Intelligent whistle limiting method and system for rail locomotive and electronic equipment

Abstract

The invention relates to the technical field of intelligent whistle limiting scheme design of rail locomotives, in particular to an intelligent whistle limiting method and system of a rail locomotive and electronic equipment. The method comprises the following steps: S100, acquiring GPS data and inertial sensor data of a rail locomotive; s200, performing data fusion based on the obtained GPS data and inertial sensor data of the rail locomotive to obtain fusion position data of the rail locomotive; and S300, judging whether the fusion position data of the rail locomotive is located outside a preset geo-fence or not, and judging whether to control the whistling function of the rail locomotive to be closed or not according to a judgment result. And by fusing the data of the GPS and the IMU, the positioning accuracy and stability are remarkably improved. The system redundancy design is realized, and the reliable positioning service can be maintained even in a severe environment. And through the use of the STM32 single-chip microcomputer, rapid and real-time data processing is realized, and the response speed of the system is improved. Unnecessary noise is reduced, and a better living environment is provided for residents.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent horn limiting scheme design for rail locomotives, and in particular to an intelligent horn limiting method and system for rail locomotives, and electronic equipment. Background Art

[0002] With the rapid development of intelligent transportation and autonomous driving technologies, the demand for high-precision and high-reliability positioning systems is growing. Traditional GPS systems perform well in open areas, but in complex environments such as urban high-rise buildings and tunnels, signals are often interfered with, resulting in reduced positioning accuracy. In addition, although the inertial navigation system can provide better location information in the short term, there is a problem of cumulative error. How to effectively combine these two technologies to achieve more stable and accurate positioning has become a hot topic in current technical research.

[0003] Therefore, the prior art needs to be further developed. Summary of the invention

[0004] The purpose of the present invention is to overcome the above technical deficiencies and provide a method and system for intelligently limiting the horn of a railway locomotive, and an electronic device to solve the problems existing in the prior art.

[0005] To achieve the above technical objectives, according to a first aspect of the present invention, the present invention provides a method for intelligently limiting horns of a railway locomotive, the method comprising: S100, obtaining GPS data and inertial sensor data of the rail vehicle; S200, performing data fusion based on the acquired GPS data and inertial sensor data of the rail locomotive to obtain fused position data of the rail locomotive; S300: Determine whether the fused position data of the rail locomotive is outside a preset geographic fence, and determine whether to control a whistle function of the rail locomotive to be turned off according to the determination result.

[0006] Specifically, determining whether to control the whistle function of the rail vehicle to be turned off according to the judgment result includes: If the fused location data of the rail locomotive is not outside the preset geographic fence, the whistle function of the rail locomotive is turned off; If the fused position data of the rail locomotive is outside the preset geographic fence, the whistle function of the rail locomotive is turned on.

[0007] Specifically, the data fusion is performed based on the acquired GPS data and inertial sensor data of the rail locomotive to obtain the fused position data of the rail locomotive, including: (1) The running state of a rail vehicle is expressed by the following formula: X k =[P k ,V k ]T Among them, X k is the running state of the rail locomotive, P k is the position information of the vehicle at time k, Vk is the velocity vector of the vehicle at time k, and T represents the matrix transpose; (2) The state prediction model based on inertial sensor data is expressed as follows: X k+1 =A k X k +B k u k +W k Among them, A k is the state transition matrix, X k is the running state of the locomotive at time k, X k+1 is the running state of the locomotive at time k+1, B k is the control input matrix, u k is the acceleration and angular velocity data output by the inertial sensor, W k is the system noise; (3) Express GPS data using observation equations: Z k =H k X k +V k Among them, Z k The observation value provided by the GPS device, X k is the running state of the locomotive at time k, H k is the observation matrix, V k To measure noise; (4) Use filtering methods to fuse the predicted values ​​of the state prediction model based on inertial sensor data and the observed values ​​provided by the GPS device: X k =X k|k-1 +K k（ Z k -H k X k|k-1） Among them, X k|k-1 is the predicted state at time k-1, X k is the running state of the locomotive at time k, K k is the filter gain, which determines the weight of the predicted value and the observed value, Z k The observation value provided by the GPS device, H k is the observation matrix.

[0008] Specifically, the filter gain K k The calculation method is: According to the state prediction error covariance matrix P k|k-1 and the observation error covariance matrix R k Calculate the filter gain K k , the specific formula is: K k =P k|k-1 （ H k P k|k-1 +R k） -1 Among them, P k|k-1为 State prediction error covariance matrix, R k is the observation error covariance matrix, H k is the observation matrix, represents the transpose of the observation error covariance matrix.

[0009] Specifically, determining whether the location data of the rail vehicle is outside a preset geographic fence includes: The running state X of the locomotive at time k obtained in step (4) k The current position data of the rail locomotive includes the current latitude and longitude coordinates of the rail locomotive. curent ,lon current ); The center coordinates of the preset geofence are expressed as (lat i， lon i ); The distance di between the current position data of the rail vehicle and the center coordinates of the preset geographic fence is calculated by the following formula: di=Calculate_Distance(lat curent ,lon current ,lat i ,lon i ) Among them, Calculate_Distance is the function used to calculate the spherical distance, lat curent is the current longitude coordinate of the locomotive, lon current is the current latitude coordinate of the rail vehicle, Calculate_Distance is the function used to calculate the spherical distance, lat i is the longitude coordinate of the center of the preset geofence, lon i is the latitude coordinate of the center of the preset geofence, and i is the number of the preset geofence.

[0010] Specifically, determining whether the location data of the rail vehicle is outside a preset geographic fence includes: Determine whether di is less than or equal to a preset radius ri of a preset geographic fence; If di is less than or equal to the preset radius ri, it is determined that the vehicle is not outside the preset geographic fence; If di is greater than the preset radius ri, it is determined that the vehicle is outside the preset geographic fence; Where i is the number of the preset geo-fence.

[0011] Specifically, the controlling the whistle function of the rail locomotive to be turned off also includes: Control the LED indicator light for indicating whistle off, which is arranged on the rail vehicle, to flash.

[0012] Specifically, the control of turning on the whistle function of the rail locomotive also includes: The LED indicator light indicating the whistle is turned off and arranged on the rail locomotive is turned off.

[0013] According to a second aspect of the present invention, there is provided a railway locomotive intelligent horn limiting system, comprising: An acquisition module is used to acquire GPS data and inertial sensor data of rail vehicles; The control module is used to perform data fusion based on the acquired GPS data and inertial sensor data of the rail locomotive to obtain the fused position data of the rail locomotive; and is used to determine whether the fused position data of the rail locomotive is outside a preset geographic fence, and determine whether to control the whistle function of the rail locomotive to be turned off according to the judgment result.

[0014] According to a third aspect of the present invention, there is provided an electronic device, comprising: a memory; and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the above-mentioned rail locomotive intelligent horn limiting method is implemented.

[0015] Beneficial effects: The present invention solves the following technical problems of the prior art: 1. Insufficient positioning accuracy: A single GPS system is unstable in complex environments and cannot meet high-precision requirements.

[0016] 2. Real-time requirements: In fast-moving application scenarios, sensor data needs to be processed in real time to ensure timely response.

[0017] 3. System reliability: When one system fails, another system can take over to ensure continuous positioning services.

[0018] 4. Difficulty in data fusion: Traditional navigation systems lack an effective data fusion mechanism, resulting in inefficient information processing.

[0019] The present invention achieves the following beneficial effects: 1. Improve positioning accuracy: By integrating GPS and IMU data, the accuracy and stability of positioning can be significantly improved.

[0020] 2. Enhance system robustness: Implement redundant system design to maintain reliable positioning services even in harsh environments.

[0021] 3. Optimize real-time processing capabilities: The use of STM32 microcontrollers can achieve fast, real-time data processing and improve system response speed.

[0022] 4. Reduce noise pollution: Accurately control locomotive horns to reduce unnecessary noise and provide residents with a better living environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a schematic flow chart of a method for intelligently limiting the sound of a railway locomotive provided in a specific embodiment of the present invention; Figure 2 1 is a schematic diagram of the system composition of the rail locomotive intelligent horn limiting system provided in a specific embodiment of the present invention; Figure 3 It is a control logic diagram of the rail locomotive intelligent horn limiting method provided in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is clearly and completely described below in conjunction with the accompanying drawings of the present invention. Based on the embodiments in this application, other similar embodiments obtained by ordinary technicians in this field without making creative work should all fall within the scope of protection of this application. In addition, the directional words mentioned in the following embodiments, such as "up", "down", "left", "right", etc., are only reference to the directions of the accompanying drawings. Therefore, the directional words used are used to illustrate rather than limit the invention.

[0025] The present invention will be further described below in conjunction with the accompanying drawings and preferred embodiments.

[0026] See also Figure 1 and Figure 3 The present invention provides a method for intelligently limiting the sound of a railway locomotive, the method comprising: S100: Obtain GPS data and inertial sensor data of the rail vehicle.

[0027] It should be noted that the step before step S100 includes: The center coordinates (lati, loni) of each preset geographic fence and the radius ri of each preset geographic fence are preset in the control module.

[0028] It can be understood that the center coordinates (lati, loni) of each preset geographic fence and the radius ri of each preset geographic fence can be specifically set according to the actual needs of the user of the present invention. The present invention does not limit the setting of the specific numerical values ​​of the above parameters, as long as it is applicable to the intelligent horn limiting method for rail locomotives proposed in the present invention.

[0029] S200: Based on the acquired GPS data and inertial sensor data of the rail vehicle, data fusion is performed to obtain fused position data of the rail vehicle.

[0030] Specifically, the data fusion is performed based on the acquired GPS data and inertial sensor data of the rail locomotive to obtain the fused position data of the rail locomotive, including: (1) The running state of a rail vehicle is expressed by the following formula: X k =[P k ,V k ] T Among them, X k is the running state of the rail locomotive, P k is the position information of the vehicle at time k, Vk is the velocity vector of the vehicle at time k, and T represents the matrix transpose; (2) The state prediction model based on inertial sensor data is expressed as follows: X k+1 =A k X k +B k u k +W k Among them, A k is the state transition matrix, X k is the running state of the locomotive at time k, X k+1 is the running state of the locomotive at time k+1, B k is the control input matrix, u k is the acceleration and angular velocity data output by the inertial sensor, W k is the system noise; (3) Express GPS data using observation equations: Z k =H k X k +V k Among them, Z k The observation value provided by the GPS device, X k is the running state of the locomotive at time k, H k is the observation matrix, Vk To measure noise; (4) Use filtering methods to fuse the predicted values ​​of the state prediction model based on inertial sensor data and the observed values ​​provided by the GPS device: X k =X k|k-1 +K k（ Z k -H k X k|k-1） Among them, X k|k-1 is the predicted state at time k-1, X k is the running state of the locomotive at time k, K k is the filter gain, which determines the weight of the predicted value and the observed value, Z k The observation value provided by the GPS device, H k is the observation matrix.

[0031] Specifically, the filter gain K k The calculation method is: According to the state prediction error covariance matrix P k|k-1 and the observation error covariance matrix R k Calculate the filter gain K k , the specific formula is: K k =P k|k-1 （ H k P k|k-1 +R k） -1 Among them, P k|k-1为 State prediction error covariance matrix, R k is the observation error covariance matrix, H k is the observation matrix, represents the transpose of the observation error covariance matrix.

[0032] Specifically, determining whether the location data of the rail vehicle is outside a preset geographic fence includes: The running state X of the locomotive at time k obtained in step (4) k The current position data of the rail locomotive includes the current latitude and longitude coordinates of the rail locomotive. curent ,lon current ); The center coordinates of the preset geofence are expressed as (lat i， lon i ); The distance di between the current position data of the rail vehicle and the center coordinates of the preset geographic fence is calculated by the following formula: di=Calculate_Distance(lat curent ,lon current ,lat i ,lon i ) Among them, Calculate_Distance is the function used to calculate the spherical distance, lat curent is the current longitude coordinate of the locomotive, lon current is the current latitude coordinate of the rail vehicle, Calculate_Distance is the function used to calculate the spherical distance, lat i is the longitude coordinate of the center of the preset geofence, lon i is the latitude coordinate of the center of the preset geo-fence, and i is the number of the preset geo-fence.

[0033] Specifically, the distance di can be calculated by a surface distance formula, such as the Haversine formula, or other algorithms applicable to geographic coordinates, which will not be elaborated in detail in the present invention.

[0034] S300: Determine whether the fused position data of the rail locomotive is outside a preset geographic fence, and determine whether to control a whistle function of the rail locomotive to be turned off according to the determination result.

[0035] Specifically, determining whether to control the whistle function of the rail vehicle to be turned off according to the judgment result includes: If the fused location data of the rail locomotive is not outside the preset geographic fence, the whistle function of the rail locomotive is turned off; If the fused position data of the rail locomotive is outside the preset geographic fence, the whistle function of the rail locomotive is turned on.

[0036] Specifically, determining whether the location data of the rail vehicle is outside a preset geographic fence includes: Determine whether di is less than or equal to a preset radius ri of a preset geographic fence; If di is less than or equal to the preset radius ri, it is determined that the vehicle is not outside the preset geographic fence; If di is greater than the preset radius ri, it is determined that the vehicle is outside the preset geographic fence; Where i is the number of the preset geo-fence.

[0037] Specifically, the controlling the whistle function of the rail locomotive to be turned off also includes: Control the LED indicator light for indicating whistle off, which is arranged on the rail locomotive, to flash.

[0038] Specifically, the control of turning on the whistle function of the rail locomotive also includes: The LED indicator light indicating the whistle is turned off and arranged on the rail locomotive is turned off.

[0039] Specifically, the present invention implements the above functions through the following control logic: 1. Control logic when entering restricted areas When the vehicle enters the horn restricted area (i.e. not outside the preset geo-fence mentioned above), the STM32 controls the relay to turn off the horn and controls the LED light to flash to provide a visual warning. The specific process is as follows: To turn off the whistle (relay control): The relay is controlled by the GPIO pin of STM32. The input of the relay is connected to the output pin of STM32, and the normally open end of the relay is used to control the circuit of the whistle. When the whistle is turned off, the STM32 outputs a low level, triggering the relay to disconnect the circuit.

[0040] Activate the warning system (LED flashes): LED flashing is achieved by controlling the on / off state of the LED in the timer interrupt. The STM32 timer triggers interrupts regularly, switching the state of the LED in the interrupt service routine to produce a flashing effect.

[0041] 2. Control logic when leaving the restricted area When the vehicle leaves the restricted area (i.e., outside the preset geographic fence mentioned above), the STM32 control relay resumes honking and stops the LED warning light from flashing. The specific process is as follows: Turn on the whistle (relay control): When the vehicle leaves the restricted area, the STM32 outputs a high level, triggering the relay to close the circuit and restart the horn.

[0042] To stop the warning system (turn off LED flashing): When the vehicle leaves the restricted area, the STM32 stops the timer interrupt, thereby stopping the LED flashing and turning off the LED.

[0043] Specifically, the present invention obtains GPS location data through UART.

[0044] Acquire acceleration and angular velocity data from IMU and perform data fusion.

[0045] Specifically, the present invention performs inertial navigation prediction: The current position and speed of the vehicle are predicted in real time through the acceleration and angular velocity sensors of the IMU.

[0046] Specifically, the present invention performs GPS update: When the GPS signal is available, the current location information is obtained as observation data input to correct the system prediction value. The system redundancy design is implemented to maintain reliable positioning services even in harsh environments.

[0047] Specifically, the present invention performs data fusion: According to the real-time filter gain, the weights of GPS and IMU data are dynamically adjusted to output the fused accurate location information. When one system fails, another system can take over to ensure continuous positioning services.

[0048] Furthermore, the present invention provides a data fusion algorithm: Through filtering algorithm, efficient data fusion is achieved.

[0049] Furthermore, the present invention provides a location-based whistle control system: Design an intelligent whistle control system to automatically manage whistle behavior according to the real-time position of the locomotive, improve safety and reduce noise.

[0050] Furthermore, the present invention adopts a modular design: The modular design allows each component of the system to be updated and maintained independently, improving the flexibility and scalability of the system.

[0051] The present invention solves the following technical problems of the prior art: 1. Insufficient positioning accuracy: A single GPS system is unstable in complex environments and cannot meet high-precision requirements.

[0052] 2. Real-time requirements: In fast-moving application scenarios, sensor data needs to be processed in real time to ensure timely response.

[0053] 3. System reliability: When one system fails, another system can take over to ensure continuous positioning services.

[0054] 4. Difficulty in data fusion: Traditional navigation systems lack an effective data fusion mechanism, resulting in inefficient information processing.

[0055] The present invention achieves the following beneficial effects: 1. Improve positioning accuracy: By integrating GPS and IMU data, the accuracy and stability of positioning can be significantly improved.

[0056] 2. Enhance system robustness: Implement redundant system design to maintain reliable positioning services even in harsh environments.

[0057] 3. Optimize real-time processing capabilities: The use of STM32 microcontrollers can achieve fast, real-time data processing and improve system response speed.

[0058] 4. Reduce noise pollution: Accurately control locomotive horns to reduce unnecessary noise and provide residents with a better living environment.

[0059] See also Figure 2 The present invention provides another embodiment, which provides a railway locomotive intelligent horn limiting system, and the railway locomotive intelligent horn limiting system includes: The acquisition module 100 is used to acquire GPS data and inertial sensor data of the rail vehicle; The control module 200 is used to perform data fusion based on the acquired GPS data and inertial sensor data of the rail locomotive to obtain fused position data of the rail locomotive; and to determine whether the fused position data of the rail locomotive is located outside a preset geographic fence, and determine whether to control the whistle function of the rail locomotive to be turned off according to the judgment result.

[0060] Specifically, the present invention uses the following main equipment: GPS module: provides the latitude and longitude information of the locomotive in real time.

[0061] Inertial Measurement Unit (IMU): includes accelerometers and gyroscopes, used to measure the motion state of the locomotive.

[0062] Control unit: Use STM32 microcontroller to process GPS and IMU data.

[0063] Geobeacon database: stores the coordinates of no-honking zones and their radius.

[0064] Warning system: LED lights provide visual warning when horn is restricted.

[0065] Specifically, the present invention obtains GPS location data through UART.

[0066] Acquire acceleration and angular velocity data from IMU and perform data fusion.

[0067] Specifically, the present invention performs inertial navigation prediction: The current position and speed of the vehicle are predicted in real time through the acceleration and angular velocity sensors of the IMU.

[0068] Specifically, the present invention performs GPS update: When the GPS signal is available, the current location information is obtained as observation data input to correct the system prediction value. The system redundancy design is implemented to maintain reliable positioning services even in harsh environments.

[0069] Specifically, the present invention performs data fusion: According to the real-time filter gain, the weight of GPS and IMU data is dynamically adjusted to output the fused accurate location information. When one system fails, another system can take over to ensure continuous positioning services.

[0070] Furthermore, the present invention provides a data fusion algorithm: Through filtering algorithm, efficient data fusion is achieved.

[0071] Furthermore, the present invention provides a location-based whistle control system: Design an intelligent whistle control system to automatically manage whistle behavior according to the real-time position of the locomotive, improve safety and reduce noise.

[0072] Furthermore, the present invention adopts a modular design: The modular design allows each component of the system to be updated and maintained independently, improving the flexibility and scalability of the system.

[0073] The present invention solves the following technical problems of the prior art: 1. Insufficient positioning accuracy: A single GPS system is unstable in complex environments and cannot meet high-precision requirements.

[0074] 2. Real-time requirements: In fast-moving application scenarios, sensor data needs to be processed in real time to ensure timely response.

[0075] 3. System reliability: When one system fails, another system can take over to ensure continuous positioning services.

[0076] 4. Difficulty in data fusion: Traditional navigation systems lack an effective data fusion mechanism, resulting in inefficient information processing.

[0077] The present invention achieves the following beneficial effects: 1. Improve positioning accuracy: By integrating GPS and IMU data, the accuracy and stability of positioning can be significantly improved.

[0078] 2. Enhance system robustness: Implement redundant system design to maintain reliable positioning services even in harsh environments.

[0079] 3. Optimize real-time processing capabilities: The use of STM32 microcontrollers can achieve fast, real-time data processing and improve system response speed.

[0080] 4. Reduce noise pollution: Accurately control locomotive horns to reduce unnecessary noise and provide residents with a better living environment.

[0081] In a preferred embodiment, the present application further provides an electronic device, the electronic device comprising: A memory; and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method for intelligently limiting the sound of a railway locomotive is implemented. The computer device can be broadly a server, a terminal, or any other electronic device with necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, a memory, a network interface, a communication interface, etc. connected via a system bus. The processor of the computer device can be used to provide necessary computing, processing and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and an internal memory. An operating system, a computer program, etc. may be stored in or on the non-volatile storage medium. The internal memory may provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, the steps of the method of the present invention are performed.

[0082] The present invention may be implemented as a computer-readable storage medium having a computer program stored thereon, which causes the steps of the method of an embodiment of the present invention to be executed when executed by a processor. In one embodiment, the computer program is distributed on a plurality of computer devices or processors coupled to a network so that the computer program is stored, accessed and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be performed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be performed by one or more computer devices or processors, and one or more other method steps / operations may be performed by one or more other computer devices or processors. One or more computer devices or processors may perform a single method step / operation, or perform two or more method steps / operations.

[0083] It will be appreciated by a person skilled in the art that the method steps of the present invention may be performed by instructing related hardware such as a computer device or a processor through a computer program, and the computer program may be stored in a non-temporary computer-readable storage medium, which causes the steps of the present invention to be performed when the computer program is executed. Depending on the circumstances, any reference to memory, storage, database, or other media herein may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0084] Furthermore, the present invention provides a data fusion algorithm: Through filtering algorithm, efficient data fusion is achieved.

[0085] Furthermore, the present invention provides a location-based whistle control system: Design an intelligent whistle control system to automatically manage whistle behavior according to the real-time position of the locomotive, improve safety and reduce noise.

[0086] Furthermore, the present invention adopts a modular design: The modular design allows each component of the system to be updated and maintained independently, improving the flexibility and scalability of the system.

[0087] The present invention solves the following technical problems of the prior art: 1. Insufficient positioning accuracy: A single GPS system is unstable in complex environments and cannot meet high-precision requirements.

[0088] 2. Real-time requirements: In fast-moving application scenarios, sensor data needs to be processed in real time to ensure timely response.

[0089] 3. System reliability: When one system fails, another system can take over to ensure continuous positioning services.

[0090] 4. Difficulty in data fusion: Traditional navigation systems lack an effective data fusion mechanism, resulting in inefficient information processing.

[0091] The present invention achieves the following beneficial effects: 1. Improve positioning accuracy: By integrating GPS and IMU data, the accuracy and stability of positioning can be significantly improved.

[0092] 2. Enhance system robustness: Implement redundant system design to maintain reliable positioning services even in harsh environments.

[0093] 3. Optimize real-time processing capabilities: The use of STM32 microcontrollers can achieve fast, real-time data processing and improve system response speed.

[0094] 4. Reduce noise pollution: Accurately control locomotive horns to reduce unnecessary noise and provide residents with a better living environment.

[0095] The various technical features described above can be combined arbitrarily. Although all possible combinations of these technical features are not described, any combination of these technical features should be considered to be covered by this specification as long as there is no contradiction in such combination.

[0096] The specific implementation of the present invention described above does not constitute a limitation on the protection scope of the present invention. Any other corresponding changes and modifications made based on the technical concept of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for intelligently limiting the sound of a railway locomotive, characterized in that: The method comprises: S100, obtaining GPS data and inertial sensor data of the rail vehicle; S200, performing data fusion based on the acquired GPS data and inertial sensor data of the rail locomotive to obtain fused position data of the rail locomotive; S300: Determine whether the fused position data of the rail locomotive is outside a preset geographic fence, and determine whether to control a whistle function of the rail locomotive to be turned off according to the determination result.

2. The intelligent horn limiting method for rail locomotives according to claim 1 is characterized in that: The step of determining whether to control the whistle function of the rail vehicle to be turned off according to the determination result includes: If the fused location data of the rail locomotive is not outside the preset geographic fence, the whistle function of the rail locomotive is turned off; If the fused position data of the rail locomotive is outside the preset geographic fence, the whistle function of the rail locomotive is turned on.

3. The intelligent horn limiting method for rail locomotives according to claim 1 is characterized in that: The method of performing data fusion based on the acquired GPS data and inertial sensor data of the rail vehicle to obtain fused position data of the rail vehicle includes: (1) The running state of a rail vehicle is expressed by the following formula: X k =[P k ,V k ] T Among them, X k is the running state of the rail locomotive, P k is the position information of the vehicle at time k, Vk is the velocity vector of the vehicle at time k, and T represents the matrix transpose; (2) The state prediction model based on inertial sensor data is expressed as follows: X k+1 =A k X k +B k u k +W k Among them, A k is the state transition matrix, X k is the running state of the locomotive at time k, X k+1 is the running state of the locomotive at time k+1, B k is the control input matrix, u k is the acceleration and angular velocity data output by the inertial sensor, W k is the system noise; (3) Express GPS data using observation equations: Z k =H k X k +V k Among them, Z k The observation value provided by the GPS device, X k is the running state of the locomotive at time k, H k is the observation matrix, V k To measure noise; (4) Use filtering methods to fuse the predicted values ​​of the state prediction model based on inertial sensor data and the observed values ​​provided by the GPS device: X k =X k|k-1 +K k（ Z k -H k X k|k-1） Among them, X k|k-1 is the predicted state at time k-1, X k is the running state of the locomotive at time k, K k is the filter gain, which determines the weight of the predicted value and the observed value, Z k The observation value provided by the GPS device, H k is the observation matrix.

4. The intelligent horn limiting method for rail locomotives according to claim 3 is characterized in that: The filter gain K k The calculation method is: According to the state prediction error covariance matrix P k|k-1 and the observation error covariance matrix R k Calculate the filter gain K k , the specific formula is: K k =P k|k-1 （ H k P k|k-1 +R k） -1 Among them, P k|k-1为 State prediction error covariance matrix, R k is the observation error covariance matrix, H k is the observation matrix, represents the transpose of the observation error covariance matrix.

5. The intelligent horn limiting method for rail locomotives according to claim 3 is characterized in that: The determining whether the location data of the rail vehicle is outside a preset geographic fence includes: The running state X of the locomotive at time k obtained in step (4) k The current position data of the rail locomotive includes the current latitude and longitude coordinates of the rail locomotive. curent ,lon current ); The center coordinates of the preset geofence are expressed as (lat i， lon i ); The distance di between the current position data of the rail vehicle and the center coordinates of the preset geographic fence is calculated by the following formula: di=Calculate_Distance(lat curent ,lon current ,lat i ,lon i ) Among them, Calculate_Distance is the function used to calculate the spherical distance, lat curent is the current longitude coordinate of the locomotive, lon current is the current latitude coordinate of the rail vehicle, Calculate_Distance is the function used to calculate the spherical distance, lat i is the longitude coordinate of the center of the preset geofence, lon i is the latitude coordinate of the center of the preset geofence, and i is the number of the preset geofence.

6. The intelligent horn limiting method for rail locomotives according to claim 5 is characterized in that: The determining whether the location data of the rail vehicle is outside a preset geographic fence includes: Determine whether di is less than or equal to a preset radius ri of a preset geographic fence; If di is less than or equal to the preset radius ri, it is determined that the vehicle is not outside the preset geographic fence; If di is greater than the preset radius ri, it is determined that the vehicle is outside the preset geographic fence; Where i is the number of the preset geo-fence.

7. The intelligent horn limiting method for rail locomotives according to claim 2 is characterized in that: The controlling the whistle function of the rail locomotive to be turned off also includes: Control the LED indicator light for indicating whistle off, which is arranged on the rail locomotive, to flash.

8. The intelligent horn limiting method for rail locomotives according to claim 2 is characterized in that: The control of the whistle function of the rail locomotive is turned on, and further comprises: The LED indicator light indicating the whistle is turned off and is set on the rail locomotive is turned off.

9. An intelligent horn limiting system for rail locomotives, characterized in that: include: An acquisition module is used to acquire GPS data and inertial sensor data of rail vehicles; A control module, used for performing data fusion based on the acquired GPS data and inertial sensor data of the rail locomotive to obtain fused position data of the rail locomotive; It is used to determine whether the fused position data of the rail locomotive is outside the preset geographic fence, and determine whether to control the whistle function of the rail locomotive to be turned off based on the judgment result.

10. An electronic device, characterized in that: include: Memory; and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the intelligent horn limiting method for railway locomotives according to any one of claims 1 to 8 is implemented.

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