Speed change strategy adjustment method and system based on combination of Internet of Vehicles and road condition perception

Abstract

The invention relates to the technical field of intelligent bicycles, in particular to a speed change strategy adjusting method and system based on Internet of Vehicles in combination with road condition perception, and the method comprises the steps: receiving a driving instruction, extracting a target path in the driving instruction based on a position monitoring system, and obtaining initial target path data of the target path, obtaining an initial speed change strategy sequence according to the initial target path data, obtaining a user position based on a position monitoring system, extracting an initial speed change strategy corresponding to the user position from the initial speed change strategy sequence according to the user position to obtain an initial target strategy, and obtaining real-time measurement data; and obtaining a preferred target strategy according to the real-time measurement data initial target strategy, and completing speed change strategy adjustment of the Internet of Vehicles in combination with road condition perception based on the preferred target strategy. Various data actually detected in the riding process are integrated, particularly the road surface flatness and real-time environment data are emphasized, reference is provided for a rider to adjust a speed change strategy, and therefore the riding process is assisted.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent bicycles, and particularly to a variable speed strategy adjustment method and system based on vehicle networking combined with road condition perception. Background Art

[0002] With the rapid development of vehicle networking technology, intelligent bicycles can obtain road condition information, navigation path data, and surrounding traffic dynamics in real time through various sensors and cloud communication to assist riders in cycling.

[0003] Traditional vehicle variable speed control methods mainly rely on preset driving modes or provide references for riders based on navigation information, and finally rely on the decisions of riders to adjust the variable speed.

[0004] Although the above methods can achieve the adjustment of intelligent bicycles, they mainly rely on information such as navigation or driving modes to provide references for riders, ignoring the actual road condition characteristics, and cannot provide references for drivers in combination with the environment and real-time driving status. Summary of the Invention

[0005] The present invention provides a variable speed strategy adjustment method and a computer-readable storage medium based on vehicle networking combined with road condition perception. Its main purpose is to integrate various data actually detected during the cycling process, especially paying attention to road surface flatness and real-time environmental data, to provide references for riders to adjust the variable speed strategy, thereby assisting the cycling process.

[0006] To achieve the above object, a variable speed strategy adjustment method based on vehicle networking combined with road condition perception provided by the present invention includes: Receiving a driving instruction, and starting a monitoring system based on the driving instruction, wherein the monitoring system includes: a road condition monitoring system, an environmental monitoring system, a rider monitoring system, a position monitoring system, and a vehicle monitoring system; Extracting a target path in the driving instruction based on the position monitoring system, and obtaining initial target path data of the target path. According to the initial target path data, an initial variable speed strategy sequence is obtained, wherein the initial target path data includes initial road condition data, initial weather data, and initial navigation data, and the initial variable speed strategy sequence includes a plurality of initial variable speed strategies; Obtaining the user position based on the position monitoring system, and extracting the initial variable speed strategy corresponding to the user position from the initial variable speed strategy sequence to obtain an initial target strategy; Obtain the road surface flatness within a preset range using a road condition monitoring system, obtain the weather status according to an environmental monitoring system, obtain the vehicle driving status and road surface inclination according to a vehicle monitoring system, obtain the real-time cycling status of the cyclist according to a cyclist monitoring system, and summarize the road surface flatness, weather status, vehicle driving status, road surface inclination, and the real-time cycling status of the cyclist to obtain real-time measurement data; Extract the initial target vehicle speed and the initial target gear from the initial target strategy, obtain an optimized target strategy based on the real-time measurement data, the initial target vehicle speed, and the initial target gear, and complete the adjustment of the variable speed strategy combining vehicle networking with road condition perception based on the optimized target strategy.

[0007] Optionally, the obtaining of the initial variable speed strategy sequence according to the initial target path data includes: Obtain a section-node sequence based on the initial navigation data, where the section-node sequence includes multiple sections and multiple nodes; Successively extract target data from the section-node sequence. If the target data is a node, generate a low-speed strategy based on the target data; If the target data is a section, obtain the limited vehicle speed of the section, obtain the section type and the average vehicle flow speed based on the initial road condition data, calculate the road congestion index based on the section type and the average vehicle flow speed, obtain the weather category based on the initial weather data, and calculate the weather impact index according to the weather category; Calculate the initial vehicle speed based on the road congestion index, the weather impact index, and the limited vehicle speed, query the speed control strategy corresponding to the initial vehicle speed from a pre-constructed speed control strategy table to obtain the section driving strategy; Successively summarize the low-speed strategy and the section driving strategy according to the section-node sequence to obtain the initial variable speed strategy sequence.

[0008] Optionally, the calculating of the road congestion index based on the section type and the average vehicle flow speed includes: Obtain the congestion classification level and the section classification level, where the congestion classification level includes multiple sub-levels, and the multiple sub-levels are: extremely congested, slightly congested, basically unobstructed, and extremely unobstructed, and the section classification level includes multiple arterial road types: expressway, main road, secondary road, and branch road; Successively extract the arterial road type from the multiple arterial road types, obtain the speed limit range of the arterial road type, perform an equal division operation on the speed limit range using the preset number of congestion classification levels to obtain multiple speed ranges, and calculate the unit congestion index of each speed range in the multiple speed ranges respectively to obtain a road congestion index set, where the multiple speed ranges are the extremely congested speed range, the slightly congested speed range, the basically unobstructed speed range, and the extremely unobstructed speed range; Associate the sub-trunk road types, multiple speed ranges, and the road congestion index set to obtain a trunk road data group, where the trunk road data group includes multiple trunk road data, and the trunk road data includes a speed range and the unit congestion index corresponding to the speed range; Summarize the trunk road data group and use the summarized trunk road data group to draw a congestion reference table; Identify the trunk road data group corresponding to the road section type from the congestion reference table to obtain a target trunk road data group, extract the trunk road data corresponding to the average vehicle speed of the vehicle flow from the target trunk road data group, and confirm the unit congestion index in the trunk road data corresponding to the average vehicle speed of the vehicle flow as the road congestion index.

[0009] Optionally, the obtaining of the road surface flatness within a preset range by using the road condition monitoring system includes: Use the road condition monitoring system to obtain an oblique photography data set within a preset range, and use the oblique photography data set and a pre-constructed GIS model to obtain a real-time road model; Intercept multiple road longitudinal lines and multiple road transverse lines from the real-time road model to obtain an intercept line set, where the road longitudinal lines are parallel to the road longitudinal section, the road transverse lines are parallel to the road cross section, the real-time road model is a model composed of three-dimensional geographic information within a preset range, the three-dimensional geographic information includes a three-dimensional coordinate system, and the three-dimensional coordinate system includes a horizontal axis, a vertical axis, and a vertical axis; Obtain a depth data set based on the intercept line set, and calculate the road surface flatness based on the depth data set.

[0010] Optionally, the obtaining of the depth data set based on the intercept line set and the calculation of the road surface flatness based on the depth data set include: Sequentially extract intercept lines from the intercept line set to obtain target lines, and perform the following operations on all target lines: Use the target line as the cross-section horizontal axis and the vertical axis in the real-time road model as the cross-section vertical axis, and extract the two-dimensional target cross-section corresponding to the target line from the real-time road model to obtain a target cross-section image; Use a pre-constructed edge recognition algorithm to extract the cross-section road surface curve corresponding to the target line, sequentially obtain the extreme points of the cross-section road surface curve to obtain an extreme point set, calculate the depth data set based on the extreme point set, and calculate the unit flatness according to the depth data set; Summarize the unit flatness to obtain a flatness set, calculate the road surface flatness based on the flatness set, where the road surface flatness is the average value of the unit flatness in the flatness set.

[0011] Optionally, the calculation of the depth data set based on the extreme point set and the calculation of the unit flatness according to the depth data set include: Sequentially extract extreme points from the extreme point set to obtain target extreme points, and perform the following operations on the target extreme points: Extract the next extreme point adjacent to the target extreme point from the set of extreme points to obtain the adjacent extreme point. Among them, if the target extreme point is a maximum point, the adjacent extreme point is a minimum point; if the target extreme point is a minimum point, the adjacent extreme point is a maximum point. Respectively obtain the ordinates of the target extreme point and the adjacent extreme point in the cross-sectional road surface curve to obtain the target extreme value and the adjacent extreme value. Calculate the absolute difference between the target extreme value and the adjacent extreme value to obtain the unit longitudinal depth data. Calculate the absolute difference between the target extreme point and the adjacent extreme point to obtain the unit transverse depth data. Aggregate the unit longitudinal depth data and the unit transverse depth data to obtain the unit depth data. Aggregate the unit depth data to obtain the initial depth data set. Construct an envelope depth data set based on the cross-sectional road surface curve. Aggregate the envelope depth data set and the initial depth data set to obtain the depth data set. Calculate the unit flatness based on the depth data set.

[0012] Optionally, the constructing the envelope depth data set based on the cross-sectional road surface curve includes: Intercept the cross-sectional road surface curve based on a preset division length to obtain multiple unit curves. Perform the following operations on each of the multiple unit curves: Obtain the envelope straight line of the unit curve, and extract multiple extreme points existing in the envelope straight line from the set of extreme points to obtain the envelope extreme point set. Perform the following operations on each envelope extreme point in the envelope extreme point set: Respectively obtain the value of the envelope extreme point on the unit curve and the value of the envelope extreme point on the envelope straight line to obtain the curve value and the straight line value. Calculate the absolute difference between the curve value and the straight line value to obtain the envelope depth data. Aggregate the unit envelope depth data to obtain multiple envelope depth data corresponding to the unit curve. Aggregate the multiple envelope depth data to obtain the envelope depth data set corresponding to the cross-sectional road surface curve.

[0013] Optionally, the calculating the unit flatness based on the depth data set includes: Successively extract extreme points from the set of extreme points to obtain flatness nodes. Perform the following operations on each flatness node: Extract the envelope depth data set and the initial depth data set from the depth data set. Extract the envelope depth data corresponding to the flatness node from the envelope depth data set to obtain the target envelope data. Extract the unit depth data corresponding to the flatness node from the initial depth data set to obtain the target depth data. Calculate the unit flatness corresponding to the flatness node based on the target envelope data and the target depth data. Among them, the calculation formula of the unit flatness is as follows: Among them, represents the unit flatness. Indicates the envelope depth data, , , and respectively represent the envelope depth weight, the unit horizontal depth weight, the unit vertical depth weight, and the aspect ratio weight, and , , and are all constants, represents the unit horizontal depth data of the target depth data, represents the unit vertical depth data of the target depth data, represents the numerical stabilization parameter.

[0014] Optionally, extracting the initial target vehicle speed and the initial target gear from the initial target strategy, and obtaining an optimal target strategy according to the real-time measurement data, the initial target vehicle speed, and the initial target gear, includes: Using a pre-constructed evaluation method to obtain a multi-modal weight set corresponding to the real-time measurement data, performing a normalization operation on the real-time measurement data to obtain a normalized data set, calculating an initial target vehicle speed correction parameter according to the normalized data set and the multi-modal weight set, and calculating the product of the initial target vehicle speed correction parameter and the initial target vehicle speed to obtain the target riding speed; If the target riding speed is within the initial target gear, extract the current driving speed from the vehicle driving state; If the target riding speed is greater than the current driving speed, confirm the target riding speed and the initial target gear as the optimal target strategy. Otherwise, calculate the absolute difference between the target riding speed and the current driving speed to obtain the speed to be adjusted, calculate the ratio of the speed to be adjusted to the target riding speed to obtain the speed difference percentage. If the speed difference percentage is greater than a preset speed difference threshold, generate an overspeed warning, and generate an optimal target strategy based on the overspeed warning, the speed to be adjusted, and the initial target gear; If the target riding speed is not within the initial target gear, obtain the corrected gear corresponding to the target riding speed, obtain the minimum speed and the maximum speed of the corrected gear. If the minimum speed is greater than the current driving speed, confirm the corrected gear and the target riding speed as the optimal target strategy. If the maximum speed is less than the current driving speed, generate an overspeed warning, and generate an optimal target strategy based on the overspeed warning, the corrected gear, and the target riding speed.

[0015] To achieve the above object, the present invention further provides a variable speed strategy adjustment system based on vehicle networking combined with road condition perception, including: A monitoring module, configured to receive a driving instruction and start a monitoring system based on the driving instruction, wherein the monitoring system includes: a road condition monitoring system, an environment monitoring system, a rider monitoring system, a position monitoring system, and a vehicle monitoring system; An initial variable speed strategy module, configured to extract a target path in the driving instruction based on a position monitoring system, obtain initial target path data of the target path, and acquire an initial variable speed strategy sequence according to the initial target path data, wherein the initial target path data includes initial road condition data, initial weather data, and initial navigation data, and the initial variable speed strategy sequence includes a plurality of initial variable speed strategies; A real-time measurement data module, configured to obtain a user position based on a position monitoring system, extract an initial variable speed strategy corresponding to the user position from the initial variable speed strategy sequence to obtain an initial target strategy, acquire the road surface flatness within a preset range by using a road condition monitoring system, obtain the weather condition by using an environment monitoring system, obtain the vehicle driving state and the road surface inclination angle by using a vehicle monitoring system, obtain the real-time riding state of a rider by using a rider monitoring system, and summarize the road surface flatness, the weather condition, the vehicle driving state, the road surface inclination angle, and the real-time riding state of the rider to obtain real-time measurement data; An optimal target strategy acquisition module, configured to extract an initial target vehicle speed and an initial target gear from the initial target strategy, obtain an optimal target strategy according to the real-time measurement data, the initial target vehicle speed, and the initial target gear, and complete the variable speed strategy adjustment of the vehicle networking combined with road condition perception based on the optimal target strategy.

[0016] To solve the above problems, the present invention further provides an electronic device, where the electronic device includes: A memory storing at least one instruction; and a processor, configured to execute the instruction stored in the memory to implement the above-mentioned variable speed strategy adjustment method based on vehicle networking combined with road condition perception.

[0017] To solve the above problems, the present invention further provides a computer-readable storage medium, where at least one instruction is stored in the computer-readable storage medium, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned variable speed strategy adjustment method based on vehicle networking combined with road condition perception.

[0018] To solve the problems described in the background art, the present invention receives a driving instruction, extracts the target path in the driving instruction based on a position monitoring system. It can be seen that the present invention provides real-time monitoring data for cyclists riding on the target path by combining various monitoring systems in the monitoring system. Obtain the initial target path data of the target path, and obtain the initial variable speed strategy sequence according to the initial target path data. The present invention divides the target path into multiple sections and multiple nodes, sorts them in the order from the starting point to the ending point of the target path, generates a low-speed strategy at the nodes, and generates a section driving strategy at the sections, and equips an initial variable speed strategy for the entire section, providing a basis for adjusting the variable speed strategy by combining real-time monitoring data later. Based on the position monitoring system, obtain the user's position, and extract the initial variable speed strategy corresponding to the user's position from the initial variable speed strategy sequence to obtain the initial target strategy. It can be seen that after the present invention confirms the user's position, it can confirm the section where the user is located, and extract the initial variable speed strategy corresponding to the user's position from the initial variable speed strategy sequence to provide a basic reference for the cyclist's riding on this section. Obtain real-time measurement data, and obtain the optimal target strategy according to the real-time measurement data and the initial target strategy. The present invention adjusts the initial variable speed strategy according to the road surface flatness obtained by real-time monitoring, including but not limited to road surface flatness, weather conditions, vehicle driving conditions, road surface inclination, and the cyclist's real-time riding state, etc., and especially pays attention to road surface flatness and real-time environmental data, so as to assist the cyclist's riding process, and complete the adjustment of the variable speed strategy by combining the vehicle networking and road condition perception based on the optimal target strategy. Therefore, the present invention integrates various data actually detected during the riding process, especially pays attention to road surface flatness and real-time environmental data, provides a reference for the cyclist to adjust the variable speed strategy, and thus assists the riding process. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 FIG. is a schematic flowchart of a method for adjusting a variable speed strategy by combining vehicle networking and road condition perception provided by an embodiment of the present invention; Figure 2 FIG. is a functional module diagram of a system for adjusting a variable speed strategy by combining vehicle networking and road condition perception provided by an embodiment of the present invention; Figure 3 FIG. is a schematic structural diagram of an electronic device for implementing the method for adjusting a variable speed strategy by combining vehicle networking and road condition perception provided by an embodiment of the present invention.

[0020] DESCRIPTION OF REFERENCE NUMERALS: 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0021] The implementation, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] It should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0023] An embodiment of the present application provides a variable speed strategy adjustment method based on vehicle networking combined with road condition perception. The execution subject of the variable speed strategy adjustment method based on vehicle networking combined with road condition perception includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in this embodiment of the present application. In other words, the variable speed strategy adjustment method based on vehicle networking combined with road condition perception can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0024] Refer to Figure 1 As shown, it is a schematic flowchart of a variable speed strategy adjustment method based on vehicle networking combined with road condition perception provided by an embodiment of the present invention. In this embodiment, the variable speed strategy adjustment method based on vehicle networking combined with road condition perception includes: S1. Receive a driving instruction, and start a monitoring system based on the driving instruction, where the monitoring system includes: a road condition monitoring system, an environment monitoring system, a rider monitoring system, a position monitoring system, and a vehicle monitoring system.

[0025] It can be understood that the driving instruction is issued by the rider of the vehicle, and the vehicle in the present invention is specifically an intelligent bicycle. The road condition monitoring system is used to monitor the road surface condition in real time, especially for monitoring the road surface flatness. The environment monitoring system is used to monitor the environmental conditions in real time, including but not limited to various environmental conditions such as temperature, humidity, sunny days, rainy days, fog, haze, etc. Monitoring the environmental conditions helps the rider perceive the environment, thereby providing a basis for the rider to execute the variable speed strategy. For example, compared with sunny days, when it suddenly rains, it will interfere with the rider's line of sight, so deceleration is required. Another example is that when the environment suddenly heats up or cools down (such as when riding to a snow mountain, etc.), it will affect the rider's physical state, and the rider needs to decelerate to adapt to the environment. Examples are not listed one by one here. The rider monitoring system is used to monitor the physical state of the rider of the intelligent bicycle, such as parameters such as heart rate, body temperature, and riding duration. For example, when the heart rate exceeds 160, it means that the rider is in a high-intensity exercise. Continuing high-intensity exercise will consume the rider's physical strength, making it difficult to make timely judgments in the face of danger. When the riding duration exceeds a preset duration (such as one hour), there may be a situation of fatigue driving, so it is necessary to monitor the physical state of the rider. The position monitoring system is a system for obtaining the real-time position of the vehicle, and the vehicle monitoring system is a system for monitoring the running state of the vehicle, such as the speed of the intelligent bicycle, the vehicle pedal frequency, the opening and closing of the lights, etc.

[0026] It should be noted that the present invention aims to integrate various data actually detected during cycling, with particular emphasis on road surface flatness and real-time environmental data, providing a reference for cyclists to adjust their shifting strategies, thereby assisting the cycling process.

[0027] S2. Extract the target path in the driving instruction based on the position monitoring system, and obtain the initial target path data of the target path. According to the initial target path data, obtain an initial shifting strategy sequence, where the initial target path data includes initial road condition data, initial weather data, and initial navigation data, and the initial shifting strategy sequence includes multiple initial shifting strategies.

[0028] It can be understood that the target path is the path that the user pre-selects and expects to cycle.

[0029] Furthermore, the obtaining of the initial shifting strategy sequence according to the initial target path data includes: Obtain a road segment-node sequence based on the initial navigation data, where the road segment-node sequence includes multiple road segments and multiple nodes; Sequentially extract target data from the road segment-node sequence. If the target data is a node, generate a low-speed strategy based on the target data; If the target data is a road segment, obtain the limited vehicle speed of the road segment, and obtain the road segment type and the average vehicle speed of traffic flow based on the initial road condition data. Calculate the road congestion index based on the road segment type and the average vehicle speed of traffic flow, obtain the weather category based on the initial weather data, and calculate the weather influence index according to the weather category; Calculate the initial vehicle speed based on the road congestion index, the weather influence index, and the limited vehicle speed, and query the shifting strategy corresponding to the initial vehicle speed from a pre-constructed shifting strategy table to obtain the road segment driving strategy; Summarize the low-speed strategy and the road segment driving strategy in sequence according to the road segment-node sequence to obtain the initial shifting strategy sequence.

[0030] It should be noted that the initial road condition data is the data composed of the road conditions in the road segment, and the road conditions in the road segment include: road segment type, average vehicle speed of traffic flow, etc. The initial navigation data refers to the data that provides navigation for cyclists. When obtaining the initial navigation data, the embodiments of the present invention will divide the target path into multiple road segments and multiple nodes. The node refers to places such as zebra crossings, intersection points (such as crossroads, T-shaped intersections, etc.), traffic lights, etc. in the target path. The road segment refers to a continuous driving route without nodes, and the two endpoints of the road segment are the nodes.

[0031] In summary, according to the definitions of nodes and road segments, the target path can be divided into multiple road segments and multiple nodes using the initial navigation data, and then the multiple road segments and multiple nodes are sorted in the order of the target path from the starting point to the ending point of the target path. The obtained sequence is the road segment-node sequence. It can be understood that if the target data is a node, in order to ensure the safety of the rider, it is necessary to slow down. Therefore, the present invention generates a low-pace strategy based on the target data.

[0032] It should be noted that the pace strategy table includes multiple pace strategies, and the multiple pace strategies include a rated pace interval and a rated pace gear. Among them, the rated pace interval is an interval composed of the riding speed per hour, the rated pace gear is an identifier for the rated pace interval, and different ratios of the flywheel to the chainring are also included in the rated pace interval. Within the same rated pace interval, the same speed per hour can be achieved by different ratios of the flywheel to the chainring.

[0033] It should be noted that in the subsequent description of the present invention, the running state of the smart bicycle (such as the ratio of the flywheel to the chainring, etc.) is adjusted with speed as the guide. After determining the riding speed per hour of the smart bicycle, after detecting the inclination of the road surface where the smart bicycle is located (uphill, downhill or flat), the riding speed per hour and the road surface inclination can be input into the pre-constructed smart pace selection model, and then the corresponding pace strategy can be found in the pace strategy table using the smart pace selection model. Therefore, in addition to including the speed per hour, when formulating the pace strategy, it can also include the cadence after fuzzy processing and the inclination of the road surface (the data can be fuzzified through a large language intelligent model) as parameters of the pace strategy table. For example, there is a pace strategy that states: riding speed per hour: 0 - 8 km / h, target cadence for pacing: 80 - 100 RPM, inclination of the road surface: 20 - 30°, and the intelligent speed ratio (ratio of the flywheel to the chainring) is 0.9. Here, the intelligent speed ratio can be calculated from the rider's habit (for example, in this case, the most commonly used intelligent speed ratio by the rider is 0.9), or summarized from big data (the most commonly used intelligent speed ratio by different riders in the parameters of the pace strategy is 0.9), or generated after training a neural network model. The present invention does not limit this. Finally, applying the pace strategy to the smart bicycle or providing it for the rider to choose can both achieve providing intelligent gear shifting decisions for the rider in combination with the road conditions.

[0034] Furthermore, in the above process, the riding speed per hour is a key factor affecting the gear shifting decision. Therefore, the present invention focuses on discussing how to determine the riding speed per hour, and the technology of adjusting the pace according to the speed of the smart bicycle can be achieved by the prior art, and the present invention will not elaborate on this.

[0035] Optionally, the multiple pacing strategies are respectively a low pacing strategy, a medium pacing strategy, and a high pacing strategy. Among them, the low pacing strategy refers to a speed that is relatively low compared to the maximum riding speed of the bicycle. Specifically, the concepts of "low", "medium", and "high" are all relative concepts, and when specifically defining the ranges of "low", "medium", and "high", it can be stipulated by humans. For example, the low pacing strategy refers to a speed of 0 - 8 km / h during riding, the medium pacing strategy refers to a speed of 8 - 40 km / h during riding, and the high pacing strategy refers to a speed of 40 km / h and above during riding.

[0036] Exemplarily, in a section of the road, the speed at which the intelligent bicycle can travel is 0 - 20 km / h. Therefore, the variable speed strategy of the intelligent bicycle is set as the low pacing strategy.

[0037] It can be understood that the present invention divides the target path into multiple sections and multiple nodes, sorts them in the order from the starting point to the ending point of the target path, generates a low pacing strategy at the nodes, generates a section driving strategy at the sections, and equips an initial variable speed strategy for the entire section, providing a basis for adjusting the variable speed strategy by combining real-time monitoring data in the future.

[0038] Furthermore, the defined speed is the maximum riding speed of the bicycle on the section under traffic rules. The average driving speed of the traffic flow is the average riding speed of the vehicles on this section. The average driving speed of the traffic flow can be directly obtained through existing technologies (such as various map apps like Gaode Map, Tencent Map, etc.). The average driving speed of the traffic flow can intuitively represent whether there is congestion on this section, and the congestion situation will also affect the speed of the intelligent bicycle. Therefore, in the embodiments of the present invention, a road congestion index is also calculated based on the average driving speed of the traffic flow to adjust the actual speed that the intelligent bicycle can travel, thereby guiding the variable speed strategy.

[0039] Furthermore, the road congestion index is a dimensionless parameter used to describe the congestion situation of the section. When the road is more congested, the road congestion index is higher. At this time, after multiplying the road congestion index by the initial speed, the obtained initial speed is smaller. The initial weather data includes the weather data obtained from the cloud of the vehicle network. The weather category is the category of the real-time weather, which can be sunny, rainy, foggy, hazy, etc. as described above. When calculating the weather impact index according to the weather category, the environmental conditions real-time monitored in the environmental monitoring system also need to be considered, including but not limited to temperature, humidity, etc. The specific calculation method can set weights for different weather categories, temperatures, and humidities and calculate using the entropy weight method, or calculate using a neural network model (setting the input data as different weather categories, temperatures, humidities, etc., and setting the output layer as the weather impact index). The weather impact index is a dimensionless parameter used to describe the impact of the weather on the riding speed.

[0040] Further, the initial vehicle speed is the speed per hour obtained by multiplying the defined vehicle speed by the road congestion index and then by the weather impact index. The initial variable speed strategy sequence is a sequence obtained by arranging the low-speed strategies or road section driving strategies corresponding to each node and each road section in the road section-node sequence in the order of nodes and road sections in the road section-node sequence, and all the strategies in the initial variable speed strategy sequence are denoted as initial variable speed strategies.

[0041] Further, calculating the road congestion index based on the road section type and the average driving speed of the vehicle flow includes: Obtain the congestion classification level and the road section classification level. Among them, the congestion classification level includes multiple sub-levels, and the multiple sub-levels are: extremely congested, slightly congested, basically unobstructed, and extremely unobstructed. The road section classification level includes multiple arterial road types: expressway, main road, secondary road, and branch road; Extract the arterial road types from the multiple arterial road types in sequence, and obtain the speed limit intervals of the arterial road types. Use the preset number of congestion classification levels to evenly divide the speed limit intervals to obtain multiple speed intervals, and calculate the unit congestion index of each speed interval in the multiple speed intervals respectively to obtain a road congestion index set. Among them, the multiple speed intervals are the extremely congested speed interval, the slightly congested speed interval, the basically unobstructed speed interval, and the extremely unobstructed speed interval; Associate the arterial road types, the multiple speed intervals, and the road congestion index set to obtain an arterial road data group. Among them, the arterial road data group includes multiple arterial road data, and the arterial road data includes a speed interval and the unit congestion index corresponding to the speed interval; Summarize the arterial road data group, and use the summarized arterial road data group to draw a congestion reference table; Identify the arterial road data group corresponding to the road section type from the congestion reference table to obtain a target arterial road data group, and extract the arterial road data corresponding to the average driving speed of the vehicle flow from the target arterial road data group, and confirm the unit congestion index in the arterial road data corresponding to the average driving speed of the vehicle flow as the road congestion index.

[0042] It is understandable that extreme congestion, mild congestion, basic smoothness, and extreme smoothness are all artificially set labels for describing the congestion conditions of road sections. And according to the arrangement of extreme congestion, mild congestion, basic smoothness, and extreme smoothness, the faster the speed at which the smart bicycle can travel in the road sections with the corresponding labels. The expressway, arterial road, secondary arterial road, and branch road are all labels obtained by classifying road sections according to the existing urban road grades, which will not be elaborated here. The speed limit range is the range composed of the speeds at which the smart bicycle can travel on the sub-arterial road type in traffic rules. For example, if the speed limit range on a certain road section is 0 - 15 km / h, then the speed at which the smart bicycle can travel on the sub-arterial road type is 0 - 15 km / h. The operation of performing equal division is to evenly divide the speed limit range into the same number of speed intervals as the number of congestion classification levels. Therefore, the extreme congestion speed interval, mild congestion speed interval, basic smoothness speed interval, and extreme smoothness speed interval respectively correspond to extreme congestion, mild congestion, basic smoothness, and extreme smoothness, which will not be elaborated here.

[0043] Exemplarily, the congestion classification levels of the present invention include extreme congestion, mild congestion, basic smoothness, and extreme smoothness, so the number of congestion classification levels is 4. If the speed limit range is [0, 60] (unit: km / h), then the operation of performing equal division is to evenly divide the speed limit range into 4 speed intervals, which are [0, 15], [15, 30], [30, 45], and [45, 60] respectively. The unit congestion index is a dimensionless parameter used to describe the congestion degree of the speed interval. For example, the unit congestion degree of [0, 15] can be set to 75%, the unit congestion degree of [15, 30] can be set to 50%, the unit congestion degree of [30, 45] can be set to 25%, and the unit congestion degree of [45, 60] can be set to 0%. The more congested the road is, the slower the speed at which it can travel, and thus the higher the unit congestion index of the corresponding speed interval.

[0044] Furthermore, the road congestion index set is a set composed of multiple unit congestion indexes. The operation of associating the sub-arterial road type, multiple speed intervals, and the road congestion index set is to store the sub-arterial road type, one speed interval, and the unit congestion index corresponding to one speed interval in the road congestion index set in one piece of data and then summarize them. At this time, the summarized data is the arterial road data group.

[0045] It is understandable that the congestion reference table is a table made using the summarized arterial road data group. The horizontal classification axis and the vertical classification axis of the table are multiple sub-levels and multiple sub-arterial road types respectively, and the filled data are the speed intervals and unit congestion indexes under the conditions of sub-levels and sub-arterial road types.

[0046] It is understandable that the cycling roads applicable in the present invention are roads adjacent to highways and accessible to smart bicycles. For example, on common highway arterials, there may be roads accessible to smart bicycles and roads inaccessible to smart bicycles. For cycling roads for bicycles, they include roads accessible to cars and roads inaccessible to cars. Roads accessible to bicycles but inaccessible to cars often have complex road conditions, such as alleys and mountain roads where vehicles cannot pass. At this time, it is of little significance to use the intelligent system to adjust the speed of the smart bicycle, and the cyclist should directly decide the cycling speed. Therefore, the application scenario of the embodiments of the present invention is roads accessible to bicycles, and there is a highway arterial adjacent to the roads accessible to the bicycles.

[0047] Furthermore, according to the application scenario defined in this embodiment, when the highway arterial is extremely congested or slightly congested, there are often large flows of people and vehicles. In these cases, the cycling speed of the vehicle should be reduced. Therefore, by using the easily monitored congestion condition of the highway arterial (the congestion condition of the highway arterial can be obtained through the Internet), calculating the road congestion index, and using the road congestion index to correct the cycling speed, it provides a reference for formulating the cycling speed, which is both practical and saves monitoring costs.

[0048] S3. Obtain the user's location based on the location monitoring system, and extract the initial speed change strategy corresponding to the user's location from the initial speed change strategy sequence to obtain the initial target strategy.

[0049] Furthermore, the user's location is the real-time location of the cyclist. After confirming the user's location, the section where the user is located can be confirmed, so that the initial speed change strategy corresponding to the user's location can be extracted from the initial speed change strategy sequence, providing a reference for the cyclist's cycling on this section.

[0050] S4. Use the road condition monitoring system to obtain the road surface flatness within a preset range.

[0051] Furthermore, the obtaining of the road surface flatness within a preset range by using the road condition monitoring system includes: Use the road condition monitoring system to obtain the oblique photography data set within the preset range, and use the oblique photography data set and the pre-constructed GIS model to obtain the real-time road model; Intercept multiple road vertical lines and multiple road horizontal lines from the real-time road model to obtain an intercepted line set. Among them, the road vertical lines are parallel to the road vertical section, and the road horizontal lines are parallel to the road cross section. The real-time road model is a model composed of three-dimensional geographic information within the preset range, and the three-dimensional geographic information includes a three-dimensional coordinate system, and the three-dimensional coordinate system includes a horizontal axis, a vertical axis, and a vertical axis; Obtain the depth data set based on the intercepted line set, and calculate the road surface flatness based on the depth data set.

[0052] It is understandable that, optionally, the present invention sets the preset range as a sphere with the centroid of the intelligent bicycle as the origin and a radius of 3 m, which can be set by researchers during actual application. The oblique photography data set is a set of images obtained by using the camera on the intelligent bicycle to photograph the road surface. The specific implementation of intercepting multiple road longitudinal lines and multiple road transverse lines from the real-time road model is as follows: fitting the road surface into a two-dimensional plane, and equally dividing the two-dimensional plane in the cross-sectional direction of the road by a preset number. Each line used for equal division is a road transverse line, and the same applies to the road longitudinal line, which will not be elaborated here.

[0053] It is understandable that the initial speed change strategy can guide the riding speed of the intelligent bicycle on a section of the road. However, during actual riding, there may still be factors such as uneven road surface, foreign object protrusions, and unrecorded speed bumps in the section that affect the riding speed and speed change strategy of the rider. When the rider rides to the position corresponding to the above factors, if the rider does not observe in advance, there may be jolts or even rollovers due to the excessive riding speed. Therefore, it is also necessary to adjust the initial speed change strategy according to the road surface flatness obtained by real-time monitoring to assist the rider during the riding process.

[0054] Furthermore, the GIS model can choose Tuxing Earth as the model, or other existing technologies can be selected to build the model. Its purpose is to be able to build three-dimensional geographical information within the preset range, especially referring to the real-scene three-dimensional model. The three-dimensional geographical information includes but is not limited to a three-dimensional coordinate system, scatter points in the three-dimensional coordinate system, road names, etc.

[0055] It should be noted that when establishing a real-time road model based on the oblique photography data set images, the speed of calculating the model and judging the road surface flatness is related to the accuracy (mesh division, resolution) of the real-time road model. In the process of applying the embodiments of the present invention, since the intelligent bicycle is equipped with a shock absorption system, it is not necessary to monitor the road surface conditions that are too small (for example, gravel). In the present invention, it is more desirable to monitor parts such as speed bumps, road cracks, manhole covers, and small steps. Therefore, the calculation amount can be reduced in various ways (for example, setting the resolution of the oblique photography data set), the running speed can be increased, and while accurately identifying the road surface flatness as much as possible, the effect of real-time detecting the road surface flatness can be achieved.

[0056] Optionally, a flatness threshold can be set manually. When the road surface flatness exceeds the preset flatness threshold, this embodiment indicates that the intelligent bicycle can issue an intelligent alarm to prompt the rider that there may be jolts. The intelligent alarm is a voice alarm and can be set by setting a ringtone, etc.

[0057] Further, obtaining the depth dataset based on the set of intercept lines and calculating the pavement evenness based on the depth dataset includes: Extract the intercept lines from the set of intercept lines in sequence to obtain target lines, and perform the following operations on all target lines: Taking the target line as the cross-section horizontal axis and the vertical axis in the real-time road model as the cross-section vertical axis, extract the two-dimensional target cross-section corresponding to the target line from the real-time road model to obtain the target cross-section image; Use the pre-constructed edge recognition algorithm to extract the cross-section pavement curve corresponding to the target line, sequentially obtain the extreme points of the cross-section pavement curve to obtain the extreme point set, calculate the depth dataset based on the extreme point set, and calculate the unit evenness according to the depth dataset; Summarize the unit evenness to obtain the evenness set, and calculate the pavement evenness based on the evenness set, where the pavement evenness is the average value of the unit evenness in the evenness set.

[0058] It should be noted that the cross-section horizontal axis is the horizontal axis in the target cross-section image, and the cross-section vertical axis is the vertical axis of the horizontal axis in the target cross-section image. In the process of constructing three-dimensional geographic information in a three-dimensional coordinate system, the vertical axis is commonly used to represent height information, and the pavement evenness is related to height information. Therefore, in the present invention, the vertical axis in the real-time road model is used as the cross-section vertical axis. The two-dimensional target cross-section is a road cross-section or a road longitudinal section obtained based on the target line. If the target line is a road horizontal line, the intercepted one is the road cross-section. If the target line is a road longitudinal line, the intercepted one is the road longitudinal section. The specific implementation steps of using the pre-constructed edge recognition algorithm to extract the cross-section pavement curve corresponding to the target line are as follows: After using the edge recognition algorithm to fit the edge that can describe the pavement evenness in the target cross-section image, then use the curve fitting method to fit the edge that can describe the pavement evenness into a curve to obtain the cross-section pavement curve. The edge detection algorithm can use the canny edge detection algorithm, and there are various existing methods to realize curve fitting, which will not be exemplified here. The extreme point set is a set composed of extreme points sequentially obtained in the direction of increasing cross-section vertical axis. Further, calculating the depth dataset based on the extreme point set and calculating the unit evenness according to the depth dataset includes: Extract the extreme points from the extreme point set in sequence to obtain target extreme points, and perform the following operations on the target extreme points: Extract the next extreme point adjacent to the target extreme in the extreme point set to obtain the adjacent extreme point. Among them, if the target extreme point is a maximum point, the adjacent extreme point is a minimum point; if the target extreme point is a minimum point, the adjacent extreme point is a maximum point; Obtain the ordinates of the target extreme point and the adjacent extreme point in the cross-section road surface curve respectively to obtain the target extreme value and the adjacent extreme value. Calculate the absolute difference between the target extreme value and the adjacent extreme value to obtain the unit longitudinal depth data. Calculate the absolute difference between the target extreme point and the adjacent extreme point to obtain the unit transverse depth data. Aggregate the unit longitudinal depth data and the unit transverse depth data to obtain the unit depth data; Aggregate the unit depth data to obtain the initial depth data set; Construct an envelope depth data set based on the cross-section road surface curve. Aggregate the envelope depth data set and the initial depth data set to obtain the depth data set. Calculate the unit flatness based on the depth data set.

[0059] Further, if the extreme point set is [x 1 , x 2 , x 3 , extract the extreme point x 1 . Then the target extreme point is x 1 , and the adjacent extreme point is x 2 . In the present invention, the cross-section road surface curve is a continuous curve. Therefore, if the target extreme point is a maximum point, the adjacent extreme point is a minimum point; if the target extreme point is a minimum point, the adjacent extreme point is a maximum point.

[0060] It can be understood that the target extreme value is the value corresponding to the ordinate of the target extreme point in the cross-section road surface curve. The adjacent extreme value is the value corresponding to the ordinate of the adjacent extreme point in the cross-section road surface curve. The unit longitudinal depth data is the absolute difference obtained by calculating the target extreme value and the adjacent extreme value. In the present invention, the maximum point is regarded as the raised part of the road surface, and the minimum point is regarded as the sunken part of the road surface. Therefore, calculating the unit longitudinal depth data can calculate the height difference between two adjacent extreme points when the road surface is uneven, thereby providing a basis for calculating the road surface flatness.

[0061] It should be noted that while the height difference between the road surface convex point and the road surface concave point is larger, if the distance between the road surface convex point and the road surface concave point in the road horizontal axis direction is also larger, then the slope between the road surface convex point and the road surface concave point is smaller. At this time, compared with the road surface where the slope between the road surface convex point and the road surface concave point is larger, the bumpy feeling of the rider when riding on this road surface is smaller.

[0062] Further, the constructing the envelope depth data set based on the cross-section road surface curve includes: Intercept the cross-section road surface curve based on a preset division length to obtain a plurality of unit curves. Perform the following operations on each of the plurality of unit curves: Obtain the envelope straight line of the unit curve, and extract a plurality of extreme points existing in the envelope straight line from the extreme point set to obtain the envelope extreme point set. Perform the following operations on each envelope extreme point in the envelope extreme point set: Obtain the values of the envelope extreme points on the unit curve and the values of the envelope extreme points on the envelope line respectively, obtain the curve values and the line values, calculate the absolute difference between the curve values and the line values, and obtain the envelope depth data; Summarize the unit envelope depth data to obtain multiple envelope depth data corresponding to the unit curve, and summarize the multiple envelope depth data to obtain the envelope depth data set corresponding to the cross-section road surface curve.

[0063] Further, the division length is a manually set length for dividing the cross-section road surface curve and dividing the cross-section road surface curve into multiple unit curves. The envelope line is an envelope line constructed based on multiple maximum points within the unit curve, and the envelope line is a straight line.

[0064] It can be understood that the ordinate with the abscissa being the envelope extreme point is extracted on the unit curve, and the obtained ordinate is the value of the envelope extreme point on the unit curve, that is, the curve value. The ordinate obtained by extracting the ordinate with the abscissa being the envelope extreme point on the envelope line is the value of the envelope extreme point on the envelope line, that is, the line value.

[0065] It can be understood that in the present invention, the envelope line is regarded as a flat road surface. Therefore, the larger the absolute difference, the farther the corresponding envelope extreme point is from the flat road surface, and when the intelligent bicycle travels to the position of the envelope extreme point, it is more likely to have a bumpy feeling. Therefore, in the embodiments of the present invention, the absolute difference is used as the envelope depth data.

[0066] Further, the calculating the unit flatness based on the depth data set includes: Extract the extreme points from the extreme point set in sequence to obtain the flatness nodes, and perform the following operations on all the flatness nodes: Extract the envelope depth data set and the initial depth data set from the depth data set, extract the envelope depth data corresponding to the flatness node from the envelope depth data set to obtain the target envelope data, and extract the unit depth data corresponding to the flatness node from the initial depth data set to obtain the target depth data; Calculate the unit flatness corresponding to the flatness node based on the target envelope data and the target depth data, where the calculation formula of the unit flatness is as follows: Among them, represents the unit flatness, represents the envelope depth data, 、 、 and respectively represent the envelope depth weight, the unit horizontal depth weight, the unit vertical depth weight and the horizontal-vertical ratio weight, and 、 、 and are both constants, represents the unit horizontal depth data of the target depth data, represents the unit vertical depth data of the target depth data, represents the numerical stabilization parameter.

[0067] It should be noted that the flatness node is the node during flatness calculation, specifically the extreme point extracted from the set of extreme points. The envelope depth weight, unit horizontal depth weight, unit vertical depth weight, and aspect ratio weight are dimensionless parameters respectively used to describe the importance degree of including depth data, unit horizontal depth data, unit vertical depth data, and the ratio of unit horizontal depth data to unit vertical depth data. The specific weights can be confirmed by existing technologies such as the analytic hierarchy process and will not be elaborated here. The numerical stabilization parameter is a manually set constant used to avoid the situation of a zero denominator caused by the unit horizontal depth data tending to zero.

[0068] S5. Obtain the weather state according to the environmental monitoring system, obtain the vehicle driving state and road surface inclination according to the vehicle monitoring system, and obtain the real-time riding state of the rider according to the rider monitoring system.

[0069] Furthermore, the weather state is the real-time weather state, which is different from the above-mentioned initial weather data, and the above-mentioned initial weather data can be obtained from the weather forecast. There will be differences between the weather forecasted by the weather forecast and the real-time weather state at certain moments. For example, a local shower suddenly appears in a certain place, while the weather forecasted by the weather forecast is still sunny. To avoid traffic accidents caused by slipping due to rain, the rider needs to decelerate at this time. It should be noted that the road surface inclination is the real-time road surface inclination and can be obtained by the sensor installed on the smart bicycle. Although the road surface inclination does not affect the riding speed of the smart bicycle, during professional bicycle riding, in order to ensure that the pedaling frequency of the rider is within the preset range, different inclinations should correspond to different gear ratios (the gear ratio refers to the ratio of the flywheel to the chainring). Although the embodiments of the present invention do not limit and elaborate on the gear ratios corresponding to different inclinations, the road surface inclination is an important factor affecting the setting of the ratio of the flywheel to the chainring during the formulation of the gear ratio strategy table of the bicycle. Therefore, the road surface inclination is still collected during data collection.

[0070] It should be noted that the real-time riding state of the rider includes but is not limited to heart rate and riding duration. The vehicle driving state includes multiple parameters during the vehicle driving process, such as the riding speed of the smart bicycle, the real-time gear of the smart bicycle, the lighting on / off state, etc.

[0071] S6. Summarize the road surface flatness, weather state, vehicle driving state, road surface inclination, and real-time riding state of the rider to obtain the real-time measurement data.

[0072] It is understandable that the real-time measurement data is the data obtained by storing the road surface flatness, weather conditions, vehicle driving state, road surface inclination and the real-time riding state of the rider in one data and identifying it.

[0073] S7. Extract the initial target vehicle speed and the initial target gear from the initial target strategy, obtain an optimized target strategy according to the real-time measurement data, the initial target vehicle speed and the initial target gear, and complete the adjustment of the variable speed strategy combining vehicle networking and road condition perception based on the optimized target strategy.

[0074] Furthermore, the extracting the initial target vehicle speed and the initial target gear from the initial target strategy and obtaining an optimized target strategy according to the real-time measurement data, the initial target vehicle speed and the initial target gear includes: Obtain a multi-modal weight set corresponding to the real-time measurement data by using a pre-constructed evaluation method, perform a normalization operation on the real-time measurement data to obtain a normalized data set, calculate an initial target vehicle speed correction parameter according to the normalized data set and the multi-modal weight set, and calculate the product of the initial target vehicle speed correction parameter and the initial target vehicle speed to obtain the target riding speed; If the target riding speed is within the initial target gear, extract the current driving speed from the vehicle driving state; If the target riding speed is greater than the current driving speed, confirm the target riding speed and the initial target gear as the optimized target strategy. Otherwise, calculate the absolute difference between the target riding speed and the current driving speed to obtain the speed to be adjusted, calculate the ratio of the speed to be adjusted to the target riding speed to obtain the speed difference percentage. If the speed difference percentage is greater than a preset speed difference threshold, generate an overspeed warning, and generate an optimized target strategy based on the overspeed warning, the speed to be adjusted and the initial target gear; If the target riding speed is not within the initial target gear, obtain the corrected gear corresponding to the target riding speed, obtain the lowest speed and the highest speed of the corrected gear. If the lowest speed is greater than the current driving speed, confirm the corrected gear and the target riding speed as the optimized target strategy. If the highest speed is less than the current driving speed, generate an overspeed warning, and generate an optimized target strategy based on the overspeed warning, the corrected gear and the target riding speed.

[0075] It is understandable that the evaluation method can be the analytic hierarchy process or can be evaluated by a neural network. There are multiple existing technologies to achieve this, so it will not be elaborated here. The multi-modal weight set is a set composed of the weights corresponding to the road surface flatness, weather conditions, vehicle driving state, road surface inclination and the real-time riding state of the rider. The normalization operation can be achieved by existing technologies, and its purpose is to eliminate the influence of the dimensions of all data in the real-time measurement data.

[0076] Further, calculating the initial target vehicle speed correction parameter based on the normalized data set and the multi-modal weight set includes: extracting normalized data from the normalized data set to obtain target normalized data, and extracting the multi-modal weight corresponding to the target normalized data from the multi-modal weight set to obtain a target weight, calculating the product of the target normalized data and the target weight to obtain a unit correction parameter, summarizing the unit correction parameters to obtain a unit correction parameter set, and calculating the initial target vehicle speed correction coefficient using the following formula: wherein, represents the initial target vehicle speed correction coefficient, represents the th unit correction parameter in the unit correction parameter set.

[0077] It should be noted that 5 in the above formula represents the total number of unit correction parameters in the unit correction parameter set.

[0078] It can be understood that the current driving speed is the speed at which the rider is actually riding in real time. If the target riding speed is within the initial target gear range, it means that the difference between the initial target vehicle speed provided by the initial target strategy and the target riding speed is not significant. Therefore, if the target riding speed is greater than the current driving speed, it means that the rider's speed is slower than the target riding speed. The present invention believes that the intelligent bicycle is relatively safer in a slower riding state. Therefore, the initial target vehicle speed and the initial target gear can be confirmed as the preferred target strategy. However, whether to select this preferred target strategy as the final strategy is up to the rider to decide.

[0079] Further, if the target riding speed is less than or equal to the current driving speed, it means that the rider's speed is faster than the target riding speed. Since the initial target vehicle speed and the initial target gear in the initial target strategy can provide a reference for the rider's riding on this section of the road, when the current driving speed is faster than the target riding speed, there may be a risk of traffic accidents due to excessive riding speed. Therefore, it is necessary to further judge the current driving speed.

[0080] Specifically, the absolute difference is the absolute value of the difference. The speed difference threshold is a preset percentage value used to limit the speed difference percentage. If the speed difference percentage is greater than the preset speed difference threshold, it means that the rider has a risk of speeding, and thus there may be a risk of traffic accidents due to excessive riding speed. Therefore, an over-speed warning is generated. The over-speed warning can be a warning composed of text or various forms such as voice broadcast for reminding the rider of over-speeding, which is not limited here.

[0081] Further, in the process of generating the optimal target strategy based on the overspeed warning, the speed to be adjusted, and the initial target gear, the target riding speed is used as the final vehicle speed, the speed to be adjusted is used as the speed that the smart bicycle needs to adjust, and the initial target gear is used as the gear for the rider's reference to generate the optimal target strategy. Moreover, the optimal target strategy also includes sending the overspeed warning to the rider.

[0082] It should be noted that the initial target gear here is similar in meaning to the rated pacing gear described above. Therefore, the applicable pacing (the ratio of the flywheel to the chainring) is also included in the initial target gear. Thus, the gear must be confirmed in the optimal target strategy.

[0083] Further, if the target riding speed is not within the initial target gear, it indicates that there is a large difference between the initial target vehicle speed provided by the initial target strategy and the target riding speed. Then, the initial target strategy cannot be directly applied. Instead, the corresponding pacing strategy needs to be found in the pacing strategy table according to the target riding speed to obtain the corrected gear. The corrected gear is similar in meaning to the rated pacing gear described above. Therefore, the corrected gear can be represented by an interval composed of speeds. Thus, the lowest speed is the lower limit of the interval corresponding to the corrected gear, that is, the minimum speed, and the highest speed is the upper limit of the interval corresponding to the corrected gear, that is, the maximum speed.

[0084] Specifically, if the lowest speed is greater than the current driving speed, it means that the rider's speed is slower than the target riding speed. The smart bicycle is relatively safer in a slower riding state. Therefore, the corrected gear and the target riding speed can be confirmed as the optimal target strategy. If the highest speed is less than the current driving speed, it means that the rider's speed is faster than the target riding speed and faster than the maximum speed of the corrected gear. Then, there may be a traffic accident due to the excessive riding speed. Therefore, an overspeed warning is generated.

[0085] Further, the process of generating the optimal target strategy based on the overspeed warning, the corrected gear, and the target riding speed is similar to the process of generating the optimal target strategy based on the overspeed warning, the speed to be adjusted, and the initial target gear, and will not be elaborated here.

[0086] It should be noted that when applying the optimal target strategy to the smart bicycle, the adjustment of the variable speed strategy combining vehicle networking and road condition perception is completed. The present invention integrates various actually detected data during the riding process to correct the preset initial target strategy. The initial target strategy is speed-oriented and provides a reference for the smart bicycle. The present invention also particularly focuses on the road surface flatness and real-time environment data, enabling the rider to adjust the variable speed strategy before encountering bumpy roads, uneven roads, etc., so as to assist the riding process.

[0087] To solve the problems described in the background art, the present invention receives a driving instruction, extracts the target path in the driving instruction based on the position monitoring system. It can be seen that the present invention provides real-time monitoring data for cyclists riding on the target path by combining various monitoring systems in the monitoring system. Obtain the initial target path data of the target path, and obtain the initial variable speed strategy sequence according to the initial target path data. The present invention divides the target path into multiple sections and multiple nodes, sorts them in the order from the starting point to the ending point of the target path, generates a low-speed strategy at the nodes, and generates a section driving strategy at the sections, and equips an initial variable speed strategy for the entire section, providing a basis for adjusting the variable speed strategy by combining real-time monitoring data in the future. Based on the position monitoring system, obtain the user's position, and extract the initial variable speed strategy corresponding to the user's position from the initial variable speed strategy sequence to obtain the initial target strategy. It can be seen that after the present invention confirms the user's position, it can confirm the section where the user is located, and extract the initial variable speed strategy corresponding to the user's position from the initial variable speed strategy sequence to provide a basic reference for the cyclist's riding on this section. Obtain real-time measurement data, and obtain the optimal target strategy according to the real-time measurement data and the initial target strategy. The present invention adjusts the initial variable speed strategy according to the road surface flatness obtained by real-time monitoring, including but not limited to road surface flatness, weather conditions, vehicle driving conditions, road surface inclination, and the cyclist's real-time riding state, etc., and especially pays attention to road surface flatness and real-time environmental data, so as to assist the cyclist's riding process, and complete the adjustment of the variable speed strategy by combining the vehicle network and road condition perception based on the optimal target strategy. Therefore, the present invention integrates various data actually detected during the riding process, especially pays attention to road surface flatness and real-time environmental data, provides a reference for the cyclist to adjust the variable speed strategy, and thus assists the riding process.

[0088] As Figure 2 shown, it is a functional module diagram of a variable speed strategy adjustment system based on vehicle network combined with road condition perception provided by an embodiment of the present invention.

[0089] The variable speed strategy adjustment system 100 based on vehicle network combined with road condition perception described in the present invention can be installed in an electronic device. According to the functions achieved, the variable speed strategy adjustment system 100 based on vehicle network combined with road condition perception can include a monitoring module 101, an initial variable speed strategy module 102, a real-time measurement data module 103, and an optimal target strategy acquisition module 104. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0090] The monitoring module 101 is used to receive a driving instruction and start the monitoring system based on the driving instruction. Among them, the monitoring system includes: a road condition monitoring system, an environment monitoring system, a cyclist monitoring system, a position monitoring system, and a vehicle monitoring system; The initial gear shifting strategy module 102 is configured to extract a target path in the driving instruction based on a position monitoring system, obtain initial target path data of the target path, and acquire an initial gear shifting strategy sequence according to the initial target path data. The initial target path data includes initial road condition data, initial weather data, and initial navigation data, and the initial gear shifting strategy sequence includes a plurality of initial gear shifting strategies. The real-time measurement data module 103 is configured to obtain a user position based on a position monitoring system, extract an initial gear shifting strategy corresponding to the user position from the initial gear shifting strategy sequence to obtain an initial target strategy, acquire the road surface flatness within a preset range by using a road condition monitoring system, obtain the weather condition by using an environment monitoring system, obtain the vehicle driving state and the road surface inclination angle by using a vehicle monitoring system, obtain the real-time riding state of a rider by using a rider monitoring system, and summarize the road surface flatness, the weather condition, the vehicle driving state, the road surface inclination angle, and the real-time riding state of the rider to obtain real-time measurement data. The optimal target strategy acquisition module 104 is configured to extract an initial target vehicle speed and an initial target gear from the initial target strategy, obtain an optimal target strategy according to the real-time measurement data, the initial target vehicle speed, and the initial target gear, and complete the gear shifting strategy adjustment combining vehicle networking and road condition perception based on the optimal target strategy.

[0091] Specifically, when the modules in the gear shifting strategy adjustment system 100 combining vehicle networking and road condition perception in the embodiments of the present invention are used, they adopt the same technical means as those Figure 1 in the gear shifting strategy adjustment method combining vehicle networking and road condition perception described above, and can produce the same technical effects, which will not be elaborated here.

[0092] As Figure 3 shown, it is a schematic structural diagram of an electronic device for implementing the gear shifting strategy adjustment method combining vehicle networking and road condition perception provided by an embodiment of the present invention.

[0093] The electronic device 1 may include a processor 10, a memory 11, and a bus 12, and may further include a computer program stored in the memory 11 and executable on the processor 10, such as a gear shifting strategy adjustment method program combining vehicle networking and road condition perception.

[0094] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disc, etc. The memory 11 can be an internal storage unit of the electronic device 1 in some embodiments, such as the mobile hard disk of the electronic device 1. The memory 11 can also be an external storage device of the electronic device 1 in some other embodiments, such as a plug-in mobile hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. equipped on the electronic device 1. Further, the memory 11 also includes the internal storage unit of the electronic device 1 and also includes an external storage device. The memory 11 can not only be used to store application software installed in the electronic device 1 and various types of data, such as the code of the variable speed strategy adjustment method program based on vehicle networking combined with road condition perception, etc., but also be used to temporarily store data that has been output or will be output.

[0095] The processor 10 can be composed of integrated circuits in some embodiments. For example, it can be composed of a single packaged integrated circuit, or can be composed of multiple integrated circuits with the same or different functions packaged, including a combination of one or more central processing units (CPU), microprocessors, digital processing chips, graphics processors, and various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, connecting various components of the entire electronic device through various interfaces and lines, and by running or executing programs or modules stored in the memory 11 (such as the variable speed strategy adjustment method program based on vehicle networking combined with road condition perception, etc.), and calling data stored in the memory 11, to execute various functions of the electronic device 1 and process data.

[0096] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is set to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0097] Figure 3 Only the electronic device with components is shown. Those skilled in the art can understand that Figure 3The structure shown does not constitute a limitation on the electronic device 1, and it may include fewer or more components than shown, or combine certain components, or have a different component arrangement.

[0098] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for powering each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management system, so as to implement functions such as charging management, discharging management, and power consumption management through the power management system. The power source may also include any components such as one or more DC or AC power sources, a recharge system, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may also include a variety of sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0099] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0100] Optionally, the electronic device 1 may also include a user interface. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display a visual user interface.

[0101] The program of the variable speed strategy adjustment method based on vehicle networking combined with road condition perception stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can implement: Receive a driving instruction and start a monitoring system based on the driving instruction. Among them, the monitoring system includes: a road condition monitoring system, an environment monitoring system, a rider monitoring system, a position monitoring system, and a vehicle monitoring system; Extract the target path in the driving instruction based on the position monitoring system, and obtain the initial target path data of the target path. According to the initial target path data, obtain an initial variable speed strategy sequence. Among them, the initial target path data includes initial road condition data, initial weather data, and initial navigation data, and the initial variable speed strategy sequence includes multiple initial variable speed strategies; The position monitoring system is used to obtain the user's position, and the initial speed change strategy corresponding to the user's position is extracted from the initial speed change strategy sequence according to the user's position to obtain the initial target strategy; The road surface flatness within a preset range is obtained by using the road condition monitoring system, the weather condition is obtained by using the environment monitoring system, the vehicle driving state and the road surface inclination are obtained by using the vehicle monitoring system, and the real-time riding state of the rider is obtained by using the rider monitoring system. The road surface flatness, the weather condition, the vehicle driving state, the road surface inclination and the real-time riding state of the rider are summarized to obtain the real-time measurement data; The initial target vehicle speed and the initial target gear are extracted from the initial target strategy, and the optimal target strategy is obtained according to the real-time measurement data, the initial target vehicle speed and the initial target gear. The speed change strategy adjustment combining the vehicle networking and the road condition perception is completed based on the optimal target strategy.

[0102] Specifically, for the specific implementation method of the above instructions by the processor 10, reference may be made to Figures 1 to 3 the description of the relevant steps in the corresponding embodiment, which will not be elaborated here.

[0103] Further, if the modules / units integrated in the electronic device 1 are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory).

[0104] The present invention also provides a computer-readable storage medium, and the readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, it can implement: Receiving a driving instruction, and starting the monitoring system based on the driving instruction, where the monitoring system includes: a road condition monitoring system, an environment monitoring system, a rider monitoring system, a position monitoring system, and a vehicle monitoring system; Extracting a target path in the driving instruction based on the position monitoring system, and obtaining initial target path data of the target path. An initial speed change strategy sequence is obtained according to the initial target path data, where the initial target path data includes initial road condition data, initial weather data, and initial navigation data, and the initial speed change strategy sequence includes a plurality of initial speed change strategies; Obtaining the user's position based on the position monitoring system, and extracting the initial speed change strategy corresponding to the user's position from the initial speed change strategy sequence to obtain the initial target strategy; Obtain the road surface evenness within a preset range using a road condition monitoring system, obtain the weather condition according to an environmental monitoring system, obtain the vehicle driving state and the road surface inclination according to a vehicle monitoring system, obtain the real-time cycling state of a cyclist according to a cyclist monitoring system, and summarize the road surface evenness, weather condition, vehicle driving state, road surface inclination, and the real-time cycling state of the cyclist to obtain real-time measurement data; Extract the initial target vehicle speed and the initial target gear from the initial target strategy, obtain an optimized target strategy according to the real-time measurement data, the initial target vehicle speed, and the initial target gear, and complete the adjustment of the variable speed strategy by combining the vehicle networking with road condition perception based on the optimized target strategy.

[0105] In several embodiments provided by the present invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and there may be other division methods in actual implementation.

[0106] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0107] In addition, in each embodiment of the present invention, the functional modules can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware, or in the form of a combination of hardware and software functional modules.

[0108] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A speed change strategy adjustment method based on vehicle networking combined with road condition perception, characterized in that: The method comprises: receiving a driving instruction, and starting a monitoring system based on the driving instruction, wherein the monitoring system includes: a road condition monitoring system, an environment monitoring system, a rider monitoring system, a position monitoring system, and a vehicle monitoring system; Extracting a target path in the driving instruction based on a position monitoring system, and acquiring initial target path data of the target path, and acquiring an initial speed change strategy sequence according to the initial target path data, wherein the initial target path data includes initial road condition data, initial weather data, and initial navigation data, and the initial speed change strategy sequence includes multiple initial speed change strategies; The user position is obtained based on the position monitoring system, and the initial speed change strategy corresponding to the user position is extracted from the initial speed change strategy sequence according to the user position to obtain the initial target strategy; The road condition monitoring system is used to obtain the road surface flatness within a preset range, the environment monitoring system is used to obtain the weather conditions, the vehicle driving status and the road surface inclination angle are obtained according to the vehicle monitoring system, and the rider's real-time riding status is obtained according to the rider monitoring system. The road surface flatness, weather conditions, vehicle driving status, road surface inclination angle and rider's real-time riding status are summarized to obtain real-time measurement data; The initial target vehicle speed and initial target gear are extracted from the initial target strategy, and the optimal target strategy is obtained according to the real-time measurement data, the initial target vehicle speed and the initial target gear. Based on the optimal target strategy, the speed change strategy adjustment of the Internet of Vehicles combined with road condition perception is completed.

2. The method for adjusting the speed change strategy based on vehicle networking combined with road condition perception as claimed in claim 1, characterized in that: The step of acquiring an initial speed change strategy sequence according to the initial target path data comprises: Acquire a road segment-node sequence based on the initial navigation data, wherein the road segment-node sequence includes a plurality of road segments and a plurality of nodes; Extracting target data from the road segment-node sequence in sequence, and if the target data is a node, generating a low-speed strategy based on the target data; If the target data is a road section, the speed limit of the road section is obtained, and the road section type and the average speed of the traffic flow are obtained based on the initial road condition data, and the road congestion index is calculated based on the road section type and the average speed of the traffic flow. The weather category is obtained based on the initial weather data, and the weather impact index is calculated according to the weather category; Calculate the initial vehicle speed based on the road congestion index, the weather impact index and the limited vehicle speed, query the speed strategy corresponding to the initial vehicle speed from the pre-built speed strategy table, and obtain the road section driving strategy; According to the road section-node sequence, the low speed strategy and the road section driving strategy are summarized in sequence to obtain the initial speed change strategy sequence.

3. The method for adjusting the speed change strategy based on the Internet of Vehicles combined with road condition perception as claimed in claim 2, characterized in that: The calculation of the road congestion index based on the road section type and the average speed of the traffic flow includes: Obtaining congestion classification levels and road section classification levels, wherein the congestion classification level includes multiple sub-levels, and the multiple sub-levels are: extremely congested, slightly congested, basically smooth and extremely smooth, and the road section classification level includes multiple sub-road types: expressway, main road, secondary road and branch road; Extracting sub-arterial road types from multiple sub-arterial road types in sequence, and obtaining speed limit intervals of the sub-arterial road types, performing an average division operation on the speed limit intervals using a preset number of congestion classification levels to obtain multiple speed intervals, and respectively calculating a unit congestion index of each speed interval in the multiple speed intervals to obtain a road congestion index set, wherein the multiple speed intervals are respectively an extremely congested speed interval, a lightly congested speed interval, a basically unobstructed speed interval, and an extremely unobstructed speed interval; Associating the sub-arterial road type, the plurality of speed intervals and the road congestion index set to obtain an arterial road data group, wherein the arterial road data group includes a plurality of arterial road data, and the arterial road data includes a speed interval and a unit congestion index corresponding to the speed interval; Summarize the trunk road data group, and use the summarized trunk road data group to draw a congestion reference table; The trunk road data group corresponding to the road section type is identified from the congestion reference table to obtain the target trunk road data group, and the trunk road data corresponding to the average speed of the vehicle flow is extracted from the target trunk road data group, and the unit congestion index in the trunk road data corresponding to the average speed of the vehicle flow is confirmed as the road congestion index.

4. The method for adjusting the speed change strategy based on vehicle networking combined with road condition perception as claimed in claim 3, characterized in that: The method of using the road condition monitoring system to obtain the road surface flatness within a preset range includes: The road condition monitoring system is used to obtain the oblique photography dataset within a preset range, and the oblique photography dataset and the pre-built GIS model are used to obtain the real-time road model; A plurality of road longitudinal lines and a plurality of road transverse lines are intercepted from the real-time road model to obtain an intercepted line set, wherein the road longitudinal lines are parallel to the road longitudinal section, and the road transverse lines are parallel to the road transverse section, and the real-time road model is a model constructed based on three-dimensional geographic information within a preset range, wherein the three-dimensional geographic information includes a three-dimensional coordinate system, and the three-dimensional coordinate system includes a horizontal axis, a longitudinal axis, and a vertical axis; A depth data set is obtained based on the intercept line set, and the road surface smoothness is calculated based on the depth data set.

5. The method for adjusting the speed change strategy based on vehicle networking combined with road condition perception as claimed in claim 4, characterized in that: The method of acquiring a depth data set based on the intercept line set and calculating the road surface flatness based on the depth data set includes: Extract the intercept lines from the intercept line set in sequence to obtain the target lines, and perform the following operations on the target lines: Taking the target line as the horizontal axis of the section and the vertical axis in the real-time road model as the longitudinal axis of the section, extracting the two-dimensional target section corresponding to the target line from the real-time road model to obtain a target section image; The cross-section road surface curve corresponding to the target line is extracted by using a pre-built edge recognition algorithm, and the extreme points of the cross-section road surface curve are obtained in sequence to obtain an extreme point set, and a depth data set is calculated based on the extreme point set, and the unit flatness is calculated based on the depth data set; The unit flatness is summarized to obtain a flatness set, and the road surface flatness is calculated based on the flatness set, wherein the road surface flatness is the average of the unit flatness in the flatness set.

6. The method for adjusting the speed change strategy based on vehicle networking combined with road condition perception as claimed in claim 5, characterized in that: The step of calculating the depth data set based on the extreme point set and calculating the unit flatness according to the depth data set includes: Extract extreme points from the extreme point set in sequence to obtain the target extreme point, and perform the following operations on the target extreme point: Extract the next extreme point adjacent to the target extreme value in the extreme point set to obtain adjacent extreme points, wherein if the target extreme point is a maximum point, the adjacent extreme point is a minimum point, and if the target extreme point is a minimum point, the adjacent extreme point is a maximum point; The ordinates of the target extreme value point and the adjacent extreme value point in the cross-section pavement curve are respectively obtained to obtain the target extreme value and the adjacent extreme value, the absolute difference between the target extreme value and the adjacent extreme value is calculated to obtain the unit longitudinal depth data, the absolute difference between the target extreme value point and the adjacent extreme value is calculated to obtain the unit transverse depth data, and the unit longitudinal depth data and the unit transverse depth data are summarized to obtain the unit depth data; Aggregate unit depth data to obtain an initial depth data set; An envelope depth dataset is constructed based on the cross-section pavement curve, the envelope depth dataset and the initial depth dataset are summarized to obtain a depth dataset, and the unit flatness is calculated based on the depth dataset.

7. The method for adjusting the speed change strategy based on the Internet of Vehicles combined with road condition perception as claimed in claim 6, characterized in that: The step of constructing an envelope depth data set based on a cross-section road surface curve includes: The cross-section road surface curve is intercepted based on a preset division length to obtain a plurality of unit curves, and the following operations are performed on the plurality of unit curves: Get the envelope straight line of the unit curve, and extract multiple extreme value points existing in the envelope straight line from the extreme value point set to obtain the envelope extreme value point set. Perform the following operations on the envelope extreme value points in the envelope extreme value point set: The values ​​of the envelope extreme value points on the unit curve and the values ​​of the envelope extreme value points on the envelope straight line are respectively obtained to obtain the curve value and the straight line value, and the absolute difference between the curve value and the straight line value is calculated to obtain the envelope depth data; The unit envelope depth data are aggregated to obtain a plurality of envelope depth data corresponding to the unit curve, and the plurality of envelope depth data are aggregated to obtain an envelope depth data set corresponding to the cross-section pavement curve.

8. The method for adjusting the speed change strategy based on the Internet of Vehicles combined with road condition perception as claimed in claim 7, characterized in that: The calculating unit flatness based on the depth data set includes: The extreme points are extracted from the extreme point set in sequence to obtain the flatness nodes, and the following operations are performed on the flatness nodes: Extracting an envelope depth data set and an initial depth data set from the depth data set, extracting envelope depth data corresponding to the flatness node from the envelope depth data set to obtain target envelope data, and extracting unit depth data corresponding to the flatness node from the initial depth data set to obtain target depth data; The unit flatness corresponding to the flatness node is calculated based on the target envelope data and the target depth data, where the calculation formula of the unit flatness is as follows: in, Indicates unit flatness, represents the envelope depth data, , , and denote the envelope depth weight, unit transverse depth weight, unit longitudinal depth weight and aspect ratio weight respectively, and , , and are constants, The unit horizontal depth data representing the target depth data, The unit depth data representing the target depth data, represents the numerical stabilization parameter.

9. The method for adjusting the speed change strategy based on vehicle networking combined with road condition perception as claimed in claim 8, characterized in that: The extracting the initial target vehicle speed and the initial target gear from the initial target strategy, and obtaining the optimal target strategy according to the real-time measurement data, the initial target vehicle speed and the initial target gear, includes: Using a pre-built evaluation method to obtain a multimodal weight set corresponding to the real-time measurement data, and performing a normalization operation on the real-time measurement data to obtain a normalized data set, calculating an initial target vehicle speed correction parameter based on the normalized data set and the multimodal weight set, and calculating the product of the initial target vehicle speed correction parameter and the initial target vehicle speed to obtain a target riding speed; If the target riding speed is within the initial target gear, the current driving speed is extracted from the vehicle driving state; If the target riding speed is greater than the current driving speed, the target riding speed and the initial target gear are confirmed as the preferred target strategy; otherwise, the absolute difference between the target riding speed and the current driving speed is calculated to obtain the speed to be adjusted, and the ratio of the speed to be adjusted to the target riding speed is calculated to obtain the speed difference percentage. If the speed difference percentage is greater than the preset speed difference threshold, an overspeed warning is generated, and a preferred target strategy is generated based on the overspeed warning, the speed to be adjusted and the initial target gear; If the target riding speed is not within the initial target gear, the corrected gear corresponding to the target riding speed is obtained, and the minimum speed and maximum speed of the corrected gear are obtained. If the minimum speed is greater than the current driving speed, the corrected gear and the target riding speed are confirmed as the preferred target strategy. If the maximum speed is less than the current driving speed, an overspeed warning is generated, and an preferred target strategy is generated based on the overspeed warning, the corrected gear and the target riding speed.

10. A speed change strategy adjustment system based on vehicle networking combined with road condition perception, characterized in that: The system comprises: A monitoring module, used to receive a driving instruction and start a monitoring system based on the driving instruction, wherein the monitoring system includes: a road condition monitoring system, an environment monitoring system, a rider monitoring system, a location monitoring system and a vehicle monitoring system; an initial speed change strategy module, for extracting a target path in the driving instruction based on a position monitoring system, and obtaining initial target path data of the target path, and obtaining an initial speed change strategy sequence according to the initial target path data, wherein the initial target path data includes initial road condition data, initial weather data and initial navigation data, and the initial speed change strategy sequence includes a plurality of initial speed change strategies; A real-time measurement data module is used to obtain the user position based on the position monitoring system, extract the initial speed change strategy corresponding to the user position from the initial speed change strategy sequence according to the user position, obtain the initial target strategy, use the road condition monitoring system to obtain the road surface flatness within a preset range, obtain the weather conditions according to the environment monitoring system, obtain the vehicle driving status and road surface inclination according to the vehicle monitoring system, obtain the real-time riding status of the rider according to the rider monitoring system, summarize the road surface flatness, weather conditions, vehicle driving status, road surface inclination and the real-time riding status of the rider, and obtain real-time measurement data; The preferred target strategy acquisition module is used to extract the initial target vehicle speed and the initial target gear from the initial target strategy, obtain the preferred target strategy based on real-time measurement data, the initial target vehicle speed and the initial target gear, and complete the speed change strategy adjustment based on the Internet of Vehicles combined with road condition perception.

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