Underground space electromagnetic positioning method and device based on track time sequence analysis

Abstract

The invention provides an underground space electromagnetic positioning method and device based on track time sequence analysis, and relates to the technical field of positioning. The method comprises the following steps: acquiring an electromagnetic field phase difference and magnetic field signal intensity of a to-be-positioned target, and constructing time sequence input data according to the electromagnetic field phase difference and the magnetic field signal intensity; inputting the time sequence input data into the constructed time sequence electromagnetic positioning model; wherein the time sequence electromagnetic positioning model comprises a screening door, a sensing door and a repositioning door; and outputting target pose information in real time according to the time sequence input data and the time sequence electromagnetic positioning model. Target positioning information is autonomously sensed and captured through the time sequence electromagnetic positioning network model, and high-precision real-time continuous positioning of the moving target is achieved.

Description

Technical Field

[0001] The present invention relates to the field of positioning technology, and in particular to an underground space electromagnetic positioning method and device based on trajectory time series analysis. Background Art

[0002] With the development of information technology, people's demand for positioning in complex indoor and underground non-exposed spaces is increasing. In underground parking lots, underground pipe corridors, subways, mines and other environments, timely acquisition of location information of personnel and mobile devices is crucial to ensure normal life and production order. Traditional indoor wireless positioning methods, such as RFID (Radio Frequency Identification), ZigBee, WIFI or UWB (Ultra Wide Band) technologies, use high-frequency wireless signals that are reflected, diffracted and scattered when propagating indoors, causing multipath effects, affecting the accuracy of indoor positioning, and cannot penetrate buildings for propagation. The near-field electromagnetic positioning system uses NFER (Near Field Electromagnetic Ranging) technology to obtain the distance between the target and the positioning base station, and then uses a multi-base station positioning algorithm to locate the target in real time. The near-field electromagnetic positioning system uses low-frequency electromagnetic signals, which have small path attenuation when penetrating the medium and are not affected by multipath effects. It is suitable for underground air positioning.

[0003] However, the transmitting antenna of the near-field electromagnetic positioning system generally adopts an orthogonal magnetic coil antenna, which has strong directivity. When using the near-field electromagnetic positioning system to locate a moving target, the target's motion trajectory and posture will affect the convergence performance of the near-field ranging algorithm, resulting in a decrease in positioning accuracy and timeliness. Summary of the invention

[0004] In order to solve the technical problems of reduced positioning accuracy and timeliness in the prior art, the embodiment of the present invention provides an underground space electromagnetic positioning method and device based on trajectory time series analysis. The technical solution is as follows:

[0005] On the one hand, a method for underground space electromagnetic positioning based on trajectory time series analysis is provided, the method is implemented by an underground space electromagnetic positioning device, and the method includes:

[0006] S1. Obtain the electromagnetic field phase difference and magnetic field signal strength of the target to be located, and construct time series input data according to the electromagnetic field phase difference and magnetic field signal strength.

[0007] S2. Input the time series input data into the constructed time series electromagnetic positioning model; wherein the time series electromagnetic positioning model includes a screening gate, a perception gate and a relocation gate.

[0008] S3. Output the target position information in real time according to the time series input data and the time series electromagnetic positioning model.

[0009] Optionally, the process of constructing the time series electromagnetic positioning model in S2 includes:

[0010] S21. Construct an electromagnetic positioning data set; wherein the electromagnetic positioning data set includes electromagnetic field phase difference samples, magnetic field signal strength samples, and corresponding target posture information samples.

[0011] S22. Construct a screening gate.

[0012] S23. Build a perception gate.

[0013] S24, construct a relocation gate.

[0014] S25. Construct a sequential electromagnetic positioning network based on the screening gate, the perception gate and the relocation gate.

[0015] S26. Training the time series electromagnetic positioning network according to the electromagnetic positioning data set to generate a time series electromagnetic positioning model.

[0016] Optionally, the construction screening gate in S22 includes:

[0017] S221. For the current moment t, obtain the target posture information sample at the previous moment t-1, the electromagnetic field phase difference sample at the current moment t, and the magnetic field signal strength sample at the current moment t.

[0018] S222. Input the target posture information sample at the previous moment t-1 into the first self-dimensionalization layer to obtain the feature map MO1.

[0019] S223. Input the feature map MO1 into the convolution module to obtain the feature map MO2.

[0020] S224, input the electromagnetic field phase difference sample at the current moment t and the magnetic field signal strength sample at the current moment t into the second self-dimensionalization layer to obtain the feature map ME1.

[0021] S225. Input the feature map ME1 into the global pooling layer and output the feature vector ME2.

[0022] S226. Input the feature vector ME2 into the SE layer to obtain the feature vector ME3.

[0023] S227, multiply the feature map MO2 by the feature vector ME3 to obtain the filtering gate output feature map ML0.

[0024] Optionally, the building of the perception gate in S23 includes:

[0025] S231. Perform a 1×1 convolution operation on the feature map MO1 to obtain a deep feature matrix MO3. Perform a 3×3 convolution operation on the deep feature matrix MO3 to obtain a deep feature matrix MO4.

[0026] S232, add the feature map MO1 and the deep feature matrix MO4 element by element to obtain the first sum matrix of the perception gate.

[0027] S233. Perform a 1×1 convolution operation on the feature map ME1 to obtain a deep feature matrix ME4, and perform a 3×3 convolution operation on the deep feature matrix ME4 to obtain a deep feature matrix ME5.

[0028] S234, adding the feature map ME1 and the deep feature matrix ME5 element by element to obtain the second sum matrix of the perception gate.

[0029] S235, perform matrix multiplication of the first perception gate sum matrix and the second perception gate sum matrix, and perform global element activation through the Sigmoid layer to obtain the perception gate voting matrix MO5.

[0030] S236, perform matrix multiplication on the first perception gate sum matrix and the second perception gate sum matrix, and activate them through the Tanh layer to obtain the perception matrix MO8.

[0031] S237, perform matrix multiplication of the perception gate voting matrix MO5 and the perception matrix MO8 to obtain the perception gate output feature map ML1.

[0032] Optionally, the construction relocation gate in S24 includes:

[0033] S241, perform a 1×1 convolution operation on the feature map MO1 to obtain a deep feature matrix MO9, and perform a 3×3 convolution operation on the deep feature matrix MO9 to obtain a deep feature matrix MO 10 .

[0034] S242, feature map MO1 and deep feature matrix MO 10 Add the elements to get the first sum matrix of the relocation gates.

[0035] S243. Perform a 1×1 convolution operation on the feature map ME1 to obtain a deep feature matrix ME8, and perform a 3×3 convolution operation on the deep feature matrix ME8 to obtain a deep feature matrix ME9.

[0036] S244, perform element-wise addition of the feature map ME1 and the deep feature matrix ME9 to obtain a second sum matrix of the relocation gate.

[0037] S245, perform matrix multiplication on the first sum matrix of the relocation gate and the second sum matrix of the relocation gate, and perform global element activation through the Sigmoid layer to obtain the relocation gate voting matrix MO 11 .

[0038] S246, obtain electromagnetic positioning memory unit L t , for the electromagnetic positioning memory unit L t Perform two 3×3 convolution operations in sequence to obtain the convolution result L3, and then combine the convolution result L3 with the electromagnetic positioning memory unit L t The elements are summed and activated through the Tanh layer to obtain the relocation matrix L4.

[0039] S247, relocate the gate voting matrix MO 11 Perform matrix multiplication with the relocation matrix L4 to obtain the relocation gate output feature map L5.

[0040] S248. Input the relocation gate output feature map L5 into the self-dimensionality reduction layer to obtain the relocation gate output feature Ot.

[0041] Optionally, in S25, a sequential electromagnetic positioning network is constructed according to the screening gate, the perception gate, and the relocation gate, including:

[0042] S251. For the current moment t, the target posture information sample at the previous moment t-1, the electromagnetic field phase difference sample at the current moment t, and the magnetic field signal strength sample at the current moment t are input into the screening gate to obtain the screening gate output feature map ML0.

[0043] S252, subtract the screening gate output characteristic map ML0 from the electromagnetic positioning memory unit Lt-1 at the previous time t-1 to obtain the electromagnetic positioning memory unit MLt-1.

[0044] S253. Input the feature map MO1 and the feature map ME1 in the screening gate into the perception gate to obtain the perception gate output feature map ML1.

[0045] S254, adding the electromagnetic positioning memory unit MLt-1 and the perception gate output characteristic map ML1 to obtain the electromagnetic positioning memory unit Lt.

[0046] S255. Input the feature map MO1, the feature map ME1 and the electromagnetic positioning memory unit Lt into the repositioning gate to obtain the repositioning gate output feature Ot, which is the target posture information at the current moment.

[0047] On the other hand, an underground space electromagnetic positioning device based on trajectory time series analysis is provided, and the device is applied to an underground space electromagnetic positioning method based on trajectory time series analysis, and the device comprises:

[0048] The acquisition module is used to acquire the electromagnetic field phase difference and magnetic field signal strength of the target to be located, and construct time series input data according to the electromagnetic field phase difference and magnetic field signal strength.

[0049] The input module is used to input the time series input data into the constructed time series electromagnetic positioning model; wherein the time series electromagnetic positioning model includes a screening gate, a perception gate and a relocation gate.

[0050] The output module is used to output the target posture information in real time according to the time series input data and the time series electromagnetic positioning model.

[0051] Optionally, the input module is further configured to:

[0052] S21. Construct an electromagnetic positioning data set; wherein the electromagnetic positioning data set includes electromagnetic field phase difference samples, magnetic field signal strength samples, and corresponding target posture information samples.

[0053] S22. Construct a screening gate.

[0054] S23. Build a perception gate.

[0055] S24, construct a relocation gate.

[0056] S25. Construct a sequential electromagnetic positioning network based on the screening gate, the perception gate and the relocation gate.

[0057] S26. Training the time series electromagnetic positioning network according to the electromagnetic positioning data set to generate a time series electromagnetic positioning model.

[0058] Optionally, the input module is further configured to:

[0059] S221. For the current moment t, obtain the target posture information sample at the previous moment t-1, the electromagnetic field phase difference sample at the current moment t, and the magnetic field signal strength sample at the current moment t.

[0060] S222. Input the target posture information sample at the previous moment t-1 into the first self-dimensionalization layer to obtain the feature map MO1.

[0061] S223. Input the feature map MO1 into the convolution module to obtain the feature map MO2.

[0062] S224, input the electromagnetic field phase difference sample at the current moment t and the magnetic field signal strength sample at the current moment t into the second self-dimensionalization layer to obtain the feature map ME1.

[0063] S225. Input the feature map ME1 into the global pooling layer and output the feature vector ME2.

[0064] S226. Input the feature vector ME2 into the SE layer to obtain the feature vector ME3.

[0065] S227, multiply the feature map MO2 by the feature vector ME3 to obtain the filtering gate output feature map ML0.

[0066] Optionally, the input module is further configured to:

[0067] S231. Perform a 1×1 convolution operation on the feature map MO1 to obtain a deep feature matrix MO3. Perform a 3×3 convolution operation on the deep feature matrix MO3 to obtain a deep feature matrix MO4.

[0068] S232, add the feature map MO1 and the deep feature matrix MO4 element by element to obtain the first sum matrix of the perception gate.

[0069] S233. Perform a 1×1 convolution operation on the feature map ME1 to obtain a deep feature matrix ME4, and perform a 3×3 convolution operation on the deep feature matrix ME4 to obtain a deep feature matrix ME5.

[0070] S234, adding the feature map ME1 and the deep feature matrix ME5 element by element to obtain the second sum matrix of the perception gate.

[0071] S235, perform matrix multiplication of the first perception gate sum matrix and the second perception gate sum matrix, and perform global element activation through the Sigmoid layer to obtain the perception gate voting matrix MO5.

[0072] S236, perform matrix multiplication on the first perception gate sum matrix and the second perception gate sum matrix, and activate them through the Tanh layer to obtain the perception matrix MO8.

[0073] S237, perform matrix multiplication of the perception gate voting matrix MO5 and the perception matrix MO8 to obtain the perception gate output feature map ML1.

[0074] Optionally, the input module is further configured to:

[0075] S241, perform a 1×1 convolution operation on the feature map MO1 to obtain a deep feature matrix MO9, and perform a 3×3 convolution operation on the deep feature matrix MO9 to obtain a deep feature matrix MO 10 .

[0076] S242, feature map MO1 and deep feature matrix MO 10 Add the elements to get the first sum matrix of the relocation gates.

[0077] S243. Perform a 1×1 convolution operation on the feature map ME1 to obtain a deep feature matrix ME8, and perform a 3×3 convolution operation on the deep feature matrix ME8 to obtain a deep feature matrix ME9.

[0078] S244, perform element-wise addition of the feature map ME1 and the deep feature matrix ME9 to obtain a second sum matrix of the relocation gate.

[0079] S245, perform matrix multiplication on the first sum matrix of the relocation gate and the second sum matrix of the relocation gate, and perform global element activation through the Sigmoid layer to obtain the relocation gate voting matrix MO 11 .

[0080] S246, obtain electromagnetic positioning memory unit L t , for the electromagnetic positioning memory unit L t Perform two 3×3 convolution operations in sequence to obtain the convolution result L3, and then combine the convolution result L3 with the electromagnetic positioning memory unit L t The elements are summed and activated through the Tanh layer to obtain the relocation matrix L4.

[0081] S247, relocate the gate voting matrix MO 11 Perform matrix multiplication with the relocation matrix L4 to obtain the relocation gate output feature map L5.

[0082] S248. Input the relocation gate output feature map L5 into the self-dimensionality reduction layer to obtain the relocation gate output feature Ot.

[0083] Optionally, the input module is further configured to:

[0084] S251. For the current moment t, the target posture information sample at the previous moment t-1, the electromagnetic field phase difference sample at the current moment t, and the magnetic field signal strength sample at the current moment t are input into the screening gate to obtain the screening gate output feature map ML0.

[0085] S252, subtract the screening gate output characteristic map ML0 from the electromagnetic positioning memory unit Lt-1 at the previous time t-1 to obtain the electromagnetic positioning memory unit MLt-1.

[0086] S253. Input the feature map MO1 and the feature map ME1 in the screening gate into the perception gate to obtain the perception gate output feature map ML1.

[0087] S254, adding the electromagnetic positioning memory unit MLt-1 and the perception gate output characteristic map ML1 to obtain the electromagnetic positioning memory unit Lt.

[0088] S255. Input the feature map MO1, the feature map ME1 and the electromagnetic positioning memory unit Lt into the repositioning gate to obtain the repositioning gate output feature Ot, which is the target posture information at the current moment.

[0089] On the other hand, an underground space electromagnetic positioning device is provided, comprising: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the underground space electromagnetic positioning methods based on trajectory timing analysis is implemented.

[0090] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned underground space electromagnetic positioning methods based on trajectory timing analysis.

[0091] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0092] In the present invention, the underground space electromagnetic positioning method based on trajectory timing analysis outputs target positioning information in real time through a timing electromagnetic positioning network. Compared with the traditional method, the motion trajectory and posture of the target will affect the positioning accuracy and timeliness of the near-field ranging algorithm. It has a stronger timing signal processing capability and can learn more essential electromagnetic positioning characterization mechanisms as data and time accumulate, thereby providing more accurate real-time target positioning information.

[0093] The sequential electromagnetic positioning network is designed for a specific task, namely, electromagnetic positioning in underground space. It contains three structures: screening gate, perception gate, and relocation gate. The three gate structures are designed to filter out redundant historical positioning information, perceive and store the current electromagnetic positioning representation, and relocate the current position of the target. The three gate structures are connected in series in sequence, which can enhance the anti-interference ability of the electromagnetic positioning method and achieve accurate positioning of the target through deep mining and interaction of long-term positioning historical experience and current signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0095] Figure 1 It is a flow chart of an underground space electromagnetic positioning method based on trajectory time series analysis provided by an embodiment of the present invention;

[0096] Figure 2 It is a structural schematic diagram of an underground space electromagnetic positioning based on trajectory time series analysis provided by an embodiment of the present invention;

[0097] Figure 3 is a flowchart of constructing a time-series electromagnetic positioning model provided by an embodiment of the present invention;

[0098] Figure 4 is a structural diagram of a screening door provided by an embodiment of the present invention;

[0099] Figure 5 is a structural diagram of a perception gate provided by an embodiment of the present invention;

[0100] Figure 6 is a structural diagram of a relocation door provided by an embodiment of the present invention;

[0101] Figure 7 is a diagram of a sequential electromagnetic positioning network structure provided by an embodiment of the present invention;

[0102] Figure 8 It is a block diagram of an underground space electromagnetic positioning device based on trajectory timing analysis provided by an embodiment of the present invention;

[0103] Fig. 9 It is a structural schematic diagram of an underground space electromagnetic positioning device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0104] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0105] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0106] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0107] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0108] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0109] The embodiment of the present invention provides an underground space electromagnetic positioning method based on trajectory time series analysis, which can be implemented by an underground space electromagnetic positioning device, which can be a terminal or a server. Figure 1 The flowchart of the underground space electromagnetic positioning method based on trajectory timing analysis is shown. The processing flow of the method may include the following steps:

[0110] S1. Obtain the electromagnetic field phase difference and magnetic field signal strength of the target to be located, and construct time series input data according to the electromagnetic field phase difference and magnetic field signal strength.

[0111] S2. Input the time series input data into the constructed time series electromagnetic positioning model; wherein the time series electromagnetic positioning model includes a screening gate, a perception gate and a relocation gate.

[0112] S3. Output the target position information in real time according to the time series input data and the time series electromagnetic positioning model.

[0113] In a feasible implementation, the present invention constructs time-series input data by collecting the phase difference between the field signal and the magnetic field signal within a sampling period and the magnetic field signal strength, and then outputs the target positioning information in real time through a time-series electromagnetic positioning network.

[0114] Furthermore, the sequential electromagnetic positioning network is designed for underground electromagnetic positioning, and includes three structures: screening gate, perception gate and repositioning gate. The three gate structures are designed to filter out redundant historical positioning information, perceive and store the current electromagnetic positioning representation, and reposition the current position of the target. The three gate structures are connected in series in sequence, which can enhance the anti-interference ability of the electromagnetic positioning method, and achieve accurate positioning of the target through deep mining and interaction of long-term positioning historical experience and current signals.

[0115] Specifically, Figure 2 As shown, the present invention can transmit the electromagnetic signal required for locating the target through the signal transmitting part; the signal receiving part receives the electric field part and the magnetic field part of the positioning signal, and the magnetic field signal is received by an array antenna to receive the signal strength of the magnetic field signal in multiple directions; the preprocessing part processes the received electric field signal and magnetic field signal to obtain the phase difference between the electric field signal and the magnetic field signal, and the field strength of the magnetic field signal; the positioning model part designs a sequential electromagnetic positioning network, and trains based on a large number of electromagnetic field phase difference and field strength data of different trajectories to obtain a positioning model. When actually used, the data to be processed can be input into the model; the positioning result part outputs the position of the target.

[0116] Furthermore, in the present invention, the signal transmitting antenna adopts an orthogonal magnetic antenna to transmit the target electromagnetic signal.

[0117] The electric field signal received by the electric field antenna at the receiving end is E, and the magnetic field signal received by the magnetic field array antenna is ,in is the number of magnetic field antennas.

[0118] In the preprocessing stage, LAMBDA (Leastsquare AMBiguity DecorrelationAdijustment) is used to obtain the phase difference between the electric field signal and the magnetic field signal. The sample mean square value is used as the magnetic field signal strength. .

[0119] Alternatively, if Figure 3 As shown, in the positioning model stage, the time-series electromagnetic positioning model can be obtained through the following steps S21-S26:

[0120] S21. Construct an electromagnetic positioning data set; wherein the electromagnetic positioning data set includes electromagnetic field phase difference samples, magnetic field signal strength samples, and corresponding target posture information samples.

[0121] In a feasible implementation, an underground target positioning test scene is arranged to collect and produce a large amount of electromagnetic field phase difference, field strength data and corresponding target posture information under different trajectories, and the collection period is T.

[0122] Furthermore, the phase difference ( ) and field strength data ( ) are all dimension T n time series signal. Let the electromagnetic field phase difference and field strength data corresponding to time t be Et, then Et consists of two dimensions 1 The data composition of n.

[0123] The target pose information is dimension T 4 Timing Signal ,in is the coordinate of the target in the space coordinate system, is the angle between the target direction and true north. Let the target posture data corresponding to time t be Ot, then the dimension of Ot is 1 4.

[0124] S22. Construct a screening gate.

[0125] Optionally, the above step S22 may include the following steps S221-S227:

[0126] S221. For the current moment t, obtain the target posture information sample at the previous moment t-1, the electromagnetic field phase difference sample at the current moment t, and the magnetic field signal strength sample at the current moment t.

[0127] S222. Input the target posture information sample at the previous moment t-1 into the first self-dimensionalization layer to obtain the feature map MO1.

[0128] S223. Input the feature map MO1 into the convolution module to obtain the feature map MO2.

[0129] S224, input the electromagnetic field phase difference sample at the current moment t and the magnetic field signal strength sample at the current moment t into the second self-dimensionalization layer to obtain the feature map ME1.

[0130] S225. Input the feature map ME1 into the global pooling layer and output the feature vector ME2.

[0131] S226. Input the feature vector ME2 into the SE layer to obtain the feature vector ME3.

[0132] S227, multiply the feature map MO2 by the feature vector ME3 to obtain the filtering gate output feature map ML0.

[0133] In a feasible implementation, Figure 4 As shown in the figure, the input of the screening gate is the target posture information Ot-1 at the previous moment and the electromagnetic field phase difference and field strength data Et at the current moment. First, Ot-1 is input into the self-dimensionalization layer, and in the self-dimensionalization layer, Ot-1 is first multiplied by its own transposition to obtain 4 4 matrix; then perform three consecutive deconvolution operations with a step size of 2 and a channel number of 32 to obtain a matrix of size 32 32 32 feature map MO1. This step enriches the spatiotemporal feature representation of the target pose information from different dimensions, providing a diversified data basis for subsequent feature mining. The feature map MO1 is then input into the convolution module, and feature mining is performed through three consecutive convolution operations. In this step, each convolution operation consists of a 3 It consists of 3 convolutional layers, a Relu activation layer and a BN layer. The output size after feature mining is 32 32 Feature map MO2 of 32.

[0134] Then, Et is input into its self-elevating dimension layer, in which dimension 1 Phase difference data of n and dimension 1 The field strength data of n are transposed and multiplied by each other to obtain two n n matrix, and perform channel concatenation to obtain n n 2; then perform three consecutive deconvolution operations on the data matrix with the corresponding step size and the number of channels being 32 to obtain a data matrix of size 32 32 This step enriches the spatiotemporal feature representation of the target electromagnetic information from different dimensions, providing a diversified data basis for subsequent feature mining. The feature map ME1 is then input into the global pooling layer for spatial dimension average pooling to obtain a size of 1 1 32, and perform SE attention activation on it to obtain a feature vector ME3 of the same size.

[0135] Multiply the feature map MO2 with the feature vector ME3 to get a final size of 32 32 The output feature map ML0 of 32.

[0136] The above is the internal structure of the screening gate. The screening gate extracts the positioning information of the previous moment and the current electromagnetic information to obtain redundant features that are irrelevant to the current moment positioning task, so as to filter them out later.

[0137] S23. Build a perception gate.

[0138] Optionally, the above step S23 may include the following steps S231-S237:

[0139] S231. Perform a 1×1 convolution operation on the feature map MO1 to obtain a deep feature matrix MO3. Perform a 3×3 convolution operation on the deep feature matrix MO3 to obtain a deep feature matrix MO4.

[0140] S232, add the feature map MO1 and the deep feature matrix MO4 element by element to obtain the first sum matrix of the perception gate.

[0141] S233. Perform a 1×1 convolution operation on the feature map ME1 to obtain a deep feature matrix ME4, and perform a 3×3 convolution operation on the deep feature matrix ME4 to obtain a deep feature matrix ME5.

[0142] S234, adding the feature map ME1 and the deep feature matrix ME5 element by element to obtain the second sum matrix of the perception gate.

[0143] S235, perform matrix multiplication of the first perception gate sum matrix and the second perception gate sum matrix, and perform global element activation through the Sigmoid layer to obtain the perception gate voting matrix MO5.

[0144] S236, perform matrix multiplication on the first perception gate sum matrix and the second perception gate sum matrix, and activate them through the Tanh layer to obtain the perception matrix MO8.

[0145] S237, perform matrix multiplication of the perception gate voting matrix MO5 and the perception matrix MO8 to obtain the perception gate output feature map ML1.

[0146] In a feasible implementation, Figure 5 As shown in Figure 1, the input of the perception gate is the output feature map MO1 and ME1 of the self-elevating layer in the screening gate, which respectively represent the positioning information of the previous moment and the current electromagnetic positioning feature. First, the voting matrix is ​​constructed, and the output feature map MO1 is input into the convolution kernel as 1 1 and 3 In the convolutional layer of 3, deep feature matrices MO3 and MO4 are obtained in turn. MO1 itself is added to its deep feature MO4 elements to obtain a sum matrix, which retains the original electromagnetic positioning information while obtaining rich positioning voting information. Figure 5 The same operation is performed as shown to obtain the sum matrix of ME1 itself and its deep feature ME5. Then the two sum matrices are matrix multiplied and the global element activation is performed through the Sigmoid layer to obtain the voting matrix MO5, that is, all output elements are between 0 and 1, which are used to vote on the importance of the part information.

[0147] Then the perception matrix is ​​constructed. The construction path of the perception matrix is ​​the same as that of the voting matrix. The difference is that after the two and matrices are multiplied, they are activated through the Tanh layer to obtain the perception matrix MO8, all of which have output elements between ±1, which effectively captures the nonlinear relationship of the positioning signal while achieving feature scaling and accelerating the convergence of subsequent network training.

[0148] Finally, the voting matrix MO5 is multiplied with the perception matrix MO8 to obtain a final size of 32 32 The output feature map ML1 of 32 represents the refresh degree of the positioning information at the current moment.

[0149] The above is the internal structure of the perception gate. The perception gate obtains useful historical positioning data by extracting the positioning information of the previous moment, and captures the key information that guides the positioning update by extracting the current electromagnetic signal characteristics.

[0150] S24, construct a relocation gate.

[0151] Optionally, the above step S24 may include the following steps S241-S248:

[0152] S241, perform a 1×1 convolution operation on the feature map MO1 to obtain a deep feature matrix MO9, and perform a 3×3 convolution operation on the deep feature matrix MO9 to obtain a deep feature matrix MO 10 .

[0153] S242, feature map MO1 and deep feature matrix MO 10 Add the elements to get the first sum matrix of the relocation gates.

[0154] S243. Perform a 1×1 convolution operation on the feature map ME1 to obtain a deep feature matrix ME8, and perform a 3×3 convolution operation on the deep feature matrix ME8 to obtain a deep feature matrix ME9.

[0155] S244, perform element-wise addition of the feature map ME1 and the deep feature matrix ME9 to obtain a second sum matrix of the relocation gate.

[0156] S245, perform matrix multiplication on the first sum matrix of the relocation gate and the second sum matrix of the relocation gate, and perform global element activation through the Sigmoid layer to obtain the relocation gate voting matrix MO 11 .

[0157] S246, obtain electromagnetic positioning memory unit L t , for the electromagnetic positioning memory unit L t Perform two 3×3 convolution operations in sequence to obtain the convolution result L3, and then combine the convolution result L3 with the electromagnetic positioning memory unit L t The elements are summed and activated through the Tanh layer to obtain the relocation matrix L4.

[0158] S247, relocate the gate voting matrix MO 11 Perform matrix multiplication with the relocation matrix L4 to obtain the relocation gate output feature map L5.

[0159] S248. Input the relocation gate output feature map L5 into the self-dimensionality reduction layer to obtain the relocation gate output feature Ot.

[0160] In a feasible implementation, Figure 6 As shown in the figure, the input of the relocation gate is the output feature map MO1 and ME1 of the self-elevating layer in the screening gate, which respectively represent the last moment positioning information and the current electromagnetic positioning feature. First, the voting matrix is ​​constructed. The construction process is the same as the perception gate voting matrix. After calculation, the voting matrix MO 11 , used for voting dimension 32 32 The importance of each element information in the feature map of 32.

[0161] Then construct the relocation matrix, the input feature is L t . L t It is an electromagnetic positioning memory unit. t Perform two consecutive convolutions with a kernel size of 3 3 convolution operation, and the convolution result L3 is combined with the input feature L t The elements are added, and the sum is input into the Tanh layer for activation to obtain the relocation matrix L4.

[0162] Finally, the voting matrix MO 11Matrix multiplication with the relocation matrix L4 gives a final size of 32 32 The output feature map L5 of 32 represents the positioning information at the current moment. L5 is input into the self-dimensionality reduction layer, where it is first pooled by global average pooling to reduce the dimension of 32. 32 The output feature map of 32 is converted to 1 1 32, and then output the vector to a fully connected layer to obtain 1 The output feature of 4 is Ot, which is the position information of the target at the current moment.

[0163] The above is the internal structure of the relocation gate. The relocation gate can extract and learn the rich electromagnetic positioning and target state representation in the electromagnetic positioning memory unit, and activate the key information through the voting matrix to generate accurate current target pose information.

[0164] S25. Construct a sequential electromagnetic positioning network based on the screening gate, the perception gate and the relocation gate.

[0165] Optionally, the above step S25 may include the following steps S251-S255:

[0166] S251. For the current moment t, the target posture information sample at the previous moment t-1, the electromagnetic field phase difference sample at the current moment t, and the magnetic field signal strength sample at the current moment t are input into the screening gate to obtain the screening gate output feature map ML0.

[0167] S252, subtract the screening gate output characteristic map ML0 from the electromagnetic positioning memory unit Lt-1 at the previous time t-1 to obtain the electromagnetic positioning memory unit MLt-1.

[0168] S253. Input the feature map MO1 and the feature map ME1 in the screening gate into the perception gate to obtain the perception gate output feature map ML1.

[0169] S254, adding the electromagnetic positioning memory unit MLt-1 and the perception gate output characteristic map ML1 to obtain the electromagnetic positioning memory unit Lt.

[0170] S255. Input the feature map MO1, the feature map ME1 and the electromagnetic positioning memory unit Lt into the repositioning gate to obtain the repositioning gate output feature Ot, which is the target posture information at the current moment.

[0171] In a feasible implementation manner, the sequential electromagnetic positioning network structure is as follows: Figure 7As shown. First, the target posture information Ot-1 at the previous moment and the electromagnetic field phase difference and field strength data Et at the current moment are input into the screening gate, and the feature map ML0 is output. ML0 contains redundant features that are irrelevant to the positioning task at the current moment. The electromagnetic positioning memory unit Lt-1 at the previous moment is subtracted from ML0 to obtain the memory unit MLt-1. This step is used to delete the redundant features in the memory unit Lt-1 that are irrelevant to the positioning task at the current moment.

[0172] Then, the feature maps MO1 and ME1 in the screening gate are input into the perception gate, and the feature map ML1 is output. The memory unit MLt-1 is added to ML1 to obtain Lt, so that the memory unit obtains valuable electromagnetic positioning information at the current moment.

[0173] The feature maps MO1, ME1 and Lt are input into the relocation gate together, and Ot is output, which is the pose information of the target at the current moment.

[0174] The above is the structure of the sequential electromagnetic positioning network. By continuously inputting the electromagnetic signal at the current moment into the network, the target position information at the corresponding moment can be output.

[0175] S26. Training the time series electromagnetic positioning network according to the electromagnetic positioning data set to generate a time series electromagnetic positioning model.

[0176] In a feasible implementation manner, the time-series electromagnetic positioning model is generated by: using the constructed electromagnetic positioning data set to train the above-mentioned time-series electromagnetic positioning network until the algorithm converges to obtain the final time-series electromagnetic positioning model.

[0177] The above is the generation process of the time series electromagnetic positioning model.

[0178] After obtaining the time-series electromagnetic positioning model, in actual application, the collected electromagnetic field phase difference and magnetic field signal strength are input into the time-series electromagnetic positioning model to obtain the position and posture of the target. ,in is the coordinate of the target in the space coordinate system, is the angle between the target direction and true north.

[0179] In an embodiment of the present invention, an underground space electromagnetic positioning method based on trajectory timing analysis outputs target positioning information in real time through a timing electromagnetic positioning network. Compared with traditional methods in which the target's motion trajectory and posture affect the positioning accuracy and timeliness of the near-field ranging algorithm, it has a stronger timing signal processing capability and can learn a more essential electromagnetic positioning characterization mechanism as data and time accumulate, thereby providing more accurate real-time target positioning information.

[0180] The sequential electromagnetic positioning network is designed for a specific task, namely, electromagnetic positioning in underground space. It contains three structures: screening gate, perception gate, and relocation gate. The three gate structures are designed to filter out redundant historical positioning information, perceive and store the current electromagnetic positioning representation, and relocate the current position of the target. The three gate structures are connected in series in sequence, which can enhance the anti-interference ability of the electromagnetic positioning method and achieve accurate positioning of the target through deep mining and interaction of long-term positioning historical experience and current signals.

[0181] Figure 7 The block diagram of an underground space electromagnetic positioning device based on trajectory time series analysis according to an exemplary embodiment is shown. The device is used in an underground space electromagnetic positioning method based on trajectory time series analysis. Figure 7 The device includes an acquisition module 310, an input module 320 and an output module 330. Among them:

[0182] The acquisition module 310 is used to acquire the electromagnetic field phase difference and the magnetic field signal strength of the target to be located, and construct time series input data according to the electromagnetic field phase difference and the magnetic field signal strength.

[0183] The input module 320 is used to input the time-series input data into the constructed time-series electromagnetic positioning model; wherein the time-series electromagnetic positioning model includes a screening gate, a perception gate and a relocation gate.

[0184] The output module 330 is used to output the target posture information in real time according to the time-series input data and the time-series electromagnetic positioning model.

[0185] Optionally, the input module 320 is further configured to:

[0186] S21. Construct an electromagnetic positioning data set; wherein the electromagnetic positioning data set includes electromagnetic field phase difference samples, magnetic field signal strength samples, and corresponding target posture information samples.

[0187] S22. Construct a screening gate.

[0188] S23. Build a perception gate.

[0189] S24, construct a relocation gate.

[0190] S25. Construct a sequential electromagnetic positioning network based on the screening gate, the perception gate and the relocation gate.

[0191] S26. Training the time series electromagnetic positioning network according to the electromagnetic positioning data set to generate a time series electromagnetic positioning model.

[0192] Optionally, the input module 320 is further configured to:

[0193] S221. For the current moment t, obtain the target posture information sample at the previous moment t-1, the electromagnetic field phase difference sample at the current moment t, and the magnetic field signal strength sample at the current moment t.

[0194] S222. Input the target posture information sample at the previous moment t-1 into the first self-dimensionalization layer to obtain the feature map MO1.

[0195] S223. Input the feature map MO1 into the convolution module to obtain the feature map MO2.

[0196] S224, input the electromagnetic field phase difference sample at the current moment t and the magnetic field signal strength sample at the current moment t into the second self-dimensionalization layer to obtain the feature map ME1.

[0197] S225. Input the feature map ME1 into the global pooling layer and output the feature vector ME2.

[0198] S226. Input the feature vector ME2 into the SE layer to obtain the feature vector ME3.

[0199] S227, multiply the feature map MO2 by the feature vector ME3 to obtain the filtering gate output feature map ML0.

[0200] Optionally, the input module 320 is further configured to:

[0201] S231. Perform a 1×1 convolution operation on the feature map MO1 to obtain a deep feature matrix MO3. Perform a 3×3 convolution operation on the deep feature matrix MO3 to obtain a deep feature matrix MO4.

[0202] S232, add the feature map MO1 and the deep feature matrix MO4 element by element to obtain the first sum matrix of the perception gate.

[0203] S233. Perform a 1×1 convolution operation on the feature map ME1 to obtain a deep feature matrix ME4, and perform a 3×3 convolution operation on the deep feature matrix ME4 to obtain a deep feature matrix ME5.

[0204] S234, adding the feature map ME1 and the deep feature matrix ME5 element by element to obtain the second sum matrix of the perception gate.

[0205] S235, perform matrix multiplication of the first perception gate sum matrix and the second perception gate sum matrix, and perform global element activation through the Sigmoid layer to obtain the perception gate voting matrix MO5.

[0206] S236, perform matrix multiplication on the first perception gate sum matrix and the second perception gate sum matrix, and activate them through the Tanh layer to obtain the perception matrix MO8.

[0207] S237, perform matrix multiplication of the perception gate voting matrix MO5 and the perception matrix MO8 to obtain the perception gate output feature map ML1.

[0208] Optionally, the input module 320 is further configured to:

[0209] S241, perform a 1×1 convolution operation on the feature map MO1 to obtain a deep feature matrix MO9, and perform a 3×3 convolution operation on the deep feature matrix MO9 to obtain a deep feature matrix MO 10 .

[0210] S242, feature map MO1 and deep feature matrix MO 10 Add the elements to get the first sum matrix of the relocation gates.

[0211] S243. Perform a 1×1 convolution operation on the feature map ME1 to obtain a deep feature matrix ME8, and perform a 3×3 convolution operation on the deep feature matrix ME8 to obtain a deep feature matrix ME9.

[0212] S244, perform element-wise addition of the feature map ME1 and the deep feature matrix ME9 to obtain a second sum matrix of the relocation gate.

[0213] S245, perform matrix multiplication on the first sum matrix of the relocation gate and the second sum matrix of the relocation gate, and perform global element activation through the Sigmoid layer to obtain the relocation gate voting matrix MO 11 .

[0214] S246, obtain electromagnetic positioning memory unit L t , for the electromagnetic positioning memory unit L t Perform two 3×3 convolution operations in sequence to obtain the convolution result L3, and then combine the convolution result L3 with the electromagnetic positioning memory unit L t The elements are summed and activated through the Tanh layer to obtain the relocation matrix L4.

[0215] S247, relocate the gate voting matrix MO 11 Perform matrix multiplication with the relocation matrix L4 to obtain the relocation gate output feature map L5.

[0216] S248. Input the relocation gate output feature map L5 into the self-dimensionality reduction layer to obtain the relocation gate output feature Ot.

[0217] Optionally, the input module 320 is further configured to:

[0218] S251. For the current moment t, the target posture information sample at the previous moment t-1, the electromagnetic field phase difference sample at the current moment t, and the magnetic field signal strength sample at the current moment t are input into the screening gate to obtain the screening gate output feature map ML0.

[0219] S252, subtract the screening gate output characteristic map ML0 from the electromagnetic positioning memory unit Lt-1 at the previous time t-1 to obtain the electromagnetic positioning memory unit MLt-1.

[0220] S253. Input the feature map MO1 and the feature map ME1 in the screening gate into the perception gate to obtain the perception gate output feature map ML1.

[0221] S254, adding the electromagnetic positioning memory unit MLt-1 and the perception gate output characteristic map ML1 to obtain the electromagnetic positioning memory unit Lt.

[0222] S255. Input the feature map MO1, the feature map ME1 and the electromagnetic positioning memory unit Lt into the repositioning gate to obtain the repositioning gate output feature Ot, which is the target posture information at the current moment.

[0223] In an embodiment of the present invention, an underground space electromagnetic positioning method based on trajectory timing analysis outputs target positioning information in real time through a timing electromagnetic positioning network. Compared with traditional methods in which the target's motion trajectory and posture affect the positioning accuracy and timeliness of the near-field ranging algorithm, it has a stronger timing signal processing capability and can learn a more essential electromagnetic positioning characterization mechanism as data and time accumulate, thereby providing more accurate real-time target positioning information.

[0224] The sequential electromagnetic positioning network is designed for a specific task, namely, electromagnetic positioning in underground space. It contains three structures: screening gate, perception gate, and relocation gate. The three gate structures are designed to filter out redundant historical positioning information, perceive and store the current electromagnetic positioning representation, and relocate the current position of the target. The three gate structures are connected in series in sequence, which can enhance the anti-interference ability of the electromagnetic positioning method and achieve accurate positioning of the target through deep mining and interaction of long-term positioning historical experience and current signals.

[0225] Figure 8 is a schematic diagram of the structure of an underground space electromagnetic positioning device provided by an embodiment of the present invention, such as Figure 8 As shown, the underground space electromagnetic positioning equipment may include the above Figure 7 The underground space electromagnetic positioning device based on trajectory time series analysis is shown. Optionally, the underground space electromagnetic positioning device 410 may include a first processor 2001 .

[0226] Optionally, the underground electromagnetic positioning device 410 may also include a memory 2002 and a transceiver 2003 .

[0227] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0228] Combine the following Figure 8 The various components of the underground space electromagnetic positioning device 410 are specifically introduced:

[0229] The first processor 2001 is the control center of the underground electromagnetic positioning device 410, which can be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

[0230] Optionally, the first processor 2001 can perform various functions of the underground space electromagnetic positioning device 410 by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002.

[0231] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 8 CPU0 and CPU1 are shown in FIG.

[0232] In a specific implementation, as an embodiment, the underground space electromagnetic positioning device 410 may also include multiple processors, such as Figure 8 The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0233] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0234] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently and access the first processor 2001 through the interface circuit ( Figure 8 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0235] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0236] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 8 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0237] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently and communicate with the first processor 2001 through the interface circuit ( Figure 8 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0238] It should be noted that Figure 8 The structure of the underground space electromagnetic positioning device 410 shown in the figure does not constitute a limitation on the router. The actual knowledge structure identification device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0239] In addition, the technical effects of the underground space electromagnetic positioning device 410 can refer to the technical effects of the underground space electromagnetic positioning method based on trajectory timing analysis described in the above method embodiment, and will not be repeated here.

[0240] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0241] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0242] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0243] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0244] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0245] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0246] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0247] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0248] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0249] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0250] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0251] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0252] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An underground space electromagnetic positioning method based on trajectory time series analysis, characterized in that: The method comprises: S1, obtaining the electromagnetic field phase difference and magnetic field signal strength of the target to be located, and constructing time series input data according to the electromagnetic field phase difference and magnetic field signal strength; S2, inputting the time series input data into the constructed time series electromagnetic positioning model; wherein the time series electromagnetic positioning model includes a screening gate, a perception gate and a relocation gate; S3. Output the target position information in real time according to the time series input data and the time series electromagnetic positioning model.

2. The underground space electromagnetic positioning method based on trajectory time series analysis according to claim 1 is characterized in that: The construction process of the time series electromagnetic positioning model in S2 includes: S21, constructing an electromagnetic positioning data set; wherein the electromagnetic positioning data set includes electromagnetic field phase difference samples, magnetic field signal strength samples and corresponding target posture information samples; S22, constructing a screening gate; S23, constructing a perception gate; S24, constructing a relocation gate; S25, constructing a time-series electromagnetic positioning network according to the screening gate, the sensing gate and the relocation gate; S26. Training the time series electromagnetic positioning network according to the electromagnetic positioning data set to generate a time series electromagnetic positioning model.

3. The underground space electromagnetic positioning method based on trajectory time series analysis according to claim 2 is characterized in that: The construction screening gate in S22 includes: S221. For the current time t, obtain the target posture information sample at the previous time t-1, the electromagnetic field phase difference sample at the current time t, and the magnetic field signal strength sample at the current time t; S222, input the target posture information sample at the previous time t-1 into the first self-elevating layer to obtain a feature map MO1; S223, inputting the feature map MO1 into a convolution module to obtain a feature map MO2; S224, inputting the electromagnetic field phase difference sample at the current time t and the magnetic field signal strength sample at the current time t into the second self-dimensionalization layer to obtain a feature map ME1; S225, inputting the feature map ME1 into the global pooling layer, and outputting a feature vector ME2; S226, inputting the feature vector ME2 into the SE layer to obtain a feature vector ME3; S227. Multiply the feature map MO2 by the feature vector ME3 to obtain the screening gate output feature map ML0.

4. The underground space electromagnetic positioning method based on trajectory time series analysis according to claim 2 is characterized in that: The construction of the perception gate in S23 includes: S231, performing a 1×1 convolution operation on the feature map MO1 to obtain a deep feature matrix MO3, and performing a 3×3 convolution operation on the deep feature matrix MO3 to obtain a deep feature matrix MO4; S232, adding the feature map MO1 and the deep feature matrix MO4 element by element to obtain a first sum matrix of the perception gate; S233, performing a 1×1 convolution operation on the feature map ME1 to obtain a deep feature matrix ME4, and performing a 3×3 convolution operation on the deep feature matrix ME4 to obtain a deep feature matrix ME5; S234, adding the feature map ME1 and the deep feature matrix ME5 element by element to obtain a second sum matrix of the perception gate; S235, performing matrix multiplication of the first perception gate sum matrix and the second perception gate sum matrix, and performing global element activation through a Sigmoid layer to obtain a perception gate voting matrix MO5; S236, performing matrix multiplication of the first perception gate sum matrix and the second perception gate sum matrix, and activating through a Tanh layer to obtain a perception matrix MO8; S237, perform matrix multiplication on the perception gate voting matrix MO5 and the perception matrix MO8 to obtain the perception gate output feature map ML1.

5. The underground space electromagnetic positioning method based on trajectory time series analysis according to claim 2 is characterized in that: The construction relocation gate in S24 includes: S241, perform a 1×1 convolution operation on the feature map MO1 to obtain a deep feature matrix MO9, and perform a 3×3 convolution operation on the deep feature matrix MO9 to obtain a deep feature matrix MO 10 ; S242, the feature map MO1 and the deep feature matrix MO 10 Add the elements to obtain the first sum matrix of the relocation gates; S243, performing a 1×1 convolution operation on the feature map ME1 to obtain a deep feature matrix ME8, and performing a 3×3 convolution operation on the deep feature matrix ME8 to obtain a deep feature matrix ME9; S244, adding the feature map ME1 and the deep feature matrix ME9 element by element to obtain a second sum matrix of relocation gates; S245, matrix multiplying the first relocation gate sum matrix and the second relocation gate sum matrix, and performing global element activation through a Sigmoid layer to obtain a relocation gate voting matrix MO 11 ; S246, obtain electromagnetic positioning memory unit L t , the electromagnetic positioning memory unit L t Perform two 3×3 convolution operations in sequence to obtain a convolution result L3, and combine the convolution result L3 with the electromagnetic positioning memory unit L t Add the elements and activate them through the Tanh layer to get the relocation matrix L4; S247, the relocation gate voting matrix MO 11 Perform matrix multiplication with the relocation matrix L4 to obtain a relocation gate output feature map L5; S248. Input the relocation gate output feature map L5 into the self-dimensionality reduction layer to obtain the relocation gate output feature Ot.

6. The underground space electromagnetic positioning method based on trajectory time series analysis according to claim 2 is characterized in that: The step S25 constructs a sequential electromagnetic positioning network according to the screening gate, the sensing gate and the relocation gate, including: S251, for the current time t, input the target posture information sample at the previous time t-1, the electromagnetic field phase difference sample at the current time t, and the magnetic field signal strength sample at the current time t into the screening gate to obtain the screening gate output feature map ML0; S252, subtracting the screening gate output characteristic graph ML0 from the electromagnetic positioning memory unit Lt-1 at the previous time t-1 to obtain the electromagnetic positioning memory unit MLt-1; S253, inputting the feature map MO1 and the feature map ME1 in the screening gate into the perception gate to obtain the perception gate output feature map ML1; S254, adding the electromagnetic positioning memory unit MLt-1 to the perception gate output characteristic map ML1 to obtain an electromagnetic positioning memory unit Lt; S255. Input the feature map MO1, the feature map ME1 and the electromagnetic positioning memory unit Lt into the repositioning gate to obtain the repositioning gate output feature Ot, that is, the target posture information at the current moment.

7. An underground space electromagnetic positioning device based on trajectory time series analysis, the underground space electromagnetic positioning device based on trajectory time series analysis is used to implement the underground space electromagnetic positioning method based on trajectory time series analysis as claimed in any one of claims 1 to 6, characterized in that: The device comprises: An acquisition module is used to acquire the electromagnetic field phase difference and magnetic field signal strength of the target to be located, and construct time series input data according to the electromagnetic field phase difference and magnetic field signal strength; An input module, used for inputting the time-series input data into the constructed time-series electromagnetic positioning model; wherein the time-series electromagnetic positioning model includes a screening gate, a perception gate and a relocation gate; The output module is used to output the target posture information in real time according to the time-series input data and the time-series electromagnetic positioning model.

8. The underground space electromagnetic positioning device based on trajectory time series analysis according to claim 7, characterized in that: The construction process of the time-series electromagnetic positioning model includes: S21, constructing an electromagnetic positioning data set; wherein the electromagnetic positioning data set includes electromagnetic field phase difference samples, magnetic field signal strength samples and corresponding target posture information samples; S22, constructing a screening gate; S23, constructing a perception gate; S24, constructing a relocation gate; S25, constructing a time-series electromagnetic positioning network according to the screening gate, the sensing gate and the relocation gate; S26. Training the time series electromagnetic positioning network according to the electromagnetic positioning data set to generate a time series electromagnetic positioning model.

9. An underground space electromagnetic positioning device, characterized in that: The underground space electromagnetic positioning equipment comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 6.

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