Vehicle instrument equipment management and control method and management and control system supporting online processing

Abstract

The invention provides a vehicle instrument equipment management and control method and management and control system supporting online processing, and relates to the technical field of vehicle instrument display, and the method comprises the steps: determining a screen display layout template library and a real-time riding scene; after the real-time screen display layout is scheduled in the screen display layout template library, the standardized screen display configuration of the vehicle instrument equipment is smoothly replaced; after real-time environment brightness is received, a brightness adjusting strategy is output; performing screen display layout analysis according to the real-time weather data and the real-time terrain data; refreshing a real-time instrument screen display by adopting a brightness adjustment strategy and a layout optimization strategy; and presetting a screen display refreshing window, and executing self-adaptive periodic online refreshing control on the real-time instrument screen display by taking the screen display refreshing window as a constraint. According to the method and the device, the technical problem of relatively low instrument equipment management and control adaptability caused by lack of functions of sensing real-time index changes and recognizing scenes in the prior art can be solved, and the instrument equipment management and control adaptability is improved by scheduling and adjusting the screen display dynamic state in real time.

Description

Technical Field

[0001] The present application relates to the field of vehicle instrument display technology, and in particular to a vehicle instrument equipment control method and control system supporting online processing. Background Art

[0002] With the rapid development of intelligent transportation technology, cycling vehicles (such as electric bicycles, shared bicycles, etc.) have gradually become an important part of modern urban transportation. As an important part of cycling vehicles, vehicle instrument equipment not only provides basic cycling data (such as speed, mileage, power, etc.), but also undertakes the key task of information interaction between users and vehicles. In order to meet the increasingly diverse needs of users, the existing cycling vehicle instrument equipment management and control methods mainly adopt a fixed screen layout and a static indicator display method, which usually only provides the display of basic data such as speed, mileage, and power, resulting in the user's information needs in different cycling scenarios (such as commuting, mountain, competition, etc.) are difficult to meet, thus affecting the efficiency and safety of cycling.

[0003] To sum up, the existing technology has technical problems such as low adaptability of instrument equipment control due to the lack of perception of real-time indicator changes and scene recognition functions, which further affects riding efficiency and safety. Summary of the invention

[0004] The purpose of this application is to provide a vehicle instrument equipment control method and control system that supports online processing, so as to solve the technical problem in the prior art that the adaptability of instrument equipment control is low due to the lack of perception of real-time indicator changes and scene recognition functions, which further affects riding efficiency and safety.

[0005] In view of the above problems, the present application provides a vehicle instrument equipment control method and control system that supports online processing.

[0006] In a first aspect, the present application provides a vehicle instrument equipment control method that supports online processing, and the vehicle instrument equipment control method that supports online processing is implemented by a vehicle instrument equipment control system that supports online processing, wherein the vehicle instrument equipment control method that supports online processing includes: receiving historical riding data, and performing retrospective focus analysis on the historical riding data to obtain a screen layout template library, wherein the screen layout template library stores a plurality of sample riding scenes and a plurality of sample screen layouts in association; fusing and analyzing instrument input data and real-time terrain data to output a real-time riding scene, wherein the real-time terrain data is obtained in real time through a cloud navigation service; and generating a real-time riding scene based on the real-time riding scene. After the screen display layout template library schedules the real-time screen display layout, the real-time screen display layout is used to smoothly replace the standardized screen display configuration of the vehicle instrument equipment to generate a real-time instrument screen display; after receiving the real-time ambient brightness detected by the light sensor, the real-time ambient brightness is input into the ambient brightness adjustment network for brightness adjustment analysis, and a brightness adjustment strategy is output; real-time weather data is obtained through an API, and a screen display layout analysis is performed based on the real-time weather data and real-time terrain data, and a layout optimization strategy is output; the real-time instrument screen display is refreshed using the brightness adjustment strategy and layout optimization strategy; a screen display refresh window is preset, and the screen display refresh window is used as a constraint to perform adaptive periodic online refresh control on the real-time instrument screen display.

[0007] In the second aspect, the present application also provides a vehicle instrument equipment control system that supports online processing, which is used to execute a vehicle instrument equipment control method that supports online processing as described in the first aspect, wherein the vehicle instrument equipment control system that supports online processing includes: a historical data analysis module, the historical data analysis module is used to receive historical riding data, and perform retrospective focus analysis on the historical riding data to obtain a screen display layout template library, wherein the screen display layout template library is associated with and stores multiple sample riding scenes and multiple sample screen display layouts; a riding scene analysis module, the riding scene analysis module is used to fuse and analyze instrument input data and real-time terrain data, and output a real-time riding scene, wherein the real-time terrain data is obtained in real time through a cloud navigation service; a screen display layout scheduling module, the screen display layout scheduling module is used to schedule the real-time screen in the screen display layout template library according to the real-time riding scene. After the display layout is completed, the standardized display configuration of the vehicle instrument equipment is smoothly replaced by the real-time display layout to generate a real-time instrument display; an environmental brightness adjustment module, the environmental brightness adjustment module is used to receive the real-time environmental brightness detected and transmitted back by the light sensor, input the real-time environmental brightness into the environmental brightness adjustment network for brightness adjustment analysis, and output a brightness adjustment strategy; a display layout optimization module, the display layout optimization module is used to obtain real-time weather data through an API, and perform display layout analysis based on the real-time weather data and real-time terrain data, and output a layout optimization strategy; a display update module, the display update module is used to refresh the real-time instrument display by adopting the brightness adjustment strategy and layout optimization strategy; an online refresh control module, the online refresh control module is used to preset a display refresh window, and use the display refresh window as a constraint to perform adaptive periodic online refresh control on the real-time instrument display.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: By receiving historical riding data and performing retrospective analysis on the historical riding data, a screen display layout template library is obtained, wherein the screen display layout template library stores multiple sample riding scenes and multiple sample screen display layouts in association; the instrument input data and real-time terrain data are integrated and analyzed to output the real-time riding scene, wherein the real-time terrain data is obtained in real time through a cloud navigation service; after the real-time screen display layout is scheduled in the screen display layout template library according to the real-time riding scene, the standardized screen display configuration of the vehicle instrument equipment is smoothly replaced by the real-time screen display layout to generate a real-time instrument screen display; after receiving the real-time environmental brightness detected by the light sensor, the real-time environmental brightness is input into the environmental brightness adjustment network for brightness adjustment analysis, and a brightness adjustment strategy is output; real-time weather data is obtained through an API, and a screen display layout analysis is performed according to the real-time weather data and real-time terrain data, and a layout optimization strategy is output; the real-time instrument screen display is refreshed by using the brightness adjustment strategy and the layout optimization strategy; a screen display refresh window is preset, and the screen display refresh window is used as a constraint to perform adaptive periodic online refresh control on the real-time instrument screen display. In other words, by determining the screen display layout template library based on historical riding data, combining real-time terrain data and instrument data to determine the current riding scene, dynamically scheduling the real-time screen display layout, intelligently adjusting the display brightness and layout according to lighting conditions and weather changes, and presetting the screen display refresh window to automatically update the screen display information according to real-time changes, the adaptability and intelligence level of the vehicle's instrument equipment and the safety and efficiency of user riding are improved.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0011] Figure 1 A flowchart of a vehicle instrument equipment control method supporting online processing for this application; Figure 2 This is a structural diagram of a vehicle instrument equipment control system that supports online processing in this application.

[0012] Explanation of the accompanying drawings: historical data analysis module 11, riding scene analysis module 12, screen display layout scheduling module 13, ambient brightness adjustment module 14, screen display layout optimization module 15, screen display update module 16, online refresh control module 17. DETAILED DESCRIPTION

[0013] This application solves the technical problem in the prior art that the lack of perception of real-time indicator changes and scene recognition functions leads to low adaptability of instrument equipment control, which further affects riding efficiency and safety by providing a vehicle instrument equipment control method and control system that supports online processing. By determining the screen layout template library based on historical riding data, combining real-time terrain data and instrument data to determine the current riding scene, dynamically scheduling the real-time screen layout, intelligently adjusting the display brightness and layout according to lighting conditions and weather changes, and presetting the screen refresh window to automatically update the screen information according to real-time changes, the adaptability and intelligence level of vehicle instrument equipment and the safety and efficiency of user riding are improved.

[0014] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0015] For example, please refer to the attached Figure 1 The present application provides a vehicle instrument equipment control method supporting online processing, wherein the vehicle instrument equipment control method supporting online processing is applied to a vehicle instrument equipment control system supporting online processing, and the vehicle instrument equipment control method supporting online processing specifically includes the following steps: S100: receiving historical riding data, and performing retrospective analysis on the historical riding data to obtain a screen display layout template library, wherein the screen display layout template library stores a plurality of sample riding scenes and a plurality of sample screen display layouts in an associated manner.

[0016] Specifically, historical cycling data is received, including cycling records in different scenarios over a period of time in the past and the user's use and query behavior of various indicators. Retrospective attention analysis is performed on historical cycling data to count the frequency of users' attention to various cycling indicators in different cycling scenarios. Based on the results of the retrospective attention analysis, that is, the frequency of attention to various cycling indicators in each scenario, a corresponding screen layout template is designed for each cycling scenario. Retrospective attention analysis refers to analyzing historical cycling data, counting the user's attention to various indicators in different scenarios (such as heart rate, power, etc.), and designing the screen layout based on the attention.

[0017] The screen layout template library is used to store a database of predefined screen layouts. Each template is associated with a certain cycling scenario (such as commuting mode, competitive mode, etc.) and is used to dynamically adjust the screen information display and optimize the user experience. For example, the historical cycling data shows that the user has 100 query records in the commuting scenario, of which navigation is 50 times, speed is 25 times, and time is 25 times; the commuting mode screen layout is: navigation occupies 50% of the screen, as the main focus, located in the center of the screen; speed occupies 25% of the screen, located on the right side of the screen; time occupies 25% of the screen, located on the left side of the screen. The historical cycling data shows that the user has 40 query records in the competitive mode, of which speed is 16 times, power output is 12 times, and heart rate is 12 times. The competitive mode screen layout is: speed occupies 40% of the screen, as the core focus, displayed at the top of the screen; power output occupies 30% of the screen, displayed on the left side of the screen; heart rate occupies 30% of the screen, displayed on the right side of the screen.

[0018] Through this allocation, the screen layout can meet the actual needs of users in different scenarios and improve the user experience. The generated screen layout template is associated with the corresponding riding scene and stored in the screen layout template library to complete the construction of the screen layout template library. The role of the template library is to achieve the standardization of the screen layout and provide a reference for the real-time layout in different scenarios. According to different riding scenarios (such as commuting and competition), an optimized screen layout is provided to meet the core concerns of users in specific scenarios.

[0019] S200: integrating and analyzing instrument input data and real-time terrain data, and outputting a real-time riding scene, wherein the real-time terrain data is obtained in real time through a cloud navigation service.

[0020] Specifically, the current instrument input data is obtained, that is, the data collected from the instrument equipment of the riding vehicle (such as speedometer, power meter, power meter, etc.), including the current riding status, user physiological status and vehicle status, such as riding speed, mileage, remaining battery power, user physiological status (such as heart rate), etc. At the same time, the current terrain data is obtained, that is, the geographical and topographic information of the riding environment, including slope, road surface type (such as asphalt, soil, gravel, etc.), road conditions (such as flat road, uphill, downhill, etc.), etc., which helps to judge the complexity and difficulty of the current riding, usually obtained in real time through GPS, sensors and cloud navigation services. Cloud navigation service is a navigation service provided by cloud computing platform, which obtains and processes geographic information data in real time, can provide detailed terrain information, real-time traffic conditions, road conditions, etc., and can be dynamically updated according to the location and status of the rider.

[0021] By combining the instrument data and terrain information, the specific situation of the current riding can be identified. The instrument input data and real-time terrain data are input into the trained riding scene recognition network to identify the current riding scene, including flat roads, uphills, downhills, turns, bumpy roads, etc. The riding scene recognition network is a model constructed based on the CNN network, and localized training is performed through historical data (such as instrument input records, terrain records, and riding scenes) to complete the construction of the riding scene recognition network. Through the riding scene recognition network, multiple riding scene probability parameters are obtained. Probabilistic transfer analysis is performed on multiple riding scene probability parameters, and the current riding scene is determined by combining the most likely riding scene after the scene transfer. By considering the rider's status and environmental conditions at the same time, the current riding scene (such as uphill, flat ground, downhill, etc.) is accurately identified to ensure the accuracy and real-time nature of the information.

[0022] S300: After the real-time screen display layout is scheduled in the screen display layout template library according to the real-time riding scene, the standardized screen display configuration of the vehicle instrument equipment is smoothly replaced by the real-time screen display layout to generate a real-time instrument screen display.

[0023] Specifically, according to the determined real-time riding scene (such as commuting mode, competitive mode, mountain biking, etc.), the screen display layout template library is traversed to determine the current real-time screen display layout. In other words, according to the current riding scene, the screen display configuration retrieved and applied from the screen display layout template library determines the display mode and distribution of various indicators on the screen. When the scene switches, the currently displayed content (standardized screen display configuration) is smoothly replaced with a new screen display layout (real-time screen display layout). Smooth replacement technology is used to gradually change the screen content in an animated manner to avoid visual abruptness caused by direct jumps. Smooth replacement is a visual transition effect, usually achieved through transition animation, which makes the switching of the screen display layout smoother and does not produce abrupt or unnatural transitions. Gradients, sliding or other animation effects can be used to smoothly display changes in content.

[0024] Standardized screen configuration refers to the initial or default display mode of the device, which may include a fixed content layout, such as the default display of speed, power, time and other information. According to the current riding needs of the user corresponding to the real-time riding scene, determine which data is the most important and displayed first, and render and display various indicators (such as speed, power, heart rate, etc.) according to the selected layout configuration. For example, the information on the screen gradually disappears and is replaced by new content, and the color or content gradually transitions to show smooth changes; different screen display areas can be switched through sliding effects, and new information slides into the old information area, while the old information slides out of the area. When the user starts to enter the competitive mode, the screen will first show that the navigation information gradually disappears, while data such as speed, power output, and heart rate gradually appear. The content of the navigation area gradually slides out of the screen, while the speed, power, and heart rate data slide in from the edge of the screen. Through the introduction of animation or transition effects, the visual abruptness is reduced, and the user can smoothly transition to the new screen layout, which enhances the smoothness and comfort of operation and ensures that the device can display the indicators that users are most concerned about according to different riding scenarios (such as commuting, competition, etc.).

[0025] S400: After receiving the real-time ambient brightness detected by the light sensor, the real-time ambient brightness is input into the ambient brightness adjustment network for brightness adjustment analysis, and a brightness adjustment strategy is output.

[0026] Specifically, the ambient brightness is monitored in real time through the light sensor, and the detected real-time ambient brightness data is transmitted to the vehicle instrument control unit by wireless or wired means. The control unit inputs the real-time ambient brightness data detected by the light sensor into the ambient brightness adjustment network for brightness adjustment analysis. A light sensor is a sensor that can sense light intensity (such as natural light or artificial light) and is usually used to measure the brightness in the environment.

[0027] The ambient brightness adjustment network is a system or network used to process and analyze real-time ambient brightness data. It usually uses machine learning or adaptive algorithms to generate brightness adjustment strategies. According to the changing trend of ambient brightness data, it determines the appropriate adjustment strategy, such as increasing or decreasing screen brightness, controlling lights, etc. After receiving real-time ambient brightness data, the ambient brightness adjustment network evaluates the brightness of the current environment through built-in analysis algorithms (such as rule-based logic, machine learning, deep learning, etc.) and determines whether the display brightness or indoor lighting needs to be adjusted. Based on the analysis results, the system will output a set of brightness adjustment strategies, including increasing brightness, decreasing brightness, optimizing energy efficiency, etc.

[0028] Obtain a large number of ambient brightness samples and related parameters for training the ambient brightness adjustment network, that is, the ambient brightness set and the corresponding brightness adjustment strategy set for different environments and different time periods in the past period of time. Select a suitable neural network type, such as a convolutional neural network (CNN) or a recurrent neural network (RNN). Design the structure of the network, including the number of layers, the number of neurons in each layer, the activation function, etc. Use the collected data to adjust the parameters in the neural network so that it can accurately predict the brightness adjustment strategy. Perform data preprocessing on the ambient brightness set and the corresponding brightness adjustment strategy set, and divide them into a training set and a validation set. Train the neural network with the training set, and use an appropriate optimization algorithm (such as gradient descent) to adjust the network parameters to minimize the difference between the predicted value and the actual value. Verify the accuracy of the model with the validation set, and adjust the network structure or parameters according to the validation results to improve its generalization ability. Stop training until the model reaches the convergence condition, and integrate the trained ambient brightness adjustment network into the control unit of the vehicle instrument, so that it can receive ambient brightness data in real time for brightness adjustment analysis and output the brightness adjustment strategy.

[0029] Determine the corresponding brightness adjustment strategy based on the changes in the real-time ambient brightness. For example, if the ambient brightness is lower than a certain threshold (such as 300 Lux), the screen brightness is automatically increased or the light is turned on; if the ambient brightness is higher than a certain threshold (such as 7000 Lux), the screen brightness is automatically reduced or even unnecessary lights are turned off. By automatically adjusting the display device and light brightness according to the ambient brightness, the user's visual comfort is prevented from being affected by an overly bright or dark environment. When the ambient brightness is high, the device brightness is automatically lowered to save energy and extend battery life.

[0030] S500: Acquire real-time weather data through an API, perform screen display layout analysis based on the real-time weather data and real-time terrain data, and output a layout optimization strategy.

[0031] Specifically, by obtaining real-time weather data, weather information including temperature, humidity, wind speed, precipitation probability, UV intensity, etc. is obtained from the weather service platform through established interfaces. API (Application Programming Interface) is an interface that allows different software systems to communicate with each other, providing a standardized method to access specific functions or data. For example, use public weather APIs (such as OpenWeatherMap, WeatherAPI, etc.) to obtain current weather data. Real-time weather data is information about environmental conditions such as temperature, humidity, wind speed, precipitation, etc. provided in real time by weather service providers.

[0032] Through the Geographic Information System (GIS) or navigation service, real-time data about terrain, including road slope, curvature, road surface type, driving path, etc., are obtained. Real-time weather data and terrain data are input into the screen layout analysis module for comprehensive analysis. For example, the screen layout is adjusted according to different weather and terrain conditions, such as increasing the area for displaying temperature and humidity when the temperature is high; when the wind speed is high, the area for displaying wind speed data is increased because the wind speed has a greater impact on the rider; when the rider is detected entering an uphill section, the display priority of power output or driving pace data is increased to help the rider better adjust the strategy. According to the results of the screen layout analysis, the layout optimization strategy is output to determine the display priority and display content of each information on the display screen. According to the output layout optimization strategy, the content of the display screen is adjusted. For example, if the possibility of rain is detected to increase, the weather warning is displayed on the screen first, and even the brightness is adjusted to improve visibility. By dynamically adjusting the display layout, users can clearly and conveniently obtain the most important information such as temperature, wind speed, slope, etc. during riding, optimizing the riding experience.

[0033] S600: Refresh the real-time instrument screen display using the brightness adjustment strategy and layout optimization strategy.

[0034] Specifically, the brightness adjustment strategy is to adjust the brightness adjustment strategy output by the network according to the ambient brightness, dynamically adjust the brightness level of the instrument screen, optimize the clarity and energy consumption of the screen display, ensure that the screen content is visible under strong light, and is not dazzling and energy-saving in low-light environments. The layout optimization strategy is to dynamically adjust the display area size, position and priority of each functional module (such as heart rate, speed, temperature, power, etc.) on the display screen according to the optimization results output by the screen layout analysis module, improve the readability of important information, and reduce the interference of unnecessary information.

[0035] Combine the brightness adjustment strategy with the layout optimization strategy to update the real-time instrument screen display, apply the brightness parameters in the brightness adjustment strategy to the screen hardware of the instrument device, and adjust the brightness by adjusting the backlight intensity or screen contrast. Reallocate the space size of the display module according to priority, smoothly replace the real-time instrument screen display of the vehicle instrument device, and use transition animations (such as gradual brightness and smooth scaling modules) to improve the user experience when the brightness or layout changes. After completing the brightness and layout refresh, load the real-time user physiological data, vehicle status data, and environmental data into the new layout. By using the brightness adjustment strategy and layout optimization strategy to refresh the real-time instrument screen display, adaptive adjustment of brightness and layout can be achieved, the intelligence level of the riding equipment can be improved, and the best display effect of important data can be provided in real time to meet the user's usage needs under various environmental conditions, while ensuring efficient and energy-saving operation of the equipment.

[0036] S700: Preset a screen display refresh window, and use the screen display refresh window as a constraint to perform adaptive periodic online refresh control on the real-time instrument screen display.

[0037] Specifically, a screen refresh window is pre-set, which is the time range or constraint used to control the screen refresh frequency and update cycle. For example, one minute is a refresh cycle to ensure that the screen data is updated within this time interval to achieve a balance between real-time performance and system resource utilization. According to the hardware performance and user needs of the riding equipment, a highly adaptable screen refresh time range is set. With the screen refresh window as a constraint, the real-time instrument screen is controlled through adaptive periodic online refresh.

[0038] Adaptive periodicity refers to dynamically adjusting the refresh cycle of the screen display, and setting a reasonable refresh frequency according to the rate of change of real-time data or the frequency of priority changes. For example, when the user's heart rate or riding speed changes dramatically, the refresh cycle is shortened. If the data change exceeds the preset threshold (such as the heart rate change exceeds 10bpm), the refresh cycle is shortened. If the data change is small or stable for a long time, the refresh cycle is extended to save energy. According to the refresh window and priority, the screen display content is updated in real time, and high-priority modules (such as speed and heart rate) are refreshed frequently; low-priority modules (such as body temperature and power) are refreshed according to a longer cycle. In each refresh cycle, the current data changes are monitored and the refresh strategy for the next cycle is dynamically adjusted. By presetting the screen refresh window and implementing adaptive periodic online refresh control, the intelligence and user experience of cycling instrument equipment can be effectively improved, which can not only provide fast feedback of real-time data, but also optimize the refresh frequency according to actual needs, taking into account performance and energy efficiency.

[0039] Furthermore, the present application S100 includes: A time series division scale is preset, and the historical riding data is divided with the time series division scale as a constraint to obtain M historical riding subsets, wherein the historical riding subsets include historical instrument input records and historical terrain records; after the M historical riding subsets are input into the riding scene recognition network for scene probability recognition, M historical riding scenes are obtained through probability transfer analysis; scene consistency aggregation is performed on the M historical riding scenes to obtain multiple historical riding scene sets of the multiple sample riding scenes; M riding indicator query records of the M historical riding subsets are aggregated according to the multiple historical riding scene sets to obtain multiple groups of riding indicator query records; and retrospective attention analysis is performed on the multiple groups of riding indicator query records to obtain the screen display layout template library.

[0040] Specifically, a time series partitioning scale is pre-set to be used for the time interval used when segmenting the time series data, such as dividing the data by dimensions such as minutes, hours, days, and riding stages. The historical riding data is obtained, and the historical riding data is segmented by the time series partitioning scale to obtain M historical riding subsets, each of which contains historical instrument input records and terrain records within a specific time period.

[0041] The M historical riding subsets obtained by segmentation are input into the riding scene recognition network to identify the riding scene probability corresponding to each subset. By analyzing the change of scene probability, that is, analyzing the scene probability distribution through the scene state transfer matrix, the scene classification of each historical riding subset is determined, and M historical riding scenes are obtained. The specific process is similar to the subsequent probability transfer analysis of multiple riding scene probability parameters and the process of outputting real-time riding scenes, which is described in detail in the corresponding subordinate step explanation. In short, the M historical riding subsets are input into the riding scene recognition network to obtain multiple riding scene probability parameters corresponding to each subset. The multiple riding scene probability parameters corresponding to each subset are input into the scene state transfer matrix to obtain multiple updated scene probability parameters, and the multiple riding scene probability parameters and the multiple updated scene probability parameters are added to obtain the scene probability sequence corresponding to the subset, and the maximum value call is performed to obtain the historical riding scene corresponding to the subset. This step is performed for all M historical riding subsets to obtain M historical riding scenes.

[0042] The M historical riding scenes that have been identified are subjected to scene consistency aggregation, and multiple similar scenes (such as uphills of different intensities) are grouped into one category to reduce redundancy and improve efficiency. Scene consistency aggregation refers to the process of integrating multiple historical riding scenes to find consistent scene features, such as merging similar scenes into one through a clustering algorithm. Through consistency aggregation, multiple historical riding scene sets of multiple sample riding scenes are obtained. A historical riding index query is performed on the M historical riding subsets to obtain M riding index query records, including average speed, power consumption, terrain type, etc. Based on multiple historical riding scene sets, the M riding index query records are aggregated to obtain multiple groups of riding index query records, and the multiple historical riding scene sets and the multiple groups of riding index query records correspond to each other.

[0043] Perform retrospective query frequency statistics on multiple groups of cycling indicator query records, that is, count the frequency of each cycling indicator appearing in historical cycling data, and analyze which indicators are the focus of user attention in different scenarios. According to the query frequency of multiple groups of indicators, determine multiple user attention sequences, including sequences that users pay attention to in different scenarios, perform indicator screen display layout analysis based on multiple user attention sequences, and determine multiple sample screen display layouts, that is, how the screen display layout should be in different scenarios. For example, in a commuting scenario, the attention of navigation is 50%, the attention of current speed is 25%, and the attention of time is 25%. In this case, navigation occupies 50% of the screen, and speed and time each occupy 25% of the screen.

[0044] Multiple sample display layouts and multiple sample riding scenes are associated and stored to obtain a display layout template library. In other words, each sample riding scene is associated with its corresponding display layout and stored in the display layout template library. Through time-series division scale and scene aggregation, the huge historical riding data is converted into a meaningful scene set. The generated display layout template library can automatically switch the display content according to real-time data, improve the information display effect during riding, and make it more in line with the needs of riders.

[0045] Furthermore, the present application also includes the following steps: Performing frequency statistics of indicator backtracking queries on the multiple groups of cycling indicator query records to obtain multiple groups of indicator query frequencies, wherein the cycling indicators include heart rate indicators, blood oxygen saturation indicators, body temperature indicators, driving pace indicators, power indicators, power output indicators, exercise load indicators, fatigue index indicators and energy efficiency indicators; constructing multiple user attention sequences according to the multiple groups of indicator query frequencies; performing indicator screen display layout analysis according to the indicator attention of the multiple user attention sequences to generate the multiple sample screen display layouts; associating and storing the multiple sample cycling scenes and the multiple sample screen display layouts to complete the construction of the screen display layout template library.

[0046] Specifically, multiple groups of cycling index query records record various physiological, environmental or vehicle status indicators related to the cycling process, such as heart rate, blood oxygen saturation, body temperature, battery power, power, etc. The frequency of retrospective queries of various cycling indicators in historical cycling data is counted, and the query frequency of each indicator in different scenarios (such as uphill, flat, etc.) is counted to obtain the query frequency of each indicator. Cycling indicators include heart rate, blood oxygen saturation, body temperature, driving pace, battery power, power output, exercise load, fatigue index and energy efficiency, etc., which reflect the status of the driving user and the vehicle.

[0047] According to the query frequency of multiple groups of indicators, an attention sequence is generated for each cycling indicator, and the query frequency (or percentage) of each indicator is sorted to obtain multiple user attention sequences. Each attention indicator has an attention percentage, which determines the indicator display size of the indicator screen layout. Indicators with high percentages indicate that users pay more attention. For example, when riding uphill, users often check their heart rate 80 times, accounting for 40% of the total query frequency and a 40% attention percentage; they check the battery level 40 times, accounting for 20% of the total query frequency and a 20% attention percentage.

[0048] According to the indicator attention of multiple user attention sequences, the indicator screen layout is analyzed to determine the display size and position of each indicator in the screen layout, and multiple sample screen layouts are generated, each layout corresponding to a sample riding scene. The user attention sequence is an attention sequence generated based on the query frequency of the riding indicator and its display size in the screen layout (determined by the attention percentage). Each indicator that a user pays attention to will be assigned a weight (percentage) based on the frequency, which determines the display size or priority of the indicator on the screen. By analyzing the attention of each riding indicator, it is determined which indicators need to be displayed in a large area and which can be displayed in a small area, thereby optimizing the space utilization and information display of the screen.

[0049] According to multiple sample riding scenarios, the sample display layout corresponding to each sample riding scenario is determined, and each sample riding scenario is associated with its corresponding display layout, and stored in the display layout template library. Each riding scenario (such as flat road, uphill, competitive, etc.) has a corresponding sample display layout, and these layouts can be switched dynamically according to real-time data. For example, in the commuting scenario, the navigation, current speed and average speed occupy a larger display area, while the heart rate and fatigue index are displayed in a smaller area; in the competitive scenario, the user pays more attention to the real-time speed, followed by the power output, and then the heart rate, and the battery and navigation occupy a smaller display area; in the fitness scenario, the user pays more attention to calorie consumption and heart rate, so they occupy a larger display area. By analyzing the query frequency of riding indicators, the display content is automatically adjusted according to the user's attention needs to ensure that the indicators that the user cares about most are displayed first, and the appropriate display ratio is allocated to each indicator to effectively use the screen space, avoid excessive information stacking, and ensure that the display is concise and rich in information.

[0050] Further, the present application S200 includes: Interactively obtain multiple sample instrument input records, multiple sample terrain records and multiple sample scene probability records of the multiple sample riding scenes, wherein the sample instrument input records include multiple historical instrument inputs, and the historical instrument inputs include historical riding state parameters, historical user physiological data and historical vehicle state data; construct a riding scene recognition network based on a CNN network; use the multiple sample instrument input records, multiple sample terrain records and multiple sample riding scenes as training data to perform parameter optimization of the riding scene recognition network, and complete the localization of the riding scene recognition network; load the instrument input data and real-time terrain data into the riding scene recognition network, and output multiple riding scene probability parameters of the multiple sample riding scenes; perform probability transfer analysis on the multiple riding scene probability parameters, and output the real-time riding scene.

[0051] Specifically, multiple sample riding scenarios are obtained by interacting with the dashboard terminal or the IoT platform, that is, in different riding environments, specific riding scenarios based on historical data records, including a variety of possible riding situations, such as commuting, competition, mountain biking, fitness, etc. Historical data related to multiple sample riding scenarios are collected, including multiple sample instrument input records, multiple sample terrain records, and multiple sample scenario probability records. Multiple sample instrument input records refer to input data recorded by historical instrument equipment, including historical riding state parameters (such as speed, power output, mileage, etc.), user physiological data (such as heart rate, body temperature, etc.) and vehicle status data (such as battery power, tire pressure, etc.). Multiple sample terrain records refer to historical terrain data related to the riding environment, including road slope, curvature, road surface type, etc. Multiple sample scenario probability records refer to recording different riding scenarios that occurred during historical riding and their probabilities of occurrence.

[0052] A cycling scene recognition network is constructed using a convolutional neural network (CNN), which includes multiple convolutional layers, pooling layers, and fully connected layers. The convolutional layer is used to extract local features of the image, the pooling layer is used to reduce the feature dimension and reduce the amount of calculation, and the fully connected layer is used to map the extracted features to the final classification label. The CNN network is a deep learning method that is particularly good at processing data with a grid structure, such as images and videos. Data preprocessing is performed on multiple sample instrument input records, multiple sample terrain records, and multiple sample cycling scenes, including data cleaning and normalization. Pair historical cycling data (such as user heart rate, cycling speed, etc.), terrain data (such as slope, curvature, etc.), and cycling scene labels (such as mountain, flat, city, etc.) with actual scene labels to form a complete training data set.

[0053] Multiple sample meter input records, multiple sample terrain records, and multiple sample riding scenes are input as training sets into the riding scene recognition network. The purpose is to let the network learn how to recognize riding scenes based on the input meter data and terrain information. The performance of the network can be improved by adjusting the hyperparameters of the model, including learning rate, network structure, etc. The learning rate controls the update stride of the model at each iteration. By using a learning rate decay strategy or trying different learning rate values, the model can be helped to converge quickly and avoid falling into a local optimum. Network structure optimization includes selecting different numbers of convolutional layers, convolution kernel sizes, pooling layers, etc. Adjusting these structural parameters can make the network extract features more effectively.

[0054] By using the above optimized parameters, the network is trained locally. Localization refers to adapting the trained general model to specific application scenarios, such as different geographical locations, user behaviors, hardware characteristics, etc. The terrain in different geographical locations may be different, so the model needs to be trained locally according to the local terrain characteristics (such as slope, road type, etc.). Different users have different riding habits. For example, some places pay more attention to speed when riding, while other places may pay more attention to endurance, so adjustments need to be made according to different riding habits. Different riding devices (such as electric bicycles, shared bicycles, etc.) may have different sensor data, so the model needs to be customized according to the characteristics of the device.

[0055] After localized training is completed, the network will be trained with new sample data (including instrument input, terrain records, and scene labels), and the parameters will be continuously optimized to achieve the best recognition effect. At this point, the model has been customized for a specific riding scene and can more accurately identify the current riding environment. The riding scene recognition network refers to a model that uses deep learning (such as a CNN network) to identify the current riding environment or scene. It is trained with input instrument data, terrain data, and historical riding scene data to identify and classify the probability parameters of real-time riding scenes.

[0056] The instrument input data and real-time terrain data are passed to the riding scene recognition network through the network input layer. The instrument input data is processed through a processing network (e.g., a feedforward neural network) to extract key features to describe the user's physiological state, riding speed, etc. The real-time terrain data is processed through a convolutional neural network (CNN) to extract geographic features such as slope, road surface type, etc. Instrument input data refers to data obtained from the instrument system of the riding vehicle, including but not limited to riding state parameters, user physiological data, and vehicle state data, such as speed, heart rate, battery power, power output, and other parameters related to riding state. Real-time terrain data refers to data on the terrain characteristics of the current riding environment, usually including information such as road slope, curvature, and road surface type.

[0057] In the cycling scene recognition network, the input instrument data and terrain data will be fused to form a comprehensive feature vector. The instrument data is feature extracted through the fully connected layer; the terrain data features are extracted through the convolutional layer network structure, and the two parts of the features are combined into a final feature vector for subsequent scene recognition. The fused feature vector will be calculated through a classification layer (usually a softmax layer) to output the probability of each cycling scene. For example, the model may output: mountain cycling 40%, commuting cycling 35%, flat cycling 15%, competitive cycling 10%.

[0058] Obtain the scene switching records in historical riding to form a historical scene alternation record. Analyze the transition probability between each pair of scenes, aggregate multiple sets of sample scene transition probabilities, and construct a scene state transition matrix. Each row of the matrix represents the current riding scene, and each column represents the target riding scene. Input multiple riding scene probability parameters into the scene state transition matrix, and calculate multiple updated scene probability parameters. In other words, through matrix operations, calculate the probability distribution of the next most likely riding scene.

[0059] The probability parameters of multiple riding scenarios are added to the updated scenario probability parameters to obtain a comprehensive sample scenario probability sequence. These summed results represent the total probability of each scenario, helping to further confirm which scenario is most likely to be the current riding scenario. By performing a maximum call on the summed sample scenario probability sequence, the riding scenario with the largest probability value is selected as the current real-time riding scenario. In other words, the most suitable current riding scenario is determined through the probability distribution of the current scenario and the probability distribution of the next most likely riding scenario. Through the training and optimization of the CNN network, the device can accurately identify the current riding scenario in real time. According to the recognition results of different riding scenarios, the instrument equipment can dynamically adjust the display content and adjust the display in time according to the riding status and environmental changes, thereby enhancing the user's awareness of the current riding status and avoiding the impact of insufficient information or inaccurate display on riding safety.

[0060] Furthermore, the present application also includes the following steps: Interactively obtain historical scene alternation records; calculate and obtain multiple groups of sample scene transition probabilities of the multiple sample riding scenes based on the historical scene alternation records; use the multiple groups of sample scene transition probabilities to construct a scene state transfer matrix; input the multiple riding scene probability parameters into the scene state transfer matrix, and calculate and obtain multiple updated scene probability parameters; add the multiple riding scene probability parameters and the multiple updated scene probability parameters, and serialize the multiple sample riding scenes according to the summed results to obtain a sample scene probability sequence; perform a maximum call on the sample scene probability sequence, and output the real-time riding scene.

[0061] Specifically, the historical scene alternation records are obtained by interacting with the IoT platform, that is, the transition records between different scenes during the riding process, including the changes from one riding scene to another, such as the transition from urban riding to mountain riding. For example, a user may transition from flat riding to mountain riding, and then to urban riding, and the historical data will help determine the frequency and method of switching between such scenes. By analyzing the historical scene alternation records, the transition probability between each pair of scenes is calculated, that is, the probability of switching from one riding scene to another. The historical scene alternation records contain the scene switching information during the riding process, and the transition probability can be calculated by counting the number of these switches. Through calculation, the probability of each scene transition can be obtained by the ratio of the number of transitions to the total number of transitions. For example, if the number of switches from flat road to uphill is 300 times and the total number of switches is 1000 times, the transition probability from flat road to uphill is 0.3.

[0062] The scene state transfer matrix is ​​constructed based on the calculated multiple sets of sample scene transition probabilities to represent different scenes. Each row of the matrix represents the current riding scene, each column represents the target scene, and each element represents the probability of transferring from the current riding scene to the target riding scene. Multiple riding scene probability parameters are input into the scene state transfer matrix, usually by multiplying the multiple riding scene probability parameters with the scene state transfer matrix to obtain multiple updated scene probability parameters, that is, the probability value of the updated riding scene, which reflects the possibility of the current scene after the scene transfer.

[0063] Multiple riding scene probability parameters and multiple update scene probability parameters are added together to form a comprehensive sample scene probability sequence. Addition refers to the superposition of multiple probability parameters, usually by weighted summing the probabilities of multiple scenes to obtain a more accurate scene prediction value. Multiple sample riding scenes are serialized by summing up the results, and the summed results at each time point are arranged in a certain order (such as the summed results) to form a sample scene probability sequence, which represents the evolution of scene probabilities throughout the riding process, and represents the possible riding scenes and their corresponding probabilities at each time point during the riding process.

[0064] Perform a maximum value call on the sample scene probability sequence, find the riding scene with the largest probability value from multiple sample scene probability sequences, and output the most likely real-time riding scene. The maximum value call is to select the maximum value by comparing the values ​​of each data point. From the entire sample scene probability sequence, select the riding scene with the highest probability at the current moment to obtain the real-time riding scene. The real-time riding scene is not static and will be continuously updated with the passage of time and changes in input data. For example, as the rider enters different terrains (such as from the city to the mountains), the new probability value is recalculated and the maximum value call is performed based on the new real-time terrain data and historical data to ensure that the output scene is always the most suitable scene for the current environment and state.

[0065] Through the alternating records of historical scenes and the probability of scene transitions, the current riding scene can be accurately predicted. Through the scene state transition matrix, the prediction of the current scene can be dynamically adjusted according to the changes during the riding process, and the most likely real-time riding scene can be determined. The current riding environment can be effectively predicted and identified, thereby providing more accurate instrument information display and riding suggestions, improving riding safety and user experience.

[0066] Further, the present application S300 includes: Physiological data of riding users are collected through wearable devices to obtain real-time user physiological data, wherein the real-time user physiological data includes real-time heart rate, real-time blood oxygen saturation and real-time body temperature; real-time vehicle status data is obtained by calling from a vehicle status sensor array, wherein the real-time vehicle status data includes real-time driving pace, real-time power information and real-time power output; the real-time user physiological data and real-time vehicle status data are fused and analyzed to obtain real-time riding status parameters, wherein the real-time riding status parameters include real-time exercise load, real-time fatigue index and real-time energy efficiency; screen display priority is determined according to the real-time riding status parameters and the real-time riding scene to obtain the real-time screen display priority; after the real-time instrument screen is dynamically rearranged according to the real-time screen display priority, the real-time riding status parameters, real-time user physiological data and real-time vehicle status data are loaded into the real-time instrument screen.

[0067] Specifically, wearable devices are used to collect the user's physiological data, such as heart rate belts, smart watches or other sensor devices worn by users, to collect the user's heart rate, blood oxygen saturation and body temperature data in real time. Real-time user physiological data includes real-time heart rate, real-time blood oxygen saturation and real-time body temperature, that is, the current heart rate of the cyclist (used to assess exercise intensity and cardiovascular burden), blood oxygen saturation (used to determine the body's oxygen supply and breathing rate), and body surface or core body temperature.

[0068] The sensor array installed on the vehicle collects the vehicle's speed, acceleration, power status, and rider's power output data. Real-time vehicle status data includes real-time driving pace, real-time power information, and real-time power output. Real-time driving pace refers to the vehicle's current riding speed (in km / h or m / s) and acceleration (rate of speed change, in m / s²), reflecting riding dynamics; real-time power information refers to the remaining power of the vehicle's battery (usually expressed as a percentage), which is used to measure endurance; real-time power output refers to the rider's physical output, which is usually related to the pedal output power monitored by the vehicle's sensors, reflecting the user's physical energy consumption.

[0069] The real-time user physiological data and real-time vehicle status data are integrated and analyzed to obtain real-time riding status parameters, including real-time exercise load, real-time fatigue index and real-time energy efficiency. The real-time exercise load is an indicator to measure the current exercise intensity, which is obtained by the joint analysis of heart rate and power output; the real-time fatigue index evaluates the user's fatigue level based on the long-term trend of heart rate and blood oxygen saturation; the real-time energy efficiency is obtained by dynamic calculation based on battery life and vehicle speed.

[0070] The user's heart rate changes reflect the heart burden, and the power output reflects the user's physical exertion. The combination of the two can comprehensively evaluate the real-time exercise load, that is, the real-time exercise load = the ratio of the real-time heart rate to the user's maximum heart rate + the ratio of the real-time power output to the maximum power output. The long-term trend of heart rate and blood oxygen level is a key indicator of the user's fatigue state. A high heart rate and a decrease in blood oxygen level indicate that fatigue is gradually increasing. For example, in the past 30 minutes, the heart rate increased from 140bpm to 165bpm, and the blood oxygen level decreased from 97% to 92%. The fatigue index is 85 (range 0-100). Energy efficiency measures the user's unit energy consumption performance, that is, the battery life at the current pace. The real-time energy efficiency can be obtained by the ratio of battery life (such as mileage) and the current vehicle pace, indicating the driving efficiency that the user can maintain at the current speed.

[0071] Determine multiple sample riding state thresholds corresponding to multiple sample riding scenarios, that is, the reference range or upper and lower limits of the state parameters of each riding scenario, which are used to judge the degree of deviation of the real-time state. Determine the corresponding real-time riding state threshold according to the real-time riding scenario, compare the real-time riding state parameters and the real-time riding state threshold item by item, calculate the deviation, and obtain the real-time riding state deviation. Sort by the absolute value of the deviation, and display the indicators with serious deviations in a prominent position first to obtain the real-time screen priority, determine which data is more urgent or important, and ensure that the indicators with the most serious deviations are displayed on the screen first. For example, when the rider's fatigue index is too high, this indicator is displayed first to prompt the rider to rest; if the energy efficiency is low (such as 1.2), the user is reminded to reduce the speed and extend the battery life.

[0072] According to the priority of the screen display, the layout of the content displayed on the instrument screen is adjusted in real time. The content with higher priority is displayed in the center of the screen or occupies a larger area, and the secondary content is placed in the corner and displayed in a smaller font. For example, when the fatigue index is high, the information in the screen display will be displayed in large fonts on the screen, while reducing the proportion of other information. Dynamic rearrangement is to adjust the display content and layout order on the screen according to the real-time screen display priority. The real-time riding status parameters (such as exercise load, fatigue index, energy efficiency) as well as real-time user physiological data and real-time vehicle status data are loaded into the vehicle instrument screen according to the adjusted layout. By dynamically adjusting the screen layout, the rider can always see the data that needs the most attention. According to the real-time monitoring of energy efficiency, it helps the rider to reasonably allocate physical strength and battery energy, improve the endurance during riding, and promptly remind the rider to pay attention to rest or adjust the riding intensity to prevent accidents caused by excessive fatigue, thereby improving riding efficiency and safety.

[0073] Furthermore, the present application also includes the following steps: Interactively obtain multiple sample riding state thresholds of the multiple sample riding scenes; call the real-time riding state threshold from the multiple sample riding state thresholds according to the real-time riding scene; use the real-time riding state parameter to traverse the real-time riding state threshold to obtain the real-time riding state deviation, wherein the real-time riding state deviation includes exercise load deviation, fatigue index deviation and energy efficiency deviation; perform screen display priority judgment according to the real-time riding state deviation to obtain the real-time screen display priority.

[0074] Specifically, multiple sample riding state thresholds of multiple sample riding scenarios are obtained by interacting with the IoT platform or the historical riding database, that is, the state parameter thresholds preset for multiple riding scenarios, including the reference range or upper and lower limits of exercise load, fatigue index and energy efficiency, which are used to judge the degree of deviation of the real-time state. According to the real-time riding scenario (such as commuting mode or competitive mode), multiple sample riding state thresholds are traversed to determine the real-time riding state threshold corresponding to the real-time riding scenario.

[0075] The real-time riding state parameters (exercise load, fatigue index, energy efficiency) are compared with the real-time riding state threshold item by item, and the deviation is calculated to obtain the real-time riding state deviation, including exercise load deviation, fatigue index deviation and energy efficiency deviation. The larger the absolute value of the deviation, the more serious the state deviation; the deviation is positive, exceeding the upper limit; the deviation is negative, below the lower limit. The display priority of each indicator is sorted according to the severity of the deviation. The more serious the deviation, the higher the priority. Sort by the absolute value of the deviation, and display the indicators with serious deviations in a prominent position first. For example, if the fatigue index deviation is the largest, the fatigue index related information occupies a more prominent position on the screen. Based on the deviation analysis, the abnormality of the state parameters is accurately located to improve the sensitivity of the rider to his own and the vehicle status. The screen layout is dynamically rearranged according to the severity of the deviation to ensure that the most critical information is always highlighted.

[0076] In summary, the vehicle instrument equipment control method supporting online processing provided by this application has the following technical effects: By receiving historical riding data and performing retrospective analysis on the historical riding data, a screen display layout template library is obtained, wherein the screen display layout template library stores multiple sample riding scenes and multiple sample screen display layouts in association; the instrument input data and real-time terrain data are integrated and analyzed to output the real-time riding scene, wherein the real-time terrain data is obtained in real time through a cloud navigation service; after the real-time screen display layout is scheduled in the screen display layout template library according to the real-time riding scene, the standardized screen display configuration of the vehicle instrument equipment is smoothly replaced by the real-time screen display layout to generate a real-time instrument screen display; after receiving the real-time environmental brightness detected by the light sensor, the real-time environmental brightness is input into the environmental brightness adjustment network for brightness adjustment analysis, and a brightness adjustment strategy is output; real-time weather data is obtained through an API, and a screen display layout analysis is performed according to the real-time weather data and real-time terrain data, and a layout optimization strategy is output; the real-time instrument screen display is refreshed by using the brightness adjustment strategy and the layout optimization strategy; a screen display refresh window is preset, and the screen display refresh window is used as a constraint to perform adaptive periodic online refresh control on the real-time instrument screen display. In other words, by determining the screen display layout template library based on historical riding data, combining real-time terrain data and instrument data to determine the current riding scene, dynamically scheduling the real-time screen display layout, intelligently adjusting the display brightness and layout according to lighting conditions and weather changes, and presetting the screen display refresh window to automatically update the screen display information according to real-time changes, the adaptability and intelligence level of the vehicle's instrument equipment and the safety and efficiency of user riding are improved.

[0077] Embodiment 2, based on the same inventive concept as the vehicle instrument equipment control method supporting online processing in the aforementioned embodiment 1, the present application also provides a vehicle instrument equipment control system supporting online processing, please refer to the attached Figure 2The vehicle instrument equipment control system supporting online processing includes: A historical data analysis module 11, the historical data analysis module 11 is used to receive historical riding data, and perform retrospective analysis on the historical riding data to obtain a screen display layout template library, wherein the screen display layout template library stores multiple sample riding scenes and multiple sample screen display layouts in association; a riding scene analysis module 12, the riding scene analysis module 12 is used to integrate and analyze instrument input data and real-time terrain data, and output a real-time riding scene, wherein the real-time terrain data is obtained in real time through a cloud navigation service; a screen display layout scheduling module 13, the screen display layout scheduling module 13 is used to smoothly replace the standardized screen display configuration of the vehicle instrument equipment with the real-time screen display layout after scheduling the real-time screen display layout in the screen display layout template library according to the real-time riding scene, and generate a real-time instrument screen display; an environmental brightness adjustment module 13; a real-time display layout scheduling ... A control module 14, wherein the environment brightness adjustment module 14 is used to receive the real-time environment brightness detected and transmitted back by the light sensor, input the real-time environment brightness into the environment brightness adjustment network for brightness adjustment analysis, and output a brightness adjustment strategy; a screen display layout optimization module 15, wherein the screen display layout optimization module 15 is used to obtain real-time weather data through an API, and perform screen display layout analysis based on the real-time weather data and real-time terrain data, and output a layout optimization strategy; a screen display update module 16, wherein the screen display update module 16 is used to refresh the real-time instrument screen display using the brightness adjustment strategy and layout optimization strategy; an online refresh control module 17, wherein the online refresh control module 17 is used to preset a screen display refresh window, and use the screen display refresh window as a constraint to perform adaptive periodic online refresh control on the real-time instrument screen display.

[0078] Furthermore, the historical data analysis module 11 in the vehicle instrument equipment management and control system supporting online processing is also used for: A time series division scale is preset, and the historical riding data is divided with the time series division scale as a constraint to obtain M historical riding subsets, wherein the historical riding subsets include historical instrument input records and historical terrain records; after the M historical riding subsets are input into the riding scene recognition network for scene probability recognition, M historical riding scenes are obtained through probability transfer analysis; scene consistency aggregation is performed on the M historical riding scenes to obtain multiple historical riding scene sets of the multiple sample riding scenes; M riding indicator query records of the M historical riding subsets are aggregated according to the multiple historical riding scene sets to obtain multiple groups of riding indicator query records; and retrospective attention analysis is performed on the multiple groups of riding indicator query records to obtain the screen display layout template library.

[0079] Furthermore, the historical data analysis module 11 in the vehicle instrument equipment management and control system supporting online processing is also used for: Performing frequency statistics of indicator backtracking queries on the multiple groups of cycling indicator query records to obtain multiple groups of indicator query frequencies, wherein the cycling indicators include heart rate indicators, blood oxygen saturation indicators, body temperature indicators, driving pace indicators, power indicators, power output indicators, exercise load indicators, fatigue index indicators and energy efficiency indicators; constructing multiple user attention sequences according to the multiple groups of indicator query frequencies; performing indicator screen display layout analysis according to the indicator attention of the multiple user attention sequences to generate the multiple sample screen display layouts; associating and storing the multiple sample cycling scenes and the multiple sample screen display layouts to complete the construction of the screen display layout template library.

[0080] Furthermore, the riding scene analysis module 12 in the vehicle instrument equipment management and control system supporting online processing is also used for: Interactively obtain multiple sample instrument input records, multiple sample terrain records and multiple sample scene probability records of the multiple sample riding scenes, wherein the sample instrument input records include multiple historical instrument inputs, and the historical instrument inputs include historical riding state parameters, historical user physiological data and historical vehicle state data; construct a riding scene recognition network based on a CNN network; use the multiple sample instrument input records, multiple sample terrain records and multiple sample riding scenes as training data to perform parameter optimization of the riding scene recognition network, and complete the localization of the riding scene recognition network; load the instrument input data and real-time terrain data into the riding scene recognition network, and output multiple riding scene probability parameters of the multiple sample riding scenes; perform probability transfer analysis on the multiple riding scene probability parameters, and output the real-time riding scene.

[0081] Furthermore, the riding scene analysis module 12 in the vehicle instrument equipment management and control system supporting online processing is also used for: Interactively obtain historical scene alternation records; calculate and obtain multiple groups of sample scene transition probabilities of the multiple sample riding scenes based on the historical scene alternation records; use the multiple groups of sample scene transition probabilities to construct a scene state transfer matrix; input the multiple riding scene probability parameters into the scene state transfer matrix, and calculate and obtain multiple updated scene probability parameters; add the multiple riding scene probability parameters and the multiple updated scene probability parameters, and serialize the multiple sample riding scenes according to the summed results to obtain a sample scene probability sequence; perform a maximum call on the sample scene probability sequence, and output the real-time riding scene.

[0082] Furthermore, the screen display layout scheduling module 13 in the vehicle instrument equipment control system supporting online processing is also used for: Physiological data of riding users are collected through wearable devices to obtain real-time user physiological data, wherein the real-time user physiological data includes real-time heart rate, real-time blood oxygen saturation and real-time body temperature; real-time vehicle status data is obtained by calling from a vehicle status sensor array, wherein the real-time vehicle status data includes real-time driving pace, real-time power information and real-time power output; the real-time user physiological data and real-time vehicle status data are fused and analyzed to obtain real-time riding status parameters, wherein the real-time riding status parameters include real-time exercise load, real-time fatigue index and real-time energy efficiency; screen display priority is determined according to the real-time riding status parameters and the real-time riding scene to obtain the real-time screen display priority; after the real-time instrument screen is dynamically rearranged according to the real-time screen display priority, the real-time riding status parameters, real-time user physiological data and real-time vehicle status data are loaded into the real-time instrument screen.

[0083] Furthermore, the screen display layout scheduling module 13 in the vehicle instrument equipment control system supporting online processing is also used for: Interactively obtain multiple sample riding state thresholds of the multiple sample riding scenes; call the real-time riding state threshold from the multiple sample riding state thresholds according to the real-time riding scene; use the real-time riding state parameter to traverse the real-time riding state threshold to obtain the real-time riding state deviation, wherein the real-time riding state deviation includes exercise load deviation, fatigue index deviation and energy efficiency deviation; perform screen display priority judgment according to the real-time riding state deviation to obtain the real-time screen display priority.

[0084] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The vehicle instrument equipment control method supporting online processing and the specific examples in the first embodiment are also applicable to the vehicle instrument equipment control system supporting online processing in the present embodiment. Through the above detailed description of the vehicle instrument equipment control method supporting online processing, those skilled in the art can clearly know the vehicle instrument equipment control system supporting online processing in the present embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0085] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0086] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A vehicle instrument equipment control method supporting online processing, characterized in that: include: Receiving historical riding data, and performing retrospective analysis on the historical riding data to obtain a screen display layout template library, wherein the screen display layout template library stores a plurality of sample riding scenes and a plurality of sample screen display layouts in association with each other; Fusion analysis of instrument input data and real-time terrain data to output a real-time riding scene, wherein the real-time terrain data is obtained in real time through a cloud navigation service; After the real-time screen display layout is scheduled in the screen display layout template library according to the real-time riding scene, the standardized screen display configuration of the vehicle instrument equipment is smoothly replaced by the real-time screen display layout to generate a real-time instrument screen display; After receiving the real-time ambient brightness detected by the light sensor, the real-time ambient brightness is input into the ambient brightness adjustment network for brightness adjustment analysis, and a brightness adjustment strategy is output; Acquire real-time weather data through an API, perform screen layout analysis based on the real-time weather data and real-time terrain data, and output a layout optimization strategy; Refreshing the real-time instrument screen display using the brightness adjustment strategy and layout optimization strategy; A screen display refresh window is preset, and the screen display refresh window is used as a constraint to perform adaptive periodic online refresh control on the real-time instrument screen display.

2. A vehicle instrument equipment control method supporting online processing as claimed in claim 1, characterized in that: Fusion analysis instrument input data and real-time terrain data to output real-time riding scenes, including: Interactively obtaining a plurality of sample instrument input records, a plurality of sample terrain records, and a plurality of sample scene probability records of the plurality of sample riding scenes, wherein the sample instrument input records include a plurality of historical instrument inputs, and the historical instrument inputs include historical riding state parameters, historical user physiological data, and historical vehicle state data; Build a cycling scene recognition network based on CNN network; Using the plurality of sample instrument input records, the plurality of sample terrain records and the plurality of sample riding scenes as training data to perform parameter optimization of the riding scene recognition network, thereby completing localization of the riding scene recognition network; Loading the instrument input data and the real-time terrain data into the riding scene recognition network, and outputting a plurality of riding scene probability parameters of the plurality of sample riding scenes; Probability transfer analysis is performed on the multiple riding scene probability parameters, and the real-time riding scene is output.

3. A vehicle instrument equipment control method supporting online processing as claimed in claim 2, characterized in that: Receive historical riding data, and perform retrospective analysis on the historical riding data to obtain a screen display layout template library, including: Preset a time series division scale, and use the time series division scale as a constraint to divide the historical riding data to obtain M historical riding subsets, wherein the historical riding subsets include historical instrument input records and historical terrain records; After inputting the M historical riding subsets into the riding scene recognition network for scene probability recognition, M historical riding scenes are obtained through probability transfer analysis; Performing scene consistency aggregation on the M historical riding scenes to obtain multiple historical riding scene sets of the multiple sample riding scenes; Aggregating the M cycling index query records of the M historical cycling subsets according to the multiple historical cycling scene sets to obtain multiple groups of cycling index query records; A retrospective analysis is performed on the plurality of groups of cycling index query records to obtain the screen display layout template library.

4. A vehicle instrument equipment control method supporting online processing as claimed in claim 3, characterized in that: The plurality of groups of cycling index query records are retrospectively analyzed to obtain the screen display layout template library, including: Performing index backtracking query frequency statistics on the multiple groups of cycling index query records to obtain multiple groups of index query frequencies, wherein the cycling indexes include a heart rate index, a blood oxygen saturation index, a body temperature index, a driving pace index, a power index, a power output index, a sports load index, a fatigue index, and an energy efficiency index; Constructing multiple user attention sequences according to the query frequencies of the multiple groups of indicators; Performing an indicator screen display layout analysis according to the indicator attention of the plurality of user attention sequences to generate the plurality of sample screen display layouts; The multiple sample riding scenes and the multiple sample screen display layouts are stored in association with each other to complete the construction of the screen display layout template library.

5. A vehicle instrument equipment control method supporting online processing as claimed in claim 1, characterized in that: The standardized screen display configuration of the vehicle instrument device is smoothly replaced by the real-time screen display layout to generate a real-time instrument screen display, and then, the method includes: The wearable device collects physiological data of the riding user to obtain real-time user physiological data, wherein the real-time user physiological data includes real-time heart rate, real-time blood oxygen saturation and real-time body temperature; Obtaining real-time vehicle status data from a vehicle status sensor array, wherein the real-time vehicle status data includes real-time driving speed, real-time power information, and real-time power output; Performing a fusion analysis on the real-time user physiological data and the real-time vehicle status data to obtain real-time riding status parameters, wherein the real-time riding status parameters include real-time exercise load, real-time fatigue index and real-time energy efficiency; Determine the screen display priority according to the real-time riding state parameter and the real-time riding scene to obtain the real-time screen display priority; After the real-time instrument screen display is dynamically rearranged according to the real-time screen display priority, the real-time riding state parameter, the real-time user physiological data and the real-time vehicle state data are loaded into the real-time instrument screen display.

6. A vehicle instrument equipment control method supporting online processing as claimed in claim 2, characterized in that: Performing probability transfer analysis on the plurality of riding scene probability parameters and outputting the real-time riding scene includes: Interactively obtain alternating records of historical scenes; Calculate and obtain multiple groups of sample scene transition probabilities of the multiple sample riding scenes according to the historical scene alternating records; Constructing a scene state transition matrix using the multiple groups of sample scene transition probabilities; Inputting the plurality of riding scene probability parameters into the scene state transfer matrix, and calculating and obtaining a plurality of updated scene probability parameters; Adding the plurality of riding scene probability parameters and the plurality of updated scene probability parameters, and serializing the plurality of sample riding scenes according to the summed results to obtain a sample scene probability sequence; The sample scene probability sequence is maximized and the real-time riding scene is output.

7. A vehicle instrument equipment control method supporting online processing as claimed in claim 5, characterized in that: The screen display priority is determined according to the real-time riding state parameter and the real-time riding scene to obtain the real-time screen display priority, including: interactively obtaining a plurality of sample riding state thresholds of the plurality of sample riding scenes; calling a real-time riding state threshold from the plurality of sample riding state thresholds according to the real-time riding scenario; Using the real-time riding state parameter to traverse the real-time riding state threshold, to obtain a real-time riding state deviation, wherein the real-time riding state deviation includes a motion load deviation, a fatigue index deviation and an energy efficiency deviation; A screen display priority is determined according to the real-time riding state deviation to obtain a real-time screen display priority.

8. A vehicle instrument equipment control system supporting online processing, characterized in that: Steps for implementing a vehicle instrument equipment control method supporting online processing as described in any one of claims 1 to 7, wherein the vehicle instrument equipment control system supporting online processing comprises: A historical data analysis module, the historical data analysis module is used to receive historical riding data, and perform retrospective analysis on the historical riding data to obtain a screen display layout template library, wherein the screen display layout template library stores a plurality of sample riding scenes and a plurality of sample screen display layouts in association with each other; A riding scene analysis module, which is used to integrate and analyze instrument input data and real-time terrain data and output a real-time riding scene, wherein the real-time terrain data is obtained in real time through a cloud navigation service; A screen display layout scheduling module, wherein the screen display layout scheduling module is used to smoothly replace the standardized screen display configuration of the vehicle instrument equipment with the real-time screen display layout after the screen display layout template library schedules the real-time screen display layout according to the real-time riding scene, so as to generate a real-time instrument screen display; An environment brightness adjustment module, which is used to receive the real-time environment brightness detected by the light sensor, input the real-time environment brightness into the environment brightness adjustment network for brightness adjustment analysis, and output a brightness adjustment strategy; A screen display layout optimization module, the screen display layout optimization module is used to obtain real-time weather data through an API, and perform screen display layout analysis based on the real-time weather data and real-time terrain data, and output a layout optimization strategy; A screen display update module, the screen display update module is used to refresh the real-time instrument screen display by adopting the brightness adjustment strategy and the layout optimization strategy; An online refresh control module is used to preset a screen refresh window, and use the screen refresh window as a constraint to perform adaptive periodic online refresh control on the real-time instrument screen.

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