Car key positioning method and device, medium and equipment

Abstract

The invention discloses a vehicle key positioning method and device, a medium and equipment. According to the method, the signal intensity data of the vehicle key is obtained, the distances between the vehicle key and the positions of the preset antennas in the vehicle are calculated according to the signal intensity data and the preset fitting relation, and finally the distance between the vehicle key and the vehicle is calculated according to the corrected centroid algorithm, the distances and the preset constraint conditions. According to the method, the problem that an algorithm needs to be developed again due to key chip model replacement is avoided, the adaptability and stability of the system are enhanced, and therefore stable and accurate positioning of the car key can be achieved in different environments. The problem that in the prior art, a car key cannot be accurately positioned is solved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle key positioning, and in particular to a vehicle key positioning method, device, medium and equipment. Background Art

[0002] In modern automotive technology, the PEPS (Push-to-Entry and Push-to-Start) system is increasingly favored by automakers and consumers for its convenience and safety. The core of the PEPS system lies in its ability to accurately detect the precise location of the smart key, ensuring that the system can correctly respond to the user's instructions only when the key is in a specific area of ​​the vehicle. This position detection technology, namely area detection technology, is essential for the normal operation of the PEPS system. It ensures that the vehicle's locking, unlocking and starting functions are only activated when the key is within the preset working range, thus avoiding misoperation and potential safety risks. However, there are some challenges and limitations in the current physical key positioning technology:

[0003] Poor positioning accuracy: The RSSI (received signal strength indication) value of the physical key is a key parameter in the positioning process, but it is significantly affected by the surrounding environment. For example, metal objects, buildings, and other electromagnetic interference sources around the vehicle may affect the RSSI value, resulting in inaccurate positioning results. If these RSSI values ​​are not properly processed and corrected, the RSSI-based positioning system will not be able to provide reliable positioning accuracy.

[0004] Chip dependency of key positioning algorithms: Existing key positioning algorithms often rely on library functions provided by specific chips. This dependency limits the versatility and adaptability of the algorithm. When the chip model of the smart key changes, the existing positioning algorithm may no longer be applicable and needs to be adjusted or redeveloped. This not only increases development costs and prolongs the time to market, but also increases system complexity and maintenance difficulty.

[0005] These problems result in the inability of the prior art to accurately locate the car key. Summary of the invention

[0006] The present invention provides a vehicle key positioning method, device, medium and equipment to solve the problem that the vehicle key cannot be accurately positioned in the prior art.

[0007] In a first aspect, the present application provides a vehicle key positioning method, comprising:

[0008] Get the signal strength data of the car key;

[0009] Calculating the distances between the vehicle key and each preset antenna position in the vehicle according to the signal strength data and a preset fitting relationship;

[0010] The distance between the car key and the vehicle is calculated based on the modified centroid algorithm, the distances and the preset constraints.

[0011] This application obtains the RSSI data of the car key and uses a preset fitting relationship to calculate the distance between the key and the antenna in the car, and then uses the modified centroid algorithm and constraints to determine the distance between the key and the vehicle. The preset fitting relationship of this application can reduce the impact of environmental factors on RSSI data and improve the accuracy of positioning data; secondly, the application of the modified centroid algorithm and constraints further improves the accuracy of positioning and ensures the reliability of positioning results; finally, this method of this application avoids the problem of redeveloping the algorithm due to the replacement of the key chip model, enhances the adaptability and stability of the system, and thus can achieve stable and accurate positioning of the car key in different environments. This solves the problem that the car key cannot be accurately positioned in the prior art.

[0012] As a preferred embodiment of the first aspect, the preset fitting relationship is specifically:

[0013] Obtain the signal strength data received by each antenna in the car and the actual distance from the car key to each position of the vehicle;

[0014] Inputting the signal strength data received by each antenna in the vehicle and each actual distance into a radial basis function network, so that the radial basis function network outputs a mapping relationship between the signal strength data received by each antenna and each actual distance;

[0015] According to the mapping relationship, a preset fitting relationship expression is calculated.

[0016] In this preferred embodiment, by collecting and inputting actual signal strength and distance data, the RBFN can learn and capture the complex nonlinear relationship between these data; secondly, the mapping relationship output by the network can be used as a preset fitting relationship for subsequent car key positioning calculations, and such a fitting relationship can more accurately reflect the signal attenuation characteristics in the actual environment; finally, using this precisely fitted relationship to calculate the distance between the car key and the vehicle can significantly improve the accuracy of positioning, because this method can more realistically simulate and adapt to the changing electromagnetic environment inside the vehicle, thereby optimizing the performance of the PEPS system and user experience.

[0017] As a preferred embodiment of the first aspect, the distance between the car key and the vehicle is calculated based on the modified centroid algorithm, the distances and the preset constraints, specifically:

[0018] Calculate each corrected distance based on the corrected centroid algorithm and each distance;

[0019] Calculating the distance between the vehicle key and the vehicle according to the various corrected distances and the preset constraints;

[0020] The preset constraint conditions include four inequalities. If one of the inequalities does not hold, the signal strength data of the vehicle key is acquired again.

[0021] The preset constraint conditions include four inequalities, specifically:

[0022] The four inequalities are: d1+d2>a;

[0023] d1+a <d4;

[0024] d1+a>d3;

[0025] d3+a>d4;

[0026] Wherein, d1, d2, d3 and d4 represent respective correction distances, and a is a preset constant.

[0027] In this preferred embodiment, the present application calculates the distance between the car key and the vehicle through a modified centroid algorithm combined with various distances and preset constraints. First, the modified centroid algorithm can calculate the corrected distance based on the distance between the car key and each antenna, which helps to reduce the impact of a single measurement error on the positioning result; secondly, the introduction of preset constraints, namely four inequalities, provides an additional verification mechanism for the positioning process, ensuring the rationality and accuracy of the positioning result; further, if any inequality does not hold, the process of re-acquiring signal strength data is triggered, which helps to correct possible errors in a timely manner and avoid erroneous positioning results; finally, this method that comprehensively considers the correction algorithm and constraints can improve the accuracy and reliability of car key positioning, ensuring that the PEPS system can operate stably in various environments, thereby improving user experience and system security.

[0028] As a preferred embodiment of the first aspect, after calculating the distances between the car key and each preset antenna position in the car according to the signal strength data and the preset fitting relationship, the method further includes:

[0029] According to the distances and the preset formula, determining whether the key is in the vehicle;

[0030] The preset formula is: L=A+B*lg(d5)+C*f;

[0031] Wherein, L represents the first signal strength received by each antenna in the vehicle, A, B and C are preset constants, d5 represents each distance, and f represents the frequency of the electromagnetic wave.

[0032] In this preferred embodiment, the present application calculates the distance between the car key and each antenna in the car by using signal strength data and a preset fitting relationship, and then determines whether the key is in the car based on these distances and the preset formula. The beneficial effects of this method are: first, by calculating the distance between the car key and each antenna, the approximate position of the key can be preliminarily determined; secondly, using the preset formula L=A+B*lg(d5)+C*f, where L represents signal strength, A, B and C are constants, d5 represents distance, and f represents frequency, it is possible to more accurately determine whether the key is in the car based on the relationship between signal strength and distance, because the formula takes into account the law of signal strength changing with distance and frequency; ultimately, this method can improve the accuracy of positioning and the reliability of the system, because it combines distance measurement and signal strength analysis, provides a dual verification mechanism, and ensures that the PEPS system will only respond to the user's instructions when the key is actually in the car, thereby enhancing the safety of the vehicle and the convenience of the user.

[0033] In a second aspect, the present application provides a vehicle key positioning device. The vehicle key positioning device includes an acquisition module, a first calculation module, and a second calculation module;

[0034] The acquisition module is used to obtain the signal strength data of the car key;

[0035] The first calculation module is used to calculate the distances between the car key and each preset antenna position in the car according to the signal strength data and a preset fitting relationship;

[0036] The second calculation module is used to calculate the distance between the car key and the vehicle according to the modified centroid algorithm, the distances and preset constraints.

[0037] This device uses three modules to divide the work and coordinate work to more accurately locate the car key. This application uses a method of obtaining the RSSI data of the car key and combining it with a preset fitting relationship to calculate the distance between the key and the antenna in the car, and then uses the revised centroid algorithm and constraints to determine the distance between the key and the vehicle. The preset fitting relationship of this application can reduce the impact of environmental factors on RSSI data and improve the accuracy of positioning data; secondly, the application of the revised centroid algorithm and constraints further improves the accuracy of positioning and ensures the reliability of the positioning results; finally, this method of the present application avoids the problem of redeveloping the algorithm due to the change of the key chip model, enhances the adaptability and stability of the system, and thus can achieve stable and accurate positioning of the car key in different environments. This solves the problem that the car key cannot be accurately located in the prior art.

[0038] As a preferred embodiment of the second aspect, the preset fitting relationship is specifically:

[0039] Obtain the signal strength data received by each antenna in the car and the actual distance from the car key to each position of the vehicle;

[0040] Inputting the signal strength data received by each antenna in the vehicle and each actual distance into a radial basis function network, so that the radial basis function network outputs a mapping relationship between the signal strength data received by each antenna and each actual distance;

[0041] According to the mapping relationship, a preset fitting relationship expression is calculated.

[0042] In this preferred embodiment, by collecting and inputting actual signal strength and distance data, the RBFN can learn and capture the complex nonlinear relationship between these data; secondly, the mapping relationship output by the network can be used as a preset fitting relationship for subsequent car key positioning calculations, and such a fitting relationship can more accurately reflect the signal attenuation characteristics in the actual environment; finally, using this precisely fitted relationship to calculate the distance between the car key and the vehicle can significantly improve the accuracy of positioning, because this method can more realistically simulate and adapt to the changing electromagnetic environment inside the vehicle, thereby optimizing the performance of the PEPS system and user experience.

[0043] As a preferred embodiment of the second aspect, the distance between the car key and the vehicle is calculated based on the modified centroid algorithm, the distances and the preset constraints, specifically:

[0044] Calculate each corrected distance based on the corrected centroid algorithm and each distance;

[0045] Calculating the distance between the vehicle key and the vehicle according to the various corrected distances and the preset constraints;

[0046] The preset constraint conditions include four inequalities. If one of the inequalities does not hold, the signal strength data of the vehicle key is acquired again.

[0047] In this preferred embodiment, the present application calculates the distance between the car key and the vehicle through a modified centroid algorithm combined with various distances and preset constraints. First, the modified centroid algorithm can calculate the corrected distance based on the distance between the car key and each antenna, which helps to reduce the impact of a single measurement error on the positioning result; secondly, the introduction of preset constraints, namely four inequalities, provides an additional verification mechanism for the positioning process, ensuring the rationality and accuracy of the positioning result; further, if any inequality does not hold, the process of re-acquiring signal strength data is triggered, which helps to correct possible errors in a timely manner and avoid erroneous positioning results; finally, this method that comprehensively considers the correction algorithm and constraints can improve the accuracy and reliability of car key positioning, ensuring that the PEPS system can operate stably in various environments, thereby improving user experience and system security.

[0048] In a third aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute a vehicle key positioning method as described above. The beneficial effect thereof is the same as the vehicle key positioning method provided in the first aspect of the present application.

[0049] In a fourth aspect, the present application provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the vehicle key locating methods described in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 : A flow chart of an embodiment of a method for locating a vehicle key provided by the present application;

[0051] Figure 2 : A structural schematic diagram of an embodiment of the vehicle and key distance provided by the present application;

[0052] Figure 3 : A flow chart of an embodiment of the calibration data collection and processing process provided by the present application;

[0053] Figure 4 : A flow chart of an embodiment of a modified centroid algorithm process provided by the present application;

[0054] Figure 5 : A structural schematic diagram of an embodiment of a vehicle key positioning device provided in the present application. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0056] Embodiment 1

[0057] Please refer to Figure 1 , which is a vehicle key positioning method provided by an embodiment of the present invention.

[0058] In this embodiment, the process of the vehicle key locating method in this application is described in detail through steps S01-S03.

[0059] S01: Acquire the signal strength data of the car key.

[0060] As a preferred embodiment of the first embodiment, the signal strength data of the vehicle key is specifically:

[0061] The signal strength data of the car key refers to the signal strength of the signal emitted by the car's smart key (composed of a key chip and peripheral circuits) and captured by the vehicle's receiver (such as the vehicle's built-in antenna). This signal strength can be used to estimate the distance between the smart key and the vehicle, and then to determine the location of the key.

[0062] S02: Calculating the distances between the vehicle key and each preset antenna position in the vehicle according to the signal strength data and a preset fitting relationship.

[0063] As a preferred embodiment of the first embodiment, the preset fitting relationship is specifically:

[0064] Obtain the signal strength data received by each antenna in the car and the actual distance from the car key to each position of the vehicle;

[0065] Furthermore, the method for obtaining each actual distance is specifically as follows:

[0066] This application has requirements on the number and placement of vehicle antennas, such as Figure 2 As shown, there are four vehicle antennas; the vehicle antennas are placed below the main driver's center console (A1), the left front door handle (A2), the right front door handle (A3), and the rear bumper (A4), and the line connecting the antenna below the main driver's center console and the rear bumper antenna is perpendicular to the line connecting the left front door handle and the right front door handle.

[0067] Furthermore, the collection and processing of calibration data is as follows Figure 3As shown in the figure, the key RSSI and actual distance received by different antennas are collected at multiple locations; the key RSSI and distance are processed using the radial basis function network (RBFN) in the neural network to obtain the fitting relationship between the key RSSI and the distance.

[0068] Inputting the signal strength data received by each antenna in the vehicle and each actual distance into a radial basis function network, so that the radial basis function network outputs a mapping relationship between the signal strength data received by each antenna and each actual distance;

[0069] According to the mapping relationship, a preset fitting relationship expression is calculated.

[0070] In this preferred embodiment, by collecting and inputting actual signal strength and distance data, the RBFN can learn and capture the complex nonlinear relationship between these data; secondly, the mapping relationship output by the network can be used as a preset fitting relationship for subsequent car key positioning calculations, and such a fitting relationship can more accurately reflect the signal attenuation characteristics in the actual environment; finally, using this precisely fitted relationship to calculate the distance between the car key and the vehicle can significantly improve the accuracy of positioning, because this method can more realistically simulate and adapt to the changing electromagnetic environment inside the vehicle, thereby optimizing the performance of the PEPS system and user experience.

[0071] S03: Calculate the distance between the car key and the vehicle according to the modified centroid algorithm, the distances and preset constraints.

[0072] As a preferred embodiment of the first embodiment, the distance between the car key and the vehicle is calculated based on the modified centroid algorithm, the distances and the preset constraints, specifically:

[0073] The modified centroid algorithm is as follows Figure 4 As shown, the algorithm can calculate the distance between the vehicle and the key through the distance between the four antennas and the key.

[0074] Furthermore, each corrected distance is calculated based on the corrected centroid algorithm and the distances;

[0075] Calculating the distance between the vehicle key and the vehicle according to the various corrected distances and the preset constraints;

[0076] The preset constraint conditions include four inequalities. If one of the inequalities does not hold, the signal strength data of the vehicle key is acquired again.

[0077] The preset constraint conditions include four inequalities, specifically:

[0078] The four inequalities are: d1+d2>a;

[0079] d1+a <d4;

[0080] d1+a>d3;

[0081] d3+a>d4;

[0082] Wherein, d1, d2, d3 and d4 represent respective correction distances, and a is a preset constant.

[0083] In this preferred embodiment, the present application calculates the distance between the car key and the vehicle through a modified centroid algorithm combined with various distances and preset constraints. First, the modified centroid algorithm can calculate the corrected distance based on the distance between the car key and each antenna, which helps to reduce the impact of a single measurement error on the positioning result; secondly, the introduction of preset constraints, namely four inequalities, provides an additional verification mechanism for the positioning process, ensuring the rationality and accuracy of the positioning result; further, if any inequality does not hold, the process of re-acquiring signal strength data is triggered, which helps to correct possible errors in a timely manner and avoid erroneous positioning results; finally, this method that comprehensively considers the correction algorithm and constraints can improve the accuracy and reliability of car key positioning, ensuring that the PEPS system can operate stably in various environments, thereby improving user experience and system security.

[0084] As a preferred embodiment of the first embodiment, after calculating the distances between the car key and each preset antenna position in the car according to the signal strength data and the preset fitting relationship, the method further includes:

[0085] like Figure 4 As shown, according to the distances and the preset formula, it is determined whether the key is in the car;

[0086] The preset formula is: L=A+B*lg(d5)+C*f;

[0087] Wherein, L represents the first signal strength received by each antenna in the vehicle, A, B and C are preset constants, d5 represents each distance, and f represents the frequency of the electromagnetic wave.

[0088] In this preferred embodiment, the present application calculates the distance between the car key and each antenna in the car by using signal strength data and a preset fitting relationship, and then determines whether the key is in the car based on these distances and the preset formula. The beneficial effects of this method are: first, by calculating the distance between the car key and each antenna, the approximate position of the key can be preliminarily determined; secondly, using the preset formula L=A+B*lg(d5)+C*f, where L represents signal strength, A, B and C are constants, d5 represents distance, and f represents frequency, it is possible to more accurately determine whether the key is in the car based on the relationship between signal strength and distance, because the formula takes into account the law of signal strength changing with distance and frequency; ultimately, this method can improve the accuracy of positioning and the reliability of the system, because it combines distance measurement and signal strength analysis, provides a dual verification mechanism, and ensures that the PEPS system will only respond to the user's instructions when the key is actually in the car, thereby enhancing the safety of the vehicle and the convenience of the user.

[0089] This application obtains the RSSI data of the car key and uses a preset fitting relationship to calculate the distance between the key and the antenna in the car, and then uses the modified centroid algorithm and constraints to determine the distance between the key and the vehicle. The preset fitting relationship of this application can reduce the impact of environmental factors on RSSI data and improve the accuracy of positioning data; secondly, the application of the modified centroid algorithm and constraints further improves the accuracy of positioning and ensures the reliability of positioning results; finally, this method of this application avoids the problem of redeveloping the algorithm due to the replacement of the key chip model, enhances the adaptability and stability of the system, and thus can achieve stable and accurate positioning of the car key in different environments. This solves the problem that the car key cannot be accurately positioned in the prior art.

[0090] Embodiment 2

[0091] Please refer to Figure 5 , which is a car key positioning device provided in an embodiment of the present application.

[0092] In this embodiment, the vehicle key locating device includes an acquisition module 10 , a first calculation module 20 and a second calculation module 30 .

[0093] The acquisition module 10 is used to acquire signal strength data of the vehicle key.

[0094] As a preferred embodiment of the second embodiment, the signal strength data of the car key is specifically:

[0095] The signal strength data of the car key refers to the signal strength of the signal emitted by the car's smart key (composed of a key chip and peripheral circuits) and captured by the vehicle's receiver (such as the vehicle's built-in antenna). This signal strength can be used to estimate the distance between the smart key and the vehicle, and then to determine the location of the key.

[0096] The first calculation module 20 is used to calculate the distances between the vehicle key and each preset antenna position in the vehicle according to the signal strength data and a preset fitting relationship.

[0097] As a preferred embodiment of the second embodiment, the preset fitting relationship is specifically:

[0098] Obtain the signal strength data received by each antenna in the car and the actual distance from the car key to each position of the vehicle;

[0099] Furthermore, the method for obtaining each actual distance is specifically as follows:

[0100] This application has requirements on the number and placement of vehicle antennas, such as Figure 2 As shown, there are four vehicle antennas; the vehicle antennas are placed below the main driver's center console (A1), the left front door handle (A2), the right front door handle (A3), and the rear bumper (A4), and the line connecting the antenna below the main driver's center console and the rear bumper antenna is perpendicular to the line connecting the left front door handle and the right front door handle.

[0101] Furthermore, the collection and processing of calibration data is as follows Figure 3 As shown in the figure, the key RSSI and actual distance received by different antennas are collected at multiple locations; the key RSSI and distance are processed using the radial basis function network (RBFN) in the neural network to obtain the fitting relationship between the key RSSI and the distance.

[0102] Inputting the signal strength data received by each antenna in the vehicle and each actual distance into a radial basis function network, so that the radial basis function network outputs a mapping relationship between the signal strength data received by each antenna and each actual distance;

[0103] According to the mapping relationship, a preset fitting relationship expression is calculated.

[0104] In this preferred embodiment, by collecting and inputting actual signal strength and distance data, the RBFN can learn and capture the complex nonlinear relationship between these data; secondly, the mapping relationship output by the network can be used as a preset fitting relationship for subsequent car key positioning calculations, and such a fitting relationship can more accurately reflect the signal attenuation characteristics in the actual environment; finally, using this precisely fitted relationship to calculate the distance between the car key and the vehicle can significantly improve the accuracy of positioning, because this method can more realistically simulate and adapt to the changing electromagnetic environment inside the vehicle, thereby optimizing the performance of the PEPS system and user experience.

[0105] The second calculation module 30 is used to calculate the distance between the car key and the vehicle according to the modified centroid algorithm, the distances and preset constraints.

[0106] As a preferred embodiment of the second embodiment, the distance between the car key and the vehicle is calculated based on the modified centroid algorithm, the distances and the preset constraints, specifically:

[0107] The modified centroid algorithm is as follows Figure 4 As shown, the algorithm can calculate the distance between the vehicle and the key through the distance between the four antennas and the key.

[0108] Furthermore, each corrected distance is calculated based on the corrected centroid algorithm and the distances;

[0109] Calculating the distance between the vehicle key and the vehicle according to the various corrected distances and the preset constraints;

[0110] The preset constraint conditions include four inequalities. If one of the inequalities does not hold, the signal strength data of the vehicle key is acquired again.

[0111] The preset constraint conditions include four inequalities, specifically:

[0112] The four inequalities are: d1+d2>a;

[0113] d1+a <d4;

[0114] d1+a>d3;

[0115] d3+a>d4;

[0116] Wherein, d1, d2, d3 and d4 represent respective correction distances, and a is a preset constant.

[0117] In this preferred embodiment, the present application calculates the distance between the car key and the vehicle through a modified centroid algorithm combined with various distances and preset constraints. First, the modified centroid algorithm can calculate the corrected distance based on the distance between the car key and each antenna, which helps to reduce the impact of a single measurement error on the positioning result; secondly, the introduction of preset constraints, namely four inequalities, provides an additional verification mechanism for the positioning process, ensuring the rationality and accuracy of the positioning result; further, if any inequality does not hold, the process of re-acquiring signal strength data is triggered, which helps to correct possible errors in a timely manner and avoid erroneous positioning results; finally, this method that comprehensively considers the correction algorithm and constraints can improve the accuracy and reliability of car key positioning, ensuring that the PEPS system can operate stably in various environments, thereby improving user experience and system security.

[0118] As a preferred embodiment of the second embodiment, after calculating the distances between the car key and each preset antenna position in the car according to the signal strength data and the preset fitting relationship, the method further includes:

[0119] like Figure 4 As shown, according to the distances and the preset formula, it is determined whether the key is in the car;

[0120] The preset formula is: L=A+B*lg(d5)+C*f;

[0121] Wherein, L represents the first signal strength received by each antenna in the vehicle, A, B and C are preset constants, d5 represents each distance, and f represents the frequency of the electromagnetic wave.

[0122] In this preferred embodiment, the present application calculates the distance between the car key and each antenna in the car by using signal strength data and a preset fitting relationship, and then determines whether the key is in the car based on these distances and the preset formula. The beneficial effects of this method are: first, by calculating the distance between the car key and each antenna, the approximate position of the key can be preliminarily determined; secondly, using the preset formula L=A+B*lg(d5)+C*f, where L represents signal strength, A, B and C are constants, d5 represents distance, and f represents frequency, it is possible to more accurately determine whether the key is in the car based on the relationship between signal strength and distance, because the formula takes into account the law of signal strength changing with distance and frequency; ultimately, this method can improve the accuracy of positioning and the reliability of the system, because it combines distance measurement and signal strength analysis, provides a dual verification mechanism, and ensures that the PEPS system will only respond to the user's instructions when the key is actually in the car, thereby enhancing the safety of the vehicle and the convenience of the user.

[0123] This device uses three modules to divide the work and coordinate work to more accurately locate the car key. This application uses a method of obtaining the RSSI data of the car key and combining it with a preset fitting relationship to calculate the distance between the key and the antenna in the car, and then uses the revised centroid algorithm and constraints to determine the distance between the key and the vehicle. The preset fitting relationship of this application can reduce the impact of environmental factors on RSSI data and improve the accuracy of positioning data; secondly, the application of the revised centroid algorithm and constraints further improves the accuracy of positioning and ensures the reliability of the positioning results; finally, this method of the present application avoids the problem of redeveloping the algorithm due to the change of the key chip model, enhances the adaptability and stability of the system, and thus can achieve stable and accurate positioning of the car key in different environments. This solves the problem that the car key cannot be accurately located in the prior art.

[0124] Embodiment three:

[0125] An embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the vehicle key positioning method;

[0126] Wherein, if the vehicle key positioning method is implemented in the form of a software functional unit and used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium.

[0127] Embodiment 4

[0128] The present application provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, any one of the vehicle key positioning methods described in Example 1 is implemented.

[0129] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for locating a vehicle key, characterized in that: include: Get the signal strength data of the car key; Calculating the distances between the vehicle key and each preset antenna position in the vehicle according to the signal strength data and a preset fitting relationship; The distance between the car key and the vehicle is calculated based on the modified centroid algorithm, the distances and the preset constraints.

2. The vehicle key positioning method according to claim 1, characterized in that: The preset fitting relationship is specifically: Obtain the signal strength data received by each antenna in the car and the actual distance from the car key to each position of the vehicle; Inputting the signal strength data received by each antenna in the vehicle and each actual distance into a radial basis function network, so that the radial basis function network outputs a mapping relationship between the signal strength data received by each antenna and each actual distance; According to the mapping relationship, a preset fitting relationship expression is calculated.

3. The vehicle key positioning method according to claim 1, characterized in that: The distance between the car key and the vehicle is calculated based on the modified centroid algorithm, the distances and the preset constraints, specifically: Calculate each corrected distance based on the corrected centroid algorithm and each distance; Calculating the distance between the vehicle key and the vehicle according to the various corrected distances and the preset constraints; The preset constraint conditions include four inequalities. If one of the inequalities does not hold, the signal strength data of the vehicle key is acquired again.

4. The vehicle key positioning method according to claim 1, characterized in that: The preset constraint conditions include four inequalities, specifically: The four inequalities are: d1+d2>a; d1+a <d4; d1+a>d3; d3+a>d4; Wherein, d1, d2, d3 and d4 represent respective correction distances, and a is a preset constant.

5. The vehicle key positioning method according to claim 1, characterized in that: After calculating the distances between the car key and each preset antenna position in the car according to the signal strength data and the preset fitting relationship, the method further includes: According to the distances and the preset formula, determining whether the key is in the vehicle; The preset formula is: L=A+B*lg(d5)+C*f; Wherein, L represents the first signal strength received by each antenna in the vehicle, A, B and C are preset constants, d5 represents each distance, and f represents the frequency of the electromagnetic wave.

6. A vehicle key positioning device, characterized in that: It includes an acquisition module, a first calculation module and a second calculation module; The acquisition module is used to obtain the signal strength data of the car key; The first calculation module is used to calculate the distances between the car key and each preset antenna position in the car according to the signal strength data and a preset fitting relationship; The second calculation module is used to calculate the distance between the car key and the vehicle according to the modified centroid algorithm, the distances and preset constraints.

7. The vehicle key positioning device according to claim 6, characterized in that: The preset fitting relationship is specifically: Obtain the signal strength data received by each antenna in the car and the actual distance from the car key to each position of the vehicle; Inputting the signal strength data received by each antenna in the vehicle and each actual distance into a radial basis function network, so that the radial basis function network outputs a mapping relationship between the signal strength data received by each antenna and each actual distance; According to the mapping relationship, a preset fitting relationship expression is calculated.

8. The vehicle key positioning device according to claim 6, characterized in that: The distance between the car key and the vehicle is calculated based on the modified centroid algorithm, the distances and the preset constraints, specifically: Calculate each corrected distance based on the corrected centroid algorithm and each distance; Calculating the distance between the vehicle key and the vehicle according to the various corrected distances and the preset constraints; The preset constraint conditions include four inequalities. If one of the inequalities does not hold, the signal strength data of the vehicle key is acquired again.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the vehicle key positioning method according to any one of claims 1 to 5.

10. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the vehicle key positioning method according to any one of claims 1 to 5 when executing the computer program.

Images