Installation method and calculation method of super-long distance TBM tunnel multi-vehicle scheduling management device

Abstract

The application relates to the technical field of tunnel multi-vehicle scheduling, and discloses a super-long-distance TBM tunnel multi-vehicle scheduling management device installation method and a calculation method. The application aims to solve the technical problems that the existing technology is unstable in positioning, the avoidance algorithm logic is simple, the existing technology cannot cope with the situation of multi-vehicle driving and rail vehicles, and the existing technology lacks a safe and reliable anti-collision avoidance scheduling. The super-long-distance TBM tunnel multi-vehicle scheduling management device installation method comprises the following steps: positioning beacon installation, communication base station installation, vehicle-mounted terminal installation and scheduling center deployment. The super-long-distance TBM tunnel multi-vehicle scheduling management method comprises the following steps: establishing a position model, analyzing the model and making a prediction, analyzing the locomotive position, transmitting the position information and multi-vehicle driving scheduling. The one-dimensional Kalman filtering algorithm is adopted, the positioning accuracy and continuity are greatly improved, and a more accurate basis is provided for the anti-collision system and the scheduling system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel multi-vehicle scheduling, and particularly relates to a method for installing a super-long distance TBM tunnel multi-vehicle scheduling management device and a calculation method. BACKGROUND

[0002] With the development of tunnel site informatization, the construction informatization management technology for people and vehicles in tunnels has developed, indoor positioning technology and tunnel vehicle avoidance technology have been applied. At present, the safety management of vehicles in tunnels is limited to positioning vehicles in tunnels or designing avoidance algorithms for trackless vehicles to achieve driving avoidance between vehicles. The avoidance principle is based on the distance from the avoidance hole and the priority when driving in the same direction or driving in opposite directions, and the server sends a prompt voice to the vehicle terminal to remind the driver to avoid.

[0003] During long-distance TBM tunnel construction, efficient material transportation is the key to TBM rapid construction. According to the size of the tunnel space and the construction characteristics, construction materials are mostly transported by trackless vehicles through long-distance inclined shafts with large slopes to the TBM construction intersection, and then transported by rail vehicles to the TBM supporting location, wherein the trackless vehicle driving road is mainly single lane and double lane, and the rail vehicle driving road is mainly double track single line and four track three line.

[0004] Trackless vehicles are mostly used for long-distance branch tunnel channels to provide materials for deep-buried TBM tunnel construction. The branch tunnel is mostly single lane, with a staggered hole in the middle for vehicle meeting and avoidance when multiple vehicles are driving. Alternatively, the middle partition wall is assembled after the tunnel is connected, and the trackless vehicle driving road is double lane during construction.

[0005] Rail vehicles are mostly used for long-distance TBM tunnel construction, and the material transportation in the main tunnel adopts a double-track single-line rail transportation mode, with staggered platforms set at regular intervals in the middle for vehicle avoidance when multiple vehicles are driving. Alternatively, horizontal transportation adopts a four-track three-line transportation mode, with a set of mobile crossover switches arranged in front of and behind the lining formation platform, and the lining platform area adopts single-line transportation, and multiple vehicles drive through the lining platform for meeting and avoidance.

[0006] Although the above material transport vehicles have different driving road modes, similar scheduling management and safety problems exist during vehicle driving. When the trackless vehicle is driving, the driver mainly relies on the light of the front vehicle to assist in judging whether to avoid meeting and scheduling the room. The real-time position of the vehicle cannot be known, and the vehicle is easily collided when reversing to avoid, driving at close range, driving at high speed, and other accidents occur, which also reduces the vehicle transportation efficiency. When the trackless vehicle is driving, the scheduling cannot determine the position and number of existing vehicles in the tunnel in real time, and the vehicle cannot be fully scheduled to enter the tunnel to transport materials, and the vehicle utilization rate is low. At the same time, when multiple vehicles are driving, the distance between vehicles, overspeed, and avoidance of the wrong platform all exist the problem of not smooth coordination, which causes low vehicle utilization efficiency and high driving risk.

[0007] The prior art has unstable positioning, the avoidance algorithm is for trackless vehicles, and the avoidance logic is simple, which cannot cope with multiple vehicle driving and trackless vehicles. For trackless vehicles in long-distance tunnels, only wrong vehicles can be used on the wrong vehicle platform, and the driving logic of the trackless vehicle is more complex, which requires safer and more reliable anti-collision avoidance scheduling. SUMMARY

[0008] In view of the above technical problems, the present disclosure provides a super-long distance TBM tunnel multi-vehicle scheduling management device installation method and calculation method, which solves the technical problems of unstable positioning, simple avoidance algorithm logic, and inability to cope with multiple vehicle driving and trackless vehicles in the prior art. For trackless vehicles in long-distance tunnels, only wrong vehicles can be used on the wrong vehicle platform, and the driving logic of the trackless vehicle is more complex, which requires safer and more reliable anti-collision avoidance scheduling.

[0009] According to one aspect of the present disclosure, a super-long distance TBM tunnel multi-vehicle scheduling management device installation method is provided, comprising the following steps:

[0010] (1) Positioning beacon installation, positioning beacons are installed at intervals in the tunnel;

[0011] (2) Communication base station installation, communication base station installation points are set at intervals in the tunnel, each installation point is equipped with a WIFI base station, the WIFI base station is connected to a UPS power supply for uninterrupted power supply, and the WIFI base station is connected by an optical fiber to form a ring network;

[0012] (3) Vehicle-mounted terminal installation, a vehicle-mounted terminal is mounted on a tunnel driving locomotive, the terminal host is fixed near the driver, and the terminal host is electrically connected to a Bluetooth antenna, a WLAN antenna, a camera, and a power supply. The Bluetooth antenna is fixed on the side of the cab to obtain beacon data; the WLAN antenna is fixed on the top of the locomotive for long-distance wireless network transmission with the communication base station; and the camera is fixed on the top of the locomotive through a support to monitor the vehicle driving condition in real time;

[0013] (4) the dispatch center is deployed and set up, and the ground dispatch room and the underground dispatch room are set up, the server is deployed in the ground dispatch room,

[0014] the display screen, the computer and the telephone; the computer and the telephone are deployed in the underground dispatch room.

[0015] In step (1), two groups of beacons are set up and installed in parallel in the tunnel, and each is independently connected with the base station, the vehicle-mounted terminal and the dispatch center to form two sets of information of the super-long distance TBM tunnel multi-vehicle dispatch management device, which are independently displayed on the display screen and correspond to each other. In step (1), a positioning beacon is installed every 20 m in the tunnel.

[0016] In step (2), a communication base station installation point is set up every 1000 m in the tunnel.

[0017] In step (2), two WIFI base stations of 5.8G frequency are deployed in a back-to-back manner at each installation point to emit signals in opposite directions, and the two WIFI base stations are connected with an UPS power supply for uninterrupted power supply.

[0018] In the tunnel, a plurality of positioning beacon installation surfaces are determined, the positioning beacon installation surfaces include at least three positioning beacons, at least one of the positioning beacons is a standby beacon, an output end of the standby beacon is connected with two information transmission branches which are selectively started, and the information transmission branches are connected with a standby information trunk. The standby information trunk is connected in parallel on the information transmission line, and a switching element is arranged on the standby information trunk to control the start and sleep of the standby information trunk. According to another aspect of the present disclosure, a super-long distance TBM tunnel multi-vehicle dispatch management calculation method is provided, which adopts one-dimensional wireless positioning technology based on Bluetooth and is deployed in the server of the dispatch center, and includes the following steps:

[0019] S1: establishing a position model: using the vehicle-mounted terminal and the adjacent beacon, obtaining the position-related position parameters, and establishing a one-dimensional Kalman filter model;

[0020] S2: analyzing the model and predicting: using the position parameters and the established Kalman filter model, analyzing the coordinate position at the present time and predicting the coordinate position at the next time;

[0021] S3: Analyzing the position of the locomotive: using the signal strength method (RSSI), using the Bluetooth sniffing module of the vehicle terminal to collect and preliminarily clean the signal strength of the Bluetooth beacon, obtaining the signal strength of several beacons, establishing a mathematical model between the geographical position of the corresponding Bluetooth beacon and the RSSI value, RSSI=A-10nlg(d); calculating and analyzing the position of the locomotive; in the formula: RSSI is the signal strength value of the vehicle at time d, A is the RSSI strength value received by the vehicle terminal when the wireless transceiver node is 1m apart, n is the path loss (PassLoss) index;

[0022] S4: Transmission of position information: transmitting the position information to the communication base station through the wireless network, and transmitting the position information to the server through the optical fiber;

[0023] S5: Multi-vehicle driving scheduling: after the server obtains the position information, through anti-collision logic operation, the scheduling instructions of vehicle avoidance / speeding / safe vehicle distance are sent to the vehicle terminal through the telephone, realizing the interactive manual auxiliary scheduling of the dispatching room and the driver, and realizing the intelligent scheduling of multi-vehicle safe driving.

[0024] The calculation steps of analyzing the position of the locomotive in step S3 are as follows:

[0025] 1) Initialization

[0026] Initial locomotive position coordinate value

[0027] 2) Prediction

[0028] According to the estimated value at the last time, the predicted value at this time is obtained:

[0029]

[0030] In the formula: is the predicted value at this time, F is the state transition matrix, is the estimated value at the last time, B is the control matrix, u t-1 is the speed of the locomotive;

[0031] According to the covariance at the last time and the prediction noise, the predicted covariance at this time is obtained:

[0032] P t - = FP t-1 F T +Q,

[0033] In the formula: P t - is the predicted covariance at this time, F is the state transition matrix, P t-1 is the covariance at the last time, F TLet F be the transpose of F, and Q be the variance of the prediction noise, taken as 10E-5m;

[0034] 3) Updated Kalman Gain Equation

[0035] Based on the covariance and hyperparameters of the previous time step, the Kalman gain is derived:

[0036] K t =P t - H T HP t - H T +R) -1 ,

[0037] In the formula: K t For Kalman gain, P t - Let H be the covariance of the predicted values ​​at this moment, and H be the observation matrix. T R is the transpose of H, and R is the deviation between the collected locomotive position coordinates and the actual locomotive position coordinates, which is taken as 0.466m.

[0038] 4) Update - State Equation

[0039] By employing Kalman filtering to eliminate data transmission errors, a Kalman prediction model is established. Based on the predicted value, the observed value, and the Kalman gain at this moment, the estimated value at this moment is derived.

[0040]

[0041] In the formula: This is the predicted value for this moment. K is the estimated value from the previous time step. t For Kalman gain, Z t H represents the locomotive status, and H is the observation matrix.

[0042] 5) Prediction

[0043] In steps 2)-4), iteratively calculate the state extrapolation equation and covariance extrapolation equation to obtain the vehicle position at a certain moment.

[0044] It also enables the prediction of vehicle trajectory.

[0045] The beneficial effects of this invention are as follows:

[0046] Compared with the prior art, the application adopts one-dimensional Kalman filtering algorithm for Bluetooth beacon data, optimizes the positioning algorithm, greatly improves the positioning accuracy and continuity, and provides more accurate basis for the anti-collision system and the scheduling system. Scientific avoidance rules are adopted for the rail vehicles to schedule and avoid collision, and the function of voice and video connection based on VOIP is added to solve the scheduling problem in emergency situations.

[0047] The anti-collision system formulates scientific avoidance rules, is compatible with the avoidance of holes or the platform for avoiding collision, and covers all motion scenarios logically. The rail vehicles and the trackless vehicles can accurately avoid collision according to the rules formulated according to the meeting position, distance and motion direction, prevent the collision of the vehicle train from the algorithm, avoid the situation that the vehicle train cannot avoid collision by reversing after meeting, and improve the transportation efficiency.

[0048] The added online audio and video intercom system is suitable for emergency call and communication in emergency situations, and is used for vehicle command and scheduling to ensure that the system is effective in scheduling and emergency measures in extreme scenarios. Real-time voice intercom and online video call are realized through IP phone and infrared camera. The vehicle anti-collision and interval scheduling are mainly based on the position information of multiple vehicles, and the scheduling algorithm automatically issues warning information to the driver to reduce the risk of multiple vehicle collision and congestion and improve the long-distance transportation efficiency.

[0049] In view of the situation that communication is inconvenient in a long-distance tunnel and electronic components are easily damaged due to a humid environment, a backup information transmission device is added, which can start the backup beacon at any time. In the application, there are two sets of beacon measurement systems, which can verify each other during operation and play a double insurance role, especially suitable for measurement in a long-distance environment. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 It is a single lane schematic diagram of a super-long distance TBM tunnel;

[0051] Figure 2 It is a double lane schematic diagram of a super-long distance TBM tunnel;

[0052] Figure 3 It is a double rail single line schematic diagram of a super-long distance TBM tunnel;

[0053] Figure 4 It is a four-rail three-line schematic diagram of a super-long distance TBM tunnel;

[0054] Figure 5 It is a positioning beacon installation structure schematic diagram;

[0055] Figure 6 It is another view of the positioning beacon installation structure schematic diagram;

[0056] The names of the components in the figure are: 1, tunnel; 2, first positioning beacon; 3, second positioning beacon; 4, third positioning beacon. DETAILED DESCRIPTION

[0057] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings, it should be understood that the preferred embodiments described here are only used to illustrate and explain the present application, and are not used to limit the present application.

[0058] Example 1

[0059] The example discloses a long-distance TBM tunnel multi-vehicle scheduling management device installation method, comprising the following steps:

[0060] (1) Positioning beacon installation, positioning beacons are installed at intervals in the tunnel;

[0061] (2) Communication base station installation, communication base station installation points are set at intervals in the tunnel, WIFI base stations are deployed at each installation point, the WIFI base stations are connected to a UPS power supply for uninterrupted power supply, and the WIFI base stations are connected by optical fibers to form a ring network;

[0062] (3) Vehicle-mounted terminal installation, a vehicle-mounted terminal is carried on a locomotive driving in the tunnel, the terminal host is fixed near the driver, and the terminal host is electrically connected to a Bluetooth antenna, a WLAN antenna, a camera, and a power supply, the Bluetooth antenna is fixed on the side of the cab to obtain beacon data; the WLAN antenna is fixed on the roof of the locomotive to perform long-distance wireless network transmission with the communication base station; the camera is fixed on the roof of the locomotive through a support to monitor the driving condition of the vehicle in real time;

[0063] (4) Dispatching center deployment, a ground dispatching room and an underground dispatching room are set, a server,

[0064] a display screen, a computer, and a telephone are disposed in the ground dispatching room; a computer and a telephone are disposed in the underground dispatching room;

[0065] In step (1), two groups of beacons are set, which are installed in parallel in the tunnel, and each is independently connected to a base station, a vehicle-mounted terminal, and a dispatching center to form two sets of long-distance TBM tunnel multi-vehicle scheduling management device information, and the two groups of information are independently displayed on the display screen and correspond to each other.

[0066] In step (1), one positioning beacon is installed at an interval of 20 m in the tunnel.

[0067] In step (2), one communication base station installation point is set at an interval of 1000 m in the tunnel.

[0068] In step (2), two WIFI base stations of 5.8G frequency are disposed in a back-to-back manner at each installation point to emit signals in opposite directions, and the two WIFI base stations are connected to a UPS power supply for uninterrupted power supply.

[0069] Referring to Figure 5 With Figure 6 , in the tunnel 1, a plurality of positioning beacon mounting surfaces are determined, the positioning beacon mounting surfaces including a first positioning beacon 2, a second positioning beacon 3, and a third positioning beacon 4, wherein the third positioning beacon 4 is a standby beacon, and an output end of the third positioning beacon 4 is connected to two information transmission branches that are selectively opened, and the information transmission branches are connected to a standby information trunk. The standby information trunk is connected in parallel to an information transmission line, and a switching element is arranged on the standby information trunk to control the start and sleep of the standby information trunk.

[0070] The example discloses a super-long-distance TBM tunnel multi-vehicle scheduling management calculation method, adopts one-dimensional wireless positioning technology based on Bluetooth, is arranged in a server of a scheduling center, and comprises the following steps:

[0071] S1: establishing a position model: obtaining position parameters related to positions by using a vehicle terminal and a nearby beacon, and establishing a one-dimensional Kalman filter model;

[0072] S2: analyzing a model and making a prediction: analyzing a coordinate position at the present moment and predicting a coordinate position at the next moment by using the position parameters and the established Kalman filter model;

[0073] S3: analyzing a locomotive position: collecting and preliminarily cleaning signal strengths of Bluetooth beacons by using a Bluetooth sniffing module of the vehicle terminal by using a signal strength method (RSSI), obtaining signal strengths of a plurality of beacons, establishing a mathematical model between geographical positions of the corresponding Bluetooth beacons and the RSSI values, RSSI=A-10nlg(d); and analyzing the position of the locomotive; in the formula, RSSI is a signal strength value of the vehicle at d moment, A is an RSSI strength value received by the vehicle terminal when the wireless transceiver node is 1 m apart, and n is a path loss (PassLoss) index;

[0074] S4: transmitting position information: transmitting the position information to a communication base station through a wireless network, and transmitting the position information to a server through an optical fiber;

[0075] S5: multi-vehicle driving scheduling: after the server obtains the position information, the scheduling instructions of vehicle avoidance / speeding / safe vehicle distance are sent to the vehicle terminal through a telephone by using anti-collision logical operation, the interactive manual auxiliary scheduling of the dispatching room and the driver is realized, and the intelligent scheduling of the safe driving of the multi-vehicle is realized.

[0076] The calculation steps of analyzing the locomotive position in step S3 are as follows:

[0077] 1) initialization

[0078] initial locomotive position coordinate value

[0079] 2) Prediction

[0080] According to the last time estimation value, the prediction value of this time is derived:

[0081]

[0082] In the formula: is the prediction value of this time, F is the state transition matrix, is the last time estimation value, B is the control matrix, u t-1 is the locomotive speed;

[0083] According to the last time covariance and prediction noise, the prediction value covariance of this time is derived:

[0084] P t - = FP t-1 F T + Q,

[0085] In the formula: P t - is the prediction value covariance of this time, F is the state transition matrix, P t-1 is the last time covariance, F T is the transpose of F, Q is the variance of prediction noise, which is 10E-5m;

[0086] 3) Update - Kalman gain equation

[0087] According to the last time covariance and hyperparameters, the Kalman gain is derived:

[0088] K t = P t - H T (HP t - H T + R) -1 ,

[0089] In the formula: K t is the Kalman gain, P t - is the prediction value covariance of this time, H is the observation matrix, H T is the transpose of H, R is the deviation value of the collected locomotive position coordinates and the actual locomotive position coordinates, which is 0.466m;

[0090] 4) Update - state equation

[0091] By using Kalman filtering technology to eliminate data transmission error, a Kalman prediction model is established, and according to the prediction value of this time, the observation value of this time, the Kalman gain, the estimation value of this time is derived:

[0092]

[0093] wherein: is the prediction value at this moment, is the estimation value at the last moment, K t is the Kalman gain, Z t is the locomotive state, H is the observation matrix;

[0094] 5) prediction

[0095] The iterative calculation of the state extrapolation equation and the covariance extrapolation equation is performed in steps 2)-4), the next estimation value is predicted from the first initial value, the noise coefficient is then obtained, and the equation is substituted again to obtain the optimal estimation value at each time, and the vehicle position at a certain moment is obtained, and the prediction of the vehicle running track is realized.

[0096] The anti-collision system is composed of a data processing center and a vehicle terminal. After the data processing center calculates the positions of all locomotives, it determines the locomotives that need to be avoided according to the avoidance rules, and sends avoidance instructions to the locomotives according to the vehicle priority. After the vehicle terminal receives the avoidance instructions, it plays the corresponding avoidance voice warning, and the driver performs the locomotive avoidance action. The data processing center is the core of the avoidance system, which needs to perform super-fast algorithm operation and timely send avoidance instructions to achieve the sensitivity and accuracy of the avoidance system. The vehicle terminal is the playing port of the instructions, which needs to clearly and accurately play the voice instructions to achieve stable transmission of the instructions of the anti-collision avoidance system. The avoidance logic is independent of the car changing platform, and can also be implemented in the scene without the car changing platform and without the avoidance tunnel.

[0097] The intelligent scheduling anti-collision logic is as follows:

[0098] (1) The road mode of the transport vehicle is double-lane mode, as shown in Figure 2 During the opposite driving of the vehicles, the meeting point is random, and the vehicles are reminded to meet when they are at a distance of DOv (the distance between opposite direction vehicles).

[0099] (2) The road mode of the transport vehicle is single-lane, as shown in Figure 1As shown, during the process of vehicles running in opposite directions, the meeting point is fixed, the vehicles meeting reminder is given when the distance between the vehicles is DOTv (the distance of opposite direction trackless vehicles), the vehicle close to the meeting point enters the wrong car hole, and the vehicle far from the meeting point passes slowly; when the single lane road has a certain slope, the vehicle with low priority reminds the vehicle to enter the wrong car hole, and the vehicle with high priority passes slowly. During the process of vehicles running in the same direction, the safety distance reminder is given to the rear vehicle when the distance between the vehicles is DSTv (the distance of same direction trackless vehicles).

[0100] (3) The running road mode of the transport vehicle is double-track single line, as shown in Figure 3 As shown, during the process of vehicles running in opposite directions, the meeting point is fixed, the meeting reminder is given when the distance between the vehicles is DORv(1) (the distance of opposite direction rail vehicles), the vehicle close to the meeting point enters the wrong car platform, and the vehicle far from the meeting point passes slowly; during the process of vehicles running in the same direction, the safety distance reminder is given to the rear vehicle when the distance between the vehicles is DSRv (the distance of same direction rail vehicles).

[0101] (4) The running road mode of the transport vehicle is four-track three-line, as shown in Figure 4 As shown, during the process of vehicles running in opposite directions, under the condition of no lining platform between the two vehicles, the slow driving reminder is given to the vehicle when the distance between the vehicles is DORv(2); under the condition of lining platform between the two vehicles, the meeting reminder is given when the distance between the vehicles is DORv(1), the vehicle close to the lining platform stops and waits, and the vehicle far from the lining platform passes slowly. During the process of vehicles running in the same direction, the safety distance reminder is given to the rear vehicle when the distance between the vehicles is DSRv.

[0102] (5) For all vehicles, no reminder is given when the vehicle distance is less than Mvs (Minimum vehicle spacing).

[0103] (6) When the speed of the trackless vehicle exceeds MsTv (Maximum speed limit for trackless vehicles), the system will remind the vehicle of overspeed; when the speed of the rail vehicle exceeds MsRv (Maximum speed limit for rail vehicles), the system will remind the vehicle of overspeed; when the vehicle overspeeds, the vehicle running information on the dispatching management platform will be marked in red.

[0104] (7) When there are two vehicles in the same running interval (two passing platforms and the line segment between them), the system will remind the nearest vehicle outside the interval to stop and wait.

[0105] (8) After the alarm broadcast command is triggered, the broadcast interval time IT (interval time) will be broadcast.

[0106] Since the intelligent dispatching management system has universality and adaptability, the above parameters can be specifically personalized according to different engineering conditions.

[0107] For example:

[0108] The length of the tunnel in a certain section is 6,444 m, the comprehensive gradient is 11%, the cross-sectional diameter is 8.5 m, there are 11 passing holes with a spacing of about 450-700 m, and after the wind duct, slag conveyor belt, water pipeline and other equipment are occupied, the effective space for safe vehicle travel is 4 m x 4 m. There are 11 trackless vehicles running in the construction. The length of the main tunnel in this section is 20 km, the TBM excavation diameter is 7.03 m, the long-distance material transport vehicle running mode is double-track single line, and the current TBM excavation length is about 10 km. Two passing platforms are set with a spacing of about 4 km. There are 3 rail vehicles in the early stage of construction, and the number is planned to increase to 5 in the later stage. According to the project construction period arrangement, the vehicle intelligent dispatching management system is preferentially applied in the long-distance main tunnel to improve the efficiency of long-distance material transport of multiple vehicles. According to the project management requirements of this section, the intelligent anti-collision parameters in the system are specifically set as follows:

[0109] DORv (1) = {1000 m, 500 m, 200 m, 100 m, 50 m, 20 m, 10 m};

[0110] DSRv = {1000 m, 500 m, 200 m, 100 m, 50 m, 20 m};

[0111] Mvs = 5 m;

[0112] MsRv = 25 km / h;

[0113] IT = 3 s.

[0114] Based on a long distance TBM tunnel, the field installation and application of the 10km transport vehicle intelligent scheduling management system are carried out, the specific anti-collision parameters are set according to the characteristics of the engineering project, the driving state statistical analysis and early warning reminder of long distance material transport vehicles are realized, and the long distance material transport efficiency and multi-vehicle driving safety are improved.

[0115] Meanwhile, during the working process, taking the design of 26 beacons as an example, the forward {A1, A2, …… AN} and the reverse {BN, BN-1, ……, B1} are installed, and the two groups of beacons send signals at the same time during the working process. On the display screen, the staff can check the corresponding situation, such as the signals of A2 and BN-1 should arrive at the same time. If the situation of asynchronization occurs, it can be checked whether the beacon falls off or the beacon is damaged.

[0116] In the present application, since it is a dynamic measurement process different from the manual measurement or individual measurement of the prior art equipment, once a beacon is damaged, it will cause local deviation of the overall data, the state of being easily affected by the deviation waveform, and the measurement result is inaccurate. In the present design, the two groups of beacons are installed in parallel in the tunnel and work independently at the same time, so that the damaged beacons can be monitored in real time. If there are damaged beacons, the spare beacons are used to realize the replacement work in time through remote control, so that the measurement accuracy can be maximized.

[0117] The dispatching system is composed of a video voice intercom host and a client. The host can dial and call a single client at any time or call multiple clients at the same time through wireless base station networking. The client can also call the host with one key, and the two parties can carry out video calls. The communication quality is stable and is not affected by harsh environments. When an emergency occurs during the construction operation process, the dispatching center can immediately call and intercom with each locomotive through the dispatching system to assist in dispatching. The locomotive can also communicate with the dispatching center through calling the host to report the emergency in time.

[0118] The control management system includes two parts of man-machine interaction and server engine. The man-machine interaction includes positioning map display, system management and asset management. The system management includes personnel management, organization management, role management, etc. The asset management includes vehicle management, terminal management and beacon management, etc. The server engine includes dispatching algorithm, database and some basic data services, etc.

[0119] Although some preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0120] Obviously, many modifications and variations of the present application are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A method for installing an ultra-long distance TBM tunnel multi-vehicle dispatching management device, characterized in that, It comprises the following steps: (1) Positioning beacon installation, installing positioning beacons at intervals in the tunnel; (2) Communication base station installation, setting up communication base station installation points at intervals in the tunnel, deploying WIFI base stations at each installation point, connecting the WIFI base stations to UPS power supply for uninterrupted power supply, and connecting the WIFI base stations through optical fibers to form a ring network; (3) Vehicle-mounted terminal installation, carrying a vehicle-mounted terminal on a locomotive driving in the tunnel, fixing the terminal host near the driver, and electrically connecting the terminal host with a Bluetooth antenna, a WLAN antenna, a camera, and a power supply, fixing the Bluetooth antenna on the side of the cab to obtain beacon data, fixing the WLAN antenna on the roof of the locomotive for long-distance wireless network transmission with the communication base station, and fixing the camera on the roof of the locomotive through a support to monitor the driving condition of the vehicle in real time; (4) Dispatching center deployment, setting up a ground dispatching room and an underground dispatching room, deploying servers, display screens, computers, and telephones in the ground dispatching room, and deploying computers and telephones in the underground dispatching room; In step (1), two groups of beacons are set up, which are installed in parallel in the tunnel and each independently connected with base stations, vehicle-mounted terminals, and dispatching centers to form two sets of information of the super-long distance TBM tunnel multi-vehicle dispatching and management device, and the two groups of information are independently displayed on the display screens and correspond to each other; In the tunnel, a plurality of positioning beacon installation surfaces are determined, each of which comprises at least three positioning beacons, at least one of which is a standby beacon, the output end of the standby beacon is connected with two information transmission branches which are selectively started, and the information transmission branches are connected with a standby information trunk; The two groups of beacons installed in parallel in the tunnel work independently at the same time to monitor in real time whether there is a damaged beacon, and if there is, the standby beacon is used to remotely control and replace the damaged beacon in time.

2. The ultra-long distance TBM tunnel multi-vehicle dispatching management device installation method of claim 1, wherein: In step (1), one positioning beacon is installed in the tunnel every 20 m.

3. The method of claim 1, wherein the method further comprises: installing the ultra-long distance TBM tunnel multi-vehicle dispatching management device in the tunnel. In step (2), one communication base station installation point is set up in the tunnel every 1000 m.

4. The method of claim 1, wherein the method further comprises: installing the ultra-long distance TBM tunnel multi-vehicle dispatching management device in the tunnel. In step (2), two WIFI base stations with a frequency of 5.8G are disposed in a back-to-back manner at each installation point to emit signals in opposite directions, and the two WIFI base stations are connected with one UPS power supply for uninterrupted power supply.

5. A super-long distance TBM tunnel multi-vehicle scheduling management calculation method, based on the super-long distance TBM tunnel multi-vehicle scheduling management device installation method of claim 1, using one-dimensional wireless positioning technology based on Bluetooth, deployed in the server of the scheduling center, characterized in that, It comprises the following steps: S1: Establishing a position model: using the vehicle-mounted terminal and the nearby beacon to obtain position-related position parameters and establishing a one-dimensional Kalman filter model; S2: Analyzing the model and predicting: using the position parameters and the established Kalman filter model to analyze the coordinate position at the current time and predict the coordinate position at the next time; S3: Analyzing the position of the locomotive: using the signal strength method (RSSI), using the Bluetooth sniffing module of the vehicle terminal to collect and preliminarily clean the signal strength of the Bluetooth beacon, obtaining the signal strength of several beacons, and establishing a mathematical model between the geographical position of the corresponding Bluetooth beacon and the RSSI value, ; calculating the position of the locomotive; in the formula: RSSI is the signal strength value of the vehicle at time d, A is the RSSI strength value received by the vehicle terminal when the wireless transceiver node is 1 m apart, and n is the path loss (Pass Loss) index; S4: Transmitting position information: transmitting the position information to the communication base station through a wireless network, and transmitting the position information to the server through optical fibers; S5: Multi-vehicle driving scheduling: After the server obtains the position information, through anti-collision logic operation, the scheduling instructions of vehicle avoidance, overspeed and safe distance are sent to the vehicle terminal through the handset, realizing the interactive manual auxiliary scheduling between the scheduling room and the driver, and realizing the intelligent scheduling of multi-vehicle safe driving.

6. The ultra-long distance TBM tunnel multi-vehicle dispatching management calculation method of claim 5, wherein: The calculation steps of analyzing the locomotive position in step S3 are as follows: 1) initialization Initial locomotive position coordinate value ; 2) prediction According to the estimation value of the last time, the prediction value of this time is derived: ; wherein: is the prediction value for this time instant, is the state transition matrix, is the estimation value for the previous time instant, is the control matrix, is the locomotive speed; According to the covariance of the last moment and the prediction noise, the prediction value covariance of this moment is derived: , In the formula: Let the covariance of the predicted value at this moment be... Here is the state transition matrix. The covariance of the previous time step. for transpose, To predict the variance of the noise, we take 10E-5m; 3) update - Kalman gain equation Based on the covariance of the last time and the hyperparameters, the Kalman gain is derived: , In the formula: For Kalman gain, Let the covariance of the predicted value at this moment be... For the observation matrix, for transpose, To measure the deviation between the locomotive's position coordinates and the actual locomotive position coordinates, a value of 0.466m was used. 4) update - state equation By using Kalman filtering technique to eliminate data transmission error, Kalman prediction model is established, and according to the prediction value at this moment, the observation value at this moment and Kalman gain, the estimated value at this moment is derived: ; wherein: is the prediction value for this time instant, is the estimate value for the previous time instant, is the Kalman gain, is the locomotive state, is the observation matrix; 5) prediction Loop steps 2) - 4) iteratively calculate state extrapolation equation and covariance extrapolation equation to obtain vehicle position at a certain time and realize prediction of vehicle running track.

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