Speed ​​change strategy adjustment method and system based on vehicle networking combined with road condition perception

Abstract

The present invention relates to the field of intelligent bicycle technology, and is a method and system for adjusting a speed change strategy based on a vehicle-to-vehicle network combined with road condition perception, comprising: receiving a driving instruction, extracting a target path in the driving instruction based on a position monitoring system, obtaining initial target path data of the target path, obtaining an initial speed change strategy sequence based on the initial target path data, obtaining a user position based on the position monitoring system, extracting an initial speed change strategy corresponding to the user position from the initial speed change strategy sequence based on the user position, obtaining an initial target strategy, obtaining real-time measurement data, obtaining a preferred target strategy based on the initial target strategy according to the real-time measurement data, and completing speed change strategy adjustment based on the vehicle-to-vehicle network combined with road condition perception. The present invention integrates various data actually detected during riding, particularly focusing on road surface smoothness and real-time environmental data, providing a reference for the rider to adjust the speed change strategy, thereby assisting the riding process.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent bicycles, and in particular to a speed change 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, smart bicycles can obtain real-time road condition information, navigation path data, and surrounding traffic dynamics through various sensors and cloud communications to assist cyclists in riding.

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

[0004] Although the above method can adjust the smart bicycle, it mainly relies on information such as navigation or driving mode to provide reference for the rider, ignores the actual road conditions, and cannot provide reference for the driver based on the environment and real-time driving status. Summary of the Invention

[0005] The present invention provides a method for adjusting a speed shift strategy based on the Internet of Vehicles combined with road condition perception and a computer-readable storage medium. The main purpose of the method is to integrate various data actually detected during riding, with particular emphasis on road surface smoothness and real-time environmental data, to provide a reference for riders to adjust their speed shift strategies, thereby assisting the riding process.

[0006] To achieve the above objectives, the present invention provides a method for adjusting a speed change strategy based on vehicle networking and road condition perception, comprising:

[0007] receiving a driving instruction and activating 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;

[0008] extracting a target path in the driving instruction based on a position monitoring system, obtaining initial target path data of the target path, and obtaining an initial speed change strategy sequence based on 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;

[0009] The user's location is obtained based on the location monitoring system, and the initial speed change strategy corresponding to the user's location is extracted from the initial speed change strategy sequence according to the user's location to obtain the initial target strategy;

[0010] The road condition monitoring system is used to obtain the road surface smoothness within a preset range, the environmental monitoring system is used to obtain the weather conditions, the vehicle driving status and road surface inclination angle are obtained from the vehicle monitoring system, and the rider's real-time riding status is obtained from the rider monitoring system. The road surface smoothness, weather conditions, vehicle driving status, road surface inclination angle, and rider's real-time riding status are summarized to obtain real-time measurement data;

[0011] The initial target vehicle speed and initial target gear are extracted from the initial target strategy, and the optimal target strategy is obtained based on 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.

[0012] Optionally, acquiring an initial speed change strategy sequence according to the initial target path data includes:

[0013] 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;

[0014] 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;

[0015] 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;

[0016] Calculate the initial vehicle speed based on the road congestion index, weather impact index and speed limit, query the speed strategy corresponding to the initial vehicle speed from the pre-built speed strategy table, and obtain the road section driving strategy;

[0017] 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.

[0018] Optionally, the calculating of the road congestion index based on the road section type and the average speed of traffic flow includes:

[0019] Obtain congestion classification levels and road section classification levels, where the congestion classification level includes multiple sub-levels, including extremely congested, slightly congested, generally unobstructed, and extremely unobstructed. The road section classification level includes multiple sub-road types: expressways, main roads, secondary roads, and branch roads.

[0020] Extracting sub-arterial road types from a plurality of sub-arterial road types in sequence, obtaining speed limit intervals for 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 a plurality of speed intervals, and calculating a unit congestion index for each of the plurality of speed intervals to obtain a road congestion index set, wherein the plurality of 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;

[0021] 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;

[0022] Summarize the arterial road data group, and use the summarized arterial road data group to draw a congestion reference table;

[0023] 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 traffic 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 traffic flow is confirmed as the road congestion index.

[0024] Optionally, obtaining the road surface smoothness within a preset range by using a road condition monitoring system includes:

[0025] Use the road condition monitoring system to obtain oblique photography datasets within a preset range, and use the oblique photography datasets and pre-built GIS models to obtain real-time road models;

[0026] intercepting a plurality of road longitudinal lines and a plurality of road transverse lines from a 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, wherein 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 vertical axis, and a vertical axis;

[0027] A depth dataset is obtained based on the intercept line set, and the road surface smoothness is calculated based on the depth dataset.

[0028] Optionally, acquiring a depth dataset based on the intercept line set, and calculating road surface smoothness based on the depth dataset, includes:

[0029] 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:

[0030] 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 a two-dimensional target section corresponding to the target line from the real-time road model to obtain a target section image;

[0031] A pre-built edge recognition algorithm is used to extract the cross-section road surface curve corresponding to the target line, and the extreme points of the cross-section road surface curve are obtained in sequence to obtain an extreme point set. A depth data set is calculated based on the extreme point set, and the unit flatness is calculated based on the depth data set.

[0032] The unit roughness is aggregated to obtain a roughness set, and the road surface roughness is calculated based on the roughness set, where the road surface roughness is the average of the unit roughness in the roughness set.

[0033] Optionally, calculating the depth data set based on the extreme point set, and calculating the unit flatness according to the depth data set, includes:

[0034] 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:

[0035] Extract the next extreme point adjacent to the target extreme point in the extreme point set to obtain the adjacent extreme point. 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.

[0036] Obtaining the ordinates of the target extreme point and the adjacent extreme point in the cross-section pavement curve respectively, obtaining the target extreme value and the adjacent extreme value, calculating the absolute difference between the target extreme value and the adjacent extreme value, obtaining the unit longitudinal depth data, calculating the absolute difference between the target extreme point and the adjacent extreme value, obtaining the unit transverse depth data, summarizing the unit longitudinal depth data and the unit transverse depth data, and obtaining the unit depth data;

[0037] Aggregate unit depth data to obtain the initial depth dataset;

[0038] 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. Unit flatness is calculated based on the depth dataset.

[0039] Optionally, constructing an envelope depth dataset based on a cross-section road surface curve includes:

[0040] The cross-section road surface curve is intercepted based on a preset division length to obtain multiple unit curves, and the following operations are performed on each of the multiple unit curves:

[0041] Get the envelope straight line of the unit curve, and extract multiple extreme points that exist within the envelope straight line from the extreme point set to obtain the envelope extreme point set. Perform the following operations on all envelope extreme points in the envelope extreme point set:

[0042] 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 respectively, obtain the curve value and the straight line value, calculate the absolute difference between the curve value and the straight line value, and obtain the envelope depth data;

[0043] The unit envelope depth data are aggregated to obtain multiple envelope depth data corresponding to the unit curve, and the multiple envelope depth data are aggregated to obtain the envelope depth data set corresponding to the cross-section pavement curve.

[0044] Optionally, calculating the unit flatness based on the depth data set includes:

[0045] The extreme points are sequentially extracted from the extreme point set to obtain the flatness nodes, and the following operations are performed on the flatness nodes:

[0046] 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;

[0047] The unit flatness corresponding to the flatness node is calculated based on the target envelope data and the target depth data. The calculation formula for the unit flatness is as follows:

[0048]

[0049] in, Indicates unit flatness, represents the envelope depth data, 、 、 and represent the envelope depth weight, unit horizontal depth weight, unit vertical depth weight and aspect ratio weight respectively, and 、 、 and are all constants, Indicates the unit horizontal depth data of the target depth data, The unit depth data representing the target depth data, represents the numerical stabilization parameter.

[0050] Optionally, 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:

[0051] 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;

[0052] If the target riding speed is within the initial target gear, the current riding speed is extracted from the vehicle's driving state;

[0053] 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. 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 a preset speed difference threshold, an overspeed warning is generated. The preferred target strategy is generated based on the overspeed warning, the speed to be adjusted, and the initial target gear.

[0054] If the target riding speed is not within the initial target gear, a 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 a preferred target strategy is generated based on the overspeed warning, the corrected gear and the target riding speed.

[0055] To achieve the above objectives, the present invention further provides a speed change strategy adjustment system based on the Internet of Vehicles combined with road condition perception, comprising:

[0056] A monitoring module, configured to receive a driving instruction and activate 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;

[0057] an initial speed change 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 obtain an initial speed change strategy sequence based on 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;

[0058] A real-time measurement data module is used to obtain a user's location based on a location monitoring system, extract an initial shifting strategy corresponding to the user's location from an initial shifting strategy sequence based on the user's location, and obtain an initial target strategy; obtain road surface roughness within a preset range using a road condition monitoring system; obtain weather conditions based on an environmental monitoring system; obtain vehicle driving conditions and road surface inclination angle based on a vehicle monitoring system; obtain a rider's real-time riding status based on a rider monitoring system; and summarize the road surface roughness, weather conditions, vehicle driving conditions, road surface inclination angle, and rider's real-time riding status to obtain real-time measurement data;

[0059] The preferred target strategy acquisition module is used to extract the initial target vehicle speed and 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.

[0060] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0061] A memory storing at least one instruction; and a processor executing the instructions stored in the memory to implement the above-mentioned speed change strategy adjustment method based on the Internet of Vehicles combined with road condition perception.

[0062] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned speed change strategy adjustment method based on the Internet of Vehicles combined with road condition perception.

[0063] To address the problems described in the background technology, the present invention receives a driving instruction and extracts the target path in the driving instruction based on a location monitoring system. This shows that the present invention provides real-time monitoring data for cyclists riding on the target path by combining multiple monitoring systems within the monitoring system. Initial target path data for the target path is obtained, and an initial speed change strategy sequence is obtained based on the initial target path data. The present invention divides the target path into multiple sections and multiple nodes, sorting them in the order from the start point to the end point of the target path. A low-speed strategy is generated at the nodes, and a section driving strategy is generated at the sections. An initial speed change strategy is provided for the entire section, providing a basis for subsequent adjustment of the speed change strategy based on real-time monitoring data. The user's location is obtained based on the location monitoring system, and the initial speed change strategy corresponding to the user's location is extracted from the initial speed change strategy sequence based on the user's location to obtain the initial target strategy. This shows that after confirming the user's location, the present invention can confirm the section where the user is located and extract the initial speed change strategy corresponding to the user's location from the initial speed change strategy sequence, thereby providing a basic reference for the cyclist riding on that section. The present invention obtains real-time measurement data and determines a preferred target strategy based on the initial target strategy of the real-time measurement data. The present invention adjusts the initial shift strategy based on the road surface smoothness obtained through real-time monitoring, including but not limited to road surface smoothness, weather conditions, vehicle driving conditions, road surface inclination, and the rider's real-time riding status, with particular emphasis on road surface smoothness and real-time environmental data. This assists the rider in the riding process and completes the speed shift strategy adjustment based on the vehicle network and road condition perception based on the preferred target strategy. Therefore, the present invention integrates various data actually detected during the riding process, with particular emphasis on road surface smoothness and real-time environmental data, providing a reference for the rider to adjust the speed shift strategy, thereby assisting the riding process. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A flow chart of a method for adjusting a speed change strategy based on vehicle networking and road condition perception according to an embodiment of the present invention;

[0065] Figure 2 This is a functional module diagram of a speed change strategy adjustment system based on vehicle networking combined with road condition perception provided by one embodiment of the present invention;

[0066] Figure 3 A schematic structural diagram of an electronic device for implementing the method for adjusting a speed change strategy based on vehicle networking combined with road condition perception, provided in one embodiment of the present invention.

[0067] Description of reference numerals:

[0068] 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0069] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0070] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0071] The embodiment of the present application provides a method for adjusting a speed change strategy based on the Internet of Vehicles combined with road condition perception. The execution subject of the method for adjusting a speed change strategy based on the Internet of Vehicles combined with road condition perception includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for adjusting a speed change strategy based on the Internet of Vehicles 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.

[0072] Reference Figure 1 FIG. 1 is a flow chart of a method for adjusting a speed change strategy based on vehicle networking and road condition perception according to an embodiment of the present invention. In this embodiment, the method for adjusting a speed change strategy based on vehicle networking and road condition perception includes:

[0073] S1. 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.

[0074] It is understandable that the driving instructions are issued by the rider of the vehicle, and the vehicle described in the present invention is specifically a smart bicycle. The road condition monitoring system is used to monitor the road surface conditions in real time, and is particularly used to monitor the flatness of the road surface. The environmental monitoring system is used to monitor environmental conditions in real time, including but not limited to temperature, humidity, sunny days, rainy days, fog, haze and other environmental conditions. Monitoring environmental conditions helps the rider perceive the environment, thereby providing a basis for the rider to implement the speed change strategy. For example, compared to sunny days, sudden rain will interfere with the rider's vision, so it is necessary to slow down. For another example, a sudden increase or decrease in temperature in the environment (such as riding to a snowy mountain, etc.) will affect the rider's physical condition, and the rider needs to slow down to adapt to the environment. Examples will not be given one by one here. The rider monitoring system is used to monitor the physical condition of the smart bicycle rider, such as heart rate, body temperature, riding time, and other parameters. For example, when the heart rate exceeds 160, it indicates that the rider is in high-intensity exercise. Continuous high-intensity exercise will consume the rider's physical strength, making it difficult to make timely judgments when facing danger. When the riding time exceeds a preset time (for example, one hour), fatigue may occur, so the rider's physical condition needs to be monitored. The position monitoring system is a system for obtaining the real-time location of the vehicle, and the vehicle monitoring system is a system for monitoring the vehicle's operating status, such as the smart bicycle's speed, vehicle cadence, and the on and off of lights.

[0075] It should be noted that the present invention aims to integrate various data actually detected during the riding process, with particular emphasis on road surface smoothness and real-time environmental data, to provide a reference for riders to adjust their gear shifting strategies, thereby assisting the riding process.

[0076] S2. Extracting the target path in the driving instruction based on the 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 multiple initial speed change strategies.

[0077] It can be understood that the target route is a desired riding route pre-selected by the user.

[0078] Furthermore, obtaining an initial speed change strategy sequence according to the initial target path data includes:

[0079] 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;

[0080] 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;

[0081] 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;

[0082] Calculate the initial vehicle speed based on the road congestion index, weather impact index and speed limit, query the speed strategy corresponding to the initial vehicle speed from the pre-built speed strategy table, and obtain the road section driving strategy;

[0083] 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.

[0084] It should be noted that the initial road condition data is data consisting of the road conditions in the road section, and the road conditions in the road section include: road section type, average speed of traffic flow, etc. The initial navigation data refers to data that provides navigation for cyclists. When obtaining the initial navigation data, the embodiment of the present invention divides the target path into multiple sections and multiple nodes. The nodes refer to places in the target path where there are zebra crossings, intersections (such as crossroads, T-junctions, etc.), traffic lights, etc. The road section refers to a continuous driving route without nodes, and the two endpoints of the road section are the nodes.

[0085] In summary, based on the definitions of nodes and segments, the target path can be divided into multiple segments and multiple nodes using the initial navigation data. These segments and multiple nodes are then sorted in the order of the target path from the starting point to the end point. The resulting sequence is the segment-node sequence. It is understood that if the target data is a node, slowing down is necessary to ensure the safety of the cyclist. Therefore, the present invention generates a slow-pacing strategy based on the target data.

[0086] It should be noted that the pacing strategy table includes multiple pacing strategies, each of which includes rated pacing intervals and rated pacing gears. The rated pacing intervals are defined by riding speeds, and the rated pacing gears are identifiers for the rated pacing intervals. The rated pacing intervals also include different flywheel-to-crankset ratios. Within the same rated pacing interval, the same speed can be achieved with different flywheel-to-crankset ratios.

[0087] It should be noted that the present invention, in the subsequent description, is speed-oriented to adjust the operating state of the smart bicycle (such as the ratio of the flywheel to the chainring, etc.). After determining the riding speed of the smart bicycle, based on the detection of the inclination of the road surface on which the smart bicycle is located (uphill, downhill, or flat), the riding speed and the road inclination can be input into a pre-built intelligent pacing selection model. The intelligent pacing selection model is then used to find the corresponding pacing strategy in the pacing strategy table. Therefore, in addition to speed, the pacing strategy can also include fuzzy-processed cadence and road inclination (data fuzzification can be achieved through a large language intelligent model) as parameters of the pacing strategy table when formulating the pacing strategy. For example, a pacing strategy may include the following: riding speed: 0-8 km / h, target cadence: 80-100 RPM, road inclination: 20-30°, and a smart speed ratio (the ratio of the flywheel to the chainring) of 0.9. The smart speed ratio may be calculated based on the rider's habits (for example, in this case, the most commonly used smart speed ratio for riders is 0.9), or may be derived from big data (different riders have a most commonly used smart speed ratio of 0.9 in the parameters of the pacing strategy), or may be generated by training a neural network model, although this is not limited in the present invention. Ultimately, applying the pacing strategy to a smart bicycle or providing riders with options can provide riders with intelligent speed change decisions based on road conditions.

[0088] Furthermore, in the above process, riding speed is a key factor affecting the speed change decision. Therefore, the present invention focuses on how to determine the riding speed. The technology of adjusting the pace according to the speed of the smart bicycle can be achieved by existing technology, and the present invention will not elaborate on this.

[0089] Optionally, the multiple pace strategies are respectively a low pace strategy, a medium pace strategy and a high pace strategy, wherein the low pace strategy is a speed lower than the maximum riding speed of the bicycle. The specific concepts of "low", "medium" and "high" are all relative concepts, and when the range of "low", "medium" and "high" is specifically defined, it can be manually specified. For example, the low pace strategy refers to a riding speed of 0-8km / h, the medium pace strategy refers to a riding speed of 8-40km / h, and the high pace strategy refers to a riding speed of 40km / h and above.

[0090] For example, the speed that the smart bicycle can travel on a road section is 0-20 km / h, so the speed change strategy of the smart bicycle is set to a low speed strategy.

[0091] It is understandable that the present invention divides the target path into multiple sections and multiple nodes, sorts them in the order of the target path from the starting point to the end point, generates a low-speed strategy at the nodes, and generates a section driving strategy at the section, and equips the entire section with an initial speed change strategy, providing a basis for subsequent adjustment of the speed change strategy in combination with real-time monitoring data.

[0092] Furthermore, the speed limit is the maximum bicycle speed allowed by traffic regulations for the road section. The average speed of traffic is the average speed of vehicles on that road section. The average speed can be directly obtained using existing technologies (e.g., various map apps such as AutoNavi and Tencent Maps). The average speed of traffic can intuitively indicate whether the road section is congested, which in turn affects the speed of the smart bicycle. Therefore, this embodiment of the present invention also calculates a road congestion index based on the average speed of traffic. This index is used to adjust the actual travel speed of the smart bicycle, thereby guiding the speed change strategy.

[0093] Furthermore, the road congestion index is a dimensionless parameter used to describe the congestion situation of a road section. The more congested the road, the higher the road congestion index. At this time, after the road congestion index is multiplied by the initial vehicle speed, the initial vehicle speed obtained is smaller. The initial weather data includes weather data obtained from the cloud of the Internet of Vehicles. The weather category is the category of real-time weather, which can be sunny, rainy, foggy, hazy, etc. as mentioned above. When calculating the weather impact index according to the weather category, it is also necessary to consider the environmental conditions monitored in real time in the environmental monitoring system, including but not limited to temperature, humidity, etc. The specific calculation method can be achieved by setting weights for different weather categories, temperatures, and humidity, and calculating using the entropy weight method, or using a neural network model (the input data is set to different weather categories, temperatures, humidity, etc., and the output layer is set to the weather impact index). The weather impact index is a dimensionless parameter used to describe the impact of weather on cycling speed.

[0094] Furthermore, the initial vehicle speed is the speed calculated by multiplying the limited vehicle speed by the road congestion index and then by the weather impact index. The initial speed change strategy sequence is a sequence obtained by arranging the low-speed strategy or segment driving strategy corresponding to each node and each segment in the segment-node sequence in the order of the nodes and segments in the segment-node sequence. All strategies in the initial speed change strategy sequence are recorded as the initial speed change strategy.

[0095] Furthermore, the calculation of the road congestion index based on the road section type and the average speed of traffic flow includes:

[0096] Obtain congestion classification levels and road section classification levels, where the congestion classification level includes multiple sub-levels, including extremely congested, slightly congested, generally unobstructed, and extremely unobstructed. The road section classification level includes multiple sub-road types: expressways, main roads, secondary roads, and branch roads.

[0097] Extracting sub-arterial road types from a plurality of sub-arterial road types in sequence, obtaining speed limit intervals for 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 a plurality of speed intervals, and calculating a unit congestion index for each of the plurality of speed intervals to obtain a road congestion index set, wherein the plurality of 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;

[0098] 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;

[0099] Summarize the arterial road data group, and use the summarized arterial road data group to draw a congestion reference table;

[0100] 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 traffic 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 traffic flow is confirmed as the road congestion index.

[0101] It is understandable that extremely congested, slightly congested, basically unobstructed and extremely unobstructed are all artificially set labels used to describe the congestion conditions of road sections, and according to the arrangement of extremely congested, slightly congested, basically unobstructed and extremely unobstructed, the speed at which smart bicycles can travel in road sections with corresponding labels is faster. Expressways, main roads, secondary roads and branch roads are all labels obtained by classifying road sections according to existing urban road grades, and will not be repeated here. The speed limit interval is the interval formed by the speed at which smart bicycles can travel on sub-main road types in traffic rules. For example, if the speed limit interval on a certain road section is 0-15km / h, then the speed at which smart bicycles can travel on sub-main road types is 0-15km / h. The execution of the averaging operation is to evenly divide the speed limit interval into the same number of speed intervals as the number of congestion classification levels. Therefore, the extremely congested speed interval, the slightly congested speed interval, the basically unobstructed speed interval and the extremely unobstructed speed interval correspond to extremely congested, slightly congested, basically unobstructed and extremely unobstructed, respectively, and are not repeated here.

[0102] Exemplarily, the congestion classification levels of the present invention include extreme congestion, mild congestion, basically unobstructed, and extremely unobstructed. The number of congestion classification levels is 4. If the speed limit interval is [0,60] (in km / h), the averaging operation is performed to divide the speed limit interval into 4 speed intervals, namely [0,15], [15,30], [30,45], and [45,60]. The unit congestion index is a dimensionless parameter used to describe the congestion level of the speed interval. For example, the unit congestion level of [0,15] can be set to 75%, the unit congestion level of [15,30] can be set to 50%, the unit congestion level of [30,45] can be set to 25%, and the unit congestion level of [45,60] can be set to 0%. The more congested the road, the slower the drivable speed, and thus the higher the unit congestion index of the corresponding speed interval.

[0103] Furthermore, the road congestion index set is a set of multiple unit congestion indices. The associating of the sub-arterial road type, multiple speed intervals, and the road congestion index set is an operation of storing the sub-arterial road type, a speed interval, and the corresponding unit congestion indices in the speed interval in the road congestion index set in one data set and then aggregating the data. The aggregated data is then the arterial road data set.

[0104] It is understood that the congestion reference table is a table created using the aggregated arterial road data set. The horizontal and vertical axes of the table are divided into multiple sub-levels and multiple sub-arterial road types, respectively, and the populated data are the speed ranges and unit congestion indexes under the conditions of the sub-levels and sub-arterial road types.

[0105] It can be understood that the cycling roads applicable to the present invention are roads on which smart bicycles can travel and which are adjacent to highways. For example, on common highways, there may be roads on which smart bicycles can travel and roads on which smart bicycles cannot travel. Bicycle riding roads include roads on which cars can travel and roads on which cars cannot travel. Roads on which bicycles can travel but cars cannot often have complex road conditions, such as alleys and mountain roads on which vehicles cannot travel. At this time, it is not meaningful to use the intelligent system to adjust the speed of the smart bicycle. The riding speed should be decided directly by the rider. Therefore, the application scenario of the embodiment of the present invention is a road on which bicycles can travel, and there is a highway on which cars can travel next to the road on which bicycles can travel.

[0106] Furthermore, according to the application scenarios defined in this embodiment, when the main roads are extremely congested or slightly congested, there is often a large amount of pedestrian and vehicle traffic, and the vehicle's riding speed should be reduced in these situations. Therefore, the congestion conditions of the main roads that are easy to monitor (the congestion conditions of the main roads can be obtained through the Internet) are used to calculate the road congestion index, and the road congestion index is used to correct the riding speed, providing a reference for the formulation of the riding speed, which is both practical and saves monitoring costs.

[0107] S3. Obtain the user's location based on the location monitoring system, extract the initial speed change strategy corresponding to the user's location from the initial speed change strategy sequence according to the user's location, and obtain the initial target strategy.

[0108] Furthermore, the user location is the real-time location of the cyclist. Once the user location is confirmed, the road section the user is on can be determined, so that the initial speed change strategy corresponding to the user location can be extracted from the initial speed change strategy sequence, providing a reference for the cyclist riding on that road section.

[0109] S4. Obtaining the road surface smoothness within a preset range using a road condition monitoring system.

[0110] Furthermore, the method of obtaining the road surface smoothness within a preset range by using the road condition monitoring system includes:

[0111] Use the road condition monitoring system to obtain oblique photography datasets within a preset range, and use the oblique photography datasets and pre-built GIS models to obtain real-time road models;

[0112] intercepting a plurality of road longitudinal lines and a plurality of road transverse lines from a 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, wherein 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 vertical axis, and a vertical axis;

[0113] A depth dataset is obtained based on the intercept line set, and the road surface smoothness is calculated based on the depth dataset.

[0114] It is understood that the present invention optionally sets the preset range to a sphere with a radius of 3m, with the center of mass of the smart bicycle as the origin. In actual application, this range can be set by researchers. The oblique photography dataset is a collection of images obtained by photographing the road surface using the camera on the smart bicycle. The specific implementation method of intercepting multiple road longitudinal lines and multiple road transverse lines from the real-time road model is as follows: the road surface is fitted into a two-dimensional plane, and the two-dimensional plane is divided into a preset number of equal parts in the cross-sectional direction of the road. Each line used for the equal division is a road transverse line. The same is true for the road longitudinal lines, which will not be repeated here.

[0115] It is understandable that the initial speed change strategy can guide the riding speed of a smart bicycle on a road section. However, in actual riding, there may be factors such as uneven road surface, protruding foreign objects, and unrecorded speed bumps that affect the rider's riding speed and speed change strategy. When the rider rides to the position corresponding to the above factors, if the rider does not observe them in advance, the rider may experience bumps or even rollovers due to riding too fast. Therefore, it is also necessary to adjust the initial speed change strategy based on the road surface flatness obtained by real-time monitoring to assist the rider in the riding process.

[0116] Furthermore, the GIS model can be constructed using Tuxin Earth or other existing technologies. Its purpose is to construct three-dimensional geographic information within a preset range, particularly a real-world three-dimensional model. The three-dimensional geographic information includes, but is not limited to, a three-dimensional coordinate system, scattered points within the three-dimensional coordinate system, and road names.

[0117] It should be noted that when establishing a real-time road model based on the images of the oblique photography dataset, the speed of the entire process of calculating the model and judging the flatness of the road surface is related to the accuracy of the real-time road model (grid division, resolution). During the application of the embodiment of the present invention, since the smart bicycle is equipped with a shock absorption system, there is no need to monitor the road conditions that are too small (for example, gravel). The present invention is more expected to monitor speed bumps, road cracks, manhole covers, small steps, etc., so the amount of calculation can be reduced in various ways (for example, setting the resolution of the oblique photography dataset, etc.), the operating speed can be increased, and the road surface flatness can be accurately identified as much as possible while achieving the effect of real-time detection of the road surface flatness.

[0118] Optionally, a flatness threshold can be set manually. When the road surface flatness exceeds the preset flatness threshold, this embodiment indicates that the smart bicycle can issue an intelligent alarm to remind the rider that bumps may occur. The intelligent alarm is a voice alarm and can be set by setting a ringtone, etc.

[0119] Furthermore, the acquiring of a depth data set based on the intercept line set and the calculation of the road surface smoothness based on the depth data set include:

[0120] 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:

[0121] 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 a two-dimensional target section corresponding to the target line from the real-time road model to obtain a target section image;

[0122] A pre-built edge recognition algorithm is used to extract the cross-section road surface curve corresponding to the target line, and the extreme points of the cross-section road surface curve are obtained in sequence to obtain an extreme point set. A depth data set is calculated based on the extreme point set, and the unit flatness is calculated based on the depth data set.

[0123] The unit roughness is aggregated to obtain a roughness set, and the road surface roughness is calculated based on the roughness set, where the road surface roughness is the average of the unit roughness in the roughness set.

[0124] 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 cross-section 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 often used to represent height information, and the road surface flatness is related to the height information. Therefore, the present invention uses the vertical axis in the real-time road model as the cross-section vertical axis. The two-dimensional target section is a road cross-section or road longitudinal section obtained with the target line as the reference. If the target line is a road cross-line, the road cross-section is intercepted. If the target line is a road longitudinal line, the road longitudinal section is intercepted. The specific implementation steps of extracting the cross-section road surface curve corresponding to the target line using the pre-built edge recognition algorithm are as follows: after fitting the edge that can describe the road surface flatness in the target cross-section image using the edge recognition algorithm, the edge that can describe the road surface flatness is fitted into a curve using curve fitting to obtain a cross-section road surface curve. The edge detection algorithm can use the canny edge detection algorithm. There are many existing methods to implement curve fitting, which will not be given examples here. The extreme point set is a set of extreme points obtained in sequence along the increasing direction of the longitudinal axis of the cross section. Further, the depth data set is calculated based on the extreme point set, and the unit flatness is calculated based on the depth data set, including:

[0125] 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:

[0126] Extract the next extreme point adjacent to the target extreme point in the extreme point set to obtain the adjacent extreme point. 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.

[0127] Obtaining the ordinates of the target extreme point and the adjacent extreme point in the cross-section pavement curve respectively, obtaining the target extreme value and the adjacent extreme value, calculating the absolute difference between the target extreme value and the adjacent extreme value, obtaining the unit longitudinal depth data, calculating the absolute difference between the target extreme point and the adjacent extreme value, obtaining the unit transverse depth data, summarizing the unit longitudinal depth data and the unit transverse depth data, and obtaining the unit depth data;

[0128] Aggregate unit depth data to obtain the initial depth dataset;

[0129] 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. Unit flatness is calculated based on the depth dataset.

[0130] Furthermore, if the extreme point set is [x1, x2, x3], the extreme point x1 is extracted, then the target extreme point is x1, and the adjacent extreme point is x2. The cross-sectional pavement curve in the present invention 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.

[0131] It is understood that the target extreme value is the numerical value corresponding to the ordinate of the target extreme value point in the cross-sectional road surface curve. The adjacent extreme value is the numerical value corresponding to the ordinate of the adjacent extreme value point in the cross-sectional road surface curve. The unit depth data is the absolute difference between the target extreme value and the adjacent extreme value. The present invention regards the maximum value point as the raised part of the road surface and the minimum value point as the sunken part of the road surface. Therefore, the calculation of the unit depth data can calculate the height difference between two adjacent extreme value points when the road surface is uneven, thereby providing a basis for calculating the road surface flatness.

[0132] It should be noted that, as the height difference between the raised and sunken points of the road surface increases, and as the distance between the raised and sunken points in the horizontal axis of the road increases, the slope between the raised and sunken points will decrease. At this time, compared with a road surface with a larger slope between the raised and sunken points, the rider will feel less bumpy riding on this road surface.

[0133] Furthermore, the construction of the envelope depth dataset based on the cross-section road surface curve includes:

[0134] The cross-section road surface curve is intercepted based on a preset division length to obtain multiple unit curves, and the following operations are performed on each of the multiple unit curves:

[0135] Get the envelope straight line of the unit curve, and extract multiple extreme points that exist within the envelope straight line from the extreme point set to obtain the envelope extreme point set. Perform the following operations on all envelope extreme points in the envelope extreme point set:

[0136] 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 respectively, obtain the curve value and the straight line value, calculate the absolute difference between the curve value and the straight line value, and obtain the envelope depth data;

[0137] The unit envelope depth data are aggregated to obtain multiple envelope depth data corresponding to the unit curve, and the multiple envelope depth data are aggregated to obtain the envelope depth data set corresponding to the cross-section pavement curve.

[0138] Furthermore, the division length is a manually set length used to divide the cross-section road surface curve and divide the cross-section road surface curve into multiple unit curves. The envelope line is an envelope line constructed based on multiple maximum points in the unit curve, and the envelope line is a straight line.

[0139] It can be understood that when the ordinate of the envelope extreme value point is extracted from the unit curve, the resulting ordinate is the value of the envelope extreme value point on the unit curve, that is, the curve value. When the ordinate of the envelope extreme value point is extracted from the envelope line, the resulting ordinate is the value of the envelope extreme value point on the envelope line, that is, the line value.

[0140] It is understandable that the present invention regards the envelope straight line as a flat road surface. Therefore, when the absolute difference is larger, the corresponding envelope extreme point is farther away from the flat road surface, and the smart bicycle is more likely to feel bumpy when it travels to the position of the envelope extreme point. Therefore, the embodiment of the present invention uses the absolute difference as envelope depth data.

[0141] Furthermore, the calculating of unit flatness based on the depth data set includes:

[0142] The extreme points are sequentially extracted from the extreme point set to obtain the flatness nodes, and the following operations are performed on the flatness nodes:

[0143] 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;

[0144] The unit flatness corresponding to the flatness node is calculated based on the target envelope data and the target depth data. The calculation formula for the unit flatness is as follows:

[0145]

[0146] in, Indicates unit flatness, represents the envelope depth data, 、 、 and represent the envelope depth weight, unit horizontal depth weight, unit vertical depth weight and aspect ratio weight respectively, and 、 、 and are all constants, Indicates the unit horizontal depth data of the target depth data, The unit depth data representing the target depth data, represents the numerical stabilization parameter.

[0147] It should be noted that the flatness node is a node when performing flatness calculations, specifically an extreme point extracted from the extreme point set. The envelope depth weight, unit transverse depth weight, unit longitudinal depth weight, and aspect ratio weight are dimensionless parameters used to describe the importance of depth data, unit transverse depth data, unit longitudinal depth data, and the ratio of unit transverse depth data to unit longitudinal depth data, respectively. The specific weights can be confirmed by existing technologies such as the hierarchical analysis method and will not be described in detail here. The numerical stabilization parameter is an artificially set constant used to avoid the situation where the denominator is zero due to the unit transverse depth data tending to zero.

[0148] S5. Obtain weather conditions based on the environmental monitoring system, obtain vehicle driving conditions and road inclination angles based on the vehicle monitoring system, and obtain the real-time riding status of the rider based on the rider monitoring system.

[0149] Furthermore, the weather conditions are real-time, different from the initial weather data described above, which can be obtained from a weather forecast. The weather forecast and the real-time weather conditions may differ at certain times. For example, a sudden localized shower may occur in a certain area, while the forecast still predicts sunny weather. To avoid traffic accidents caused by slipping due to the rain, the rider must slow down. It should be noted that the road inclination is real-time and can be obtained by sensors installed on the smart bicycle. While the road inclination does not affect the riding speed of the smart bicycle, when riding a professional bicycle, to ensure that the rider's cadence is within a preset range, different inclination angles should correspond to different paces (the pace refers to the ratio of the flywheel to the chainring). Although the embodiments of the present invention do not limit or elaborate on the paces corresponding to different inclination angles, the road inclination is an important factor influencing the setting of the flywheel to chainring ratio when developing a bicycle's pace strategy table. Therefore, the road inclination angle is still collected during data collection.

[0150] It should be noted that the rider's real-time riding status includes but is not limited to heart rate and riding time. The vehicle's driving status includes multiple parameters during the vehicle's driving process, such as the smart bicycle's riding speed, the smart bicycle's real-time gear position, and the light on / off status.

[0151] S6. Summarize the road surface smoothness, weather conditions, vehicle driving conditions, road surface inclination, and the rider's real-time riding status to obtain real-time measurement data.

[0152] It is understandable that the real-time measurement data is data obtained by storing and marking the road surface flatness, weather conditions, vehicle driving conditions, road surface inclination and the rider's real-time riding status in one data.

[0153] S7. Extract the initial target vehicle speed and the initial target gear from the initial target strategy, obtain the optimal target strategy based on the 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 and road condition perception based on the optimal target strategy.

[0154] Furthermore, extracting the initial target vehicle speed and the initial target gear from the initial target strategy, and obtaining the optimal target strategy based on the real-time measurement data, the initial target vehicle speed and the initial target gear, includes:

[0155] 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;

[0156] If the target riding speed is within the initial target gear, the current riding speed is extracted from the vehicle's driving state;

[0157] 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. 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 a preset speed difference threshold, an overspeed warning is generated. The preferred target strategy is generated based on the overspeed warning, the speed to be adjusted, and the initial target gear.

[0158] If the target riding speed is not within the initial target gear, a 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 a preferred target strategy is generated based on the overspeed warning, the corrected gear and the target riding speed.

[0159] It is understood that the evaluation method can be a hierarchical analysis method or a neural network, and there are many existing technologies that can be used to implement this, so it will not be described in detail here. The multimodal weight set is a set of weights corresponding to road surface smoothness, weather conditions, vehicle driving conditions, road surface inclination, and the rider's real-time riding status. The normalization operation can be implemented using existing technologies, and its purpose is to eliminate the dimensional influence of all data in the real-time measurement data.

[0160] Furthermore, the method of calculating the initial target vehicle speed correction parameter based on the normalized data set and the multimodal weight set includes: extracting normalized data from the normalized data set to obtain target normalized data, extracting multimodal weights corresponding to the target normalized data from the multimodal weight set to obtain target weights, calculating the product of the target normalized data and the target weights to obtain unit correction parameters, 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:

[0161]

[0162] in, represents the initial target speed correction coefficient, Indicates the first Unit correction parameter.

[0163] It should be noted that, in the above formula, 5 represents the total number of unit correction parameters in the unit correction parameter set.

[0164] It is understood that the current speed is the rider's real-time riding speed. If the target riding speed is within the initial target gear, it indicates that the initial target speed provided by the initial target strategy and the target riding speed are not much different. Therefore, if the target riding speed is greater than the current speed, it indicates that the rider's speed is slower than the target riding speed. The present invention believes that smart bicycles are relatively safer when riding at slower speeds. Therefore, the initial target speed and initial target gear can be determined as the preferred target strategy. However, whether to select this preferred target strategy as the final strategy is determined by the rider.

[0165] Furthermore, 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 initial target gear in the initial target strategy can provide a reference for the rider's riding on this road section, if the current driving speed is faster than the target riding speed, it is possible that a traffic accident will be caused by riding too fast, so further judgment of the current driving speed is required.

[0166] 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 indicates that the rider is at risk of speeding, and may cause a traffic accident due to excessive riding speed, so a speeding warning is generated. The speeding warning can be a text warning, a voice broadcast, or other forms used to remind the rider of speeding, which are not limited here.

[0167] Furthermore, in the process of generating the preferred target strategy based on the overspeed warning, the speed to be adjusted, and the initial target gear, the preferred target strategy is generated with the target riding speed as the final vehicle speed, the speed to be adjusted as the speed to which the smart bicycle needs to be adjusted, and the initial target gear as the gear for the rider's reference, and the preferred target strategy also includes sending an overspeed warning to the rider.

[0168] It should be noted that the initial target gear here is similar to the rated speed gear mentioned above. Therefore, the initial target gear also includes the applicable speed (the ratio of the flywheel to the chainring), so the gear must be determined in the preferred target strategy.

[0169] Furthermore, if the target speed is not within the initial target gear, it indicates that the initial target speed provided by the initial target strategy differs significantly from the target speed. Therefore, the initial target strategy cannot be directly applied. Instead, the corresponding pacing strategy must be found in the pacing strategy table based on the target speed to determine the corrected gear. The corrected gear has a similar meaning to the rated speed gear described above, and can be represented by a speed interval. Therefore, the minimum speed is the lower limit of the corrected gear interval, i.e., the minimum speed, and the maximum speed is the upper limit of the corrected gear interval, i.e., the maximum speed.

[0170] Specifically, if the lowest speed is greater than the current speed, it indicates that the rider's speed is slower than the target speed. A smart bicycle is relatively safer when riding at a slower speed. Therefore, the modified gear and target speed can be determined as the preferred target strategy. If the highest speed is less than the current speed, it indicates that the rider's speed is faster than the target speed and faster than the maximum speed of the modified gear. This may cause a traffic accident due to excessive speed, so a speeding warning is generated.

[0171] Furthermore, the generation of the preferred target strategy based on the overspeed warning, the corrected gear and the target riding speed is similar to the generation of the preferred target strategy based on the overspeed warning, the speed to be adjusted and the initial target gear, and will not be repeated here.

[0172] It should be noted that applying the preferred target strategy to smart bicycles allows for the adjustment of speed change strategies based on the Internet of Vehicles (IoV) and road condition perception. The present invention integrates various data actually detected during the riding process to modify the preset initial target strategy. The initial target strategy is speed-based, providing a reference for smart bicycles. The present invention also places particular emphasis on road surface smoothness and real-time environmental data, allowing riders to adjust their speed change strategies before encountering bumpy or uneven sections of road, thereby assisting the riding process.

[0173] To address the problems described in the background technology, the present invention receives a driving instruction and extracts the target path in the driving instruction based on a location monitoring system. This shows that the present invention provides real-time monitoring data for cyclists riding on the target path by combining multiple monitoring systems within the monitoring system. Initial target path data for the target path is obtained, and an initial speed change strategy sequence is obtained based on the initial target path data. The present invention divides the target path into multiple sections and multiple nodes, sorting them in the order from the start point to the end point of the target path. A low-speed strategy is generated at the nodes, and a section driving strategy is generated at the sections. An initial speed change strategy is provided for the entire section, providing a basis for subsequent adjustment of the speed change strategy based on real-time monitoring data. The user's location is obtained based on the location monitoring system, and the initial speed change strategy corresponding to the user's location is extracted from the initial speed change strategy sequence based on the user's location to obtain the initial target strategy. This shows that after confirming the user's location, the present invention can confirm the section where the user is located and extract the initial speed change strategy corresponding to the user's location from the initial speed change strategy sequence, thereby providing a basic reference for the cyclist riding on that section. The present invention obtains real-time measurement data and determines a preferred target strategy based on the initial target strategy of the real-time measurement data. The present invention adjusts the initial shift strategy based on the road surface smoothness obtained through real-time monitoring, including but not limited to road surface smoothness, weather conditions, vehicle driving conditions, road surface inclination, and the rider's real-time riding status, with particular emphasis on road surface smoothness and real-time environmental data. This assists the rider in the riding process and completes the speed shift strategy adjustment based on the vehicle network and road condition perception based on the preferred target strategy. Therefore, the present invention integrates various data actually detected during the riding process, with particular emphasis on road surface smoothness and real-time environmental data, providing a reference for the rider to adjust the speed shift strategy, thereby assisting the riding process.

[0174] like Figure 2 , which is a functional module diagram of a speed change strategy adjustment system based on vehicle networking combined with road condition perception provided by one embodiment of the present invention.

[0175] The speed shift strategy adjustment system 100 based on the Internet of Vehicles and road condition perception described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the system 100 can include a monitoring module 101, an initial speed shift strategy module 102, a real-time measurement data module 103, and a preferred target strategy acquisition module 104. A module, also referred to as a unit, is a series of computer program segments that can be executed by a processor in an electronic device and perform a fixed function. These modules are stored in the memory of the electronic device.

[0176] The monitoring module 101 is configured to receive a driving instruction and activate 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;

[0177] The initial speed change 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 obtain an initial speed change strategy sequence based on 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;

[0178] The real-time measurement data module 103 is configured to obtain a user location based on a location monitoring system, extract an initial shifting strategy corresponding to the user location from an initial shifting strategy sequence based on the user location, obtain an initial target strategy, obtain road surface roughness within a preset range using a road condition monitoring system, obtain weather conditions based on an environmental monitoring system, obtain vehicle driving conditions and road surface inclination angle based on a vehicle monitoring system, obtain a rider's real-time riding status based on a rider monitoring system, and summarize the road surface roughness, weather conditions, vehicle driving conditions, road surface inclination angle, and rider's real-time riding status to obtain real-time measurement data;

[0179] The preferred target strategy acquisition module 104 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 the 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 based on the preferred target strategy.

[0180] In detail, the modules in the speed change strategy adjustment system 100 based on the vehicle network combined with road condition perception in the embodiment of the present invention are used in the same manner as above. Figure 1 The technical means described in the method for adjusting the speed strategy based on the Internet of Vehicles combined with road condition perception are the same and can produce the same technical effects, so they will not be repeated here.

[0181] like Figure 3 , which is a structural diagram of an electronic device for implementing a speed change strategy adjustment method based on vehicle networking combined with road condition perception, provided by an embodiment of the present invention.

[0182] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a speed change strategy adjustment method program based on the Internet of Vehicles combined with road condition perception.

[0183] The memory 11 includes at least one type of readable storage medium, including flash memory, a removable hard drive, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a removable hard drive of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in removable hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software installed in the electronic device 1 and various data, such as the code of a method for adjusting a speed change strategy based on the Internet of Vehicles and road condition perception, but also to temporarily store data that has been output or is about to be output.

[0184] In some embodiments, the processor 10 may be comprised of an integrated circuit, such as a single packaged integrated circuit or a combination of multiple packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (control unit) of the electronic device, connecting the various components of the electronic device using various interfaces and circuits. It executes programs or modules stored in the memory 11 (e.g., a program for adjusting a speed change strategy based on vehicle networking and road condition perception) and accesses data stored in the memory 11 to execute various functions and process data.

[0185] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0186] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 3The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0187] For example, although not shown, the electronic device 1 may further include a power source (e.g., a battery) to power various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management system, thereby enabling functions such as charge management, discharge management, and power consumption management through the power management system. The power source may further include any components such as one or more DC or AC power sources, a recharging system, a power failure detection circuit, a power converter or inverter, and a power status indicator. The electronic device 1 may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which are not further described here.

[0188] 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.

[0189] Optionally, the electronic device 1 may further include a user interface, which may be a display or an input unit (such as a 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-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display may also be appropriately referred to as a display screen or a display unit, and is used to display information processed by the electronic device 1 and to display a visual user interface.

[0190] The program for adjusting the speed change strategy based on the Internet of Vehicles 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 achieve the following:

[0191] receiving a driving instruction and activating 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;

[0192] extracting a target path in the driving instruction based on a position monitoring system, obtaining initial target path data of the target path, and obtaining an initial speed change strategy sequence based on 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;

[0193] The user's location is obtained based on the location monitoring system, and the initial speed change strategy corresponding to the user's location is extracted from the initial speed change strategy sequence according to the user's location to obtain the initial target strategy;

[0194] The road condition monitoring system is used to obtain the road surface smoothness within a preset range, the environmental monitoring system is used to obtain the weather conditions, the vehicle driving status and road surface inclination angle are obtained from the vehicle monitoring system, and the rider's real-time riding status is obtained from the rider monitoring system. The road surface smoothness, weather conditions, vehicle driving status, road surface inclination angle, and rider's real-time riding status are summarized to obtain real-time measurement data;

[0195] The initial target vehicle speed and initial target gear are extracted from the initial target strategy, and the optimal target strategy is obtained based on 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.

[0196] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0197] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. The computer-readable storage medium may 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 drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0198] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0199] receiving a driving instruction and activating 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;

[0200] extracting a target path in the driving instruction based on a position monitoring system, obtaining initial target path data of the target path, and obtaining an initial speed change strategy sequence based on 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;

[0201] The user's location is obtained based on the location monitoring system, and the initial speed change strategy corresponding to the user's location is extracted from the initial speed change strategy sequence according to the user's location to obtain the initial target strategy;

[0202] The road condition monitoring system is used to obtain the road surface smoothness within a preset range, the environmental monitoring system is used to obtain the weather conditions, the vehicle driving status and road surface inclination angle are obtained from the vehicle monitoring system, and the rider's real-time riding status is obtained from the rider monitoring system. The road surface smoothness, weather conditions, vehicle driving status, road surface inclination angle, and rider's real-time riding status are summarized to obtain real-time measurement data;

[0203] The initial target vehicle speed and initial target gear are extracted from the initial target strategy, and the optimal target strategy is obtained based on 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.

[0204] In the 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 only exemplary, and actual implementations may have other division methods.

[0205] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0206] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0207] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0208] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents 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 activating 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; A target path in the driving instruction is extracted based on a position monitoring system, and initial target path data of the target path is obtained. An initial speed change strategy sequence is obtained based on 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 initial speed change strategy sequence is obtained based on the initial target path data, including: 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, weather impact index and speed limit, 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; The user's location is obtained based on the location monitoring system, and the initial speed change strategy corresponding to the user's location is extracted from the initial speed change strategy sequence according to the user's location to obtain the initial target strategy; The road condition monitoring system is used to obtain the road surface smoothness within a preset range, the environmental monitoring system is used to obtain the weather conditions, the vehicle driving status and road surface inclination angle are obtained from the vehicle monitoring system, and the rider's real-time riding status is obtained from the rider monitoring system. The road surface smoothness, 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 based on 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 the Internet of Vehicles combined with road condition perception according to claim 1, characterized in that: The calculation of the road congestion index based on the road section type and the average speed of traffic flow includes: Obtain congestion classification levels and road section classification levels, where the congestion classification level includes multiple sub-levels, including extremely congested, slightly congested, generally unobstructed, and extremely unobstructed. The road section classification level includes multiple sub-road types: expressways, main roads, secondary roads, and branch roads. Extracting sub-arterial road types from a plurality of sub-arterial road types in sequence, obtaining speed limit intervals for 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 a plurality of speed intervals, and calculating a unit congestion index for each of the plurality of speed intervals to obtain a road congestion index set, wherein the plurality of 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 arterial road data group, and use the summarized arterial 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 traffic 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 traffic flow is confirmed as the road congestion index.

3. The method for adjusting the speed change strategy based on the Internet of Vehicles combined with road condition perception according to claim 2, characterized in that: The method of obtaining the road surface smoothness within a preset range by using the road condition monitoring system includes: Use the road condition monitoring system to obtain oblique photography datasets within a preset range, and use the oblique photography datasets and pre-built GIS models to obtain real-time road models; intercepting a plurality of road longitudinal lines and a plurality of road transverse lines from a 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, wherein 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 vertical axis, and a vertical axis; A depth dataset is obtained based on the intercept line set, and the road surface smoothness is calculated based on the depth dataset.

4. The method for adjusting the speed change strategy based on the Internet of Vehicles combined with road condition perception according to claim 3, characterized in that: The method of obtaining a depth data set based on the intercept line set and calculating the road surface smoothness 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 a two-dimensional target section corresponding to the target line from the real-time road model to obtain a target section image; A pre-built edge recognition algorithm is used to extract the cross-section road surface curve corresponding to the target line, and the extreme points of the cross-section road surface curve are obtained in sequence to obtain an extreme point set. 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 roughness is aggregated to obtain a roughness set, and the road surface roughness is calculated based on the roughness set, where the road surface roughness is the average of the unit roughness in the roughness set.

5. The method for adjusting the speed change strategy based on the Internet of Vehicles combined with road condition perception according to claim 4, characterized in that: The step of calculating a depth dataset based on an extreme point set and calculating unit flatness according to the depth dataset 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 point in the extreme point set to obtain the adjacent extreme point. 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. Obtaining the ordinates of the target extreme point and the adjacent extreme point in the cross-section pavement curve respectively, obtaining the target extreme value and the adjacent extreme value, calculating the absolute difference between the target extreme value and the adjacent extreme value, obtaining the unit longitudinal depth data, calculating the absolute difference between the target extreme point and the adjacent extreme value, obtaining the unit transverse depth data, summarizing the unit longitudinal depth data and the unit transverse depth data, and obtaining the unit depth data; Aggregate unit depth data to obtain the initial depth dataset; 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. Unit flatness is calculated based on the depth dataset.

6. The method for adjusting the speed change strategy based on the Internet of Vehicles combined with road condition perception according to claim 5, characterized in that: The step of constructing an envelope depth dataset 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 multiple unit curves, and the following operations are performed on each of the multiple unit curves: Get the envelope straight line of the unit curve, and extract multiple extreme points that exist within the envelope straight line from the extreme point set to obtain the envelope extreme point set. Perform the following operations on all envelope extreme points in the envelope extreme point set: 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 respectively, obtain the curve value and the straight line value, calculate the absolute difference between the curve value and the straight line value, and obtain the envelope depth data; The unit envelope depth data are aggregated to obtain multiple envelope depth data corresponding to the unit curve, and the multiple envelope depth data are aggregated to obtain the envelope depth data set corresponding to the cross-section pavement curve.

7. The method for adjusting the speed change strategy based on the Internet of Vehicles combined with road condition perception according to claim 6, characterized in that: The calculating unit flatness based on the depth data set includes: The extreme points are sequentially extracted from the extreme point set 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. The calculation formula for the unit flatness is as follows: in, Indicates unit flatness, represents the envelope depth data, 、 、 and represent the envelope depth weight, unit horizontal depth weight, unit vertical depth weight and aspect ratio weight respectively, and 、 、 and are all constants, Indicates the unit horizontal depth data of the target depth data, The unit depth data representing the target depth data, represents the numerical stabilization parameter.

8. The method for adjusting the speed change strategy based on the Internet of Vehicles combined with road condition perception according to claim 7, characterized in that: The extracting of 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 riding speed is extracted from the vehicle's 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. 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 a preset speed difference threshold, an overspeed warning is generated. The 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, a 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 a preferred target strategy is generated based on the overspeed warning, the corrected gear and the target riding speed.

9. A speed change strategy adjustment system based on vehicle networking combined with road condition perception, applied to the speed change strategy adjustment method based on vehicle networking combined with road condition perception as claimed in claim 1, characterized in that: The system comprises: A monitoring module, configured to receive a driving instruction and activate 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 is used to extract the target path in the driving instruction based on the position monitoring system, and obtain initial target path data of the target path, and obtain an initial speed change strategy sequence based on 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, wherein the obtaining of the initial speed change strategy sequence based on the initial target path data includes: obtaining a road segment-node sequence based on the initial navigation data, wherein the road segment-node sequence includes multiple road segments and multiple nodes, and extracting target data from the road segment-node sequence in sequence, If the target data is a node, a low speed strategy is generated based on the target data; if the target data is a road section, a limited speed 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; a road congestion index is calculated based on the road section type and the average speed of the traffic flow; a weather category is obtained based on the initial weather data, a weather impact index is calculated based on the weather category, an initial speed is calculated based on the road congestion index, the weather impact index, and the limited speed; a speed strategy corresponding to the initial speed is queried from a pre-constructed speed strategy table to obtain a road section driving strategy; the low speed strategy and the road section driving strategy are sequentially summarized according to the road section-node sequence to obtain an initial speed change strategy sequence; A real-time measurement data module is used to obtain a user's location based on a location monitoring system, extract an initial shifting strategy corresponding to the user's location from an initial shifting strategy sequence based on the user's location, and obtain an initial target strategy; obtain road surface roughness within a preset range using a road condition monitoring system; obtain weather conditions based on an environmental monitoring system; obtain vehicle driving conditions and road surface inclination angle based on a vehicle monitoring system; obtain a rider's real-time riding status based on a rider monitoring system; and summarize the road surface roughness, weather conditions, vehicle driving conditions, road surface inclination angle, and rider's real-time riding status to obtain real-time measurement data; The preferred target strategy acquisition module is used to extract the initial target vehicle speed and 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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