Intelligent speed limiting method and device for electric bicycle, electronic equipment and storage medium

Abstract

The invention provides an intelligent speed limiting method and device for an electric bicycle, electronic equipment and a storage medium, and the method comprises the steps: obtaining environment sensing data in the riding process of the electric bicycle, and the environment sensing data comprises weather data, road condition data and traffic scene data; based on an intelligent speed limit model, applying the environment sensing data to obtain a speed limit threshold value; speed limiting is carried out on the electric bicycle based on the speed limiting threshold value; the intelligent speed limiting model is constructed based on a machine learning algorithm. According to the method and the device provided by the invention, the weather data, the road condition data and the traffic scene data in the riding process of the electric bicycle are acquired as the environment sensing data, and the environment sensing data are analyzed based on the intelligent speed limiting model, so that the speed limiting threshold value fitting the current environment sensing data is obtained; and the flexibility, intelligence and accuracy of the speed limit threshold are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric motorcycles, and in particular to an intelligent speed limiting method, device, electronic equipment and storage medium for electric motorcycles. Background Art

[0002] With the booming development of the sharing economy, shared electric motorcycles have become an important choice for short-distance travel in cities. However, due to the uncertainty of riders' behavior, the variability of weather and road conditions, and the complexity of traffic scenarios, shared electric motorcycles bring convenience but also bring safety risks. The traditional speed limit method is usually for staff to set a unified speed limit threshold.

[0003] However, traditional speed limit methods are often too simple and cannot be flexibly adjusted according to actual conditions, and are not ideal for improving the safety of riding motorcycles. Summary of the invention

[0004] The present invention provides an intelligent speed limiting method, device, electronic equipment and storage medium for an electric motorcycle, so as to solve the defect that the speed limiting method in the prior art cannot be flexibly adjusted according to actual conditions.

[0005] The present invention provides an intelligent speed limiting method for an electric motorcycle, comprising: Acquire environmental perception data during motorcycle riding, wherein the environmental perception data includes weather data, road condition data, and traffic scene data; Applying the environmental perception data based on the intelligent speed limit model to obtain a speed limit threshold; Enforcing a speed limit on the motorcycle based on the speed limit threshold; The intelligent speed limit model is constructed based on a machine learning algorithm.

[0006] According to an intelligent speed limit method for an electric motorcycle provided by the present invention, the method of applying the environmental perception data based on the intelligent speed limit model to obtain a speed limit threshold includes: Obtaining the current speed and current position of the motorcycle; determining a regional reference speed matching the current position; In a case where the current speed exceeds the regional reference speed, the speed limit threshold is obtained by applying the environmental perception data based on the intelligent speed limit model.

[0007] According to an intelligent speed limiting method for an electric motorcycle provided by the present invention, the speed limiting method for the electric motorcycle based on the speed limit threshold comprises: When the current speed exceeds the speed limit threshold, generating first speed limit warning information based on the speed limit threshold; displaying the first speed limit warning information, and executing speed limit on the motorcycle based on the speed limit threshold; The first speed limit warning information is used to remind the user that the speed limit will be implemented on the motorcycle.

[0008] According to an intelligent speed limiting method for an electric motorcycle provided by the present invention, the speed limiting of the electric motorcycle based on the speed limit threshold value further includes: When the current speed does not exceed the speed limit threshold, generating second speed limit warning information based on the speed limit threshold; The second speed limit warning information is displayed.

[0009] According to an intelligent speed limiting method for an electric motorcycle provided by the present invention, the training steps of the intelligent speed limiting model include: Acquire sample environmental perception data, speed threshold labels corresponding to the sample environmental perception data, and an initial speed limit model; Applying the sample environment perception data based on the initial speed limit model to obtain a sample speed limit threshold; A training loss is calculated based on the sample speed limit threshold and the speed threshold label corresponding to the sample environmental perception data, so as to adjust the parameters of the initial speed limit model based on the training loss to obtain the intelligent speed limit model.

[0010] According to an intelligent speed limiting method for an electric motorcycle provided by the present invention, the weather data is obtained based on a temperature sensor and a humidity sensor; The road condition data is obtained by comprehensive analysis based on acceleration data and displacement data, wherein the acceleration data is obtained based on an acceleration sensor, and the displacement data is obtained based on a displacement sensor; The traffic scene data is obtained based on image recognition of a riding environment image; the riding environment image is obtained based on a camera, and the camera is set on the motorcycle.

[0011] The present invention also provides an intelligent speed limiting device for an electric motorcycle, comprising: An acquisition unit, for acquiring environmental perception data during the riding of an electric motorcycle, wherein the environmental perception data includes weather data, road condition data, and traffic scene data; A threshold determination unit, applying the environmental perception data based on an intelligent speed limit model to obtain a speed limit threshold; A speed limiting unit, which limits the speed of the motorcycle based on the speed limit threshold; The intelligent speed limit model is constructed based on a machine learning algorithm.

[0012] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, an intelligent speed limiting method for an electric motorcycle as described above is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the intelligent speed limiting method for an electric motorcycle as described in any one of the above-mentioned methods is implemented.

[0014] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the intelligent speed limiting method for an electric motorcycle as described above is implemented.

[0015] The intelligent speed limit method, device, electronic device and storage medium for an electric motorcycle provided by the present invention obtain weather data, road condition data and traffic scene data during the riding of the electric motorcycle as environmental perception data, analyze the environmental perception data based on an intelligent speed limit model, and obtain a speed limit threshold that fits the current environmental perception data, thereby improving the flexibility, intelligence and accuracy of the speed limit threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a schematic diagram of the flow of the intelligent speed limiting method for an electric motorcycle provided by the present invention; Figure 2 It is a structural schematic diagram of the intelligent speed limiting device for an electric motorcycle provided by the present invention; Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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.

[0019] It should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0020] In response to the above problems, the present invention provides an intelligent speed limit method for an electric motorcycle to achieve intelligent speed limit based on actual riding scenarios, thereby effectively improving the safety of riding an electric motorcycle. Figure 1 is a flow chart of the intelligent speed limiting method for motorcycles provided by the present invention, such as Figure 1 As shown, the method includes: Step 110, obtaining environmental perception data during motorcycle riding, wherein the environmental perception data includes weather data, road condition data, and traffic scene data.

[0021] Step 120, applying the environmental perception data based on an intelligent speed limit model to obtain a speed limit threshold; the intelligent speed limit model is constructed based on a machine learning algorithm.

[0022] Step 130: Limit the speed of the motorcycle based on the speed limit threshold.

[0023] Specifically, first, corresponding sensor data can be collected through the multi-source sensors installed on the motorcycle to obtain environmental perception data during the riding of the motorcycle, and the environmental perception data can be uploaded to the big data processing platform through the central control on the motorcycle.

[0024] The environmental perception data here includes weather data, road condition data and traffic scene data. Among them, the weather data can be obtained by collecting sensor signals through the temperature and humidity sensors set on the motorcycle, or the weather forecast information of the motorcycle riding area can be obtained through the Internet. Alternatively, the temperature and humidity sensors can be combined to collect sensor signals and weather forecasts to obtain weather data.

[0025] The road condition data here can obtain acceleration data and displacement data through the acceleration sensor and displacement sensor installed on the motorcycle, and then perform data analysis based on the acceleration data and displacement data, analyze the magnitude, direction, and change rate of acceleration and displacement, and obtain the road condition data during the motorcycle riding process. For example, when the magnitude and change rate of the acceleration data and displacement data in this direction are small, the road condition data may be uneven, with potholes or obstacles; when the magnitude and change rate of the acceleration data and displacement data in this direction are large, the road condition data may be flat.

[0026] In addition, the traffic scene data here can obtain the environmental image around the motorcycle through the camera installed on the motorcycle, and perform image recognition on the environmental image through the image recognition algorithm to determine whether the riding environment is a congested section or a non-congested section.

[0027] Next, after the environmental perception data during motorcycle riding is obtained on the big data processing platform, the environmental perception data can be input into the intelligent speed limit model, and the environmental perception data can be applied by the intelligent speed limit model for comprehensive analysis to obtain a speed limit threshold that matches the environmental perception data item. The intelligent speed limit model here can be constructed based on a machine learning algorithm, such as a neural network, and pre-trained based on pre-labeled sample environmental perception data and speed threshold labels that correspond one-to-one to the sample environmental perception data.

[0028] Finally, the speed limit threshold can be sent to the corresponding motorcycle, and the speed limit and warning prompt can be executed on the motorcycle based on the speed limit threshold. For example, a speed limit instruction can be sent to the central control to adjust the power output of the motorcycle to achieve speed limit.

[0029] It should be noted that intelligent speed limit based on environmental perception data effectively avoids speeding in bad weather, complex road conditions and high-risk traffic scenarios, reducing the risk of traffic accidents. In addition, intelligent speed limit based on different environmental perception conditions not only ensures safety, but also meets the travel needs of users as much as possible and improves the riding experience. At the same time, big data analysis and intelligent speed limit will help urban traffic management departments better understand the operating status of shared electric motorcycles and optimize urban traffic management.

[0030] The method provided by the embodiment of the present invention obtains weather data, road condition data and traffic scene data during the riding of an electric motorcycle as environmental perception data, analyzes the environmental perception data based on an intelligent speed limit model, and obtains a speed limit threshold that fits the current environmental perception data, thereby improving the flexibility, intelligence and accuracy of the speed limit threshold.

[0031] Based on any of the above embodiments, step 120 includes: Obtaining the current speed and current position of the motorcycle; determining a regional reference speed matching the current position; In a case where the current speed exceeds the regional reference speed, the speed limit threshold is obtained by applying the environmental perception data based on the intelligent speed limit model.

[0032] Here, the regional benchmark speed refers to the average speed of motorcycles in the area, which can be calculated by counting the daily speeds of multiple motorcycles in the area and taking the average value.

[0033] Specifically, first, the current speed of the motorcycle can be obtained through the motorcycle's speed sensor, and the current position of the motorcycle can be obtained through the motorcycle's positioning unit. The positioning unit here can be a GPS (Global Positioning System) positioning unit or a Beidou satellite positioning unit. Then, the regional benchmark speed that matches the current position can be determined based on the current position. For example, the regional benchmark speed in the central area is 15km / h, and the regional benchmark speed in the non-central area is 25km / h.

[0034] It should be noted that the basic speeds of motorcycles in different areas are different. The current position of the motorcycle is used to determine the regional benchmark speed that matches the current position, so that the speed threshold can be calculated more flexibly and computing resources can be saved.

[0035] Furthermore, after obtaining the regional reference speed matching the current position, the current speed of the motorcycle can be compared with the regional reference speed. When the current speed exceeds the regional reference speed, the speed limit threshold is obtained based on the intelligent speed limit model using environmental perception data. When the current speed does not exceed the regional reference speed, the speed limit threshold may not be calculated to save computing resources.

[0036] It should be noted that if the current speed exceeds the regional benchmark speed, it means that the user may be at risk of speeding when riding the motorcycle in the future, and the speed limit threshold calculation is required to ensure the safety of riding. If the current speed does not exceed the regional benchmark speed, it means that the user's riding speed is far from the speed limit threshold, and the current riding safety is relatively high, so the speed limit threshold calculation does not need to be performed.

[0037] The method provided by the embodiment of the present invention determines the regional benchmark speed that matches the current position, compares the current speed of the motorcycle with the regional benchmark speed, and decides whether to perform speed limit threshold calculation to achieve speed limit control of the motorcycle, thereby achieving flexible speed limit threshold calculation and saving computing resources.

[0038] Based on any of the above embodiments, step 130 includes: When the current speed exceeds the speed limit threshold, generating first speed limit warning information based on the speed limit threshold; displaying the first speed limit warning information, and executing speed limit on the motorcycle based on the speed limit threshold; The first speed limit warning information is used to remind the user that the speed limit will be implemented on the motorcycle.

[0039] Here, the first speed limit warning information is used to warn the user that the current driving speed has exceeded the speed limit threshold, and to inform the user that the speed of the motorcycle will be limited based on the speed limit threshold.

[0040] Specifically, when the current speed exceeds the speed limit threshold, the first speed limit warning information can be generated according to the speed limit threshold. The first speed limit warning information here can be "You are currently speeding! Your driving speed will be reduced to below the current speed limit of 15km / h."

[0041] Then, the first speed limit warning information can be displayed through the central control display screen of the motorcycle or the riding APP, and the speed limit can be implemented on the motorcycle to reduce the current speed of the motorcycle to below the speed limit threshold.

[0042] The method provided by an embodiment of the present invention generates a first speed limit warning message based on the speed limit threshold when the current speed exceeds the speed limit threshold, so as to inform the user that the speed of the motorcycle will be limited. This allows the user to know in advance that the speed of the motorcycle will be limited based on the speed limit threshold, thereby ensuring riding safety.

[0043] Based on any of the above embodiments, step 130 further includes: When the current speed does not exceed the speed limit threshold, generating second speed limit warning information based on the speed limit threshold; The second speed limit warning information is displayed.

[0044] Here, the second speed limit warning information may be used to remind the user of a specific numerical value corresponding to the current speed limit, so as to instruct the user that the driving speed does not exceed the speed limit threshold during subsequent riding.

[0045] Specifically, when the current speed does not exceed the speed limit threshold, the second speed limit warning information can be generated based on the speed limit threshold. For example, the second speed limit warning information can be "The current speed limit threshold is 15km / h, you are not speeding, please keep it up!" Similarly, the second speed limit warning information can be displayed on the central control display screen of the motorcycle or the riding APP, and the speed limit of the motorcycle is not enforced here.

[0046] Based on any of the above embodiments, the training step of the intelligent speed limit model includes: Acquire sample environmental perception data, speed threshold labels corresponding to the sample environmental perception data, and an initial speed limit model; Applying the sample environment perception data based on the initial speed limit model to obtain a sample speed limit threshold; A training loss is calculated based on the sample speed limit threshold and the speed threshold label corresponding to the sample environmental perception data, so as to adjust the parameters of the initial speed limit model based on the training loss to obtain the intelligent speed limit model.

[0047] Specifically, first, sample environmental perception data, speed threshold labels corresponding to the sample environmental perception data, and an initial speed limit model are obtained. Here, the initial speed limit model can be a neural network model whose parameters are to be adjusted. The sample environmental perception data here can be obtained based on environmental perception data collected during the historical riding of the motorcycle. Then, the environmental perception data can be manually labeled to obtain speed threshold labels corresponding to the sample environmental perception data.

[0048] Next, in the training phase, the sample environment perception data is applied based on the initial speed limit model to obtain a sample speed limit threshold. For example, the sample environment perception data can be input into the initial speed limit model, and the sample environment perception data can be analyzed by the initial speed limit model to obtain a sample speed limit threshold.

[0049] Furthermore, in each training round, the training loss can be calculated by the sample speed limit threshold and the speed threshold label corresponding to the sample environmental perception data, so as to adjust the parameters of the initial speed limit model based on the training loss. Finally, after all the training rounds, the final intelligent speed limit model is obtained, that is, the intelligent speed limit model that can analyze and obtain accurate speed limit thresholds based on environmental perception data.

[0050] Based on any of the above embodiments, the weather data is obtained based on a temperature sensor and a humidity sensor; The road condition data is obtained by comprehensive analysis based on acceleration data and displacement data, wherein the acceleration data is obtained based on an acceleration sensor, and the displacement data is obtained based on a displacement sensor; The traffic scene data is obtained based on image recognition of a riding environment image; the riding environment image is obtained based on a camera, and the camera is set on the motorcycle.

[0051] Here, the weather data is collected by the temperature sensor and humidity sensor installed on the motorcycle. Then, the temperature data and humidity data are comprehensively analyzed to obtain the weather data. It can be understood that compared with directly obtaining the weather data of the current location of the motorcycle based on the Internet, the temperature sensor and humidity sensor installed on the motorcycle collect real-time data, and the real-time collected data is comprehensively analyzed to obtain more accurate weather data, thereby improving the accuracy of the speed limit threshold obtained by analyzing the weather data.

[0052] Here, the road condition data is obtained by comprehensive analysis based on acceleration data and displacement data, the acceleration data is obtained based on an acceleration sensor, and the displacement data is obtained based on a displacement sensor.

[0053] Similarly, by combining multiple sensors from different sources to obtain sensor data, and performing comprehensive analysis based on the multiple sensor data, more accurate road condition data can be obtained, thereby improving the accuracy of the speed limit threshold obtained based on the analysis of the road condition data.

[0054] In addition, the traffic scene data may be obtained by performing image recognition on a riding environment image; the riding environment image is obtained based on a camera, and the camera is disposed on the electric motorcycle.

[0055] Based on any of the above embodiments, Figure 2 FIG. 1 is a schematic diagram of the structure of the intelligent speed limiting device for an electric motorcycle provided by the present invention. Figure 2 As shown, the device comprises: An acquisition unit 210 acquires environmental perception data during the riding process of the motorcycle, wherein the environmental perception data includes weather data, road condition data, and traffic scene data; A threshold determination unit 220, applying the environmental perception data based on the intelligent speed limit model to obtain a speed limit threshold; A speed limiting unit 230, which limits the speed of the motorcycle based on the speed limit threshold; The intelligent speed limit model is constructed based on a machine learning algorithm.

[0056] The device provided by the embodiment of the present invention obtains weather data, road condition data and traffic scene data during the riding of an electric motorcycle as environmental perception data, analyzes the environmental perception data based on an intelligent speed limit model, and obtains a speed limit threshold that fits the current environmental perception data, thereby improving the flexibility, intelligence and accuracy of the speed limit threshold.

[0057] Based on any of the above embodiments, the threshold determination unit is specifically used for: Obtaining the current speed and current position of the motorcycle; determining a regional reference speed matching the current position; In a case where the current speed exceeds the regional reference speed, the speed limit threshold is obtained by applying the environmental perception data based on the intelligent speed limit model.

[0058] Based on any of the above embodiments, the speed limiting unit is specifically used for: When the current speed exceeds the speed limit threshold, generating first speed limit warning information based on the speed limit threshold; displaying the first speed limit warning information, and executing speed limit on the motorcycle based on the speed limit threshold; The first speed limit warning information is used to remind the user that the speed limit will be implemented on the motorcycle.

[0059] Based on any of the above embodiments, the speed limiting unit is specifically used for: When the current speed does not exceed the speed limit threshold, generating second speed limit warning information based on the speed limit threshold; The second speed limit warning information is displayed.

[0060] Based on any of the above embodiments, the device further includes a training unit, and the training unit is specifically used for: Acquire sample environmental perception data, speed threshold labels corresponding to the sample environmental perception data, and an initial speed limit model; Applying the sample environment perception data based on the initial speed limit model to obtain a sample speed limit threshold; A training loss is calculated based on the sample speed limit threshold and the speed threshold label corresponding to the sample environmental perception data, so as to adjust the parameters of the initial speed limit model based on the training loss to obtain the intelligent speed limit model.

[0061] Based on any of the above embodiments, the weather data is obtained based on a temperature sensor and a humidity sensor; The road condition data is obtained by comprehensive analysis based on acceleration data and displacement data, wherein the acceleration data is obtained based on an acceleration sensor, and the displacement data is obtained based on a displacement sensor; The traffic scene data is obtained based on image recognition of a riding environment image; the riding environment image is obtained based on a camera, and the camera is set on the motorcycle.

[0062] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3 As shown, the electronic device may include: a processor 310, a communications interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communications interface 320 and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the intelligent speed limit method of the motorcycle, the method comprising: obtaining environmental perception data during the riding of the motorcycle, the environmental perception data including weather data, road condition data and traffic scene data; applying the environmental perception data based on the intelligent speed limit model to obtain a speed limit threshold; executing speed limit on the motorcycle based on the speed limit threshold; the intelligent speed limit model is constructed based on a machine learning algorithm.

[0063] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0064] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the intelligent speed limit method for electric motorcycles provided by the above methods. The method includes: obtaining environmental perception data during the riding of an electric motorcycle, the environmental perception data including weather data, road condition data and traffic scene data; applying the environmental perception data based on an intelligent speed limit model to obtain a speed limit threshold; executing speed limit on the electric motorcycle based on the speed limit threshold; the intelligent speed limit model is constructed based on a machine learning algorithm.

[0065] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the intelligent speed limit method for electric motorcycles provided by the above methods. The method includes: obtaining environmental perception data during the riding of the electric motorcycle, the environmental perception data including weather data, road condition data and traffic scene data; applying the environmental perception data based on an intelligent speed limit model to obtain a speed limit threshold; executing speed limit on the electric motorcycle based on the speed limit threshold; the intelligent speed limit model is constructed based on a machine learning algorithm.

[0066] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0067] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent speed limiting method for an electric motorcycle, characterized in that: include: Acquire environmental perception data during motorcycle riding, wherein the environmental perception data includes weather data, road condition data, and traffic scene data; Applying the environmental perception data based on the intelligent speed limit model to obtain a speed limit threshold; Enforcing a speed limit on the motorcycle based on the speed limit threshold; The intelligent speed limit model is constructed based on a machine learning algorithm.

2. The intelligent speed limiting method for an electric motorcycle according to claim 1, characterized in that: The applying the environment perception data based on the intelligent speed limit model to obtain the speed limit threshold includes: Obtaining the current speed and current position of the motorcycle; determining a regional reference speed matching the current position; In a case where the current speed exceeds the regional reference speed, the speed limit threshold is obtained by applying the environmental perception data based on the intelligent speed limit model.

3. The intelligent speed limiting method for electric motorcycles according to claim 2, characterized in that: The step of limiting the speed of the motorcycle based on the speed limit threshold comprises: When the current speed exceeds the speed limit threshold, generating first speed limit warning information based on the speed limit threshold; displaying the first speed limit warning information, and executing speed limit on the motorcycle based on the speed limit threshold; The first speed limit warning information is used to remind the user that the speed limit will be implemented on the motorcycle.

4. The intelligent speed limiting method for an electric motorcycle according to claim 2, characterized in that: The executing of speed limit on the motorcycle based on the speed limit threshold further includes: When the current speed does not exceed the speed limit threshold, generating second speed limit warning information based on the speed limit threshold; The second speed limit warning information is displayed.

5. The intelligent speed limiting method for an electric motorcycle according to any one of claims 2 to 4, characterized in that: The training steps of the intelligent speed limit model include: Acquire sample environmental perception data, speed threshold labels corresponding to the sample environmental perception data, and an initial speed limit model; Applying the sample environment perception data based on the initial speed limit model to obtain a sample speed limit threshold; A training loss is calculated based on the sample speed limit threshold and the speed threshold label corresponding to the sample environmental perception data, so as to adjust the parameters of the initial speed limit model based on the training loss to obtain the intelligent speed limit model.

6. The intelligent speed limiting method for an electric motorcycle according to any one of claims 1 to 4, characterized in that: The weather data is obtained based on a temperature sensor and a humidity sensor; The road condition data is obtained by comprehensive analysis based on acceleration data and displacement data, wherein the acceleration data is obtained based on an acceleration sensor, and the displacement data is obtained based on a displacement sensor; The traffic scene data is obtained based on image recognition of a riding environment image; the riding environment image is obtained based on a camera, and the camera is set on the motorcycle.

7. An intelligent speed limiter for an electric motorcycle, characterized in that: include: An acquisition unit, for acquiring environmental perception data during the riding of an electric motorcycle, wherein the environmental perception data includes weather data, road condition data, and traffic scene data; A threshold determination unit, applying the environmental perception data based on an intelligent speed limit model to obtain a speed limit threshold; A speed limiting unit, which limits the speed of the motorcycle based on the speed limit threshold; The intelligent speed limit model is constructed based on a machine learning algorithm.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the intelligent speed limiting method for an electric motorcycle as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the intelligent speed limiting method for an electric motorcycle as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the intelligent speed limiting method for an electric motorcycle as described in any one of claims 1 to 6 is implemented.

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