Dual-polarized antenna adjustment methods, systems, and antennas

Abstract

This application relates to a dual-polarized antenna adjustment method, system, and antenna. The method includes: acquiring signal quality data of antennas of multiple users in historical paths; analyzing the signal quality data in historical paths to determine the loss mutation interval under the target path; acquiring environmental interference data and polarization state data of the antenna under the target path based on the loss mutation interval; analyzing the polarization state data and environmental interference data based on a neural network prediction model to obtain environmental interference prediction results, antenna polarization state offset prediction results, and compensation prediction results for signal loss under polarization offset and environmental interference; performing real-time analysis of the antenna under the user path based on the compensation prediction results; generating control commands for the adjustment strategy through the compensation prediction results; and sending them to the signal processing circuit for execution.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of wireless communication, in particular to a dual-polarized antenna adjusting method and system and an antenna. BACKGROUND

[0002] With the popularity of wireless digital television signals, more and more people receive television signals outdoors or on the move, not just indoors. In outdoor, especially in mobile, the application scenario is that the mobile vehicles such as vehicles and ships will change the pose and direction constantly during driving, resulting in the change of the position of the receiving antenna.

[0003] Firstly, the existing vehicle antenna has strong directivity and single polarization, but the effective gain range is narrow, and is greatly affected by the base station and the surrounding environment. The television signal is relatively strong in a fixed direction, and is weak in other directions. After the vehicle moves, the antenna direction needs to be adjusted to achieve the best receiving effect.

[0004] Secondly, the change of the signal direction during the driving of the vehicle is dynamically complex, and many influencing dimensions need to be considered, such as polarization shift and environmental interference. Especially for the vehicle antenna with fixed antenna direction, if the real-time adjustment method is used, a large amount of data calculation is required, and the adjustment process is easy to lack accuracy and timeliness. SUMMARY

[0005] The primary purpose of the present application is to solve at least one of the above problems and provide a dual-polarized antenna adjusting method, system and antenna.

[0006] To meet the various purposes of the present application, the present application adopts the following technical solutions:

[0007] A dual-polarized antenna adjusting method provided for one of the purposes of the present application comprises the following steps:

[0008] Obtaining signal quality data of the antenna in the historical path of a plurality of users, analyzing the signal quality data in the historical path to determine a loss mutation interval in a target path;

[0009] Obtaining environmental interference data and polarization state data of the antenna in the target path according to the loss mutation interval, analyzing the polarization state data and the environmental interference data based on a neural network prediction model to obtain an interference prediction result of the environment, a shift prediction result of the polarization state of the antenna, and a compensation prediction result of the signal loss under the polarization shift and the environmental interference;

[0010] Real-time analyzing the antenna in the user path according to the compensation prediction result, generating a control instruction of the adjustment strategy through the compensation prediction result, and sending the control instruction to a signal processing circuit for execution.

[0011] In some optional embodiments, the acquiring the signal quality data of the antennas in the historical path of the plurality of users comprises the following steps:

[0012] The signal-to-noise ratio, the bit error rate and the signal strength of the antennas in the historical path of the plurality of users are acquired, and the signal-to-noise ratio, the bit error rate and the signal strength are weighted by using a weight matrix determined according to prior experience to obtain the signal quality data;

[0013] Before the signal quality data is acquired, the following steps are further included:

[0014] The historical path data and the time data of the users are collected by using the vehicle-mounted sensors, and the corresponding signal quality data is marked with the path and the time stamp when the signal quality data is acquired;

[0015] The historical paths of different users are clustered according to the similarity according to the difference between the historical paths and the path time, and the clustering center of the reference path is obtained to filter the historical paths.

[0016] In some optional embodiments, the analyzing the signal quality data in the historical path to determine the loss mutation interval in the target path comprises the following steps:

[0017] The signal quality data of all users is weighted and averaged to obtain a reference quality threshold, and the reference quality threshold is used to match the reference mutation interval in the reference path according to the signal quality data;

[0018] According to the signal loss time node of the reference path fed back by the user, a standard quality threshold is determined by weighted average, so as to judge whether a mutation point exists in the continuous signal quality data;

[0019] A first signal quality data set before a first time in the reference mutation interval is collected, the change rate of the first signal quality data set is calculated to judge whether a mutation positive critical point and a mutation negative critical point exist in the first signal quality data set, and the first signal quality data at least includes four continuous signal quality data;

[0020] A second signal quality data set before a second time in the reference mutation interval is collected, the same rate change rate of the second signal data is calculated, whether a signal mutation point exists in the second signal data is judged according to the standard quality threshold, a signal mutation interval is determined according to the signal mutation point, and the second signal quality data set at least includes four first signal quality data sets.

[0021] Further, the collecting the first signal quality data set before the first time in the reference mutation interval, and calculating the change rate of the first signal quality data set to judge whether a mutation positive critical point and a mutation negative critical point exist in the first signal quality data set comprises the following steps:

[0022] Comparing the first threshold value and the rate of change of the first signal quality data set, if the rate of change is greater than or equal to the first threshold value, a first cumulative value is accumulated, and if the rate of change is less than the first threshold value, the first signal quality data set is reselected;

[0023] According to the first cumulative threshold value, if the first cumulative value is greater than the first cumulative threshold value, it is judged that there is a mutation critical point in the first signal quality data set, and according to the increasing or decreasing direction of the rate of change, the mutation critical point is judged to be a mutation positive critical point or a mutation negative critical point, and the time point and the path point corresponding to the mutation positive critical point and the mutation negative critical point are recorded. The judgment of the rate of change is carried out, and if the first cumulative value is less than the first cumulative threshold value, the first signal quality data set is reselected.

[0024] Further, the second signal quality data set before the second time in the reference mutation interval is collected, the rate of change of the second signal data is calculated, and it is judged according to the standard quality threshold value whether there is a signal mutation point in the second signal data. According to the signal mutation point, a signal mutation interval is determined, including the following steps:

[0025] The first signal quality data set is taken as an element unit of the second signal quality data set, and the rate of change of the second signal quality data set is calculated according to the signal mean value of the element unit;

[0026] According to the standard quality threshold value, a second threshold value of the second signal quality data set is determined, and the quantitative relationship between the second threshold value and the rate of change is compared. If the rate of change is greater than or equal to the second threshold value, the judgment result is marked and a second cumulative value is accumulated, and if the rate of change is less than the second threshold value, the second signal quality data set is reselected;

[0027] According to the second cumulative threshold value, if the second cumulative value is greater than or equal to the second cumulative value, the signal mutation point in the reference mutation interval is determined, and if the second cumulative value is less than the second cumulative value, the second signal quality data set is reselected;

[0028] Based on the signal mutation point, a loss quality interval in the reference mutation interval is selected, and a target path corresponding to the loss quality interval is determined according to the path annotation and the time annotation.

[0029] In some optional embodiments, based on the neural network prediction model, interference prediction results of the environment and offset prediction results of the antenna polarization state are analyzed, including the following steps:

[0030] The environmental interference data of each loss mutation interval in the target path is input into the trained environmental interference prediction neural network to obtain the interference prediction parameters of all environmental interference types in the loss mutation interval, the interference prediction result includes multipath effect interference prediction parameters, reflection loss interference prediction parameters and shielding interference prediction parameters, and the environmental interference prediction neural network is obtained through a plurality of training environmental interference information and a training data set of corresponding multipath effect interference labels, reflection loss interference labels and shielding interference labels;

[0031] The interference prediction parameters greater than the preset interference threshold are screened for different interference types, the mean of the interference change rates of different interference prediction parameters is calculated, the influence factor coefficient of the interference prediction parameters of each interference type at the current time is determined, and the interference prediction result is output through the product of the interference prediction parameters and the influence factor coefficient;

[0032] The polarization state data of each loss mutation interval in the target path is input into the trained first polarization offset prediction neural network to obtain the first offset prediction result of each polarization state in the mutation interval, the first offset prediction result includes a first horizontal polarization offset result, a first vertical polarization offset result and a first authenticity probability of the first offset prediction result, and the polarization offset prediction neural network is obtained through a first training data set including a plurality of training polarization state information and corresponding polarization offset label data;

[0033] The first offset prediction result output by the first polarization offset prediction neural network and the interference prediction result are input into the trained second polarization offset prediction neural network to obtain the second offset prediction result in the mutation interval, the second offset prediction result includes a second horizontal polarization offset result, a vertical polarization offset result and a second authenticity probability of the second offset prediction result, and the second polarization offset prediction neural network is obtained through a second training set including a plurality of real polarization state information, corresponding polarization offset label data and label data of the influence of corresponding environmental interference on polarization offset;

[0034] The first offset prediction result and the second offset prediction result are subjected to authenticity probability weighted summation according to the first authenticity probability and the second authenticity probability, to obtain the offset prediction result, the first authenticity probability is directly proportional to the accuracy of the first polarization offset prediction neural network, and the second authenticity probability is directly proportional to the number of influences of the interference prediction result on the second offset prediction result.

[0035] Further, the polarization state data and the environmental interference data are analyzed based on the neural network prediction model to obtain the compensation prediction result of the signal loss under the polarization offset and the environmental interference, including the following steps:

[0036] The signal loss compensation prediction neural network is constructed based on a compensation classification network and a compensation prediction network. The compensation classification network includes a classifier model, a time recurrent neural network and an output discrimination model. The compensation classification network is used to predict signal losses of different polarization offset types and environmental interference types. The time recurrent neural network is used to predict two continuous first compensation prediction results corresponding to the predicted signal losses, and compare the similarity of the first compensation prediction results and compensation results under actual signal losses. The output discrimination model is used to screen valid compensation prediction results from the first compensation prediction results according to the similarity being greater than a similarity threshold. The compensation prediction network is used to determine a compensation prediction result corresponding to a compensation prediction value of the antenna according to the valid compensation prediction results. The compensation prediction value includes a compensation index of horizontal polarization, a compensation index of vertical polarization and a compensation index of impedance adjustment corresponding to the environmental interference type.

[0037] The interference prediction result and the polarization offset result are input into the signal loss compensation prediction neural network to obtain a compensation prediction result corresponding to the signal loss. The signal loss compensation prediction neural network is obtained by training a compensation training data set including a plurality of polarization offset type data sets corresponding to antenna signal losses and environmental interference data sets, and a signal loss label.

[0038] In some optional embodiments, the antenna on the user path is analyzed in real time according to the compensation prediction result, a control instruction of an adjustment strategy is generated through the compensation prediction result, and the control instruction is sent to a signal processing circuit for execution, including the following steps:

[0039] A pre-response time threshold of the adjustment control instruction is set according to the distance between the user position on the real-time path and the loss mutation interval in the target path, and the real-time analysis time is synchronized according to the pre-response time threshold;

[0040] The demand power of the corresponding adjustment device in the antenna is determined according to the compensation index of the compensation prediction result, and the demand power is sent to the signal processing circuit to adjust the polarization direction and the impedance of the antenna. The adjustment device includes a power distribution unit, a horizontal polarization unit, a vertical polarization unit, an impedance unit, a filter unit and an amplifier unit.

[0041] On the other hand, a dual-polarized antenna adjustment system is provided for performing a dual-polarized antenna adjustment method provided for one of the purposes of the present application. The dual-polarized antenna adjustment system:

[0042] The adjustment interval determination module is configured to obtain signal quality data of the antenna in the historical path of a plurality of users, and analyze the signal quality data in the historical path to determine a loss mutation interval in the target path.

[0043] The adjustment compensation prediction module is configured to obtain environmental interference data and polarization state data of the antenna under the target path according to the loss mutation interval, analyze the polarization state data and the environmental interference data based on a neural network prediction model, and obtain an interference prediction result of the environment, a shift prediction result of the polarization state of the antenna, and a compensation prediction result of the signal loss under polarization shift and environmental interference.

[0044] The real-time adjustment control module is configured to perform real-time analysis on the antenna under the user path according to the compensation prediction result, generate a control instruction of the adjustment strategy based on the compensation prediction result, and send the control instruction to the signal processing circuit for execution.

[0045] In another aspect, an antenna is provided for one of the purposes of the present application, comprising an adjustment system for performing the adjustment method of the dual-polarized antenna according to the first aspect of the present application, and further comprising:

[0046] The shell, the horizontal polarization unit, the vertical polarization unit, and the antenna decoupling;

[0047] The horizontal polarization unit comprises arrow-shaped dipoles and arc-shaped dipoles arranged in an array, the arc-shaped dipoles being arranged above the arrow-shaped dipoles, each of the arrow-shaped dipoles comprising an arrowhead and an arrow shaft, the arrow shaft being provided with insulated parallel wires;

[0048] The vertical polarization unit comprises a columnar antenna and an assembly circuit board, the arc-shaped dipoles, the arrow-shaped dipoles, the columnar antenna, and the dual-polarized antenna adjustment system are all electrically connected to the assembly circuit board, and the assembly circuit board comprises a power distribution unit, an impedance unit, a filter unit, and an amplifier unit;

[0049] The antenna decoupling is arranged between every two of the arrow-shaped dipoles.

[0050] In yet another aspect, a dual-polarized antenna adjustment device is provided for one of the purposes of the present application, comprising a central processing unit and a memory, the central processing unit being configured to invoke a computer program stored in the memory to execute the steps of the dual-polarized antenna adjustment method described in the present application.

[0051] In yet another aspect, a computer readable storage medium is provided for one of the purposes of the present application, the computer readable storage medium storing computer executable instructions for causing a computer to execute the dual-polarized antenna adjustment method described in any one of the present application.

[0052] The technical solution of the present application has multiple advantages, including but not limited to the following aspects:

[0053] Firstly, the application adopts multiple arrow-shaped dipoles and arc-shaped dipoles arranged in an array as the horizontal polarization part, and adopts a columnar antenna and an assembled circuit board as the vertical polarization part; the array arrangement increases the range of effective gain of the antenna, enhances the signal strength of the antenna in the horizontal direction, and thus expands the communication coverage; two signals of different polarizations can be received, and both are omnidirectional reception, so that the effective reception range can be achieved without adjusting the direction of the antenna, and the reception effect is better than that of a single-polarization antenna;

[0054] Secondly, the application determines the loss mutation interval of the target path that needs to be adjusted by analyzing the signal quality data collected by multiple users in the historical path, which can greatly reduce the calculation amount of the real-time adjustment process, and the environmental interference data and polarization state data in the loss mutation interval are effective data, the prediction result output by the neural network prediction model improves the accuracy of the prediction data of polarization deviation and environmental interference and the timeliness of the real-time adjustment process, which can be used as data basis for signal loss compensation.

[0055] Finally, the compensation prediction result obtained by real-time analysis generates an adjustment strategy in the real-time path movement of the user, which can quickly respond to the signal loss problem of the vehicle antenna, improve the signal reception capability of the antenna through real-time adjustment, and especially for the dual-polarized antenna in the moving state, the accurate data of the antenna adjustment can be provided without changing the pose of the antenna, and the stability of the signal received by the vehicle antenna is ensured. BRIEF DESCRIPTION OF DRAWINGS

[0056] The above and / or additional aspects and advantages of the present application will become apparent and more readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:

[0057] Figure 1 a flow chart of an embodiment of the dual-polarized antenna adjustment method of the present application;

[0058] Figure 2 a schematic diagram of the dual-polarized antenna adjustment system adopted by the present application;

[0059] Figure 3 an exploded structural schematic diagram of the exemplary dual-polarized antenna of the present application;

[0060] Figure 4 an exploded structural schematic diagram of the exemplary dual-polarized antenna of the present application, with the shell omitted and the columnar antenna in an exploded state;

[0061] Figure 5 an exploded structural schematic diagram of the exemplary dual-polarized antenna of the present application, with the shell omitted and the columnar antenna in an exploded state;

[0062] Figure 6The schematic diagram of the overall structure of the exemplary dual-polarized antenna of the present application;

[0063] Figure 7 The schematic diagram of the structure of the barrel shell of the exemplary dual-polarized antenna of the present application in an exploded state;

[0064] Figure 8 The schematic diagram of the signal processing in the assembled circuit board of the exemplary dual-polarized antenna of the present application;

[0065] Reference signs: 1, shell, 11, barrel shell, 12, disc top shell, 13, disc bottom shell, 131, clamping position, 132, cylinder seat, 133, first through hole, 14, support, 141, second through hole, 15, bolt nut, 2, horizontal polarization part, 3, vertical polarization part, 4, arrow type vibrator, 41, arrow head, 42, arrow body, 421, insulated parallel line, 5, arc dipole, 6, columnar antenna, 61, antenna rod, 62, screw rod, 63, gasket, 64, nut, 65, antenna head, 7, assembled circuit board, 71, power distribution unit, 72, impedance unit, 73, filter unit, 74, amplifier unit, 8, antenna decoupling. DETAILED DESCRIPTION

[0066] The technical solution of the present application is suitable for the field of wireless communication technology, and particularly suitable for the scene of dual-polarized antenna adjustment. In this context, the technical solution of the present application can be applied in a typical vehicle antenna embodiment.

[0067] Firstly, the vehicle antenna needs to adjust the pose of the antenna according to the gain direction of the base station signal transmission, so as to ensure that a stronger signal is received in this direction. The vehicle antenna is usually an adjustable pose omnidirectional antenna, and the effective gain range is narrow and is greatly affected by the base station and the surrounding environment. The television signal is relatively strong in a fixed direction and weak in other directions. After the vehicle moves, the antenna direction needs to be readjusted to achieve the best reception effect.

[0068] Secondly, the change of signal direction during the vehicle driving process is dynamically complex, and many influencing dimensions need to be considered, such as polarization shift and environmental interference. Especially for the vehicle antenna with fixed antenna direction, if the real-time adjustment method is adopted, a large amount of data calculation is required, and the adjustment process is easy to lack accuracy and timeliness.

[0069] Further, in order to solve the above technical problems, the present application proposes a dual-polarized antenna adjustment method, which aims to improve the quality of received signals through the self-adjustment of the antenna during the movement of the vehicle.

[0070] The technical solution of the present application and how the technical solution of the present application solves the above technical problems will be described in detail below with specific embodiments.

[0071] Referring to Figure 1 The application discloses a dual-polarized antenna adjusting method, and in a typical dual-polarized antenna adjusting embodiment thereof, comprises the following steps:

[0072] S110, acquiring signal quality data of the antenna in the historical path of a plurality of users, and analyzing the signal quality data in the historical path to determine a loss mutation interval in a target path;

[0073] In a specific embodiment, before acquiring the signal quality data of the antenna in the historical path, the method further comprises: collecting historical path data and time data of the user through a vehicle-mounted sensor, and performing path labeling and time stamp labeling on the corresponding signal quality data according to the historical path data and the time data when the signal quality data is acquired.

[0074] Further, the historical paths of different users are analyzed according to the similarity to obtain a clustering center of a reference path, for example, when a user drives to work, a plurality of users may drive on the same urban trunk road to the same destination area in the same time period, and the clustering center represents the path as a reference path. It can be understood that the dual-polarized antenna in the application can meet the use in various scenarios through the adjusting method, and the path data of the user needs to be obtained comprehensively, and the user needs to authorize the data acquisition before the data is acquired, so that the real data fed back by the user in the initial adjusting stage is used to support the adjusting process. In addition to the vehicle, the user may also drive other vehicles and use the antenna of the vehicle. It can be understood that the data collected in different vehicles are all path data, and the difference between the path data of different vehicles is only that the path types are different. The data of the same path type can be preliminarily screened through the clustering method to obtain the reference path. Especially when stored in the cloud, the signal quality data in different paths and at different times can be classified and stored, and the non-effective and non-real data can be screened and deleted, thereby reducing the load of the cloud storage and improving the utilization rate of the collected data.

[0075] Optionally, the obtained signal quality data includes signal-to-noise ratio, bit error rate and signal strength; wherein the signal-to-noise ratio represents the proportion of signal and noise, the bit error rate refers to the ratio of the number of error-received data bits to the total number of transmitted data bits in the data transmission process, and the signal strength refers to the signal power level received. In this embodiment, the signal quality data can be determined according to the sensitivity of the signal-to-noise ratio, the bit error rate and the signal strength to the signal quality of the historical path in the prior experience, so as to determine the corresponding weight matrix. The signal quality data is obtained by weighting the obtained signal-to-noise ratio, bit error rate and signal strength through the weight matrix. It can be understood that since the signal-to-noise ratio, the bit error rate and the signal strength are all time series data reflecting the transient characteristics and stability of the signal, and the collected signal quality data also has the spatial dimension of the path marker, the preprocessed signal quality data is space-time data with path markers and time markers, which is used to reflect the signal quality received by the antenna of each user under different paths and different times. It can be understood that the path and the time in the signal quality data are related to each other, which can be estimated according to the historical driving habits of the user.

[0076] In some embodiments, the signal quality data in the historical path is analyzed to determine the loss mutation interval in the target path, including the following steps:

[0077] S1110, the signal quality data of all users is weighted and averaged to obtain a reference quality threshold, and the reference quality threshold is compared with the signal quality data to match the reference mutation interval in the reference path;

[0078] Specifically, the weight value of the weighted average is calculated according to the similarity between the path corresponding to the signal quality data and the reference path. The reference quality threshold calculated by the weighted average is used to filter the signal quality data in the historical path of all users to determine the reference mutation interval where the signal loss may exist;

[0079] S1120, according to the user feedback signal loss time node in the reference path, the standard quality threshold is determined by weighted average, which is used to judge whether there is a mutation point in the continuous signal quality data;

[0080] Specifically, the time node fed back by the user is used as the reference value of the signal loss in the time scale to highlight the authenticity of the antenna state in the actual use process. Since the time node fed back by the user may have a time lag problem, the time lag weight of the time scale also needs to be considered when the weight of the standard quality threshold is considered, so as to reduce the judgment error of the standard quality threshold.

[0081] S1130, collect the first signal quality data set before the first time in the reference mutation interval, calculate the change rate of the first signal quality data set to determine whether there is a mutation positive critical point and a mutation negative critical point in the first signal quality data set, and the first signal quality data includes at least four continuous signal quality data;

[0082] It should be noted that the first first time is randomly selected for the sample, and a second judgment can be made according to the calculated signal change rate, such as a positive signal change rate, and the second time is selected as the time interval before the first time for sampling.

[0083] In a specific real-time, the change rate of the first signal quality data set is calculated to determine whether there is a mutation critical point in the first signal quality data set, including the following steps:

[0084] S1131, compare the first threshold value and the quantity relationship of the change rate of the first signal quality data set, if the change rate is greater than or equal to the first threshold value, then accumulate to obtain the first cumulative value, if the change rate is less than the first threshold value, then reselect the first signal quality data set;

[0085] Specifically, the expression of the change rate is:

[0086]

[0087] Among them, S represents the signal change rate corresponding to the signal quality data at time t, S t S represents the signal data in the signal quality data at time t, S (t-i) S represents the signal data in the signal quality data corresponding to i unit time before time t.

[0088] S1132, compare the first cumulative value according to the first cumulative threshold value, if greater than the first cumulative threshold value, then determine that there is a mutation critical point in the first signal quality data set, determine the mutation positive critical point or the mutation negative critical point according to the increase or decrease direction of the change rate, record the time point and the path point corresponding to the mutation positive critical point and the mutation negative critical point, and judge the same change rate, if less than the first cumulative threshold value, then reselect the first signal quality data set;

[0089] Optionally, the mutation positive critical point and the mutation negative critical point are verified by a minimum length of a preset change baseline period, according to the distance length between the time point corresponding to the mutation positive critical point and the mutation negative critical point and the initial time of the first signal quality data set, and the quantity relationship between the minimum length, if greater than or equal to the minimum length, it is determined that there is a mutation critical point, if less than the minimum length, it is determined that there is no mutation critical point, it is necessary to reselect the first signal quality data, wherein the minimum length is obtained according to the experience verification of signal mutation sample.

[0090] S1140, collect the second signal quality data set before the second time in the reference mutation interval, calculate the same rate of change of the second signal data, judge whether there is a signal mutation point in the second signal data according to the standard quality threshold, determine the signal mutation interval according to the signal mutation point, and the second signal quality data set includes at least four first signal quality data sets.

[0091] Optionally, the second signal quality data can be collected according to the time starting point corresponding to the mutation critical point, specifically, according to the determined time starting point, a second signal data set composed of a plurality of adjacent first signal data sets of the same unit time interval before the time starting point is collected, and the position of the loss mutation interval can be further evaluated according to the data of a plurality of adjacent time points, so as to increase the accuracy of the data.

[0092] In specific implementation, collecting the second signal quality data set before the second time in the reference mutation interval includes the following steps:

[0093] S1141, the first signal quality data set is taken as an element unit of the second signal quality data set, and the same rate of change in the second signal quality data set is calculated according to the signal mean value of the element unit;

[0094] Specifically, the expression of the signal mean value is:

[0095]

[0096] Specifically, the expression of the same rate of change is:

[0097]

[0098] Wherein, S represents the mean value of the first signal quality data set from t time, S t S represents the signal data corresponding to t time, and i represents the i th unit time point before t time, S represents the mean value of the first signal quality data set corresponding to the adjacent unit time interval from t time, S v S represents the signal data corresponding to the first signal quality data set of the adjacent unit time interval from t time, j represents the j th unit time point corresponding to the first signal data set of the adjacent unit time interval from t time, and Y t S represents the same rate of change of the adjacent first signal quality data set from t time to the adjacent unit time point interval.

[0099] It can be seen that in the above embodiment, the change of the signal quality data is described by the same change rate, which is used to evaluate whether there is a mutation in the signal quality, so as to further adjust the antenna according to the mutation in the deep diagnosis, which can quickly respond to the event of weak received signal caused by signal loss, so as to adjust the antenna state in real time to achieve the best receiving effect.

[0100] In specific implementation, the same change rate of the second signal data is calculated, whether there is a signal mutation point in the second signal data is judged according to the standard quality threshold, and the signal mutation interval is determined according to the signal mutation point, including the following steps:

[0101] S1142, determining a second threshold of the second signal quality data set according to the standard quality threshold, comparing the quantity relationship between the second threshold and the same change rate, marking and accumulating the second cumulative value for the judgment result of the same change rate greater than or equal to the second threshold, and reselecting the second signal quality data set for the judgment result of the same change rate less than the second threshold;

[0102] S1143, comparing the second cumulative threshold with the second cumulative value, if greater than or equal to the second cumulative value, determining the signal mutation point in the reference mutation interval, if less than the second cumulative value, reselecting the second signal quality data set;

[0103] In specific implementation, the loss quality interval in the reference mutation interval is selected based on the signal mutation point, and the target path corresponding to the loss quality interval is determined according to the path label and the time label.

[0104] It can be seen that in the above embodiment, the signal quality data of the historical path is analyzed in the time domain and the space domain, the signal quality loss mutation interval and the corresponding target path required for antenna adjustment are determined, the data collected by multiple users can be cross-verified, and the further analysis of the loss mutation interval of the target path can improve the analysis accuracy required for adjustment and reduce the calculation loss. Especially in the antenna adjustment system without attenuator and phase shifter, the adjustment demand of the antenna can be quickly and accurately responded, and the corresponding cloud data storage can save storage resources.

[0105] S210, acquiring the environmental interference data and the polarization state data of the antenna under the target path according to the loss mutation interval, analyzing the polarization state data and the environmental interference data based on the neural network prediction model, obtaining the interference prediction result of the environment, the offset prediction result of the polarization state of the antenna, and the compensation prediction result of the signal loss under the polarization offset and the environmental interference;

[0106] Optionally, real-time data collection is performed on the determined target path during user travel, continuously receiving polarization state data and environmental interference data of the antenna on the target path, wherein the polarization state data is recorded by a high-precision synchronous sampling device to record the polarization state data in the vertical polarization direction and the horizontal polarization direction at the same time, the environmental interference data is obtained by ray tracing software to clean up the antenna signal data of the antenna to obtain multipath effect interference data, reflection loss data and shielding interference data, and the polarization deviation of the signal and the environmental interference are predicted and evaluated through the above data, which can provide data support for antenna adjustment. At the same time, the above data are collected in the loss mutation interval, and the data volume is significantly reduced compared with the data in the historical path, which can quickly respond to the prediction demand of antenna adjustment.

[0107] In a specific implementation, the polarization state data and the environmental interference data are analyzed based on the neural network prediction model to obtain an environmental interference prediction result, including the following steps:

[0108] S2110, input the environmental interference data of each loss mutation interval in the target path into the trained environmental interference prediction neural network to obtain the interference prediction parameters of all environmental interference types in the loss mutation interval, the interference prediction result includes multipath effect interference prediction parameters, reflection loss interference prediction parameters and shielding interference prediction parameters, and the environmental interference prediction neural network is obtained through a plurality of training environmental interference information and a training data set corresponding to the multipath effect interference label, reflection loss interference label and shielding interference label in the historical path;

[0109] S2111, screen the interference prediction parameters greater than the preset interference threshold for different interference types, calculate the mean value of the interference change rate of different interference prediction parameters, determine the influence factor coefficient of the interference prediction parameter of each interference type at the current time, and output the interference prediction result through the product of the interference prediction parameter and the influence factor coefficient.

[0110] As can be seen, through the above optional embodiments, the environmental interference data of each mutation interval in the target path is input into the trained environmental interference prediction neural network to obtain the interference prediction result of each interference type, the interference types with prediction parameters exceeding the preset interference threshold are screened out, the influence factor coefficient of the prediction parameter under different interference types is calculated, and the influence of environmental interference on the antenna signal quality is comprehensively evaluated according to the influence factor coefficient and the interference prediction parameter, so as to realize precise interference prediction analysis based on environmental interference types and real-time environmental interference, improve the accuracy and efficiency of the antenna adjustment signal quality process, and reduce the influence of environmental interference on the antenna signal quality.

[0111] In a specific implementation, the polarization state data and the environmental interference data are analyzed based on the neural network prediction model to obtain the offset prediction result of the antenna polarization state, including the following steps:

[0112] S2112, input the polarization state data of each loss mutation interval in the target path into the trained first polarization offset prediction neural network to obtain the first offset prediction result of each polarization state in the mutation interval, the first offset prediction result including a first horizontal polarization offset result, a first vertical polarization offset result, and a first authenticity probability of the first offset prediction result, the polarization offset prediction neural network being obtained through a first training data set including a plurality of training polarization state information and corresponding polarization offset labeled data;

[0113] S2112, input the first offset prediction result and the interference prediction result output by the first polarization offset prediction neural network into the trained second polarization offset prediction neural network to obtain the second offset prediction result in the mutation interval, the second offset prediction result including a second horizontal polarization offset result, a vertical polarization offset result, and a second authenticity probability of the second offset prediction result, the second polarization offset prediction neural network being obtained through a second training set including a plurality of real polarization state information, corresponding polarization offset labeled data, and labeled data of the influence of corresponding environmental interference on the polarization offset;

[0114] S2113, perform authenticity probability weighted summation on the first offset prediction result and the second offset prediction result according to the first authenticity probability and the second authenticity probability, obtain the offset prediction result, the first authenticity probability being directly proportional to the accuracy of the first polarization offset prediction neural network, and the second authenticity probability being directly proportional to the number of times of the influence of the interference prediction result on the second offset prediction result.

[0115] It can be understood that without adjusting the antenna direction, the effective polarization direction of the antenna will be offset, and the quality of the received signal will be affected by the multipath effect, reflection loss, and shielding, among which the polarization offset is real-time and dynamic and cannot be directly measured and controlled. Therefore, a neural network is used for offset prediction to determine the compensation control amount corresponding to the offset. In the present application, the user path that needs to be adjusted in real time is first screened, the data processing amount and the calculation complexity are reduced, and the reaction efficiency of antenna adjustment is improved.

[0116] As can be seen, through the above embodiments, the authenticity of the offset prediction result is determined comprehensively through two neural network models, and the influence of environmental interference on the offset prediction result is considered, so as to improve the prediction accuracy of the required compensation value corresponding to each loss mutation interval according to the offset prediction result in the subsequent process, and realize more intelligent and comprehensive monitoring and adjustment of antenna signal enhancement.

[0117] In a specific implementation, the polarization state data and the environmental interference data are analyzed based on the neural network prediction model to obtain a compensation prediction result of signal loss under polarization deviation and environmental interference, including the following steps:

[0118] S2120, a signal loss compensation prediction neural network is constructed based on a compensation classification network and a compensation prediction network, the compensation classification network includes a classifier model, a time recurrent neural network and an output discrimination model, the compensation classification network is used to predict signal loss of different polarization deviation types and environmental interference types, the time recurrent neural network is used to predict two continuous first compensation prediction results corresponding to the signal loss, and the similarity of the first compensation prediction result and a compensation result under actual signal loss is compared, the output discrimination model is used to filter valid compensation prediction results from the first compensation prediction result according to the similarity being greater than a similarity threshold, and the compensation prediction network is used to determine a compensation prediction result corresponding to a compensation prediction value of the antenna, the compensation prediction value includes a compensation index of horizontal polarization, a compensation index of vertical polarization and a compensation index of impedance adjustment corresponding to the environmental interference type;

[0119] S2121, the interference prediction result and the polarization deviation result are input into the signal loss compensation prediction neural network to obtain a compensation prediction result corresponding to signal loss.

[0120] Optionally, the signal loss compensation prediction neural network is obtained by training a compensation training data set including a plurality of polarization deviation type data sets and environmental interference data sets corresponding to antenna signal loss, and a signal loss label.

[0121] Optionally, the compensation index of impedance adjustment corresponding to the environmental interference type includes a compensation index of multipath effect interference, a compensation index of reflection loss interference and a compensation index of shielding interference.

[0122] As can be seen, through the above optional embodiments, the neural network type and architecture corresponding to the compensation prediction result in the application are limited, the correlation between the signal loss of different data types representing the signal loss and the compensation prediction can be realized, so as to accurately predict the compensation value required for antenna adjustment under different signal loss reasons, to accurately predict the adjustment value required for antenna adjustment in real-time mobile state, and the accuracy and efficiency of antenna adjustment are improved based on the joint judgment of multiple neural networks to output the prediction result, especially for the dual-polarized antenna in mobile state, accurate data reference for antenna adjustment can be provided without changing the antenna pose, and the stability of antenna signal reception is improved.

[0123] S310, the antenna on the user path is analyzed in real time according to the compensation prediction result, a control instruction of adjustment strategy is generated based on the compensation prediction result, and is sent to a signal processing circuit for execution.

[0124] In a specific implementation, the real-time analysis of the antenna under the user path according to the compensation prediction result, the generation of the control instruction of the adjustment strategy through the compensation prediction result, and the sending to the signal processing circuit for execution include the following steps:

[0125] S3101, set a pre-response time threshold of the adjustment control instruction according to the distance between the real-time path user position and the loss mutation interval in the target path, and synchronize the real-time analysis time according to the pre-response time threshold;

[0126] Specifically, in real-time analysis, the neural network model requires a certain calculation time. Since the neural network model has prediction capability, it can calculate the adjustment control in advance according to historical data and training data. Although the application further reduces the calculation consumption by determining the loss mutation interval, the setting of the pre-response time threshold can obtain the corresponding adjustment data in advance for antenna adjustment, which can improve the response speed and adjustment efficiency of the adjustment, and maintain the stability of the antenna in receiving signals in the path.

[0127] S3102, determine the demand power of the corresponding adjustment device in the antenna according to the compensation index of the compensation prediction result, and send the demand power to the signal processing circuit to adjust the polarization direction and impedance of the antenna.

[0128] Optionally, the adjustment device includes a power distribution unit, a horizontal polarization unit, a vertical polarization unit, an impedance unit, a filter unit, and an amplifier unit.

[0129] Specifically, the horizontal polarization unit and the vertical polarization unit can change the polarization synthesis direction through the regulation of the demand power, so that the polarization synthesis direction approaches the gain direction corresponding to the loss mutation point, thereby improving the quality of the received signal.

[0130] Further, the shielding interference is also a case of changing the signal gain direction, and the effective gain direction under the shielding interference can also be obtained by changing the polarization synthesis direction through the power regulation of the horizontal polarization unit and the vertical polarization unit.

[0131] Further, the power regulation of the impedance unit can ensure the adaptability of the antenna to the back-end device through impedance matching, thereby reducing the influence of signal reflection loss on signal quality. The filter unit and the amplifier unit can separate, filter and amplify signals of different polarization directions and different frequency bands through power regulation, thereby improving the quality and strength of the signals.

[0132] Further, please refer to Figure 8, the power distributor can limit the power of each unit, set a power interval according to the historical demand power, if the demand power of the adjustment device is higher than the maximum power set by the power interval, the actual power of the adjustment device is reduced to improve the stability of the antenna, if the demand power of the adjustment device is lower than the minimum power set by the power interval, the actual power of the adjustment device is increased to reach the effective working state, and the stability of the antenna is also improved.

[0133] Finally, the unique technical advantage of the present application is that the present application determines the loss mutation interval of the target path that needs to be adjusted by the antenna through the signal quality data collected by multiple users in the historical path, which can greatly reduce the calculation amount of the real-time adjustment process, the environmental interference data and the polarization state data in the loss mutation interval are effective data, the prediction result output by the neural network prediction model improves the accuracy of the prediction data of the polarization deviation and the environmental interference and the timeliness of the real-time adjustment process, which can be used as the data basis for signal loss compensation, the compensation prediction result generated by real-time analysis generates an adjustment strategy in the real-time path movement of the user, which can quickly respond to the signal loss problem of the vehicle antenna, improve the signal receiving capability of the antenna through real-time adjustment, especially the dual-polarized antenna in the moving state, without changing the pose of the antenna, accurate data for antenna adjustment can be provided to ensure the stability of the signal received by the vehicle antenna, and has high application prospect in the field of wireless communication technology of vehicle antenna.

[0134] Please refer to Figure 2 According to one aspect of the present application, a dual-polarized antenna adjustment system is provided, which comprises:

[0135] An adjustment interval determination module is configured to obtain signal quality data of the antenna in the historical path of multiple users, and analyze the signal quality data in the historical path to determine the loss mutation interval in the target path; an adjustment compensation prediction module is configured to obtain environmental interference data and polarization state data of the antenna in the target path according to the loss mutation interval, analyze the polarization state data and the environmental interference data based on a neural network prediction model, and obtain the interference prediction result of the environment, the deviation prediction result of the polarization state of the antenna, and the compensation prediction result of the signal loss under the polarization deviation and the environmental interference; a real-time adjustment control module is configured to analyze the antenna in the path of the user in real time according to the compensation prediction result, generate a control instruction of the adjustment strategy through the compensation prediction result, and send the control instruction to a signal processing circuit for execution.

[0136] On the basis of any embodiment of the system of the present application, the system of the present application further comprises a data acquisition module configured to obtain the signal-to-noise ratio, the bit error rate and the signal strength of the antenna in the historical path of multiple users, and obtain the signal quality data by weighting the signal-to-noise ratio, the bit error rate and the signal strength according to a weight matrix determined by prior experience.

[0137] On the basis of any embodiment of the system of the application, the system of the application further comprises: a path screening module configured to collect historical path data and time data of a user through a vehicle-mounted sensor, and to perform path labeling and timestamp labeling on corresponding signal quality data when the signal quality data is acquired according to the historical path data and the time data; and to perform clustering analysis on historical paths of different users according to the similarity of the historical paths and the path time to obtain a clustering center of a reference path to screen the historical paths. On the basis of any embodiment of the system of the application, the system of the application further comprises: a signal loss mutation module configured to obtain a reference quality threshold by weighted averaging of signal quality data of all users, match a reference mutation interval in a reference path according to the reference quality threshold by comparing the signal quality data, determine a standard quality threshold by weighted averaging according to a signal loss time node of the reference path fed back by a user, to be used for judging whether a mutation point exists in continuous signal quality data, collect a first signal quality data set before a first time in the reference mutation interval, calculate a change rate of the first signal quality data set to judge whether a mutation positive critical point and a mutation negative critical point exist in the first signal quality data set, the first signal quality data set comprising at least four continuous signal quality data, collect a second signal quality data set before a second time in the reference mutation interval, calculate a same-period change rate of the second signal data, judge whether a signal mutation point exists in the second signal data according to the standard quality threshold, determine a signal mutation interval according to the signal mutation point, and the second signal quality data set comprising at least four first signal quality data sets.

[0138] On the basis of any embodiment of the system of the application, the system of the application further comprises: a mutation critical module configured to compare a quantity relationship between a first threshold and a change rate of the first signal quality data set, if the change rate is greater than or equal to the first threshold, accumulate to obtain a first accumulated value, and if the change rate is less than the first threshold, reselect the first signal quality data set; compare the first accumulated value with a first accumulated threshold, if the first accumulated value is greater than the first accumulated threshold, judge that a mutation critical point exists in the first signal quality data set, judge whether the mutation critical point is a mutation positive critical point or a mutation negative critical point according to an increasing or decreasing direction of the change rate, record time points and path points corresponding to the mutation positive critical point and the mutation negative critical point, and judge a same-period change rate, and if the first accumulated value is less than the first accumulated threshold, reselect the first signal quality data set.

[0139] Based on any embodiment of the system in this application, the system further includes: a signal mutation module, configured to use a first signal quality dataset as an element unit of a second signal quality dataset, calculate the year-on-year change rate within the second signal quality dataset based on the signal mean of the element unit; determine a second threshold for the second signal quality dataset based on a standard quality threshold, compare the quantitative relationship between the second threshold and the year-on-year change rate, mark the judgment results where the year-on-year change rate is greater than or equal to the second threshold and accumulate a second cumulative value, and reselect the second signal quality dataset for the judgment results where the year-on-year change rate is less than the second threshold; compare the second cumulative value with the second cumulative threshold, and if it is greater than or equal to the second cumulative value, determine the signal mutation point within the benchmark mutation interval; if it is less than the second cumulative value, reselect the second signal quality dataset; select a loss quality interval within the benchmark mutation interval based on the signal mutation point, and determine the target path corresponding to the loss quality interval based on path labeling and time labeling.

[0140] Based on any embodiment of the system in this application, the system further includes: a first prediction module, configured to input environmental interference data of each loss mutation interval in the target path into a trained environmental interference prediction neural network to obtain interference prediction parameters for all environmental interference types within the loss mutation interval. The interference prediction results include multipath effect interference prediction parameters, reflection loss interference prediction parameters, and occlusion interference prediction parameters. The environmental interference prediction neural network is obtained through a training dataset of multiple training environmental interference information and corresponding multipath effect interference labels, reflection loss interference labels, and occlusion interference labels in historical paths. The module filters interference prediction parameters greater than a preset interference threshold for different interference types, calculates the mean of the interference change rate of different interference prediction parameters, determines the influence factor coefficient of the interference prediction parameter for each interference type at the current time, and outputs the interference prediction result through the product of the interference prediction parameter and the influence factor coefficient. The module also inputs polarization state data of each loss mutation interval in the target path into a trained first polarization offset prediction neural network to obtain a first offset prediction result for each polarization state within the mutation interval. The first offset prediction result includes... The first horizontal polarization shift result, the first vertical polarization shift result, and the first shift prediction result have a first probability of authenticity. The polarization shift prediction neural network is obtained through a first training dataset including multiple training polarization state information and corresponding polarization shift annotation data. The first shift prediction result and interference prediction result output by the first polarization shift prediction neural network are input into a trained second polarization shift prediction neural network to obtain a second shift prediction result within the abrupt change interval. The second shift prediction result includes the second horizontal polarization shift result, the vertical polarization shift result, and the second shift prediction result, and has a second probability of authenticity. The second polarization shift prediction neural network is obtained through a second training set including multiple true polarization state information, corresponding polarization shift annotation data, and annotation data corresponding to the influence of environmental interference on polarization shift. The first probability of authenticity and the second probability of authenticity are weighted and summed to obtain the shift prediction result. The first probability of authenticity is proportional to the accuracy of the first polarization shift prediction neural network, and the second probability of authenticity is proportional to the number of times the interference prediction result influences the second shift prediction result.

[0141] Based on any embodiment of the system in this application, the system further includes: a second prediction module, configured to construct a signal loss compensation prediction neural network based on a compensation classification network and a compensation prediction network. The compensation classification network includes a classifier model, a time recurrent neural network, and an output discriminant model. The classification network is used to predict signal loss for different polarization offset types and environmental interference types. The time recurrent neural network is used to compare the similarity between any two predictions corresponding to the signal loss and two consecutive first compensation prediction results, and the similarity between the first compensation prediction results and the compensation results under actual signal loss. The output discriminant model is used to filter effective compensation prediction results based on the similarity being greater than a similarity threshold. The compensation prediction network is used to determine the compensation prediction result corresponding to the antenna's compensation prediction value based on the effective compensation prediction results. The compensation prediction value includes the compensation index for horizontal polarization, the compensation index for vertical polarization, and the compensation index for impedance adjustment corresponding to the environmental interference type. The interference prediction result and the polarization offset result are input into the signal loss compensation prediction neural network to obtain the compensation prediction result corresponding to the signal loss. The signal loss compensation prediction neural network is trained using a dataset including multiple antenna signal loss corresponding to polarization offset types and environmental interference datasets, as well as a compensation training dataset labeled with corresponding signal loss.

[0142] Based on any embodiment of the system in this application, the system of this application further includes: a power adjustment module, configured to set a pre-response time threshold for the adjustment control command based on the distance between the user's position and the loss mutation interval in the target path under the real-time path, and to synchronously analyze the time in real time based on the pre-response time threshold; determine the required power of the corresponding adjustment device in the antenna based on the compensation index of the compensation prediction result, and send the required power to the signal processing circuit to adjust the polarization direction and impedance of the antenna, wherein the adjustment device includes a power distribution unit, a horizontal polarization unit, a vertical polarization unit, an impedance unit, a filtering unit, and an amplifier unit.

[0143] Please see Figures 3-8Another embodiment of this application provides a dual-polarized antenna, including a housing 1, a horizontally polarized portion 2, and a vertically polarized portion 3. The horizontally polarized portion 2 includes four arrow-shaped elements 4 and an arc-shaped dipole 5 arranged in an array. The arc-shaped dipole 5 is disposed above the four arrow-shaped elements 4. The vertically polarized portion 3 includes a cylindrical antenna 6 and an assembly circuit board 7. The arc-shaped dipole 5, the four arrow-shaped elements 4, and the cylindrical antenna 6 are all electrically connected to the assembly circuit board 7. It is capable of simultaneously receiving horizontally and vertically polarized signals. In a practical environment, the polarity of the signal... The polarization direction is complex and variable, and may change due to factors such as the location of the transmitter and reflections from surrounding objects. Traditional single-polarized antennas can only receive signals with one polarization direction. When the signal polarization direction changes, the reception effect will drop significantly, requiring the antenna to be readjusted to maintain reception. However, the dual-polarized antenna of this application can process signals with two polarization directions simultaneously, maintaining good reception performance regardless of changes in signal polarization direction, and reducing the need for readjustment of the antenna due to changes in signal polarization direction. The four arrow-shaped elements 4 arranged in the array can... To enhance the antenna's horizontal radiation and reception capabilities, and through reasonable design of array spacing and feeding methods, a wider horizontal beamwidth can be achieved, increasing the effective gain range. The arc-shaped dipole 5, positioned above the four arrow-shaped elements 4, further optimizes the antenna's radiation pattern and expands the signal coverage. The cylindrical antenna 6 of the vertical polarization section 3 also provides a certain degree of vertical coverage. In summary, this antenna has good coverage capabilities in both the horizontal and vertical directions, making it more likely to remain within the effective signal coverage range during movement, reducing the possibility of needing to readjust the antenna direction due to signal loss caused by movement. Compared to traditional linear dipoles, the arc-shaped structure of the arc-shaped dipole 5 can radiate and receive electromagnetic waves more uniformly, reducing dependence on a specific direction. When various reflectors and obstacles exist in the surrounding environment, the arc-shaped dipole 5 can better adapt to different reflection paths, reducing signal interference and fading caused by environmental reflections. At the same time, the arc-shaped structure can also reduce the antenna's own directivity sensitivity to the surrounding environment, enabling the antenna to maintain relatively stable performance under incident signals at different angles.

[0144] Specifically, the four arrow-shaped oscillators 4 arranged in the array are for the UHF band, and the arc-shaped dipoles 5 are for the VHF band.

[0145] Optional, please refer to Figure 8The signal processing circuitry integrated on the assembly circuit board 7 includes a power distribution unit 71, an impedance unit 72, a filtering unit 73, and an amplifier unit 74. These circuits can separate, filter, and amplify signals with different polarization directions and frequency bands, improving signal quality and strength. Simultaneously, the assembly circuit board 7 also performs impedance matching, ensuring good matching between the antenna and backend equipment, reducing signal reflection and loss. Thus, even if the antenna experiences some interference or positional changes during movement, the circuit board's processing ensures stable signal reception, reducing the need for readjusting the antenna direction.

[0146] Furthermore, it also includes four antenna decouplers 8, which are located between every two arrow-shaped elements 4. These antenna decouplers 8 are metal sheets, which can reduce mutual interference between the four arrow-shaped elements 4.

[0147] Furthermore, each of the arrow-shaped oscillators 4 includes an arrowhead portion 41 and an arrow body portion 42, wherein the arrow body portion 42 is provided with insulated parallel lines 421.

[0148] Furthermore, the columnar antenna 6 includes an antenna rod 61, a screw 62, a washer 63, a nut 64, and an antenna head. One end of the antenna rod 61 is electrically connected to the assembly circuit board 7. One end of the screw 62 is engaged with the other end of the rod. The washer 63 is fitted inside the screw 62. The nut 64 is threadedly engaged with the screw 62 and fixes the washer 63 onto the screw 62. The antenna head is threadedly connected to the other end of the screw 62 and is located above the nut 64.

[0149] Furthermore, the housing 1 includes a barrel-shaped shell 11, a disc top shell 12, a disc bottom shell 13, and a support 14. The disc top shell 12 covers the disc bottom shell 13, the barrel-shaped shell 11 is disposed on the disc top shell 12, and the support 14 is disposed below the disc bottom shell 13.

[0150] Furthermore, the disc bottom shell 13 is provided with a plurality of locking positions 131, which are used to place the arrow-shaped oscillator 4 and the arc-shaped dipole 5.

[0151] Furthermore, a cylindrical base 132 extends from the bottom of the disc base 13. The cylindrical base 132 is provided with a first through hole 133, and the support 14 is provided with a second through hole 141. The first through hole 133 and the second through hole 141 are aligned and arranged, and the cylindrical base 132 and the support 14 are fixed by bolts and nuts.

[0152] Another embodiment of this application provides a dual-polarized antenna adjustment device, which includes a processor, a computer-readable storage medium, a memory, and a network interface connected via a system bus. The computer-readable, non-volatile storage medium of the dual-polarized antenna adjustment device stores an operating system, a database, and computer-readable instructions. The database may store information sequences, and when the computer-readable instructions are executed by the processor, the processor can implement a dual-polarized antenna adjustment method.

[0153] The processor of this dual-polarized antenna adjustment device provides computing and control capabilities, supporting the operation of the entire device. The device's memory can store computer-readable instructions, which, when executed by the processor, cause the processor to perform the dual-polarized antenna adjustment method of this application. The network interface of the device is used for communication with a terminal.

[0154] In this embodiment, the processor is used to execute... Figure 2 The system defines the specific functions of each module, and the memory stores the program code and various data required to execute these modules or submodules. The network interface is used to enable data transmission between user terminals or servers.

[0155] The non-volatile readable storage medium in this embodiment stores the program code and data required to execute all modules in the dual-polarized antenna adjustment system of this application. The server can call the program code and data of the server to execute the functions of all modules.

[0156] This application also provides a non-volatile readable storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the dual-polarized antenna adjustment method of any embodiment of this application.

[0157] This application also provides a computer program product, including a computer program / instructions that, when executed by one or more processors, implement the steps of the method described in any embodiment of this application.

Claims

1. A method for adjusting a dual-polarized antenna, characterized in that, The method comprises the following steps: Obtaining signal quality data of the antenna in the historical path of a plurality of users, analyzing the signal quality data in the historical path to determine a loss mutation interval in a target path; According to the loss mutation interval, obtaining environmental interference data and polarization state data of the antenna in the target path, analyzing the polarization state data and the environmental interference data based on a neural network prediction model to obtain an environmental interference prediction result, an antenna polarization state offset prediction result, and a compensation prediction result of signal loss under polarization offset and environmental interference; According to the compensation prediction result, the antenna in the user path is analyzed in real time, the compensation prediction result is used to generate a control instruction of the adjustment strategy, and the control instruction is sent to the signal processing circuit for execution; The neural network prediction model is used to analyze the polarization state data and the environmental interference data to obtain the environmental interference prediction result and the antenna polarization state offset prediction result, which comprises the following steps: The environmental interference data of each loss mutation interval in the target path is input into the trained environmental interference prediction neural network to obtain interference prediction parameters of all environmental interference types in the loss mutation interval, the interference prediction result comprises multipath effect interference prediction parameters, reflection loss interference prediction parameters, and shielding interference prediction parameters, and the environmental interference prediction neural network is obtained through a plurality of training environmental interference information and a training data set corresponding to multipath effect interference annotation, reflection loss interference annotation, and shielding interference annotation in the historical path; For different interference types, interference prediction parameters greater than a preset interference threshold are screened, the mean value of the interference change rate of different interference prediction parameters is calculated, the influence factor coefficient of each interference type in the current time is determined, and the interference prediction result is output through the product of the interference prediction parameter and the influence factor coefficient; The polarization state data of each loss mutation interval in the target path is input into the trained first polarization offset prediction neural network to obtain a first offset prediction result of each polarization state in the mutation interval, the first offset prediction result comprises a first horizontal polarization offset result, a first vertical polarization offset result, and a first authenticity probability of the first offset prediction result, and the polarization offset prediction neural network is obtained through a first training data set comprising a plurality of training polarization state information and corresponding polarization offset annotation data; The first offset prediction result output by the first polarization offset prediction neural network and the interference prediction result are input into the trained second polarization offset prediction neural network to obtain a second offset prediction result in the mutation interval, the second offset prediction result comprises a second horizontal polarization offset result, a vertical polarization offset result, and a second authenticity probability of the second offset prediction result, and the second polarization offset prediction neural network is obtained through a second training set comprising a plurality of real polarization state information, corresponding polarization offset annotation data, and annotation data of the influence of corresponding environmental interference on the polarization offset; The first and second real probability is used to weight the first and second offset prediction results to obtain an offset prediction result, wherein the first real probability is proportional to the accuracy of the first polarization offset prediction neural network, and the second real probability is proportional to the number of times of the influence of the interference prediction result on the second offset prediction result. The neural network prediction model is used to analyze the polarization state data and the environmental interference data to obtain a compensation prediction result of signal loss under the polarization offset and the environmental interference, and the compensation prediction result comprises the following steps: A signal loss compensation prediction neural network is constructed based on a compensation classification network and a compensation prediction network, the compensation classification network comprises a classifier model, a time recurrent neural network and an output discrimination model, the compensation classification network is used to predict signal loss of different polarization offset types and environmental interference types, the time recurrent neural network is used to predict two continuous first compensation prediction results corresponding to the signal loss, and the similarity of the first compensation prediction results and a compensation result under actual signal loss is compared, the output discrimination model is used to screen valid compensation prediction results from the first compensation prediction results according to the similarity being greater than a similarity threshold, and the compensation prediction network is used to determine a compensation prediction result corresponding to a compensation prediction value of the antenna, wherein the compensation prediction value comprises a compensation index of horizontal polarization, a compensation index of vertical polarization and a compensation index corresponding to the environmental interference type of impedance adjustment. The interference prediction result and the polarization offset result are input into the signal loss compensation prediction neural network to obtain a compensation prediction result corresponding to signal loss, and the signal loss compensation prediction neural network is obtained by training a compensation training data set comprising a polarization offset type data set and an environmental interference data set corresponding to a plurality of antenna signal losses and a signal loss label.

2. The dual polarized antenna adjustment method of claim 1, wherein, The signal quality data of the antenna in the historical path of the plurality of users is obtained, and the signal quality data comprises the following steps: The signal-to-noise ratio, the bit error rate and the signal strength of the antenna in the historical path of the plurality of users are obtained, and the signal quality data is obtained by weighting the signal-to-noise ratio, the bit error rate and the signal strength according to a weight matrix determined according to prior experience. Before the signal quality data is obtained, the following steps are further included: The historical path data and the time data of the user are collected by the vehicle-mounted sensor, and the corresponding signal quality data is path-labeled and time-stamped according to the historical path data and the time data when the signal quality data is obtained; The historical paths of different users are clustered and analyzed according to the similarity to obtain a clustering center of the reference path to screen the historical paths.

3. The dual polarized antenna adjustment method of claim 2, wherein, The signal quality data in the historical path is analyzed to determine a loss mutation interval in the target path, and the signal quality data comprises the following steps: The signal quality data of all users is weighted and averaged to obtain a reference quality threshold, and the reference quality threshold is used to match the reference mutation interval in the reference path according to the signal quality data; The standard quality threshold is determined by weighted averaging according to the signal loss time node of the reference path fed back by the user, so as to determine whether a mutation point appears in the continuous signal quality data. The first signal quality data set before the first time in the reference mutation interval is collected, the change rate of the first signal quality data set is calculated to determine whether there is a mutation positive critical point and a mutation negative critical point in the first signal quality data set, and the first signal quality data set includes at least four continuous signal quality data; The second signal quality data set before the second time in the reference mutation interval is collected, the same period change rate of the second signal data is calculated, whether there is a signal mutation point in the second signal data is determined according to the standard quality threshold, and the signal mutation interval is determined according to the signal mutation point. The second signal quality data set includes at least four first signal quality data sets.

4. The dual polarized antenna adjustment method of claim 3, wherein, The first signal quality data set before the first time in the reference mutation interval is collected, the change rate of the first signal quality data set is calculated to determine whether there is a mutation positive critical point and a mutation negative critical point in the first signal quality data set, and the first signal quality data set includes at least four continuous signal quality data; The first threshold is compared with the quantity relationship of the change rate of the first signal quality data set, if the change rate is greater than or equal to the first threshold, the first cumulative value is obtained by accumulation, and if the change rate is less than the first threshold, the first signal quality data set is reselected; If the first cumulative value is greater than the first cumulative threshold, it is determined that there is a mutation critical point in the first signal quality data set, the mutation critical point is determined to be a mutation positive critical point or a mutation negative critical point according to the increase or decrease direction of the change rate, the time point and the path point corresponding to the mutation positive critical point and the mutation negative critical point are recorded, and the same period change rate is determined, and if the first cumulative value is less than the first cumulative threshold, the first signal quality data set is reselected.

5. The dual polarized antenna adjustment method of claim 3, wherein, The first signal quality data set is used as an element unit of the second signal quality data set, the same period change rate in the second signal quality data set is calculated according to the signal mean value of the element unit; The second threshold of the second signal quality data set is determined according to the standard quality threshold, the quantity relationship between the second threshold and the same period change rate is compared, the judgment result of the same period change rate greater than or equal to the second threshold is marked and the second cumulative value is accumulated, and the judgment result of the same period change rate less than the second threshold is reselected second signal quality data set; If the second cumulative value is greater than or equal to the second cumulative threshold, it is determined that there is a signal mutation point in the reference mutation interval, and if the second cumulative value is less than the second cumulative threshold, the second signal quality data set is reselected; The loss quality interval in the reference mutation interval is selected based on the signal mutation point, and the target path corresponding to the loss quality interval is determined according to the path annotation and the time annotation. The antenna under the user path is analyzed in real time according to the compensation prediction result, the control instruction of the adjustment strategy is generated through the compensation prediction result, and is sent to the signal processing circuit for execution, and the following steps are included:

6. The dual polarized antenna adjustment method of claim 1, wherein, ​ The pre-response time threshold of the adjustment control instruction is set according to the distance between the user position in real time and the loss mutation interval in the target path, and the real-time analysis time is synchronized according to the pre-response time threshold; The demand power of the corresponding adjustment device in the antenna is determined according to the compensation index of the compensation prediction result, and the demand power is sent to the signal processing circuit to adjust the polarization direction and impedance of the antenna, and the adjustment device includes a power distribution unit, a horizontal polarization unit, a vertical polarization unit, an impedance unit, a filter unit and an amplifier unit.

7. A dual polarized antenna conditioning system, characterized by, The system is used to perform the dual-polarized antenna adjustment method of any one of claims 1-6, and the system comprises: The adjustment interval determination module is used to acquire signal quality data of the antenna in the historical path of a plurality of users, and analyze and determine the loss mutation interval in the target path based on the signal quality data in the historical path; The adjustment compensation prediction module is used to acquire environmental interference data and polarization state data of the antenna in the target path based on the loss mutation interval, analyze the polarization state data and the environmental interference data based on a neural network prediction model, and obtain an interference prediction result of the environment, an offset prediction result of the polarization state of the antenna, and a compensation prediction result of signal loss under polarization offset and environmental interference; The real-time adjustment control module is used to perform real-time analysis on the antenna in the user path based on the compensation prediction result, generate control instructions of the adjustment strategy through the compensation prediction result, and send the control instructions to the signal processing circuit for execution.

8. An antenna comprising the dual-polarized antenna conditioning system of claim 7 for performing the method of conditioning a dual-polarized antenna of any one of claims 1-6, wherein, Comprise: A shell, a horizontal polarization unit, a vertical polarization unit and an antenna decoupling; The horizontal polarization unit comprises a plurality of arrow-shaped dipoles and arc-shaped dipoles arranged in an array, and the arc-shaped dipoles are arranged above the arrow-shaped dipoles; each arrow-shaped dipole comprises an arrowhead and an arrow shaft, and the arrow shaft is provided with insulated parallel lines; The vertical polarization unit comprises a columnar antenna and an assembly circuit board, and the arc-shaped dipoles, the arrow-shaped dipoles, the columnar antenna and the dual-polarized antenna adjustment system are all electrically connected to the assembly circuit board, and the assembly circuit board comprises a power distribution unit, an impedance unit, a filter unit and an amplifier unit; The antenna decoupling is arranged between every two arrow-shaped dipoles.

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