Method, device and equipment for positioning personnel in tunnel and storage medium

Abstract

The invention relates to the technical field of positioning, and discloses a method, a device and equipment for positioning personnel in a tunnel, and a storage medium. The method comprises the following steps: acquiring first reference data, second reference data and third reference data; inputting the first reference data, the second reference data and the third reference data into a distance prediction model to predict the distance between the to-be-positioned terminal and the base station to obtain a distance prediction value; and determining the terminal position of the to-be-positioned terminal according to the distance prediction value. According to the embodiment of the invention, multiple radio signals are combined for positioning the personnel in the tunnel, so that the positioning precision of the personnel in the tunnel can be improved.

Description

Technical Field

[0001] The present application relates to the field of positioning technology, and in particular to a method, device, equipment and storage medium for positioning personnel in a tunnel. Background Art

[0002] At present, the methods used to locate people in tunnels are Ultra Wide Band (UWB), Radio Frequency Identification (RFID) or Wi-Fi positioning methods, that is, the terminal to be located and the base station send and receive indoor radio signals to each other, calculate the propagation time, signal strength and multipath fading of the indoor radio signals and other parameters to determine the distance or position between the terminal to be located and the base station, and locate the person carrying the terminal to be located. However, due to the serious space obstruction and frequent signal interference in the tunnel, the above positioning methods are difficult to maintain the continuity and visibility of the positioning signal, and thus cannot normally locate the people in the tunnel. Summary of the invention

[0003] The purpose of this application is to provide a method, device, equipment and storage medium for locating people in a tunnel, which can improve the accuracy of locating people in a tunnel by combining multiple radio signals.

[0004] The present application provides a method for locating a person in a tunnel, including: Acquire first reference data, second reference data, and third reference data; the first reference data is communication quality data when the terminal to be located and the base station perform long-distance wireless communication, the second reference data is distance measurement data obtained by frequency hopping ranging between the terminal to be located and the base station, and the third reference data is communication quality data when the terminal to be located and the base station perform short-distance wireless communication; Inputting the first reference data, the second reference data and the third reference data into a distance prediction model to predict the distance between the terminal to be located and the base station, and obtaining a distance prediction value; The terminal position of the terminal to be located is determined according to the distance prediction value.

[0005] In some embodiments, before acquiring the first reference data, the second reference data and the third reference data, the method further includes: Acquire a reference data packet forwarded by the base station from the terminal to be located; Parse the reference data packet to obtain the first reference data, the second reference data and the third reference data; the reference data packet is obtained by encapsulating the first reference data, the second reference data and the third reference data when the terminal to be located performs long-distance wireless communication, frequency hopping ranging and short-distance wireless communication with the base station in sequence.

[0006] In some embodiments, the second reference data is generated by: Determine reference distance data based on a distance measurement data set obtained by frequency hopping ranging; Performing polynomial fitting on the reference distance data to obtain distance measurement fitting data; Determine whether the distance measurement fitting data meets a preset distance measurement data condition; the preset distance measurement data condition is that the distance measurement fitting data is within a preset distance measurement data interval and the frequency error between the terminal to be located and the base station is within a preset frequency error interval; If it meets the requirements, the distance measurement fitting data is output as the second reference data.

[0007] In some embodiments, inputting the first reference data, the second reference data, and the third reference data into a distance prediction model to predict the distance between the terminal to be located and the base station to obtain a distance prediction value includes: Performing local feature extraction on the first reference data, the second reference data, and the third reference data to obtain a local data vector sequence including a plurality of local data vectors; Performing position encoding on the local data vector sequence to obtain a position embedding vector sequence; Performing linear transformation processing on the position embedding vector sequence, and performing attention weight operation on the local data vector after the linear transformation processing to obtain a global distance feature; The distance between the terminal to be located and the base station is predicted according to the global distance feature to obtain the distance prediction value.

[0008] In some embodiments, determining the terminal position of the terminal to be located according to the distance prediction value includes: Within a preset waiting time, determining whether the distance prediction model outputs two distance prediction values ​​obtained by predicting the distance between the same terminal to be located and the two base stations; If not, determining the relative direction between the terminal to be located and the base station according to the historical positioning data, and determining the terminal position of the terminal to be located in combination with the distance prediction value; If so, the terminal position of the terminal to be located is calculated according to the two distance prediction values ​​and the distance value between the two base stations.

[0009] In some embodiments, the distance prediction model training method includes: Obtaining a current real distance value between the terminal to be located and the base station; Inputting the first reference data, the second reference data and the third reference data into a deep neural network model to be trained to obtain a training distance prediction value; Determining model loss information according to the training distance prediction value and the true distance value; Determining whether the model loss information is within a loss threshold range; If not, adjust the weight parameters of the neural network model to be trained; return to the step of inputting the first reference data, the second reference data and the third reference data into the deep neural network model to be trained to obtain a training distance prediction value; If so, determine whether the number of training resets reaches the reset threshold; If not reached, changing the position of the terminal to be located; returning to the step of obtaining the current real distance value between the terminal to be located and the base station; If it is reached, the training is terminated and the distance prediction model is obtained.

[0010] In some embodiments, the first reference data includes the long-distance wireless communication frequency error between the terminal to be located and the base station, the long-distance wireless communication signal strength value recorded by the base station, the long-distance wireless communication signal strength value recorded by the terminal to be located, and the long-distance wireless communication channel signal-to-noise ratio; the second reference data is the distance measurement data obtained by frequency hopping ranging when the terminal to be located and the base station perform long-distance wireless communication; the third reference data includes the short-distance wireless communication signal strength value recorded by the base station and the short-distance wireless communication signal strength value recorded by the terminal to be located.

[0011] The present application provides a device for locating people in a tunnel, comprising: The first module is used to obtain first reference data, second reference data and third reference data; the first reference data is the communication quality data when the terminal to be located and the base station perform long-distance wireless communication, the second reference data is the distance measurement data obtained by the terminal to be located and the base station performing frequency hopping ranging, and the third reference data is the communication quality data when the terminal to be located and the base station perform short-distance wireless communication; A second module is used to input the first reference data, the second reference data and the third reference data into a distance prediction model to predict the distance between the terminal to be located and the base station, and obtain a distance prediction value; The third module is used to determine the terminal position of the terminal to be located according to the distance prediction value.

[0012] An embodiment of the present application provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned method for locating people in a tunnel when executing the computer program.

[0013] An embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for locating personnel in a tunnel is implemented.

[0014] The beneficial effects of the present application are as follows: using a pre-trained distance prediction model, based on the communication quality data when the terminal to be located and the base station perform long-distance wireless communication, the distance measurement data obtained by frequency hopping ranging between the terminal to be located and the base station, and the communication quality data when the terminal to be located and the base station perform short-distance wireless communication, the distance between the terminal to be located and the base station is predicted, thereby realizing the positioning of personnel in the tunnel. Since the communication quality data obtained by combining the two communication modes of long-distance wireless communication and short-distance wireless communication are mutually verified, and multiple distance measurement data obtained by using different channels are obtained by frequency modulation ranging, the pre-trained distance prediction model can predict the distance between the terminal to be located and the base station based on reference data of multiple dimensions, thereby enhancing the accuracy and reliability of the distance prediction value and improving the accuracy of positioning personnel in the tunnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a diagram of the implementation environment of the method for locating personnel in a tunnel provided in an embodiment of the present application.

[0016] Figure 2 It is a flow chart of a method for locating personnel in a tunnel provided in an embodiment of the present application.

[0017] Figure 3 It is a flowchart of a method for generating second reference data provided in an embodiment of the present application.

[0018] Figure 4 It is a flowchart of the specific method of step S202 provided in an embodiment of the present application.

[0019] Figure 5 It is a flowchart of the specific method of step S203 provided in an embodiment of the present application.

[0020] Figure 6 It is a flowchart of the training method of the distance prediction model provided in the embodiment of the present application.

[0021] Figure 7 It is a schematic diagram of the structure of a device for locating personnel in a tunnel provided in an embodiment of the present application.

[0022] Figure 8 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application.

[0023] Fig. 9 It is a schematic diagram of the model structure of the distance prediction model provided in the embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0025] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown can be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second" and the like in the specification, claims and drawings are used to distinguish similar objects, and are not used to describe a specific order or sequence.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0027] The execution body of the method for locating personnel in a tunnel provided in the embodiment of the present application is applied to the terminal side device and can also be applied to the cloud side device, which is not limited in this embodiment.

[0028] The method provided in the embodiment of the present application can be applied to Figure 1 The implementation environment shown includes a terminal 110 to be located, multiple base stations 120 and an execution subject 130. The base stations 120 are arranged in the tunnel and spaced along the length of the tunnel. When the construction personnel carry the terminal 110 to be located into the tunnel, the terminal 110 to be located and the base station 120 can perform long-distance wireless communication or short-distance wireless communication, and the execution subject 130 can communicate with the base station 120 through a communication network. Among them, the communication network uses standard communication technology and / or protocols, usually the Internet, but it can also be any network, including but not limited to any combination of Bluetooth, local area network, metropolitan area network, wide area network, mobile, private network or virtual private network. In some embodiments, customized or dedicated data communication technology can be used to replace or supplement the above data communication technology.

[0029] See also Figure 2 , Figure 2 is a flow chart of a method for locating personnel in a tunnel provided by an embodiment of the present application. In some embodiments, Figure 2 The method includes but is not limited to steps S201 to S203.

[0030] Step S201, obtaining first reference data, second reference data and third reference data.

[0031] Among them, the first reference data is the communication quality data when the terminal to be located and the base station perform long-distance wireless communication, the second reference data is the distance measurement data obtained by the terminal to be located and the base station performing frequency hopping ranging, and the third reference data is the communication quality data when the terminal to be located and the base station perform short-distance wireless communication.

[0032] During the specific implementation, construction workers carry the terminal to be located into the tunnel, and the terminal to be located interacts with the two nearest base stations through wireless communication and obtains the first reference data, the second reference data and the third reference data. After the interaction between the terminal to be located and the base station is completed, the execution entity obtains the first reference data, the second reference data and the third reference data from the terminal to be located or the base station.

[0033] Step S202: input the first reference data, the second reference data and the third reference data into a distance prediction model to predict the distance between the terminal to be located and the base station, and obtain a distance prediction value.

[0034] Among them, the distance prediction model is obtained by training the deep neural network model based on the first reference data, the second reference data, the third reference data and the actual distance value between the terminal to be located and the base station.

[0035] In the specific implementation, the first reference data, the second reference data and the third reference data are input into the distance prediction model, the distance prediction model extracts local features from the first reference data, the second reference data and the third reference data, and understands the long-distance dependency relationship between the extracted local features based on the multi-head attention mechanism and the feedforward neural network. The distance between the terminal to be located and the base station is predicted in combination with the local features and the corresponding long-distance dependency relationship to obtain a distance prediction value.

[0036] Step S203: determining the terminal position of the terminal to be located according to the distance prediction value.

[0037] In a specific implementation, in a one-dimensional environment of a tunnel, a terminal to be located communicates with at least two base stations. After the distance prediction model predicts and outputs two distance prediction values, the terminal position of the terminal to be located can be determined. Alternatively, after the distance prediction model predicts and outputs a distance prediction value, the terminal position of the terminal to be located can be determined based on historical positioning records.

[0038] In a specific embodiment, before step S201, the method further includes: obtaining a reference data packet forwarded by the base station from the terminal to be located; parsing the reference data packet to obtain first reference data, second reference data, and third reference data. The reference data packet is obtained by encapsulating the first reference data, the second reference data, and the third reference data when the terminal to be located performs long-distance wireless communication, frequency hopping ranging, and short-distance wireless communication with the base station in sequence.

[0039] In a specific implementation, after the interaction between the terminal to be located and the base station is completed, the terminal to be located encapsulates the first reference data, the second reference data, and the third reference data obtained during the interaction in a reference data packet and sends it to the base station. The execution subject obtains the reference data packet forwarded by the base station from the terminal to be located, and obtains the first reference data, the second reference data, and the third reference data by parsing the reference data packet.

[0040] The specific process of the interaction between the terminal to be located and the base station to generate the first reference data, the second reference data and the third reference data includes the first interaction stage, the second interaction stage and the third interaction stage. In the first interaction stage, the terminal to be located and the base station perform long-distance wireless communication, the terminal to be located sends a long-distance communication request message to the base station, the base station receives the long-distance communication request message and verifies the identity information of the terminal to be located in the message, if the verification result is that the terminal to be located is a legal terminal, the base station records the communication quality data when receiving the long-distance communication request message and writes it into the long-distance communication feedback message, sends the long-distance communication feedback message to the terminal to be located, the terminal to be located records the communication quality data when receiving the long-distance communication consent message and the base station records the communication quality data when receiving the long-distance communication request message, and obtains the first reference data. In the second interactive stage, the terminal to be located and the base station conduct long-distance wireless communication and switch to the frequency hopping ranging mode, divide the entire communication bandwidth into multiple channels according to the frequency value, and the terminal to be located and the base station complete message sending and message receiving through each channel in turn, determine the distance measurement data obtained by using the channel for ranging according to the message sending and receiving time, and after traversing all channels, select the distance measurement data that can represent the distance between the terminal to be located and the base station to obtain the second reference data. In the third interactive stage, the terminal to be located and the base station conduct short-distance wireless communication, the terminal to be located sends a short-distance communication request message to the base station, the base station receives the short-distance communication request message and verifies the identity information of the terminal to be located in the message, if the verification result is that the terminal to be located is a legal terminal, the base station records the communication quality data when receiving the short-distance communication request message and writes it into the short-distance communication feedback message, sends the short-distance communication feedback message to the terminal to be located, the terminal to be located records the communication quality data when receiving the short-distance communication consent message and the base station records the communication quality data when receiving the short-distance communication request message, and obtains the third reference data.

[0041] Figure 3 is a flow chart of a method for generating second reference data provided by an embodiment of the present application. Figure 3 In some embodiments, the method includes but is not limited to steps S301 to S304.

[0042] Step S301, determining reference distance data according to a distance measurement data set obtained by frequency hopping ranging.

[0043] Step S302: Perform polynomial fitting on the reference distance data to obtain distance measurement fitting data.

[0044] Step S303, determining whether the distance measurement fitting data meets the preset distance measurement data condition.

[0045] The preset ranging data condition is that the distance measurement fitting data is within a preset ranging data interval and the frequency error between the terminal to be located and the base station is within a preset frequency error interval.

[0046] If yes, execute step S304; if no, discard the distance measurement fitting data.

[0047] Step S304: output the distance measurement fitting data as the second reference data.

[0048] In a specific implementation, when the second interaction phase is completed, multiple distance measurement data based on multiple channels for message reception and transmission are calculated to obtain a distance measurement data set, and distance measurement data as a median is selected from the distance measurement data set, or the average value of each distance measurement data in the distance measurement data set is calculated to obtain reference distance data, and then a polynomial fitting is performed on the reference distance data using an expert experience library to obtain distance measurement fitting data, and it is determined whether the distance measurement fitting data meets the preset distance measurement data conditions. When the preset distance measurement data conditions are met, the distance measurement fitting data is used as the second reference data.

[0049] In a specific embodiment, the first reference data includes the long-distance wireless communication frequency error between the terminal to be located and the base station, the long-distance wireless communication signal strength value recorded by the base station, the long-distance wireless communication signal strength value recorded by the terminal to be located, and the long-distance wireless communication channel signal-to-noise ratio; the second reference data is the distance measurement data obtained by frequency hopping ranging when the terminal to be located and the base station perform long-distance wireless communication; the third reference data includes the short-distance wireless communication signal strength value recorded by the base station and the short-distance wireless communication signal strength value recorded by the terminal to be located.

[0050] Preferably, LoRa communication is used for long-distance wireless communication between the terminal to be located and the base station, which has good signal penetration and can better maintain the continuity and visibility of the positioning signal. BLE communication is used for short-distance wireless communication between the terminal to be located and the base station.

[0051] Figure 4 is a flowchart of a specific method of step S202 provided in an embodiment of the present application. Figure 4 In some embodiments, the method includes but is not limited to steps S401 to S404.

[0052] Step S401 , extracting local features from the first reference data, the second reference data and the third reference data to obtain a local data vector sequence including a plurality of local data vectors.

[0053] Step S402, position encoding is performed on the local data vector sequence to obtain a position embedding vector sequence.

[0054] Step S403, linear transformation is performed on the position embedding vector sequence, and attention weight operation is performed on the local data vector after the linear transformation to obtain a global distance feature.

[0055] Step S404: predicting the distance between the terminal to be located and the base station according to the global distance feature to obtain a distance prediction value.

[0056] In some embodiments, the first reference data, the second reference data, and the third reference data are preprocessed, and then local features are extracted from the preprocessed first reference data, the second reference data, and the third reference data. For example, the number of successful frequency modulation ranging is normalized, the timestamp is formatted, and other data is standardized.

[0057] See also Fig. 9 In an embodiment of the present application, the distance prediction model includes a convolutional network layer, a position encoding network layer, an encoder network layer and a decoder network layer.

[0058] The local feature extraction of the first reference data, the second reference data and the third reference data is performed by calling the convolutional network layer of the distance prediction model. In the specific implementation, the first reference data, the second reference data and the third reference data are input into the convolutional network layer, and a layer-by-layer convolution operation is performed in the convolutional network layer to extract the local features of the first reference data, the second reference data and the third reference data. The last hidden layer of the convolutional network layer is connected to an activation module, and the activation module uses an activation function to activate the result of the last convolution operation of the convolutional network layer, thereby obtaining a local data vector. After summarizing the local data vectors, a local data vector sequence is obtained.

[0059] The position encoding of the local data vector sequence is performed by calling the position encoding network layer of the distance prediction model. In the specific implementation, the local data vector sequence is input into the position encoding network layer, and the corresponding position information is embedded in each local data vector in the local data vector sequence to represent the time sequence relationship of the corresponding local features. After the position encoding network layer performs position encoding on the local data vector sequence, the local data vector sequence is mapped into a vector sequence composed of multiple local data vectors with embedded position information, that is, a position embedded vector sequence.

[0060] The encoder network layer of the distance prediction model is called to perform linear transformation processing on the position embedding vector sequence and to perform attention weight calculation on the local data vector after the linear transformation. In the specific implementation, after obtaining the position embedding vector sequence, the global distance feature is obtained by performing linear change processing on the local data vector after each embedded position information in the position embedding vector sequence, and performing attention weight calculation on each local data vector after the linear transformation. More specifically, the position embedding vector sequence is input into the encoder network layer, and the encoder network layer connects the multi-head mechanism and the feedforward layer through the residual network result. The multi-head mechanism performs multiple linear transformations on the input vector to obtain different linear values, and then calculates the attention weight. The attention weight calculation formula is as follows: , , Among them, Q, K and V are the input local data vector matrices, , and is a trainable weight matrix, Attention is the attention weight operation, Concat is the concatenation process, is the i-th hyperparameter head, i is an integer greater than or equal to 1 and less than or equal to h. Through the above formula, each local data vector after embedding position information in the position embedding vector sequence is linearly transformed, and the principle of attention weight operation is to perform Attention operation after mapping Q, K and V through the corresponding weight matrix, and concatenate the calculation results after repeating h times to obtain the global distance feature reflecting the long-distance dependency relationship between the local features.

[0061] According to the global distance feature, the distance between the terminal to be located and the base station is predicted by calling the decoder network layer of the distance prediction model. After obtaining the global distance feature, the global distance information of the first reference data, the second reference data and the third reference data is understood layer by layer through the global distance feature to predict the distance between the terminal to be located and the base station. In the specific implementation, the global distance feature is input into the decoder network layer, and the decoder network layer connects the multi-head mechanism and the feedforward layer through the residual network result. The multi-head mechanism performs multiple linear transformations on the input vector to obtain different linear values, and then operates on the attention weights to capture the representation of different features obtained by different linear transformations, thereby capturing the global information in the first reference data, the second reference data and the third reference data. Combined with the global information in the first reference data, the second reference data and the third reference data, the distance between the terminal to be located and the base station is predicted to obtain a distance prediction value.

[0062] In some embodiments, the terminal to be located may also collect geomagnetic data. The execution subject obtains the geomagnetic data collected by the terminal to be located and inputs it into the distance prediction model together with the first reference data, the second reference data and the third reference data. When predicting the distance between the terminal to be located and the base station, the distance prediction model determines the moving direction of the terminal to be located through the geomagnetic data to obtain a more accurate distance prediction value.

[0063] In some embodiments, the base stations can also communicate with each other to detect the distance and obtain the base station interval data. The execution subject obtains the base station interval data, can determine the location information and location change information of the base station, inputs the base station interval data together with the first reference data, the second reference data and the third reference data into the distance prediction model, and provides more reference information when predicting the distance between the terminal to be located and the base station, so as to obtain a more accurate distance prediction value.

[0064] Figure 5 is a flowchart of a specific method of step S203 provided in an embodiment of the present application. Figure 5 In some embodiments, the method includes but is not limited to steps S501 to S503.

[0065] Step S501: within a preset waiting time, determine whether the distance prediction model outputs two distance prediction values ​​obtained by predicting the distance between the same terminal to be located and two base stations.

[0066] If not, execute step S502; if so, execute step S503.

[0067] Step S502: determine the relative direction between the terminal to be located and the base station according to the historical positioning data, and determine the terminal position of the terminal to be located in combination with the distance prediction value.

[0068] Step S503: Calculate the terminal position of the terminal to be located according to the two distance prediction values ​​and the distance value between the two base stations.

[0069] When the distance prediction model outputs two distance prediction values ​​obtained by predicting the distance between the same terminal to be located and two base stations, the calculation formula for the terminal position of the terminal to be located is: , , , in, is the terminal position of the terminal to be located, and are weight coefficients, is the predicted value of the distance between the terminal to be located and one of its base stations, is the predicted value of the distance between the terminal to be located and another base station, is the distance between two base stations.

[0070] Figure 6 is a flow chart of a distance prediction model training method provided in an embodiment of the present application. Figure 6 In some embodiments, the method includes but is not limited to steps S601 to S608.

[0071] Step S601: Acquire the current real distance value between the terminal to be located and the base station.

[0072] Step S602: input the first reference data, the second reference data and the third reference data into the deep neural network model to be trained to obtain a training distance prediction value.

[0073] Step S603, determining model loss information according to the training distance prediction value and the actual distance value.

[0074] Step S604, determining whether the model loss information is within a loss threshold range.

[0075] If not, execute step S605; if so, execute step S606.

[0076] Step S605, adjusting the weight parameters of the neural network model to be trained. Return to step S602.

[0077] Step S606, determining whether the number of training reset times reaches a reset threshold.

[0078] If not, execute step S607; if reached, execute step S608.

[0079] Step S607: Change the position of the terminal to be located. Return to step S601.

[0080] Step S608, end the training and obtain the distance prediction model.

[0081] In the specific implementation, a loss threshold interval and a reset threshold are preset as the training end condition. When the model loss information is within the loss threshold interval and the number of training resets reaches the reset threshold, the training is terminated, and the deep neural network model to be trained obtained in the last iteration is the distance prediction model. When the number of training resets does not reach the reset threshold, the weight parameters of the neural network model to be trained are adjusted by the back propagation method according to the degree of deviation of the model loss information from the loss threshold interval, so that the model loss information gradually approaches the loss threshold interval during the iteration process and finally falls within the loss threshold interval, and then the first reference data, the second reference data and the third reference data are input into the deep neural network model to be trained again until the model loss information is within the loss threshold interval, the position of the terminal to be located is changed, and the current real distance value between the terminal to be located and the base station is obtained again, and the neural network model to be trained is iteratively trained. The above steps are repeated until the number of training resets reaches the reset threshold, and then the training is terminated to obtain the distance prediction model.

[0082] See also Figure 7 The embodiment of the present application further provides a device for locating personnel in a tunnel, which can implement the above-mentioned method for locating personnel in a tunnel, and the device includes: The first module 701 is used to obtain first reference data, second reference data and third reference data; the first reference data is the communication quality data when the terminal to be located and the base station perform long-distance wireless communication, the second reference data is the distance measurement data obtained by the terminal to be located and the base station performing frequency hopping ranging, and the third reference data is the communication quality data when the terminal to be located and the base station perform short-distance wireless communication; The second module 702 is used to input the first reference data, the second reference data and the third reference data into the distance prediction model to predict the distance between the terminal to be located and the base station, and obtain a distance prediction value; The third module 703 is used to determine the terminal position of the terminal to be located according to the distance prediction value.

[0083] The specific implementation of the device for locating personnel in a tunnel is substantially the same as the specific implementation of the method for locating personnel in a tunnel described above, and will not be described in detail herein.

[0084] Figure 8 It is a block diagram of an electronic device according to an exemplary embodiment.

[0085] Refer to the following Figure 8 The electronic device 800 according to this embodiment of the present disclosure is described. Figure 8The electronic device 800 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0086] like Figure 8 As shown, the electronic device 800 is in the form of a general computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one storage unit 820, a bus 830 connecting different system components (including the storage unit 820 and the processing unit 810), a display unit 840, etc.

[0087] The storage unit stores program codes, which can be executed by the processing unit 810, so that the processing unit 810 executes the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned method for locating personnel in a tunnel of this specification.

[0088] The storage unit 820 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 8201 and / or a cache memory unit 8202 , and may further include a read-only memory unit (ROM) 8203 .

[0089] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0090] Bus 830 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0091] The electronic device 800 may also communicate with one or more external devices 800' (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 800, and / or communicate with any device that enables the electronic device 800 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 850. Furthermore, the electronic device 800 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 860. The network adapter 860 may communicate with other modules of the electronic device 800 via a bus 830. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0092] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned method for locating personnel in a tunnel is implemented.

[0093] The method, device, equipment and storage medium for positioning personnel in a tunnel provided in the embodiments of the present application use a pre-trained distance prediction model to predict the distance between the terminal to be positioned and the base station based on the communication quality data when the terminal to be positioned and the base station perform long-distance wireless communication, the distance measurement data obtained by frequency hopping ranging between the terminal to be positioned and the base station, and the communication quality data when the terminal to be positioned and the base station perform short-distance wireless communication, thereby realizing the positioning of personnel in the tunnel. Since the communication quality data obtained by combining the two communication modes of long-distance wireless communication and short-distance wireless communication are mutually verified, and multiple distance measurement data obtained by using different channels are obtained by frequency modulation ranging, the pre-trained distance prediction model can predict the distance between the terminal to be positioned and the base station based on reference data of multiple dimensions, thereby enhancing the accuracy and reliability of the distance prediction value and improving the accuracy of positioning personnel in the tunnel.

[0094] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the implementation of the present disclosure.

[0095] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0096] Computer readable storage media may include data signals propagated in baseband or as part of a carrier wave, wherein readable program codes are carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program codes contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0097] Those skilled in the art will appreciate that the above modules can be distributed in the device according to the description of the embodiment, or can be changed accordingly and only used in one or more devices different from the embodiment. The modules of the above embodiments can be combined into one module, or further divided into multiple sub-modules.

[0098] The exemplary embodiments of the present disclosure are specifically shown and described above. It should be understood that the present disclosure is not limited to the detailed structures, configurations or implementations described herein; on the contrary, the present disclosure is intended to cover various modifications and equivalent configurations included in the spirit and scope of the appended claims.

Claims

1. A method for locating personnel in a tunnel, characterized in that: include: Acquire first reference data, second reference data, and third reference data; The first reference data is communication quality data when the terminal to be located and the base station perform long-distance wireless communication, the second reference data is distance measurement data obtained by frequency hopping ranging between the terminal to be located and the base station, and the third reference data is communication quality data when the terminal to be located and the base station perform short-distance wireless communication; Inputting the first reference data, the second reference data and the third reference data into a distance prediction model to predict the distance between the terminal to be located and the base station, and obtaining a distance prediction value; The terminal position of the terminal to be located is determined according to the distance prediction value.

2. The method for locating personnel in a tunnel according to claim 1, characterized in that: Before acquiring the first reference data, the second reference data and the third reference data, the method further includes: Acquire a reference data packet forwarded by the base station from the terminal to be located; Parse the reference data packet to obtain the first reference data, the second reference data and the third reference data; the reference data packet is obtained by encapsulating the first reference data, the second reference data and the third reference data when the terminal to be located performs long-distance wireless communication, frequency hopping ranging and short-distance wireless communication with the base station in sequence.

3. The method for locating personnel in a tunnel according to claim 1, characterized in that: The method for generating the second reference data is: Determine reference distance data based on a distance measurement data set obtained by frequency hopping ranging; Performing polynomial fitting on the reference distance data to obtain distance measurement fitting data; Determining whether the distance measurement fitting data meets the preset distance measurement data condition; The preset ranging data condition is that the distance measurement fitting data is within a preset ranging data interval and the frequency error between the terminal to be located and the base station is within a preset frequency error interval; If it meets the requirements, the distance measurement fitting data is output as the second reference data.

4. The method for locating personnel in a tunnel according to claim 1, characterized in that: The step of inputting the first reference data, the second reference data, and the third reference data into a distance prediction model to predict the distance between the terminal to be located and the base station to obtain a distance prediction value includes: Performing local feature extraction on the first reference data, the second reference data, and the third reference data to obtain a local data vector sequence including a plurality of local data vectors; Performing position encoding on the local data vector sequence to obtain a position embedding vector sequence; Performing linear transformation processing on the position embedding vector sequence, and performing attention weight operation on the local data vector after the linear transformation processing to obtain a global distance feature; The distance between the terminal to be located and the base station is predicted according to the global distance feature to obtain the distance prediction value.

5. The method for locating personnel in a tunnel according to claim 1, characterized in that: The determining the terminal position of the terminal to be located according to the distance prediction value includes: Within a preset waiting time, determining whether the distance prediction model outputs two distance prediction values ​​obtained by predicting the distance between the same terminal to be located and the two base stations; If not, determining the relative direction between the terminal to be located and the base station according to the historical positioning data, and determining the terminal position of the terminal to be located in combination with the distance prediction value; If so, the terminal position of the terminal to be located is calculated according to the two distance prediction values ​​and the distance value between the two base stations.

6. The method for locating personnel in a tunnel according to claim 1, characterized in that: The training method of the distance prediction model comprises: Obtaining a current real distance value between the terminal to be located and the base station; Inputting the first reference data, the second reference data and the third reference data into a deep neural network model to be trained to obtain a training distance prediction value; Determining model loss information according to the training distance prediction value and the true distance value; Determining whether the model loss information is within a loss threshold range; If not, adjust the weight parameters of the neural network model to be trained; return to the step of inputting the first reference data, the second reference data and the third reference data into the deep neural network model to be trained to obtain a training distance prediction value; If so, determine whether the number of training resets reaches the reset threshold; If not reached, changing the position of the terminal to be located; returning to the step of obtaining the current real distance value between the terminal to be located and the base station; If it is reached, the training is terminated and the distance prediction model is obtained.

7. The method for locating personnel in a tunnel according to any one of claims 1 to 6, characterized in that: The first reference data includes the long-distance wireless communication frequency error between the terminal to be located and the base station, the long-distance wireless communication signal strength value recorded by the base station, the long-distance wireless communication signal strength value recorded by the terminal to be located, and the long-distance wireless communication channel signal-to-noise ratio; the second reference data is the distance measurement data obtained by frequency hopping ranging when the terminal to be located and the base station perform long-distance wireless communication; the third reference data includes the short-distance wireless communication signal strength value recorded by the base station and the short-distance wireless communication signal strength value recorded by the terminal to be located.

8. A device for locating personnel in a tunnel, characterized in that: include: A first module is used to obtain first reference data, second reference data and third reference data; The first reference data is communication quality data when the terminal to be located and the base station perform long-distance wireless communication, the second reference data is distance measurement data obtained by frequency hopping ranging between the terminal to be located and the base station, and the third reference data is communication quality data when the terminal to be located and the base station perform short-distance wireless communication; A second module is used to input the first reference data, the second reference data and the third reference data into a distance prediction model to predict the distance between the terminal to be located and the base station, and obtain a distance prediction value; The third module is used to determine the terminal position of the terminal to be located according to the distance prediction value.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method for locating people in a tunnel as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for locating people in a tunnel according to any one of claims 1 to 7 is implemented.

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