CN119946815A - A positioning visualization system, device and method for battery vehicles in shield tunnels - Google Patents
A positioning visualization system, device and method for battery vehicles in shield tunnels Download PDFInfo
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Abstract
The application relates to the technical field of wireless communication, in particular to a positioning visualization system, a positioning visualization device and a positioning visualization method for a shield tunnel storage battery car, wherein the method comprises the steps of collecting the received signal strength of each WIFI router in a shield tunnel in real time; the method comprises the steps of establishing a logarithmic transmission loss model, obtaining dynamic weights of WIFI routers at any acquisition time, combining the dynamic weights with all path loss indexes of the WIFI routers before any acquisition time, estimating the path loss indexes of the WIFI routers at any acquisition time by using a Kalman filtering algorithm, calculating measurement distances between the WIFI routers and the battery car at any acquisition time by using the logarithmic transmission loss model through the path loss indexes and the received signal intensities of the WIFI routers at any acquisition time, constructing an objective function for positioning the battery car, and positioning the battery car. The application aims to improve the positioning precision of the battery car.
Description
Technical Field
The application relates to the technical field of wireless communication, in particular to a positioning visualization system, device and method for a shield tunnel storage battery car.
Background
The shield tunnel storage battery car is one of important supporting equipment of a tunnel boring machine, and is used for carrying sand and stone earthwork, materials, equipment and the like in tunnel boring construction. The position of the shield tunnel storage battery car is accurately positioned, the operation of the vehicle can be reasonably scheduled, the engineering efficiency is improved, positioning visualization can be realized by combining working condition videos of the position of the shield tunnel storage battery car, potential hazards can be found in advance, and accordingly measures are taken to avoid accidents.
Patent application CN107124701a discloses a positioning method and a positioning device for a WIFI terminal, which establishes communication connection between the WIFI terminal and a plurality of communication devices, analyzes wireless signals sent by the WIFI terminal and received by the plurality of communication devices, obtains different coordinate information, and completes positioning of the WIFI terminal through coordinate information comparison. However, the tunnel is a long and narrow closed environment, and the tunnel in construction also has the characteristics of large gradient, small radius and the like, under the environment, the wireless signal propagation can have the factors of attenuation, multipath fading and the like, and if the disclosed positioning method is adopted, the difference of the attenuation of the wireless signals received by different communication devices can not be fully considered, so that the positioning precision of the shield tunnel storage battery car is reduced.
Disclosure of Invention
In view of the above, it is necessary to provide a positioning visualization system, device and method for a shield tunnel battery car, which improves positioning accuracy for an underground target compared with the traditional target positioning based on ground penetrating radar data:
in a first aspect, an embodiment of the present application provides a positioning visualization method for a shield tunnel battery truck, where the method includes the following steps:
The method comprises the steps of collecting the received signal strength of each WIFI router in a shield tunnel in real time, wherein the signals are transmitted by an electric vehicle;
Based on the relation between the received signal strength and the transmission distance, constructing a logarithmic transmission loss model, and acquiring the dynamic weight of each WIFI router at any acquisition time through the distribution of all received signal strengths of each WIFI router before any acquisition time and the difference of the received signal strengths of each WIFI router before any acquisition time compared with each adjacent WIFI router;
Estimating the path loss index of each WIFI router at any acquisition time by using a Kalman filtering algorithm by combining the dynamic weight and all path loss indexes of each WIFI router before any acquisition time, wherein the initial value of the path loss index is a preset value;
Calculating the measurement distance between each WIFI router and the battery car at any acquisition time by using a logarithmic transmission loss model through the path loss index and the received signal strength of each WIFI router at any acquisition time;
selecting each positioning router from all WIFI routers;
And under any acquisition time, constructing an objective function for positioning the battery car by combining the estimated error of the Kalman filtering algorithm on the path loss index according to the distance between each positioning router and the estimated coordinates of the battery car and comparing the difference of the measured distances, and positioning the battery car by solving the estimated coordinates.
In one embodiment, the logarithmic transmission loss model is expressed as:
In the formula (I), in the formula (II), Representing the received signal strength at a position at a distance d from the transmitting end, a representing the preset received signal strength at a position at a preset value from the transmitting end, γ representing the real-time path loss index, lg () representing a logarithmic function based on 10, d representing the distance from the transmitting end.
In one embodiment, the expression of the dynamic weight is:
In the formula (I), in the formula (II), Dynamic weight of an ith WIFI router at the jth acquisition time is represented; Representing the average value of all received signal intensities of the ith WIFI router before the jth acquisition time; The method comprises the steps of representing the average value of differences between received signal intensities of an ith WIFI router and all neighbor WIFI routers at the ith acquisition time, wherein T represents the total number of acquisition times before the jth acquisition time, alpha and beta represent weights which are preset to be larger than 0, and the sum of the alpha and the beta is 1.
In one embodiment, the estimating, using a kalman filter algorithm, a path loss index of each WIFI router at the any acquisition time includes:
the Kalman gain when the path loss index is estimated by the Kalman filtering algorithm is adjusted through the dynamic weight;
All path loss indexes of the WIFI routers before any acquisition time are arranged according to time sequence to form a path loss index vector;
And taking the path loss index vector as a state vector of a Kalman filtering algorithm, and obtaining the path loss index of each WIFI router at any acquisition time by combining the Kalman filtering algorithm through the adjusted Kalman gain.
In one embodiment, the method for estimating the kalman gain when estimating the path loss exponent by the dynamic weight adjustment kalman filter algorithm includes:
And taking the product of the Kalman gain of the Kalman filtering algorithm before adjustment and the dynamic weight as the Kalman gain of the Kalman filtering algorithm after adjustment.
In one embodiment, the selecting method of the positioning router is as follows:
and under any acquisition time, arranging all distances obtained through calculation of the logarithmic transmission loss model in an ascending order, and taking the WIFI routers corresponding to the preset number of distances as each positioning router.
In one embodiment, the expression of the objective function is:
; The method comprises the steps of acquiring a target function of positioning an electric vehicle at a j-th acquisition time, wherein M represents the number of positioning routers; representing coordinates of an mth positioning router at a jth acquisition time; representing estimated coordinates of the battery car at the j-th acquisition time; The distance between the mth positioning router and the battery car, which is calculated by the logarithmic transmission loss model at the jth acquisition time, is represented; The method is used for representing the trace of an error covariance matrix when a Kalman filtering algorithm estimates the path loss index of an mth positioning router at the jth acquisition time, and epsilon represents a preset positive number.
In one embodiment, the positioning of the battery car by solving the estimated coordinates comprises that the estimated coordinates of the battery car are the actual positions of the battery car when the value of the objective function is minimum at any acquisition time.
In a second aspect, the embodiment of the application also provides a positioning visualization device of the shield tunnel storage battery car, which 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 for receiving wireless signals transmitted by the storage battery car in the shield tunnel;
The working condition cameras are used for acquiring working condition videos of the tunnel positions of the battery cars and accessing the working condition videos to the visualization module;
The visualization module is used for displaying the working condition video;
The signal receiving module is used for collecting the received signal intensity of each WIFI router in the shield tunnel in real time, and the signals are transmitted by the battery car;
The distance calculation module is used for constructing a logarithmic transmission loss model based on the relation between the received signal intensity and the transmission distance, and acquiring the dynamic weight of each WIFI router at any acquisition time through the distribution of all received signal intensities of each WIFI router before any acquisition time and the difference of the received signal intensities of each WIFI router compared with each adjacent WIFI router before any acquisition time;
Estimating the path loss index of each WIFI router at any acquisition time by using a Kalman filtering algorithm by combining the dynamic weight and all path loss indexes of each WIFI router before any acquisition time, wherein the initial value of the path loss index is a preset value;
Calculating the measurement distance between each WIFI router and the battery car at any acquisition time by using a logarithmic transmission loss model through the path loss index and the received signal strength of each WIFI router at any acquisition time;
The positioning module is used for selecting each positioning router from all the WIFI routers;
And under any acquisition time, constructing an objective function for positioning the battery car by combining the estimated error of the Kalman filtering algorithm on the path loss index according to the distance between each positioning router and the estimated coordinates of the battery car and comparing the difference of the measured distances, and positioning the battery car by solving the estimated coordinates.
In a third aspect, an embodiment of the present application further provides a positioning visualization system for a shield tunnel battery truck, where the positioning visualization system includes a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of the positioning visualization method for a shield tunnel battery truck according to any one of the above when executing the computer program.
The application has at least the following beneficial effects:
The method and the device have the advantages that the WIFI router with more serious signal attenuation is given smaller weight through calculation of dynamic weight, the WIFI router with more serious signal attenuation difference with the adjacent WIFI router is given larger weight, the difference of different position signal attenuation is fully considered, the accuracy of subsequent signal transmission loss estimation is improved, further, the signal transmission loss is dynamically estimated through combination of dynamic weight and a Kalman filtering algorithm, error accumulation of the signal transmission loss can be reduced, the change of a shield tunnel environment can be adapted in real time, the distance between the WIFI router and a storage battery car is accurately calculated through a logarithmic transmission loss model by utilizing the dynamically estimated signal transmission loss, further, the storage battery car is positioned through selecting a positioning router and constructing an objective function, unnecessary calculation can be reduced, the running efficiency is improved, and the positioning result of the storage battery car is optimized. The application can effectively cope with complex environments such as 'large gradient', 'small radius' and the like in the shield tunnel, reduce the influence of multipath fading and signal attenuation on the positioning of the storage battery car, and improve the positioning precision of the storage battery car.
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In order to more clearly illustrate the embodiments of the application or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of a positioning visualization method for a shield tunnel battery truck according to an embodiment of the present application;
fig. 2 is a schematic diagram of installation of a WIFI router in a shield tunnel;
Fig. 3 is a schematic flow chart of positioning the battery car.
Detailed Description
In describing embodiments of the present application, words such as "exemplary," "or," "such as," and the like are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," "such as," and the like are intended to present related concepts in a concrete fashion.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It is to be understood that, unless otherwise indicated, a "/" means or.
It should be further noted that the terms "first" and "second" are used herein to distinguish similar objects from each other and are not used to describe a particular order or sequence.
The application provides a positioning visualization system, a positioning visualization device and a positioning visualization method for a shield tunnel storage battery car.
Referring to fig. 1, a step flow chart of a positioning visualization method for a shield tunnel battery car according to an embodiment of the application is shown, and the method includes the following steps:
And S1, collecting the received signal intensity of each WIFI router in the shield tunnel in real time, wherein the signals are transmitted by the battery car.
And arranging a plurality of WIFI routers in the shield tunnel, wherein the WIFI routers are used for receiving the signal intensity of wireless signals transmitted by the battery car in real time and obtaining the received signal intensity of each WIFI router.
In the embodiment, the WIFI router is arranged in the shield tunnel by being installed on tunnel walls on two sides of the shield tunnel in a crossing and equidistant mode, wherein the distance between two adjacent WIFI routers on the same side is 50m, an installation schematic diagram of the WIFI router in the shield tunnel is shown in fig. 2, 1 is the WIFI router, and 2 is the tunnel wall. It should be noted that 50 is only one embodiment of the present application, and the embodiment may be defined according to practical situations, and the present application is not limited in particular.
And S2, constructing a logarithmic transmission loss model, estimating the path loss index of each WIFI router at any acquisition time by using a Kalman filtering algorithm, and calculating the distance between each WIFI router and the battery car at any acquisition time by using the logarithmic transmission loss model through the path loss index and the received signal strength of each WIFI router at any acquisition time.
The application adopts a positioning method based on a transmission loss model, and utilizes the relation that the signal intensity of a wireless signal decreases along with the increase of the transmission distance in the transmission process to estimate the distance between a to-be-measured point and a reference point.
And step S2.1, constructing a logarithmic transmission loss model based on the relation between the received signal strength and the transmission distance.
According to the transmission attenuation characteristic of the wireless signal in the free space, the application adopts a logarithmic transmission loss model, and the expression is as follows:
In the formula (I), in the formula (II), The method comprises the steps of representing received signal strength at a position with a distance d from a transmitting end, representing preset received signal strength at a position with a preset value from the transmitting end, representing real-time path loss index (gamma), reflecting the influence degree of the received signal strength of a WIFI router 1 on the current communication environment, taking a jth acquisition time as an example, when the distance between the WIFI router 1 and an electric vehicle is calculated at the jth acquisition time, representing the path loss index (gamma) at the jth acquisition time, representing a logarithmic function with a base of 10 by lg (), and representing the distance from the transmitting end.
In this embodiment, the preset value is 1m, the received signal strength at 1m is 40dB, the range of the pathloss index is [2,4], the preset value and the range of the pathloss index are preset manually, and the operator can adjust the preset value and the range of the pathloss index according to the specific tunnel construction environment, which is not particularly limited in the application.
And S2.2, acquiring the dynamic weight of each WIFI router at any acquisition time through the distribution of all received signal intensities of each WIFI router before any acquisition time and the difference of the received signal intensities of each WIFI router before any acquisition time compared with each adjacent WIFI router.
Further, considering that the tunnel is a long and narrow closed environment, and the tunnel in construction also has the characteristics of 'large gradient', 'small radius', and the like, the wireless signal propagation can generate phenomena of attenuation, multipath fading, and the like, under the environment, the loss of the transmitted signal of the battery car can be correspondingly changed along with the position change of the battery car in the tunnel.
When the storage battery car passes through different areas in the tunnel in the working process, the fluctuation difference of the received signal intensity of the adjacent WIFI router 1 can reflect the change of the wireless signal loss degree of different positions in the tunnel. The larger the fluctuation difference of the received signal intensity of the adjacent WIFI router 1 is, the larger the transmission loss change between the storage battery car and the WIFI router 1 is caused by the movement of the storage battery car position.
In order to reduce errors generated when the distance between the WIFI router 1 and the battery car is acquired and improve the subsequent positioning accuracy of the battery car, a logarithmic transmission loss model needs to be dynamically adjusted.
Firstly, considering that under normal conditions, the signal loss difference caused by the position change of the WIFI router 1 with a relatively close distance is similar, but when the storage battery car is positioned at a 'large gradient' and a 'small radius' position in the shield tunnel, the signal loss difference caused by the position change of the adjacent WIFI routers 1 is obviously enlarged.
Secondly, the Kalman filtering algorithm can gradually accumulate historical information and quickly respond to environmental changes according to the state vector of the input data to realize accurate estimation of the input data in consideration of the strong dynamic modeling capability and good noise processing performance of the Kalman filtering algorithm. Therefore, the application adopts a Kalman filtering algorithm, adjusts the estimation process by utilizing the variation trend of the received signal strength, recursively estimates the path loss index of each WIFI router at each acquisition time according to the variation rule of the path loss index of each WIFI router 1 before each acquisition time, thereby realizing the accurate estimation of the path loss index and reducing the error generated when the distance between the storage battery car and the WIFI router 1 is acquired.
Taking the j-th acquisition time as an example, on one hand, if the difference of the received signal strength of each WIFI router 1 and each adjacent WIFI router 1 before the j-th acquisition time is larger, the larger the signal loss between each WIFI router 1 and the storage battery car caused by the position change of the storage battery car is reflected, the larger the communication environment between each WIFI router 1 and the storage battery car is changed, the larger the influence on the path loss index of each WIFI router 1 estimated by the Kalman filtering algorithm is, the larger the dynamic weight is set for each WIFI router 1 at the j-th acquisition time, and the effect of input data in the Kalman filtering algorithm is improved, so that the estimated value of the path loss index is adjusted faster to adapt to the environment change.
In this embodiment, the number of neighboring WIFI routers 1 of each WIFI router 1 is 6, and an implementer can set the number of neighboring WIFI routers 1 by himself, which is not particularly limited in the present application. The acquisition method of the neighbor WIFI router 1 of any WIFI router 1 comprises the steps of arranging distances among other WIFI routers 1 of any WIFI router 1 in an ascending order, and taking the WIFI router 1 corresponding to the first 6 distances as the neighbor WIFI router 1 of any WIFI router 1.
On the other hand, the smaller the received signal strength of each WIFI router 1 is, the more the distance between each WIFI router 1 and the battery car is likely, the more the wireless signal loss is serious, the lower the accuracy of estimating the distance between the two is, and the estimation of the path loss index is further affected, so that the smaller dynamic weight is set for each WIFI router 1 at the j-th acquisition time, and the increase of estimation errors caused by the dependence of the kalman filtering algorithm on input data is avoided.
Finally, based on the analysis, the dynamic weight of each WIFI router 1 at the j-th acquisition time is obtained through the distribution of all received signal intensities of each WIFI router 1 before the j-th acquisition time and the difference of the received signal intensities of each WIFI router 1 before the j-th acquisition time compared with each neighboring WIFI router 1, where the expression is:
In the formula (I), in the formula (II), Dynamic weight of an ith WIFI router at the jth acquisition time is represented; Representing the average value of all received signal intensities of the ith WIFI router before the jth acquisition time; The method comprises the steps of representing the average value of differences between received signal intensities of an ith WIFI router and all neighbor WIFI routers at the ith acquisition time, wherein T represents the total number of acquisition times before the jth acquisition time, alpha and beta represent weights which are preset to be larger than 0, and the sum of the alpha and the beta is 1.
In this embodiment, the difference between the received signal strengths is an absolute value of the difference, and as other embodiments, the practitioner may use other calculation methods, such as square of the difference, ratio, etc., on the basis that the difference between the received signal strengths can be measured, which is not particularly limited in the present application.
In this embodiment, the arctangent function pair is usedNormalization was performed.
In this embodiment, the values of α and β are all 0.5, and the values of α and β are preset by human beings, so that the operator can limit the values according to the actual situation, and the application is not particularly limited.
And S2.3, estimating the path loss index of each WIFI router at any acquisition time by using a Kalman filtering algorithm by combining the dynamic weight and all path loss indexes of each WIFI router before any acquisition time, wherein the initial value of the path loss index is a preset value, and calculating the measurement distance between each WIFI router and the battery car at any acquisition time by using a logarithmic transmission loss model through the path loss index and the received signal intensity of each WIFI router at any acquisition time.
Further, by means of dynamic weight, the kalman gain when the kalman filtering algorithm estimates the path loss index of each WIFI router 1 at each acquisition time is adjusted, and the expression is:
In the formula (I), in the formula (II), The Kalman gain after the adjustment of the Kalman filtering algorithm for estimating the path loss index of the ith WIFI router at the jth acquisition time is represented, and the dependence degree of the Kalman filtering algorithm on input data when estimating the path loss index is reflected; representing the dynamic weight of an ith WIFI router at the jth acquisition time; The Kalman gain before adjustment of the Kalman filtering algorithm to estimate the path loss index of the ith WIFI router at the jth acquisition time is shown. The calculation of the kalman gain before the adjustment of the kalman filtering algorithm is a known technology, and the application is not repeated.
The smaller the average value of all received signal intensities of the ith WIFI router 1 before the jth acquisition time is, the farther the distance between the ith WIFI router 1 and the battery car is likely to be, the smaller the dynamic weight is, the smaller the Kalman gain is after adjustment, and the increase of estimation errors caused by dependence of a Kalman filtering algorithm on input data is avoided;
If the difference of the received signal strength between the ith WIFI router 1 and each adjacent WIFI router 1 is larger, the probability that the transmission characteristics of the wireless communication channel between the ith WIFI router 1 and the battery car are changed is indicated to be larger, the dynamic weight is larger, and the adjusted Kalman gain is larger, so that the Kalman filtering algorithm can rapidly adjust the estimated value of the path loss index according to input data, and adapt to the change of the communication environment.
In this embodiment, when the path loss index of the ith WIFI router 1 is estimated by using a kalman filter algorithm, specific inputs are set as follows:
The initial error covariance matrix is set to be a 6-level diagonal matrix with equal diagonal elements and 0.01 in size, reflecting the estimated error of the path loss index in the initial calculation. The practitioner can adjust according to the specific circumstances.
The initial state transition matrix is set as a 20-level identity matrix, reflecting the initial change relation of the state vector. The practitioner can adjust according to the specific circumstances.
The method comprises the steps of arranging all path loss indexes of an ith WIFI router 1 before the jth acquisition time according to time sequence to form a path loss index vector of the ith WIFI router 1 at the jth acquisition time, taking the path loss index vector of the ith WIFI router 1 at the jth acquisition time as a state vector of a Kalman filtering algorithm, and estimating the adjusted Kalman gain of the path loss index of the ith WIFI router at the jth acquisition time through the Kalman filtering algorithmAnd combining a Kalman filtering algorithm to acquire the path loss index of the ith WIFI router 1 at the jth acquisition time.
And calculating the distance between the ith WIFI router 1 and the battery car at the jth acquisition time by using the dynamic weight and the received signal strength of the ith WIFI router 1 at the jth acquisition time and using a logarithmic transmission loss model.
According to the calculation method of the distance between the ith WIFI router 1 and the battery car at the jth acquisition time, the distance between each WIFI router 1 and the battery car at the jth acquisition time is calculated.
It should be noted that, at the preset number of acquisition moments, since the data volume is insufficient, the log transmission loss model cannot be updated accurately and dynamically, so that the preset number of acquisition moments need to use the preset log transmission loss model to calculate the distance, specifically:
The method comprises the steps that at the preset number of collection moments, the path loss index of each WIFI router 1 at each collection moment is 3, and the distance between each WIFI router 1 and the battery car at each collection moment is calculated through the received signal strength and the path loss index of each WIFI router 1 at each collection moment by using a logarithmic transmission loss model. Wherein 3 is merely one embodiment of the present application, and the practitioner may adjust its specific value according to the specific construction environment.
In this embodiment, the preset number of values is 20, and the preset number of values is manually preset, so that the operator can set the preset number of values by himself, and the present application is not particularly limited.
And 3, under any acquisition time, constructing an objective function for positioning the battery car by combining the estimated error of the Kalman filtering algorithm on the path loss index and comparing the difference of the measured distances with the distance between each positioning router and the estimated coordinates of the battery car, and positioning the battery car by solving the estimated coordinates.
The application adopts a maximum likelihood estimation method, and estimates the position of the battery car at the j-th acquisition time according to the distance between each WIFI router 1 and the battery car at the j-th acquisition time, and the specific process is as follows:
because the accuracy of estimating the position of the battery car is higher when the distance between the WIFI router 1 and the battery car is closer, distances between all the WIFI routers 1 obtained by calculation of the logarithmic transmission loss model at the j-th acquisition time and the battery car are arranged in ascending order, and the WIFI routers 1 corresponding to the preset number of distances are used as positioning routers.
In this embodiment, the preset number of values is 6, and the preset number of values is manually preset, so that the operator can set the preset number of values by himself, and the present application is not limited in particular.
Further, the integral errors of all path loss indexes can be reflected by considering the trace of the error covariance matrix of the Kalman filtering algorithm, and the estimation error of the path loss index at the jth acquisition time is further represented, so that the effect of the WIFI router 1 corresponding to the path loss index with larger estimation error can be reduced when the position of the battery car is estimated subsequently, and the positioning precision of the battery car is improved.
At the j-th acquisition time, the objective function for positioning the battery car is obtained by combining the estimated error of the Kalman filtering algorithm on the path loss index compared with the difference of the distances calculated by the digital transmission loss model through the distances between the positioning routers and the estimated coordinates of the battery car, and the expression is as follows:
; The method comprises the steps of acquiring a target function of positioning an electric vehicle at a j-th acquisition time, wherein M represents the number of positioning routers; representing coordinates of an mth positioning router at a jth acquisition time; representing estimated coordinates of the battery car at the j-th acquisition time; The distance between the mth positioning router and the battery car, which is calculated by the logarithmic transmission loss model at the jth acquisition time, is represented; The method is used for representing the trace of an error covariance matrix when a Kalman filtering algorithm estimates the path loss index of an mth positioning router at the jth acquisition time, epsilon represents a preset positive number and is used for avoiding that a denominator is 0, the value of epsilon is preset by human beings, and an implementer can set the value of epsilon at self by himself, and in the embodiment, the value of epsilon is 0.01.
In this embodiment, the arctangent function pair is usedNormalization was performed.
In this embodiment, the distance between the coordinates of the positioning router and the estimated coordinates of the battery car is the euclidean distance.
And under the j-th acquisition time, when the value of the objective function is minimum, the estimated coordinate of the battery car is the actual position of the battery car. A schematic diagram of the process of positioning the battery car is shown in fig. 3.
In this embodiment, the minimum value of the objective function is solved by adopting the genetic algorithm, and the implementer can select other available optimizing algorithms by himself, so that the application is not particularly limited.
Based on the same inventive concept as the method, the embodiment of the application also provides a positioning visualization device of the shield tunnel storage battery car, which comprises a plurality of WIFI routers, two working condition cameras, a visualization module, a signal receiving module, a distance calculation module and a positioning module;
The WIFI routers are arranged on two sides of the shield tunnel in a crossing mode at equal intervals and used for receiving wireless signals transmitted by the battery cars in the shield tunnel;
the two working condition cameras are respectively arranged at the head part and the tail part of the battery car, are used for acquiring working condition videos of the tunnel position of the battery car, are connected with the visualization module for displaying, and realize positioning visualization of the battery car;
The signal receiving module is used for collecting the received signal intensity of each WIFI router in the shield tunnel in real time, and the signals are transmitted by the battery car;
The distance calculation module is used for constructing a logarithmic transmission loss model based on the relation between the received signal intensity and the transmission distance, and acquiring the dynamic weight of each WIFI router at any acquisition time through the distribution of all received signal intensities of each WIFI router before any acquisition time and the difference of the received signal intensities of each WIFI router compared with each adjacent WIFI router before any acquisition time;
Estimating the path loss index of each WIFI router at any acquisition time by using a Kalman filtering algorithm by combining the dynamic weight and all path loss indexes of each WIFI router before any acquisition time, wherein the initial value of the path loss index is a preset value;
Calculating the measurement distance between each WIFI router and the battery car at any acquisition time by using a logarithmic transmission loss model through the path loss index and the received signal strength of each WIFI router at any acquisition time;
The positioning module is used for selecting each positioning router from all the WIFI routers;
And under any acquisition time, constructing an objective function for positioning the battery car by combining the estimated error of the Kalman filtering algorithm on the path loss index according to the distance between each positioning router and the estimated coordinates of the battery car and comparing the difference of the measured distances, and positioning the battery car by solving the estimated coordinates.
Based on the same inventive concept as the method, the embodiment of the application also provides a positioning visualization system of the shield tunnel storage battery car, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor realizes the steps of any one of the positioning visualization methods of the shield tunnel storage battery car when executing the computer program.
In summary, the WIFI router with more serious signal attenuation is given smaller weight by calculating dynamic weight, the WIFI router with more serious signal attenuation is given larger weight to the WIFI router with more difference in received signal intensity with the adjacent WIFI router, the difference of different position signal attenuation is fully considered, the accuracy of the subsequent estimation of signal transmission loss is improved, further, the signal transmission loss is dynamically estimated by combining a Kalman filtering algorithm through the dynamic weight, error accumulation of the signal transmission loss can be reduced, the change of shield tunnel environment can be adapted in real time, the distance between the WIFI router and a storage battery car is accurately calculated through a logarithmic transmission loss model by utilizing the dynamically estimated signal transmission loss, further, unnecessary calculation can be reduced by selecting a positioning router and constructing an objective function to position the storage battery car, the operation efficiency is improved, and the positioning result of the storage battery car is optimized. The application can effectively cope with complex environments such as 'large gradient', 'small radius' and the like in the shield tunnel, reduce the influence of multipath fading and signal attenuation on the positioning of the storage battery car, and improve the positioning precision of the storage battery car.
The flowcharts and block diagrams in the figures 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 diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than that disclosed in the description, and sometimes no specific order exists between different operations or steps. For example, two consecutive operations or steps may actually be performed substantially in parallel, they may sometimes be performed in reverse order, which may be dependent on the functions involved. Each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the essential characteristics thereof. The above-described embodiments of the application should therefore be regarded as illustrative in all respects and not restrictive.
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