Underground personnel positioning method, readable storage medium and electronic equipment

Abstract

The invention relates to the technical field of wireless positioning, in particular to an underground personnel positioning method, a readable storage medium and electronic equipment. The underground personnel positioning method comprises the following steps: installing an underground personnel positioning system and recording an MAC address of a WiFi positioning base station located at the top of a roadway; the distance data between the underground personnel and the WiFi positioning base station is measured through the WiFi chip in the activity process of the underground personnel; the inertial sensor data of the underground personnel is measured through the MEMS inertial sensor chip; and the fusion positioning subsystem calculates the distance data and the inertial sensor data in real time to obtain the position of the underground personnel. And in combination with WiFi high-precision real-time distance measurement information, a personnel positioning identification card acquires inertial sensor data such as acceleration, angular velocity and geomagnetism in the underground movement process of personnel in real time, and a positioning subsystem is fused for real-time calculation, so that continuous and high-precision positioning of the underground personnel can be realized.

Description

Technical Field

[0001] The present invention relates to the field of wireless positioning technology, and in particular to an underground personnel positioning method, a readable storage medium and an electronic device. Background Art

[0002] It is emphasized that the advantages of new technologies such as AI intelligent identification, underground precise positioning, and the Internet of Things should be fully utilized in the identification of major disaster risks in mines and emergency rescue, and higher requirements are put forward for the underground personnel positioning system in terms of real-time accuracy, emergency rescue, and digital management. The underground personnel positioning system should be able to query and display the location of the specified object and the historical movement trajectory of the specified object at any time. For a specified area, the number of located objects and their specific distribution at any time can be queried. When an accident occurs, the system can determine the approximate location of the personnel based on the information currently received, providing a basis for rescue.

[0003] At present, the non-coal mining industry as a whole has established a regional underground personnel positioning system, such as WiFi-based personnel positioning. The principle of traditional WiFi positioning is based on location fingerprint matching. It is necessary to deploy multiple signal access points (Access Point, AP) in the indoor space to build a location fingerprint library and regularly maintain and update the fingerprint. It is not applicable in the underground mine environment, so it can only be located at the underground entrances and exits, main tunnels and other key locations according to the WiFi positioning base station. When the personnel carry a uniquely identified positioning card close to the base station with a calibrated position, the positioning card is sensed and identified, and then the area where the personnel is located is displayed. There are problems such as low positioning accuracy, many blind spots, large position jumps, and high transformation costs, which hinder mine safety management and emergency rescue work. How to make the best use of existing facilities in underground mines, achieve full coverage of underground areas at the lowest cost, reduce positioning errors, enhance positioning continuity, and improve anti-interference capabilities are issues that need to be solved urgently.

[0004] In summary, there is an urgent need for a positioning method that can achieve continuous and high-precision positioning of underground personnel to solve the problems existing in the prior art. Summary of the invention

[0005] The purpose of this utility model is to provide a method for underground personnel positioning that can achieve continuous and high-precision positioning of underground personnel. The specific technical solution is as follows:

[0006] A method for locating underground personnel comprises the following steps:

[0007] The first step is to install an underground personnel positioning system, which includes a WiFi communication subsystem, a personnel positioning identification card and a fusion positioning subsystem; the WiFi communication subsystem includes a WiFi positioning base station deployed in the tunnel; a personnel positioning identification card is set on the underground personnel, and the personnel positioning identification card has a built-in MEMS inertial sensor chip and a WiFi chip, and the WiFi chip and the WiFi positioning base station perform distance measurement; the MEMS inertial sensor chip is used to collect data on underground personnel activities in real time; the personnel positioning identification card is connected to the fusion positioning subsystem signal through the WiFi positioning base station;

[0008] Record the MAC address of the WiFi positioning base station located at the top of the alley;

[0009] The second step is to measure the distance data between the underground personnel and the WiFi positioning base station through the WiFi chip during the underground personnel's activities, and to measure the inertial sensor data of the underground personnel through the MEMS inertial sensor chip;

[0010] In the third step, the fusion positioning subsystem calculates the distance data and inertial sensor data obtained in the second step in real time to obtain the position of the underground personnel.

[0011] Preferably, in the third step, the fusion positioning subsystem solves the distance data and the inertial sensor data in real time by: using an extended Kalman filter algorithm to fuse the distance data and the inertial sensor data to correct the PDR positioning error.

[0012] Preferably, the real-time solution of distance data and inertial sensor data by the fusion positioning subsystem in the third step includes the following steps: obtaining a real-time WiFi ranging value based on the distance data; calculating the position based on the step size and heading output by the PDR algorithm according to the inertial sensor data to obtain a position estimate; and performing EKF fusion based on the real-time WiFi ranging value and the position estimate to obtain the position of the underground personnel.

[0013] Preferably, the following state transition model is established when the position is calculated using the step length and heading output by the PDR algorithm:

[0014]

[0015] Where: (x t ,y t ) represents the estimated value of the position at step t; (x t-1 ,y t-1 ) represents the estimated value of the position at step t-1; l t represents the step length of the t-th step estimate; α t is the estimated heading at step t; F is the state transition matrix of the system equation, w tis the noise of system state transition.

[0016] Preferably, obtaining a real-time WiFi distance measurement value according to the distance data specifically includes the following steps:

[0017] Establish the following WiFi-RTT ranging model:

[0018] D RTT =D T +D B +ο R ;

[0019] Where: D RTT is the distance value measured by WiFi-RTT, which includes the inherent error D of the WiFi chip B and random noise in the environment. R , D T It is the actual distance between the WiFi positioning base station and the personnel positioning identification card;

[0020] According to the following WiFi-RSSI ranging model:

[0021] RSSI = C-10mlog 10 (D RSSI )+r R ;

[0022] Where: RSSI is D RSSI The signal strength value of WiFi received at the time, D RSSI is the distance between the WiFi positioning base station and the personnel positioning identification card; C represents the hardware constant; m represents the path loss index; r R Indicates that RSSI measurements have zero mean and standard deviation σ RSSI Gaussian random noise;

[0023] Based on the WiFi-RTT ranging model, the distance D between the WiFi positioning base station and the personnel positioning identification card is obtained RSSI The expression is as follows:

[0024]

[0025] WiFi-RTT and WiFi-RSSI are weightedly fused to obtain the distance measurement value D of WiFi-RTT and RSSI in t steps of activity t wifi As shown below:

[0026]

[0027] Where: N is the number of WiFi ranging collections by underground personnel in t steps of activities; is the ith WiFi-RTT distance measurement value of the underground personnel in the t-step activity The weight of is the ith WiFi-RSSI ranging value of the underground personnel in the t-step activity The weight of λ represents a hyperparameter, RSSI t,i is the measured value of the RSSI of the i-th WiFi signal strength during the t-step activity of the underground personnel.

[0028] Preferably, performing EKF fusion according to the real-time WiFi ranging value and the position estimation value specifically includes:

[0029] The following observation equation is established:

[0030]

[0031] Among them, D t PDR-AP is the inverse distance from the coordinates estimated by the PDR of the underground personnel in the t-step activity to the coordinates of the WiFi positioning base station AP, (x AP ,y AP ,z AP ) represents the three-dimensional coordinates of the WiFi positioning base station AP; z0 is the vertical height of the WiFi positioning base station AP when it is deployed at the top of the lane; v t is the observation noise of underground personnel during t-step activities;

[0032] Linearizing the above observation equation, we get the Jacobian matrix H of the observation equation relative to the state vector: t :

[0033]

[0034] The EKF fusion equation of WiFi and PDR is as follows:

[0035]

[0036] Where: X t =[x t ,y t ] T , represents the state vector of the system after fusion correction, that is, the position of the underground personnel at step t; is the state vector of the system state transfer; Q is the Gaussian white noise with zero mean and covariance Q in the state transfer equation; R is the Gaussian white noise with zero mean and covariance R in the measurement equation; is the prior error covariance matrix of the t-th step; H t is the Jacobian matrix; K is the Kalman gain; P t Represents the error covariance matrix of the posterior state at step t; T is the transpose.

[0037] Preferably, the underground personnel positioning system also includes a data management subsystem connected to the WiFi communication subsystem and the fusion positioning subsystem, and the data management subsystem is used to display and store the location of the underground personnel.

[0038] Preferably, the data management subsystem can manage and identify the location of the WiFi positioning base station and can construct a real-life model of the underground scene; display the location of the underground personnel obtained by the fusion positioning subsystem on a computer / digital screen, and store the number, location and trajectory of the underground personnel in real time.

[0039] Applying the technical solution of the present invention, the effects are as follows:

[0040] 1. The underground personnel positioning method of the present invention includes three steps: installing an underground personnel positioning system and recording the MAC address of a WiFi positioning base station located at the top of the tunnel, measuring the distance data between the underground personnel and the WiFi positioning base station through a WiFi chip during the activities of the underground personnel, measuring the inertial sensor data of the underground personnel through a MEMS inertial sensor chip, and the fusion positioning subsystem real-time solving the distance data and the inertial sensor data to obtain the position of the underground personnel. Combined with the high-precision real-time distance measurement information of WiFi, the real-time collection of inertial sensor data such as acceleration, angular velocity, geomagnetism, etc. of the personnel during the underground movement by the personnel positioning identification card, and the real-time solving by the fusion positioning subsystem, continuous and high-precision positioning of the underground personnel can be achieved.

[0041] 2. The present invention preferably utilizes an extended Kalman filter algorithm to fuse distance data and inertial sensor data to correct the PDR positioning error and improve the performance of the WiFi positioning system.

[0042] 3. The present invention preferably obtains real-time WiFi ranging values ​​based on distance data, specifically including establishing a WiFi-RTT ranging model, establishing a WiFi-RTT ranging model, and performing weighted fusion of WiFi-RTT and WiFi-RSSI to obtain ranging values ​​of WiFi-RTT and RSSI. By fusion ranging of WiFi-RTT and RSSI, traditional WiFi fingerprint positioning is not required. Only the ranging information of a single WiFi positioning base station needs to be combined with pedestrian dead reckoning PDR, which effectively overcomes the drawbacks of traditional WiFi regional positioning, improves positioning accuracy, enhances positioning continuity, reduces the cost of deploying additional WiFi positioning base stations, and can achieve the transformation and upgrading of the existing mining area positioning system at a relatively low cost.

[0043] The present invention also discloses a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned underground personnel positioning method is implemented.

[0044] The present invention also discloses an electronic device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the above-mentioned underground personnel positioning method by executing the executable instructions.

[0045] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0047] Figure 1 is a schematic diagram of the structure of an underground personnel positioning system in an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of the installation structure of the WiFi positioning base station underground;

[0049] Figure 3 is a schematic diagram of obtaining the position of underground personnel in an embodiment of the present invention;

[0050] Among them, 1. WiFi communication subsystem, 2. Personnel positioning identification card, 3. Fusion positioning subsystem, 4. Data management subsystem. DETAILED DESCRIPTION

[0051] The embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0052] Example:

[0053] A method for locating underground personnel, which is implemented by using an underground personnel positioning system, see Figure 1 The underground personnel positioning system includes a WiFi communication subsystem 1, a personnel positioning identification card 2, a fusion positioning subsystem 3 and a data management subsystem 4, the details of which are as follows:

[0054] WiFi communication subsystem 1: A WiFi positioning base station with FTM (Fine Time Measurement) function is deployed in the underground space. It is not only used for personnel positioning, but also responsible for distance measurement communication with the WiFi chip in the personnel positioning identification card. It is also connected to the underground wired communication ring network to realize communication transmission between the well and the underground, and forward the personnel's positioning system to the fusion positioning subsystem 3 through the ring network. The WiFi positioning base station has the FTM function and can be installed and fixed at a certain distance in the tunnel according to its coverage range. FTM is a technology that uses WiFi signals for precise distance measurement and positioning. It is the basis for the realization of WiFi-RTT (Round-Trip Time) measurement and provides accurate time measurement capabilities.

[0055] Personnel positioning identification card 2: The personnel positioning identification card is worn by personnel entering the underground. It has a built-in MEMS-based inertial sensor chip (i.e., inertial sensor) and a WiFi chip. The personnel positioning identification card connects the personnel positioning data collected in real time by the sensor chip to the nearest WiFi positioning base station for forwarding, and then transmits it to the fusion positioning subsystem 3 by the WiFi communication subsystem 1.

[0056] Fusion positioning subsystem 3: Receives real-time data of personnel positioning collected from personnel positioning identification card 2 and transmitted from WiFi communication subsystem 1, calculates the position through the algorithm integrating WiFi and pedestrian track calculation, and stores and publishes the real-time positioning results by data management subsystem 4.

[0057] Data management subsystem 4: mainly manages and identifies the location of underground WiFi positioning base stations. Through the constructed underground real-life model, the positioning position solved by the fusion positioning subsystem 3 is displayed on the computer / digital screen through a specific expression, and personnel positioning related information such as number of people, location, trajectory, etc. is stored in real time for viewing and management.

[0058] The underground personnel positioning method using the underground personnel positioning system specifically includes the following steps:

[0059] The first step is to install the underground personnel positioning system. Deploy the WiFi positioning base station in the tunnel and some at the top of the tunnel. Obtain its vertical height z0 and record the MAC address of the WiFi positioning base station at the top of the tunnel. The schematic diagram of the installation structure of the WiFi positioning base station in the mine is as follows: Figure 2 As shown; the personnel positioning identification card 2 is worn on the underground personnel.

[0060] The second step is to measure the distance data between the underground personnel and the WiFi positioning base station through the WiFi chip during the underground personnel's activities, and to measure the inertial sensor data of the underground personnel through the MEMS inertial sensor chip;

[0061] In the third step, the fusion positioning subsystem 3 calculates the distance data and inertial sensor data obtained in the second step in real time to obtain the position of the underground personnel.

[0062] The schematic diagram of obtaining the position of the underground personnel in this embodiment is as follows Figure 3 As shown, it includes: the WiFi chip measures the distance value D obtained by measuring the WiFi-RTT RTT The distance D between the WiFi positioning base station and the personnel positioning identification card RSSI , D RTT and D RSSI Weighted fusion is performed to obtain a weighted fusion result; the inertial sensor obtains inertial sensor data including an accelerometer, a gyroscope and a magnetometer, and the PDR algorithm is used to calculate the calculated result by combining the cadence detection, step length estimation and heading estimation; the weighted fusion result and the calculated result are fused by EKF, and finally the position of the underground personnel is output.

[0063] In this embodiment, the fusion positioning subsystem 3 in the third step solves the distance data and the inertial sensor data in real time by using an extended Kalman filter algorithm to fuse the distance data and the inertial sensor data to correct the PDR positioning error, which specifically includes the following steps:

[0064] The real-time WiFi ranging value is obtained based on the distance data. The position is estimated by calculating the step length and heading output by the PDR algorithm based on the inertial sensor data. The EKF fusion is performed based on the real-time WiFi ranging value and the position estimate to obtain the position of the underground personnel.

[0065] Further preferably, the following state transition model is established when the position is estimated using the step length and heading output by the PDR algorithm:

[0066]

[0067] Where: (x t ,y t ) represents the estimated value of the position at step t; (x t-1 ,y t-1 ) represents the estimated value of the position at step t-1; l t represents the step length of the t-th step estimate; α t is the estimated heading at step t; F is the state transition matrix of the system equation, w t is the noise of system state transition.

[0068] Further preferably, obtaining the real-time WiFi ranging value according to the distance data specifically includes the following steps:

[0069] Establish the following WiFi-RTT ranging model:

[0070] D RTT =D T +D B +ο R ;

[0071] Where: D RTT is the distance value measured by WiFi-RTT, which includes the inherent error D of the WiFi chip B and random noise in the environment. R , D T It is the actual distance between the WiFi positioning base station and the personnel positioning identification card;

[0072] According to the following WiFi-RSSI ranging model (i.e. distance-signal attenuation propagation model):

[0073] RSSI = C-10mlog 10 (D RSSI )+r R ;

[0074] Where: RSSI is D RSSI The signal strength value of the WiFi received at the time, its unit is dBm; D RSSI is the distance between the WiFi positioning base station and the personnel positioning identification card; C represents the hardware constant; m represents the path loss index, which is generally 2 to 6 in indoor space; r R Indicates that RSSI measurements have zero mean and standard deviation σ RSSI Gaussian random noise;

[0075] Based on the WiFi-RTT ranging model, the distance D between the WiFi positioning base station and the personnel positioning identification card is obtained RSSI The expression is as follows:

[0076]

[0077] The standard deviation of RSSI ranging is d B Generally, it can be obtained by the following formula:

[0078] d B =ln(10)×D RSSI ×σ RSSI 10×m.

[0079] Based on d B The expression shows that: d B and D RSSIIt is linearly proportional to , and it can be seen that the error of RSSI distance measurement will increase with the increase of . It can be deduced that the ranging performance of WiFi-RTT in short distances is relatively poor, because the transmission time of short-distance signals between the WiFi base station and the personnel positioning identification card is too small, which increases the uncertainty of measurement. WiFi-RSSI is based on the signal attenuation loss model for ranging, which can effectively overcome the influence of non-line-of-sight errors. Therefore, WiFi-RTT and RSSI ranging will have complementary performance, so weighted fusion is performed.

[0080] Since the distance measurement accuracy of WiFi-RTT and RSSI fusion is generally at the meter level, and the normal walking step is usually less than 1 meter, it is not sensitive to the distance change within one step. The distance change within one step is generally calculated by accumulating the distance measurement and then taking the average value. WiFi-RTT and WiFi-RSSI are weightedly fused to obtain the distance measurement value D of WiFi-RTT and RSSI in t steps of activity. t wifi As shown below:

[0081]

[0082] Where: N is the number of WiFi ranging collections by underground personnel in t steps of activities; is the ith WiFi-RTT distance measurement value of the underground personnel in the t-step activity The weight of is the ith WiFi-RSSI ranging value of the underground personnel in the t-step activity The weight of λ represents a hyperparameter, RSSI t,i is the measured value of the RSSI of the i-th WiFi signal strength during the t-step activity of the underground personnel.

[0083] In this embodiment, further preferably, performing EKF fusion according to the real-time WiFi ranging value and the position estimation value specifically includes:

[0084] The following observation equation is established:

[0085]

[0086] Among them, D t PDR-AP is the inverse distance from the coordinates estimated by the PDR of the underground personnel in the t-step activity to the coordinates of the WiFi positioning base station AP, (x AP ,y AP ,z AP ) represents the three-dimensional coordinates of the WiFi positioning base station AP; z0 is the vertical height of the WiFi positioning base station AP when it is deployed at the top of the lane; v t is the observation noise of underground personnel during t-step activities;

[0087] Linearizing the above observation equation, we get the Jacobian matrix H of the observation equation relative to the state vector: t :

[0088]

[0089] The EKF fusion equation of WiFi and PDR is as follows:

[0090]

[0091] Where: X t =[x t ,y t ] T , represents the state vector of the system after fusion correction, that is, the position of the underground personnel at step t; is the state vector of the system state transfer; Q is the Gaussian white noise with zero mean and covariance Q in the state transfer equation; R is the Gaussian white noise with zero mean and covariance R in the measurement equation; is the prior error covariance matrix of the t-th step; H t is the Jacobian matrix; K is the Kalman gain; P t Represents the error covariance matrix of the posterior state at step t; T is the transpose.

[0092] Through the loop, until the output X t , that is, the coordinates (i.e., positions) of the underground personnel are obtained after fusion.

[0093] By applying the technical solution of this embodiment, in view of the current situation that the existing WiFi-based personnel positioning system in underground mines can only perform regional positioning, the deployed facilities can be maximized. It is only necessary to replace the original WiFi base station with a base station with FTM function, integrate the MEMS-based inertial sensor chip in the personnel positioning identification card, and perform fusion positioning based on single WiFi ranging information and pedestrian track estimation PDR. The original WiFi positioning system can be upgraded and transformed at the lowest cost, solving the problem of inability to perform continuous positioning, providing more accurate personnel positioning information and activity trajectories for mine safety management, and improving positioning reliability.

[0094] In the algorithm, in order to minimize the impact of non-line-of-sight errors on WiFi-RTT ranging, a ranging method based on WiFi-RTT and RSSI fusion is proposed to obtain more accurate ranging information. The principle is: through the complementary weighted fusion of WiFi-RTT (Round-TripTime) high-precision ranging and RSSI (Received Signal Strength Indicator) anti-interference ranging, the impact of non-line-of-sight (NLOS) measurement errors is corrected, and finally high-precision real-time WiFi ranging information is obtained. An extended Kalman filter model of single WiFi ranging information and PDR is established, that is, the extended Kalman filter algorithm (Extended Kalman Filter, EKF) is used for fusion to suppress the PDR cumulative error (that is, to correct the PDR positioning error) and improve the accuracy and stability of the fusion positioning system.

[0095] This embodiment also discloses a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned underground personnel positioning method is implemented.

[0096] This embodiment also discloses an electronic device, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the underground personnel positioning method as described above by executing the executable instructions.

[0097] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for locating underground personnel, characterized in that: The following steps are involved: The first step is to install an underground personnel positioning system, which includes a WiFi communication subsystem (1), a personnel positioning identification card (2) and a fusion positioning subsystem (3); the WiFi communication subsystem (1) includes a WiFi positioning base station deployed in the tunnel; the personnel positioning identification card (2) is set on the underground personnel, the personnel positioning identification card has a built-in MEMS inertial sensor chip and a WiFi chip, and the WiFi chip and the WiFi positioning base station perform distance measurement; the MEMS inertial sensor chip is used to collect data on the activities of underground personnel in real time; the personnel positioning identification card (2) is connected to the fusion positioning subsystem (3) through the WiFi positioning base station signal; Record the MAC address of the WiFi positioning base station located at the top of the alley; The second step is to measure the distance data between the underground personnel and the WiFi positioning base station through the WiFi chip during the underground personnel's activities, and to measure the inertial sensor data of the underground personnel through the MEMS inertial sensor chip; In the third step, the fusion positioning subsystem (3) calculates the distance data and inertial sensor data obtained in the second step in real time to obtain the position of the underground personnel.

2. The underground personnel positioning method according to claim 1, characterized in that: In the third step, the fusion positioning subsystem (3) calculates the distance data and the inertial sensor data in real time by using an extended Kalman filter algorithm to fuse the distance data and the inertial sensor data, thereby correcting the PDR positioning error.

3. The underground personnel positioning method according to claim 2, characterized in that: In the third step, the fusion positioning subsystem (3) calculates the distance data and the inertial sensor data in real time, including the following steps: obtaining a real-time WiFi distance measurement value based on the distance data; calculating the position using the step length and heading output by the PDR algorithm based on the inertial sensor data to obtain a position estimation value; and performing EKF fusion based on the real-time WiFi distance measurement value and the position estimation value to obtain the position of the underground personnel.

4. The underground personnel positioning method according to claim 3, characterized in that: The following state transition model is established when the position is calculated using the step length and heading output by the PDR algorithm: Where: (x t ,y t ) represents the estimated value of the position at step t; (x t-1 ,y t-1 ) represents the estimated value of the position at step t-1; l t represents the step length of the t-th step estimate; α t is the estimated heading at step t; F is the state transition matrix of the system equation, w t is the noise of system state transition.

5. The underground personnel positioning method according to claim 4, characterized in that: Obtaining the real-time WiFi distance measurement value based on the distance data specifically includes the following steps: Establish the following WiFi-RTT ranging model: D RTT =D T +D B +ο R ; Where: D RTT is the distance value measured by WiFi-RTT, which includes the inherent error D of the WiFi chip B and random noise in the environment. R , D T It is the actual distance between the WiFi positioning base station and the personnel positioning identification card; According to the following WiFi-RSSI ranging model: RSSI=C-10mlog 10 (D RSSI )+r R ; Where: RSSI is D RSSI The signal strength value of the WiFi received at the time, D RSSI is the distance between the WiFi positioning base station and the personnel positioning identification card; C represents the hardware constant; m represents the path loss index; r R Indicates that RSSI measurements have zero mean and standard deviation σ RSSI Gaussian random noise; Based on the WiFi-RTT ranging model, the distance D between the WiFi positioning base station and the personnel positioning identification card is obtained RSSI The expression is as follows: WiFi-RTT and WiFi-RSSI are weightedly fused to obtain the distance measurement value D of WiFi-RTT and WiFi-RSSI in t steps of activity t wifi As shown below: Where: N is the number of WiFi ranging collections by underground personnel in t steps of activities; is the ith WiFi-RTT distance measurement value of the underground personnel in the t-step activity The weight of is the ith WiFi-RSSI ranging value of the underground personnel in the t-step activity The weight of λ represents a hyperparameter, RSSI t,i is the measured value of the RSSI of the i-th WiFi signal strength during the t-step activity of the underground personnel.

6. The underground personnel positioning method according to claim 5, characterized in that: EKF fusion based on real-time WiFi ranging values ​​and position estimates specifically includes: The following observation equation is established: Among them, D t PDR-AP is the inverse distance from the coordinates estimated by the PDR of the underground personnel in the t-step activity to the coordinates of the WiFi positioning base station AP, (x AP ,y AP ,z AP ) represents the three-dimensional coordinates of the WiFi positioning base station AP; z0 is the vertical height of the WiFi positioning base station AP when it is deployed at the top of the lane; v t is the observation noise of underground personnel during t-step activities; Linearizing the above observation equation, we get the Jacobian matrix H of the observation equation relative to the state vector: t : The EKF fusion equation of WiFi and PDR is as follows: Where: X t =[x t ,y t ] T , represents the state vector of the system after fusion correction, that is, the position of the underground personnel at step t; is the state vector of the system state transfer; Q is the Gaussian white noise with zero mean and covariance Q in the state transfer equation; R is the Gaussian white noise with zero mean and covariance R in the measurement equation; is the prior error covariance matrix of the t-th step; H t is the Jacobian matrix; K is the Kalman gain; P t Represents the error covariance matrix of the posterior state at step t; T is the transpose.

7. The underground personnel positioning method according to any one of claims 1 to 6, characterized in that: The underground personnel positioning system also includes a data management subsystem (4) connected to the WiFi communication subsystem (1) and the fusion positioning subsystem (3), and the data management subsystem (4) is used to display and store the position of the underground personnel.

8. The underground personnel positioning method according to claim 7, characterized in that: The data management subsystem (4) can manage and identify the location of the WiFi positioning base station and can construct a real-life model of the underground mine; the location of the underground personnel obtained by the integrated positioning subsystem (3) is displayed on a computer / digital screen, and the number, location and trajectory of the underground personnel are stored in real time.

9. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the underground personnel positioning method described in any one of claims 1-8 is implemented.

10. An electronic device, characterized in that: include: processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the underground personnel positioning method as described in any one of claims 1-8 by executing the executable instructions.

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