-th WIFI router 1 before the j-th acquisition time in time sequence to form the path loss index vector of the i-th WIFI router 1 at the j-th acquisition time; use the path loss index vector of the i-th WIFI router 1 at the j-th acquisition time as the state vector of the Kalman filter algorithm, and use the Kalman filter algorithm to estimate the path loss index of the i-th WIFI router at the j-th acquisition time. , combined with the Kalman filter algorithm, the path loss index of the i-th WIFI router 1 at the j-th collection time is obtained.

[0048] The distance between the i-th WIFI router 1 and the battery vehicle at the j-th collection moment is calculated by using the dynamic weight of the i-th WIFI router 1 and the received signal strength at the j-th collection moment and the logarithmic transmission loss model.

[0049] According to the calculation method of the distance between the i-th WIFI router 1 and the battery vehicle at the j-th collection time, the distance between each WIFI router 1 and the battery vehicle at the j-th collection time is calculated.

[0050] It should be noted that: at the first preset number of collection moments, due to insufficient data volume, the logarithmic transmission loss model cannot be accurately and dynamically updated. Therefore, the preset logarithmic transmission loss model needs to be used for distance calculation at the first preset number of collection moments, specifically: At the preset number of collection moments, the path loss index of each WIFI router 1 at each collection moment is 3. The distance between each WIFI router 1 and the battery vehicle at each collection moment is calculated by using the received signal strength and path loss index of each WIFI router 1 at each collection moment and using the logarithmic transmission loss model. 3 is only an embodiment of the present application, and the implementer can adjust its specific value according to the specific construction environment.

[0051] In this embodiment, the value of the preset number is 20. The value of the preset number is preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0052] Step 3, at any of the acquisition moments, by comparing the distance between each positioning router and the estimated coordinates of the battery vehicle with the difference in the measured distance, combined with the estimated error of the path loss exponent by the Kalman filter algorithm, construct an objective function for positioning the battery vehicle, and locate the battery vehicle by solving the estimated coordinates.

[0053] This application adopts the maximum likelihood estimation method to estimate the position of the battery car at the jth collection time according to the distance between each WIFI router 1 and the battery car at the jth collection time. The specific process is as follows: Since the closer the distance between the WIFI router 1 and the battery vehicle is, the greater the accuracy of estimating the position of the battery vehicle is, therefore, the distances between all WIFI routers 1 and the battery vehicle calculated by the logarithmic transmission loss model at the jth acquisition moment are arranged in ascending order, and the WIFI routers 1 corresponding to the first preset number of distances are used as positioning routers.

[0054] In this embodiment, the value of the preset number is 6. The value of the preset number is preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0055] Furthermore, considering the trace of the error covariance matrix of the Kalman filter algorithm, it can reflect the overall error of all path loss indices, and then characterize the estimation error of the path loss index at the j-th acquisition moment. When estimating the position of the electric vehicle in the subsequent time, the role of the WIFI router 1 corresponding to the path loss index with a large estimation error can be reduced, thereby improving the accuracy of the positioning of the electric vehicle.

[0056] At the jth acquisition time, the distance between each positioning router and the estimated coordinates of the battery vehicle is compared with the difference in distance calculated by the transmission loss model, combined with the estimation error of the path loss index by the Kalman filter algorithm, and the objective function of battery vehicle positioning is obtained, which is expressed as: ; 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 m-th positioning router at the j-th acquisition moment; ε represents a preset positive number used to avoid the denominator being 0. The value of ε is preset manually and can be set by the implementer. In this embodiment, the value of ε is 0.01.

[0057] In this embodiment, the inverse tangent function is used to Normalize.

[0058] In this embodiment, the distance between the coordinates of the positioning router and the estimated coordinates of the battery vehicle is the Euclidean distance.

[0059] At the jth acquisition time, when the value of the objective function is the minimum, the estimated coordinates of the battery car are the actual position of the battery car. The flowchart of the battery car positioning is as follows: Figure 3 shown.

[0060] In this embodiment, a genetic algorithm is used to solve the minimum value of the objective function. The implementer can select other existing feasible optimization algorithms at will, and this application does not impose any special restrictions.

[0061] 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 vehicle, including: multiple WIFI routers, two working condition cameras, a visualization module, a signal receiving module, a distance calculation module and a positioning module; The multiple WIFI routers are cross-mounted and evenly spaced on both sides of the shield tunnel to receive wireless signals emitted by battery vehicles in the shield tunnel; Two working condition cameras are installed at the head and tail of the battery vehicle respectively to obtain working condition videos of the battery vehicle in the tunnel, and are connected to the visualization module for display to realize the positioning visualization of the battery vehicle; 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.

[0062] Based on the same inventive concept as the above method, an embodiment of the present application also provides a positioning visualization system for a shield tunnel electric vehicle, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned positioning visualization methods for a shield tunnel electric vehicle when executing the computer program.

[0063] In summary, this application calculates dynamic weights, assigns smaller weights to WIFI routers with more severe signal attenuation, and assigns larger weights to WIFI routers with greater differences in received signal strength between adjacent WIFI routers, fully considering the differences in signal attenuation at different locations, and facilitating the improvement of the accuracy of subsequent estimation of signal transmission loss; and then through dynamic weights, 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 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 a positioning router and constructing an objective function to locate the battery car, it can reduce unnecessary calculations, improve operating efficiency, and optimize the positioning results of the battery car. This application can effectively cope with complex environments such as "large slopes" and "small radii" in shield tunnels, reduce the impact of multipath fading and signal attenuation on the positioning of battery cars, and improve the positioning accuracy of battery cars.

[0064] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0065] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic features of the present application. Therefore, no matter from which point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive.

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 d away 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 index; lg( ) represents a logarithmic function with base 10; d represents the distance from the transmitter.

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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