A method, apparatus, device, and storage medium for region determination

Abstract

The present invention discloses a method, apparatus, device, and storage medium for area determination. The method includes: obtaining the attribute information of a target terminal and the attribute information of a current vehicle; receiving a Bluetooth signal emitted by the target terminal; inputting the attribute information of the target terminal, the attribute information of the current vehicle, and the Bluetooth signal into a target decision tree model to obtain distance information corresponding to the Bluetooth signal, wherein the target decision tree model is obtained by training a neural network model with a target sample set, and the target samples include: attribute information samples of the target terminal, attribute information samples of the current vehicle, Bluetooth signal samples, and distance information corresponding to the Bluetooth signal samples; determining a target area corresponding to the Bluetooth signal according to the distance information corresponding to the Bluetooth signal. By adding sample features, distinguishing different terminal devices and vehicle models, and removing the calibration process, the embodiments of the present invention improve the accuracy, accuracy, and stability of Bluetooth positioning.

Description

Technical Field

[0001] The present invention relates to the technical field of Bluetooth positioning, and in particular, to a method, device, equipment and storage medium for determining a region. Background Art

[0002] Bluetooth digital keys have put forward higher requirements for various aspects of Bluetooth positioning, such as positioning accuracy, latency, region hopping, calibration accuracy loss and other indicators. How to further improve the accuracy of Bluetooth positioning has become the main direction of the development of Bluetooth data keys.

[0003] To meet the positioning requirements of different Bluetooth-connected devices and different vehicle models, recalibration and post-processing fusion are required, which greatly reduces the accuracy of Bluetooth positioning, and the calibration takes a long time and the accuracy is also greatly affected. Summary of the Invention

[0004] The present invention provides a method, device, equipment and storage medium for determining a region, so as to increase sample features, distinguish different terminal devices and vehicle models, remove the calibration process, and improve the accuracy, accuracy and stability of Bluetooth positioning.

[0005] According to one aspect of the present invention, a method for determining a region is provided, and the method includes:

[0006] Obtain the attribute information of the target terminal and the attribute information of the current vehicle;

[0007] Receive the Bluetooth signal sent by the target terminal;

[0008] Input the attribute information of the target terminal, the attribute information of the current vehicle, and the Bluetooth signal into a target decision tree model to obtain distance information corresponding to the Bluetooth signal, where the target decision tree model is obtained by training a neural network model with a target sample set, and the target sample includes: an attribute information sample of the target terminal, an attribute information sample of the current vehicle, a Bluetooth signal sample, and distance information corresponding to the Bluetooth signal sample;

[0009] Determine the target region corresponding to the Bluetooth signal according to the distance information corresponding to the Bluetooth signal.

[0010] According to another aspect of the present invention, a device for determining a region is provided, and the device includes:

[0011] An obtaining module, configured to obtain the attribute information of the target terminal and the attribute information of the current vehicle;

[0012] A receiving module, configured to receive the Bluetooth signal sent by the target terminal;

[0013] An input module, configured to input the attribute information of the target terminal, the attribute information of the current vehicle, and the Bluetooth signal into a target decision tree model to obtain distance information corresponding to the Bluetooth signal, where the target decision tree model is obtained by training a neural network model with a target sample set, and the target sample includes: an attribute information sample of the target terminal, an attribute information sample of the current vehicle, a Bluetooth signal sample, and distance information corresponding to the Bluetooth signal sample;

[0014] A determination module, configured to determine a target area corresponding to the Bluetooth signal according to the distance information corresponding to the Bluetooth signal.

[0015] According to another aspect of the present invention, there is provided an electronic device, including:

[0016] At least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the area determination method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the area determination method according to any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention obtains the attribute information of the target terminal and the attribute information of the current vehicle, receives the Bluetooth signal sent by the target terminal, inputs the attribute information of the target terminal, the attribute information of the current vehicle, and the Bluetooth signal into the target decision tree model to obtain the distance information corresponding to the Bluetooth signal, and determines the target area corresponding to the Bluetooth signal according to the distance information corresponding to the Bluetooth signal. The technical solution of the embodiment of the present invention has the beneficial effects of improving the accuracy, accuracy, and stability of Bluetooth positioning by adding sample features, distinguishing different terminal devices and vehicle models, and removing the calibration process.

[0019] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Description of the Drawings

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 is a flowchart of a method for determining a region provided in Embodiment 1 of the present invention;

[0022] Figure 2 is a schematic structural diagram of a device for determining a region provided in Embodiment 2 of the present invention;

[0023] Figure 3 is a schematic structural diagram of an electronic device for implementing the method for determining a region in an embodiment of the present invention. Detailed implementation manners

[0024] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0025] It should be noted that the terms "first", "target", etc. in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0026] Embodiment 1

[0027] The existing Bluetooth positioning technology generally uses an empirical formula for converting RSSI to distance for Bluetooth positioning:

[0028]

[0029]

[0030] where d represents the calculated Bluetooth distance (unit: meter); RSSI(d) represents the signal strength received at a distance of d (unit: dBm); d0 is the reference distance, generally taken as 1 meter; n represents the environmental attenuation factor, which is affected by the terminal device, the vehicle itself, and the environment; N σrepresents random noise, which follows a normal distribution; A represents the signal strength when the transmitting end (terminal device) and the receiving end (vehicle) are 1 meter apart.

[0031] From the above two equations, it can be known that the corresponding distance d can be obtained directly from the three parameters of RSSI(d), A, and n. However, for the calibration of different vehicle models and different terminal devices, the parameters A and n are not the same, and it is necessary to calibrate the data for each different situation, so as to obtain the corresponding calibration parameters A and n for each Bluetooth, and the post-processing of positioning needs to be adjusted, which greatly affects the positioning accuracy.

[0032] The technical solution of the embodiment of the present invention adds two sample features, namely the attribute information of the target terminal and the attribute information of the current vehicle, on the basis of the prior art, distinguishes different terminal devices and vehicle models, removes the calibration process, and improves the accuracy, accuracy and stability of Bluetooth positioning.

[0033] Figure 1 is a flowchart of a method for determining a region provided by Embodiment 1 of the present invention. This embodiment is applicable to the situation of region determination. This method can be executed by a region determination device, and the region determination device can be implemented in the form of hardware and / or software. The region determination device can be integrated in any electronic device that provides the function of region determination. As Figure 1 shown, the method includes:

[0034] S101. Obtain the attribute information of the target terminal and the attribute information of the current vehicle.

[0035] In this embodiment, the target terminal may be an intelligent terminal device that establishes a Bluetooth connection with the current vehicle. Exemplarily, the target terminal may include, but is not limited to, a smart phone, an iPad, a laptop computer, etc. Among them, the attribute information of the target terminal may be information such as the brand name, version model, and / or device code of the target terminal.

[0036] In this embodiment, the current vehicle may be the vehicle that currently establishes a Bluetooth connection with the target terminal. Among them, the attribute information of the current vehicle may be information such as the brand name and / or vehicle model of the current vehicle.

[0037] In the embodiments of the present invention, two sample features, namely the attribute information of the target terminal and the attribute information of the current vehicle, are added, which can distinguish different terminal devices and vehicle models. Specifically, the attribute information of the target terminal and the attribute information of the current vehicle are obtained, a mapping table is established between the attribute information of the target terminal and the attribute information of the current vehicle, and the attribute information of the target terminal and the attribute information of the current vehicle are formed into digital codes. Exemplarily, n target terminals can be digitally coded. For example, target terminal 1 can correspond to digital code 1, target terminal 2 can correspond to digital code 2,..., target terminal n can correspond to digital code n; similarly, m vehicles can be digitally coded. For example, vehicle 1 can correspond to digital code 1, vehicle 2 can correspond to digital code 2,..., vehicle m can correspond to digital code m. Then, the two groups of digital codes can be stored in the form of lists respectively.

[0038] S102. Receive the Bluetooth signal sent by the target terminal.

[0039] Among them, the Bluetooth signal can be the Bluetooth signal sent by the target terminal to the current vehicle.

[0040] Specifically, receive the Bluetooth signal sent by the target terminal to the current vehicle. After that, the received Bluetooth signal can be associated with the target terminal and the current vehicle that sent the Bluetooth signal, that is, the signal strength of the received Bluetooth signal can be associated with the attribute information of the target terminal and the attribute information of the current vehicle that sent the Bluetooth signal.

[0041] S103. Input the attribute information of the target terminal, the attribute information of the current vehicle, and the Bluetooth signal into the target decision tree model to obtain the distance information corresponding to the Bluetooth signal.

[0042] It can be known that decision tree classification is a tree-shaped classification algorithm that uses the recursive idea to continuously divide. In the training stage, the current sample is divided by calculating the Gini index of a certain attribute in the sample, thereby forming the judgment condition of the current node. In the prediction stage, it is equivalent to a conditional judgment, and the prediction speed is fast.

[0043] In this embodiment, the target decision tree model can be a model used to obtain the distance information corresponding to the Bluetooth signal according to the attribute information of the target terminal, the attribute information of the current vehicle, and the Bluetooth signal.

[0044] Among them, the target decision tree model is obtained by training a neural network model with a target sample set.

[0045] Among them, the target samples include: the attribute information samples of the target terminal, the attribute information samples of the current vehicle, the Bluetooth signal samples, and the distance information corresponding to the Bluetooth signal samples.

[0046] In the implementation process, the digital codes corresponding to the target terminal and the current vehicle can be used to distinguish the Bluetooth signal samples received by different target terminals and vehicles, improving the compatibility with different target terminals and vehicle models and enhancing the positioning accuracy.

[0047] Exemplarily, the correspondence between the attribute information samples of some target terminals, the attribute information samples of the current vehicle, and the Bluetooth signal samples in the target samples can be represented as shown in Table 1.

[0048] It should be noted that the first 8 columns of fields rssi_1 to 8 in Table 1 can represent the Bluetooth signal strengths of 8 Bluetooth signals (unit: dBm), and the latter two fields are the digital codes corresponding to the target terminal and the current vehicle respectively. Exemplarily, the digital code 1 corresponding to the target terminal can represent target terminal 1, such as a smartphone of XX brand and XX model, and the digital code 1 corresponding to the current vehicle can represent vehicle 1, such as a car of XX brand and XX model.

[0049] Table 1

[0050]

[0051] In this embodiment, the distance information corresponding to the Bluetooth signal can be understood as the distance information between the target terminal and the current vehicle, because the Bluetooth signal is emitted by the target terminal. Exemplarily, the distance information corresponding to the Bluetooth signal can be 3 meters, that is, the distance between the target terminal and the current vehicle is 3 meters.

[0052] Specifically, the trained target decision tree model is obtained by training the neural network model with the attribute information samples of the target terminals, the attribute information samples of the current vehicle, the Bluetooth signal samples, and the distance information corresponding to the Bluetooth signal samples in the target sample set. The attribute information of the target terminal, the attribute information of the current vehicle, and the Bluetooth signal are input into the target decision tree model to obtain the distance information corresponding to the Bluetooth signal.

[0053] S104. Determine the target area corresponding to the Bluetooth signal according to the distance information corresponding to the Bluetooth signal.

[0054] In this embodiment, different areas are divided according to the different distances between the target terminal and the current vehicle. Specifically, the division criteria for different distance areas can be set by the user according to the actual situation, and this embodiment does not limit this.

[0055] Among them, the target area corresponding to the Bluetooth signal can be the area where the target terminal that emits the Bluetooth signal is located.

[0056] Specifically, determine the target area corresponding to the Bluetooth signal according to the distance information corresponding to the Bluetooth signal, that is, the target area where the target terminal that emits the Bluetooth signal is located.

[0057] In the technical solution of the embodiment of the present invention, the attribute information of the target terminal and the attribute information of the current vehicle are obtained, the Bluetooth signal sent by the target terminal is received, the attribute information of the target terminal, the attribute information of the current vehicle, and the Bluetooth signal are input into the target decision tree model to obtain the distance information corresponding to the Bluetooth signal, and the target area corresponding to the Bluetooth signal is determined according to the distance information corresponding to the Bluetooth signal. In the technical solution of the embodiment of the present invention, by adding sample features, different terminal devices and vehicle models are distinguished, the calibration process is removed, and the beneficial effects of improving the accuracy, accuracy and stability of Bluetooth positioning are achieved.

[0058] Optionally, determining the target area corresponding to the Bluetooth signal according to the distance information corresponding to the Bluetooth signal includes:

[0059] Determine the current prediction area corresponding to the Bluetooth signal according to the distance information corresponding to the Bluetooth signal.

[0060] It should be noted that the current prediction area may be the current area where the Bluetooth signal is located predicted by the target decision tree model according to the attribute information of the target terminal, the attribute information of the current vehicle, and the Bluetooth signal.

[0061] Specifically, the target decision tree model makes a prediction according to the attribute information of the target terminal, the attribute information of the current vehicle, and the Bluetooth signal, outputs the distance information corresponding to the Bluetooth signal, and then determines the current prediction area corresponding to the Bluetooth signal according to the predicted distance information corresponding to the Bluetooth signal.

[0062] If the current operation is the first area determination operation, the current prediction area corresponding to the Bluetooth signal is determined as the target area corresponding to the Bluetooth signal.

[0063] It should be explained that the first area determination operation can be understood as that no area determination has been performed before, and this area determination is the first operation, that is, this operation occurs at the initial moment.

[0064] Specifically, if the current operation is the first area determination operation, the current prediction area corresponding to the Bluetooth signal is directly determined as the target area corresponding to the Bluetooth signal.

[0065] Optionally, the area determination method further includes:

[0066] Obtain the area at the (N-1)th moment and the area at the (N-2)th moment corresponding to the Bluetooth signal received at the Nth moment.

[0067] Where N is a positive integer greater than or equal to 2.

[0068] In this embodiment, if the current operation is not the first area determination operation, the moment of the current operation can be represented as the Nth moment. Then the previous moment of the Nth moment is the (N - 1)th moment, and the area corresponding to the (N - 1)th moment is the area of the (N - 1)th moment; similarly, the previous moment of the (N - 1)th moment is the (N - 2)th moment, and the area corresponding to the (N - 2)th moment is the area of the (N - 2)th moment.

[0069] Specifically, if the current operation is not the first area determination operation, obtain the area of the (N - 1)th moment and the area of the (N - 2)th moment corresponding to the Bluetooth signal received at the Nth moment.

[0070] Determine the target area corresponding to the Bluetooth signal based on the area of the (N - 1)th moment, the area of the (N - 2)th moment corresponding to the Bluetooth signal received at the Nth moment, and the area of the Nth moment corresponding to the Bluetooth signal received at the Nth moment.

[0071] Specifically, if the current operation is not the first area determination operation, determine the target area corresponding to the Bluetooth signal based on the area of the (N - 1)th moment, the area of the (N - 2)th moment corresponding to the Bluetooth signal received at the Nth moment, and the area of the Nth moment corresponding to the Bluetooth signal received at the Nth moment.

[0072] Optionally, determining the target area corresponding to the Bluetooth signal based on the area of the (N - 1)th moment, the area of the (N - 2)th moment corresponding to the Bluetooth signal received at the Nth moment, and the area of the Nth moment corresponding to the Bluetooth signal received at the Nth moment includes:

[0073] If the area of the Nth moment corresponding to the Bluetooth signal received at the Nth moment is the same as the area of the (N - 1)th moment, determine the area of the Nth moment corresponding to the Bluetooth signal received at the Nth moment as the target area corresponding to the Bluetooth signal.

[0074] Specifically, if the area of the Nth moment corresponding to the Bluetooth signal received at the Nth moment, that is, the current predicted area corresponding to the Bluetooth signal at the Nth moment, is the same as the area of the (N - 1)th moment, it indicates that the area corresponding to the Bluetooth signal detected between the (N - 1)th moment and the current moment, i.e., the Nth moment, has not changed. Then determine the area of the Nth moment corresponding to the Bluetooth signal received at the Nth moment as the target area corresponding to the Bluetooth signal.

[0075] If the area of the Nth moment corresponding to the Bluetooth signal received at the Nth moment is different from the area of the (N - 1)th moment, and the area of the (N - 1)th moment is the same as the area of the (N - 2)th moment, determine the area of the (N - 1)th moment corresponding to the Bluetooth signal received at the Nth moment as the target area corresponding to the Bluetooth signal.

[0076] Specifically, if the area corresponding to the Bluetooth signal received at the Nth moment is different from the area at the (N - 1)th moment, that is, the current predicted area corresponding to the Bluetooth signal at the Nth moment is different from the area at the (N - 1)th moment, and the area at the (N - 1)th moment is the same as the area at the (N - 2)th moment, it indicates that the area corresponding to the Bluetooth signal detected between the (N - 1)th moment and the current moment (i.e., the Nth moment) has had a first jump. Then, the area at the (N - 1)th moment corresponding to the Bluetooth signal received at the Nth moment is determined as the target area corresponding to the Bluetooth signal, that is, the area of the previous moment (i.e., the (N - 1)th moment) is retained unchanged once first.

[0077] If the area corresponding to the Bluetooth signal received at the Nth moment is different from the area at the (N - 1)th moment, and the area at the (N - 1)th moment is different from the area at the (N - 2)th moment, then the area corresponding to the Bluetooth signal received at the Nth moment is determined as the target area corresponding to the Bluetooth signal.

[0078] Specifically, if the area corresponding to the Bluetooth signal received at the Nth moment is different from the area at the (N - 1)th moment, that is, the current predicted area corresponding to the Bluetooth signal at the Nth moment is different from the area at the (N - 1)th moment, and the area at the (N - 1)th moment is different from the area at the (N - 2)th moment, it indicates that the area corresponding to the Bluetooth signal has had a jump between the (N - 1)th moment and the (N - 2)th moment, and another jump has occurred in the area corresponding to the Bluetooth signal detected between the Nth moment and the (N - 1)th moment. Then, the area corresponding to the Bluetooth signal received at the Nth moment is determined as the target area corresponding to the Bluetooth signal.

[0079] The above operation method for determining the target area corresponding to the Bluetooth signal based on the distance information corresponding to the Bluetooth signal can be summarized as performing post - processing with a secondary lock on the prediction result obtained from the target decision tree model, aiming to improve the stability of target area determination. Now, a specific example is used to illustrate the post - processing operation with a secondary lock:

[0080] Table 2

[0081] The Nth moment 1 2 3 4 5 6 7 8 9 10 11 12 The regional state value at the (N - 1)th moment -1 1 1 2 2 2 2 2 1 1 1 1 The regional state value at the Nth moment 1 2 2 1 2 2 1 1 1 1 2 1 The target regional state value 1 1 2 2 2 2 2 1 1 1 1 1

[0082] As shown in Table 2, the first row of numbers in Table 2 represents the Nth moment, where N = 1, 2, ……, 12. The second row of numbers in Table 2 represents the state value of the region at the (N - 1)th moment. The third row of numbers in Table 2 represents the state value of the region at the Nth moment, that is, the state value of the current predicted region. The fourth row of numbers in Table 2 represents the state value of the finally determined actual target region. Among them, the state values 1 and 2 are used to distinguish whether the regions determined at two adjacent moments have changed. That is, assuming that the region determined at the previous moment is Region A and the region state value at the previous moment is set to 1, if the region determined at the current moment is still Region A, then the region state value at the current moment is still 1; if the region determined at the current moment is Region B, then the region state value at the current moment becomes 2. For the 1st moment, there is no previous moment, so the region state value at the 0th moment can be set to -1.

[0083] For the 1st moment, since the current operation is the first region determination operation, the current predicted region corresponding to the Bluetooth signal is directly determined as the target region corresponding to the Bluetooth signal. For the 2nd moment, since the region at the 1st moment and the region at the 2nd moment are different, and the operation at the 1st moment is the first region determination operation, it means that the region corresponding to the Bluetooth signal detected between the 2nd moment and the 1st moment has undergone the first jump. Then, the region at the 1st moment is still determined as the target region corresponding to the Bluetooth signal, that is, the region at the previous moment (i.e., the 1st moment) is retained unchanged once. For the 3rd moment, since the region at the 2nd moment and the region at the 3rd moment are different, and the region at the 1st moment and the region at the 2nd moment are different, it means that the region corresponding to the Bluetooth signal has undergone a jump between the 1st moment and the 2nd moment, and the region corresponding to the Bluetooth signal detected between the 2nd moment and the 3rd moment has undergone another jump. Then, the region at the 3rd moment is determined as the target region corresponding to the Bluetooth signal at this time. And so on, the same applies to the subsequent 4th, 5th, ……, 12th moments, which will not be elaborated here one by one.

[0084] Optionally, training the neural network model through the target sample set includes:

[0085] Establish a neural network model.

[0086] In this embodiment, the neural network model can specifically be a decision tree classification model.

[0087] Input the attribute information samples of the target terminals, the attribute information samples of the current vehicle, and the Bluetooth signal samples in the target sample set into the neural network model to obtain the predicted distance information corresponding to the Bluetooth signal samples.

[0088] Among them, the predicted distance information can be the distance information corresponding to the Bluetooth signal samples predicted by the neural network model based on the attribute information samples of the target terminals, the attribute information samples of the current vehicle, and the Bluetooth signal samples in the target sample set.

[0089] Specifically, associate the attribute information samples of the target terminals in the target sample set, the attribute information samples of the current vehicle, and the Bluetooth signal samples. Distinguish the Bluetooth signal samples through the attribute information samples of the target terminals and the attribute information samples of the current vehicle, and then input them into the neural network model for prediction to obtain the predicted distance information corresponding to the Bluetooth signal samples.

[0090] Train the parameters of the neural network model according to the distance information and the predicted distance information corresponding to the Bluetooth signal samples.

[0091] Specifically, train the parameters of the neural network model according to the objective function formed by the distance information and the predicted distance information corresponding to the Bluetooth signal samples.

[0092] Return to perform the operation of inputting the attribute information samples of the target terminals in the target sample set, the attribute information samples of the current vehicle, and the Bluetooth signal samples into the neural network model to obtain the predicted distance information corresponding to the Bluetooth signal samples, until the target decision tree model is obtained.

[0093] Specifically, return to perform the operation of inputting the attribute information samples of the target terminals in the target sample set, the attribute information samples of the current vehicle, and the Bluetooth signal samples into the neural network model to obtain the predicted distance information corresponding to the Bluetooth signal samples, and continuously train the parameters of the neural network model until the trained target decision tree model is obtained.

[0094] Optionally, the target area includes at least one of: the in-vehicle area, the unlocking area, the reserved area, the locking area, the welcome area, and the connection area.

[0095] In this embodiment, different areas can be divided according to the different distances of the target terminal from the current vehicle. Exemplarily, the division criteria for different distance areas and the names of each area can be:

[0096] Area 1: In-vehicle area, distance: <0 meters;

[0097] Area 2: Unlocking area, distance: 0 - 2 meters;

[0098] Area 3: Reserved area, distance: 2 - 3 meters;

[0099] Area 4: Locking area, distance: 3 - 6 meters;

[0100] Area 5: Welcome area, distance: 6 - 10 meters;

[0101] Area 6: Connection area, distance: >10 meters.

[0102] It should be noted that: the above distance refers to the distance between the target terminal and the edge of the vehicle body.

[0103] The technical solution of the embodiment of the present invention adds digital coding labels to different terminals and vehicle models during the data collection process, associates the received Bluetooth signal with the target terminal and the current vehicle that emits the Bluetooth signal, adds these two features to the sample itself, increases the sample features of the target decision tree model, can distinguish different terminal devices and vehicle models, eliminates the calibration process, improves the accuracy, precision, and stability of Bluetooth positioning, and saves manpower and resource investment.

[0104] Embodiment 2

[0105] Figure 2 is a schematic structural diagram of a region determination device provided according to Embodiment 2 of the present invention. As Figure 2 shown, the device includes: an acquisition module 201, a reception module 202, an input module 203, and a determination module 204.

[0106] Among them, the acquisition module 201 is configured to acquire the attribute information of the target terminal and the attribute information of the current vehicle;

[0107] The reception module 202 is configured to receive the Bluetooth signal emitted by the target terminal;

[0108] The input module 203 is configured to input the attribute information of the target terminal, the attribute information of the current vehicle, and the Bluetooth signal into a target decision tree model to obtain the distance information corresponding to the Bluetooth signal, where the target decision tree model is obtained by training a neural network model with a target sample set, and the target sample includes: an attribute information sample of the target terminal, an attribute information sample of the current vehicle, a Bluetooth signal sample, and the distance information corresponding to the Bluetooth signal sample;

[0109] The determination module 204 is configured to determine the target region corresponding to the Bluetooth signal according to the distance information corresponding to the Bluetooth signal.

[0110] Optionally, the determination module 204 includes:

[0111] A first determination unit, configured to determine the current prediction region corresponding to the Bluetooth signal according to the distance information corresponding to the Bluetooth signal;

[0112] A second determination unit, configured to, if the current operation is the first region determination operation, determine the current prediction region corresponding to the Bluetooth signal as the target region corresponding to the Bluetooth signal.

[0113] Optionally, the region determination device further includes:

[0114] An acquisition unit, configured to acquire the region at the (N - 1)th moment and the region at the (N - 2)th moment corresponding to the Bluetooth signal received at the Nth moment, where N is a positive integer greater than or equal to 2;

[0115] A third determination unit, configured to determine a target area corresponding to the Bluetooth signal according to an area at the (N - 1)th moment, an area at the (N - 2)th moment corresponding to the Bluetooth signal received at the Nth moment, and an area at the Nth moment corresponding to the Bluetooth signal received at the Nth moment.

[0116] Optionally, the third determination unit includes:

[0117] A first determination subunit, configured to, if the area at the Nth moment corresponding to the Bluetooth signal received at the Nth moment is the same as the area at the (N - 1)th moment, determine the area at the Nth moment corresponding to the Bluetooth signal received at the Nth moment as the target area corresponding to the Bluetooth signal;

[0118] A second determination subunit, configured to, if the area at the Nth moment corresponding to the Bluetooth signal received at the Nth moment is different from the area at the (N - 1)th moment, and the area at the (N - 1)th moment is the same as the area at the (N - 2)th moment, determine the area at the (N - 1)th moment corresponding to the Bluetooth signal received at the Nth moment as the target area corresponding to the Bluetooth signal;

[0119] A third determination subunit, configured to, if the area at the Nth moment corresponding to the Bluetooth signal received at the Nth moment is different from the area at the (N - 1)th moment, and the area at the (N - 1)th moment is different from the area at the (N - 2)th moment, determine the area at the Nth moment corresponding to the Bluetooth signal received at the Nth moment as the target area corresponding to the Bluetooth signal.

[0120] Optionally, the input module 203 includes:

[0121] An establishment unit, configured to establish a neural network model;

[0122] An input unit, configured to input an attribute information sample of a target terminal, an attribute information sample of a current vehicle, and a Bluetooth signal sample in the target sample set into the neural network model to obtain predicted distance information corresponding to the Bluetooth signal sample;

[0123] A training unit, configured to train parameters of the neural network model according to distance information corresponding to the Bluetooth signal sample and the predicted distance information;

[0124] An execution unit, configured to return and execute an operation of inputting an attribute information sample of a target terminal, an attribute information sample of a current vehicle, and a Bluetooth signal sample in the target sample set into the neural network model to obtain predicted distance information corresponding to the Bluetooth signal sample until a target decision tree model is obtained.

[0125] Optionally, the target area includes at least one of an in-vehicle area, an unlocking area, a reserved area, a locking area, a welcome area, and a connection area.

[0126] The area determination device provided by the embodiments of the present invention can execute the area determination method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0127] Embodiment III

[0128] Figure 3 FIG. shows a schematic structural diagram of an electronic device 30 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0129] As Figure 3 shown, the electronic device 30 includes at least one processor 31, and a memory communicatively connected to the at least one processor 31, such as a read-only memory (ROM) 32, a random access memory (RAM) 33, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 31 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 32 or the computer program loaded from the storage unit 38 into the random access memory (RAM) 33. In the RAM 33, various programs and data required for the operation of the electronic device 30 can also be stored. The processor 31, the ROM 32, and the RAM 33 are connected to each other through a bus 34. The input / output (I / O) interface 35 is also connected to the bus 34.

[0130] Multiple components in the electronic device 30 are connected to the I / O interface 35, including: an input unit 36, such as a keyboard, a mouse, etc.; an output unit 37, such as various types of displays, speakers, etc.; a storage unit 38, such as a magnetic disk, an optical disc, etc.; and a communication unit 39, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 39 allows the electronic device 30 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0131] The processor 31 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 31 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 31 executes the various methods and processes described above, such as the area determination method:

[0132] Obtain the attribute information of the target terminal and the attribute information of the current vehicle;

[0133] Receive the Bluetooth signal emitted by the target terminal;

[0134] Input the attribute information of the target terminal, the attribute information of the current vehicle, and the Bluetooth signal into a target decision tree model to obtain the distance information corresponding to the Bluetooth signal, where the target decision tree model is obtained by training a neural network model with a target sample set, and the target samples include: attribute information samples of the target terminal, attribute information samples of the current vehicle, Bluetooth signal samples, and the distance information corresponding to the Bluetooth signal samples;

[0135] Determine the target area corresponding to the Bluetooth signal according to the distance information corresponding to the Bluetooth signal.

[0136] In some embodiments, the area determination method can be implemented as a computer program, which is tangibly included in a computer-readable storage medium, such as the storage unit 38. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 30 via the ROM 32 and / or the communication unit 39. When the computer program is loaded into the RAM 33 and executed by the processor 31, one or more steps of the area determination method described above can be executed. Alternatively, in other embodiments, the processor 31 can be configured to execute the area determination method by any other suitable means (e.g., by means of firmware).

[0137] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0138] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0139] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0140] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0141] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0142] A computing system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship between the client and the server is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0143] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0144] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for determining a region, characterized in that Including: Obtain the attribute information of the target terminal and the attribute information of the current vehicle; Receive the Bluetooth signal emitted by the target terminal; Input the attribute information of the target terminal, the attribute information of the current vehicle, and the Bluetooth signal into the target decision tree model to obtain the distance information corresponding to the Bluetooth signal, where the target decision tree model is obtained by training a neural network model with a target sample set, and the target samples include: attribute information samples of the target terminal, attribute information samples of the current vehicle, Bluetooth signal samples, and the distance information corresponding to the Bluetooth signal samples; Determine the target area corresponding to the Bluetooth signal according to the distance information corresponding to the Bluetooth signal; Wherein, the area determination method further includes: Obtain the area of the (N - 1)th moment and the area of the (N - 2)th moment corresponding to the Bluetooth signal received at the Nth moment, where N is a positive integer greater than or equal to 2; Determine the target area corresponding to the Bluetooth signal according to the area of the (N - 1)th moment corresponding to the Bluetooth signal received at the Nth moment, the area of the (N - 2)th moment, and the area of the Nth moment corresponding to the Bluetooth signal received at the Nth moment; Wherein, determining the target area corresponding to the Bluetooth signal according to the area of the (N - 1)th moment corresponding to the Bluetooth signal received at the Nth moment, the area of the (N - 2)th moment, and the area of the Nth moment corresponding to the Bluetooth signal received at the Nth moment includes: If the area of the Nth moment corresponding to the Bluetooth signal received at the Nth moment is the same as the area of the (N - 1)th moment, then determine the area of the Nth moment corresponding to the Bluetooth signal received at the Nth moment as the target area corresponding to the Bluetooth signal; If the area of the Nth moment corresponding to the Bluetooth signal received at the Nth moment is different from the area of the (N - 1)th moment, and the area of the (N - 1)th moment is the same as the area of the (N - 2)th moment, then determine the area of the (N - 1)th moment corresponding to the Bluetooth signal received at the Nth moment as the target area corresponding to the Bluetooth signal; If the area of the Nth moment corresponding to the Bluetooth signal received at the Nth moment is different from the area of the (N - 1)th moment, and the area of the (N - 1)th moment is different from the area of the (N - 2)th moment, then determine the area of the Nth moment corresponding to the Bluetooth signal received at the Nth moment as the target area corresponding to the Bluetooth signal.

2. The method according to claim 1, wherein Determining the target area corresponding to the Bluetooth signal according to the distance information corresponding to the Bluetooth signal includes: Determine the current prediction area corresponding to the Bluetooth signal according to the distance information corresponding to the Bluetooth signal; If the current operation is the first area determination operation, then determine the current prediction area corresponding to the Bluetooth signal as the target area corresponding to the Bluetooth signal.

3. The method according to claim 1, wherein Training the neural network model with the target sample set includes: Establish a neural network model; Input the attribute information samples of the target terminal, the attribute information samples of the current vehicle, and the Bluetooth signal samples in the target sample set into the neural network model to obtain the predicted distance information corresponding to the Bluetooth signal samples; Train the parameters of the neural network model according to the distance information corresponding to the Bluetooth signal samples and the predicted distance information; Return to perform the operation of inputting the attribute information sample of the target terminal, the attribute information sample of the current vehicle, and the Bluetooth signal sample in the target sample set into the neural network model to obtain the predicted distance information corresponding to the Bluetooth signal sample until the target decision tree model is obtained.

4. The method according to claim 1, wherein The target area includes at least one of: an in-vehicle area, an unlocking area, a reserved area, a locking area, a welcoming area, and a connection area.

5. A region determination device, characterized in that, Including: An acquisition module, configured to acquire the attribute information of the target terminal and the attribute information of the current vehicle; A receiving module, configured to receive the Bluetooth signal emitted by the target terminal; An input module, configured to input the attribute information of the target terminal, the attribute information of the current vehicle, and the Bluetooth signal into the target decision tree model to obtain the distance information corresponding to the Bluetooth signal, where the target decision tree model is obtained by training a neural network model with a target sample set, and the target sample includes: an attribute information sample of the target terminal, an attribute information sample of the current vehicle, a Bluetooth signal sample, and the distance information corresponding to the Bluetooth signal sample; A determination module, configured to determine the target area corresponding to the Bluetooth signal according to the distance information corresponding to the Bluetooth signal; Wherein, the area determination device further includes: An acquisition unit, configured to acquire the area at the (N - 1)th moment and the area at the (N - 2)th moment corresponding to the Bluetooth signal received at the Nth moment, where N is a positive integer greater than or equal to 2; A third determination unit, configured to determine the target area corresponding to the Bluetooth signal according to the area at the (N - 1)th moment, the area at the (N - 2)th moment corresponding to the Bluetooth signal received at the Nth moment, and the area at the Nth moment corresponding to the Bluetooth signal received at the Nth moment; Wherein, the third determination unit includes: A first determination subunit, configured to, if the area at the Nth moment corresponding to the Bluetooth signal received at the Nth moment is the same as the area at the (N - 1)th moment, determine the area at the Nth moment corresponding to the Bluetooth signal received at the Nth moment as the target area corresponding to the Bluetooth signal; A second determination subunit, configured to, if the area at the Nth moment corresponding to the Bluetooth signal received at the Nth moment is different from the area at the (N - 1)th moment, and the area at the (N - 1)th moment is the same as the area at the (N - 2)th moment, determine the area at the (N - 1)th moment corresponding to the Bluetooth signal received at the Nth moment as the target area corresponding to the Bluetooth signal; A third determination subunit, configured to, if the area at the Nth moment corresponding to the Bluetooth signal received at the Nth moment is different from the area at the (N - 1)th moment, and the area at the (N - 1)th moment is different from the area at the (N - 2)th moment, determine the area at the Nth moment corresponding to the Bluetooth signal received at the Nth moment as the target area corresponding to the Bluetooth signal.

6. The device according to claim 5, characterized in that, The determination module includes: A first determination unit, configured to determine the current predicted area corresponding to the Bluetooth signal according to the distance information corresponding to the Bluetooth signal; A second determination unit, configured to, if the current operation is the first area determination operation, determine the current predicted area corresponding to the Bluetooth signal as the target area corresponding to the Bluetooth signal.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, the memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, enables the at least one processor to execute the area determination method according to any one of claims 1-4.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for implementing the area determination method according to any one of claims 1-4 when the computer instructions are executed by a processor.

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