State detection method, device, terminal and storage medium

Abstract

The present application discloses a state detection method, device, terminal and storage medium, the method comprising: obtaining channel indicator data of a device in a target scenario; determining channel change degree indicator data based on the channel indicator data; determining a state detection threshold corresponding to the target scenario based on the channel change degree indicator data and environmental indicator data; detecting the target state of the device in the target scenario through the state detection threshold and the channel change degree indicator data. The present invention measures the channel change degree of the device through channel indicator data, such as RSSI, CSI, etc., and uses environmental indicator data to analyze the current wireless environment, so that the state detection threshold can be adjusted in real time and adaptively in different scenarios to achieve state detection of the device. The present invention is not only simple and convenient, and applicable to different scenarios, but also improves the accuracy of device state detection.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a state detection method, device, terminal and storage medium. Background Art

[0002] Due to the rapid development of positioning services, the demand for mobile detection services for devices is gradually increasing, especially for mobile detection services for devices connected to wireless APs. When a device moves, the channel environment between the device and the wireless AP will change, increasing the difficulty of detecting the device status. Therefore, how to accurately detect the status of the device has become an urgent problem to be solved.

[0003] At present, the status of a device is generally obtained by positioning detection or signal change detection. Positioning detection uses an algorithm to predict whether the device is in a moving state, while signal change detection uses a simple filtering operation and a fixed threshold to determine whether the device is moving.

[0004] However, the above methods are unable to accurately detect the status of the device in different scenarios. Summary of the invention

[0005] The main purpose of the present application is to provide a state detection method, device, terminal and storage medium to solve the problem existing in the related art that the state of the device cannot be accurately detected in different scenarios.

[0006] In order to achieve the above objectives, in a first aspect, the present application provides a state detection method, comprising:

[0007] Obtain channel indicator data of the device in the target scenario;

[0008] Based on the channel indicator data, determining channel change degree indicator data;

[0009] Based on the channel change degree index data and the environmental index data, determine the state detection threshold corresponding to the target scene;

[0010] The target state of the device in the target scenario is detected through the state detection threshold and channel change degree indicator data.

[0011] In a possible implementation, determining a state detection threshold corresponding to a target scene based on the channel change degree index data and the environmental index data includes:

[0012] Based on the channel change degree index data and the environmental index data, determining the target environmental index data and the mapping function, wherein the mapping function is used to characterize the mapping relationship between the channel change degree index data and the environmental index data;

[0013] The target environment indicator data is input into the mapping function and the state detection threshold is output.

[0014] In a possible implementation, determining target environment indicator data and a mapping function based on the channel variation degree indicator data and the environment indicator data includes:

[0015] Extract distribution parameters from environmental indicator data;

[0016] Calculate the correlation between the distribution parameter and the channel change degree index data using the first preset method, and use the distribution parameter corresponding to the maximum correlation value as the target environment index data;

[0017] The target environment index data and the channel change degree index data are fitted using a second preset method to obtain a mapping function.

[0018] In a possible implementation, detecting a target state of a device in a target scenario by using a state detection threshold and channel change degree indicator data includes:

[0019] Compare the state detection threshold and the channel change degree index data;

[0020] If the state detection threshold is less than the channel change degree indicator data, the device is in a mobile state in the target scenario;

[0021] If the state detection threshold is greater than the channel change degree indicator data, the device is in a stationary state in the target scenario.

[0022] In a possible implementation, the channel indicator data includes RSSI data at the nth moment and / or CSI data at the nth moment, and the channel change degree indicator data includes RSSI change amount indicator data at the nth moment and / or CSI change amount indicator data at the nth moment;

[0023] Based on the channel indicator data, channel change degree indicator data is determined, including:

[0024] In the case of n=1, the RSSI data at the nth moment is subjected to noise reduction processing, and based on the processed RSSI data, the RSSI change index data at the nth moment is determined;

[0025] Based on the CSI data at the nth moment, the CSI change indicator data at the nth moment is determined.

[0026] In a possible implementation, the channel indicator data includes RSSI data at the nth moment and / or CSI data at the nth moment, and the channel change degree indicator data includes RSSI change amount indicator data at the nth moment and / or CSI change amount indicator data at the nth moment;

[0027] Based on the channel indicator data, channel change degree indicator data is determined, including:

[0028] When n≧2 and is an integer, the RSSI data at the nth moment is subjected to noise reduction processing, and the processed RSSI data is subtracted from the RSSI data at the n-1th moment to obtain difference data, and the difference data is used as the RSSI change index data at the nth moment;

[0029] The CSI data at the nth moment and the CSI data at the n-1th moment are processed, and the processed CSI data is used as the CSI change amount indicator data at the nth moment.

[0030] In a possible implementation manner, the channel variation degree indicator data includes at least one of RSSI variation amount indicator data and CSI variation amount indicator data.

[0031] In a second aspect, an embodiment of the present invention provides a state detection device, including:

[0032] A data acquisition module is used to obtain channel indicator data of the device in the target scenario;

[0033] A change amount determination module, used to determine channel change degree index data based on the channel index data;

[0034] A threshold determination module, used to determine a state detection threshold corresponding to a target scene based on the channel change degree index data and the environmental index data;

[0035] The state detection module is used to detect the target state of the device in the target scene through the state detection threshold and the channel change degree indicator data.

[0036] In a third aspect, an embodiment of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above state detection methods when executing the computer program.

[0037] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above state detection methods are implemented.

[0038] The embodiment of the present invention provides a state detection method, device, terminal and storage medium, including: obtaining channel indicator data of the device in the target scene, then determining channel change degree indicator data based on the channel indicator data, then determining the state detection threshold corresponding to the target scene based on the channel change degree indicator data and environmental indicator data, and finally detecting the target state of the device in the target scene through the state detection threshold and the channel change degree indicator data. The present invention measures the channel change degree of the device through channel indicator data, such as RSSI, CSI, etc., and uses environmental indicator data to analyze the current wireless environment, so that the state detection threshold can be adjusted in real time and adaptively in different scenes to realize the state detection of the device. The present invention is not only simple and convenient, and applicable to different scenes, but also improves the accuracy of device state detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The schematic embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0040] Figure 1 This is an application scenario diagram of a state detection method provided by an embodiment of the present invention;

[0041] Figure 2 is a flow chart of an implementation of a state detection method provided by an embodiment of the present invention;

[0042] Figure 3 is a relationship diagram of a mapping function provided by an embodiment of the present invention;

[0043] Figure 4 is a structural schematic diagram of a state detection device provided by an embodiment of the present invention;

[0044] Figure 5 is a schematic diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein.

[0047] It should be understood that in various embodiments of the present invention, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0048] It should be understood that in the present invention, "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0049] It should be understood that in the present invention, "plurality" refers to two or more than two. "And / or" is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "Contains A, B and C", "Contains A, B, C" means that A, B, and C are all included, "Contains A, B or C" means that one of A, B, and C is included, and "Contains A, B and / or C" means that any one, any two, or any three of A, B, and C are included.

[0050] It should be understood that in the present invention, "B corresponding to A", "B corresponding to A", "A corresponds to B" or "B corresponds to A" means that B is associated with A and B can be determined based on A. Determining B based on A does not mean determining B based only on A, but B can also be determined based on A and / or other information. A and B match when the similarity between A and B is greater than or equal to a preset threshold.

[0051] Depending on the context, "if" as used herein may be interpreted as "when" or "when" or "in response to determining" or "in response to detecting."

[0052] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0054] The present application provides a state detection method which can be applied to Figure 1 In the application environment shown. Among them, the wireless AP (Access Point) 102 obtains the channel indicator data of the device 104 in the target scene, and then the wireless AP 102 determines the channel change degree indicator data based on the channel indicator data, and determines the state detection threshold corresponding to the target scene based on the channel change degree indicator data and the environmental indicator data, and then detects the target state of the device in the target scene through the state detection threshold and the channel change degree indicator data. Among them, the wireless AP 102 and the device 104 perform wireless communication, and the device 104 is any device that can perform wireless communication with the wireless AP 102, such as a client such as a user's computer, a cloud server, etc.

[0055] In one embodiment, Figure 2 As shown, a state detection method is provided, which is applied to Figure 1 The scenario shown includes the following steps:

[0056] Step S201: Acquire channel indicator data of the device in a target scenario.

[0057] Among them, the target scenario is mainly used to characterize the different channel environments in which the equipment is located, such as shielded environment, home environment (including area size, openness and fullness, different building materials, etc.), dense deployment scenarios (such as shopping malls, stadiums, parks, etc.), outdoor scenes, and other scenarios.

[0058] Channel indicators refer to variables that measure changes in the channel environment, such as RSSI (Received Signal Strength Indicator), latency, throughput, transmission rate, CSI (Channel State Information), and packet loss rate. Since the jitter fluctuation of latency is large, the accuracy rate is very low when used to measure the channel environment; throughput and packet loss rate only have a good reference value when the device has a certain traffic behavior; RSSI and CSI are easy to obtain in real time and can directly reflect the current channel environment, so this application uses RSSI data and CSI data as channel indicator data. In other words, for any channel environment, the channel indicator data obtained by wireless AP102 are the RSSI data and CSI data in that channel environment.

[0059] Step S202: Determine channel change degree indicator data based on the channel indicator data.

[0060] The channel indicator data includes at least one of RSSI data and CSI data, wherein the RSSI data is RSSI data when the device is moving and stationary, and the CSI data is CSI data when the device is moving and stationary. The channel change degree index refers to the degree of change of a variable that measures the change in the channel environment, and the channel change degree index data includes at least one of RSSI change amount index data and CSI change amount index data.

[0061] Since the state detection of the device proposed in this application is performed in real time, the device will obtain the channel indicator data in the target scene at each moment, and determine the channel change degree indicator data at each moment based on the channel indicator data at each moment. Therefore, the process of determining the channel change degree indicator data based on the channel indicator data is described below based on the changes at each moment, as follows:

[0062] In some embodiments, based on the channel indicator data, determining the channel change degree indicator data includes: when n=1, performing noise reduction processing on the RSSI data at the nth moment, and determining the RSSI change amount indicator data at the nth moment based on the processed RSSI data; and determining the CSI change amount indicator data at the nth moment based on the CSI data at the nth moment. Wherein, the channel indicator data includes the RSSI data at the nth moment and / or the CSI data at the nth moment, and the channel change degree indicator data includes the RSSI change amount indicator data at the nth moment and / or the CSI change amount indicator data at the nth moment;

[0063] Specifically, at the first moment, when the channel indicator data is the RSSI data at the first moment, the wireless AP 102 performs noise reduction processing on the RSSI data at the first moment, and obtains the RSSI change indicator data at the first moment based on the processed RSSI data, wherein the noise reduction processing method includes but is not limited to mean filtering, Gaussian filtering, etc. Similarly, at the first moment, when the channel indicator data is the CSI data at the first moment, the wireless AP 102 determines the CSI change indicator data at the first moment based on the CSI data at the first moment.

[0064] When the channel indicator data includes RSSI data and CSI data at the first moment, the wireless AP 102 obtains RSSI change indicator data and CSI change indicator data at the first moment based on the RSSI data and CSI data at the first moment, respectively. The method of determining RSSI change indicator data through RSSI data and determining CSI change indicator data through CSI data is the same as above and will not be repeated here.

[0065] In other embodiments, based on the channel indicator data, the channel change degree indicator data is determined, including: when n≧2 and is an integer, performing noise reduction processing on the RSSI data at the nth moment, and subtracting the processed RSSI data from the RSSI data at the n-1th moment to obtain difference data, and using the difference data as the RSSI change amount indicator data at the nth moment; processing the CSI data at the nth moment and the CSI data at the n-1th moment, and using the processed CSI data as the CSI change amount indicator data at the nth moment. Wherein, the channel indicator data includes the RSSI data at the nth moment and / or the CSI data at the nth moment, and the channel change degree indicator data includes the RSSI change amount indicator data at the nth moment and / or the CSI change amount indicator data at the nth moment.

[0066] Specifically, suppose that at the second moment, when the channel indicator data is the RSSI data at the second moment, the wireless AP102 performs noise reduction processing on the RSSI data at the second moment, and subtracts the processed RSSI data from the RSSI data at the first moment to obtain difference data, and uses the difference data as the RSSI change indicator data at the second moment. Similarly, at the second moment, when the channel indicator data is the CSI data at the second moment, the wireless AP102 processes the CSI data at the second moment and the CSI data at the first moment, and uses the processed CSI data as the CSI change indicator data at the second moment, wherein the processing methods here include but are not limited to covariance, cosine similarity, Pearson correlation coefficient, time reversal focusing, autocorrelation, etc.

[0067] At the second moment, when the channel indicator data includes the RSSI data and the CSI data at the second moment, the wireless AP 102 obtains the RSSI change indicator data and the CSI change indicator data at the second moment based on the RSSI data and the CSI data at the second moment, respectively. The method of determining the RSSI change indicator data through the RSSI data and determining the CSI change indicator data through the CSI data is the same as the calculation method at the second moment, which will not be repeated here.

[0068] The above only specifically explains the calculation method of the channel change degree index data at the second moment. The calculation method of the channel change degree index data at any moment after the second moment, such as the third moment, the fourth moment, etc., is similar to the calculation method of the channel change degree index data at the second moment, and will not be repeated here.

[0069] Step S203: Determine a state detection threshold corresponding to the target scene based on the channel change degree index data and the environmental index data.

[0070] Among them, environmental indicator data refers to environmental-related statistical data of wireless APs in different scenarios, including but not limited to plcp detection errors, AP idle time ratio, background noise, proportion of receiving other BSS data packets, PHY layer packet parsing error ratio, and channel utilization.

[0071] After obtaining the channel variation index data, the wireless AP 102 determines the state detection threshold corresponding to the target scene based on the channel variation index data and the environmental index data. The target environmental index data and the mapping function are mainly determined based on the channel variation index data and the environmental index data. Specifically, the distribution parameters are first extracted from the environmental index data, and then the correlation between the distribution parameters and the channel variation index data is calculated using the first preset method, and the distribution parameters corresponding to the maximum correlation value are used as the target environmental index data, and then the target environmental index data and the channel variation index data are fitted using the second preset method to obtain the mapping function. The mapping function is used to characterize the mapping relationship between the channel variation index data and the environmental index data.

[0072] After determining the target environment index data and the mapping function, the target environment index data is input into the mapping function and the state detection threshold is output. The present application adaptively adjusts the state detection threshold through the channel change degree index data and the environment index data to improve the state detection precision and accuracy in different scenarios.

[0073] The following takes the channel change degree indicator data as RSSI change amount indicator data as an example to explain the process of determining the state detection threshold corresponding to the target scene based on the channel change degree indicator data and the environmental indicator data, as follows:

[0074] The present application analyzes and compares the environmental indicator data and RSSI variation indicator data in different scenarios through wireless AP102, so as to select the target environmental indicator data that can distinguish different scenarios, and determine the correspondence between the state detection threshold and the scene based on the target environmental indicator data and the RSSI variation indicator data, that is, determine the state detection threshold in different scenarios.

[0075] The wireless AP 102 first extracts the corresponding distribution parameters from the environmental indicator data in different scenarios, where the distribution parameters can be a single distribution or a joint distribution, such as the mean of the background noise, the joint distribution parameters of the background noise and the AP idle time ratio, etc. Then the distribution parameters and the RSSI change indicator data in different scenarios are correlated to obtain the calculation results in different scenarios, where the correlation calculation can be determined by common correlation analysis methods, including but not limited to chart analysis, covariance matrix, correlation coefficient method, regression method, and information entropy method. After the calculation results in different scenarios are obtained, the distribution parameter corresponding to the maximum correlation value in the calculation results is used as the target environmental indicator data to determine the target environmental indicator data in different scenarios.

[0076] Specifically, taking the correlation coefficient method as an example, the distribution parameters (such as the background noise mean) and the RSSI change index data are used as one-to-one corresponding points according to the scene and the correlation coefficient (such as Pearson, Spearman and other coefficients) are calculated. After completing the correlation coefficient calculation for all scenes, one or more parameters with higher correlation (such as the result coefficient is closer to 1) are selected from each scene as the target environment indicator data for the scene.

[0077] After determining the target environment index data for each scene, fit the target environment index data and the RSSI change index data at the first moment to obtain a mapping function, that is, analyze the mapping relationship between the RSSI change index data at the first moment and the target environment index data as a basis for adjusting the state detection threshold. Among them, the analysis method may include but is not limited to linear fitting, data fitting, and regression methods. After obtaining the mapping function, substitute the target environment index data as input into the mapping function, and use the output as the state detection threshold.

[0078] by Figure 3 For example, assume that the mapping function is the relationship between the background noise and the RSSI change indicator data Δrssi obtained by logarithmic fitting, specifically: background noise = -5.274ln (Δrssi) -94.036.

[0079] The above mapping relationship shows the relationship between the background noise and the RSSI variation index data Δrssi (ie, the RSSI fluctuation degree), wherein the present application regards the RSSI variation index data Δrssi as the state detection threshold when the real-time background noise is equal to x.

[0080] The above method for determining the state detection threshold is only an example. For example, the fitting method is not limited to logarithmic fitting. Linear, regression and other methods may all obtain a suitable fitting formula or fitting table. The adjustment of the state detection threshold is not limited to one threshold. If the mobile state of the device has multiple definitions, multiple corresponding thresholds can also be divided.

[0081] It should be noted that when the channel change degree index data is CSI change amount index data, the method of determining the state detection threshold corresponding to the target scene is similar to when the channel change degree index data is RSSI change amount index data, and will not be repeated here.

[0082] Step S204: Detect the target state of the device in the target scene through the state detection threshold and the channel change degree index data.

[0083] After the state detection threshold is determined, it is necessary to detect the target state of the device in the target scenario through the state detection threshold and the channel change degree index data. Specifically, the state detection threshold and the channel change degree index data are compared. If the state detection threshold is less than the channel change degree index data, the device is in a mobile state in the target scenario. If the state detection threshold is greater than the channel change degree index data, the device is in a stationary state in the target scenario.

[0084] Taking the mapping function as background noise = -5.274ln(Δrssi)-94.036 as an example, when the real-time background noise is x, the calculated Δrssi is used as the state detection threshold, and then the state detection threshold is compared with the RSSI change indicator data after real-time processing. If the RSSI change indicator data after real-time processing is less than the state detection threshold, it indicates that the device is in a stationary state, otherwise it is in a moving state.

[0085] The embodiment of the present invention provides a state detection method, including: obtaining channel indicator data of a device in a target scenario, then determining channel change degree indicator data based on the channel indicator data, then determining a state detection threshold corresponding to the target scenario based on the channel change degree indicator data and environmental indicator data, and finally detecting the target state of the device in the target scenario through the state detection threshold and the channel change degree indicator data. The present invention measures the channel change degree of the device through channel indicator data, such as RSSI, CSI, etc., and uses environmental indicator data to analyze the current wireless environment, so that the state detection threshold can be adjusted in real time and adaptively in different scenarios to achieve state detection of the device. The present invention is not only simple and convenient, and applicable to different scenarios, but also improves the accuracy of device state detection.

[0086] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0087] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0088] Figure 4A schematic diagram of the structure of a state detection device provided by an embodiment of the present invention is shown. For the convenience of description, only the part related to the embodiment of the present invention is shown. A state detection device includes a data acquisition module 41, a change amount determination module 42, a threshold determination module 43 and a state detection module 44, which are specifically as follows:

[0089] The data acquisition module 41 is used to acquire the channel indicator data of the device in the target scenario;

[0090] A change amount determination module 42 is used to determine channel change degree indicator data based on the channel indicator data;

[0091] A threshold determination module 43, used to determine a state detection threshold corresponding to a target scene based on the channel change degree index data and the environmental index data;

[0092] The state detection module 44 is used to detect the target state of the device in the target scene through the state detection threshold and the channel change degree indicator data.

[0093] In a possible implementation, the threshold determination module 43 includes:

[0094] A function determination submodule, used to determine target environmental index data and a mapping function based on the channel change degree index data and the environmental index data, wherein the mapping function is used to characterize the mapping relationship between the channel change degree index data and the environmental index data;

[0095] The threshold determination submodule is used to input the target environmental indicator data into the mapping function and output the state detection threshold.

[0096] In a possible implementation, the function determination submodule includes:

[0097] A parameter extraction unit, used for extracting distribution parameters from environmental indicator data;

[0098] An environmental indicator determination unit, configured to calculate the correlation between the distribution parameter and the channel variation index data using a first preset method, and use the distribution parameter corresponding to the maximum correlation value as the target environmental indicator data;

[0099] The function determination unit is used to fit the target environment index data and the channel change degree index data using a second preset method to obtain a mapping function.

[0100] In a possible implementation, the state detection module 44 includes:

[0101] A data comparison submodule is used to compare the state detection threshold and the channel change degree index data;

[0102] A mobile state determination submodule, used to determine that if the state detection threshold is less than the channel change degree indicator data, the device is in a mobile state in the target scenario;

[0103] The stationary state determination submodule is used to determine that if the state detection threshold is greater than the channel change degree indicator data, the device is in a stationary state in the target scenario.

[0104] In a possible implementation, the channel indicator data includes RSSI data at the nth moment and / or CSI data at the nth moment, and the channel change degree indicator data includes RSSI change amount indicator data at the nth moment and / or CSI change amount indicator data at the nth moment;

[0105] The variation determination module 42 includes:

[0106] The first variation determination submodule is used to perform noise reduction processing on the RSSI data at the nth moment when n=1, and determine the RSSI variation index data at the nth moment based on the processed RSSI data;

[0107] The second change amount determination submodule is used to determine the CSI change amount indicator data at the nth moment based on the CSI data at the nth moment.

[0108] In a possible implementation, the channel indicator data includes RSSI data at the nth moment and / or CSI data at the nth moment, and the channel change degree indicator data includes RSSI change amount indicator data at the nth moment and / or CSI change amount indicator data at the nth moment;

[0109] The variation determination module 42 includes:

[0110] The third variation determination submodule is used for, when n≧2 and is an integer, performing noise reduction processing on the RSSI data at the nth moment, and subtracting the processed RSSI data from the RSSI data at the n-1th moment to obtain difference data, and using the difference data as the RSSI variation indicator data at the nth moment;

[0111] The fourth variation determination submodule is used to process the CSI data at the nth moment and the CSI data at the (n-1)th moment, and use the processed CSI data as the CSI variation indicator data at the nth moment.

[0112] In a possible implementation manner, the channel variation degree indicator data includes at least one of RSSI variation amount indicator data and CSI variation amount indicator data.

[0113] Figure 5 is a schematic diagram of a terminal provided by an embodiment of the present invention. Figure 5As shown, the terminal 5 of this embodiment includes: a processor 51, a memory 52, and a computer program 53 stored in the memory 52 and executable on the processor 51. When the processor 51 executes the computer program 53, the steps in the above-mentioned various state detection method embodiments are implemented, for example Figure 2 Alternatively, when the processor 51 executes the computer program 53, the functions of each module / unit in the above-mentioned state detection device embodiments are realized, for example Figure 4 Functionality of the modules / units 41 to 44 shown.

[0114] The present invention also provides a readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the state detection methods provided by the various embodiments described above.

[0115] Among them, the readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application-specific integrated circuit (Application Specific Integrated Circuits, abbreviated as: ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the readable storage medium can also exist in a communication device as discrete components. The readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0116] The present invention also provides a program product, which includes an execution instruction, which is stored in a readable storage medium. At least one processor of a device can read the execution instruction from the readable storage medium, and at least one processor executes the execution instruction so that the device implements the state detection method provided by the above various embodiments.

[0117] In the embodiments of the above-mentioned devices, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0118] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A state detection method, characterized in that: include: Obtain channel indicator data of the device in the target scenario; Based on the channel indicator data, determining channel change degree indicator data; Based on the channel change degree index data and the environmental index data, determine the state detection threshold corresponding to the target scenario, wherein the environmental index data refers to the environment-related statistical data of the wireless AP in different scenarios, including plcp detection error, AP idle time ratio, background noise, proportion of receiving other BSS data packets, PHY layer packet parsing error ratio and channel utilization; Detecting a target state of the device in the target scenario by using the state detection threshold and the channel change degree index data; The determining, based on the channel change degree index data and the environmental index data, a state detection threshold corresponding to the target scene includes: Based on the channel change degree index data and the environmental index data, determining target environmental index data and a mapping function, wherein the mapping function is used to characterize a mapping relationship between the channel change degree index data and the environmental index data; Input the target environment indicator data into the mapping function and output the state detection threshold; The determining target environment indicator data and a mapping function based on the channel change degree indicator data and the environment indicator data comprises: extracting distribution parameters from the environmental indicator data; Calculating the correlation between the distribution parameter and the channel change degree index data using a first preset method, and using the distribution parameter corresponding to the maximum correlation value as the target environment index data; Using a second preset method to fit the target environment index data and the channel change degree index data to obtain the mapping function; The detecting the target state of the device in the target scenario by using the state detection threshold and the channel change degree indicator data includes: Comparing the state detection threshold with the channel change degree index data; If the state detection threshold is less than the channel change degree indicator data, the device is in a mobile state in the target scenario; If the state detection threshold is greater than the channel change degree indicator data, the device is in a stationary state in the target scenario.

2. The state detection method according to claim 1, characterized in that: The channel indicator data includes RSSI data at the nth moment and / or CSI data at the nth moment, and the channel change degree indicator data includes RSSI change amount indicator data at the nth moment and / or CSI change amount indicator data at the nth moment; The determining, based on the channel indicator data, channel change degree indicator data comprises: In the case where n=1, the RSSI data at the nth moment is subjected to noise reduction processing, and based on the processed RSSI data, the RSSI change amount indicator data at the nth moment is determined; Based on the CSI data at the nth moment, determine the CSI change indicator data at the nth moment.

3. The state detection method according to claim 1, characterized in that: The channel indicator data includes RSSI data at the nth moment and / or CSI data at the nth moment, and the channel change degree indicator data includes RSSI change amount indicator data at the nth moment and / or CSI change amount indicator data at the nth moment; The determining, based on the channel indicator data, channel change degree indicator data comprises: When n≧2 and is an integer, perform noise reduction processing on the RSSI data at the nth moment, and subtract the processed RSSI data from the RSSI data at the n-1th moment to obtain difference data, and use the difference data as the RSSI change amount indicator data at the nth moment; The CSI data at the nth moment and the CSI data at the (n-1)th moment are processed, and the processed CSI data are used as the CSI change amount indicator data at the nth moment.

4. The state detection method according to claim 1, characterized in that: The channel variation index data includes at least one of RSSI variation index data and CSI variation index data.

5. A state detection device, characterized in that: include: A data acquisition module is used to obtain channel indicator data of the device in the target scenario; A change amount determination module, used to determine channel change degree indicator data based on the channel indicator data; A threshold determination module is used to determine the state detection threshold corresponding to the target scenario based on the channel change degree index data and the environmental index data, wherein the environmental index data refers to the environment-related statistical data of the wireless AP in different scenarios, including plcp detection error, AP idle time ratio, background noise, the proportion of receiving other BSS data packets, PHY layer packet parsing error ratio and channel utilization; A state detection module, used to detect the target state of the device in the target scenario through the state detection threshold and the channel change degree index data; The determining, based on the channel change degree index data and the environmental index data, a state detection threshold corresponding to the target scene includes: Based on the channel change degree index data and the environmental index data, determining target environmental index data and a mapping function, wherein the mapping function is used to characterize a mapping relationship between the channel change degree index data and the environmental index data; Input the target environment indicator data into the mapping function and output the state detection threshold; The determining target environment indicator data and a mapping function based on the channel change degree indicator data and the environment indicator data comprises: extracting distribution parameters from the environmental indicator data; Calculating the correlation between the distribution parameter and the channel change degree index data using a first preset method, and using the distribution parameter corresponding to the maximum correlation value as the target environment index data; Using a second preset method to fit the target environment index data and the channel change degree index data to obtain the mapping function; The detecting the target state of the device in the target scenario by using the state detection threshold and the channel change degree indicator data includes: Comparing the state detection threshold with the channel change degree index data; If the state detection threshold is less than the channel change degree indicator data, the device is in a mobile state in the target scenario; If the state detection threshold is greater than the channel change degree indicator data, the device is in a stationary state in the target scenario.

6. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the state detection method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the state detection method according to any one of claims 1 to 4 are implemented.

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