Multi-target dynamic sensing method, device and equipment and readable storage medium

Abstract

The invention discloses a multi-target dynamic sensing method, device and equipment and a readable storage medium, and relates to the technical field of dynamic sensing. Comprising the following steps: acquiring optical fiber sensing data and wireless sensing data of each target object; performing cooperative processing on the optical fiber sensing data and the wireless sensing data of each target object to obtain optical fiber cooperative data and wireless cooperative data; and performing data fusion processing on the optical fiber collaborative data and the wireless collaborative data to obtain the dynamic information of each target object, thereby realizing simultaneous dynamic sensing of a plurality of moving targets, and improving the accuracy of dynamic sensing.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic perception, and more particularly to a multi-target dynamic perception method, device, equipment, and readable storage medium. Background Art

[0002] Dynamic perception is a technology for capturing the positioning and trajectory of target objects. Currently, there are usually two methods for dynamic perception, namely fiber optic sensor technology and wireless positioning technology. Fiber optic sensing technology has high precision and strong anti-electromagnetic interference ability in long-distance scenarios, but its application scope is mainly limited to fixed linear scenarios, and when monitoring multiple targets, it is unable to distinguish these targets, so it is impossible to effectively identify the movement trajectories of each object; while wireless positioning technology, although having the advantages of flexible deployment, supporting non-linear scenarios, and being able to identify multiple targets, has poor positioning stability and is easily affected by electromagnetic interference and path occlusion.

[0003] Therefore, there is an urgent need for a multi-target dynamic perception method that can overcome the above disadvantages. Summary of the Invention

[0004] The purpose of the present invention is to provide a multi-target dynamic perception method, device, equipment, and readable storage medium, which simultaneously performs dynamic perception on multiple moving targets by fusing fiber optic perception data and wireless perception data, and improves the accuracy of dynamic perception.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a multi-target dynamic perception method, which includes:

[0007] Obtain the fiber optic perception data and wireless perception data of each target object;

[0008] Perform collaborative processing on the fiber optic perception data and wireless perception data of each target object to obtain fiber optic collaborative data and wireless collaborative data;

[0009] Perform data fusion processing on the fiber optic collaborative data and wireless collaborative data to obtain the dynamic information of each target object.

[0010] In some embodiments, performing collaborative processing on the fiber optic perception data and wireless perception data of each target object to obtain fiber optic collaborative data and wireless collaborative data includes:

[0011] Perform time collaborative processing on the fiber optic perception data and wireless perception data of each target object to obtain fiber optic transition data and wireless transition data;

[0012] Perform space collaborative processing on the fiber optic transition data and wireless transition data to obtain fiber optic collaborative data and wireless collaborative data.

[0013] In some embodiments, time synchronization processing is performed on the optical fiber sensing data and the wireless sensing data of each target object to obtain optical fiber transition data and wireless transition data, including:

[0014] For each target object, obtain the first timestamp of the optical fiber sensing data of the target object and the second timestamp of the wireless sensing data of the target object;

[0015] Unify the first timestamp and the second timestamp into the same timeline to obtain optical fiber transition data and wireless transition data.

[0016] In some embodiments, spatial synchronization processing is performed on the optical fiber transition data and the wireless transition data to obtain optical fiber synchronization data and wireless synchronization data, including:

[0017] For each target object, obtain the first coordinate of the optical fiber transition data of the target object and the second coordinate of the wireless transition data of the target object;

[0018] Unify the first coordinate and the second coordinate into the same coordinate system to obtain optical fiber synchronization data and wireless synchronization data.

[0019] In some embodiments, data fusion processing is performed on the optical fiber synchronization data and the wireless synchronization data to obtain the dynamic information of each target object, including:

[0020] For each target object, perform a correlation calculation on the optical fiber synchronization data and the wireless synchronization data of the target object to obtain the dynamic information of the target object.

[0021] In some embodiments, obtaining the optical fiber sensing data and the wireless sensing data of each target object includes:

[0022] By detecting the phase change of the Rayleigh scattering signal of each target object, determine the optical fiber sensing data of each target object;

[0023] Obtain the relative distance information and identity information of each target object as the wireless sensing data of each target object.

[0024] In a second aspect, the present invention also provides a multi-target dynamic sensing device, and the device includes:

[0025] A data acquisition module, configured to acquire the optical fiber sensing data and the wireless sensing data of each target object;

[0026] A data synchronization module, configured to perform synchronization processing on the optical fiber sensing data and the wireless sensing data of each target object to obtain optical fiber synchronization data and wireless synchronization data;

[0027] A data fusion module, configured to perform data fusion processing on the fiber-optic collaborative data and the wireless collaborative data to obtain the dynamic information of each target object.

[0028] In a third aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the multi-target dynamic perception method provided in the first aspect is implemented.

[0029] In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the multi-target dynamic perception method provided in the first aspect is implemented.

[0030] In a fifth aspect, the present invention further provides a computer program product, including a computer program. When the computer program is executed by a processor, the multi-target dynamic perception method provided in the first aspect is implemented.

[0031] The beneficial effects of the present invention are as follows:

[0032] In the method of the present application, first, the fiber-optic perception data and the wireless perception data of each target object are obtained; then, the fiber-optic perception data and the wireless perception data of each target object are collaboratively processed to obtain fiber-optic collaborative data and wireless collaborative data; finally, the fiber-optic collaborative data and the wireless collaborative data are subjected to data fusion processing to obtain the dynamic information of each target object. The above method combines the advantages of high precision and strong anti-electromagnetic interference of fiber-optic sensing technology in long-distance scenarios, and the advantages of flexible deployment, support for non-linear scenarios, and the ability to identify multiple targets of wireless positioning technology. Therefore, this method not only improves the accuracy of dynamic perception, but also realizes the dynamic perception of multiple moving targets simultaneously, overcoming the defects in the traditional technology.

[0033] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly and implement it in accordance with the content of the specification, the following describes in detail with reference to the preferred embodiments of the present invention and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic flow chart of a multi-target dynamic perception method shown in an embodiment of the present invention;

[0035] Figure 2 It is a schematic diagram of the principle of obtaining fiber-optic perception data shown in an embodiment of the present invention;

[0036] Figure 3 It is a schematic diagram of the principle of obtaining wireless perception data shown in an embodiment of the present invention;

[0037] Figure 4The basic principle of a trilateral positioning algorithm shown in an embodiment of the present invention;

[0038] Figure 5 A schematic diagram of the principle of obtaining wireless sensing data using wireless sensing technology in an outdoor scene shown in an embodiment of the present invention;

[0039] Figure 6 A schematic flowchart of a method for obtaining fiber optic collaborative data and wireless collaborative data shown in an embodiment of the present invention;

[0040] Figure 7 A schematic flowchart of another multi-target dynamic sensing method shown in an embodiment of the present invention;

[0041] Figure 8 A schematic diagram of the principle of multi-target dynamic sensing in an indoor scene shown in an embodiment of the present invention;

[0042] Figure 9 A schematic diagram of the principle of multi-target dynamic sensing in an outdoor scene shown in an embodiment of the present invention;

[0043] Figure 10 An experimental result diagram of a multi-target dynamic sensing method shown in an embodiment of the present invention;

[0044] Figure 11 A schematic structural diagram of a multi-target dynamic sensing device shown in an embodiment of the present invention;

[0045] Figure 12 A schematic structural diagram of another multi-target dynamic sensing device shown in an embodiment of the present invention;

[0046] Figure 13 A schematic structural diagram of yet another multi-target dynamic sensing device shown in an embodiment of the present invention;

[0047] Figure 14 A schematic structural diagram of an electronic device provided in an embodiment of the present application. Detailed implementation manners

[0048] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] It should be noted that the references to "an embodiment", "embodiment", "exemplary embodiment", etc. in this specification mean that the described embodiment may include specific features, structures or characteristics. However, not every embodiment must include these specific features, structures or characteristics. In addition, such expressions do not refer to the same embodiment. Further, when combining specific features, structures or characteristics with an embodiment, it has been shown that combining such features, structures or characteristics with other embodiments is within the knowledge of those skilled in the art, whether or not explicitly described.

[0050] In addition, the technical features involved in different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0051] In some embodiments, as Figure 1 shown, a multi-target dynamic perception method is provided, and the method includes:

[0052] S101, obtaining the fiber optic sensing data and wireless sensing data of each target object.

[0053] Among them, the fiber optic sensing data is the position and trajectory information of each target object collected by fiber optic sensing technology, and the wireless sensing data is the position and trajectory information of each target object collected by wireless positioning technology.

[0054] Specifically, the fiber optic sensing data and wireless sensing data of each target object can be directly obtained through sensors.

[0055] Optionally, the method for obtaining the fiber optic sensing data and wireless sensing data of each target object can also be: determining the fiber optic sensing data of each target object by detecting the phase change of the Rayleigh scattering signal of each target object; obtaining the relative distance information and identity information of each target object as the wireless sensing data of each target object.

[0056] Exemplarily, the fiber optic sensing data of each target object can be obtained based on a phase-sensitive optical time domain reflectometer (Φ-OTDR). Its core principle is to use the coherence effect between Rayleigh scattering signals for sensing. Its schematic diagram is as Figure 2As shown, its light source uses a narrow linewidth laser with a long coherence length, enabling the Rayleigh scattering signal in the optical fiber to form an interference with high visibility. When an external perturbation acts on the sensing optical fiber, it will change the local optical path length in the optical fiber, thereby causing a phase change in the scattering signal. By measuring the phase difference (Δφ = (4πn / λ)ΔL), quantitative detection and positioning of external perturbations can be achieved, where ΔL is the optical path change caused by the perturbation, λ is the optical wave wavelength, and n is the refractive index of the optical fiber. Compared with traditional OTDR and C-OTDR, Φ-OTDR focuses on phase changes and realizes high-sensitivity detection of vibrations and perturbations through phase modulation and demodulation. The Φ-OTDR system achieves high-precision environmental monitoring through multiple key modules. First, the continuous laser emitted by the narrow linewidth laser (NLL) is divided into probe light and local oscillator light by a coupler (OC1). After the probe light is chopped and frequency-shifted by an acousto-optic modulator (AOM), it is amplified by an erbium-doped fiber amplifier (EDFA). The system can ensure that the signal maintains sufficient intensity during transmission to avoid attenuation, and finally it is injected into the sensing optical fiber by a circulator. When an external perturbation signal acts on the optical fiber in the sensing optical fiber, it will modulate the optical path length of the corresponding area, thereby changing the local phase relationship of the RBS signal. The scattered light signal is then guided to a photodetector (BPD) through an optical coupler (OC2) to convert the optical signal into an electrical signal. The intermediate-frequency signal output by the photoelectric conversion is processed through signal processing such as filtering and amplification, and then sent to a computer after data acquisition. The data acquisition module (DAQ) is responsible for digitally processing the collected electrical signals, analyzing the delay information of the backscattered light, obtaining the loss measurement results, and finally the computer realizes the identification and positioning of external perturbation events by the sensing system based on this, and at this time the acquisition of fiber sensing data is completed.

[0057] In addition, wireless sensing data of each target object can be obtained based on the positioning technology of ultra-wideband UWB. The UWB positioning technology based on time-of-flight (TOF) calculates the distance between nodes by measuring the propagation time of the signal from the transmitting node to the receiving node. Its core principle utilizes the wideband characteristic of the UWB signal (bandwidth greater than 500 MHz, and the center frequency is usually greater than 2.5 GHz). The schematic diagram is as Figure 3 shown. Specifically, the UWB signal is transmitted in the form of pulse modulation. By sending short pulse signals with steep rise and fall times and combining the high time-resolution characteristic, the time of arrival (TOA) of the signal is measured, and the propagation path length is obtained by multiplying the propagation time by the speed of light. In the case of unsynchronized clocks, distance estimation can be achieved by measuring the round-trip time (RTT) of the signal. The time calculation formula for the UWB signal between the anchor node and the tag is Then the distance between the anchor node and the tag node can be calculated by the formula Calculate, then with the distance between the anchor node and the label, the principle of three-sided positioning can be used to determine the location information of each target object. In addition, the identity information of each target object can be obtained, thereby realizing the distinction of the location information of each target object. Figure 4 The basic principle of the three-side positioning algorithm is shown. Assuming that the electronic tags marked in the positioning map need to be located, and the positions of each base station are known, before calculating the tag position, the distances d1, d2, and d3 from each base station to the tag are first obtained by the aforementioned ranging method. Then, with each base station as the center, circles with radii d1, d20, and d3 are drawn. By solving the following set of equations, the intersection of the arcs of multiple base stations in space can be searched, so that the exact position of the tag can be calculated:

[0058]

[0059] Among them, (x1, y1), (x2, y2) and (x3, y3) are the coordinates of anchor nodes 1, 2 and 3.

[0060] For example, Figure 5 As shown in the figure, when wireless sensing technology is applied to outdoor scenarios, such as dynamically sensing the position of a vehicle, a uniform linear array (ULA) antenna can be used to communicate with vehicles on the road through a line of sight (LoS) channel. Millimeter wave massive (MIMO) antenna arrays are equipped on both sides of the vehicle. Assume that the vehicle is traveling along a straight path parallel to the road side unit (RSU) antenna array. The ULA antenna of the road side unit (RSU) can be adjusted to be parallel to the road, allowing for a small angle deviation, which can be adjusted by a fixed correction amount. The angle, distance, and speed of the vehicle relative to the RSU are denoted as θ(t), d(t), and v(t), respectively. These parameters are functions of time t∈[0,T]. To simplify the representation, time T is discretized into several time periods, each of which is ΔT. In each time period, the vehicle's motion parameter θ n , d n and v n Assume it is a constant. (1) Initial perception. First, the RSU initializes the perception of the vehicle parameters entering its coverage area. At this stage, the RSU can be used as a single-function radar to calculate the initial parameters of the vehicle through echo reflection. 0 , d 0 and v 0 (2) State perception. At the n-1th moment, the RSU senses the motion parameters and Perform one-step and two-step predictions on the angle parameter, and simultaneously perform one-step perception predictions on the distance and speed. At the nth moment, the RSU uses the one-step perception prediction value to form a transmission beam and send an ISAC communication signal containing the two-step perception prediction value . After receiving this information, the vehicle adjusts the receiving beam at the (n + 1)th moment according to the predicted angle. The two-step prediction is used to avoid angle obsolescence, and all predictions are based on the kinematic equations of the vehicle. If the perception and prediction are accurate, the beams of the RSU and the vehicle will be aligned. (3) Vehicle tracking. At the nth moment, part of the signal sent by the RSU is reflected by the vehicle, and the other part is received by the vehicle antenna. The vehicle uses the predicted angle information at the (n + 1)th moment in the received data to complete receiving beamforming. At the same time, the RSU estimates θ n , d n and v n by receiving the echo signal of the vehicle, and optimizes the perception prediction parameters at the nth moment. These optimized parameters are used to generate the prediction results of the RSU at the (n + 1)th and (n + 2)th moments. At this time, the wireless perception data of the vehicle is obtained.

[0061] S102. Coordinate the fiber optic perception data and wireless perception data of each target object to obtain fiber optic coordinated data and wireless coordinated data.

[0062] Specifically, for the fiber optic perception data and wireless perception data of each target object, unify them to the same time line and the same coordinate system to obtain fiber optic coordinated data and wireless coordinated data.

[0063] S103. Perform data fusion processing on the fiber optic coordinated data and wireless coordinated data to obtain the dynamic information of each target object.

[0064] Specifically, fuse the fiber optic coordinated data and wireless coordinated data into a set of data, and this set of data is the dynamic information of each target object.

[0065] Optionally, for each target object, calculate the correlation between the fiber optic coordinated data and wireless coordinated data of this target object to obtain the dynamic information of this target object.

[0066] Exemplarily, the correlation between the fiber optic coordinated data and wireless coordinated data can be calculated according to preset parameters, so that when the amount of wireless coordinated data is insufficient, it can be matched and replaced by the fiber optic coordinated data, and then more accurate dynamic information of the target object can be obtained.

[0067] Optionally, the method for performing data fusion processing on fiber optic collaborative data and wireless collaborative data can also calculate the fiber optic collaborative data and wireless collaborative data based on the least squares method to obtain a fitting curve, which is the motion trajectory of each target object. Combining the time stamps corresponding to the fitting curve, the positions of each target object at each moment can be obtained.

[0068] In the above embodiments, first, the fiber optic sensing data and wireless sensing data of each target object are obtained; then, the fiber optic sensing data and wireless sensing data of each target object are collaboratively processed to obtain fiber optic collaborative data and wireless collaborative data; finally, the fiber optic collaborative data and wireless collaborative data are subjected to data fusion processing to obtain the dynamic information of each target object. The above method combines the advantages of high precision and strong anti-electromagnetic interference of fiber optic sensing technology in long-distance scenarios, and the advantages of flexible deployment, support for non-linear scenarios, and the ability to identify multiple targets of wireless positioning technology. Therefore, this method not only improves the accuracy of dynamic perception, but also realizes the dynamic perception of multiple moving targets at the same time, overcoming the defects in traditional technologies.

[0069] In another embodiment, as Figure 6 shown, the process of obtaining fiber optic collaborative data and wireless collaborative data is elaborated in detail. The specific method includes:

[0070] S201, perform time collaborative processing on the fiber optic sensing data and wireless sensing data of each target object to obtain fiber optic transition data and wireless transition data.

[0071] Optionally, for each target object, obtain the first time stamp of the fiber optic sensing data of the target object and the second time stamp of the wireless sensing data of the target object; unify the first time stamp and the second time stamp into the same time line to obtain fiber optic transition data and wireless transition data.

[0072] Exemplarily, first perform data cleaning and data supplementation on the first time stamp and the second time stamp, and unify their units. Subsequently, according to the sequence relationship between the first time stamp and the second time stamp, reset the first time stamp and the second time stamp on the same time line, that is, fiber optic transition data and wireless transition data are obtained.

[0073] S202, perform space collaborative processing on the fiber optic transition data and wireless transition data to obtain fiber optic collaborative data and wireless collaborative data.

[0074] Optionally, for each target object, obtain the first coordinate of the fiber optic transition data of the target object and the second coordinate of the wireless transition data of the target object; unify the first coordinate and the second coordinate into the same coordinate system to obtain fiber optic collaborative data and wireless collaborative data.

[0075] Exemplarily, the first coordinate of the fiber-optic transition data of the target object and the second coordinate of the wireless transition data of the target object may not be in the same coordinate system. A world coordinate system can be established, and according to the relationship between the first coordinate, the second coordinate, and the world coordinate system, the first coordinate and the second coordinate can be unified into the world coordinate system, and then the fiber-optic collaborative data and the wireless collaborative data can be obtained.

[0076] In the method of the above embodiment, first, time synchronization processing is performed on the fiber-optic sensing data and the wireless sensing data of each target object to obtain fiber-optic transition data and wireless transition data; then, spatial synchronization processing is performed on the fiber-optic transition data and the wireless transition data to obtain fiber-optic collaborative data and wireless collaborative data, unifying the fiber-optic sensing data and the wireless sensing data of each target object in terms of time and space, which is convenient for more intuitively comparing the two, and is also more convenient for determining more accurate dynamic information according to the relationship between the two.

[0077] To more comprehensively demonstrate the present solution, an optional manner of a multi-target dynamic sensing method is given in this embodiment, as Figure 7 shown:

[0078] S301. Determine the fiber-optic sensing data of each target object by detecting the phase change of the Rayleigh scattering signal of each target object.

[0079] S302. Obtain the relative distance information and identity information of each target object as the wireless sensing data of each target object.

[0080] S303. For each target object, obtain the first timestamp of the fiber-optic sensing data of the target object and the second timestamp of the wireless sensing data of the target object.

[0081] S304. Unify the first timestamp and the second timestamp into the same timeline to obtain fiber-optic transition data and wireless transition data.

[0082] S305. For each target object, obtain the first coordinate of the fiber-optic transition data of the target object and the second coordinate of the wireless transition data of the target object.

[0083] S306. Unify the first coordinate and the second coordinate into the same coordinate system to obtain fiber-optic collaborative data and wireless collaborative data.

[0084] S307. For each target object, perform correlation calculation on the fiber-optic collaborative data and the wireless collaborative data of the target object to obtain the dynamic information of the target object.

[0085] The specific processes of the above S301 - S307 can refer to the description of the method embodiment above, and their implementation principles and technical effects are similar, so they will not be elaborated here.

[0086] Exemplarily, the above multi-target dynamic perception method can be applied to indoor and outdoor scenarios.

[0087] When applied to an indoor scenario, as Figure 8 shown, first, for fiber optic sensing data acquisition, the system first monitors the indoor environment in real time through a fiber optic sensing module (Φ-OTDR system) to obtain environmental change data such as temperature, vibration, and displacement information. This data provides environmental status information for the subsequent positioning system; at the same time, for UWB positioning data acquisition, the UWB system measures the distance data between the target and the UWB base stations through wireless signals for real-time positioning of the target. Each UWB positioning node exchanges signals with multiple base stations and calculates the target position through methods such as triangulation; next, the data fusion processing center receives the fiber optic sensing data and UWB positioning data and combines the information of both for synchronization and matching. Using spatio-temporal correlation and data fusion algorithms, the system can accurately combine the fiber optic sensing data and UWB positioning data to optimize the target position and environmental perception information; then, for real-time feedback and optimization, the result after data fusion is fed back to the system, that is, the dynamic information of the target is obtained. Fiber optic sensing provides long-distance and high-precision monitoring, and UWB positioning provides flexible and real-time target position recognition. The combination of the two effectively improves the overall performance of the system.

[0088] When applied to an outdoor scenario, taking the vehicle networking scenario as an example, as Figure 9 shown, first, in the data acquisition stage, the fiber optic sensing system perceives the environment through the fiber optic to obtain the change information of the vehicle and its surrounding environment in real time. At the same time, the ISAC base station tracks the target vehicle through radar beams and conducts necessary communication and sensing data acquisition; then, for data fusion and processing, the fiber optic sensing data and ISAC sensing data are integrated through a data fusion processor, and the optical signal and wireless signal are optimized and synchronized based on spatio-temporal correlation and signal matching algorithms. The data fusion module simultaneously uses algorithms such as Kalman filtering to predict the system state and optimizes the positioning accuracy through feedback information; finally, for positioning and beam tracking, the system determines the precise position of the target vehicle and adjusts the communication beam through the fusion of fiber optic and ISAC data. The ISAC system further improves the positioning accuracy through real-time beam alignment technology. At the same time, the fiber optic sensing system compensates for the signal attenuation problem in complex environments such as multi-path and occlusion, improving the anti-interference ability of the overall system. In the vehicle networking scenario, fiber optic sensing and the ISAC system can complement each other. The ISAC base station uses its communication and radar sensing capabilities to optimize the beam alignment and tracking of in-vehicle targets, while the fiber optic sensing technology provides additional environmental perception support through its long-distance and electromagnetic interference-resistant characteristics. Through signal synchronization and data fusion algorithms, the fiber optic and wireless sensing signals are effectively integrated, further improving the reliability and positioning accuracy of the system.

[0089] In another embodiment, as Figure 10 shown, an experimental result graph of a multi-target dynamic perception method is provided. According to the experimental results, it can be known that: the wireless perception data (Data 2 and Data 4) is successfully matched with the optical fiber perception data (Data 1 and Data 3), corresponding to the trajectories of different target tags respectively. After being processed by the fusion algorithm, the positioning trajectory is complemented and optimized. The wireless perception data is effectively supplemented by the optical fiber perception data in the data missing section caused by interference, and the overall trajectory shows continuity and high precision. In addition, the fused trajectory is highly consistent with the real trajectory, verifying the effectiveness of the fusion algorithm in improving the positioning accuracy, stability and multi-target matching ability.

[0090] Based on the same inventive concept, an embodiment of the present application also provides a multi-target dynamic perception device for implementing the above-mentioned multi-target dynamic perception method. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the multi-target dynamic perception device provided below can refer to the limitations on the multi-target dynamic perception method in the above text, and will not be elaborated here.

[0091] In one embodiment, as Figure 11 shown, a multi-target dynamic perception device is provided, and the device includes:

[0092] A data acquisition module 40, configured to acquire the optical fiber perception data and wireless perception data of each target object;

[0093] A data collaboration module 41, configured to perform collaborative processing on the optical fiber perception data and wireless perception data of each target object to obtain optical fiber collaborative data and wireless collaborative data;

[0094] A data fusion module 42, configured to perform data fusion processing on the optical fiber collaborative data and wireless collaborative data to obtain the dynamic information of each target object.

[0095] In another embodiment, as Figure 12 shown, the above Figure 11 data collaboration module 41 includes:

[0096] A time collaboration unit 410, configured to perform time collaboration processing on the optical fiber perception data and wireless perception data of each target object to obtain optical fiber transition data and wireless transition data;

[0097] A space collaboration unit 411, configured to perform space collaboration processing on the optical fiber transition data and wireless transition data to obtain optical fiber collaborative data and wireless collaborative data.

[0098] In another embodiment, the above Figure 12The time coordination unit 410 therein is specifically configured to: for each target object, obtain the first timestamp of the fiber optic sensing data of the target object and the second timestamp of the wireless sensing data of the target object; unify the first timestamp and the second timestamp into the same timeline to obtain the fiber optic transition data and the wireless transition data.

[0099] In another embodiment, the above Figure 12 The space coordination unit 411 therein is specifically configured to: for each target object, obtain the first coordinate of the fiber optic transition data of the target object and the second coordinate of the wireless transition data of the target object; unify the first coordinate and the second coordinate into the same coordinate system to obtain the fiber optic coordination data and the wireless coordination data.

[0100] In another embodiment, the above Figure 11 The data fusion module 42 therein is specifically configured to: for each target object, perform a correlation calculation on the fiber optic coordination data and the wireless coordination data of the target object to obtain the dynamic information of the target object.

[0101] In another embodiment, as Figure 13 shown, the above Figure 11 The data acquisition module 40 therein includes:

[0102] The first acquisition unit 400 is configured to determine the fiber optic sensing data of each target object by detecting the phase change of the Rayleigh scattering signal of each target object.

[0103] The second acquisition unit 401 is configured to obtain the relative distance information and identity information of each target object as the wireless sensing data of each target object.

[0104] The embodiment of the present application further provides an electronic device. In some embodiments, referring to Figure 14 shown, the electronic device 700 includes an input unit 710, a memory 720, a processor 730, and an output unit 740. The memory 720 stores program instructions that can be run on the processor 730, and the processor 730 can execute the multi-target dynamic sensing method and / or technical solution based on the foregoing embodiments by invoking the program instructions. The electronic device 700 can be a mobile terminal device such as a mobile phone or a computer.

[0105] In addition, the embodiment of the present application further provides a computer-readable storage medium for storing a computer program for executing the multi-target dynamic sensing method. For example, computer program instructions, when executed by a computer, can call or provide the method and / or technical solution according to the present application through the operation of the computer. The program instructions for calling the method of the present application may be stored in a fixed or removable storage medium, and / or transmitted and / or stored in a storage medium running according to the program instructions through a data stream in a broadcast or other signal-bearing medium.

[0106] Obviously, those skilled in the art should understand that the various modules or steps of the present application described above can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. In this way, the present application is not limited to any specific combination of hardware and software.

[0107] The technical features of the above embodiments can be arbitrarily integrated. For the sake of brevity of description, not all possible integrations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the integration of these technical features, it should be considered as falling within the scope described in this specification.

[0108] The above embodiments only represent several implementation manners of the present invention, and the description thereof is relatively specific and detailed. However, it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the appended claims.

Claims

1. A multi-target dynamic perception method, characterized in that: The method comprises: Obtain optical fiber sensing data and wireless sensing data of each target object; Coordinately process the optical fiber sensing data and the wireless sensing data of each target object to obtain optical fiber coordinated data and wireless coordinated data; The optical fiber collaborative data and the wireless collaborative data are subjected to data fusion processing to obtain dynamic information of each target object.

2. The multi-target dynamic perception method according to claim 1, characterized in that: The optical fiber sensing data and the wireless sensing data of each target object are collaboratively processed to obtain optical fiber collaborative data and wireless collaborative data, including: Performing time-coordinated processing on the optical fiber sensing data and the wireless sensing data of each target object to obtain optical fiber transition data and wireless transition data; The optical fiber transition data and the wireless transition data are spatially coordinated to obtain the optical fiber coordination data and the wireless coordination data.

3. The multi-target dynamic perception method according to claim 2, characterized in that: The optical fiber sensing data and the wireless sensing data of each target object are subjected to time coordinated processing to obtain optical fiber transition data and wireless transition data, including: For each target object, obtaining a first timestamp of the optical fiber sensing data of the target object and a second timestamp of the wireless sensing data of the target object; The first timestamp and the second timestamp are unified into the same timeline to obtain the optical fiber transition data and the wireless transition data.

4. The multi-target dynamic perception method according to claim 2, characterized in that: Performing spatial collaborative processing on the optical fiber transition data and the wireless transition data to obtain the optical fiber collaborative data and the wireless collaborative data includes: For each target object, obtaining a first coordinate of the optical fiber transition data of the target object and a second coordinate of the wireless transition data of the target object; The first coordinate and the second coordinate are unified into the same coordinate system to obtain the optical fiber collaborative data and the wireless collaborative data.

5. The multi-target dynamic perception method according to claim 1, characterized in that: The optical fiber collaborative data and the wireless collaborative data are subjected to data fusion processing to obtain dynamic information of each target object, including: For each target object, correlation calculation is performed on the optical fiber coordination data and the wireless coordination data of the target object to obtain dynamic information of the target object.

6. The multi-target dynamic perception method according to claim 1, characterized in that: Obtain the optical fiber sensing data and wireless sensing data of each target object, including: Determining the optical fiber sensing data of each target object by detecting the phase change of the Rayleigh scattering signal of each target object; The relative distance information and identity information of each target object are obtained as the wireless sensing data of each target object.

7. A multi-target dynamic perception device, characterized in that: The device comprises: A data acquisition module, used to acquire optical fiber sensing data and wireless sensing data of each target object; A data collaboration module, used for collaboratively processing the optical fiber sensing data and the wireless sensing data of each target object to obtain optical fiber collaborative data and wireless collaborative data; The data fusion module is used to perform data fusion processing on the optical fiber collaborative data and the wireless collaborative data to obtain dynamic information of each target object.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the multi-target dynamic perception method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the multi-target dynamic perception method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the multi-target dynamic perception method described in any one of claims 1 to 6 is implemented.

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