An indoor personnel positioning method and device, electronic equipment and storage medium

Abstract

Embodiments of the present application disclose a positioning method and device for indoor personnel, electronic equipment and storage medium. The method comprises: determining an indoor location scene of the indoor personnel according to continuous fingerprint positioning information of the indoor personnel; detecting the motion quality of the indoor personnel according to a terminal device worn by the indoor personnel to obtain a motion quality index; calculating a fusion positioning system innovation value and an innovation covariance noise matrix according to the indoor location scene and the motion quality index; estimating the measurement noise of the fusion positioning system according to the innovation covariance noise matrix, and adjusting the process noise of the fusion positioning system according to the motion quality index; performing autonomous detection of gross errors of the indoor personnel according to the innovation value and the innovation covariance noise matrix of the fusion positioning system; and determining a target indoor positioning result of the indoor personnel according to the result of the autonomous detection of gross errors. The technical solution of the embodiments of the present application can improve the accuracy and reliability of indoor personnel positioning.

Description

Technical Field

[0001] This invention relates to the field of intelligent positioning technology, and in particular to a method, device, electronic device, and storage medium for locating people indoors. Background Technology

[0002] In recent years, the safety management of indoor workers in some high-risk industries has become closely related to the development of indoor positioning technology.

[0003] With the continuous development of positioning technology, existing technologies are generally based on the fusion of Wi-Fi (Wireless Fidelity) and Pedestrian Dead Reckoning (PDR) or Bluetooth and PDR to achieve wide-area positioning of people indoors, with positioning accuracy generally reaching 2-5 meters.

[0004] In the process of realizing this invention, the inventors discovered the following defects in the prior art: traditional indoor personnel positioning methods ignore the impact of factors such as the complexity of the indoor environment, the differences in terminal devices, and the diversity of indoor personnel movements on positioning accuracy, thus reducing the accuracy and reliability of indoor personnel positioning. Summary of the Invention

[0005] This invention provides a method, device, electronic device, and storage medium for locating people indoors, which can improve the accuracy and reliability of indoor personnel location.

[0006] In a first aspect, embodiments of the present invention provide a method for locating people indoors, comprising:

[0007] The indoor location scenario of the indoor occupants is determined based on the continuous fingerprint positioning information of the indoor occupants;

[0008] The exercise quality of the indoor personnel is detected by the terminal device worn by the indoor personnel, and exercise quality indicators are obtained.

[0009] Calculate the innovation value and innovation covariance noise matrix of the fusion positioning system based on the indoor location scene and the motion quality index;

[0010] The measurement noise of the fusion positioning system is estimated based on the new information covariance noise matrix, and the process noise of the fusion positioning system is adjusted based on the motion quality index.

[0011] Based on the information value of the fusion positioning system and the information covariance noise matrix, gross error autonomous detection is performed on the indoor personnel.

[0012] The target indoor positioning result of the personnel is determined based on the results of gross error detection.

[0013] Secondly, embodiments of the present invention also provide an indoor personnel positioning device, comprising:

[0014] The indoor location scene determination module is used to determine the indoor location scene of the indoor person based on the continuous fingerprint positioning information of the indoor person;

[0015] The exercise quality index acquisition module is used to detect the exercise quality of the indoor personnel based on the terminal device worn by the indoor personnel, and obtain the exercise quality index.

[0016] The innovation value noise matrix calculation module is used to calculate the innovation value and innovation covariance noise matrix of the fusion positioning system based on the indoor location scene and the motion quality index.

[0017] The noise estimation and adjustment module is used to estimate the measurement noise of the fusion positioning system based on the information covariance noise matrix, and to adjust the process noise of the fusion positioning system based on the motion quality index.

[0018] The personnel gross error autonomous detection module is used to perform gross error autonomous detection on the indoor personnel based on the information value of the fusion positioning system and the information covariance noise matrix.

[0019] The target indoor positioning result determination module is used to determine the target indoor positioning result of the indoor personnel based on the results of gross error autonomous detection.

[0020] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising:

[0021] At least one processor; and

[0022] A memory communicatively connected to the at least one processor; wherein,

[0023] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the indoor personnel positioning method according to any embodiment of the present invention.

[0024] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute and implement the indoor personnel positioning method described in any embodiment of the present invention.

[0025] This invention determines the indoor location of individuals based on continuous fingerprint positioning information. It then performs motion quality detection on the individuals using their worn devices to obtain motion quality indicators. Further, it calculates the innovation value and innovation covariance noise matrix of the fusion positioning system based on the indoor location scene and motion quality indicators. The measurement noise of the fusion positioning system is estimated based on the innovation value and innovation covariance noise matrix, and the process noise of the fusion positioning system is adjusted according to the motion quality indicators. Finally, it performs gross error autonomous detection on the individuals based on the innovation value and innovation covariance noise matrix, and determines the target indoor positioning result based on the gross error autonomous detection results. This solves the problem in existing indoor personnel positioning methods that ignore the impact of factors such as the complexity of the indoor environment, the differences in terminal devices, and the diversity of indoor personnel movements on positioning accuracy, thereby improving the accuracy and reliability of indoor personnel positioning.

[0026] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart of an indoor personnel positioning method provided in Embodiment 1 of the present invention;

[0029] Figure 2 This is a flowchart of an indoor personnel positioning method provided in Embodiment 2 of the present invention;

[0030] Figure 3 This is a flowchart illustrating an indoor personnel positioning method provided in Embodiment 2 of the present invention;

[0031] Figure 4 This is a schematic diagram of the structure of an indoor personnel positioning device provided in Embodiment 3 of the present invention;

[0032] Figure 5 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0033] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0035] Example 1

[0036] Figure 1 This is a flowchart of an indoor personnel positioning method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations requiring precise positioning of indoor personnel. The method can be executed by an indoor personnel positioning device, which can be implemented in software and / or hardware, and is generally integrated into an electronic device. This electronic device can be a terminal device with indoor personnel positioning functionality, or a server device, etc. The present invention does not limit the specific type of electronic device. Correspondingly, as... Figure 1 As shown, the method includes:

[0037] S110. Determine the indoor location scenario of the indoor personnel based on the continuous fingerprint positioning information of the indoor personnel.

[0038] Fingerprint positioning information can be understood as positioning information constructed based on the signal characteristics of each sampling point in an indoor scene using various basic positioning methods. Indoor location scenes can include airports, hotels, museums, convention centers, or indoor workstations, etc.

[0039] In this embodiment of the invention, a fusion positioning system can be used to accurately locate people indoors. The fusion positioning system can be a system that integrates multiple positioning signal results for accurate positioning. Specifically, the fusion positioning system can first use the continuously detected fingerprint positioning information of people indoors as input to determine whether the people are in an indoor location scenario.

[0040] S120. Based on the terminal devices worn by indoor personnel, the exercise quality of indoor personnel is detected, and exercise quality indicators are obtained.

[0041] Among them, kinematic mass can be used to describe information such as the dynamics and continuity of an object's motion.

[0042] Correspondingly, after determining the indoor location of people, the fusion positioning system can perform motion quality detection based on the information emitted by the motion sensors in the terminal devices worn by the people, thereby obtaining motion quality indicators.

[0043] Optionally, the terminal device worn by indoor personnel can be a wearable terminal such as a badge or wristband.

[0044] S130. Calculate the innovation value and innovation covariance noise matrix of the fusion positioning system based on the indoor location scene and motion quality index.

[0045] The innovation value can be used to describe the error between the actual measured value and the best predicted value based on existing information.

[0046] Accordingly, the fusion positioning system can calculate the innovation value of the fusion positioning system based on the indoor location scene and motion quality indicators, and calculate the innovation covariance noise to establish the innovation covariance noise matrix.

[0047] S140. Estimate the measurement noise of the fusion positioning system based on the new information covariance noise matrix, and adjust the process noise of the fusion positioning system according to the motion quality index.

[0048] In this embodiment, the fusion positioning system can estimate the measurement noise of the fusion positioning system based on the information covariance noise matrix, and adjust the process noise of the fusion positioning system in real time based on the motion quality index and the measurement noise of the fusion positioning system, so as to optimize the relevant parameters in the fusion positioning system and improve the positioning accuracy of the fusion positioning system.

[0049] S150. Perform gross error autonomous detection on indoor personnel based on the information value and information covariance noise matrix of the fusion positioning system.

[0050] Correspondingly, the fusion positioning system can autonomously detect gross errors in the indoor personnel's information measurements based on the relative magnitudes of the fusion positioning system's information values ​​and the information covariance noise matrix.

[0051] S160. Determine the target indoor positioning results of personnel based on the results of gross error self-detection.

[0052] Among them, the target indoor positioning result is the positioning result in the current indoor scene.

[0053] Correspondingly, the fusion positioning system can calculate and update the target indoor positioning results of personnel based on the results of the gross error detection of the new information measurement values.

[0054] This invention determines the indoor location of individuals based on continuous fingerprint positioning information. It then performs motion quality detection on the individuals using their worn devices to obtain motion quality indicators. Further, it calculates the innovation value and innovation covariance noise matrix of the fusion positioning system based on the indoor location scene and motion quality indicators. The measurement noise of the fusion positioning system is estimated based on the innovation value and innovation covariance noise matrix, and the process noise of the fusion positioning system is adjusted according to the motion quality indicators. Finally, it performs gross error autonomous detection on the individuals based on the innovation value and innovation covariance noise matrix, and determines the target indoor positioning result based on the gross error autonomous detection results. This solves the problem in existing indoor personnel positioning methods that ignore the impact of factors such as the complexity of the indoor environment, the differences in terminal devices, and the diversity of indoor personnel movements on positioning accuracy, thereby improving the accuracy and reliability of indoor personnel positioning.

[0055] Example 2

[0056] Figure 2 This is a flowchart of an indoor personnel positioning method provided in Embodiment 2 of the present invention. This embodiment further optimizes and expands upon the above embodiment, providing various specific optional implementation methods for calculating the indoor positioning results of indoor personnel. For example... Figure 2 As shown, the method may include:

[0057] S210. Obtain the location of indoor personnel in the target indoor scene based on the basic fingerprint positioning method.

[0058] The basic fingerprint positioning method can be a method of positioning based on existing fingerprint feature signals in the existing indoor scene. For example, it can be a fingerprint positioning method based on Bluetooth, WiFi, or other electromagnetic signals. The target indoor scene can be understood as the indoor location scene where indoor personnel positioning needs to be performed, such as the indoor scene of a substation.

[0059] S220. Determine whether a continuously set number of positioning positions all belong to the same positioning position. If yes, proceed to S230; otherwise, proceed to S240.

[0060] Specifically, the fusion positioning system can obtain the location of people in the target indoor scene based on the basic fingerprint positioning method, determine whether several consecutive fingerprint positioning locations belong to the same positioning location, and determine the next operation based on the judgment result.

[0061] S230. Determine the indoor location scene of the indoor personnel as the target indoor scene.

[0062] Correspondingly, if several consecutive fingerprint locations obtained by the fusion positioning system belong to the same location, then the indoor location scene where the current indoor person is located can be determined as the target indoor scene.

[0063] S240, Scenario where the indoor location of people cannot be determined.

[0064] It is understandable that if several consecutive fingerprint locations obtained by the fusion positioning system do not belong to the same location, it indicates that the person to be located has not entered the indoor scene or their movement in the indoor scene is unstable, which may be because the person to be located has not entered the working state.

[0065] S250: Calculate the dynamic index of the terminal device based on the first target sensor of the terminal device worn by the person indoors.

[0066] The first target sensor can be a sensor capable of detecting the direction of an object's motion, such as a gyroscope, a geomagnetic sensor, or an electronic compass. The dynamic index can be understood as an indicator describing the direction of an object's motion.

[0067] Correspondingly, the fusion positioning system can calculate the dynamic indicators of the terminal devices worn by people indoors, i.e., the direction of movement of people indoors, based on the first target sensor of the terminal devices worn by people indoors.

[0068] Optionally, calculating the dynamic indicators of the terminal device based on the first target sensor of the terminal device worn by indoor personnel may include:

[0069] The dynamic indicators of the terminal device are calculated based on the following formula:

[0070]

[0071] Among them, A indicator ω represents a dynamic index. e ω represents the measured angular velocity of the first target sensor in the east direction. n This represents the northward angular velocity measurement value of the first target sensor.

[0072] S260. Calculate the motion continuity index of the indoor personnel based on the second target sensor of the terminal device worn by the indoor personnel.

[0073] The second target sensor can be a sensor capable of detecting the motion characteristics of an object, such as an accelerometer or a velocity sensor. Motion continuity indicators can be understood as indicators describing the motion characteristics of an object; for example, motion continuity indicators may include, but are not limited to, velocity and acceleration.

[0074] Correspondingly, the fusion positioning system can calculate the dynamic index of the terminal device, i.e. the movement speed of the person in the room, based on the second target sensor of the terminal device worn by the person in the room.

[0075] Optionally, calculating the motion continuity index of the indoor occupants based on the second target sensor of the terminal device worn by the occupants may include:

[0076] The motion continuity index of indoor personnel is calculated based on the following formula:

[0077] B indicator =DTW(M i-1 M i )

[0078]

[0079]

[0080] Among them, B indicator The index represents the continuity of movement of people indoors; DTW represents the time warping algorithm method; M i Let M represent the acceleration sequence of the i-th step of an indoor person. i-1 Let a represent the (i-1)th step acceleration sequence of an indoor person. t acc represents the acceleration measurement amplitude at time t. x acc y and acc z These represent the triaxial measurements from the accelerometer.

[0081] S270. Calculate the motion quality index based on the dynamic index and the motion continuity index.

[0082] In this embodiment of the invention, after obtaining the dynamic indicators of the terminal device and the motion continuity indicators of the indoor personnel, the fusion positioning system can calculate motion quality indicators based on the obtained dynamic indicators and motion continuity indicators, such as the motion trajectory information and positioning information of the indoor personnel.

[0083] Optionally, the motion quality index calculated based on dynamic and motion continuity indicators may include:

[0084] The sports quality index is calculated based on the following formula:

[0085] C indicator =A indicator ·B indicator

[0086] Among them, C indicator Indicates the quality indicators of exercise.

[0087] Optionally, the fusion positioning system can achieve multi-sensor tightly coupled positioning based on the unscented Kalman filter multi-source fusion algorithm. The calculation method of the fusion algorithm state vector x involved in the algorithm can refer to the following formula:

[0088] x = [e,n,v,θ,s,b] T

[0089] Where e and n represent the east and north coordinates in the local coordinate system, respectively, v represents the pedestrian's walking speed, s represents the pedestrian step length model scale factor, and θ and b represent the deviation between the pedestrian's heading angle and the gyroscope's heading angular velocity.

[0090] Accordingly, the time update equation for the state vector x of the fusion algorithm is shown in the following formula:

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097] Where, the subscript k represents the epoch time, the superscripts - and + represent the predicted and updated state vector values, respectively, Δv represents the pedestrian velocity increment estimated by the PDR step size model, Δθ represents the heading angle increment estimated by the PDR relative heading angle, Δt represents the time interval, and w i＝1,2,…,6 This indicates process noise.

[0098] The measurement noise update equation is shown in the following formula:

[0099]

[0100] Among them, z k Let h() represent the measurement vector of the fusion positioning system, h() represent the nonlinear function between the state quantity and the measurement, and l represent the measurement noise.

[0101] S280. Calculate the innovation value and innovation covariance noise matrix of the fusion positioning system based on the indoor location scene and motion quality index.

[0102] Optionally, calculating the innovation value and innovation covariance noise matrix of the fusion positioning system based on the indoor location scene and motion quality indicators may include:

[0103] Assuming the indoor location scene is valid and the motion quality index is less than a preset threshold, the innovation value of the fused positioning system is calculated based on the following formula:

[0104]

[0105] Where, α k H represents the information value of the fused positioning system, and H represents the measurement matrix of the fused positioning system. represents the time prediction vector of the state variables of the fused positioning system, where k represents the epoch time;

[0106] The innovation covariance noise is calculated based on the following formula:

[0107]

[0108] in, denoted as the information covariance noise, and m represents the sliding window length.

[0109] S290. Estimate the measurement noise of the fusion positioning system based on the new information covariance noise matrix, and adjust the process noise of the fusion positioning system according to the motion quality index.

[0110] Optionally, estimating the measurement noise of the fusion positioning system based on the new information covariance noise matrix and adjusting the process noise of the fusion positioning system based on motion quality indicators may include:

[0111] The measurement noise of the fusion positioning system is estimated based on the following formula:

[0112]

[0113] Among them, R k The covariance noise matrix represents the measurement vector. The time-update covariance noise matrix represents the state of the fused positioning system;

[0114] The noise in the fusion positioning system is adjusted based on the following formula:

[0115] Q k =C indicator ·Q0

[0116] Among them, Q kLet Q0 represent the process noise matrix of the fusion positioning system after adaptive adjustment at time k, and let Q0 represent the initial process noise matrix of the fusion positioning system.

[0117] S2100: Perform gross error autonomous detection on indoor personnel based on the information value and information covariance noise matrix of the fusion positioning system.

[0118] S2110. Determine the target indoor positioning results of personnel based on the results of gross error self-detection.

[0119] Optionally, performing gross error autonomous detection on indoor personnel based on the innovation values ​​and innovation covariance noise matrix of the fused positioning system may include:

[0120] The following formula is used to perform gross error self-detection by indoor personnel:

[0121]

[0122] Where, α ′ k Indicates the target information measurement value. This represents the innovation covariance noise value corresponding to the target innovation measurement. The target innovation measurement is also one of the innovation measurements.

[0123] Optionally, the fusion positioning system can perform gross error detection based on the obtained innovation value and innovation covariance noise matrix, using the principle of three times the standard deviation. That is, when the innovation measurement value is greater than three times the standard deviation of the innovation noise, it indicates that a gross error has occurred. At this time, the gross error can be detected autonomously in the fusion positioning system to achieve a more robust output state vector x, thereby obtaining the positioning results e and n, which are the east and north coordinates in the local coordinate system.

[0124] Figure 3 This is a flowchart illustrating an indoor personnel positioning method provided in Embodiment 2 of the present invention. To more clearly illustrate the technical solution provided by the embodiments of the present invention, a specific example is given using a substation environment, such as... Figure 3 As shown, the method for locating indoor personnel in a substation environment can be divided into six steps: substation environment fingerprint positioning results and inertial sensor data, indoor scene identification of personnel, indoor personnel motion quality detection, adaptive estimation system measurement noise and adaptive adjustment system process noise, gross error autonomous detection, and realization of heterogeneous multi-source adaptive fusion positioning.

[0125] Step 1: Based on the substation indoor scene information, the substation environmental fingerprint positioning method is used to obtain the location of indoor personnel in the substation scene. The new information sliding window is used to record continuous fingerprint positioning results, and further obtain the data of the wearable terminal built-in inertial sensor in the substation indoor scene.

[0126] Step 2: Based on the continuous fingerprint positioning results recorded in Step 1, if several consecutive fingerprint positioning results belong to the same scene, then it is determined that the person currently indoors is in that indoor scene.

[0127] Step 3: Based on the data from the wearable terminal's built-in inertial sensor obtained in Step 1, calculate the terminal's dynamic performance index using horizontal angle changes. Combined with acceleration sensor data from personnel walking in the substation, use a dynamic time warping algorithm to determine the similarity of acceleration amplitude waveforms between adjacent steps to calculate the motion continuity index of the personnel. Further, calculate the motion quality index based on the dynamic performance index and the motion continuity index.

[0128] Step 4: When the terminal of a person inside the substation is identified as being in a certain indoor scene and the motion quality index is less than a preset threshold, the innovation value of the fusion positioning system is calculated. Further, combined with the innovation sliding window, the innovation covariance noise matrix is ​​calculated. Based on the innovation covariance noise matrix, the measurement covariance noise matrix is ​​calculated to achieve adaptive estimation of system measurement noise. Furthermore, based on the motion quality index obtained in Step 3, the process noise matrix of the fusion positioning system is calculated to achieve adaptive adjustment of system process noise.

[0129] Step 5: Gross error autonomous detection. Combining the innovation value obtained in Step 4 with the innovation covariance noise matrix, gross error autonomous detection is performed based on the principle of three times the standard deviation of innovation noise. Specifically, when the innovation measurement value is greater than three times the standard deviation of innovation noise, it indicates that a gross error has occurred in the measurement value. At this time, neither the measurement update of the fusion positioning system nor the innovation sliding window will occur.

[0130] Step 6: The multi-source adaptive fusion localization system outputs a stable state vector, and finally obtains the fusion localization result, realizing heterogeneous multi-source adaptive fusion localization.

[0131] It should be noted that any arrangement or combination of the technical features in the above embodiments also falls within the protection scope of this invention.

[0132] The technical solution of this invention uses a terminal device worn by indoor personnel to perform real-time indoor scene positioning, thereby identifying the current indoor scene based on continuous fingerprint positioning information. Based on the inertial sensor built into the terminal device worn by the indoor personnel, the dynamic index of the terminal device and the motion continuity index of the indoor personnel are calculated to obtain the detection basis of motion quality index. Then, based on the motion quality index, the measurement noise and process noise of the fusion positioning system are adaptively estimated and adjusted, and combined with gross error autonomous detection, heterogeneous multi-source adaptive fusion positioning of indoor personnel is realized, which improves the accuracy and precision of indoor personnel positioning.

[0133] Example 3

[0134] Figure 4This is a schematic diagram of an indoor personnel positioning device provided in Embodiment 3 of the present invention. This embodiment is applicable to scenarios requiring precise positioning of indoor personnel, and is not specifically limited thereto. Figure 4 As shown, the indoor personnel positioning device includes: an indoor location scene determination module 310, a motion quality index acquisition module 320, a novelty noise matrix calculation module 330, a noise estimation and adjustment module 340, a personnel gross error autonomous detection module 350, and a target indoor positioning result determination module 360.

[0135] The system includes: an indoor location scene determination module 310, used to determine the indoor location scene of personnel based on continuous fingerprint positioning information; a motion quality index acquisition module 320, used to perform motion quality detection on personnel based on the terminal devices worn by them, and obtain motion quality indices; a novelty value noise matrix calculation module 330, used to calculate the novelty value and novelty covariance noise matrix of the fusion positioning system based on the indoor location scene and motion quality indices; a noise estimation and adjustment module 340, used to estimate the measurement noise of the fusion positioning system based on the novelty covariance noise matrix, and adjust the process noise of the fusion positioning system based on the motion quality indices; a personnel gross error autonomous detection module 350, used to perform gross error autonomous detection on personnel based on the novelty value and novelty covariance noise matrix of the fusion positioning system; and a target indoor positioning result determination module 360, used to determine the target indoor positioning result of personnel based on the results of the gross error autonomous detection.

[0136] This invention determines the indoor location of individuals based on continuous fingerprint positioning information. It then performs motion quality detection on the individuals using their worn devices to obtain motion quality indicators. Further, it calculates the innovation value and innovation covariance noise matrix of the fusion positioning system based on the indoor location scene and motion quality indicators. The measurement noise of the fusion positioning system is estimated based on the innovation value and innovation covariance noise matrix, and the process noise of the fusion positioning system is adjusted according to the motion quality indicators. Finally, it performs gross error autonomous detection on the individuals based on the innovation value and innovation covariance noise matrix, and determines the target indoor positioning result based on the gross error autonomous detection results. This solves the problem in existing indoor personnel positioning methods that ignore the impact of factors such as the complexity of the indoor environment, the differences in terminal devices, and the diversity of indoor personnel movements on positioning accuracy, thereby improving the accuracy and reliability of indoor personnel positioning.

[0137] Optionally, the indoor location scene determination module 310 is specifically used to: obtain the location of an indoor person in the target indoor scene based on the basic fingerprint positioning method; and determine the indoor location scene of the indoor person as the target indoor scene when it is determined that a set number of consecutive positioning locations belong to the same positioning location.

[0138] Optionally, the motion quality index acquisition module 320 is specifically used for: calculating the dynamic index of the terminal device based on the first target sensor of the terminal device worn by the person indoors; calculating the motion continuity index of the person indoors based on the second target sensor of the terminal device worn by the person indoors; and calculating the motion quality index based on the dynamic index and the motion continuity index.

[0139] Optionally, the motion quality index acquisition module 320 is specifically used to calculate the dynamic index of the terminal device based on the following formula:

[0140]

[0141] Among them, A indicator ω represents a dynamic index. e ω represents the measured angular velocity of the first target sensor in the east direction. n This represents the northward angular velocity measurement value of the first target sensor;

[0142] The motion continuity index of indoor personnel is calculated based on the following formula:

[0143] B indicator =DTW(M i-1 M i )

[0144]

[0145]

[0146] Among them, B indicator The index represents the continuity of movement of people indoors; DTW represents the time warping algorithm method; M i Let M represent the acceleration sequence of the i-th step of an indoor person. i-1 Let a represent the (i-1)th step acceleration sequence of an indoor person. t acc represents the acceleration measurement amplitude at time t. x acc y and acc z These represent the triaxial measurements of the accelerometer;

[0147] The sports quality index is calculated based on the following formula:

[0148] C indicator =A indicator ·B indicator

[0149] Among them, C indicator Indicates the quality indicators of exercise.

[0150] Optionally, the innovation value noise matrix calculation module 330 is specifically used to: calculate the innovation value of the fused positioning system based on the following formula, provided that the indoor location scene is determined to be valid and the motion quality index is less than a preset threshold:

[0151]

[0152] Where, α k z represents the information value of the fused positioning system. k H represents the measurement vector of the fused positioning system, and H represents the measurement matrix of the fused positioning system. represents the time prediction vector of the state variables of the fused positioning system, where k represents the epoch time;

[0153] The innovation covariance noise is calculated based on the following formula:

[0154]

[0155] in, denoted as the information covariance noise, and m represents the sliding window length.

[0156] Optionally, the noise estimation and adjustment module 340 is specifically used to estimate the measurement noise of the fusion positioning system based on the following formula:

[0157]

[0158] Among them, R k This represents the measurement noise of the fused positioning system, specifically the covariance noise matrix of the measurement vectors. The time-update covariance noise matrix represents the state of the fused positioning system;

[0159] The noise in the fusion positioning system is adjusted based on the following formula:

[0160] Q k =C indicator ·Q0

[0161] Among them, Q k Let Q0 represent the process noise matrix of the fusion positioning system after adaptive adjustment at time k, and let Q0 represent the initial process noise matrix of the fusion positioning system.

[0162] Optional, the personnel gross error autonomous detection module 350 is specifically used for: autonomously detecting gross errors of indoor personnel based on the following formula:

[0163]

[0164] Where, α ′ k Indicates the target information measurement value. This represents the innovation covariance noise value corresponding to the target innovation measurement.

[0165] The indoor personnel positioning device provided in this embodiment of the invention can execute the indoor personnel positioning method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0166] Example 4

[0167] Figure 5 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0168] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0169] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0170] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the indoor personnel positioning method described in various embodiments of the present invention.

[0171] That is, the indoor location scene of the person is determined based on the continuous fingerprint positioning information of the person; motion quality detection is performed on the person based on the terminal device worn by the person to obtain motion quality index; the innovation value and innovation covariance noise matrix of the fusion positioning system are calculated based on the indoor location scene and the motion quality index; the measurement noise of the fusion positioning system is estimated based on the innovation covariance noise matrix, and the process noise of the fusion positioning system is adjusted based on the motion quality index; gross error autonomous detection is performed on the person based on the innovation value and innovation covariance noise matrix of the fusion positioning system; and the target indoor positioning result of the person is determined based on the result of the gross error autonomous detection.

[0172] In some embodiments, the indoor occupant location method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded into and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the indoor occupant location method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured by any other suitable means (e.g., by means of firmware) to perform the indoor occupant location method as described in the embodiments of the present invention.

[0173] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0174] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0175] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0176] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0177] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0178] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0179] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0180] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for locating people indoors, characterized in that, include: The indoor location scenario of the indoor occupants is determined based on the continuous fingerprint positioning information of the indoor occupants; The dynamic index of the terminal device is calculated based on the first target sensor of the terminal device worn by the person indoors; the dynamic index of the terminal device is calculated based on the following formula: ； in, This indicates the dynamic index, This represents the measured angular velocity value in the east direction from the first target sensor. This represents the northward angular velocity measurement value of the first target sensor; The motion continuity index of the indoor personnel is calculated based on the second target sensor of the terminal device worn by the indoor personnel; the motion continuity index of the indoor personnel is calculated based on the following formula: ； ； ； in, This indicates the continuity of the actions of the people inside the room. This represents the time warping algorithm method. The first person in the room A sequence of step accelerations The first person in the room A sequence of step accelerations express The amplitude of acceleration measurement at any time , and These represent the triaxial measurements of the accelerometer; The motion quality index is calculated based on the dynamic index and the motion continuity index; the motion quality index is calculated based on the following formula: ； in, This refers to the aforementioned motion quality index; Calculate the innovation value and innovation covariance noise matrix of the fusion positioning system based on the indoor location scene and the motion quality index; The measurement noise of the fusion positioning system is estimated based on the new information covariance noise matrix, and the process noise of the fusion positioning system is adjusted based on the motion quality index. Based on the information value of the fusion positioning system and the information covariance noise matrix, gross error autonomous detection is performed on the indoor personnel. The target indoor positioning result of the personnel is determined based on the results of gross error detection.

2. The method according to claim 1, characterized in that, The scenario of determining the indoor location of an indoor person based on continuous fingerprint positioning information includes: The location of the indoor personnel in the target indoor scene is obtained based on the basic fingerprint positioning method; If it is determined that a continuous set number of positioning locations all belong to the same positioning location, the indoor location scene of the indoor personnel is determined as the target indoor scene.

3. The method according to claim 1, characterized in that, The step of calculating the innovation value and innovation covariance noise matrix of the fusion positioning system based on the indoor location scene and the motion quality index includes: If the indoor location scene is determined to be valid and the motion quality index is less than a preset threshold, the innovation value of the fusion positioning system is calculated based on the following formula: ； in, This represents the information value of the fused positioning system. This represents the measurement vector of the fused positioning system. This represents the measurement matrix of the fused positioning system. This represents the time prediction vector of the state variables in the fused positioning system. Indicates an epochal time; The innovation covariance noise is calculated based on the following formula: ； in, This represents the new information covariance noise. Indicates the length of the sliding window.

4. The method according to claim 1, characterized in that, The step of estimating the measurement noise of the fusion positioning system based on the new information covariance noise matrix and adjusting the process noise of the fusion positioning system based on the motion quality index includes: The measurement noise of the fusion positioning system is estimated based on the following formula: ； in, This indicates the measurement noise of the fusion positioning system. The time-update covariance noise matrix represents the state of the fused positioning system; The noise in the fusion positioning system is adjusted based on the following formula: ； in, Represents the epoch The process noise matrix of the fusion positioning system after time-adaptive adjustment. This represents the initial process noise matrix of the fusion positioning system.

5. The method according to claim 1, characterized in that, The step of performing gross error autonomous detection on indoor personnel based on the innovation value of the fused positioning system and the innovation covariance noise matrix includes: Gross error detection for the indoor personnel is performed based on the following formula: in, Indicates the target information measurement value. This represents the innovation covariance noise value corresponding to the target innovation measurement.

6. A positioning device for indoor personnel, characterized in that, include: The indoor location scene determination module is used to determine the indoor location scene of the indoor person based on the continuous fingerprint positioning information of the indoor person; The motion quality index acquisition module includes a dynamic index acquisition unit, a motion continuity index acquisition unit, and a motion quality index acquisition unit. The dynamic index acquisition unit is used to calculate the dynamic index of the terminal device based on the first target sensor of the terminal device worn by indoor personnel; the dynamic index of the terminal device is calculated based on the following formula: ;in, This indicates the dynamic index, This represents the measured angular velocity value in the east direction from the first target sensor. This represents the northward angular velocity measurement value of the first target sensor; The motion continuity index acquisition unit is used to calculate the motion continuity index of the indoor person based on the second target sensor of the terminal device worn by the indoor person; the motion continuity index of the indoor person is calculated based on the following formula: ; ; ;in, This indicates the continuity of the actions of the people inside the room. This represents the time warping algorithm method. The first person in the room A sequence of step accelerations The first person in the room A sequence of step accelerations express The amplitude of acceleration measurement at any time , and These represent the triaxial measurements of the accelerometer; The motion quality index acquisition unit is used to calculate the motion quality index based on the dynamic index and the motion continuity index; the motion quality index is calculated based on the following formula: ;in, This refers to the aforementioned motion quality index; The innovation value noise matrix calculation module is used to calculate the innovation value and innovation covariance noise matrix of the fusion positioning system based on the indoor location scene and the motion quality index. The noise estimation and adjustment module is used to estimate the measurement noise of the fusion positioning system based on the information covariance noise matrix, and to adjust the process noise of the fusion positioning system based on the motion quality index. The personnel gross error autonomous detection module is used to perform gross error autonomous detection on the indoor personnel based on the information value of the fusion positioning system and the information covariance noise matrix. The target indoor positioning result determination module is used to determine the target indoor positioning result of the indoor personnel based on the results of gross error autonomous detection.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the indoor personnel positioning method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the indoor personnel positioning method according to any one of claims 1-5.

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