Bluetooth low energy performance radio frequency test method, system, storage medium and device

Abstract

The present application provides a method, system, storage medium and device for testing the radio frequency performance of Bluetooth low energy consumption, which relates to the field of Bluetooth testing technology and can improve the technical problem that the testing method in the related technology is insufficient in marking the signal level. The method comprises: obtaining the signal strength information of the beacon device corresponding to multiple positions, wherein the signal strength information is used to indicate the signal strength of the beacon device measured by the test device at the corresponding position; constructing a first matrix according to the multiple signal strength information and a preset matrix construction rule, wherein the preset matrix construction rule includes a position rule; generating a signal mark of the beacon device based on the first matrix and the preset matrix, wherein the signal mark is used to indicate the signal level of the beacon device.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of Bluetooth testing technology, and in particular to a method, system, storage medium and device for testing Bluetooth low energy performance radio frequency. Background Art

[0002] Bluetooth Low Energy (BLE) is a wireless communication technology designed for low-power devices and widely used in scenarios such as health and fitness, home automation, beacons, and location services. When performing Bluetooth low energy performance testing, signal testing is an important component that can help evaluate the communication performance of the device under different conditions.

[0003] As one of the key technologies in the field of Internet of Things (IoT), accurate assessment of signal levels of beacon devices is essential for achieving effective indoor positioning, navigation, and information push. However, existing testing methods have some shortcomings, especially in terms of signal level marking. Summary of the invention

[0004] The embodiments of the present application provide a Bluetooth low energy performance radio frequency test method, system, storage medium and device, which are used to improve the technical problem that the test method in the related art is insufficient in marking the signal level.

[0005] To achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

[0006] An embodiment of the present application provides a Bluetooth low energy performance radio frequency testing method, the method comprising: obtaining signal strength information of a beacon device corresponding to multiple positions, the signal strength information being used to indicate the signal strength of the beacon device measured by a test device at the corresponding position; constructing a first matrix according to the multiple signal strength information and preset matrix construction rules, wherein the preset matrix construction rules include position rules; generating a signal mark of the beacon device based on the first matrix and the preset matrix, the signal mark being used to indicate the signal level of the beacon device; and evaluating the communication performance of the beacon device according to the signal mark.

[0007] In some possible implementations, the signal strength information includes signal strength and test position, and constructing a first matrix based on multiple signal strength information and preset matrix construction rules includes: normalizing the signal strength to obtain first data; gray-scaling the first data to obtain second data; acquiring distribution information of the corresponding second data in the matrix according to the test position; and constructing the first matrix based on the distribution information and the second data.

[0008] In some possible implementations, the grayscale processing includes converting the first data according to a first formula, where the first formula is:

[0009] G=255×ln(1+Xnor)

[0010] Among them, G is the second data, and Xnor is the first data.

[0011] In some possible implementations, an area where the test device performs a test is defined as a test area, the test area includes a first sub-area and a second sub-area, and the acquiring the signal mark of the beacon device based on the first matrix and the preset matrix includes: acquiring the second matrix according to the first matrix and the preset matrix; acquiring an intermediate mark of each sub-area according to the data in the second matrix, the intermediate mark being the sum of the data in the second matrix corresponding to the sub-area; acquiring the signal mark based on the intermediate mark and a second formula, the second formula being:

[0012]

[0013] Wherein, j is the signal mark, j1 is the middle mark of the first sub-area, j2 is the middle mark of the second sub-area, d1 is the distance between the first sub-area and the beacon device, and d2 is the distance between the second sub-area and the beacon device.

[0014] In some possible implementations, before the step of constructing the first matrix based on the distribution information and the second data, the step further includes: obtaining image information between the beacon device and the test position; obtaining an obstacle correction factor of the signal strength corresponding to the test position based on the image information, the obstacle correction factor including the length and height of the cross section of the obstacle between the beacon device and the test position, the reflectivity of the obstacle surface to electromagnetic waves, and the roughness of the obstacle surface; obtaining an obstacle correction coefficient based on the obstacle correction factor and a third formula, the third formula being:

[0015]

[0016] Where A is the obstacle correction coefficient, k and α are correction constants, L is the length of the obstacle cross section, H is the height of the obstacle cross section, R is the reflectivity of the object surface, and σ is the roughness of the object surface;

[0017] The second data is corrected according to the correction factor.

[0018] In some possible implementations, the method further includes: acquiring display data of a pixel area in a marker image according to the first matrix, the display data including the coordinates of the pixel points and the grayscale values ​​of the pixel points, the marker image being used to indicate the signal strength distribution of the beacon device; and determining the signal weak points of the beacon device based on the marker image.

[0019] In the second aspect, an embodiment of the present application provides a Bluetooth low energy performance RF test system, which is applied to the method described in the first aspect, and the system includes: a test module, which is used to obtain the signal strength information of the beacon device at the corresponding position; a matrix construction module, which is coupled to the test module, and the matrix construction module is used to construct a first matrix according to the signal strength information; a mark generation module, which is coupled to the matrix construction module, and the mark generation module is used to generate a signal mark according to the first matrix and a preset matrix, and the signal mark is used to indicate the signal level of the beacon device.

[0020] In some possible implementations, the system further includes an acquisition module, which is coupled to the matrix building module and is used to acquire image information between the beacon device and the test location.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored, and the computer program is configured to execute the method described in the above implementation manner when running.

[0022] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the method described in the above implementation manner.

[0023] The present application obtains signal strength information of a beacon device corresponding to multiple locations, constructs a first matrix according to the multiple signal strength information and preset matrix construction rules, and finally generates a signal mark of the beacon device based on the first matrix and the preset matrix, so that the communication performance of the beacon device can be evaluated according to the value of the signal mark, thereby improving the technical problem that the test method in the related art is insufficient in marking the signal level. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of a test method provided for some embodiments of the present application;

[0025] Figure 2 A schematic diagram of the structure of the test area provided for some embodiments of the present application;

[0026] Figure 3A schematic diagram of a test method provided for some embodiments of the present application;

[0027] Figure 4 A schematic diagram of a marked image provided for some embodiments of the present application;

[0028] Figure 5 A schematic diagram of a preset marked image provided for some embodiments of the present application;

[0029] Figure 6 A schematic diagram of the structure of a test system provided for some embodiments of the present application. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0031] In the following, the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0032] In addition, in the present application, directional terms such as "up", "down", "left" and "right" may be defined including but not limited to the orientation relative to the schematic placement of the components in the drawings. It should be understood that these directional terms may be relative concepts, which are used for relative description and clarification, and may change accordingly according to the change in the orientation of the components in the drawings.

[0033] In this application, unless otherwise specified or limited, the term "connection" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium. In addition, the term "coupling" can be a way of achieving electrical connection for signal transmission.

[0034] As used herein, "about," "substantially," or "approximately" includes the stated value and an average value that is within an acceptable range of deviation from the particular value, where the acceptable range of deviation is determined by one of ordinary skill in the art taking into account the measurements in question and the errors associated with the measurement of the particular quantity (i.e., the limitations of the measurement system).

[0035] In the embodiments of the present application, the words "exemplarily" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to present related concepts in a specific way.

[0036] Bluetooth Low Energy (BLE) is a wireless communication technology designed for low-power devices and widely used in scenarios such as health and fitness, home automation, beacons, and location services. When performing Bluetooth low energy performance testing, signal testing is an important component that can help evaluate the communication performance of the device under different conditions.

[0037] As one of the key technologies in the field of Internet of Things (IoT), accurate evaluation of beacon device signal levels is crucial for achieving effective indoor positioning, navigation, and information push. Existing test methods usually only detect the signal at the corresponding position of the beacon device and determine the communication performance of the beacon device based on the test results. However, this test result is relatively one-sided and cannot well evaluate the overall communication performance of the beacon device in actual scenarios. In particular, when multiple beacon devices are set up in the usage scenario, it is impossible to make a reasonable layout based on the communication capabilities of each beacon device.

[0038] The embodiment of the present application provides a Bluetooth low energy performance radio frequency testing method, which is used to improve the above-mentioned technical problems in related technologies.

[0039] like Figure 1 As shown, the method includes:

[0040] S100. Acquire signal strength information of a beacon device corresponding to multiple locations, where the signal strength information is used to indicate the signal strength of the beacon device measured by a test device at a corresponding location.

[0041] Exemplarily, the beacon device working area is used as the test area that the test device needs to test, and the test area is divided into at least two sub-areas.

[0042] like Figure 2 As shown, the test area may include a first sub-area S1 and a second sub-area S2. The first sub-area S1 and the second sub-area S2 are distributed in a direction away from the beacon device 100, that is, the first sub-area S1 is closer to the beacon device 100, and the second sub-area S2 is farther from the beacon device 100. Therefore, the signal strength in the second sub-area S2 is weaker than the signal strength in the first sub-area S1. Each sub-area may include multiple test positions (C1-C6).

[0043] For example, the distance between the first sub-area S1 and the beacon device 100 may be d1, and the distance between the second sub-area S2 and the beacon device 100 may be d2. The selection points for calculating the distance between the sub-area and the beacon device 100 may be selected by those skilled in the art according to requirements.

[0044] Multiple test positions in the same sub-area may be located on the same arc, so that the distances between each test position in the same area and the beacon device 100 are equal. In other examples, multiple test positions in the same sub-area may also be located on the same straight line, which may be perpendicular to the center line of the transmission path of the beacon device 100.

[0045] The test device detects the signal strength of the beacon device 100 at different test locations to obtain the signal strength at different locations. Therefore, the signal strength information in the embodiment of the present application may include the signal strength and the test location.

[0046] The test equipment may be a mobile measuring equipment commonly used by those skilled in the art. Those skilled in the art may select a suitable test equipment according to their needs, and select a suitable measurement method to obtain the signal strength of the beacon device 100 at the aforementioned test location.

[0047] S200: construct a first matrix according to a plurality of signal strength information and a preset matrix construction rule, wherein the preset matrix construction rule includes a position rule.

[0048] like Figure 3 As shown, S200 includes:

[0049] S210: Normalize the signal strength to obtain first data.

[0050] The signal strength information is processed, such as cleaning, filtering, and normalization, to remove abnormal data and facilitate subsequent data processing, thereby reducing the impact of abnormal data on the processing results. The data processing may adopt a conventional data processing scheme, such as the normalization processing may use the minimum-maximum normalization method to scale the signal strength in the signal strength information to the interval of [0-1]. It is understandable that the scaling interval of the signal strength is not limited to the above example, and the signal strength may also be scaled to a specific interval as required.

[0051] S220, grayscale the first data to obtain second data. Exemplarily, the first data may be grayscaled according to a first formula, which is:

[0052] G=255×ln(1+Xnor)

[0053] Among them, G is the second data, and Xnor is the first data.

[0054] According to the first formula, the signal intensity can be converted into a grayscale value for image data. That is, in this embodiment, the second data can be applied to image display, and the value range of the second data can be 0-255.

[0055] S230. Obtain distribution information of corresponding second data in the matrix according to the test position.

[0056] S240: Construct a first matrix based on the distribution information and the second data.

[0057] Exemplarily, the preset matrix construction rule is: the arrangement order of the test positions corresponding to the signal strength is used as the position of the corresponding second data in the first matrix. Therefore, the test positions corresponding to the signal strength are the distribution information of the corresponding second data in the matrix. The first matrix can be constructed according to the distribution information of the second data and the second data. For example, the second data corresponding to the first test position C1 can be used as the data of the first row and first column of the first matrix, and the second data corresponding to the second test position C2 can be used as the data of the first row and second column of the first matrix.

[0058] A first matrix for example could be as follows:

[0059]

[0060] Among them, 68 is the second data at the first test position C1, 82 is the second data at the second test position C2, 71 is the second data at the third test position C3, 52 is the second data at the fourth test position C4, 65 is the second data at the fifth test position C5, and 53 is the second data at the sixth test position C6.

[0061] S300 . Generate a signal mark of the beacon device 100 based on the first matrix and the preset matrix, where the signal mark is used to indicate a signal level of the beacon device 100 .

[0062] Exemplarily, S300 may include:

[0063] S310: Obtain a second matrix according to the first matrix and a preset matrix.

[0064] The construction rule of the preset matrix may be the same as that of the first matrix. The data in the preset matrix may be second data corresponding to the minimum signal strength required to maintain communication at the corresponding test position. For example, an exemplary preset matrix may be:

[0065]

[0066] Among them, 55 is the second data corresponding to the minimum signal strength required to maintain communication.

[0067] In another exemplary embodiment, the data in the preset matrix may be second data corresponding to the expected signal strength at the corresponding test position. For example, an exemplary preset matrix may be:

[0068]

[0069] Subtract the first matrix from the preset matrix to obtain the second matrix. For example, the second matrix can be:

[0070]

[0071] S320 , obtaining an intermediate mark of each sub-region according to the data in the second matrix, where the intermediate mark is the sum of the data in the second matrix corresponding to the sub-region.

[0072] Exemplarily, according to the above example, the middle mark j1 of the first sub-region S1 is j2=-2+7+1=6, and the middle mark j2 of the second sub-region S2 is j2=-7+5=-2.

[0073] S330, obtaining a signal mark based on the intermediate mark and the second formula, the second formula is:

[0074]

[0075] Among them, j is a signal mark, j1 is the middle mark of the first sub-area S1, j2 is the middle mark of the second sub-area S2, d1 is the distance between the first sub-area S1 and the beacon device 100, and d2 is the distance between the second sub-area S2 and the beacon device 100.

[0076] Since the signal strength will attenuate as the propagation distance increases, the signal strength of the farther sub-area can better reflect the signal level of the beacon device 100, and its influence on the signal marking should also be greater.

[0077] It can be understood that those skilled in the art can also add corresponding coefficients to the above formulas to adjust the above formulas based on needs, such as adding the constant coefficient C to adjust the calculation part of the middle mark of the second sub-area S2 in the second formula. The specific adjustment calculation method can also be selected according to needs.

[0078] S400 , evaluating the communication performance of the beacon device 100 according to the signal mark.

[0079] The communication performance of the corresponding beacon device 100 can be evaluated according to the calculated signal tag of the beacon device 100. When the application scenario has multiple beacon devices 100, the signal capabilities between the beacon devices 100 can be compared according to the value of the beacon tag.

[0080] For example, the signal tag j of the first beacon device 100 is 13, and the signal tag j of the second beacon device 100 is 7, then the signal transmission capability of the second beacon device 100 is higher than that of the first beacon device 100. In actual use scenarios, what is needed more is the comprehensive signal capability of the beacon device 100, so the signal tag as a representation of its overall signal level can be more conducive to the use of actual scenarios.

[0081] In some embodiments, before the step of constructing the first matrix based on the distribution information and the second data, the method further includes:

[0082] S510: Obtain image information between the beacon device 100 and the test location. Exemplarily, the image information between the beacon device 100 and the corresponding location is obtained by a camera or other device with an image acquisition function.

[0083] S520. Obtain an obstacle correction factor of the signal strength corresponding to the test position based on the image information, where the obstacle correction factor includes the length and height of the cross section of the obstacle between the beacon device 100 and the test position, the reflectivity of the obstacle surface to electromagnetic waves, and the roughness of the obstacle surface.

[0084] After the image information is obtained, the obstacle and the type of obstacle between the beacon device 100 and the test location can be identified based on the image recognition model. For example, it can be identified that the obstacle between the beacon device 100 and the test device is a metal advertisement / sign.

[0085] The image recognition model can be trained using specific data. For example, the training data may include information such as the object type, the object's reflectivity, and the corresponding roughness, so that the image recognition model can identify the type of obstacle, the obstacle's reflectivity, and the roughness, etc., based on the image information.

[0086] The image recognition model may be a convolutional neural network, a recurrent neural network or a residual network, etc. The selection and training of the image recognition model are conventional technical means for those skilled in the art, and will not be elaborated in this application.

[0087] S530, obtaining an obstacle correction coefficient based on the obstacle correction factor and a third formula, the third formula being:

[0088]

[0089] Wherein, A is the obstacle correction coefficient, k and α are correction constants, L is the length of the obstacle cross section, H is the height of the obstacle cross section, R is the reflectivity of the object surface, and σ is the roughness of the object surface. Wherein, the value range of k and α can be selected according to the debugging requirements of the technicians in this field for the third formula, such as k=0.5, α=0.5. It should be noted that this is only an example and does not represent a restriction on the value of this application. The values ​​of both can be controlled to constrain the value of A between 0-1. The smaller A is, the greater its impact on the signal. Alternatively, the value of A can be constrained according to other requirements. The debugging of the model is a conventional technical means for those skilled in the art, and this application will not go into details here.

[0090] S540: Correct the second data according to the obstacle correction coefficient. Based on the debugging of the obstacle correction coefficient, those skilled in the art may select a corresponding correction calculation method to correct the second data.

[0091] Exemplarily, the second data is corrected according to a fourth formula, and the fourth formula is:

[0092] Y=Y1+1 / A

[0093] Among them, Y is the corrected second data, and Y1 is the second data.

[0094] In some embodiments, the method may further include:

[0095] S610. Obtain display data of a pixel area in a marker image according to a first matrix, where the display data includes coordinates of the pixel points and grayscale values ​​of the pixel points. The marker image is used to indicate a signal strength distribution of the beacon device 100.

[0096] S620 : Determine a signal weak point of the beacon device 100 based on the marker image.

[0097] The marked image is constructed by taking the value of the second data in the first matrix as the gray value of the pixel point of the pixel area, and taking the coordinate of the second data in the first matrix as the coordinate of the pixel point of the pixel area.

[0098] like Figure 4 As shown, the marking image may include multiple pixel areas, each pixel area corresponds to a test position, and the grayscale value of the pixel point in each pixel area is the second data of the corresponding test position. For example, the brightness of the area corresponding to the test position with weaker signal strength can be set to be lower, and the brightness of the area corresponding to the test position with higher signal strength can be set to be higher, so that the signal strength of the corresponding test position can be directly known through the brightness of the pixel area of ​​the marking image.

[0099] For example, a preset marker image may be pre-constructed, such as constructing a preset marker image with the second data corresponding to the expected signal strength at the corresponding test position, and the distribution thereof is as follows: Figure 5 By comparing the brightness of the pixel area corresponding to the mark image with the brightness of the pixel area corresponding to the preset mark image, the signal strength of the beacon device 100 in the corresponding pixel area can be determined, thereby determining the signal weak point of the beacon device 100 at the corresponding test position.

[0100] The present application embodiment provides a Bluetooth low energy performance radio frequency test system, which is applied to the above method. Figure 6 As shown, the system may include a testing module 201 , a matrix building module 202 , and a marker generating module 203 .

[0101] The testing module 201 may be used to obtain signal strength information of the beacon device 100 at a corresponding location.

[0102] Exemplarily, the test module 201 may be a handheld signal strength tester to meet the need to detect the signal strength of each test position. This is a conventional technical means used by those skilled in the art and will not be elaborated herein.

[0103] The matrix construction module 202 is coupled to the test module 201 , and the matrix construction module 202 is used to construct a first matrix according to the signal strength information.

[0104] Exemplarily, the matrix construction module 202 may construct a correlation matrix according to a preset matrix construction rule, such as including a first matrix construction rule to construct a first matrix. The matrix construction process may refer to the above embodiment.

[0105] The mark generation module 203 is coupled to the matrix construction module 202 . The mark generation module 203 is used to generate a signal mark according to the first matrix and the preset matrix. The signal mark is used to indicate the signal level of the beacon device 100 .

[0106] In some embodiments, the system may further include an acquisition module 204 and a processing module 205. The acquisition module is coupled to the matrix construction module 202, and is also coupled to the mark generation module 203. The acquisition module is used to acquire image information between the beacon device 100 and the test location.

[0107] Exemplarily, the acquisition module 204 may include a camera or other device with an image acquisition function to acquire image information between the beacon device 100 and the corresponding test location.

[0108] After obtaining the image information, the processing module 205 can identify the obstacle and the type of obstacle between the beacon device 100 and the test location based on a preset image recognition model, such as identifying that the obstacle between the beacon device 100 and the test device is a metal advertisement / sign.

[0109] The image recognition model can be trained using specific data. For example, the training data may include information such as the object type, the object's reflectivity, and the corresponding roughness, so that the image recognition model can identify the type of obstacle, the obstacle's reflectivity, and the roughness, etc., based on the image information.

[0110] The image recognition model may be a convolutional neural network, a recurrent neural network or a residual network, etc. The selection and training of the image recognition model are conventional technical means for those skilled in the art, and will not be elaborated in this application.

[0111] The processing module 205 calculates the obstacle correction coefficient according to the obstacle correction factor and a preset formula, so as to correct the second data. For the specific calculation process, please refer to the above embodiment.

[0112] In some examples, the system of the present application may further include an image construction module. The image construction module may construct a marked image based on the data in the second matrix.

[0113] An embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. The computer program is configured to execute the method in the above implementation manner when running.

[0114] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the method in the above implementation manner.

[0115] Through the description of the above implementation methods, the technicians in the relevant field can clearly understand that the diagnostic method in the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0116] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0117] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0118] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0119] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated units may be implemented in the form of hardware.

[0120] The modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any particular combination of hardware and software.

[0121] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application shall be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for testing the radio frequency performance of Bluetooth low energy consumption, characterized in that: The method comprises: Acquire signal strength information of a beacon device corresponding to multiple positions, wherein the signal strength information is used to indicate the signal strength of the beacon device measured by a test device at a corresponding position; constructing a first matrix based on the plurality of signal strength information and a preset matrix construction rule, wherein the preset matrix construction rule includes a position rule; Generate a signal mark of the beacon device based on the first matrix and a preset matrix, where the signal mark is used to indicate a signal level of the beacon device: The signal strength information includes signal strength and test position, and the first matrix is ​​constructed based on the plurality of signal strength information and a preset matrix construction rule, including: Normalizing the signal intensity to obtain first data; Gray-scale the first data to obtain second data; Acquire distribution information of the corresponding second data in the matrix according to the test position; constructing the first matrix based on the distribution information and the second data; The area where the test device is tested is defined as a test area, the test area includes a first sub-area and a second sub-area, and the signal mark of the beacon device is generated based on the first matrix and a preset matrix, including: Generate a second matrix according to the first matrix and the preset matrix; Obtaining an intermediate mark of each sub-region according to the data in the second matrix, wherein the intermediate mark is the sum of the data in the second matrix corresponding to the sub-region; The signal mark is obtained based on the intermediate mark and the second formula, wherein the second formula is: Wherein, j is the signal mark, j1 is the middle mark of the first sub-area, j2 is the middle mark of the second sub-area, d1 is the distance between the first sub-area and the beacon device, and d2 is the distance between the second sub-area and the beacon device.

2. The method according to claim 1, characterized in that The grayscale processing includes converting the first data according to a first formula, and the first formula is: G=255×ln(1+Xnor) Among them, G is the second data, and Xnor is the first data.

3. The method according to claim 2, characterized in that Before the step of constructing the first matrix based on the distribution information and the second data, the method further includes: Acquiring image information between the beacon device and a test location; Obtaining an obstacle correction factor of the signal strength corresponding to the test position according to the image information, the obstacle correction factor including the length and height of the cross section of the obstacle between the beacon device and the test position, the reflectivity of the obstacle surface to electromagnetic waves, and the roughness of the obstacle surface; The obstacle correction coefficient is obtained based on the obstacle correction factor and the third formula, and the third formula is: Among them, A is the obstacle correction coefficient, k, is the correction constant, L is the length of the obstacle cross section, H is the height of the obstacle cross section, R is the reflectivity of the object surface, and σ is the roughness of the object surface; The second data is corrected according to the obstacle correction coefficient.

4. The method according to claim 2 or 3, characterized in that: The method further comprises: Acquire display data of a pixel area in a marker image according to the first matrix, wherein the display data includes coordinates of a pixel point and a grayscale value of the pixel point, and the marker image is used to indicate a signal strength distribution of the beacon device; A signal weak point of the beacon device is determined based on the marker image.

5. A Bluetooth low energy performance radio frequency test system, characterized in that: As applied to the method according to claim 1, the system comprises: A test module, the test module is used to obtain signal strength information of a beacon device at a corresponding position; A matrix construction module, the matrix construction module is coupled to the test module, and the matrix construction module is used to construct a first matrix according to the signal strength information; A mark generation module, the mark generation module is coupled to the matrix construction module, the mark generation module is used to generate a signal mark according to the first matrix and a preset matrix, and the signal mark is used to indicate the signal level of the beacon device.

6. The system according to claim 5, characterized in that The system further comprises an acquisition module, which is coupled to the matrix construction module and is used to acquire image information between the beacon device and a test location.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is configured to execute the method according to any one of claims 1 to 4 when running.

8. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

Images