Terminal classification method and device based on Bluetooth RSSI characteristics, electronic equipment and medium

Abstract

The embodiment of the present invention relates to the technical field of digital key calibration, and discloses a terminal classification method and device based on Bluetooth RSSI features, an electronic device and a medium. The method comprises: collecting RSSI data of terminals to be classified; RSSI data comprises RSSI values ​​of each terminal to be classified respectively received at m sampling positions; m is a natural number greater than 1; RSSI features of each terminal to be classified are calculated according to RSSI data; RSSI features comprise: environmental attenuation factor n and A; classification of each terminal to be classified is obtained according to RSSI features. By classifying terminals according to RSSI features n and A of Bluetooth terminals, it is not only easy to implement, but also enables terminals of the same type to have good consistency when positioning based on RSSI values, thereby significantly reducing calibration costs.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of digital key calibration, and in particular to a terminal classification method and device based on Bluetooth RSSI features, an electronic device and a medium. Background Art

[0002] With the rapid development of smart cars, it is inevitable that digital keys will replace traditional physical keys. Currently, the digital keys on the market are mainly Bluetooth keys. Bluetooth digital keys receive the signal sent by the mobile phone Bluetooth through the Bluetooth base station installed in the car, and identify the unlocking position of the mobile phone relative to the car according to the RSSI (Received Signal Strength Indication) value of the mobile phone Bluetooth signal received by the car-side Bluetooth base station, so as to perform the unlocking or locking function on the vehicle. However, due to the differences in the structure of mobile phones of different brands and models and the Bluetooth chips used in mobile phones, the Bluetooth RSSI values ​​of mobile phones of different brands or models are quite different at the same distance. Therefore, mobile phones of different brands and models are usually calibrated one by one in real vehicles to ensure better unlocking or locking functions.

[0003] Since there are many mobile phone models on the market and dozens of new models are released every year, if each model of mobile phone is calibrated one by one, the Bluetooth calibration cost will be too high. Therefore, it is necessary to classify mobile phones according to their Bluetooth characteristics so that most mobile phones in the same category can reuse the same set of calibration parameters, thereby greatly reducing the workload of mobile phone Bluetooth calibration. Therefore, it is desirable to provide a method that can better classify mobile phones to ensure that mobile phones of the same type can use the same calibration parameters and have good positioning consistency. Summary of the invention

[0004] The embodiments of the present invention provide a terminal classification method and device based on Bluetooth RSSI characteristics, an electronic device and a medium. By classifying according to the RSSI characteristics of Bluetooth terminals, it is not only easy to implement, but also enables similar terminals to have good consistency when positioning based on RSSI values, thereby significantly reducing calibration costs.

[0005] In a first aspect, an embodiment of the present invention provides a terminal classification method based on Bluetooth RSSI features, comprising:

[0006] Collect RSSI data of the terminal to be classified; the RSSI data includes RSSI values ​​of each terminal to be classified received at m sampling positions respectively; m is a natural number greater than 1;

[0007] Calculate the RSSI characteristics of each terminal to be classified according to the RSSI data; the RSSI characteristics include: environmental attenuation factor n and A;

[0008] The classification of each terminal to be classified is obtained according to the RSSI feature.

[0009] As an embodiment, the RSSI feature of each terminal to be classified is calculated according to the RSSI data, including:

[0010] Calculate and obtain multiple groups of test values ​​of n and A for each terminal to be tested;

[0011] n and A are calculated based on multiple test values ​​of n and A;

[0012] Among them, a set of test values ​​of n and A are calculated using the following formula and ;

[0013] (one);

[0014] (two);

[0015] in, , are different sampling distances, , They are , RSSI value at.

[0016] As an embodiment, the RSSI feature of each terminal to be classified is calculated according to the RSSI data, including:

[0017] Calculate the average RSSI value of each terminal to be classified at each sampling position;

[0018] Accordingly, the test values ​​of n and A are calculated using the average RSSI value at each sampling position.

[0019] As an embodiment, the method of calculating the test values ​​of n and A using the average RSSI value at each sampling position includes:

[0020] Filter the average RSSI according to the rule that the average RSSI value decreases as the distance increases;

[0021] Calculate the test values ​​of n and A based on the filtered average RSSI value.

[0022] As an embodiment, obtaining the classification of each terminal to be classified according to the RSSI feature includes:

[0023] Obtaining a score for each terminal to be classified according to the RSSI feature;

[0024] The category of each terminal to be classified is obtained according to the score.

[0025] As an embodiment, obtaining a score of each terminal to be classified according to the RSSI feature includes:

[0026] Calculate the product of the environmental attenuation factor n of each terminal to be classified and a preset coefficient; the size of the product is the same as the order of magnitude of A;

[0027] The weighted sum of the product and A is calculated to obtain the score of each terminal to be classified.

[0028] As an embodiment, the collecting RSSI data of the terminal to be classified includes:

[0029] The RSSI data of each terminal to be classified is obtained by sampling in the following manner:

[0030] The sampling times of each sampling position are preset times, the distance between adjacent sampling positions is preset, and the terminal to be classified is vertically downward and remains stationary during sampling.

[0031] In a second aspect, an embodiment of the present invention provides a terminal classification device based on Bluetooth RSSI features, including:

[0032] A sampling module, used to collect RSSI data of the terminal to be classified; the RSSI data includes the RSSI value of each terminal to be classified received at m sampling positions respectively; m is a natural number greater than 1;

[0033] A feature calculation module, used to calculate the RSSI feature of each terminal to be classified according to the RSSI data; the RSSI feature includes: an environmental attenuation factor n and A;

[0034] The category determination module is used to obtain the category of each terminal to be classified according to the RSSI feature.

[0035] In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor;

[0036] The memory is used to store computer programs; the processor is used to read the computer program in the memory and implement the terminal classification method based on Bluetooth RSSI characteristics as described above when executing the program.

[0037] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the terminal classification method based on Bluetooth RSSI features as described in the first aspect.

[0038] Compared with the prior art, the technical solution provided by the embodiment of the present invention has at least the following positive effects:

[0039] In the technical solution of the embodiment of the present invention, RSSI data of the terminal to be classified is collected, and the RSSI characteristics of each terminal to be classified are calculated based on the RSSI data. The RSSI characteristics are the environmental attenuation factor n and A, and then the classification of each terminal to be classified is obtained based on the RSSI characteristics. Therefore, by combining the RSSI characteristics n and A to accurately reflect the RSSI characteristics of each terminal at different distances, not only the terminal classification method is greatly simplified and easy to implement, but also the consistency of the RSSI of the terminal after classification can be guaranteed, which well meets the application requirements of the industry and is conducive to significantly reducing the cost of Bluetooth calibration. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 A schematic diagram of a process flow of a terminal classification method based on Bluetooth RSSI features provided in Embodiment 1 of the present invention;

[0042] Figure 2 A schematic diagram showing a comparison of RSSI characteristic n values ​​between 110 types of mobile phones and 3 types of base stations provided in an embodiment of the present invention;

[0043] Figure 3 A schematic diagram showing a comparison of RSSI characteristic A values ​​between 110 types of mobile phones and 3 types of base stations provided in an embodiment of the present invention;

[0044] Figure 4 A schematic diagram showing a comparison of the scoring values ​​between 110 types of mobile phones and 3 types of base stations provided in an embodiment of the present invention;

[0045] Figure 5~Figure 8 A schematic diagram of classification based on rating values ​​of 142 types of mobile phones provided in an embodiment of the present invention;

[0046] Fig. 9 A schematic diagram of the structure of a terminal classification device based on Bluetooth RSSI characteristics provided in Embodiment 2 of the present invention;

[0047] Fig.10 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0049] The following describes the terminal classification method and device based on Bluetooth RSSI features, electronic equipment and storage medium of the embodiment of the present application with reference to the accompanying drawings. For the terminal mentioned in the background technology, for example, when a smart phone is used as a Bluetooth digital key, the RSSI values ​​of different models of mobile phones at the same distance are quite different due to the differences in the internal structure and Bluetooth chip of different models of mobile phones. The large number and increasing number of mobile phone models bring about a large calibration workload and it is difficult to classify the performance of mobile phones based on RSSI value positioning. The present application provides a terminal classification method based on Bluetooth RSSI features. In this method, by collecting RSSI data of the terminal to be classified, the RSSI features of each terminal to be classified are calculated according to the RSSI data. The RSSI features include: environmental attenuation factor n and A. The classification of each terminal to be classified is obtained according to the RSSI features, so that the differences in Bluetooth performance of different terminals are reflected through RSSI features n and A. This is a unique approach that not only ensures the accuracy of classification and the consistency of RSSI performance of similar terminals, but is also easy to implement, which is conducive to significantly reducing the calibration cost.

[0050] Figure 1 A flowchart of a terminal classification method based on Bluetooth RSSI features provided in an embodiment of the present invention, the method is used to classify Bluetooth digital key terminals, terminals of the same classification can use the same calibration parameters, such as the RSSI value of Bluetooth digital key unlocking or locking, the unlocking RSSI value is the RSSI value corresponding to the set unlocking distance. The method can be executed by a terminal classification device based on Bluetooth RSSI features provided in an embodiment of the present invention, the device can be implemented in software and / or hardware, and configured in an electronic device. Figure 1 As shown, the terminal classification method based on Bluetooth RSSI characteristics of the present application includes the following steps:

[0051] Step 101: Collect RSSI data of the terminal to be classified.

[0052] The terminal to be classified can be a portable smart device such as a smart phone or a smart watch, and no specific restrictions are made here. For each terminal to be classified, sufficient RSSI data needs to be collected to more accurately calculate its RSSI characteristics. At the same time, RSSI data of as many models of terminals as possible need to be collected to determine the classification category. The RSSI data includes the RSSI values ​​of each terminal to be classified received at m sampling positions respectively; m is a natural number greater than 1. The RSSI values ​​at different positions are conducive to more accurate calculation of RSSI characteristics.

[0053] As an example but not a limitation, in step 101, the RSSI data of the terminal to be classified can be sampled in the following manner to obtain the RSSI data of each terminal to be classified: the number of samplings at each sampling position is a preset number of times, the distance between adjacent sampling positions is a preset distance, and the terminal is vertically downward and remains stationary during sampling. The preset number of times is, for example, 500 times, and 500 RSSI values ​​are collected at each sampling position, so as to obtain sufficient RSSI value data to ensure the accuracy of RSSI value sampling. The preset distance is, for example, 1m, and the distance between adjacent sampling positions is 1m. The preset distance of 1m is conducive to simplifying the calculation. This embodiment does not impose excessive restrictions on the value of the preset distance. The sampling positions are, for example, 10, for example, within the range of 1~10m, and sampling is performed at sampling positions such as 1m, 2m...10m. The number of sampling positions can be set according to actual application requirements, and this embodiment does not impose specific restrictions on this. When sampling, the terminal, such as a smart phone, is kept in a vertically downward stationary state for sampling, so that the sampling data has high consistency and is more conducive to accurate classification. It is understandable that the collected RSSI data only needs to be sufficient to reflect the RSSI characteristics of the terminal, and this embodiment does not specifically limit the RSSI data collection method.

[0054] Step 102: Calculate the RSSI characteristics of each terminal to be classified according to the RSSI data.

[0055] RSSI features include: environmental attenuation factor n and A. A is defined as the absolute value of the RSSI value received when the Bluetooth base station is 1m away from the terminal. Environmental attenuation factor n and A are inherent characteristics of the terminal. Due to different terminal structures and Bluetooth chip models, the environmental attenuation factors n and A of different terminals are different. The effective combination of n and A can reflect the size of the terminal RSSI value.

[0056] Step 102 calculates the RSSI characteristics of each terminal to be classified according to the RSSI data, which may include: calculating the average RSSI value of each terminal to be classified at each sampling position, filtering the average RSSI according to the rule that the average RSSI value decreases as the distance increases, and calculating the test values ​​of n and A according to the filtered average RSSI value. When calculating the average RSSI value at each sampling position, the sampled RSSI value is first filtered, such as removing the minimum RSSI value, and 3sigma can also be used to process abnormal values, and then the average RSSI value is calculated. In order to further improve the effectiveness of the average RSSI value, the average RSSI is filtered according to the rule that the average RSSI value decreases as the distance increases, that is, if the average RSSI of the sampling position with a smaller distance is less than the average RSSI value of the sampling position with a larger distance, the average RSSI value of the sampling position with a smaller distance should be discarded (see below for an exemplary description). By filtering the sampled RSSI data, the effectiveness of the RSSI data can be improved.

[0057] Calculating the test values ​​of n and A according to the filtered average RSSI value may include: calculating multiple groups of test values ​​of n and A for each terminal to be tested, and calculating n and A according to the multiple groups of test values ​​of n and A. Wherein, a group of test values ​​of n and A is calculated using the following formula: and ;

[0058] (one);

[0059] (two);

[0060] in, , are different sampling distances, , They are , The average RSSI value at the location. Formulas (I) and (II) are both derived from formula Using the above calculation method, a quadratic equation is established through the average RSSI value of the two sampling positions, and the formula (1) is used to calculate , and then calculated by formula (2) , you can get a set of test values and Similarly, more groups of test values ​​of n and A can be calculated by averaging RSSI at more sampling positions. For example, there are 5 groups of test values, which are: ( , )、( , )、( , )、( , )、( , ), and then calculate ~ The average value of the terminal's environmental attenuation factor n is calculated. ~ The average value of is used to obtain the RSSI feature A of the terminal.

[0061] Step 103: Obtain the classification of each terminal to be classified according to the RSSI feature.

[0062] Specifically, step 103 of obtaining the classification of each terminal to be classified according to the RSSI feature may include: obtaining a score of each terminal to be classified according to the RSSI feature, and obtaining the category of each terminal to be classified according to the score. By establishing a unified standard for terminal classification through the score, it is convenient to accurately classify terminals of different batches.

[0063] Optionally, obtaining a score for each terminal to be classified according to the RSSI feature may include: calculating the product of the environmental attenuation factor n of each terminal to be classified and a preset coefficient, the size of the product being the same order of magnitude as A, and calculating the weighted sum of the product and A to obtain the score for each terminal to be classified. First, the environmental attenuation factor n of the terminal is unified with the order of magnitude of the RSSI feature A, and then the weighted sum of the two is calculated to facilitate setting the weights of n and A. The detailed calculation method of the score is shown in the example below. It can be understood that this embodiment does not make excessive restrictions on the calculation method of the score.

[0064] When the category of each terminal to be classified is obtained according to the score, the score segments corresponding to different categories can be divided. The score difference of the score segment can be determined according to the impact of the score on the RSSI value, and can be set according to the needs of specific applications. For detailed description, please refer to the example below.

[0065] It is worth mentioning that when collecting RSSI data in the above embodiment, one type of Bluetooth base station can be used to collect RSSI data. In order to ensure the effectiveness of RSSI data collection, the inventors verified the effectiveness of RSSI data collected by different types of Bluetooth base stations.

[0066] The classification method of the embodiment of the present invention is described in detail below through specific test data:

[0067] In order to ensure the correctness of the mobile phone grouping strategy, base stations with three different types of Bluetooth chips are used to receive mobile phone Bluetooth signals. The Bluetooth chip models of the three base stations A, B, and C are: Nordic 52832, TI2642; TLSR8269.

[0068] In an open outdoor environment, 110 mobile phones of different models were collected at a distance of 1 to 10 meters from base stations A, B, and C, and the RSSI of sampling points at every 1 meter interval (during data collection, the mobile phone and the base station were at the same height, the mobile phone remained vertically stationary, and at least 500 RSSIs were collected at each sampling point). The RSSI of each sampling point was first processed for outliers (i.e., RSSI = 127 was removed), and then RSSI outside 3 sigma was removed, and finally the average RSSI value was calculated.

[0069] The n and A values ​​are calculated by the average RSSI value of the sampling points at every 1m interval when the distance between the mobile phone and the base station is 1~10m. Among the RSSIs of 1~10m, the average RSSI of the close distance is greater than the average RSSI of the long distance to participate in the calculation of n and A, otherwise the average RSSI value of the long distance is discarded. For example: in the following Table 1, the average RSSI at a distance of 4m = -62.49dBm, which is greater than the average RSSI at a distance of 3m = -63.72dBm, so the average RSSI value at a distance of 4m is discarded; the average RSSI at distances of 8m, 9m, and 10m are all greater than the average RSSI at a distance of 7m = -68.7dBm, so the average RSSI values ​​at distances of 8m, 9m, and 10m are discarded, so the average RSSI values ​​at distances of 1m, 2m, 3m, 5m, 6m, and 7m are used to calculate n and A. Table 1 shows the average RSSI values ​​of iPhone 13 at a distance of 1~10m from base station C.

[0070] Table 1

[0071] iPhone13 dist(m) mean_RSSI(dBm) std_RSSI(dBm) 1 -56.09 1.14 2 -58.7 0.62 3 -63.72 1.82 4 -62.49 0.88 5 -64.61 1.5 6 -64.95 1.17 7 -68.7 2.03 8 -65.08 1.17 9 -64 1.2 10 -66.84 0.73

[0072] In Table 1, dist represents the distance between the mobile phone and the base station (1-10m, with one sampling point every 1m), mean_RSSI represents the average RSSI at the current sampling point (RSSI is collected at least 500 times at each sampling point), and std_RSSI represents the standard deviation of RSSI at the current sampling point (std_RSSI reflects the volatility of RSSI when the current point is repeatedly sampled).

[0073] The formula for calculating distance based on Bluetooth RSSI is: .

[0074] Where, d: the distance between the transmitting and receiving ends, in meters; RSSI: the RSSI of the mobile phone received by the base station, in dBm; n: the environmental attenuation factor; A: the RSSI value received by the base station at a distance of 1 meter from the mobile phone, in dBm.

[0075] formula Convertible to .

[0076] according to The average and The average ,get

[0077] , . Substitute first

[0078] , , , After calculating A, substitute the calculated value of A into the formula to get the value of n. By calculating multiple sets of test values ​​of n and A and averaging them, the environmental attenuation factor of the mobile phone can be obtained.

[0079] n and A.

[0080] 110 mobile phones are numbered from 1 to 110. The average RSSI of the 110 mobile phones at sampling points with a distance of 1 to 10 meters and a spacing of 1 meter is used to calculate the n and A values. The n and A values ​​of each mobile phone and the three base stations are compared, as shown in the figure below. Figure 2 and Figure 3 As shown, Figure 2 The comparison of n values ​​of each mobile phone, the horizontal axis is the mobile phone number, and the vertical axis is the n value between each mobile phone and the three base stations; Figure 3 This is a comparison of the A values ​​of each mobile phone. The horizontal axis is the mobile phone number, and the vertical axis is the A value between each mobile phone and the three base stations.

[0081] from Figure 2 and Figure 3 The approximate range of n and A can be determined. The n values ​​between 110 mobile phones and three base stations are mainly concentrated between [1 3.5], and A is mainly concentrated between [30 60]. Therefore, when using n and A to calculate the score, it is necessary to first amplify the n value by 20 times so that the amplified n value is the same order of magnitude as A, and then set the weights of n and A, set the weight value of n to 0.2, and the weight value of A to 0.8, and obtain the weighted sum of n and A as the score, as shown in Figure 4 In the score comparison chart shown, the horizontal axis is the mobile phone number, and the vertical axis is the score value of the mobile phone and the three base stations.

[0082] Figure 4 The curves of the three base stations and the scoring values ​​of each mobile phone are similar in shape, and the consistency between a single mobile phone and the three base stations is also good. It can be determined that the Bluetooth chip model at the base station end has little effect on the mobile phone grouping, so any base station can be used to collect RSSI data during classification.

[0083] Using the above method, we first collected RSSI data from 142 mobile phones of different models and base station C, then calculated the RSSI features and scores of the 142 mobile phones and base station C in turn, sorted the scores from small to large, and classified them according to the sorted scores, with a difference of 2 points as one category. The classification results are as follows: Figures 5 to 8 shown. Figure 5~Figure 8 In the figure, the horizontal axis is the mobile phone number, which can be found in the attached figure and will not be listed here one by one. The vertical axis is the score value. The 142 mobile phones are divided into 10 categories with a difference of 2 points in the mobile phone score value. ① to ⑩ in the table are the 10 categories divided according to the score value, and the mobile phones in the same group are framed by dotted lines. It can be understood that the difference in points corresponding to each category can be determined according to the positioning accuracy requirements and the impact of the score value on the RSSI value. For example, the difference can also be set to 1.5 points, and no excessive restrictions are made here.

[0084] In order to adapt to different application scenarios of Bluetooth digital keys, for example, according to the positioning accuracy requirements, the weights of n and A can be adjusted. The effective combination of n and A can well reflect the characteristics of the mobile phone Bluetooth RSSI, thereby ensuring the rationality of mobile phone classification based on the scoring value.

[0085] After the existing mobile phones on the market are classified using the above method, when classifying new mobile phones on the market, their classification can be quickly determined by sampling RSSI data and calculating the score values ​​respectively, so that the calibration parameters corresponding to the classification can be directly reused.

[0086] Compared with the prior art, the terminal classification method based on Bluetooth RSSI features in the embodiment of the present invention collects RSSI data of the terminal to be classified, calculates the RSSI features of each terminal to be classified based on the RSSI data, and the RSSI features are the environmental attenuation factors n and A, and then obtains the classification of each terminal to be classified based on the RSSI features. Therefore, by combining RSSI features n and A to accurately reflect the RSSI characteristics of each terminal at different distances, not only the terminal classification method is greatly simplified and easy to implement, but also the consistency of the RSSI of the terminal after classification can be guaranteed, which well meets the needs of industry applications and is conducive to significantly reducing the cost of Bluetooth calibration. In addition, by filtering the collected RSSI outliers and average outliers, a more accurate RSSI feature can be obtained. At the same time, the scoring value is used for classification, which is easy to calculate and classify and more convenient to classify.

[0087] Embodiment 2 of the present invention provides a terminal classification device based on Bluetooth RSSI characteristics, which is configured in an electronic device, and the electronic device may be a host computer such as a PC or a notebook. Fig. 9 As shown, the classification device 900 includes: a sampling module 902 , a feature calculation module 904 and a category determination module 906 .

[0088] The sampling module 902 is used to collect RSSI data of the terminal to be classified. The RSSI data includes the RSSI value of each terminal to be classified received at m sampling positions respectively; m is a natural number greater than 1.

[0089] The feature calculation module 904 is used to calculate the RSSI feature of each terminal to be classified according to the RSSI data; the RSSI feature includes: environmental attenuation factor n and A.

[0090] The category determination module 906 is used to obtain the category of each terminal to be classified according to the RSSI feature.

[0091] Optionally, the feature calculation module 904 is specifically used to calculate multiple groups of test values ​​of n and A for each terminal to be tested; calculate n and A according to the multiple groups of test values ​​of n and A; wherein a group of test values ​​of n and A is calculated using the following formula and :

[0092] (one);

[0093] (two);

[0094] in, , are different sampling distances, , They are , The average RSSI value at .

[0095] Optionally, the feature calculation module 904 is specifically configured to calculate an average RSSI value of each terminal to be classified at each sampling position; accordingly, the average RSSI value at each sampling position is used to calculate the test values ​​of n and A.

[0096] Optionally, the feature calculation module 904 is specifically configured to filter the average RSSI according to a rule that the average RSSI value decreases as the distance increases; and calculate the test values ​​of n and A according to the filtered average RSSI value.

[0097] Optionally, the category determination module 906 is specifically configured to obtain a score of each terminal to be classified according to the RSSI feature; and obtain a category of each terminal to be classified according to the score.

[0098] Optionally, the category determination module 906 is specifically used to calculate the product of the environmental attenuation factor n of each terminal to be classified and a preset coefficient; the size of the product is the same order of magnitude as A; and the weighted sum of the product and A is calculated to obtain the score of each terminal to be classified.

[0099] Optionally, the sampling module 902 is specifically configured to obtain RSSI data of each terminal to be classified by sampling in the following manner: the sampling times of each sampling position is a preset number, the distance between adjacent sampling positions is a preset distance, and the terminal to be classified is vertically downward and remains stationary during sampling.

[0100] Compared with the prior art, the terminal classification device based on Bluetooth RSSI features in the embodiment of the present invention collects RSSI data of the terminal to be classified, calculates the RSSI features of each terminal to be classified based on the RSSI data, and the RSSI features are the environmental attenuation factors n and A, and then obtains the classification of each terminal to be classified based on the RSSI features. Therefore, by combining RSSI features n and A to accurately reflect the RSSI characteristics of each terminal at different distances, not only the terminal classification method is greatly simplified and easy to implement, but also the consistency of the RSSI of the terminal after classification can be guaranteed, which well meets the needs of industry applications and is conducive to significantly reducing the cost of Bluetooth calibration. In addition, by filtering the collected RSSI outliers and average outliers, a more accurate RSSI feature can be obtained. At the same time, the scoring value is used for classification, which is easy to calculate and classify and more convenient to classify.

[0101] Fig.10 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. The electronic device 110 includes a memory 111 and a processor 112;

[0102] The memory 111 is used to store computer programs; the processor 112 is used to read the computer program in the memory 111 and implement the terminal classification method based on Bluetooth RSSI characteristics as described in the above embodiment when executing the program.

[0103] Embodiment 4 of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a computer processor, it is used to execute the technical solution of any method embodiment.

[0104] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course 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 is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a grid device, etc.) to execute the methods described in each embodiment of the present invention.

[0105] It is worth noting that in the embodiment of the above-mentioned device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0106] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A terminal classification method based on Bluetooth RSSI characteristics, characterized in that: include: Collecting RSSI data of the terminal to be classified; the RSSI data includes RSSI values ​​of each terminal to be classified received at m sampling positions respectively; m is a natural number greater than 1; The RSSI characteristics of each terminal to be classified are calculated according to the RSSI data; the RSSI characteristics include: an environmental attenuation factor n and A, where A is the absolute value of the RSSI value received when the Bluetooth base station is 1m away from the terminal; Obtaining a classification of each terminal to be classified according to the RSSI feature; The step of calculating the RSSI characteristics of each terminal to be classified according to the RSSI data includes: Calculate and obtain multiple groups of test values ​​of n and A for each terminal to be tested; n and A are calculated based on multiple test values ​​of n and A; Among them, a set of test values ​​of n and A are calculated using the following formula and : (one); (two); in, , are different sampling distances, , They are , RSSI value at The obtaining the classification of each terminal to be classified according to the RSSI feature includes: Obtaining a score for each terminal to be classified according to the calculated n and A; The category of each terminal to be classified is obtained according to the score.

2. The terminal classification method based on Bluetooth RSSI characteristics according to claim 1 is characterized in that: The step of calculating the RSSI characteristics of each terminal to be classified according to the RSSI data includes: Calculate the average RSSI value of each terminal to be classified at each sampling position; Accordingly, the test values ​​of n and A are calculated using the average RSSI value at each sampling position.

3. The terminal classification method based on Bluetooth RSSI characteristics according to claim 2 is characterized in that: The method of calculating the test values ​​of n and A using the average RSSI value at each sampling position includes: Filter the average RSSI according to the rule that the average RSSI value decreases as the distance increases; Calculate the test values ​​of n and A based on the filtered average RSSI value.

4. The terminal classification method based on Bluetooth RSSI characteristics according to claim 1 is characterized in that: Obtaining a score for each terminal to be classified according to the RSSI feature includes: Calculate the product of the environmental attenuation factor n of each terminal to be classified and a preset coefficient; the size of the product is the same as the order of magnitude of A; The weighted sum of the product and A is calculated to obtain the score of each terminal to be classified.

5. The terminal classification method based on Bluetooth RSSI characteristics according to claim 1 is characterized in that: The collecting of RSSI data of the terminal to be classified includes: The RSSI data of each terminal to be classified is obtained by sampling in the following manner: The sampling times of each sampling position are preset times, the distance between adjacent sampling positions is preset, and the terminal to be classified is vertically downward and remains stationary during sampling.

6. A terminal classification device based on Bluetooth RSSI characteristics, characterized in that: include: A sampling module, used to collect RSSI data of the terminal to be classified; the RSSI data includes RSSI values ​​of each terminal to be classified received at m sampling positions respectively; m is a natural number greater than 1; A feature calculation module, used to calculate the RSSI feature of each terminal to be classified according to the RSSI data; the RSSI feature includes: an environmental attenuation factor n and A, where A is the absolute value of the RSSI value received when the Bluetooth base station is 1m away from the terminal; A category determination module, used to obtain the category of each terminal to be classified according to the RSSI feature; The feature calculation module is specifically used for: Calculate and obtain multiple groups of test values ​​of n and A for each terminal to be tested; n and A are calculated based on multiple test values ​​of n and A; Among them, a set of test values ​​of n and A are calculated using the following formula and : (one); (two); in, , are different sampling distances, , They are , RSSI value at The category determination module is specifically used for: Obtaining a score for each terminal to be classified according to the calculated n and A; The category of each terminal to be classified is obtained according to the score.

7. The terminal classification device based on Bluetooth RSSI characteristics according to claim 6, characterized in that: The feature calculation module is specifically used for: Calculate the average RSSI value of each terminal to be classified at each sampling position; The average RSSI value at each sampling location is used to calculate the test values ​​of n and A.

8. An electronic device, characterized in that: including a memory and a processor; A memory for storing a computer program; the processor for reading the computer program in the memory and implementing the terminal classification method based on Bluetooth RSSI features as described in any one of claims 1-5 when executing the program.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the terminal classification method based on Bluetooth RSSI features as described in any one of claims 1 to 5 is implemented.

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