High-stability low-frequency antenna matching method

Abstract

The invention provides a high-stability low-frequency antenna matching method, and belongs to the technical field of antenna matching, and the method comprises the steps: obtaining initial parameters of a low-frequency antenna, and constructing an initial performance data file of the antenna; environmental factors are analyzed based on historical data, and quantitative analysis is carried out on environmental data, collected based on environmental monitoring equipment, of the position where the low-frequency antenna is located; performing antenna modeling on the initial performance data file by using electromagnetic simulation software, and simulating the performance of the antenna in different matching states to obtain a plurality of matching schemes; the antenna performance is monitored in real time based on a preset test instrument, and each matching scheme is evaluated; and determining an optimal scheme based on an evaluation result and a quantitative analysis result, and adjusting the low-frequency antenna. The matching accuracy and stability are improved, and a better guarantee is provided for stable operation of the low-frequency antenna in a complex environment.

Description

Technical Field

[0001] The present invention relates to the technical field of antenna matching, and particularly to a high-stability low-frequency antenna matching method. Background Art

[0002] The frequencies of low-frequency antennas are such as 50M, 100M, etc. The detection depth of low-frequency antennas is relatively deep, but the accuracy is relatively low. It is applicable to deep geological surveys such as deep large structure detection, goaf in coal mines, and water-bearing area detection.

[0003] The efficiency of low-frequency antennas is affected by environmental factors. Low-frequency antennas with longer wavelengths require larger antenna sizes to achieve efficient transmission, usually requiring antenna arrays that stretch for several miles. In addition, electromagnetic interference and temperature, humidity, etc. will also affect the transmission efficiency and signal quality of the antennas. However, the common method is to set the existing low-frequency antennas at the required positions without considering the inherent parameters of the antennas and whether the installation structure is reasonable, as long as normal signal reception and transmission can be ensured, thus it is impossible to guarantee the stable operation of low-frequency antennas in complex environments.

[0004] Therefore, the present invention proposes a high-stability low-frequency antenna matching method. Summary of the Invention

[0005] The present invention provides a high-stability low-frequency antenna matching method, which comprehensively considers the initial performance of the antenna, environmental factors, and experimental tests, and through multiple rounds of screening and adjustment, realizes the efficient matching of low-frequency antennas, providing a reliable guarantee for the stable operation of low-frequency antennas. Compared with the prior art, it fully considers the influence of environmental factors on the antenna performance, determines the optimal matching scheme by measuring the initial parameters of low-frequency antennas, electromagnetic simulation modeling, multi-scheme screening, and real-time monitoring and evaluation, and synthesizing environmental data, improving the accuracy and stability of the matching, and providing a better guarantee for the stable operation of low-frequency antennas in complex environments.

[0006] The present invention provides a high-stability low-frequency antenna matching method, including:

[0007] Step 1: Obtain the initial parameters of the low-frequency antenna and construct an initial performance data file of the antenna;

[0008] Step 2: Analyze environmental factors based on historical data and conduct quantitative analysis on the environmental data of the location where the low-frequency antenna is located collected by environmental monitoring equipment;

[0009] Step 3: Use electromagnetic simulation software to perform antenna modeling on the initial performance data file and simulate the performance of the antenna in different matching states to obtain several matching schemes;

[0010] Step 4: Based on a preset test instrument, monitor the antenna performance in real time and evaluate each matching scheme;

[0011] Step 5: Determine the optimal scheme based on the evaluation results and the quantitative analysis results, and adjust the low-frequency antenna.

[0012] Preferably, obtain the initial parameters of the low-frequency antenna and construct an initial performance data file of the antenna, including:

[0013] Obtain the initial design drawing and design objectives of the low-frequency antenna;

[0014] Determine the first parameters of the low-frequency antenna according to the design objectives, where the first parameters include: operating frequency, gain, input impedance, polarization mode, bandwidth, and efficiency;

[0015] At the same time, determine the directivity of the low-frequency antenna according to the initial design drawing and in combination with the design objectives;

[0016] Among them, the initial performance data file is composed of the first parameters and the directivity.

[0017] Preferably, analyze environmental factors based on historical data, including:

[0018] Set predefined factors, match the historical influence subsets of historical antennas for each predefined factor in different environmental scenarios from the historical database, and construct a first influence matrix for each environmental scenario and a second influence matrix for each predefined factor;

[0019] Calculate the average value of the element values of each row vector in the first influence matrix respectively;

[0020] Determine the first difference between the average value of the corresponding row vector and the right boundary value of the design influence range in the corresponding environmental scenario respectively;

[0021] Judge the first difference to determine the processing result of the corresponding row vector, obtain the final vector of the corresponding row vector, and obtain a new matrix in the corresponding environmental scenario;

[0022] Sort the element values in each row vector in the first influence matrix in ascending order;

[0023] Based on the element order sorted in ascending order in the first influence matrix respectively, adjust the element positions of the remaining row vectors successively to obtain an adjusted matrix of the corresponding row vector;

[0024] Obtain the first feature of the first influence matrix, the second feature of each adjusted matrix, and the third feature of the new matrix in the same environmental scenario;

[0025] Based on the first feature, the second feature, and the third feature, construct a first influence function based on predefined factors in the corresponding environmental scenario;

[0026] Determine the factor features of each second influence matrix respectively;

[0027] Fuse the factor features of each predefined factor with the first influence function in different environmental scenarios through function fusion to obtain a second influence function based on all predefined factors in the corresponding environmental scenario, where the second influence function includes the influence coefficient and influence weight of each predefined factor in the corresponding environmental scenario.

[0028] Preferably, judge the first difference to determine the processing result of the corresponding row vector, including:

[0029] If the first difference is greater than 0, at this time, count the first quantity N1 of the element values greater than the average value in the corresponding row vector, and determine the replacement quantity N2 of the element values in the corresponding row vector;

[0030]

[0031] Among them, N0 represents the total quantity of the existing element values in the corresponding row vector; a1 and a2 respectively represent set constants; rand() represents a random function; Represents the ceiling symbol;

[0032] When the replacement quantity N2 is not 0, lock the element values sorted from small to large in the corresponding row vector according to the replacement quantity N2, and calculate the replacement values of the corresponding element values;

[0033]

[0034] Among them, A tc Represents the average value of the element values under the corresponding replacement quantity; A yc Represents the right boundary value of the design influence range in the corresponding environmental scenario; y1 1 Represents the first numerical value greater than the average value after sorting the element values of the corresponding row vector from small to large; y1 i1 Represents the i1th numerical value greater than the average value after sorting the element values of the corresponding row vector from small to large; D2 j1 Represents the replacement value of the j1th replacement element; F ave Represents the average value of all the element values involved in the corresponding row vector; B j1 Represents the element value of the j1th replacement element; min represents the minimum value symbol; Represents the variance based on all |y1 i1 -y1 1 |;

[0035] When the replacement quantity N2 is 0, keep the element values in the corresponding row vector unchanged;

[0036] If the first difference is not greater than 0, keep the element values in the corresponding row vector unchanged.

[0037] Preferably, perform quantitative analysis on the environmental data of the location where the low-frequency antenna is located collected by the environmental monitoring device, including:

[0038] Compare and analyze the environmental data with the standard data under different environmental scenarios respectively to lock the matching scenario;

[0039] Based on the influence coefficients and influence weights of each predefined factor involved in the matching scenario, perform quantitative analysis on the environmental parameters under the corresponding predefined factors;

[0040] Among them, the environmental factor is a predefined factor with an influence coefficient and an influence weight.

[0041] Preferably, the matching state is related to impedance matching, frequency matching, polarization matching, and phase matching.

[0042] Preferably, simulate the performance of the antenna under different matching states to obtain several matching schemes, including:

[0043] Obtain the performance of the low-frequency antenna under different matching states respectively, and compare and analyze the performance with the standard performance to determine the optimized recommended parameters for each performance;

[0044] Input the optimized recommended parameters under each performance into the scheme generation model to obtain the corresponding matching scheme.

[0045] Preferably, determine the best scheme based on the evaluation result and the quantitative analysis result, including:

[0046] Obtain the current performance of the low-frequency antenna according to the real-time monitoring result;

[0047] Compare the current performance with the performance under the corresponding matching state in sequence according to the initial parameters to obtain a performance difference array, where the performance difference array includes the parameter value differences of different initial parameters and the parameter weights of the relevant initial parameters under the corresponding matching state;

[0048] Calculate the matching coefficient corresponding to the matching state according to the performance difference array;

[0049] Regard the matching schemes under the first M1 largest coefficients selected from all the matching coefficients as the schemes to be analyzed;

[0050] According to the correspondence between environmental factors and initial parameters, and combined with the results of quantitative analysis, an influence deviation list of each initial parameter is obtained, where the influence deviation list includes the influence of each initial parameter on each environmental factor in different environmental scenarios;

[0051] Determine the constraint requirements of each initial parameter according to the design goal, and continue to analyze each solution to be analyzed according to the influence deviation list to obtain the optimal solution.

[0052] Compared with the prior art, the beneficial effects of the present application are as follows:

[0053] Comprehensively consider the initial performance of the antenna, environmental factors and experimental tests. Through multiple rounds of screening and adjustment, efficient matching of the low-frequency antenna is achieved, providing a reliable guarantee for the stable operation of the low-frequency antenna. Compared with the prior art, the influence of environmental factors on the antenna performance is fully considered. Through the measurement of the initial parameters of the low-frequency antenna, electromagnetic simulation modeling, multi-scheme screening and real-time monitoring and evaluation, the best matching scheme is determined by integrating environmental data, improving the accuracy and stability of the matching, and providing a better guarantee for the stable operation of the low-frequency antenna in a complex environment.

[0054] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structure specifically pointed out in the written specification and the drawings.

[0055] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0056] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0057] Figure 1 It is a flowchart of a high-stability low-frequency antenna matching method in an embodiment of the present invention. Detailed Embodiments

[0058] The following describes the preferred embodiments of the present invention with reference to the drawings. It should be understood that the preferred embodiments described herein are only for explaining and illustrating the present invention and are not used to limit the present invention.

[0059] The present invention provides a high-stability low-frequency antenna matching method, as Figure 1 shown, including:

[0060] Step 1: Obtain the initial parameters of the low-frequency antenna and construct an initial performance data file of the antenna;

[0061] Step 2: Analyze environmental factors based on historical data, and perform quantitative analysis on the environmental data of the location where the low-frequency antenna is located collected by environmental monitoring equipment;

[0062] Step 3: Use electromagnetic simulation software to perform antenna modeling on the initial performance data file, and simulate the performance of the antenna under different matching states to obtain several matching schemes;

[0063] Step 4: Based on a preset test instrument, monitor the antenna performance in real time and evaluate each matching scheme;

[0064] Step 5: Determine the optimal scheme based on the evaluation results and the quantitative analysis results, and adjust the low-frequency antenna.

[0065] Preferably, the matching state is related to impedance matching, frequency matching, polarization matching, and phase matching.

[0066] In this embodiment, the initial parameters include: operating frequency, gain, input impedance, polarization mode, bandwidth, efficiency, directivity, etc., providing a parameter analysis basis for subsequent adjustment of the low-frequency antenna in the corresponding environment, so as to ensure the stable operation of the antenna.

[0067] In this embodiment, the initial performance data file is a document containing the initial parameters.

[0068] In this embodiment, each low-frequency antenna has its set standard conditions based on the initial parameters, for example, the standard range of the operating frequency.

[0069] In this embodiment, the historical data includes the influence of the environment on the relevant parameters of the antenna in different environmental scenarios, for example, the influence of temperature, humidity, electromagnetic interference, etc. on the initial parameters of the antenna.

[0070] In this embodiment, weather such as rain, snow, and fog will increase the air humidity, resulting in changes in the signal propagation speed and intensity. Raindrops and snowflakes may also scatter the signal, causing energy loss during signal transmission. Therefore, it is necessary to consider the influence of humidity and temperature on the antenna, and the electromagnetic interference generated by other low-frequency or similar frequency band electromagnetic signal sources in the surrounding area, such as industrial equipment and power lines, will be superimposed on the signal of the low-frequency antenna, resulting in signal distortion or an increase in the error rate. It is necessary to consider the influence of electromagnetic interference, etc. on the antenna, that is, the environmental factors are related to the factors affecting the stability of the antenna, such as temperature, humidity, and electromagnetic interference.

[0071] In this embodiment, the environmental monitoring equipment can be a temperature sensor, a humidity sensor, and an electromagnetic field strength meter.

[0072] In this embodiment, the environmental data is the collected temperature data, humidity data, and electromagnetic interference data, etc.

[0073] In this embodiment, the quantitative analysis is to set relevant weights for different factors. Since the influences of different factors on the antenna stability in different environmental scenarios are different, specific scenarios need to be analyzed specifically to facilitate the subsequent acquisition of the best solution.

[0074] In this embodiment, after the antenna modeling, the simulation is carried out by setting the matching state on this software, automatically running the antenna simulation process to obtain the performance, and the performance is related to the initial parameters, rising to the specific simulation result values for the initial parameters.

[0075] In this embodiment, the matching solution refers to the antenna adjustment solution for this low-frequency antenna under different matching states, including but not limited to the adjustment of materials, the adjustment of the antenna structure, the adjustment of solder joints, etc.

[0076] In this embodiment, the preset test instrument can be an antenna analyzer.

[0077] In this embodiment, the evaluation of the matching solution is to initially screen several solutions.

[0078] In this embodiment, the best solution is to further ensure the stability of the operation of the low-frequency antenna after adjustment.

[0079] The beneficial effects of the above technical solutions are as follows: comprehensively considering the initial performance of the antenna, environmental factors and experimental tests, through multiple rounds of screening and adjustment, the efficient matching of the low-frequency antenna is realized, providing a reliable guarantee for the stable operation of the low-frequency antenna. Compared with the existing technology, the influence of environmental factors on the antenna performance is fully considered. Through the measurement of the initial parameters of the low-frequency antenna, electromagnetic simulation modeling, multi-scheme screening and real-time monitoring and evaluation, the best matching solution is determined by integrating environmental data, improving the accuracy and stability of the matching, and providing a better guarantee for the stable operation of the low-frequency antenna in complex environments.

[0080] The present invention provides a method for matching a low-frequency antenna with high stability, which obtains the initial parameters of the low-frequency antenna and constructs an initial performance data file of the antenna, including:

[0081] Obtaining the initial design drawing and design target of the low-frequency antenna;

[0082] Determining the first parameters of the low-frequency antenna according to the design target, where the first parameters include: operating frequency, gain, input impedance, polarization mode, bandwidth and efficiency;

[0083] At the same time, determining the directivity of the low-frequency antenna according to the initial design drawing and in combination with the design target;

[0084] Among them, the initial performance data file is composed of the first parameters and the directivity.

[0085] In this embodiment, the first parameter and the directivity involved are the basic parameters required for the antenna.

[0086] In this embodiment, the design objective refers to the working scenario to which the low-frequency antenna is to be applied.

[0087] In this embodiment, the initial design drawing refers to the basic structure diagram of the low-frequency antenna.

[0088] The beneficial effects of the above technical solution are: By starting from two aspects of the design drawing and the design objective, initial parameters are obtained, providing a basis for subsequent antenna simulation.

[0089] The present invention provides a method for matching a low-frequency antenna with high stability, which analyzes environmental factors based on historical data, including:

[0090] Setting predefined factors, and matching historical influence subsets of historical antennas for each predefined factor in different environmental scenarios from a historical database, and constructing a first influence matrix for each environmental scenario and a second influence matrix for each predefined factor;

[0091] Calculating the average value of the element values of each row vector in the first influence matrix respectively;

[0092] Determining the first difference between the average value of the corresponding row vector and the right boundary value of the design influence range in the corresponding environmental scenario respectively;

[0093] Judging the first difference to determine the processing result of the corresponding row vector, obtaining the final vector of the corresponding row vector, and obtaining a new matrix in the corresponding environmental scenario;

[0094] Sorting the element values in each row vector in the first influence matrix in ascending order;

[0095] Based on the element order sorted in ascending order in the first influence matrix, adjusting the element positions of the remaining row vectors successively to obtain an adjusted matrix of the corresponding row vector;

[0096] Obtaining the first feature of the first influence matrix, the second feature of each adjusted matrix, and the third feature of the new matrix in the same environmental scenario;

[0097] Based on the first feature, the second feature, and the third feature, constructing a first influence function based on the predefined factor in the corresponding environmental scenario;

[0098] Determining the factor features of each second influence matrix respectively;

[0099] Fuse the factor features of each predefined factor with the first influence function for different environmental scenarios to obtain a second influence function based on all predefined factors in the corresponding environmental scenario, where the second influence function includes the influence coefficients and influence weights of each predefined factor in the corresponding environmental scenario.

[0100] In this embodiment, the predefined factors are related to temperature, humidity, and electromagnetic interference.

[0101] In this embodiment, the historical database contains the stable influence factors of antennas with the same design structure on different predefined factors at each measurement moment in different environmental scenarios, and the value range is from 0 to 1. The greater the influence, the greater the corresponding value, and the stable influence factor = (the actual value under the corresponding factor - the theoretical value set for the antenna - the actual value under the corresponding factor) / the theoretical value set for the antenna.

[0102]

[0103] In this embodiment,

[0104] In this embodiment, the design influence range is preset and obtained by matching with a scenario-range comparison table. The table contains different environmental scenarios and the set influence ranges for different factors in that scenario, and the set influence ranges for different factors are different. For example, the set influence range for a temperature of 39 degrees Celsius is (0, 10%).

[0105] In this embodiment, the first difference = the average value of the corresponding row vector - the right boundary value of the corresponding range.

[0106] In this embodiment, the processing result refers to whether the relevant elements in the row vector need to be adjusted in value, and each element is the corresponding stable influence factor, that is, the matrix obtained according to the adjusted and updated values is the new matrix.

[0107] In this embodiment, the element value is the stable influence factor.

[0108] In this embodiment, for example, the first influence matrix is: And each row vector in this matrix is composed of the stable influence factors under the corresponding factors. For example, u1 > u3 > u2. At this time, the adjusted matrix is: And so on, the corresponding adjusted matrices can be obtained.

[0109] In this embodiment, the first feature, the second feature, and the third feature are all obtained by a matrix analysis model. The model is trained with a matrix composed of different element values and the feature analysis results of the matrix as samples. Generally, the feature analysis result refers to the eigenvector of the matrix.

[0110] In this embodiment, the first influence matrix, the adjustment matrix, and the new matrix are for different predefined factors in the same environmental scenario, and the obtained eigenvector is: Y = F1X1 + F2X2 +... + FKXK, where F1,..., FK represent the coefficients in the eigenvector; X1,..., XK represent the predefined factors involved in the eigenvector.

[0111] In this embodiment, the first influence function is obtained by averaging the feature coefficients in the eigenvectors related to the first feature, the second feature, and the third feature, that is: Yu = F1`X1 + F2`X2 +... + FK`XK, where F1`,..., FK` represent the average coefficients in the eigenvector, that is, the determined influence coefficients.

[0112] In this embodiment, the factor feature refers to the eigenvector corresponding to the second influence matrix, that is: y = b1z1 + b2z2 +... + bhzh, where b1,..., bh represent the coefficients in the eigenvector corresponding to the factor feature; z1,..., zh represent the environmental scenarios involved in the eigenvector corresponding to the factor feature.

[0113] In this embodiment, the coefficients corresponding to different predefined factors in the same environmental scenario are respectively extracted from the factor features, and the influence weights of different predefined factors in the corresponding environmental scenario are obtained.

[0114] The first influence function in the corresponding environmental scenario is updated according to the influence weights to obtain the second influence function, and the update process is: Yu`` = (F1`, Q1`)X1 + (F2`, Q2`)X2 +... + (FK`, QK`)XK, where Q1`,..., QK` represent the influence weights of different predefined factors in the corresponding environmental scenario.

[0115] The beneficial effects of the above technical solutions are: By constructing a matrix based on the environmental scenario and a matrix of predefined factors, and combining the adjustment matrix obtained by sorting the element values and the new matrix obtained by replacing the element values, it provides a basis for obtaining relevant features, ensures the accuracy of obtaining the influence coefficients subsequently, and the influence weights are mainly realized by relying on the factor features, providing a basis for subsequent scheme matching and indirectly improving the stability of antenna operation.

[0116] The present invention provides a high-stability low-frequency antenna matching method, which judges the first difference and determines the processing result of the corresponding row vector, including:

[0117] If the first difference is greater than 0, at this time, count the first quantity N1 of the element values greater than the average value in the corresponding row vector, and determine the replacement quantity N2 of the element values in the corresponding row vector;

[0118]

[0119] Among them, N0 represents the total quantity of the existing element values in the corresponding row vector; a1 and a2 respectively represent set constants; rand() represents a random function; represents the ceiling symbol;

[0120] When the replacement quantity N2 is not 0, lock the element values sorted from small to large in the corresponding row vector according to the replacement quantity N2, and calculate the replacement values of the corresponding element values;

[0121]

[0122] Among them, A tc represents the average value of the element values under the corresponding replacement quantity; A yc represents the right boundary value of the design influence range under the corresponding environmental scenario; y1 1 represents the first value greater than the average value after sorting the element values of the corresponding row vector from small to large; y1 i1 represents the i1-th value greater than the average value after sorting the element values of the corresponding row vector from small to large; D2 j1 represents the replacement value of the j1-th replacement element; F ave represents the average value of all the element values involved in the corresponding row vector; B j1 represents the element value of the j1-th replacement element; min represents the minimum value symbol; represents the variance based on all |y1 i1 -y1 1 |;

[0123] When the replacement quantity N2 is 0, keep the element values in the corresponding row vector unchanged;

[0124] If the first difference is not greater than 0, keep the element values in the corresponding row vector unchanged.

[0125] In this embodiment, since there may be a situation where the current measurement error causes the factor to be too large during the process of determining the stability influence factor, in order to minimize the matching error caused by this situation as much as possible, the size comparison between the first difference and 0 is performed to make reasonable adjustments in different situations.

[0126] In this embodiment, the determination of the replacement quantity is to determine the replacement elements in different situations, so as to reduce the error caused by measurement errors.

[0127] The beneficial effects of the above technical solution are as follows: By analyzing the situation where the first difference is greater than 0, the replacement quantity under different ratios of N1 to N0 is determined, and through reasonable calculation of the replacement value, reasonable replacement is achieved, providing an accurate data basis for the subsequent second influence function.

[0128] The present invention provides a high-stability low-frequency antenna matching method, which quantifies and analyzes the environmental data of the location where the low-frequency antenna is located based on the environmental monitoring equipment, including:

[0129] Comparing and analyzing the environmental data with the standard data under different environmental scenarios respectively to lock the matching scenario;

[0130] Based on the influence coefficient and influence weight of each predefined factor involved in the matching scenario, quantifying and analyzing the environmental parameters under the corresponding predefined factors;

[0131] Among them, the environmental factor is a predefined factor with an influence coefficient and an influence weight.

[0132] In this embodiment, each scenario has its corresponding standard data, so by comparing the actual environmental data with the standard data, the matching scenario can be initially locked.

[0133] In this embodiment, the quantitative analysis refers to assigning the influence coefficient and influence weight of different predefined factors in the corresponding matching environment obtained to the corresponding environmental parameters.

[0134] The beneficial effects of the above technical solution are as follows: By comparing and analyzing the actual and standard environmental data, the matching scenario can be conveniently and directly locked, so as to obtain the influence coefficient and influence weight, providing a basis for determining the best solution subsequently.

[0135] The present invention provides a high-stability low-frequency antenna matching method, which simulates the performance of the antenna in different matching states to obtain several matching schemes, including:

[0136] Respectively obtaining the performance of the low-frequency antenna in different matching states, and comparing and analyzing the performance with the standard performance to determine the optimized recommended parameters for each performance;

[0137] Inputting the optimized recommended parameters under each performance into the scheme generation model to obtain the corresponding matching scheme.

[0138] In this embodiment, the performance is the simulation result of the antenna under different set states, and the standard performance of the antenna in this application scenario is set in advance, that is, the antenna needs to meet this operating standard. Therefore, after performing a comparative analysis of the performance, optimized recommended parameters can be obtained, such as recommended parameters for structural design, recommended parameters for solder joint elimination, etc.

[0139] In this embodiment, the solution generation model is trained on a neural network model using different combinations of optimized recommended parameters and expert-set improvement solutions for these parameters as samples. Therefore, corresponding matching solutions can be directly obtained.

[0140] The beneficial effects of the above technical solution are: comparing and analyzing the performance under different matching states with the standard performance to determine the optimized recommended parameters, and based on the model's analysis of the parameters, a matching solution can be obtained, providing a solution basis for determining the best solution.

[0141] The present invention provides a method for matching a high-stability low-frequency antenna, which determines the best solution based on the evaluation result and the quantitative analysis result, including:

[0142] Obtaining the current performance of the low-frequency antenna according to the real-time monitoring result;

[0143] Successively comparing the current performance with the performance under the corresponding matching states according to the initial parameters to obtain a performance difference array, where the performance difference array includes the parameter value differences of different initial parameters and the parameter weights of the relevant initial parameters under the corresponding matching states;

[0144] Calculating the matching coefficient corresponding to the matching state according to the performance difference array;

[0145] Selecting the matching solutions under the top M1 largest coefficients from all the matching coefficients as the solutions to be analyzed;

[0146] According to the correspondence between the environmental factors and the initial parameters, and in combination with the quantitative analysis result, obtaining an influence deviation list for each initial parameter, where the influence deviation list includes the influence of each initial parameter under different environmental scenarios based on each environmental factor;

[0147] Determining the constraint requirements for each initial parameter according to the design goal, and continuing to analyze each solution to be analyzed according to the influence deviation list to obtain the best solution.

[0148] In this embodiment, the successive comparison according to the initial parameters is to compare the performance values of the parameters to obtain a performance difference array: {parameter value difference of each initial parameter, parameter weight of the matching state parameter}.

[0149] In this embodiment, the parameter weights are preset, and the sum of all parameter weights is 1.

[0150] In this embodiment, the matching coefficient is: the sum of the results of (the difference in corresponding parameter values / the standard value of the parameter in the corresponding matching state) × the parameter weight.

[0151] In this embodiment,

[0152] The influence situation = YU(control relationship, (1 +

[0153] the influence coefficient of the corresponding factor in the corresponding environmental scenario)), determine the influence situation based on the control relationship and (1 + the influence coefficient of the corresponding factor in the corresponding environmental scenario) from the situation-relationship-influence comparison table, and this comparison table includes the control relationship between environmental factors and different parameters and the influence situations corresponding to the influence information of the corresponding environmental factors. It can be directly matched and obtained, and the adjustment situation of the constraint requirements of the environmental factors on the initial parameter can be determined. 影响权重 ) 影响权重 The influence situation, and the adjustment situation of the constraint requirements of the environmental factors on the initial parameter can be determined.

[0154] In this embodiment, the constraint requirements can be directly determined according to the design objective. For example, the value range of the standing wave ratio is (p1, p2), etc.

[0155] First, after determining the matching scenario, the influence situation of the matching parameters based on the environmental factors in the matching scenario can be determined;

[0156] Second, adjust the corresponding constraint requirements based on the influence situation;

[0157] Finally, determine whether the value of each initial parameter in the solution to be analyzed meets the corresponding adjustment requirements, and obtain the solution that best meets the requirements as the optimal solution.

[0158] The beneficial effects of the above technical solution are: comparing the current performance with the performance in the matching state to obtain a difference array, and then calculating the matching coefficient to obtain a preliminary screening of the solution. And subsequently, through the control relationship and the influence deviation list, the adjustment of the constraints is realized, and then the optimal solution is obtained, effectively ensuring the operation stability of the antenna.

[0159] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A high-stability low-frequency antenna matching method, characterized in that: include: Step 1: Obtain the initial parameters of the low-frequency antenna and build the initial performance data file of the antenna; Step 2: Analyze environmental factors based on historical data, and conduct quantitative analysis on environmental data of the location of the low-frequency antenna collected by environmental monitoring equipment; Step 3: Use electromagnetic simulation software to model the antenna based on the initial performance data file, and simulate the performance of the antenna under different matching conditions to obtain several matching solutions; Step 4: Monitor the antenna performance in real time based on the preset test instrument and evaluate each matching solution; Step 5: Determine the best solution based on the evaluation results and the quantitative analysis results, and adjust the low-frequency antenna.

2. The high stability low frequency antenna matching method according to claim 1, characterized in that: Obtain the initial parameters of the low-frequency antenna and build the initial performance data file of the antenna, including: Obtain the initial design drawings and design goals of the low-frequency antenna; Determining first parameters of the low-frequency antenna according to the design goal, wherein the first parameters include: operating frequency, gain, input impedance, polarization mode, bandwidth, and efficiency; At the same time, the directivity of the low-frequency antenna is determined according to the initial design drawing and in combination with the design objectives; The initial performance data file is composed of the first parameter and directionality.

3. The high stability low frequency antenna matching method according to claim 1, characterized in that: Analyze environmental factors based on historical data, including: Setting predefined factors, matching historical impact subsets of historical antennas in different environmental scenarios for each predefined factor from a historical database, and constructing a first impact matrix for each environmental scenario and a second impact matrix for each predefined factor; Calculate the average value of the element values ​​of each row vector in the first influence matrix respectively; respectively determining the first difference between the average value of the corresponding row vector and the right boundary value of the design influence range in the corresponding environmental scenario; The first difference is judged to determine the processing result of the corresponding row vector, to obtain the final vector of the corresponding row vector, and to obtain a new matrix under the corresponding environmental scenario; Sort the element values ​​in each row vector of the first influence matrix in ascending order; Based on the order of the elements in the first influence matrix sorted in ascending order, the positions of the elements of the remaining row vectors are adjusted successively to obtain an adjustment matrix of the corresponding row vectors; Obtaining a first feature of a first impact matrix, a second feature of each adjustment matrix, and a third feature of a new matrix under the same environmental scenario; Based on the first feature, the second feature and the third feature, construct a first influence function based on predefined factors in the corresponding environmental scenario; Determine the factor characteristics of each second impact matrix respectively; The factor characteristics of each predefined factor are functionally fused with the first influence function under different environmental scenarios to obtain a second influence function based on all predefined factors in the corresponding environmental scenario, wherein the second influence function includes the influence coefficient and influence weight of each predefined factor in the corresponding environmental scenario.

4. The high-stability low-frequency antenna matching method according to claim 3, characterized in that: Judging the first difference to determine a processing result of a corresponding row vector includes: If the first difference is greater than 0, at this time, a first number N1 of element values ​​in the corresponding row vector that are greater than the average value is counted, and a replacement number N2 of element values ​​in the corresponding row vector is determined; Wherein, N0 represents the total number of element values ​​in the corresponding row vector; a1 and a2 represent set constants; rand() represents a random function; Indicates the rounding up symbol; When the replacement number N2 is not 0, the element values ​​in the corresponding row vector sorted from small to large are locked in a corresponding number according to the replacement number N2, and the replacement values ​​of the corresponding element values ​​are calculated; Among them, A tc Represents the average value of the element under the corresponding number of replacements; A yc Indicates the right boundary value of the design impact range in the corresponding environmental scenario; y11 indicates the first value greater than the average value after the element values ​​of the corresponding row vector are sorted from small to large; y1 i1 Indicates the i1th value greater than the average value after the element values ​​of the corresponding row vector are sorted from small to large; D2 j1 represents the replacement value of the j1th replacement element; F ave Represents the average value of all element values ​​involved in the corresponding row vector; B j1 represents the element value of the j1th replacement element; min represents the minimum value symbol; Indicates that based on all |y1 i1 -variance of y11|; When the replacement number N2 is 0, the element value in the corresponding row vector remains unchanged; If the first difference is not greater than 0, the element value in the corresponding row vector is kept unchanged.

5. The high stability low frequency antenna matching method according to claim 1, characterized in that: Quantitative analysis of environmental data collected by environmental monitoring equipment at the location of the low-frequency antenna, including: Compare and analyze the environmental data with the standard data in different environmental scenarios to lock in the matching scenarios; Based on the influence coefficient and influence weight of each predefined factor involved in the matching scenario, quantitatively analyze the environmental parameters under the corresponding predefined factors; The environmental factors are predefined factors with influence coefficients and influence weights.

6. The high stability low frequency antenna matching method according to claim 1, characterized in that: The matching state is related to impedance matching, frequency matching, polarization matching and phase matching.

7. The high-stability low-frequency antenna matching method according to claim 1, characterized in that: The performance of the antenna under different matching conditions is simulated to obtain several matching schemes, including: Obtaining the performance of the low-frequency antenna under different matching conditions respectively, and comparing and analyzing the performance with the standard performance, and determining the optimization recommended parameters for each performance; The optimization recommended parameters under each performance are input into the solution generation model to obtain the corresponding matching solution.

8. The high-stability low-frequency antenna matching method according to claim 1, characterized in that: Determine the best solution based on the evaluation results and quantitative analysis results, including: Obtaining the current performance of the low-frequency antenna according to the real-time monitoring result; Comparing the current performance with the performance under the corresponding matching state in turn according to the initial parameters to obtain a performance difference array, wherein the performance difference array includes parameter value differences of different initial parameters and parameter weights of relevant initial parameters under the corresponding matching state; Calculate the matching coefficient corresponding to the matching state according to the performance difference array; The matching schemes with the first M1 maximum coefficients are selected from all matching coefficients and regarded as the schemes to be analyzed; According to the comparison relationship between the environmental factors and the initial parameters, and in combination with the quantitative analysis results, an impact deviation list of each initial parameter is obtained, wherein the impact deviation list includes the impact of each initial parameter based on each environmental factor in different environmental scenarios; The constraint requirements of each initial parameter are determined according to the design goal, and each solution to be analyzed is further analyzed according to the impact deviation list to obtain the best solution.

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