Matching method and system based on 5G network, Beidou positioning and non-line-of-sight microwave

Abstract

The invention discloses a matching method and system based on a 5G network, Beidou positioning and non-line-of-sight microwave, and relates to the technical field of target signal positioning, and the matching method comprises the steps: building a static and dynamic combined calibration data set through obtaining high-precision geographic position information and real-time signal intensity data, and carrying out the calibration of the calibration data set; and adaptive direction adjustment and rapid focusing of the antenna are supported, and efficient communication and accurate positioning in a complex scene are realized. The calibration matching model is trained based on the calibration data set, so that the precision and adaptability of the model under multiple geographical environments and non-line-of-sight conditions are improved, an efficient antenna positioning mechanism is provided, and the stability and real-time performance of a communication link are guaranteed; the method is particularly suitable for complex environments such as disaster rescue, accident personnel can be quickly and accurately positioned, and rescue efficiency and action reliability are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of target signal positioning technology, and in particular to a matching method and system based on 5G network and Beidou positioning and non-line-of-sight microwave. Background Art

[0002] With the frequent occurrence of large-scale gatherings and sudden disasters, the existing communication system is facing tremendous pressure, which may lead to communication interruption or even paralysis. Therefore, improving the resilience and response speed of emergency support has become the focus of the communications industry. By combining Beidou satellite positioning, 5G communication and non-line-of-sight microwave antenna technology, it is possible to quickly locate and dispatch emergency vehicles, personnel and materials to ensure the efficient operation of the emergency communication system. The system can support the monitoring and prediction of major emergencies, on-site command and emergency repairs, self-rescue and calls for help by people in disaster areas, and the satisfaction of communication needs in disaster areas, thereby improving emergency response capabilities and ensuring the rapid recovery and protection of communication services in disaster areas; To sum up, how to improve emergency response capabilities and ensure the rapid recovery and protection of communication services in disaster areas is an urgent problem to be solved and optimized based on the matching system of 5G networks, Beidou positioning and non-line-of-sight microwaves. Summary of the invention

[0003] The present invention provides a matching method and system based on 5G network, Beidou positioning and non-line-of-sight microwave to solve the technical problem of how to improve emergency response capabilities and ensure the rapid recovery and protection of communication services in disaster areas.

[0004] In order to solve the above technical problems, the present invention provides a matching method and system based on 5G network and Beidou positioning and non-line-of-sight microwave. The specific technical solutions are as follows: First, a matching method based on 5G network, Beidou positioning and non-line-of-sight microwave includes the following steps: S100, acquiring high-precision geographic location information data to obtain a first calibration data set; and enabling the antenna to adaptively and quickly focus and adjust direction according to the first calibration data set; S200, based on preliminary adjustment of the antenna direction, so that the antenna collects signals and analyzes changes in signal strength to obtain a second calibration data set; dynamically adjusting the antenna direction to match the target signal under non-line-of-sight conditions according to the second calibration data set; S300, combining the first calibration data set and the second calibration data set to obtain a calibration data set; training the calibration data set to construct a calibration matching model; the calibration matching model outputs an identification result indicating a calibration match between antenna position information and target signal position information; S400, based on the output result of the calibration matching model, using non-line-of-sight microwave in the disaster environment to enable the antenna to receive the target signal and determine the precise location of the accident personnel.

[0005] As a further optimization solution of the present invention, according to the first calibration data set, the antenna is adaptively and quickly focused and oriented, including: Based on the first calibration data set, the position of the antenna is preliminarily adjusted to obtain an initial reference direction of the antenna ; In the formula, θ ref represents the initial elevation angle of the antenna and ϕ ref Indicates the initial azimuth of the antenna; the initial reference direction of the antenna indicates that the starting direction of the antenna will be calibrated according to the coordinates of the target location; Based on the antenna in the initial reference direction, the target signal is received. , so that the received target signal is interfered with to obtain the real target signal; where r(t) represents the real signal vector received by the antenna; A(t) represents the gain matrix, representing the attenuation and gain of the signal; S(t) represents the real transmitted signal; n(t) represents the noise or interference signal; Based on receiving the real target signal, The search antenna is initially adjusted in direction so that the antenna can adaptively adjust its own receiving signal direction according to the initial reference direction. When the antenna automatically adjusts and optimizes the receiving angle of the target signal, the target direction of the antenna is determined. Where, Indicates the target direction after the antenna is initially adjusted; Indicates the real target signal strength; represents the adjustment function of the initial reference direction of the antenna; Based on the antenna, preliminary focusing is performed to focus on the signal source in the target area; through preliminary focusing, the antenna receives the target signal and provides real-time feedback to adjust the direction accuracy so that the reception error between the antenna and the target signal is small.

[0006] As a further optimization scheme of the present invention, based on the preliminary adjustment of the antenna direction, the antenna collects signals and analyzes the signal strength change to obtain a second calibration data set; according to the second calibration data set, the initial direction of the antenna is dynamically adjusted to match the target signal under non-line-of-sight conditions, including: When the antenna in the initial reference direction receives the target signal in real time, the target signals received multiple times are marked to obtain a signal reception mark point set; the signal reception mark point set includes the signal sending strength, the signal strength when the antenna receives, and the antenna angle of the received signal; Based on the signal reception mark point set, the information data of multiple signal reception mark points are recorded and compared to obtain the signal strength difference of the multiple signal reception mark points; according to the signal strength difference of the signal mark points, the initial reference direction of the antenna is preliminarily adjusted; Based on the signal reception mark point set, the initial signal strength Further analysis is performed to obtain the second calibration data set; where P represents the received initial signal strength; P0 represents the reference distance; n represents the path loss index; d represents the actual distance from the antenna; X α Represents log-normal shadow falloff, with random variations due to changes in large-scale environmental attenuation.

[0007] As a further optimization solution of the present invention, the information data of the plurality of signal receiving mark points are recorded and compared to obtain the signal strength difference of the plurality of signal receiving mark points, including: Based on the signal receiving mark point set, a plurality of triplet data sets are formed by obtaining the received mark points , where P out Indicates the signal strength; P recv Indicates the received signal strength; θ recv Indicates the angle of receiving signal; Based on the acquired triple data set; multiple triple data sets are compared to form signal reception mark point information pairs; each pair is composed of two triple data sets and ; In the formula, i and j represent data items in different triple data sets respectively; Based on the signal, a pair of marking point information is received, by ; To obtain the logarithmic difference of signal strength at the signal receiving mark point; where, Indicates the logarithmic difference of signal strength at the signal reception mark point; the logarithmic difference of signal strength at the signal reception mark point is the signal strength difference at the signal reception mark point; As a further optimization solution of the present invention, the initial signal intensity is further analyzed to obtain a second calibration data set, including: Obtain the initial true target signal strength at different locations and time periods, and analyze the signal changes by comparing the signal strength data at different times or locations; According to the change of signal, ; Obtain the mean and standard deviation of signal strength at different locations or time periods to evaluate signal stability and variation range, where μ represents the mean of signal strength; h represents the standard deviation of signal strength; N represents the number of signal receiving markers; P i Indicates the signal strength data collected in real time; The smaller the standard deviation h is or the higher the signal-to-noise ratio SNR is, the more stable the signal is; the range of variation is ; In the formula, Indicates the range of variation; Indicates the upper limit of signal strength change; Indicates the lower limit of intensity change; According to the signal stability and variation range, ; To determine the relationship between signal strength and antenna direction; where, represents the correlation coefficient between signal strength and antenna direction; f(θ) represents the fitting function; The correlation coefficient between the signal strength and the antenna direction is used to obtain the relationship between the signal strength and the antenna direction. Under non-line-of-sight conditions, the signal strength usually changes with the change of the antenna angle; the relationship between the known signal strength and the antenna direction is used to enable the antenna to dynamically adjust the antenna direction for a second time to optimize the direction of receiving the target signal.

[0008] As a further optimization scheme of the present invention, the calibration data set is trained to construct a calibration matching model; the calibration matching model outputs an identification result indicating the calibration matching between the antenna position information and the target signal position information, including: Generate structure data from the historical calibration data set, and encode the structure data into sequence data to train the calibration matching model; The sequence data is input into the calibration matching model; the calibration matching model includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer, the intermediate representation data of multiple hidden layers are transmitted to the output layer, and the output layer outputs the recognition result representing the calibration matching of the antenna position information and the target signal position information; The newly acquired calibration data set is input into the calibration matching model to output a newly acquired output layer output representing a prediction result of the calibration matching between the antenna position information and the target signal position information.

[0009] As a further optimization scheme of the present invention, based on the output result of the calibration matching model, non-line-of-sight microwave is used in the disaster environment to realize the antenna receiving the target signal to determine the precise location of the accident personnel, including: Based on the prediction results of the calibration match between the antenna position information and the target signal position information; and in combination with the dynamic adjustment of the antenna direction and the signal strength of the target signal, signal modeling is performed through non-line-of-sight microwave frequency to describe the signal band changes under multipath propagation and obstacle reflection and diffraction of non-line-of-sight propagation; By combining signal band changes with signal strength, and then according to terrain complexity and obstacle distribution, the channel conditions of the antenna in a complex environment are obtained; antenna modulation and coding signals are dynamically adjusted according to channel conditions; and the signal receiving environment is divided into diversity to obtain spatial diversity, time diversity and frequency diversity; Based on the obtained signal receiving spatial diversity, time diversity and frequency diversity, the signal band amplitudes of different diversity information are obtained; the three kinds of diversity information band amplitudes of the corresponding nodes are integrated to obtain a band amplitude data integration set; according to the band amplitude data integration set, the highest point band amplitude integration data is obtained; the band amplitude highest point data is the target position of the preliminary prediction of the accident personnel; Based on multi-source data of antenna received signals and combined with calibration matching models, the precise location of accident personnel is updated and predicted in real time.

[0010] As a further optimization scheme of the present invention, based on the obtained signal receiving space diversity, time diversity and frequency diversity, the signal band amplitudes of different diversity information are obtained; the three kinds of diversity information band amplitudes of the corresponding nodes are integrated to obtain a band amplitude data integration set, including: The spatial diversity means that multiple antennas are arranged and the receiving signal is adjusted to point to the receiving target signal; the time diversity means sampling the target signal at different time nodes; the frequency diversity means multiple frequency channels; Based on the spatial diversity, the time diversity and the frequency diversity, the band amplitude of the signal is extracted for the diversity conditions of the three signals; and the signal band amplitude data is aligned and normalized to obtain unified standard band amplitude data; The unified standard band amplitude data are integrated into a unified data set to enhance the signal of the unified data set; and the communication in complex environments is optimized and the target position of accident personnel is tracked.

[0011] As a further optimization scheme of the present invention, based on the multi-source data of the antenna receiving signal, combined with the calibration matching model, the accurate location of the accident personnel is updated and predicted in real time, including: By constructing a three-dimensional terrain map of the accident area and combining it with signal modeling, we can obtain the initial prediction of the target location signal information of the accident personnel; by using the diversity of channel conditions and signal receiving environment, we can verify the initial prediction of the spatial location of the accident personnel's signal information; in order to determine the precise location of the victims; and through Update the positioning data of the target personnel in real time to improve the accuracy of the predicted target position; where X k represents the predicted position; z k Indicates the measurement position; K k represents the filter gain; H represents the observation matrix.

[0012] In a second aspect, the system sets an electronic device including a memory, a processor, and a matching method program based on 5G network and Beidou positioning and non-line-of-sight microwaves stored in the memory and run on the processor. When the matching method program based on 5G network and Beidou positioning and non-line-of-sight microwaves is executed by the processor, the steps of the matching method based on 5G network and Beidou positioning and non-line-of-sight microwaves are implemented. The system includes: Information acquisition module: used to obtain high-precision geographic location information data to obtain a first calibration data set; based on the first calibration data set, the antenna is adaptively and quickly focused and oriented; Detection and adjustment module: It is used to make the antenna collect signals and analyze signal strength changes based on preliminary adjustment of the antenna direction to obtain a second calibration data set; according to the second calibration data set, dynamically adjust the antenna direction to match the target signal under non-line-of-sight conditions; A calibration matching module: used to combine the first calibration data set and the second calibration data set to obtain a calibration data set; train the calibration data set to construct a calibration matching model; the calibration matching model outputs an identification result indicating a calibration match between the antenna position information and the target signal position information; Positioning prediction module: It is used for the output result of the calibration matching model, and uses non-line-of-sight microwaves in the disaster environment to enable the antenna to receive the target signal to determine the precise location of the accident personnel.

[0013] The present invention has at least the following beneficial effects: the present invention forms a first calibration data set by acquiring high-precision geographic location information data; provides an initial benchmark for adaptive adjustment of the antenna to ensure the accuracy of subsequent steps; high-precision location information can effectively reduce antenna direction misalignment caused by initial errors and improve calibration efficiency; then, based on the first calibration data set, the antenna is adaptively quickly focused and oriented to quickly align the antenna direction; the antenna is enabled to quickly capture the target direction and shorten the debugging time; accurate focus and direction adjustment can avoid multiple experiments, improve efficiency and reduce resource consumption; and lay the foundation for signal acquisition and analysis to avoid signal quality degradation due to direction errors.

[0014] The second calibration data set is obtained by collecting signals through the antenna and analyzing the changes in signal strength. Based on the data of real-time signal strength changes, the antenna's adaptability to the environment is optimized. A dynamic adjustment basis is provided to ensure that the antenna can maintain efficient communication in complex scenarios (such as non-line-of-sight conditions). The ability to identify and adapt to environmental interference factors is enhanced.

[0015] Based on the second calibration data set, the antenna direction is dynamically adjusted to match the signal under non-line-of-sight conditions. The dynamic adjustment mechanism ensures that the antenna can automatically optimize the signal path in non-line-of-sight scenarios. The connection stability of the antenna is improved, overcoming the influence of complex terrain or obstacles. The reliability of the communication link is ensured, reducing signal loss or weak signal problems.

[0016] The first calibration data set and the second calibration data set are combined to form a calibration data set; static geographic location information is combined with dynamic signal strength changes to improve the integrity and accuracy of the data set; a more comprehensive calibration benchmark is provided to support the training of subsequent calibration matching models; the model deviation caused by a single data source is reduced and the adaptability of the model is enhanced; based on the calibration data set, a calibration matching model is trained and constructed; multi-dimensional calibration data is used to improve the accuracy of the calibration matching model; the model can adapt to different geographical environments and communication conditions and enhance the prediction ability; an efficient antenna position prediction mechanism is provided to shorten the positioning response time.

[0017] The calibration and matching model outputs the antenna position information and the predicted results of the position matching; it achieves high-precision prediction of the antenna position, providing a basis for subsequent communication optimization; it provides real-time position information support to ensure the accuracy of antenna adjustment; it responds quickly in dynamic communication scenarios, improving the real-time and reliability of the overall system.

[0018] Through non-line-of-sight microwave communication technology, precise connection of antennas and stability of communication links can be achieved in complex terrain or blocked by obstacles. The limitations of traditional line-of-sight communication can be overcome to ensure communication capabilities in complex scenarios. The adaptability and deployment range of the system are improved, making it suitable for special environments such as disaster relief. The stability of the communication link is guaranteed to provide continuous data support for subsequent precise positioning.

[0019] Combined with the calibration matching model, the precise location of accident personnel is predicted in real time; through the efficient prediction ability of the model, the location of accident personnel in complex environments can be quickly determined; the rescue efficiency is improved and the rescue time loss caused by information delay is reduced; stable and reliable personnel location data support is provided to improve the accuracy and safety of rescue operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of a matching method based on 5G network, Beidou positioning and non-line-of-sight microwave provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of a matching system based on 5G network, Beidou positioning and non-line-of-sight microwave provided by an embodiment of the present invention; Figure 3 This is a terminal architecture diagram based on 5G network, Beidou positioning and non-line-of-sight microwave provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The present application is further described in detail below in conjunction with the accompanying drawings. It is necessary to point out here that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technical personnel in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0022] The matching method and system based on 5G network, Beidou positioning and non-line-of-sight microwave provided in this embodiment are specifically implemented as follows: like Figure 1 As shown, the matching method based on 5G network and Beidou positioning and non-line-of-sight microwave includes the following steps: S100, acquiring high-precision geographic location information data to obtain a first calibration data set; and enabling the antenna to adaptively and quickly focus and adjust direction according to the first calibration data set; S200, based on preliminary adjustment of the antenna direction, so that the antenna collects signals and analyzes changes in signal strength to obtain a second calibration data set; dynamically adjusting the antenna direction to match the target signal under non-line-of-sight conditions according to the second calibration data set; S300, combining the first calibration data set and the second calibration data set to obtain a calibration data set; training the calibration data set to construct a calibration matching model; the calibration matching model outputs an identification result indicating a calibration match between antenna position information and target signal position information; S400, based on the output result of the calibration matching model, using non-line-of-sight microwave in the disaster environment to enable the antenna to receive the target signal and determine the precise location of the accident personnel.

[0023] In the embodiment, step S100 mainly collects high-precision geographic location information of the target area; obtains and transmits the geographic information to the system in combination with the 5G network through a satellite positioning system (such as GPS) or a geographic information system (GIS) and updates it in real time. This information helps to clarify the initial position of the antenna, the geographical distribution of the target area, and possible signal interference sources in the environment; through this step, the antenna can be preliminarily calibrated according to its initial position and geographic information, providing basic data for subsequent steps; providing a basis for subsequent antenna direction adjustment and signal reception; improving antenna adaptability: using high-precision geographic information can help the antenna focus and adjust the direction more accurately, avoiding blind adjustment.

[0024] After step S200 obtains the first calibration data set through step S100, the antenna will make preliminary directional adjustments based on the data set. Then, the antenna starts to collect target signals and monitor the signal strength changes in real time; the changes in signal strength reflect the relative position between the antenna and the target and the signal quality. Through the changes in signal strength data, the system can further infer the position of the target and generate a second calibration data set; by monitoring the changes in signal strength in real time, the antenna can automatically adjust according to environmental changes to ensure optimal signal reception; improve signal reception quality: accurate signal strength analysis can help the antenna quickly adapt to non-line-of-sight environments and reduce interference.

[0025] S300 combines the geographic location information (first calibration data set) in S100 with the signal strength data (second calibration data set) in S200 to generate a complete calibration data set; by inputting these data sets into the machine learning algorithm, a calibration matching model is constructed. The model can identify the matching relationship between the location information of the antenna and the location information of the target signal; combined with geographic information and signal strength data, it can comprehensively consider environmental factors and perform accurate positioning and matching; the machine learning model can automatically extract effective features from the data to improve the accuracy of calibration; after the model is trained, it can be dynamically adjusted to automatically optimize the antenna calibration process according to environmental changes.

[0026] Finally, based on the constructed calibration matching model, step S400 will receive signals through non-line-of-sight microwave technology (such as signals in the UHF / VHF band). This technology can work effectively in disaster-stricken environments (such as buildings, obstacles or other complex terrain); the output results of the calibration matching model can accurately determine the relationship between the antenna position and the target signal, thereby inferring the precise location of the target, such as the specific location of the accident personnel; it can accurately locate the target signal in complex and obstructed environments, such as accident personnel or distress signals in the disaster area; microwave technology can effectively penetrate obstacles and maintain efficient work in disaster-stricken or harsh environments; the system can output target location information in real time, greatly improving rescue efficiency.

[0027] The above steps work together to enable the antenna to locate the target more accurately in a non-line-of-sight environment through multi-stage calibration and signal acquisition steps, which is especially suitable for disaster relief scenarios. The working principle of each step provides support for improving the antenna's adaptability, signal reception quality and positioning accuracy, and ensures the effectiveness of the system in complex environments through data set training and optimization of calibration models. Ultimately, it achieves accurate determination of the location of accident personnel under non-line-of-sight conditions, improving the efficiency and accuracy of disaster relief.

[0028] In a preferred embodiment of the present invention, step S100 further includes the following steps: S101, based on the first calibration data set, preliminarily adjust the position of the antenna to obtain an initial reference direction of the antenna ; In the formula, θ ref represents the initial elevation angle of the antenna and ϕ ref Indicates the initial azimuth of the antenna; the initial reference direction of the antenna indicates that the starting direction of the antenna will be calibrated according to the coordinates of the target location; S102, receiving a target signal based on the antenna in the initial reference direction, by , so that the received target signal is interfered with to obtain the real target signal; where r(t) represents the real signal vector received by the antenna; A(t) represents the gain matrix, representing the attenuation and gain of the signal; S(t) represents the real transmitted signal; n(t) represents the noise or interference signal; S103, based on receiving the real target signal, The search antenna is initially adjusted in direction so that the antenna can adaptively adjust its own receiving signal direction according to the initial reference direction. When the antenna automatically adjusts and optimizes the receiving angle of the target signal, the target direction of the antenna is determined. Where, Indicates the target direction after the antenna is initially adjusted; Indicates the real target signal strength; represents the adjustment function of the initial reference direction of the antenna; S104, performing preliminary focusing based on the antenna to focus on the signal source in the target area; through the preliminary focusing, the antenna receives the target signal and provides real-time feedback to adjust the direction accuracy so that the reception error between the antenna and the target signal is small.

[0029] In an embodiment, step S101 uses the first calibration data set (usually data from a high-precision positioning system) obtained in step S100 to calculate the initial direction of the antenna. This includes calculating the initial elevation angle (θ ref ) and azimuth (ϕ ref ), which is the starting angle of the antenna relative to the target position; by using this data, the initial reference direction of the antenna is determined and calibrated to a starting direction (θ ref , ϕ ref ). This reference direction ensures that the antenna adjustment starts from a known starting point; thus providing an accurate direction basis for subsequent signal reception and analysis; by accurately calibrating the initial orientation, the initial reception inaccuracy caused by antenna position error is reduced; the initial orientation of the antenna is determined according to the target's coordinates, so that the antenna can better receive the target signal.

[0030] Step S102 enables the antenna to receive the target signal according to the initial reference direction determined in S101. The received signal may contain various interferences (such as noise or other non-target signals), so interference processing is required to extract the real target signal.

[0031] In the mathematical model, r(t) represents the signal vector received by the antenna, which includes the target signal and the interference signal; A(t) represents the gain matrix, which is used to describe the attenuation and gain of the signal; S(t) is the real signal of the target; n(t) is the noise or interference signal. By filtering and denoising the signal, the real target signal can be separated from the interference signal; the interference signal can be effectively filtered to ensure that the received signal is as close to the real target signal as possible; the quality of the received signal can be improved through gain adjustment and interference suppression, thereby improving the accuracy of subsequent processing; and the influence of environmental noise or other external interference can be reduced to ensure signal stability.

[0032] After obtaining the real target signal through step S102, step S103 further fine-tunes the antenna according to the initial reference direction to ensure that the antenna can be optimally aligned with the target signal source. This fine-tuning process is an automatic adjustment process. The antenna automatically optimizes the direction according to the change in the received signal strength to maximize the received signal strength; by adjusting the direction of the antenna, the target pointing of the antenna (i.e., the pointing angle of the antenna) is accurately optimized. Mathematically, the target pointing of the antenna after adjustment can be expressed as a function, which is fine-tuned as the signal strength changes. The goal of the adjustment function is to align the antenna with the signal source to minimize the receiving error; through automatic adjustment, the antenna can automatically optimize the receiving angle according to the real-time received signal strength to ensure accurate alignment with the target; the optimization of signal strength can minimize the deviation of the antenna and accurately receive the target signal; the antenna can be dynamically adjusted according to different environmental conditions (such as obstacles, interference, etc.) without manual intervention.

[0033] In step S104, the antenna needs to focus on the signal source in the target area through preliminary focusing. The antenna begins fine adjustment to reduce the error when receiving the signal; after receiving the target signal, the antenna continues to adjust the direction and feedback the direction accuracy in real time. Through this fine adjustment, the antenna can focus on the strongest target signal source to ensure that the reception error is minimized. The feedback mechanism ensures that the antenna can be adjusted in real time to continuously reduce the reception error; through fine adjustment, the antenna can focus on the target signal source more accurately and reduce any possible reception error; the system can monitor and optimize the antenna direction in real time to ensure that the quality of the received signal is in the best state; as the adjustment process proceeds, the reception error gradually decreases, improving the positioning accuracy and ensuring the reliability of signal acquisition.

[0034] These steps together form a complete process from preliminary positioning to accurate reception of target signals. Each step includes fine adjustment of the antenna direction and the quality of the received signal to ensure that the antenna can adapt to environmental changes and ultimately achieve high-precision target signal reception; Step S101 ensures the accuracy of the initial reference direction of the antenna to avoid errors in the initial stage of reception; Step S102 provides effective signal processing and interference removal technology to improve the quality of the received target signal; Step S103 automatically adjusts the antenna direction so that the antenna can be optimally aligned with the target signal source; Step S104 further reduces the reception error through fine focusing to ensure the best signal reception effect.

[0035] In a preferred embodiment of the present invention, step S200 further includes the following steps: S201, when the antenna in the initial reference direction receives the target signal in real time, the target signals received multiple times are marked to obtain a signal reception mark point set; the signal reception mark point set includes the signal sending strength, the signal strength when the antenna receives, and the antenna angle of the received signal; S202, based on the signal reception mark point set, recording and comparing the information data of the plurality of signal reception mark points to obtain a signal strength difference of the plurality of signal reception mark points; and preliminarily adjusting the initial reference direction of the antenna according to the signal strength difference of the signal mark points; S203, based on the signal reception mark point set, the initial signal strength Further analysis is performed to obtain the second calibration data set; where P represents the received initial signal strength; P0 represents the reference distance; n represents the path loss index; d represents the actual distance from the antenna; X α Represents log-normal shadow falloff, with random variations due to changes in large-scale environmental attenuation.

[0036] In the embodiment, in step S201, the antenna is located at an initial reference direction and starts to receive the target signal in real time. Each received target signal will be marked and recorded to form a marked point set. This marked point set contains the following key information: Signal strength (e.g., power output of the transmitting device); The signal strength received by the antenna (the strength of the signal after propagation and attenuation through the path); The antenna angle when receiving the signal (here refers to the directivity parameter of the antenna); Through such a signal marking process, the propagation characteristics of the signal under different conditions can be recorded.

[0037] Provide multi-dimensional signal information (strength, angle, etc.) to provide basic data for subsequent calibration and adjustment; ensure the time serialization and spatial labeling of the signal to facilitate comparison and analysis; and provide an objective signal basis for the optimization of the initial direction of the antenna.

[0038] Step S202 compares the recorded signal reception data based on the signal reception mark point set obtained in S201. The core is to calculate the signal strength difference between each signal reception mark point; by comparing the received signal strength in the same direction or different directions, the change pattern in signal propagation can be identified; based on the characteristics of the strength difference, the signal propagation path, the impact of obstacles in the environment on the signal, and the deviation of the initial antenna reference direction can be inferred; finally, the initial reference direction of the antenna is preliminarily adjusted using the signal strength difference information to approach a more ideal direction.

[0039] Improve the accuracy of antenna direction: Through preliminary adjustments, the antenna is brought closer to the direction with the strongest signal, improving the quality of signal reception. When there is a significant deviation in the difference in signal strength, it may indicate an abnormal area of ​​​​blockage or signal attenuation in the path. Preliminary adjustments can reduce the complexity of subsequent analysis and optimization and speed up the convergence of calibration.

[0040] In step S203, the initial signal strength is further analyzed, and the main parameters in the signal propagation process can be quantitatively analyzed. The signal strength is obtained from the marker point set, and the received signal strength is fitted and analyzed to obtain a second calibration data set for more accurate antenna direction and signal quality optimization. By introducing the path loss model and random attenuation factors, the calibration of the antenna direction takes into account a more complex propagation environment. For example, the characteristics of the path loss exponent and shadow attenuation can reflect the characteristics of the propagation environment and help optimize the communication quality. Through the second calibration, errors caused by factors such as the environment and antenna position can be further eliminated to ensure optimal signal reception.

[0041] The above three steps work together. S201 provides the basic data for the target signal position positioning for the whole process, and records the information of signal propagation through a multi-dimensional marker point set; S202 performs preliminary analysis and adjustment, and performs preliminary direction optimization based on the strength difference to reduce the complexity of subsequent analysis; S203 performs precise calibration, further analyzes the signal characteristics, and accurately calculates the calibration data to optimize the signal reception quality; from coarse adjustment to fine adjustment, the error is reduced through step-by-step analysis; combining the signal strength difference and the path loss model, it can better adapt to the dynamically changing propagation environment; the optimized antenna direction makes the signal reception stronger and more stable, thereby improving the communication quality and reliability.

[0042] In a preferred embodiment of the present invention, step S202 further includes the following steps: S2021, receiving a set of marking points based on the signal to obtain a plurality of triplet data sets of the received marking points , where P out Indicates the signal strength; P recv Indicates the received signal strength; θ recv Indicates the angle of receiving signal; S2022, based on the acquired triple data set; compare the multiple triple data sets to form signal reception mark point information pairs; each pair consists of two triple data sets and ; In the formula, i and j represent data items in different triple data sets respectively; S2023, receiving a pair of marking point information based on the signal, by ; To obtain the logarithmic difference of signal strength at the signal receiving mark point; where, Represents the logarithmic difference of the signal strength of the signal reception mark point; the logarithmic difference of the signal strength of the signal reception mark point is the signal strength difference of the signal reception mark point.

[0043] In the embodiment, in step S2021, based on the signal reception mark point set obtained in step S201 (signal data received from the antenna), the following three key information are extracted for each mark point and combined into a ternary data set: the signal transmission strength (signal strength at the transmitting end), the received signal strength (the received signal strength after attenuation through the propagation path), and the antenna direction angle when receiving the signal (the azimuth of the received signal).

[0044] Each receiving marker point will form such a triplet. After processing, all the receiving marker points will form multiple triplet data sets. By converting each receiving marker point into a standardized triplet form, the subsequent data analysis, comparison and processing can be simplified. The triplet contains important information such as signal strength and receiving angle, which makes the description of signal propagation more comprehensive and helps to accurately analyze signal characteristics. The structured form of triplet data facilitates comparison, calculation and optimization in subsequent steps, providing a precise basis for signal analysis.

[0045] In step S2022, a comparison operation is performed based on the multiple triplet data sets formed in step S2021. Each pair of data sets consists of two different triplets, forming a signal reception mark point information pair. Specifically, for any two triplet data sets (i and j) in the set, an information pair is formed by comparing their corresponding values; this comparison is mainly to find the relationship or difference between the two triplets. In this way, the propagation characteristics of the signal can be more deeply understood, especially the interaction between different receiving angles and receiving strengths; by comparing multiple triplets, the difference of the signal at different receiving angles or different transmission powers can be identified, helping to determine the regularity of signal propagation; generate data pairs: each pair of signal reception mark point information pairs can be used as the basis for further signal analysis, such as calculating the logarithmic difference of signal strength, etc.; by comparing different triplets, the propagation of the signal under different conditions can be better understood, such as the relationship between the receiving strength and the transmitting strength, and the influence of the receiving angle on the signal strength.

[0046] In step S2023, based on the signal reception mark point information pairs that have been formed, the logarithmic difference of the signal strength is further calculated. The core of this step is to quantify the change of the signal reception strength by calculating the logarithmic difference; ΔP i，j The logarithmic difference of the signal strength of the two marked points reflects the relative strength difference between them; this logarithmic difference can help us analyze the degree of signal attenuation and the impact of environmental factors on signal propagation; the logarithmic difference method can better handle the comparison of signal strength differences, because the signal strength usually spans a large dynamic range, and digital processing can simplify the analysis and improve the accuracy of calculations; the logarithmic difference has a certain debiasing property, which can more effectively remove the nonlinear effects caused by environmental changes in the data, thereby making the signal strength analysis more accurate; by calculating the logarithmic difference of signal strength, the attenuation characteristics of the signal at different locations or under different conditions can be more intuitively understood. This is very useful for tasks such as optimizing antenna adjustment and predicting signal coverage; the logarithmic difference of signal strength is a commonly used parameter in signal propagation models (such as free space path loss models), and further analysis of these differences can help optimize antenna layout, transmission power, and communication quality of equipment.

[0047] The above steps work together. By comparing the triplet data and calculating the logarithmic difference, the characteristics of signal propagation can be analyzed in more detail, and key factors affecting signal quality, such as propagation loss and interference, can be identified. The analysis provides a theoretical basis for antenna adjustment and signal optimization, so that the signal strength difference can be accurately quantified, thereby optimizing the performance of the communication system. By conducting in-depth analysis of the logarithmic difference of signal strength, it is possible to better cope with attenuation phenomena in complex propagation environments (such as multipath propagation, shadow attenuation, etc.), thereby improving the robustness and stability of the system.

[0048] In a preferred embodiment of the present invention, step S203 further includes the following steps: S2031, obtaining initial real target signal strength at different locations and different time periods, and analyzing signal changes by comparing signal strength data at different times or locations; S2032, according to the change of the signal, ; Obtain the mean and standard deviation of signal strength at different locations or time periods to evaluate signal stability and variation range, where μ represents the mean of signal strength; h represents the standard deviation of signal strength; N represents the number of signal receiving markers; P i Indicates the signal strength data collected in real time; S2033, the smaller the standard deviation h is or the higher the signal-to-noise ratio SNR is, the more stable the signal is; the range of variation ; In the formula, Indicates the range of variation; Indicates the upper limit of signal strength change; Indicates the lower limit of intensity change; S2034, based on signal stability and variation range, ; To determine the relationship between signal strength and antenna direction; where, represents the correlation coefficient between signal strength and antenna direction; f(θ) represents the fitting function; S2035, using the correlation coefficient between the signal strength and the antenna direction to obtain the relationship between the signal strength and the antenna direction. Under non-line-of-sight conditions, the signal strength usually changes with the change of the antenna angle; using the known relationship between the signal strength and the antenna direction, the antenna is enabled to dynamically adjust the antenna direction for a second time to optimize the direction of the received target signal.

[0049] In the embodiment, step S2031 records the strength data of the target signal by real-time acquisition of signal strength at different locations and different time periods. The purpose of this is to obtain preliminary changes in the signal under various environmental conditions; by covering different spatial and temporal ranges, representative signal strength data can be obtained. These data provide a basis for subsequent analysis of signal changes, stability assessment, and antenna adjustment.

[0050] Step S2032 obtains the mean (μ) and standard deviation (h) of the signal strength in different time periods or different locations based on the signal strength data obtained in step S2031. The mean reflects the concentration trend of the signal strength, and the standard deviation indicates the volatility of the signal strength; by calculating the mean and standard deviation, the stability of the signal can be quantified. If the standard deviation is small, it means that the signal strength is relatively stable; if the standard deviation is large, it means that the signal strength fluctuates greatly. This information can help judge the quality of the signal.

[0051] Step S2033 calculates the range of signal strength by comparing the upper and lower limits of signal strength. The range reflects the fluctuation of the signal at different times and locations; thus, analyzing the range and stability of the signal helps to understand the quality of the signal. If the range is too large, it may indicate interference or an unstable signal source. Further adjusting the antenna direction or optimizing the receiving system can effectively reduce these problems.

[0052] Step S2034 further studies the correlation between signal strength and antenna direction based on the stability and variation range of the signal. By analyzing historical data, a function model between signal strength and antenna direction is fitted; the purpose of this step is to identify the relationship between signal strength and antenna direction and find the best antenna pointing angle. Through accurate correlation analysis, the quality of signal reception can be significantly improved, interference can be reduced, and system performance can be improved.

[0053] Step S2035, based on the established relationship model between signal strength and antenna direction, in practical applications, adjusts the direction (or angle) of the antenna so that it receives the direction with the strongest signal strength to the greatest extent. This is usually accomplished by dynamically adjusting the antenna direction twice; by optimizing the antenna pointing direction, the signal reception quality can be effectively improved under non-line-of-sight conditions. Especially in complex environments (such as those with obstructions, interference sources, etc.), by intelligently adjusting the antenna direction, the stability and strength of the signal can be greatly improved, thereby improving the reliability and performance of the communication system.

[0054] By combining the above steps and working together, by collecting signal data at different locations and time periods, and calculating the mean and standard deviation, the stability and range of the signal can be fully analyzed. This provides a quantitative basis for judging signal quality; by analyzing the relationship between signal strength and antenna direction, an accurate model can be established to help optimize antenna pointing and reduce signal loss and interference; by utilizing the relationship between signal strength and antenna direction, the direction of the antenna can be dynamically adjusted to achieve optimal signal reception. This can effectively improve communication quality under non-line-of-sight conditions, and is especially suitable for scenarios such as wireless communications and satellite communications that require high signal quality; thereby improving signal stability and quality, optimizing the antenna pointing angle, reducing signal loss due to interference or environmental factors, and improving the efficiency and reliability of wireless communication systems, especially in complex environments or dynamically changing conditions.

[0055] In a preferred embodiment of the present invention, step S300 further includes the following steps: S301, generating structure data from the historical calibration data set, encoding the structure data into sequence data, so as to train and obtain the calibration matching model; S302, inputting the sequence data into the calibration matching model; the calibration matching model includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer, transmitting the intermediate representation data of multiple hidden layers to the output layer, and the output layer outputting the recognition result representing the calibration matching between the antenna position information and the target signal position information; S303: Input the newly acquired calibration data set into the calibration matching model to output a newly acquired output layer output representing a prediction result of the calibration matching between the antenna position information and the target signal position information.

[0056] In an embodiment, step S301 processes the historical calibration data set to form structured data. The calibration data generally includes information such as antenna position, target signal position, signal strength, etc. The structured data is encoded into a sequence form so as to be input into a deep learning model. Sequence data is a format with sequential characteristics in time or space, which is convenient for capturing dynamic relationships between data.

[0057] Example: Encode the data into a time series of [antenna position, signal position, signal strength], and use a deep learning framework (such as a multi-layer neural network) to train the sequence data. The model receives data through the input layer, gradually extracts features through multiple hidden layers, and finally generates calibration matching recognition results at the output layer; Encode the calibration data into sequence data to facilitate the model to extract relevant features of the time series or spatial series; By training the calibration matching model, the implicit relationship between the antenna position and the target signal position can be learned from historical data, laying the foundation for subsequent calibration matching.

[0058] Step S302 inputs the sequence data generated in step S301 into the calibration matching model. The model structure is: Input layer: receives sequence data, such as [antenna position, signal position, signal strength].

[0059] Hidden Layer: The first hidden layer: preliminarily extracts the underlying features of the input data.

[0060] The second hidden layer: further extracts complex nonlinear features.

[0061] The third hidden layer: integrates the features of the first two layers and generates an intermediate representation.

[0062] Output layer: Based on the intermediate representation data of the hidden layer, output the calibration matching results.

[0063] Intermediate representation transmission: The data transmitted between hidden layers gradually extracts the matching relationship features between the antenna position and the signal position.

[0064] Output layer results: The output layer generates recognition results, which indicate the degree of calibration match between the antenna position information and the target signal position information.

[0065] The data is processed in layers through multiple hidden layers to extract multi-level information from the data; the calibration matching results generated by the output layer can reflect the accurate matching relationship between the antenna and the signal position; the multi-layer structure of the deep learning model can effectively capture nonlinear relationships and improve the accuracy of calibration.

[0066] Step S303 inputs the newly acquired calibration data set into the trained calibration matching model; through the transfer and calculation of the input layer, hidden layer and output layer, the model predicts the new data based on the feature relationship learned in the previous training process; the output layer generates a prediction result, which indicates the degree of calibration matching between the new antenna position information and the target signal position information.

[0067] The trained model can quickly process new data without retraining and directly generate calibration matching results; it can update the matching results in real time based on the new calibration data to improve the dynamic adaptability of the system; the model trained based on historical data can handle complex calibration matching tasks and provide high calibration accuracy even in dynamic environments.

[0068] In a preferred embodiment of the present invention, step S400 further includes the following steps: S401, based on the prediction result of the calibration match between the antenna position information and the target signal position information; and in combination with the dynamic adjustment of the antenna direction and the signal strength of the target signal, signal modeling is performed through non-line-of-sight microwave frequency to describe the signal band change under multipath propagation and obstacle reflection and diffraction of non-line-of-sight propagation; S402, by combining signal band changes with signal strength, and then according to terrain complexity and obstacle distribution, to obtain the channel conditions of the antenna in a complex environment; dynamically adjust antenna modulation and coding signals according to the channel conditions; and perform diversity on the signal receiving environment to obtain spatial diversity, time diversity and frequency diversity; S403, based on the obtained signal receiving space diversity, time diversity and frequency diversity, to obtain signal band amplitudes of different diversity information; integrate the three kinds of diversity information band amplitudes of the corresponding nodes to obtain a band amplitude data integration set; according to the band amplitude data integration set, obtain the highest point band amplitude integration data; the band amplitude highest point data is the target position of the preliminary prediction of the accident personnel; S404, based on the multi-source data of the antenna received signal, combined with the calibration matching model, to update the precise location of the accident personnel in real time.

[0069] In an embodiment, step S401 utilizes the known position of the antenna and the adjusted optimal direction to receive the target signal, and then utilizes the signal transmission strength, arrival strength and signal arrival time information to calibrate and match with the location information of the target signal; by dynamically adjusting the direction of the antenna to maximize the signal strength, the received target signal is optimized to determine the specific direction of the target signal.

[0070] Non-line-of-sight (NLOS) signal modeling: In non-line-of-sight propagation, signals are affected by multipath effects, such as reflection, diffraction, and scattering from obstacles. The modeling process combines the changing characteristics of signal bands with multipath propagation and obstacle environments to describe the band changes in signal propagation. This improves the positioning system's ability to perceive target signals in non-line-of-sight environments. In complex environments with multipath propagation and obstacles, it improves the antenna's recognition accuracy and anti-interference ability for target signals.

[0071] Step S402 evaluates the impact of the propagation path on the signal characteristics through the signal band change and signal strength data received by the antenna, and uses the terrain data and obstacle distribution information to calculate the channel conditions, including path loss, scattering gain and shielding effect; according to the channel conditions, optimize the antenna modulation method (such as QAM or PSK) and coding signal to ensure the reliability and efficiency of signal transmission; obtain the multi-dimensional characteristics of signal propagation through space diversity (multi-antenna reception), time diversity (multiple measurements) and frequency diversity (multi-band reception); dynamically optimize the modulation and coding methods to make the signal transmission more efficient in complex environments, and enhance the anti-interference and robustness of the system; space, time and frequency diversity technologies improve the signal reception quality and reduce the signal loss caused by single-path fading.

[0072] Step S403 measures the signal band amplitudes in space diversity, time diversity and frequency diversity respectively to obtain amplitude data under each diversity; integrates the diversity information of different nodes to form a complete band amplitude data set; extracts the highest point of the band amplitude in the integrated data as the preliminary positioning point of the target signal; through the fusion of multi-diversity information, effectively eliminates the influence of environmental noise and interference on single diversity information, and significantly improves the positioning accuracy; the method of the highest point of the band amplitude is simple and direct, and can quickly provide a preliminary position prediction of the target, laying the foundation for subsequent precise positioning.

[0073] Step S404 analyzes the dynamic characteristics of the target signal in real time based on the multi-source data of the signal received by the antenna (the known position of the antenna and the adjusted optimal direction to receive the target signal, and then uses the signal emission strength, arrival strength and signal arrival time information, etc.), and uses the calibration matching model established in S401 to adjust the target position prediction in real time, correct the deviation caused by non-line-of-sight propagation or multipath effect, and dynamically adjust the target position coordinates through the real-time updated prediction algorithm to provide more accurate positioning results; combined with multi-source data and real-time model update, it can quickly respond to environmental changes and make the positioning results more real-time and reliable; reduce errors caused by signal path changes or noise, and make the predicted target position more accurate, especially suitable for personnel rescue in complex scenes; improve the positioning accuracy and efficiency of the target in non-line-of-sight complex environments; enhance the adaptability of the system and the stability of signal transmission by dynamically adjusting the modulation and coding method of the antenna; use signal diversity technology and real-time model update to quickly respond to dynamic environmental changes and ensure high-precision positioning.

[0074] The above steps are designed based on steps S100, S200 and S300 and their sub-steps, and are suitable for scenarios such as disaster rescue and complex terrain positioning. They help to find the location of accident personnel faster and more accurately, thereby improving rescue efficiency and success rate; and can quickly respond to environmental changes, making positioning results more real-time and reliable; reducing errors caused by signal path changes or noise, making the predicted target position more accurate.

[0075] In a preferred embodiment of the present invention, step S403 further includes the following steps: S4031, the spatial diversity means that multiple antennas are arranged and the receiving signal is adjusted to point to the receiving target signal; the time diversity means sampling the target signal at different time nodes; the frequency diversity means multiple frequency channels; S4032, extracting the band amplitude of the signal based on the diversity conditions of the three signals based on the spatial diversity, the time diversity and the frequency diversity; and aligning and normalizing the signal band amplitude data; to obtain unified standard band amplitude data; S4033, integrating the unified standard band amplitude data into a unified data set to enhance the signal of the unified data set; optimizing the communication in a complex environment and tracking the target location of the accident personnel.

[0076] In the embodiment, step S4031 arranges multiple antenna arrays through spatial diversity and adjusts the receiving direction of each antenna to cover different spatial ranges; the independent characteristics of the signals received by the multiple antennas can be used to effectively reduce the impact of channel fading; the receiving signals of the multiple antennas are optimized through beamforming technology to optimize the spatial distribution characteristics of the target signal.

[0077] Through time diversity, the target signal is sampled at different times and the signal strength and amplitude changes are recorded; through multiple sampling, the impact of multipath fading can be balanced and dynamic channel characteristics can be captured.

[0078] Through frequency diversity, the target signal is received on multiple frequency channels and the propagation characteristics at different frequencies are analyzed. Different frequency channels respond differently to obstacles, reflections and diffraction, and frequency diversity can be used to enhance signal stability. Space diversity improves the spatial coverage of the signal and reduces the loss of received signals due to obstruction or scattering. Time diversity smoothes the impact of dynamic environmental changes on the signal by sampling the signal at different times. Frequency diversity utilizes the characteristics of multi-band channels to enhance the signal's anti-interference ability and propagation stability.

[0079] Step S4032 extracts the band amplitude of the signal from the signals of space diversity, time diversity and frequency diversity respectively; the band amplitude is the energy distribution of the signal strength in a specific frequency or spatial range, which is used to reflect the propagation characteristics of the signal; since the data from space, time and frequency diversity may have different time bases or spatial distribution characteristics, the signal band amplitude data needs to be aligned; during the alignment process, the data is unified under the same time and space base through algorithms such as interpolation and time synchronization; the extracted signal band amplitude is normalized so that its amplitude range is limited to a standard range (for example, [0,1]); normalization can eliminate the difference in amplitude ranges of different signal diversities, which is convenient for subsequent data fusion; data alignment makes the signal amplitudes extracted by different diversity methods consistent, avoiding errors caused by time and space asynchrony; normalization processing eliminates the scale difference between data amplitudes, ensuring the reliability and stability of subsequent data fusion; the extracted and standardized band amplitude data provides more accurate signal characteristics for positioning targets.

[0080] Step S4033 integrates the normalized band amplitude data of space diversity, time diversity and frequency diversity to form a unified data set; during the integration process, a weighted average or feature fusion method is used to adjust the weights of different diversity signals according to the importance of the diversity method; through data integration, the complementary characteristics of the diversity signals are utilized to enhance the overall amplitude strength of the target signal; after data integration, the noise of the signal is further suppressed, while the characteristics of the target signal are significantly amplified; for signal propagation problems in complex environments (such as multipath fading, obstacle obstruction, etc.), the integrated data can optimize the signal propagation characteristics through modeling analysis; while optimizing, the target position of the accident personnel is further tracked, and the position information is dynamically updated.

[0081] Therefore, data integration improves the overall quality and stability of the signal by utilizing the diversity and complementarity of diversity signals; signal enhancement effectively amplifies the amplitude characteristics of the target signal while reducing the impact of background noise; complex environment optimization improves the system's anti-interference ability in non-line-of-sight environments, making target position tracking more accurate and real-time.

[0082] In a preferred embodiment of the present invention, step S404 further includes the following steps: S4041, by constructing a three-dimensional terrain map of the accident area, and combining signal modeling to obtain the initial prediction of the target location signal information of the accident personnel; using the diversity of channel conditions and signal receiving environment, verify the initial prediction of the spatial location of the signal information of the accident personnel; in order to determine the precise location of the victims; S4042, and through Update the positioning data of the target personnel in real time to improve the accuracy of the predicted target position; where X krepresents the predicted position; z k Indicates the measurement position; K k represents the filter gain; H represents the observation matrix.

[0083] In an embodiment, step S4041 uses remote sensing data (such as LiDAR, SAR images) or field data acquisition technology to scan the accident area and generate a three-dimensional terrain model; the three-dimensional terrain map accurately depicts the terrain characteristics of the accident area (such as ups and downs, obstacle distribution, etc.), providing a physical scene basis for signal modeling; based on the three-dimensional terrain map, combined with the radio wave propagation model (such as multipath fading, diffraction and reflection effects), the propagation characteristics of the signal in a complex environment are modeled; in the modeling process, the signal sent or reflected by the accident personnel (such as RFID signals or radio wave signals) is used to estimate the possible target position range; based on the channel conditions (such as channel gain, signal-to-noise ratio) and the signal receiving environment (such as receiving sky The preliminary predicted target position is verified by the three-dimensional terrain map, which provides a real physical scene for signal modeling, making the signal propagation analysis closer to reality. Signal modeling combined with terrain information can effectively compensate for the influence of complex terrain on signal propagation, thereby improving the accuracy of target position prediction. Channel condition verification further reduces the prediction error caused by channel noise or environmental interference, making the preliminary position estimation more reliable. This step can provide a high-precision initial position estimate for subsequent position tracking, laying the foundation for accurate positioning.

[0084] S4042 updates the location data in real time based on the movement characteristics of the accident personnel and the dynamic changes of the received signal; by calculating the measurement error covariance and the prediction error covariance, the filter gain is dynamically adjusted to make the position update closer to the actual position; when the channel quality is high, the measurement data is trusted more; when the channel conditions are poor, the prediction model is trusted more; combining real-time signal data and prediction models, the location information of the target personnel is continuously corrected and gradually converged to the actual position; thereby effectively integrating the long-term trend of signal modeling and the short-term dynamic changes of real-time measurement; by correcting the predicted position in real time, the target position estimation always maintains dynamic accuracy; the weight distribution of prediction and measurement is adjusted according to the channel conditions to ensure that better positioning accuracy can be obtained in different environments; comprehensive use of long-term trend prediction and short-term dynamic measurement can avoid deviations caused by a single data source; the real-time updated positioning results make the location tracking of the accident personnel more accurate and provide reliable location information support for rescue operations.

[0085] like Figure 2 As shown in the figure, the matching system based on 5G network, Beidou positioning and non-line-of-sight microwave includes: Information acquisition module: used to obtain high-precision geographic location information data to obtain a first calibration data set; based on the first calibration data set, the antenna is adaptively and quickly focused and oriented; Detection and adjustment module: It is used to make the antenna collect signals and analyze signal strength changes based on preliminary adjustment of the antenna direction to obtain a second calibration data set; according to the second calibration data set, dynamically adjust the antenna direction to match the target signal under non-line-of-sight conditions; A calibration matching module: used to combine the first calibration data set and the second calibration data set to obtain a calibration data set; train the calibration data set to construct a calibration matching model; the calibration matching model outputs an identification result indicating a calibration match between the antenna position information and the target signal position information; Positioning prediction module: It is used for the output result of the calibration matching model, and uses non-line-of-sight microwaves in the disaster environment to enable the antenna to receive the target signal to determine the precise location of the accident personnel.

[0086] Specifically, by combining Beidou positioning and signal detection analysis, it is possible to achieve fast and accurate alignment of non-line-of-sight microwave antennas in complex environments, ensuring the stability of communication links in mobile environments or unpredictable complex environments. The key technologies for fast alignment of non-line-of-sight microwave antennas based on Beidou + 5G are as follows: 1) Beidou positioning technology: Install a Beidou positioning module on the communication end and use the high-precision positioning capability of the Beidou system to quickly align the antenna even in adverse weather conditions, reducing signal attenuation or loss caused by position errors.

[0087] 2) 5G communication technology: 5G portable terminals use 5G networks for end-to-end data exchange, ensuring that location information between antennas can be transmitted in real time under non-line-of-sight conditions. 5G's high rate and low latency characteristics help to adjust the direction of the antenna in a timely manner to maintain the best connection.

[0088] 3) Signal maximum detection technology: During the alignment process, the signal maximum detection technology is used to further optimize the direction of the antenna. Based on the maximum likelihood estimation algorithm for signal detection, the received signal r(t) is considered to be composed of ideal signals. By adjusting the antenna direction to find the maximum value of the target signal, that is, the position where the signal is the strongest, the direction of the antenna can be accurately corrected to achieve the optimal signal transmission effect.

[0089] This portable terminal is suitable for emergency response after natural disasters, field operations, and any scenario where an emergency communication link needs to be established quickly.

[0090] like Figure 3As shown in the figure, the 5G portable terminal integrates 5G base station equipment, power equipment, transmission equipment, antenna and other equipment, and is lightweight and flexible. The terminal adopts an integrated design and is usually used for emergency communications, temporary network construction or temporary 5G coverage in remote areas. The terminal consists of: 5G base station equipment, which has a miniaturized 5G base station built in, supports data and voice communications and usually includes the functions of the core network part, such as EPC, so that it can operate independently or interconnect with the existing network.

[0091] Power supply equipment integrates batteries or other forms of portable power to ensure normal operation without mains power, and can be assisted by solar panels or other renewable energy sources to extend the working time of the equipment.

[0092] Transmission equipment: The terminal has a built-in satellite communication module or non-line-of-sight microwave communication equipment to transmit data in places where there is a lack of ground network infrastructure. It supports multiple transmission protocols and technical modules, such as Wi-Fi, Bluetooth, etc., to communicate with other devices over short distances.

[0093] The antenna is compact and easy to install. It can be automatically deployed under special circumstances, especially in complex geographical environments. It consists of an antenna, an antenna pan-tilt mount, and an actuator installed on the antenna mount. Its functions are as follows: Antenna: The antenna is one of the core components of the entire system. It is responsible for transmitting and receiving radio signals and is designed as a high-gain antenna to ensure good signal transmission performance under non-line-of-sight conditions.

[0094] Antenna gimbal mount, the antenna gimbal mount is a mechanical structure that supports the antenna and allows it to rotate in multiple axes, including a horizontal rotation mechanism (azimuth adjustment) and a vertical rotation mechanism (pitch angle adjustment), so that the antenna can be adjusted as needed. The mount has sufficient stability and precision to ensure that the antenna does not produce unnecessary vibration or deviation during adjustment.

[0095] The actuator installed on the antenna base is a mechanical component that drives the antenna pan-tilt base, including motors, servo systems, etc. These components are usually driven by the control system through electronic signals to achieve precise azimuth and pitch angle adjustment. The actuator needs to have high precision and fast response capabilities to meet the requirements of fast alignment.

[0096] Fast alignment and precise matching of application terminals, based on Beidou precise positioning technology and signal maximum detection technology, can achieve adaptive fast focusing, greatly improve the response speed and transmission rate of the system, and achieve adaptive fast alignment of non-line-of-sight microwave antennas. It can play a key role in a variety of application scenarios, especially in situations where high-quality wireless communication links need to be established quickly.

[0097] The system terminal can adaptively adjust the antenna direction according to the changes in the received signal strength to achieve fast focusing. This adaptive mechanism allows the antenna to maintain the best signal reception status in a dynamic environment; the system can establish an effective communication connection faster and reduce the delay caused by antenna misalignment. This is especially important for applications that require real-time communication, such as video backhaul and remote control; when the antenna is precisely aligned, the signal strength is maximized, which directly improves the quality and efficiency of data transmission. Stronger signals mean lower bit error rates and higher data throughput.

[0098] After a natural disaster, it is crucial to quickly restore communications. The device supports rapid deployment and establishes communication links; in places where there is no fixed infrastructure, it can help the front establish and maintain contact with the headquarters; in emergency environments, fire and other emergency departments can use this device to ensure smooth communication in emergencies. In short, the fast alignment and precise matching application terminal combines Beidou positioning and signal detection technology to provide an efficient and reliable wireless communication solution, which is particularly suitable for application scenarios that require rapid deployment and high-quality communication.

[0099] When the functions of the above modules are implemented in the form of software functional units and 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 to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks or optical disks.

[0100] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general intelligent device. Therefore, the purpose of the present invention can also be achieved by simply providing a program product containing a program code that implements the method or system. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order. Some steps can be performed in parallel or independently of each other.

Claims

1. A matching method based on 5G network, Beidou positioning and non-line-of-sight microwave, characterized in that: The following steps are involved: S100, acquiring high-precision geographic location information data to obtain a first calibration data set; and enabling the antenna to adaptively and quickly focus and adjust direction according to the first calibration data set; S200, based on preliminary adjustment of the antenna direction, the antenna is enabled to collect signals and analyze changes in signal strength to obtain a second calibration data set; According to the second calibration data set, dynamically adjusting the antenna direction to match the target signal under non-line-of-sight conditions; S300, combining the first calibration data set and the second calibration data set to obtain a calibration data set; Training the calibration data set to construct a calibration matching model; The calibration matching model outputs an identification result indicating a calibration match between the antenna position information and the target signal position information; S400, based on the output result of the calibration matching model, using non-line-of-sight microwave in the disaster environment to enable the antenna to receive the target signal and determine the precise location of the accident personnel.

2. The matching method based on 5G network, Beidou positioning and non-line-of-sight microwave according to claim 1 is characterized in that: According to the first calibration data set, the antenna is adaptively and quickly focused and oriented, including: Based on the first calibration data set, the position of the antenna is preliminarily adjusted to obtain the initial reference direction of the antenna θ0=(θ ref ,φ ref );where θ ref represents the initial elevation angle of the antenna and φ ref Indicates the initial azimuth of the antenna; the initial reference direction of the antenna indicates that the starting direction of the antenna will be calibrated according to the coordinates of the target location; Based on the target signal received by the antenna in the initial reference direction, r(t)=A(t)·S(t)+n(t) is used to perform interference processing on the received target signal to obtain the real target signal; wherein r(t) represents the real signal vector received by the antenna; A(t) represents the gain matrix, which represents the attenuation and gain of the signal; S(t) represents the real transmitted signal; and n(t) represents the noise or interference signal; Based on receiving the real target signal, The search antenna initially adjusts its direction so that the antenna can adaptively adjust its own receiving signal direction according to the initial reference direction. When the antenna automatically adjusts and optimizes the receiving angle of the target signal, the target direction of the antenna is determined. Where, θ * (t) represents the target direction after the antenna is initially adjusted; argmax r (t) represents the real target signal strength; An adjustment function representing the initial reference direction of the antenna; Based on the antenna, preliminary focusing is performed to focus on the signal source in the target area; through preliminary focusing, the antenna receives the target signal and provides real-time feedback to adjust the direction accuracy so that the reception error between the antenna and the target signal is small.

3. The matching method based on 5G network, Beidou positioning and non-line-of-sight microwave according to claim 2 is characterized in that: Based on the preliminary adjustment of the antenna direction, the antenna is enabled to collect signals and analyze the signal strength change to obtain a second calibration data set; Dynamically adjusting the initial direction of the antenna to match the target signal under non-line-of-sight conditions according to the second calibration data set includes: When the antenna in the initial reference direction receives the target signal in real time, the target signals received multiple times are marked to obtain a signal reception mark point set; the signal reception mark point set includes the signal sending strength, the signal strength when the antenna receives, and the antenna angle of the received signal; Based on the signal reception mark point set, the information data of multiple signal reception mark points are recorded and compared to obtain the signal strength difference of the multiple signal reception mark points; according to the signal strength difference of the signal mark points, the initial reference direction of the antenna is preliminarily adjusted; Based on the signal reception mark point set, the initial signal strength P = P0 + 10nlog 10 (d)+X α Further analysis is performed to obtain the second calibration data set; where P represents the received initial signal strength; P0 represents the reference distance; n represents the path loss index; d represents the actual distance from the antenna; X α Represents log-normal shadow falloff, with random variations due to changes in large-scale environmental attenuation.

4. The matching method based on 5G network, Beidou positioning and non-line-of-sight microwave according to claim 3 is characterized in that: Recording and comparing the information data of the plurality of signal receiving mark points to obtain the signal strength difference of the plurality of signal receiving mark points includes: Based on the signal receiving mark point set, a plurality of triplet data sets (P out , P recv ,θ recv ), where P out Indicates the signal strength; P recv Indicates the received signal strength; θ recv Indicates the angle of receiving signal; Based on the acquired triple data set; multiple triple data sets are compared to form signal reception mark point information pairs; each pair is composed of two triple data sets (P out，i , P recv，i ,θ recv，i ) and (P out，j , P recv，j ,θ recv，j ), where i and j represent data items in different triple data sets respectively; Based on the signal, a pair of marking point information is received, by To obtain the logarithmic difference of the signal strength at the signal receiving mark point; where ΔP i，j Represents the logarithmic difference of the signal strength of the signal reception mark point; the logarithmic difference of the signal strength of the signal reception mark point is the signal strength difference of the signal reception mark point.

5. The matching method based on 5G network, Beidou positioning and non-line-of-sight microwave according to claim 4 is characterized in that: The initial signal strength is further analyzed to obtain a second calibration data set, including: Obtain the initial true target signal strength at different locations and time periods, and analyze the signal changes by comparing the signal strength data at different times or locations; According to the change of signal, The mean and standard deviation of signal strength at different locations or in different time periods are obtained to evaluate the signal stability and variation range. In the formula, μ represents the mean of signal strength; h represents the standard deviation of signal strength; N represents the number of signal receiving markers; P i Indicates the signal strength data collected in real time; The smaller the standard deviation h is or the higher the signal-to-noise ratio SNR is, the more stable the signal is; the variation range ΔP rang =P recv，max -P recv，min ; In the formula, ΔP range Indicates the range of change; P recv，max Indicates the upper limit of signal strength change; P recv，min Indicates the lower limit of intensity change; According to the signal stability and variation range, To determine the relationship between signal strength and antenna direction; where, represents the correlation coefficient between signal strength and antenna direction; f(θ) represents the fitting function; The correlation coefficient between the signal strength and the antenna direction is used to obtain the relationship between the signal strength and the antenna direction. Under non-line-of-sight conditions, the signal strength usually changes with the change of the antenna angle; the relationship between the known signal strength and the antenna direction is used to enable the antenna to dynamically adjust the antenna direction for a second time to optimize the direction of the received target signal.

6. The matching method based on 5G network, Beidou positioning and non-line-of-sight microwave according to claim 5 is characterized in that: The calibration data set is trained to construct a calibration matching model; the calibration matching model outputs an identification result indicating the calibration matching between the antenna position information and the target signal position information, including: Generate structure data from the historical calibration data set, and encode the structure data into sequence data to train the calibration matching model; The sequence data is input into the calibration matching model; the calibration matching model includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer, the intermediate representation data of multiple hidden layers are transmitted to the output layer, and the output layer outputs the recognition result representing the calibration matching of the antenna position information and the target signal position information; The newly acquired calibration data set is input into the calibration matching model to output a newly acquired output layer output representing a prediction result of the calibration matching between the antenna position information and the target signal position information.

7. The matching method based on 5G network, Beidou positioning and non-line-of-sight microwave according to claim 6 is characterized in that: Based on the output results of the calibration matching model, non-line-of-sight microwaves are used in the disaster environment to realize the antenna receiving the target signal to determine the precise location of the accident personnel, including: Based on the prediction results of the calibration match between the antenna position information and the target signal position information; and in combination with the dynamic adjustment of the antenna direction and the signal strength of the target signal, signal modeling is performed through non-line-of-sight microwave frequency to describe the signal band changes under multipath propagation and obstacle reflection and diffraction of non-line-of-sight propagation; By combining signal band changes with signal strength, and then according to terrain complexity and obstacle distribution, the channel conditions of the antenna in a complex environment are obtained; antenna modulation and coding signals are dynamically adjusted according to channel conditions; and the signal receiving environment is divided into diversity to obtain spatial diversity, time diversity and frequency diversity; Based on the obtained signal receiving spatial diversity, time diversity and frequency diversity, the signal band amplitudes of different diversity information are obtained; the three kinds of diversity information band amplitudes of the corresponding nodes are integrated to obtain a band amplitude data integration set; according to the band amplitude data integration set, the highest point band amplitude integration data is obtained; the band amplitude highest point data is the target position of the preliminary prediction of the accident personnel; Based on multi-source data of antenna received signals and combined with calibration matching models, the precise location of accident personnel is updated and predicted in real time.

8. The matching method based on 5G network, Beidou positioning and non-line-of-sight microwave according to claim 7 is characterized in that: Receiving spatial diversity, time diversity and frequency diversity based on the acquired signal to obtain signal band amplitudes of different diversity information; The three types of diversity information band amplitudes of the corresponding nodes are integrated to obtain a band amplitude data integration set, including: The spatial diversity means that multiple antennas are arranged and the receiving signal is adjusted to point to the receiving target signal; the time diversity means sampling the target signal at different time nodes; the frequency diversity means multiple frequency channels; Based on the spatial diversity, the time diversity and the frequency diversity, the band amplitude of the signal is extracted for the diversity conditions of the three signals; and the signal band amplitude data is aligned and normalized to obtain unified standard band amplitude data; The unified standard band amplitude data are integrated into a unified data set to enhance the signal of the unified data set; and the communication in complex environments is optimized and the target position of accident personnel is tracked.

9. The matching method based on 5G network, Beidou positioning and non-line-of-sight microwave according to claim 8 is characterized in that: Based on multi-source data of antenna received signals and combined with calibration matching models, the precise location of accident personnel is updated and predicted in real time, including: By constructing a three-dimensional terrain map of the accident area and combining it with signal modeling, we can obtain the initial prediction of the target location signal information of the accident personnel; by using the diversity of channel conditions and signal receiving environment, we can verify the initial prediction of the spatial location of the accident personnel's signal information; in order to determine the precise location of the victims; And through X k =X k-1 +X k ·(z k -H·X k-1 ) updates the positioning data of the target personnel in real time to improve the accuracy of the predicted target position; where X k represents the predicted position; z k Indicates the measurement position; K k represents the filter gain; H represents the observation matrix.

10. A matching system based on 5G network, Beidou positioning and non-line-of-sight microwave, characterized in that: The system sets an electronic device including a memory, a processor, and a matching method program based on 5G network and Beidou positioning and non-line-of-sight microwaves stored in the memory and run on the processor. When the matching method program based on 5G network and Beidou positioning and non-line-of-sight microwaves is executed by the processor, the steps of the matching method based on 5G network and Beidou positioning and non-line-of-sight microwaves according to any one of claims 1 to 9 are implemented. The system includes: Information acquisition module: used to obtain high-precision geographic location information data to obtain a first calibration data set; based on the first calibration data set, the antenna is adaptively and quickly focused and oriented; Detection and adjustment module: It is used to make the antenna collect signals and analyze signal strength changes based on preliminary adjustment of the antenna direction to obtain a second calibration data set; according to the second calibration data set, dynamically adjust the antenna direction to match the target signal under non-line-of-sight conditions; A calibration matching module: used to combine the first calibration data set and the second calibration data set to obtain a calibration data set; train the calibration data set to construct a calibration matching model; the calibration matching model outputs an identification result indicating a calibration match between the antenna position information and the target signal position information; Positioning prediction module: It is used to use non-line-of-sight microwaves in a disaster environment to receive target signals through antennas to determine the precise location of accident personnel based on the output results of the calibration matching model.

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