Bluetooth information monitoring method and Bluetooth information monitoring system

Abstract

A Bluetooth information monitoring method and monitoring system, the monitoring method includes: constructing multiple monitoring channels based on the Bluetooth monitoring environment, configuring corresponding dynamic monitoring parameters and dynamic thresholds; collecting historical Bluetooth information and historical environment information in each monitoring channel through a BLE Bluetooth device; generating a new dynamic threshold through a threshold adjustment algorithm based on the historical Bluetooth information of each monitoring channel; generating a monitoring parameter adjustment strategy and a new dynamic monitoring parameter for each monitoring channel through a random frequency hopping sequence and a frequency hopping integration algorithm based on the historical environment information and historical Bluetooth information of each monitoring channel; replacing the original dynamic threshold and dynamic monitoring parameter of the monitoring channel with the new dynamic threshold and the new dynamic monitoring parameter; processing the RSSI signals corresponding to the historical Bluetooth information of multiple monitoring channels through a fusion algorithm to obtain a target Bluetooth RSSI signal; repeating the above steps. This improves the accuracy of the Bluetooth RSSI signal.

Description

Technical Field

[0001] The present application relates to the technical field of artificial intelligence and signal monitoring, and in particular to a method and system for monitoring the strength of Bluetooth information. Background Art

[0002] As a short-range wireless communication technology, Bluetooth technology is widely used in the Internet of Things and smart hardware devices. Bluetooth Low Energy (BLE) is widely used in various scenarios due to its advantages such as low power consumption, low cost and high-speed transmission. In the BLE Bluetooth communication process, the RSSI signal (Received Signal Strength Indicator) is an important signal parameter that can reflect the transmission quality and distance of BLE Bluetooth information. Monitoring BLE Bluetooth RSSI signals is the main way to improve the identification of Bluetooth transmission quality and distance. The existing BLE Bluetooth RSSI monitoring methods have problems such as low detection sensitivity, poor stability, high false alarm rate and missed alarm rate, which leads to poor accuracy of the signal receiving strength value of the monitored Bluetooth information. Therefore, how to improve the accuracy of monitoring BLE Bluetooth RSSI signals is the current research focus, among which multi-channel technology is a solution.

[0003] In the prior art of multi-channel Bluetooth, patent number CN202011266995.8 discloses a Bluetooth multi-channel receiving method. The method comprises: receiving cyclic redundancy check information and load data of each Bluetooth receiving branch in multiple Bluetooth receiving branches; in one mode, when the cyclic redundancy check information in at least one Bluetooth receiving branch is correct, it is determined not to retransmit. The present application also proposes a Bluetooth multi-channel receiving system and electronic device. The number of retransmissions of the Bluetooth system is effectively reduced to improve communication efficiency.

[0004] In the prior art of multi-channel Bluetooth, Patent No. CN202011436612.7 discloses an information collection gateway for multi-channel BLE Bluetooth devices and its implementation method, the implementation method includes: generating control instructions for different modules in the gateway body through the main control module; according to the control instructions, realizing data communication with external BLE Bluetooth devices through the multi-channel Bluetooth module; wherein the multi-channel Bluetooth module includes multiple Bluetooth sub-modules; when the gateway body and the external BLE Bluetooth device are in a low-speed communication state, each of the Bluetooth sub-modules is used to collect data from multiple external BLE Bluetooth devices; when the gateway body and the external BLE Bluetooth device are in a high-speed communication state, each of the Bluetooth sub-modules is used to collect data from one external BLE Bluetooth device. In this way, multi-channel and high-speed data collection of external BLE Bluetooth devices is realized, and one gateway is connected to multiple external BLE Bluetooth devices, which expands the scope of application.

[0005] Even so, the above-mentioned prior art has not completely solved the accuracy problem of monitoring BLE Bluetooth RSSI signals. Summary of the invention

[0006] The main purpose of the present application is to provide a Bluetooth information monitoring method and a Bluetooth information monitoring system, aiming to solve the problems of low sensitivity, poor stability, high false alarm rate and missed alarm rate of the signal reception strength value of Bluetooth information monitoring in the prior art.

[0007] To achieve the above object, the present application provides a Bluetooth information monitoring method, the method comprising:

[0008] S100: Acquire a Bluetooth monitoring environment, and generate environmental monitoring parameters and Bluetooth information collection thresholds for the Bluetooth monitoring environment;

[0009] S200: construct multiple monitoring channels, each monitoring channel is configured with a corresponding dynamic monitoring parameter and a dynamic threshold for collecting Bluetooth information, the initial values ​​of the dynamic monitoring parameters of all monitoring channels are environmental monitoring parameters, the initial values ​​of the dynamic thresholds of all monitoring channels are Bluetooth information collection thresholds, and the dynamic monitoring parameters include a dynamic sampling rate and a dynamic filter coefficient;

[0010] S300: Based on the dynamic monitoring parameters and dynamic thresholds, collect information on each monitoring channel through the BLE Bluetooth device, collect historical Bluetooth information and historical environmental information, and the frequency of information collection is not less than 10 times per second;

[0011] S400: Based on the historical Bluetooth information of each monitoring channel, a new dynamic threshold value of each monitoring channel is generated through a threshold adjustment algorithm;

[0012] S500: Based on the historical environment information of each monitoring channel and the historical Bluetooth information obtained by each monitoring channel, a monitoring parameter adjustment strategy of each monitoring channel is generated through a random frequency hopping sequence and a frequency hopping integration algorithm, and the monitoring parameter adjustment strategy includes a dynamic sampling rate adjustment strategy and a dynamic filter coefficient adjustment strategy;

[0013] S600: Generate new dynamic monitoring parameters for each monitoring channel through a monitoring parameter adjustment strategy;

[0014] S700: Replace the original dynamic threshold of the monitoring channel with the new dynamic threshold, and replace the original dynamic monitoring parameter of the monitoring channel with the new dynamic monitoring parameter;

[0015] S800: Processing RSSI signals corresponding to historical Bluetooth information of multiple monitoring channels through a fusion algorithm to obtain a target Bluetooth RSSI signal;

[0016] S900: Return to execute steps S300 to S800.

[0017] Optionally, step S400 further includes:

[0018] Based on the historical Bluetooth information, the RSSI signal trend and RSSI signal distribution information of the historical Bluetooth information are identified, and a new dynamic threshold for each monitoring channel is generated through a threshold adjustment algorithm; the threshold adjustment algorithm is the average value of the RSSI signal distribution information of the historical Bluetooth information minus the standard deviation of a specific multiple, and the minimum value of the specific multiple is 2; the RSSI signal of the historical Bluetooth information that is less than the dynamic threshold is excluded.

[0019] Optionally, identifying the RSSI signal trend of the historical Bluetooth information further includes performing sliding average processing, differential processing, or nonlinear fitting processing on the RSSI signal of the historical Bluetooth information.

[0020] Optionally, the historical environment information includes historical interference information and historical noise information, and step S500 further includes:

[0021] Based on the historical interference information, the interference trend of the historical interference information, the distribution information of the interference intensity, and the interference range to which the historical interference information belongs are identified, and a dynamic sampling rate adjustment strategy for the monitoring channel is generated through a frequency hopping integration algorithm;

[0022] Based on the historical noise information, the noise trend of the historical noise information and the distribution information of the noise intensity are identified, and the dynamic filter coefficient adjustment strategy of the monitoring channel is generated through the frequency hopping integration algorithm.

[0023] Optionally, the frequency hopping integration algorithm integrates a hopping algorithm and a Bluetooth frequency hopping algorithm, and the hopping algorithm is selected from the group consisting of the following algorithms: an adaptive frequency hopping algorithm, a fixed frequency hopping algorithm, a random frequency hopping algorithm, and a hopping algorithm based on a data packet transmission success rate; the Bluetooth frequency hopping algorithm includes both a CSA1 frequency hopping algorithm and a CSA2 frequency hopping algorithm.

[0024] Optionally, in step S800, based on the RSSI signal trend of historical Bluetooth information of each monitoring channel and RSSI signal distribution information, the signal quality and signal reliability of each monitoring channel are identified, and based on the signal quality and signal reliability of each monitoring channel, the RSSI signal of each monitoring channel is processed through a fusion algorithm to obtain the target Bluetooth RSSI signal.

[0025] Optionally, the fusion algorithm assigns a corresponding weight value to each monitoring channel based on the signal quality and signal reliability of each monitoring channel, and then performs weighted summation on the RSSI signals of each monitoring channel to obtain the target Bluetooth RSSI signal.

[0026] Optionally, the sum of the weight values ​​corresponding to all monitoring channels is 1.

[0027] Optionally, in step S800, the signal quality and signal reliability of each monitoring channel are calculated by identifying and calculating the RSSI signal trend and RSSI signal distribution information of the historical Bluetooth information of each monitoring channel through a neural network or a Kalman filter.

[0028] In addition, the present application also proposes a Bluetooth information monitoring system for implementing the above-mentioned Bluetooth information monitoring method.

[0029] The present application provides a Bluetooth information monitoring method and a Bluetooth information monitoring system, which adopts multi-channel monitoring, and can collect effective Bluetooth information by adjusting the dynamic threshold in real time; the dynamic monitoring parameters of each channel are adjusted by integrating Bluetooth CSA1 and CSA2 through the frequency hopping integration algorithm, thereby improving the detection sensitivity and stability of the BLE Bluetooth RSSI signal. By filtering the RSSI signal, noise and interference are reduced, and the signal quality is improved; by adjusting the dynamic monitoring parameters in real time, the obtained Bluetooth information and RSSI signal are made more accurate, and the false alarm rate and missed alarm rate are reduced; according to the signal quality and / or signal reliability of the monitoring channel, the weights of each monitoring channel are adjusted in real time to perform RSSI signal fusion, and an accurate and stable target RSSI signal is obtained. Then, the present application improves the transmission quality and anti-interference ability of the monitored Bluetooth information during the frequency hopping process. Finally, because the present application can perform a high-precision signal monitoring process for different Bluetooth monitoring environments, the present application is applicable to various complex signal environments, and has good versatility and practicality, thereby comprehensively improving the accuracy of the RSSI signal of the monitored Bluetooth information. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1 It is a flow chart of the Bluetooth information monitoring method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] The Bluetooth information monitoring method provided in the embodiment of the present application is applied to the monitoring system of Bluetooth information. Unless otherwise defined, all technical and scientific terms used in this article have the same meaning as those generally understood by technicians in the technical field of this application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned figures are used to distinguish different objects, not to describe a specific order.

[0033] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0034] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0035] The Bluetooth information monitoring method provided in the embodiment of the present application can be applied to a terminal, a server, or a monitoring system including a terminal and a server, and is implemented through the interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, etc.

[0036] like Figure 1 As shown, the first preferred embodiment of the present application provides a Bluetooth information monitoring method, including the following steps S100 to S900.

[0037] Step S100, obtaining a Bluetooth monitoring environment, and generating environmental monitoring parameters and a Bluetooth information collection threshold of the Bluetooth monitoring environment.

[0038] In this step, the interference information and noise levels in different Bluetooth monitoring environments are not the same. After being interfered with and affected by noise, the Bluetooth information strength will also change. Therefore, in theory, for different Bluetooth monitoring environments, the environmental monitoring parameters and Bluetooth information collection thresholds that should be used should also be different. However, in practice, this operation is difficult, so this embodiment uses the default environmental monitoring parameters and Bluetooth information collection thresholds, and then dynamically adjusts them later.

[0039] Step S200, construct multiple monitoring channels, each monitoring channel is configured with corresponding dynamic monitoring parameters and dynamic thresholds for collecting Bluetooth information, the initial values ​​of the dynamic monitoring parameters of all monitoring channels are environmental monitoring parameters, the initial values ​​of the dynamic thresholds of all monitoring channels are Bluetooth information collection thresholds, and the dynamic monitoring parameters include dynamic sampling rate and dynamic filter coefficient.

[0040] In this step, multiple monitoring channels are constructed to collect the Bluetooth information of the BLE Bluetooth device in each monitoring channel. BLE Bluetooth uses 40 frequency hopping channels, of which 37 are general channels for connection communication; the other 3 are broadcast channels for sending broadcast information. The interval between broadcast channels is at least 24MHz, and the corresponding center frequencies are 2402MHz, 2426MHz, and 2480MHz, respectively. Since each monitoring channel has different frequencies, the environmental information is different, that is, the interference and noise are different, so different monitoring parameters and thresholds are required to collect appropriate Bluetooth information. In this embodiment, each monitoring channel uses dynamically adjusted dynamic monitoring parameters and dynamic thresholds, but the same monitoring parameters and dynamic thresholds are used at the beginning, and then dynamic adjustments are made based on the information collected later.

[0041] Step S300: Based on the dynamic monitoring parameters and dynamic thresholds, information is collected on each monitoring channel through the BLE Bluetooth device to collect historical Bluetooth information and historical environmental information. The frequency of information collection is not less than 10 times per second.

[0042] As mentioned above, each monitoring channel has different frequencies, so the environmental information is different, that is, the interference and noise are different, so the collected Bluetooth information is also different, especially the Bluetooth RSSI signal of the Bluetooth information is also different, so it needs to be optimized. The first thing to optimize in this step is the frequency of information collection. The frequency of information collection should not be less than 10 times per second. The higher the frequency, the more information can be collected, which is conducive to using algorithms to optimize dynamic monitoring parameters and dynamic thresholds. However, the higher the frequency, the greater the power consumption, so it should not be higher than 40 times per second.

[0043] In this step, the Bluetooth monitoring environment is first comprehensively tested, Bluetooth information and environmental information are collected at a frequency of not less than 10 times per second, and sufficient historical data is collected to identify the RSSI signal level, interference level, and noise level of each monitoring channel.

[0044] Step S400: Based on the historical Bluetooth information of each monitoring channel, a new dynamic threshold of each monitoring channel is generated through a threshold adjustment algorithm.

[0045] In this step, the so-called threshold adjustment algorithm is based on the collected historical Bluetooth information, identifying the RSSI signal trend of the historical Bluetooth information in each monitoring channel, and the RSSI signal distribution information, and calculating the average value and standard deviation of the RSSI signal. Furthermore, the RSSI signal of the historical Bluetooth information can also be processed by sliding average, differential processing, or nonlinear fitting processing, and then the average value and standard deviation are calculated.

[0046] In this step, the standard deviation of the mean value minus a specific multiple is used as a new dynamic threshold, and the RSSI signal of the historical Bluetooth information that is less than the dynamic threshold is excluded. The minimum value of the specific multiple here is 2, that is, twice the standard deviation. For the data of normal distribution, the amount of data between the positive and negative two standard deviations of the mean value is 95.46%, so the amount of data greater than the mean value minus two times the standard deviation is 97.73%, that is, 2.27% of the data is discarded. The higher the specific multiple is set, the larger the amount of data. If the specific multiple is set to 3, that is, three times the standard deviation, for the data of normal distribution, the amount of data between the positive and negative three standard deviations of the mean value is 99.73%, so the amount of data greater than the mean value minus three times the standard deviation is 99.87%, and only 0.13% of the data is discarded. Setting the specific multiple needs to depend on the distribution of the RSSI signal. The closer the distribution is to the normal distribution, the larger the specific multiple can be set, the better the regularity of the data, and less data can be discarded and more data can be collected. However, if the distribution deviates from the normal distribution and the data has poor regularity, a smaller specific multiple can be set. Although less data is collected, it can avoid being affected by abnormal deviations.

[0047] Assuming that a set of RSSI signals of historical Bluetooth information is collected with a dynamic threshold of -65, this application adjusts the dynamic threshold in the following way:

[0048] The initial value of the dynamic threshold is -65. RSSI information less than -65 is discarded.

[0049] RSSI signal array: [-60, -55, -58, -62, -59, -57, -61]

[0050] RSSI signal average = (-60 -55 -58 -62 -59 -57 -61) / 7 = -58.857

[0051] RSSI signal standard deviation = 2.410

[0052] Threshold adjustment algorithm: RSSI signal average value - k*RSSI signal standard deviation,

[0053] Where k is a specific multiple,

[0054] If k=2, the new dynamic threshold = -58.857 - 2*2.410=-63.678

[0055] If k=3, the new dynamic threshold = -58.857 - 3*2.410=-66.088

[0056] If k=3 is used, the new dynamic threshold -66.088 replaces the original -65 to collect new Bluetooth information and the corresponding Bluetooth RSSI signal. If two new RSSI signals [-63, -58] are collected, the RSSI signal array becomes [-60, -55, -58, -62, -59, -57, -61, -63, -58], RSSI signal average = (-60 -55 -58 -62 -59-57 -61 -63 -58) / 9 = -59.222, RSSI signal standard deviation = 2.539, new dynamic threshold = -59.222 -3*2.539= -66.838. Then the new dynamic threshold -66.838 replaces the original -66.088 to collect new Bluetooth information and the corresponding Bluetooth RSSI signal, and repeats the above dynamic threshold update process.

[0057] The above is just a simple illustrative example. The number of historical Bluetooth information used in this application to calculate the RSSI signal average and standard deviation is much larger than the above illustrative example. Based on the statistical rule of thumb, for an analysis variable, the minimum number of samples for analysis and modeling is 25 to 30. Preferably, this application uses 120 samples. If the information collection frequency is 10 times per second, the first time a new dynamic threshold is generated, it is necessary to collect 12 seconds of Bluetooth information to obtain 120 RSSI signals. If the information collection frequency is 40 times per second, the first time a new dynamic threshold is generated, it is necessary to collect 3 seconds of Bluetooth information to obtain 120 RSSI signals. When updating the dynamic threshold later, just replace the earliest Bluetooth information with the latest collected Bluetooth information, use rolling replacement iteration, and still maintain 120 RSSI signals, so it can be updated in real time.

[0058] Based on the solution proposed in this application, by continuously iterating historical Bluetooth information and corresponding RSSI signals to calculate the dynamic threshold, the accuracy and environmental adaptability of the dynamic threshold can be effectively improved, and the dynamic threshold is adjusted after each information collection to make the information collection results more accurate and reduce the false alarm rate and missed alarm rate.

[0059] Step S500, based on the historical environment information of each monitoring channel and the historical Bluetooth information obtained by each monitoring channel, a monitoring parameter adjustment strategy for each monitoring channel is generated through a random frequency hopping sequence and a frequency hopping integration algorithm. The monitoring parameter adjustment strategy includes a dynamic sampling rate adjustment strategy and a dynamic filter coefficient adjustment strategy.

[0060] In this step, the frequency hopping integration algorithm integrates the hopping algorithm and the Bluetooth frequency hopping algorithm. The hopping algorithm can be an adaptive frequency hopping algorithm, a fixed frequency hopping algorithm, a random frequency hopping algorithm, or a hopping algorithm based on the success rate of data packet transmission, etc. The Bluetooth frequency hopping algorithm also includes the CSA1 frequency hopping algorithm and the CSA2 frequency hopping algorithm.

[0061] The characteristics of the above-mentioned hopping algorithms are as follows: Adaptive Frequency Hopping (AFH) algorithm: can dynamically avoid the monitoring channel with serious interference and improve the communication quality. Fixed frequency hopping algorithm: hopping according to the preset mode, simple but may not be flexible enough to deal with complex environments. Random frequency hopping algorithm: the hopping order is random, which enhances the anti-interference ability, but may affect the stability. Hopping algorithm based on the success rate of data packet transmission: adjust the hopping frequency according to the data transmission effect, which can optimize the communication efficiency.

[0062] The Bluetooth frequency hopping algorithms used in this application include both the CSA1 frequency hopping algorithm and the CSA2 frequency hopping algorithm. CSA1 and CSA2 (Connectionless Slave Broadcast - Channel Selection Algorithm #2) frequency hopping algorithms, where the CSA1 (Connectionless Slave Broadcast - Channel Selection Algorithm #1) frequency hopping algorithm is used for broadcast monitoring channel selection of BLE 4.x devices, which can improve the stability of data transmission. The CSA2 (Connectionless Slave Broadcast - Channel Selection Algorithm #2) frequency hopping algorithm is used for BLE 5.0 and above devices, and increases the randomness, security and efficiency of transmission by improving the monitoring channel selection mechanism. The requirements of each algorithm are different. CSA1 requires the device to follow a fixed monitoring channel hopping sequence, which is simple but may not be effective enough in a high-interference environment. CSA2 requires the device to dynamically select the monitoring channel according to an advanced algorithm, which requires higher computing power but can provide better performance and security.

[0063] This step also includes: generating a random frequency hopping sequence and using it in the CSA1 frequency hopping algorithm and the CSA2 frequency hopping algorithm.

[0064] The CSA1 frequency hopping algorithm uses a relatively simple pseudo-random number generation method. The generated CSA1 frequency hopping sequence example is as follows: [10, 15, 20, 25, 30, 35, 3, 8, 13, 18, 23, 28, 33, 1, 6, 11, 16, 21, 26,31, 0, 5, 10, 15, 20, 25, 30, 35, 3, 8, 13, 18, 23, 28, 33, 1, 6]. The CSA2 frequency hopping algorithm improves on the CSA1 frequency hopping algorithm by introducing more parameters and complex calculations, making the frequency hopping sequence more unpredictable and enhancing security and anti-interference capabilities. An example of a generated CSA2 frequency hopping sequence is as follows: [5, 10, 15,20, 25, 30, 35, 1, 6, 11, 16, 21, 26, 31, 36, 2, 7, 12, 17, 22, 27, 32, 0, 3,8, 13, 18, 23, 28, 33, 4, 9, 14, 19, 24, 29, 34].

[0065] Based on the random frequency hopping sequence, the frequency hopping sequence corresponding to each frequency hopping algorithm is generated, thereby improving the transmission and reception accuracy of Bluetooth information.

[0066] When using the frequency hopping integration algorithm, this step further includes: based on historical interference information, identifying the interference trend of the historical interference information, the distribution information of the interference intensity, and the interference range to which the historical interference information belongs, and then generating a dynamic sampling rate adjustment strategy for the monitoring channel through the frequency hopping integration algorithm.

[0067] When using the frequency hopping integration algorithm, this step further includes: based on the historical noise information, identifying the noise trend of the historical noise information and the distribution information of the noise intensity, and then generating a dynamic filter coefficient adjustment strategy for the monitoring channel through the frequency hopping integration algorithm.

[0068] Step S600: Generate new dynamic monitoring parameters for each monitoring channel through the monitoring parameter adjustment strategy.

[0069] Specifically, the frequency hopping algorithm itself focuses on changing the operating frequency of wireless communication to avoid interference and optimize channel utilization efficiency, and does not directly adjust dynamic monitoring parameters such as dynamic sampling rate or dynamic filter coefficient. However, this application uses the frequency hopping integration algorithm to obtain environmental feedback related to frequency hopping to adjust these dynamic monitoring parameters to optimize BLE Bluetooth communication performance and adaptability. The following is a simplified example:

[0070] Adjust the dynamic sampling rate:

[0071] Scenario: During BLE communication, if continuous high interference is detected in a certain frequency band, the frequency hopping algorithm may switch the communication monitoring channel to a relatively clear frequency band.

[0072] Adjustment strategy: According to the interference level of the monitoring channel, the dynamic sampling rate is adjusted. For example, the dynamic sampling rate can be reduced on the monitoring channel with lower interference to save power consumption, while the dynamic sampling rate can be increased on the monitoring channel with higher interference to improve the accuracy and robustness of the data.

[0073] Adjust the dynamic filter coefficients:

[0074] Scenario: Frequency hopping algorithms help devices avoid communicating on noisy listening channels, but the background noise characteristics may be different on different operating listening channels.

[0075] Adjustment strategy: Adaptively adjust the filter coefficients according to the noise characteristics of the current monitoring channel. On monitoring channels with less noise, use a lower filter strength to retain more signal details; on monitoring channels with more noise, increase the filter strength to suppress noise.

[0076] Based on the above scheme, the monitoring parameter adjustment strategy obtained by the frequency hopping integration algorithm adaptively generates new dynamic monitoring parameters for each monitoring channel, including a new dynamic sampling rate and a new dynamic filter coefficient, thereby adaptively adjusting the dynamic monitoring parameters and improving the accuracy of the monitoring parameters.

[0077] Step S700: Replace the original dynamic threshold of the monitoring channel with the new dynamic threshold, and replace the original dynamic monitoring parameter of the monitoring channel with the new dynamic monitoring parameter.

[0078] For each monitoring channel, a new dynamic threshold, a new dynamic sampling rate and a new dynamic filter coefficient replace the original dynamic threshold, the original dynamic sampling rate and the original dynamic filter coefficient of the original monitoring channel, thereby improving the accuracy of the monitoring parameters and ensuring the accuracy of the Bluetooth information collected by the adjusted monitoring channel.

[0079] Step S800: Process RSSI signals corresponding to historical Bluetooth information of multiple monitoring channels through a fusion algorithm to obtain a target Bluetooth RSSI signal.

[0080] In this step, before operating the fusion algorithm, the signal quality and / or signal reliability of each monitoring channel must be identified first. The basic approach is to calculate the average value, standard deviation, and RSSI signal distribution area within the information collection time based on the RSSI signal trend of the historical Bluetooth information of each monitoring channel and the RSSI signal distribution information, thereby defining the signal quality and / or signal reliability of each monitoring channel. More specifically, the higher the RSSI signal average value, the smaller the RSSI signal standard deviation, and the closer the RSSI signal distribution is to the normal distribution, the better the signal quality and signal reliability; otherwise, the worse the signal quality and signal reliability. A relatively simple approach is to set different weights for the RSSI signal average value, RSSI signal standard deviation, and RSSI signal distribution area, and then the weighted total can be used as the signal quality and / or signal reliability weight value of each monitoring channel.

[0081] Optionally, the signal quality and / or signal reliability of each monitoring channel can also be defined by identifying and calculating the RSSI signal trend and RSSI signal distribution information of historical Bluetooth information of each monitoring channel through a neural network or a Kalman filter.

[0082] When the signal quality and / or signal reliability weight value of each monitoring channel are calculated, the RSSI signals (for example, average value) of each monitoring channel are processed through a fusion algorithm to obtain the target Bluetooth RSSI signal.

[0083] Optionally, the fusion algorithm may be to adjust the weight values ​​corresponding to all monitoring channels so that the sum of the weight values ​​is 1, and then perform weighted summation.

[0084] It should be noted that the environmental information of each monitoring channel will change, so the signal quality and / or signal reliability of each monitoring channel in this application is not static, but dynamically changed and adjusted, so as to continuously obtain high-quality target Bluetooth RSSI signals.

[0085] Step S900, returns to execute steps S300 to S800, thereby forming a fast adaptive adjustment process of dynamic threshold, dynamic monitoring parameter, signal quality and / or signal reliability of the monitoring channel, ensuring the correctness and integrity of the collected Bluetooth information and the accuracy of the target Bluetooth RSSI signal.

[0086] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence as indicated by the arrows, these steps are not executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction for the execution of these steps, nor is it necessarily performed at the same time. Some steps can be performed synchronously, and some steps can be swapped one after another. For example: Steps S800 and S700 can be executed synchronously, or S700 can be executed first and then S800, or S800 can be executed first and then S700, and the effect on this application is basically the same. Steps S800 and S700 can be executed synchronously, or S700 can be executed first and then S800, or S800 can be executed first and then S700, and the effect on this application is basically the same. Steps S400 and [S500+S600] may be executed simultaneously, or S400 may be executed first and then [S500+S600], or [S500+S600] may be executed first and then S400, and the effect on the present application is basically the same.

[0087] Based on the above scheme, this embodiment adopts multi-channel monitoring, and can collect effective Bluetooth information by adjusting the dynamic threshold in real time; the dynamic monitoring parameters of each channel are adjusted by integrating Bluetooth CSA1 and CSA2 through the frequency hopping integration algorithm, thereby improving the detection sensitivity and stability of the BLE Bluetooth RSSI signal. By filtering the RSSI signal, noise and interference are reduced, and the signal quality is improved; by adjusting the dynamic monitoring parameters in real time, the obtained Bluetooth information and RSSI signal are more accurate, and the false alarm rate and missed alarm rate are reduced; according to the signal quality and / or signal reliability of the monitoring channel, the weights of each monitoring channel are adjusted in real time to perform RSSI signal fusion, and an accurate and stable target RSSI signal is obtained. After that, the present application improves the transmission quality and anti-interference ability of the monitored Bluetooth information during the frequency hopping process. Finally, because the present application can perform a high-precision signal monitoring process for different Bluetooth monitoring environments, the present application is applicable to various complex signal environments, and has good versatility and practicality, thereby comprehensively improving the accuracy of the RSSI signal of the monitored Bluetooth information.

[0088] The various technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0089] Based on the same inventive concept, the present application further proposes an embodiment, which is a Bluetooth information monitoring system for implementing the above-mentioned Bluetooth information monitoring method. The system includes hardware and software set in the hardware, wherein the hardware includes a BLE Bluetooth device and a computer device, etc. The implementation solution provided by this embodiment to solve the problem is similar to the implementation solution recorded in the above-mentioned Bluetooth information monitoring method, and will not be repeated here.

[0090] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A Bluetooth information monitoring method, characterized in that: The following steps are involved: S100: Acquire a Bluetooth monitoring environment, and generate environmental monitoring parameters and a Bluetooth information collection threshold of the Bluetooth monitoring environment; S200: constructing multiple monitoring channels, each of which is configured with a corresponding dynamic monitoring parameter and a dynamic threshold for collecting Bluetooth information, the initial values ​​of the dynamic monitoring parameters of all the monitoring channels are the environmental monitoring parameters, the initial values ​​of the dynamic thresholds of all the monitoring channels are the Bluetooth information collection thresholds, and the dynamic monitoring parameters include a dynamic sampling rate and a dynamic filter coefficient; S300: Based on the dynamic monitoring parameter and the dynamic threshold, collect information on each monitoring channel through a BLE Bluetooth device, collect historical Bluetooth information and historical environmental information, and the frequency of information collection is not less than 10 times per second; S400: Based on the historical Bluetooth information of each of the monitoring channels, a new dynamic threshold value of each of the monitoring channels is generated by a threshold adjustment algorithm; S500: Based on the historical environment information of each of the monitoring channels and the historical Bluetooth information obtained by each of the monitoring channels, a monitoring parameter adjustment strategy of each of the monitoring channels is generated through a random frequency hopping sequence and a frequency hopping integration algorithm, wherein the monitoring parameter adjustment strategy includes a dynamic sampling rate adjustment strategy and a dynamic filter coefficient adjustment strategy; S600: Generate new dynamic monitoring parameters for each of the monitoring channels through the monitoring parameter adjustment strategy; S700: replacing the original dynamic threshold of the monitoring channel with the new dynamic threshold, and replacing the original dynamic monitoring parameter of the monitoring channel with the new dynamic monitoring parameter; S800: Processing RSSI signals corresponding to the historical Bluetooth information of the plurality of monitoring channels through a fusion algorithm to obtain a target Bluetooth RSSI signal; S900: Return to execute steps S300 to S800.

2. The Bluetooth information monitoring method according to claim 1, characterized in that: The step S400 further includes: Based on the historical Bluetooth information, the RSSI signal trend and RSSI signal distribution information of the historical Bluetooth information are identified, and a new dynamic threshold value of each monitoring channel is generated through a threshold adjustment algorithm; the threshold adjustment algorithm is the average value of the RSSI signal distribution information of the historical Bluetooth information minus a specific multiple of the standard deviation, and the minimum value of the specific multiple is 2; the RSSI signal of the historical Bluetooth information that is less than the dynamic threshold is excluded.

3. The Bluetooth information monitoring method according to claim 2, characterized in that: Identifying the RSSI signal trend of the historical Bluetooth information further includes performing sliding average processing, differential processing, or nonlinear fitting processing on the RSSI signal of the historical Bluetooth information.

4. The Bluetooth information monitoring method according to claim 2, characterized in that: The historical environment information includes historical interference information and historical noise information, and the step S500 further includes: Based on the historical interference information, identifying the interference trend of the historical interference information, the distribution information of the interference intensity, and the interference range to which the historical interference information belongs, and generating the dynamic sampling rate adjustment strategy of the monitoring channel through the frequency hopping integration algorithm; Based on the historical noise information, the noise trend of the historical noise information and the distribution information of the noise intensity are identified, and the dynamic filter coefficient adjustment strategy of the monitoring channel is generated through the frequency hopping integration algorithm.

5. The Bluetooth information monitoring method according to claim 4, characterized in that: The frequency hopping integration algorithm integrates the hopping algorithm and the Bluetooth frequency hopping algorithm, and the hopping algorithm is selected from the group consisting of the following algorithms: adaptive frequency hopping algorithm, fixed frequency hopping algorithm, random frequency hopping algorithm, and hopping algorithm based on data packet transmission success rate; the Bluetooth frequency hopping algorithm also includes the CSA1 frequency hopping algorithm and the CSA2 frequency hopping algorithm.

6. The Bluetooth information monitoring method according to claim 2, characterized in that: In step S800, based on the RSSI signal trend of the historical Bluetooth information of each of the monitoring channels and the RSSI signal distribution information, the signal quality and / or signal reliability of each of the monitoring channels is identified, and based on the signal quality and signal reliability of each of the monitoring channels, the RSSI signal of each of the monitoring channels is processed through a fusion algorithm to obtain the target Bluetooth RSSI signal.

7. The Bluetooth information monitoring method according to claim 6, characterized in that: The fusion algorithm assigns a corresponding weight value to each monitoring channel based on the signal quality and / or signal reliability of each monitoring channel, and then performs weighted summation on the RSSI signals of each monitoring channel to obtain the target Bluetooth RSSI signal.

8. The Bluetooth information monitoring method according to claim 7, characterized in that: The sum of the weight values ​​corresponding to all the monitoring channels is 1.

9. The Bluetooth information monitoring method according to claim 7, characterized in that: In step S800, the signal quality and / or the signal reliability of each of the monitoring channels is calculated by identifying and calculating the RSSI signal trend and the RSSI signal distribution information of the historical Bluetooth information of each of the monitoring channels through a neural network or a Kalman filter.

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