Highway vehicle speed control method and system based on control unit

Abstract

The present invention provides a method and system for controlling the speed of vehicles on highways based on a control unit. The method obtains the to-be-controlled tasks of the target control unit of the target highway section, and for the to-be-controlled speed knowledge points of the to-be-controlled vehicles, and when the speed data exists at the current tracking node, combines the current tracking node, the end node of the first tracking period, and the tracking period duration parameter to determine the speed distribution characteristic parameters of the to-be-controlled speed knowledge points of the to-be-controlled vehicles in the second tracking period. Furthermore, a target speed prediction scheme is determined based on the speed distribution characteristic parameters, and the speed characteristic information of the to-be-controlled speed knowledge points of the to-be-controlled vehicles in the second tracking period is determined based on the target speed prediction scheme. This method can effectively predict and control the speed of vehicles on highways, improve the accuracy and real-time performance of vehicle speed management, help optimize traffic flow on highways, and improve road traffic efficiency and safety.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a method and system for controlling the speed of vehicles on a highway based on a control unit. Background Art

[0002] With the rapid development of expressways and the increasing number of vehicles, expressway traffic management faces increasing challenges. Vehicle speed control is a crucial aspect of expressway traffic management, playing a crucial role in ensuring road efficiency and reducing traffic accidents.

[0003] Traditional methods for controlling vehicle speed on highways often rely on manual monitoring and fixed speed limits, which have numerous drawbacks. For one thing, manual monitoring requires significant manpower and resources, and the monitoring range is limited, making comprehensive, real-time speed control difficult to achieve. Furthermore, fixed speed limits lack flexibility and specificity, preventing them from being adjusted promptly based on actual road conditions and vehicle traffic. Summary of the Invention

[0004] In view of the above-mentioned problems, in combination with the first aspect of the present invention, an embodiment of the present invention provides a method for controlling the speed of vehicles on a highway based on a control unit, the method comprising:

[0005] Obtaining pending control tasks of a target control unit of a target highway section, wherein the pending control tasks include pending control vehicles and pending control vehicle speed knowledge points;

[0006] If speed data exists for the speed knowledge point to be controlled of the vehicle to be controlled at the current tracking node, determining a speed distribution characteristic parameter of the speed knowledge point to be controlled for the vehicle to be controlled within a second tracking period corresponding to the current tracking node based on the current tracking node, the end node of the first tracking period, and a tracking period duration parameter, where the first tracking period is a tracking period preceding the second tracking period;

[0007] determining a target vehicle speed prediction scheme based on the vehicle speed distribution characteristic parameters of the to-be-controlled vehicle speed knowledge point during the second tracking period;

[0008] The vehicle speed characteristic information of the vehicle to be controlled at the vehicle speed knowledge point to be controlled within the second tracking period is determined based on the target vehicle speed prediction scheme.

[0009] On the other hand, an embodiment of the present invention also provides a highway vehicle speed control system based on a control unit, including a processor and a machine-readable storage medium, the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0010] Based on the above aspects, the embodiment of the present application obtains the to-be-controlled tasks of the target control unit of the target highway section, and for the to-be-controlled speed knowledge points of the to-be-controlled vehicles, when there is speed data at the current tracking node, combines the current tracking node, the end node of the first tracking period, and the tracking period duration parameters to determine the speed distribution characteristic parameters of the to-be-controlled speed knowledge points of the to-be-controlled vehicles in the second tracking period. Furthermore, a target speed prediction scheme is determined based on the speed distribution characteristic parameters, and the speed characteristic information of the to-be-controlled speed knowledge points of the to-be-controlled vehicles in the second tracking period is determined based on the target speed prediction scheme. This method can effectively predict and control the speed of vehicles on highways, improve the accuracy and real-time performance of vehicle speed management, help optimize traffic flow on highways, and improve road traffic efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the execution flow of the highway vehicle speed control method based on the control unit provided in an embodiment of the present invention.

[0012] Figure 2 Schematic diagram of the hardware architecture of a highway vehicle speed control system based on a control unit provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 The figure is a flow chart of a highway vehicle speed control method based on a control unit provided by an embodiment of the present invention. The highway vehicle speed control method based on the control unit is introduced in detail below.

[0014] Step S110: obtaining tasks to be controlled of a target control unit of a target highway section, wherein the tasks to be controlled include vehicles to be controlled and vehicle speed knowledge points to be controlled.

[0015] In this example, consider a scenario where a busy highway section is divided into multiple control units for traffic management. The target control unit is a specific control unit, such as the section between mileposts K1 and K2 on the highway. To ensure traffic safety and efficient passage on this section, the traffic management department has established a series of control tasks.

[0016] Suppose a large transport truck is identified as a target vehicle for control. Due to its large size and heavy cargo capacity, speed control is critical. The speed knowledge points required for control may cover multiple aspects, such as safe speed ranges under different road conditions, speed requirements for specific sections (such as curves and hills), and speed matching to maintain a safe distance from vehicles ahead and behind. For example, within the target control unit, there is a continuous curve. Based on road design and traffic flow analysis, the safe speed knowledge point for curves stipulates that the vehicle speed should not exceed 80 km / h. This speed requirement is part of the target speed knowledge points. After the management system identifies the truck entering the target control unit through monitoring equipment, it obtains the target control task containing the truck and the aforementioned speed knowledge points.

[0017] For example, within this target control unit, there are special construction areas. Construction safety regulations require vehicles within a certain range of these areas to reduce their speed to below 60 km / h. When a vehicle is detected approaching a construction area, this speed limit knowledge becomes the speed control knowledge point. The vehicle approaching the construction area is also identified as a target vehicle, resulting in a target control task that includes both vehicle and speed knowledge points.

[0018] Step S120: If the speed knowledge point to be controlled of the vehicle to be controlled has speed data at the current tracking node, then based on the current tracking node, the end node of the first tracking period and the tracking period duration parameter, determine the speed distribution characteristic parameters of the speed knowledge point to be controlled of the vehicle to be controlled in the second tracking period corresponding to the current tracking node, where the first tracking period is the previous tracking period of the second tracking period.

[0019] Assume that a monitoring system on a target highway section has tracking nodes set up at regular intervals or time intervals. For example, at the current tracking node (let's say node N), the speed measurement device captures the vehicle's speed data for a specific speed control point (such as the speed limit on a curve), which is 75 km / h.

[0020] First, determine the end node of the first tracking period. Assume that the first tracking period starts from a previous time point or distance point and ends at node M. The tracking period duration parameter is set to every 10 minutes as a tracking period.

[0021] Based on the current tracking node N and the ending node M of the first tracking period, determine the node interval with no speed data. If there are three nodes between node M and node N that do not have speed data for the vehicle at that speed knowledge point, these three nodes constitute the node interval with no speed data.

[0022] The speed distribution characteristic threshold is determined based on the tracking period duration parameter (10 minutes) and a predefined determination strategy. For example, the predefined determination strategy is based on historical data and traffic engineering theory. For a 10-minute tracking period, the speed distribution characteristic threshold is set at 5 nodes, the uniformity criterion is that the interval between adjacent nodes without speed data does not exceed 2 nodes, and the proportion threshold is that the interval between nodes without speed data does not exceed 30% of the entire second tracking period.

[0023] The first analysis result is generated by determining the number of nodes in the intervals without speed data, the uniformity of the node distribution, and the proportion of the intervals without speed data within the entire second tracking period. In this example, there are three nodes, and the node distribution is relatively uniform (each interval without speed data is one node). Assuming that the second tracking period contains a total of 10 nodes from node M to node N, the proportion of the intervals without speed data is 30%.

[0024] The first analysis results were preliminarily classified based on the speed distribution characteristic thresholds. Since the number of nodes without speed data (3) was less than the node number threshold of 5, the preliminary classification result was marked as the first category. The node distribution uniformity met the uniformity standard and was marked as the second category. The proportion of node intervals without speed data (30%) was equal to the proportion threshold of 30%, which did not meet the conditions for the third category.

[0025] Perform an integrated judgment on the preliminary classification results. Since the preliminary classification results include the first and second categories, a second integrated judgment logic is performed. Assume that based on the specific feature combination of the first and second category results, a fourth integrated judgment result is output, marked as category D.

[0026] Based on the integrated judgment results of category D, a speed distribution characteristic parameter framework based on the combined features of the first and second categories is constructed. The specific content of the framework covers the speed data change prediction factors corresponding to the feature combinations related to the first and second categories, and the interaction prediction factors between the speed data without and the speed data with in this feature combination. For example, the speed data change prediction factor may be set based on historical data and vehicle dynamics models so that for each missing node of speed data, the subsequent speed data may have a fluctuation range of 5 kilometers per hour; the interaction prediction factor between the speed data without and the speed data with in this feature combination is set so that the speed data after the speed data-free node will move closer to the speed value of the adjacent node with speed data with a certain probability (such as 30%).

[0027] This preliminary framework will be further refined. The specific calculation logic for the speed data change prediction factor might be weighted based on factors such as inter-node distance, vehicle type, and road slope. A quantitative model for the interaction between speed data without and with this feature combination could be constructed based on probability statistics and vehicle inertia. For example, by analyzing subsequent speed change data from a large number of similar vehicles under similar conditions without speed data, a mathematical model could be established to quantify this interaction.

[0028] The final speed distribution characteristic parameters are determined based on the refined speed distribution characteristic parameter framework. For example, the speed data comprehensive change rate characteristic parameter under the combined feature is determined to be 0.1 for each missing node speed data (indicating a possible 10% change in speed), and the speed data correction coefficient characteristic parameter under the combined feature is 0.8 (indicating that subsequent speed data needs to be multiplied by 0.8 to correct for the impact of the missing speed data).

[0029] Step S130 : determining a target vehicle speed prediction scheme based on the vehicle speed distribution characteristic parameters of the vehicle speed knowledge point to be controlled during the second tracking period.

[0030] Using a large transport truck as an example, based on the speed distribution characteristic parameters for the target speed control point (curve speed limit) during the second tracking period determined in the previous step, we assume the speed distribution characteristic parameters are concentrated, meaning the speed data is generally relatively concentrated with no significant dispersion.

[0031] Among the multiple previously defined target speed prediction schemes, there are two main schemes. The first target speed prediction scheme ignores the intervals between nodes without speed data and aggregates the speed data of the speed knowledge points to be controlled within the current tracking period. The second target speed prediction scheme comprehensively calculates the speed data of the speed knowledge points to be controlled within the previous tracking period, the speed data of the speed knowledge points to be controlled corresponding to the current tracking node, and the speed data of the speed knowledge points to be controlled corresponding to the candidate tracking period. The candidate tracking period is the tracking period from the tracking trigger node of the previous tracking period to the tracking trigger node of the current tracking period.

[0032] Because the speed distribution characteristic parameters are concentrated, the second target speed prediction scheme is output as the target speed prediction scheme according to the rules. This is because when the speed data is concentrated, integrating speed data from multiple time periods can more accurately predict the vehicle speed. Considering the vehicle's driving consistency at different time periods and the combined influence of various factors, this comprehensive calculation method can better reflect the vehicle's actual driving status and future speed trends.

[0033] Step S140 : determining the vehicle speed characteristic information of the vehicle to be controlled at the vehicle speed knowledge point to be controlled within the second tracking period based on the target vehicle speed prediction scheme.

[0034] Because the target speed prediction scheme is the second target speed prediction scheme, the speed characteristic information of the speed knowledge point to be controlled (curve speed limit) of the vehicle to be controlled (large transport truck) during the second tracking period is determined according to the corresponding rules.

[0035] First, retrieve the first speed data for the first tracking period for the speed knowledge point to be controlled from the cloud storage system. Assume that during the first tracking period, the vehicle's average speed on the curve, as recorded by speed measuring equipment at each node, was 70 km / h. This is the first speed data. Then, retrieve the second speed data for the speed knowledge point to be controlled at the current tracking node. As mentioned previously, the vehicle's speed at the current tracking node N is 75 km / h. This is the second speed data.

[0036] The candidate tracking period is determined based on the tracking trigger nodes of the first tracking period and the tracking trigger nodes of the second tracking period. Assuming that the first tracking period starts from time point T1 and ends at T2, and the second tracking period starts from T2 and ends at T3, the candidate tracking period is from T1 to T3. The third vehicle speed data of the vehicle speed knowledge point to be controlled in the candidate tracking period is determined. For example, by taking a weighted average of the vehicle speed data of each node in this time period (based on factors such as node distance and time weight), the third vehicle speed data is obtained as 72 km / h.

[0037] The first, second, and third speed data are comprehensively calculated to generate speed signature information for the vehicle under control during the second tracking period. For example, using a pre-defined calculation method (e.g., a weighted average method with a weight of 0.3 for the first speed data, 0.4 for the second speed data, and 0.3 for the third speed data), the calculated speed signature is (70 × 0.3 + 75 × 0.4 + 72 × 0.3) = 72.6 km / h. This speed signature accurately reflects the vehicle's speed characteristics with respect to the curve speed limit during the second tracking period, providing important information for subsequent traffic control decisions. For example, this speed signature can be used to determine whether the vehicle complies with the curve speed limit and whether driver reminders or other control measures are necessary.

[0038] Based on the above steps, the embodiment of the present application obtains the to-be-controlled tasks of the target control unit of the target highway section, and for the to-be-controlled speed knowledge points of the to-be-controlled vehicles, when there is speed data at the current tracking node, combines the current tracking node, the end node of the first tracking period, and the tracking period duration parameters to determine the speed distribution characteristic parameters of the to-be-controlled speed knowledge points of the to-be-controlled vehicles in the second tracking period. Furthermore, a target speed prediction scheme is determined based on the speed distribution characteristic parameters, and the speed characteristic information of the to-be-controlled speed knowledge points of the to-be-controlled vehicles in the second tracking period is determined based on the target speed prediction scheme. This method can effectively predict and control the speed of vehicles on highways, improve the accuracy and real-time performance of vehicle speed management, help optimize traffic flow on highways, and improve road traffic efficiency and safety.

[0039] In a possible implementation, step S120 includes:

[0040] Step S121 : determining a node interval without vehicle speed data based on the current tracking node and the end node of the first tracking period.

[0041] Step S122 : determining a vehicle speed distribution characteristic limit value based on the tracking period duration parameter and a predefined determination strategy.

[0042] Step S123 , determining the speed distribution characteristic parameters of the to-be-controlled speed knowledge point of the to-be-controlled vehicle within the second tracking period corresponding to the current tracking node based on the interval between nodes without speed data and the speed distribution characteristic limit value.

[0043] In a possible implementation, step S123 includes:

[0044] Step S1231 , determining the number of nodes in the node interval without vehicle speed data, the uniformity characteristics of the node distribution, and the proportion range of the node interval without vehicle speed data in the entire second tracking period, and generating a first analysis result.

[0045] Step S1232: Preliminary classification of the first analysis result is performed based on the speed distribution characteristic limit value. If the number of nodes in the node interval without speed data is less than the node number threshold value set by the speed distribution characteristic limit value, the preliminary classification result is marked as the first category. If the uniformity characteristics of the node distribution in the node interval without speed data meet the uniformity standard set by the speed distribution characteristic limit value, the preliminary classification result is marked as the second category. If the proportion of the node interval without speed data in the entire second tracking period is less than the proportion threshold value set by the speed distribution characteristic limit value, the preliminary classification result is marked as the third category, and the preliminary classification result is output.

[0046] Step S1233: Perform an integrated judgment on the preliminary classification results. If the preliminary classification results include the first, second, and third categories at the same time, a first integrated judgment logic is performed. Under the first integrated judgment logic, the relative position of the node interval without vehicle speed data within the entire second tracking period is further analyzed. If the node interval without vehicle speed data is located at the beginning of the second tracking period, one integrated judgment result is output and marked as Class A. If the node interval without vehicle speed data is located in the middle of the second tracking period, another integrated judgment result is output and marked as Class B. If the node interval without vehicle speed data is located at the end of the second tracking period, a third integrated judgment result is output and marked as Class C. Furthermore, if the preliminary classification results include only the first and second categories, a second integrated judgment logic is performed. Based on the specific feature combination of the first and second category results, a fourth integrated judgment result is output and marked as Class D or Class E. Also, if the preliminary classification results only include the second and third categories, the third integrated judgment logic is performed, and according to the specific feature combination of the second and third categories, the corresponding integrated judgment results are output, marked as category F or category G, and the fifth integrated judgment result is output.

[0047] Step S1234 determines a preliminary framework for vehicle speed distribution characteristic parameters based on the various integrated judgment results. If the integrated judgment result is Category A, a vehicle speed distribution characteristic parameter framework is constructed based on the initial portion of the vehicle speed data as the influencing factor. Specifically, the framework includes an estimated factor for the impact of the initial portion of the vehicle speed data on the subsequent vehicle speed data trend, and an estimated factor for the fluctuation range of subsequent vehicle speed data under the influence of the initial portion of the vehicle speed data. Furthermore, if the integrated judgment result is Category B, a vehicle speed distribution characteristic parameter framework is constructed based on the middle portion of the vehicle speed data as the consideration factor. Specifically, the framework includes an estimated factor for the impact of the middle portion of the vehicle speed data on the segmentation of the front and rear vehicle speed data, and an estimated factor for the correlation of the front and rear vehicle speed data under the segmentation of the middle portion of the vehicle speed data. Furthermore, if the integrated judgment result is Category C, a vehicle speed distribution characteristic parameter framework is constructed based on the final portion of the vehicle speed data as the consideration factor. Specifically, the framework includes an estimated factor for the impact of the final portion of the vehicle speed data on the integrity of the overall vehicle speed data, and an estimated factor for the proportion of valid data in the overall vehicle speed data under the presence of the final portion of the vehicle speed data. Furthermore, if the integrated judgment result is Class D, a vehicle speed distribution characteristic parameter framework is constructed based on the combined features of the first and second categories. The specific framework content includes the vehicle speed data change prediction factors corresponding to the feature combinations related to the first and second categories, and the interaction prediction factors between the presence and absence of speed data under this feature combination. Furthermore, if the integrated judgment result is Class E, another vehicle speed distribution characteristic parameter framework is constructed based on the combined features of the first and second categories. The specific framework content includes the vehicle speed data fluctuation correlation prediction factors corresponding to other combined features of the first and second categories, and the prediction factors of the impact of the absence of speed data on the continuity of vehicle speed data under specific combinations. Furthermore, if the integrated judgment result is Class F, a vehicle speed distribution characteristic parameter framework is constructed based on the combined features of the second and third categories. The specific framework content includes the vehicle speed data stability prediction factors corresponding to the feature combinations related to the second and third categories, and the prediction factors of the impact of the absence of speed data on the overall vehicle speed data trend under this feature combination. Furthermore, if the integrated judgment result is Class G, another vehicle speed distribution characteristic parameter framework based on the combined features of the second and third categories is constructed. The specific framework content covers the dynamic change prediction factors of vehicle speed data corresponding to other combined features of the second and third categories, and the prediction factors of the impact of no vehicle speed data on the extreme values of vehicle speed data under specific combinations.

[0048] Step S1235: Further refine each preliminary framework to generate a refined speed distribution characteristic parameter framework. If the framework focuses on the initial portion without speed data, the refinement includes determining the specific impact of the length of time without speed data in the initial portion on the estimated factor, and the specific functional relationship between the initial portion without speed data and subsequent speed data. Furthermore, if the framework focuses on the middle portion without speed data, the refinement includes determining specific quantitative indicators for the impact of the middle portion without speed data on the segmentation of the front and rear speed data, and the specific correlation function of the front and rear speed data under the segmentation without speed data in the middle portion. Furthermore, if the framework focuses on the end portion without speed data, the refinement includes determining a quantitative assessment method for the impact of the end portion without speed data on the integrity of the overall speed data, and a specific calculation method for the proportion of valid data in the overall speed data when there is no speed data at the end. Furthermore, if the framework is based on a combination of first and second category features, the refinement operation includes determining the specific calculation logic for the vehicle speed data change prediction factor for the combination of the first and second category related features, and a quantitative model for the interaction between the presence and absence of vehicle speed data for this feature combination. Furthermore, if the framework is based on a combination of second and third category features, the refinement operation includes determining the specific numerical range of the vehicle speed data stability prediction factor for the combination of the second and third category related features, and a quantitative relationship for the impact of the absence of vehicle speed data on the overall vehicle speed data trend for this feature combination.

[0049] Step S1236: Determine the final vehicle speed distribution characteristic parameters based on the refined vehicle speed distribution characteristic parameter framework. For each refined vehicle speed distribution characteristic parameter framework, if the framework has no speed data associated with the initial portion, determine, based on the refined content, characteristic parameters such as the average vehicle speed characteristic parameter under the influence of the initial portion without speed data and the vehicle speed fluctuation range characteristic parameter under the influence of the initial portion without speed data. Furthermore, if the framework has no speed data associated with the middle portion, determine characteristic parameters such as the front-to-back vehicle speed difference characteristic parameter under the middle portion without speed data and the vehicle speed dispersion characteristic parameter under the middle portion without speed data. Furthermore, if the framework has no speed data associated with the ending portion, determine characteristic parameters such as the overall vehicle speed deviation characteristic parameter under the ending portion without speed data and the vehicle speed extreme value characteristic parameter under the ending portion without speed data. Furthermore, if the framework is based on a combination of first and second type features, determine characteristic parameters such as the comprehensive rate of change characteristic parameter of the speed data under the combined features and the vehicle speed data correction coefficient characteristic parameter under the combined features. Furthermore, if it is a framework based on the second and third types of combined features, the vehicle speed data stability adjustment parameters under the combined features and the vehicle speed data trend correction feature parameters under the combined features are determined.

[0050] In this example, a highway with a dense monitoring system for traffic control is used as an example. Assume that the target vehicle is a long-distance bus traveling on a specific section of the highway. The traffic control unit for that section is responsible for controlling its speed. The speed knowledge point to be controlled is the safe speed range for normal driving (e.g., 60-100 km / h).

[0051] In highway monitoring systems, tracking nodes are set up at fixed distances or time intervals. For example, a node is set up every 1 kilometer, and each node has speed measurement capabilities. The first tracking period is set to the first 10 minutes after the bus enters a specific road section, and the end node is the tracking node corresponding to the 10-minute mark. The current tracking node is the node after the first tracking period ends.

[0052] The interval between nodes with no speed data is determined based on the current tracking node and the end node of the first tracking period. During bus travel, speed measurement equipment at certain nodes may malfunction or be interfered with, resulting in some nodes failing to obtain speed data related to safe speed information. For example, between the end node of the first tracking period and the current tracking node, there are five nodes without speed data; these five nodes constitute the interval between nodes with no speed data.

[0053] Speed distribution characteristic thresholds are determined based on the tracking period duration parameter (10 minutes) and a predefined determination strategy. This determination strategy is based on extensive historical traffic data and the design and operational characteristics of expressways. For a 10-minute tracking period, the node count threshold is set at 8, the uniformity criterion is that the interval between adjacent nodes with no speed data should not exceed 3 nodes, and the proportion threshold is that the interval between nodes with no speed data should not exceed 40% of the total second tracking period.

[0054] Determine the number of nodes in the interval with no speed data (5), the uniformity of the node distribution (assuming that the intervals between adjacent nodes with no speed data are 1, 2, 1, and 1 nodes, respectively, which generally meet the uniformity criteria), and the proportion of the interval with no speed data in the entire second tracking period. Assuming that the second tracking period contains a total of 15 nodes from the end node of the first tracking period to the current tracking node, the proportion of the interval with no speed data is 5 / 15 ≈ 33.3%, generating the first analysis result.

[0055] The first analysis results are preliminarily classified based on the speed distribution characteristic thresholds. Since the number of nodes in the intervals without speed data (5) is less than the node number threshold of 8, the preliminary classification result is marked as the first category. The uniformity of the node distribution generally meets the uniformity standard, so the preliminary classification result is marked as the second category. Since the proportion of intervals without speed data (33.3%) is less than the proportion threshold of 40%, the preliminary classification result is marked as the third category. The output preliminary classification results include the first, second, and third categories.

[0056] An integrated judgment is performed on the preliminary classification results. Because the preliminary classification results include the first, second, and third categories, the first integrated judgment logic is performed. The relative position of the interval between nodes without speed data within the entire second tracking period is further analyzed. Assuming that the interval between nodes without speed data is located in the middle of the second tracking period, the integrated judgment result is output and marked as Class B.

[0057] Based on the integrated judgment results of Category B, a speed distribution characteristic parameter framework is constructed, focusing on the factors of the middle section without speed data. The specific content of the framework includes the estimated factors for the impact of the middle section without speed data on the segmentation of the front and rear speed data, and the estimated factors for the correlation of the front and rear speed data under the middle section without speed data. The setting of the estimated factors for the impact of the middle section without speed data on the segmentation of the front and rear speed data is based on the principle of continuity of vehicle driving. For example, according to historical data and vehicle dynamics models, the presence of no speed data may cause a certain degree of disconnection between the front and rear speed data. The estimated factor is set to 0.3, indicating that there may be a 30% disconnection effect; the estimated factor for the correlation of the front and rear speed data under the middle section without speed data is set to 0.6, indicating that the front and rear speed data still have a 60% correlation under the middle section without speed data.

[0058] This preliminary framework is further refined. Specific quantitative indicators for the impact of the absence of speed data in the middle section on the segmentation of the front and rear speed data can be determined based on factors such as the distance between nodes, the vehicle's acceleration characteristics, and the road slope. For example, the greater the distance between nodes, the smaller the vehicle acceleration, and the flatter the road slope, the smaller the segmentation impact. A specific correlation function can be constructed based on probability theory and time series analysis, taking into account factors such as the temporal order of the front and rear speed data and the speed change trend. Assuming the constructed correlation function is a multivariate linear regression function, the function's coefficients are obtained by fitting historical data.

[0059] The final speed distribution characteristic parameters are determined based on the refined speed distribution characteristic parameter framework. For the framework related to the middle portion without speed data, the characteristic parameters of the front and rear speed difference are determined for the middle portion without speed data. For example, by analyzing the distribution of front and rear speed data and the impact of the middle portion without speed data, it is determined that the front and rear speed difference in this case may be between 10 and 15 km / h. The characteristic parameter of the speed dispersion for the middle portion without speed data is obtained by calculating the standard deviation of the front and rear speed data, assuming it is 8 km / h, which represents the degree of speed dispersion between the front and rear portions.

[0060] For example, let's assume the target vehicle is a truck, and the speed knowledge point to be controlled is the safe speed on a specific slope (assuming it's 40-60 km / h). The tracking period is set to 15 minutes. Between the end of the first tracking period and the current tracking node, there are three nodes with no speed data, constituting the no speed data node interval. The entire second tracking period, from the end of the first tracking period to the current tracking node, consists of 12 nodes.

[0061] The same determination strategy was used to determine the speed distribution characteristic thresholds. For the 15-minute tracking period, the node number threshold was 6, the uniformity criterion was that the intervals between adjacent nodes without speed data did not exceed 2 nodes, and the proportion threshold was that the proportion of intervals without speed data did not exceed 25% of the entire second tracking period. The number of nodes in the intervals without speed data was 3, less than the node number threshold of 6, and was marked as the first category. The intervals between adjacent nodes without speed data were 1, 1, and 1, respectively, meeting the uniformity criterion and being marked as the second category. The proportion of intervals without speed data was 3 / 12 = 25%, which was exactly equal to the proportion threshold and did not yet meet the third category. The preliminary classification result included both the first and second categories.

[0062] The second integrated judgment logic is performed. Based on the specific feature combinations of the first and second category results, a fourth integrated judgment result is hypothetically output, labeled Category D. A speed distribution feature parameter framework is constructed based on the combined features of the first and second categories. This framework includes the speed data change prediction factors corresponding to the feature combinations related to the first and second categories, as well as the interaction prediction factors between speed data with and without speed data under this feature combination. The speed data change prediction factors are set based on vehicle type, road conditions, and historical data. For each missing node's speed data, the subsequent speed data may fluctuate by 3 km / h. The interaction prediction factor between speed data with and without speed data under this feature combination is set so that the speed data after the node without speed data will converge with the speed value of the adjacent node with speed data with a certain probability (e.g., 20%).

[0063] This framework was refined, and the specific calculation logic for the speed data change prediction factor was weighted based on factors such as vehicle load, tire wear, and road friction. A quantitative model for the interaction between speed data without and with this feature combination was constructed based on probability statistics and the physics of vehicle movement. By analyzing the subsequent speed change data of a large number of trucks under similar conditions without speed data, a mathematical model was established to quantify this interaction.

[0064] The final speed distribution characteristic parameters were determined based on the refined framework. For the framework based on a combination of the first and second types of features, the speed data comprehensive change rate characteristic parameter was determined to be 0.08 for each missing node speed data point (indicating an 8% change in speed). The speed data correction coefficient characteristic parameter was 0.9 (indicating that subsequent speed data must be multiplied by 0.9 to correct for the effects of missing speed data).

[0065] Let's assume the target vehicle is a small sedan, and the speed requirement is the safe speed in a tunnel (assuming it's 80-100 km / h). The tracking period is set to 20 minutes. There are four nodes between the end of the first tracking period and the current tracking node where no speed data is obtained. The entire second tracking period, from the end of the first tracking period to the current tracking node, includes 18 nodes.

[0066] The speed distribution characteristic thresholds were determined according to the judgment strategy. For a 20-minute tracking period, the node number threshold was 7, the uniformity criterion was that the intervals between adjacent nodes without speed data did not exceed 3 nodes, and the proportion threshold was that the proportion of intervals without speed data did not exceed 30% of the entire second tracking period. The number of nodes in the intervals without speed data was 4, which was less than the node number threshold of 7, and was marked as the first category. The intervals between adjacent nodes without speed data were 1, 2, and 1, respectively, meeting the uniformity criterion and being marked as the second category. The proportion of intervals without speed data was 4 / 18, approximately 22.2%, which was less than the proportion threshold and was marked as the third category. The preliminary classification result included both categories.

[0067] The third integrated judgment logic is then performed. Based on the specific feature combinations of the second and third categories, a fifth integrated judgment result is hypothetically output, labeled Category F. A speed distribution parameter framework is constructed based on the combined features of the second and third categories. This includes a speed data stability estimation factor corresponding to the feature combinations associated with the second and third categories, and an estimation factor for the impact of no speed data on the overall speed data trend under these feature combinations. The speed data stability estimation factor is set to 0.8, indicating relatively stable speed data, based on the vehicle's handling performance, ventilation conditions, and lighting conditions within the tunnel. The estimation factor for the impact of no speed data on the overall speed data trend under these feature combinations is set to indicate that no speed data may cause a 10% fluctuation in the overall speed data trend.

[0068] This framework was refined, and the specific range of the speed data stability estimation factor was adjusted to between 0.7 and 0.9, taking into account factors such as the vehicle's engine performance and braking system status. A quantitative model was established to quantify the impact of speed data on the overall speed data trend under this feature combination, based on factors such as traffic flow in the tunnel and the following distance between vehicles.

[0069] The final speed distribution characteristic parameters are determined based on the refined framework. For the framework based on the combined features of the second and third categories, the speed data stability adjustment parameter under the combined features is determined to be 0.05 (indicating a 5% adjustment to the speed data stability), and the speed data trend correction characteristic parameter under the combined features is determined to be 0.95 (indicating a 0.95 correction to the overall speed data trend). Through this series of steps, the speed distribution characteristic parameters of the speed knowledge points to be controlled for the vehicles under control under different circumstances and within the corresponding tracking period can be accurately determined, providing an important basis for subsequent speed prediction and control.

[0070] In a possible implementation, step S130 includes:

[0071] In step S131, if the speed distribution characteristic parameters of the speed knowledge points to be controlled during the second tracking period are discrete, the first target speed prediction scheme among the multiple target speed prediction schemes previously defined is output as the target speed prediction scheme. The first target speed prediction scheme includes: ignoring the intervals of nodes without speed data, and summarizing and calculating the speed data of the speed knowledge points to be controlled during the current tracking period.

[0072] In step S132, if the speed distribution characteristic parameter of the speed knowledge point to be controlled during the second tracking period is concentrated, a second target speed prediction scheme among the previously defined multiple target speed prediction schemes is output as the target speed prediction scheme. The second target speed prediction scheme includes: comprehensively calculating the speed data of the speed knowledge point to be controlled during the previous tracking period, the speed data of the speed knowledge point to be controlled corresponding to the current tracking node, and the speed data of the speed knowledge point to be controlled corresponding to a candidate tracking period. The candidate tracking period is the tracking period consisting of the tracking trigger node of the previous tracking period to the tracking trigger node of the current tracking period.

[0073] In this example, we use the aforementioned highway traffic control scenario as an example to examine the control of long-distance buses. Assuming a long-distance bus is traveling on a specific highway section, the speed knowledge point to be controlled is the safe speed range for normal driving (60-100 km / h). After analyzing the intervals between nodes without speed data and the speed distribution characteristic thresholds, the speed distribution characteristic parameters of the speed knowledge point to be controlled during the second tracking period are determined.

[0074] If the speed distribution characteristic parameter is discrete, this means that during the second tracking period, the long-distance bus's speed data is quite dispersed at each node. For example, at some nodes, the speed is close to the lower limit of the safe speed range of 60 km / h, while at others, the speed is close to the upper limit of 100 km / h, with no clear central trend. This dispersion may be caused by complex changes in road conditions (such as large differences in slope between sections and changes in road friction coefficient) or unstable driving conditions of the bus itself (such as frequent acceleration and deceleration).

[0075] At this point, according to predefined rules, the first target speed prediction scheme among the multiple previously defined target speed prediction schemes is output as the target speed prediction scheme. The first target speed prediction scheme ignores the intervals between nodes without speed data and aggregates the speed data for the target speed knowledge points within the current tracking period. This is because, when speed data is discrete, the intervals between nodes without speed data have a relatively small impact on the overall speed data, and the overall speed data actually acquired within the current tracking period is more important. Specifically, the speed data acquired by each node within the current tracking period is aggregated, for example, by averaging the data to obtain a single value representing the bus's speed within the current tracking period. Assume that within the current tracking period, 10 nodes acquired speed data at 62, 65, 95, 90, 68, 92, 63, 98, 66, and 93 km / h, respectively. Adding these speed data and dividing by 10 yields an average value of 79.2 km / h. This average value can be used as an important vehicle speed characteristic indicator under the target vehicle speed prediction scheme, and can be used for subsequent vehicle speed control decision-making and other operations.

[0076] Consider the case of a truck traveling on a specific slope. The speed knowledge point to be controlled is the safe speed for that slope (40-60 km / h). When determining the speed distribution characteristic parameters for the speed knowledge point to be controlled during the second tracking period, if the speed distribution characteristic parameters are found to be concentrated, this indicates that the truck's speed data during that second tracking period is relatively concentrated within a certain range or around a certain value. For example, most nodes acquire speed data between 45-50 km / h. This may be due to the combined influence of factors such as the truck's load, the slope's restrictions on vehicle movement, and the driver's desire to maintain a relatively constant speed to ensure safe and stable driving.

[0077] According to the rules, the second target speed prediction scheme from the multiple previously defined target speed prediction schemes is now output as the target speed prediction scheme. The second target speed prediction scheme is calculated by comprehensively calculating the speed data of the target speed knowledge point to be controlled during the previous tracking period, the speed data of the target speed knowledge point to be controlled corresponding to the current tracking node, and the speed data of the target speed knowledge point to be controlled corresponding to the candidate tracking period. Assume that during the previous tracking period, the average speed data acquired by each node was 48 km / h (calculated by summing the speed data of all nodes during that period and dividing it by the number of nodes). At the current tracking node, the truck's speed was measured to be 47 km / h. For the candidate tracking period (i.e., the tracking period from the tracking trigger node of the previous tracking period to the tracking trigger node of the current tracking period), the average speed data during that period was calculated to be 46 km / h (also calculated by summing the speed data of all nodes during that period and dividing it by the number of nodes). Using a pre-set weighted calculation method (for example, the weight of the previous tracking period is 0.3, the weight of the current tracking node is 0.4, and the weight of the candidate tracking period is 0.3), the calculated speed is (48 × 0.3 + 47 × 0.4 + 46 × 0.3) = 47 km / h. This calculated speed value is the truck's speed characteristic indicator for the target speed control point during the second tracking period, determined based on the second target speed prediction scheme. It comprehensively considers speed data from different time periods and more accurately reflects the truck's actual speed during that period, thus providing a basis for further speed control measures (such as determining whether the truck is speeding and whether speed adjustment is necessary).

[0078] For small cars traveling in tunnels, the target speed knowledge point is the safe speed within the tunnel (80-100 km / h). When the speed distribution characteristic parameters for the target speed knowledge point are determined as discrete during the second tracking period, similar to the case of long-distance buses, speed data appears dispersed across various nodes within the tunnel. This may be due to factors such as varying vehicle density within the tunnel and varying driver responses to the tunnel environment (e.g., some drivers suddenly slow down upon entering the tunnel, while others maintain a higher speed). As required, the first target speed prediction scheme is used, ignoring intervals between nodes with no speed data and summarizing speed data for the current tracking period. Assuming that the vehicle speed data obtained during the current tracking period are 82, 95, 85, 98, and 88 km / h, respectively, the calculated average is (82 + 95 + 85 + 98 + 88) ÷ 5 = 89.6 km / h. This average value serves as the speed characteristic indicator under the target speed prediction scheme. It can be used to evaluate the speed of small cars in tunnels and determine whether they meet the safe speed requirements in the tunnel.

[0079] If a small car is traveling in a tunnel, the speed distribution characteristic parameters of the target speed knowledge point during the second tracking period are concentrated, for example, most speed data is concentrated between 85 and 90 km / h. This may be due to the relatively stable environment in the tunnel (such as stable lighting and ventilation conditions) and drivers' general understanding of safe speeds in tunnels. According to the rules, the second target speed prediction scheme is used. Assuming that the average speed data in the previous tracking period is 88 km / h, the speed of the current tracking node is 86 km / h, and the average speed data in the candidate tracking period is 87 km / h, a comprehensive calculation based on the set weights (such as 0.3, 0.4, and 0.3) is obtained as (88 × 0.3 + 86 × 0.4 + 87 × 0.3) = 87.1 km / h. This calculation result, as a speed characteristic indicator determined based on the second target speed prediction scheme, helps to accurately grasp the speed status of small cars in the tunnel and provide an accurate basis for traffic control, such as determining whether the speed needs to be adjusted to ensure traffic safety and efficient passage in the tunnel.

[0080] In a possible implementation, the target vehicle speed prediction scheme is the first target vehicle speed prediction scheme, and step S140 may include:

[0081] A first time period duration parameter corresponding to the interval of no speed data nodes of the to-be-controlled speed knowledge point within the second tracking time period is obtained from the cloud storage system.

[0082] The vehicle speed data of the vehicle speed knowledge point to be controlled and having a second time period duration parameter is obtained from the cloud storage system, where the second time period duration parameter is each time period duration parameter within the second tracking period except the first time period duration parameter.

[0083] The speed data of the to-be-controlled speed knowledge point of the second time period duration parameter are summarized and calculated to generate speed feature information of the to-be-controlled vehicle at the to-be-controlled speed knowledge point during the second tracking period.

[0084] In a possible implementation, the target vehicle speed prediction scheme is the second target vehicle speed prediction scheme, and step S140 may include:

[0085] The first vehicle speed data of the vehicle speed knowledge point to be controlled in the first tracking period is obtained from the cloud storage system, and the second vehicle speed data of the vehicle speed knowledge point to be controlled at the current tracking node is obtained.

[0086] A candidate tracking period is determined based on the tracking trigger node of the first tracking period and the tracking trigger node of the second tracking period, and third vehicle speed data of the to-be-controlled vehicle speed knowledge point in the candidate tracking period is determined.

[0087] The first vehicle speed data, the second vehicle speed data, and the third vehicle speed data are comprehensively calculated to generate vehicle speed characteristic information of the vehicle to be controlled at the vehicle speed knowledge point to be controlled during the second tracking period.

[0088] In this embodiment, taking a long-distance bus traveling on a specific section of a highway as an example, the control unit in that section controls the speed knowledge points to be controlled (the normal safe driving speed range is 60-100 km / h). When the target speed prediction scheme is the first target speed prediction scheme, the intervals between nodes without speed data are ignored, and the speed data for the speed knowledge points to be controlled during the current tracking period is aggregated and calculated.

[0089] First, the first time duration parameter corresponding to the intervals between nodes with no speed data for the speed knowledge points to be controlled during the second tracking period is obtained from the cloud storage system. The cloud storage system records detailed information about each node on the highway, including the time period for each node. For long-distance buses, during the second tracking period, the first time duration parameter corresponding to the intervals between nodes with no speed data is assumed to be from node 10 to node 15, which lasts for 5 minutes. This information is accurately obtained from the cloud storage system and represents the period with no speed data.

[0090] Next, the speed data of the to-be-controlled speed knowledge points with the second-period continuous parameters is obtained from the cloud storage system. The second-period continuous parameters are the continuous parameters of each period in the second tracking period except for the first-period continuous parameters. For example, in the second tracking period from node 5 to node 20, except for node 10 to node 15, which has no speed data, the speed data of other nodes needs to be obtained. Assume that the speed data from node 5 to node 9 are 70, 72, 75, 73, and 71 kilometers per hour, respectively, and the speed data from node 16 to node 20 are 78, 80, 82, 85, and 83 kilometers per hour, respectively.

[0091] The speed data for the speed points to be controlled during the second tracking period are then aggregated and calculated to generate the speed signature information for the vehicle to be controlled at those speed points. In this example, the acquired speed data (70, 72, 75, 73, 71, 78, 80, 82, 85, and 83) are aggregated and calculated, for example, to calculate the average. These speed data are first added together: 70 + 72 + 75 + 73 + 71 + 78 + 80 + 82 + 85 + 83 = 769 km / h. This average is then divided by the number of data points, 10, to obtain an average of 76.9 km / h. This 76.9 km / h represents the speed signature information for the long-distance bus at the speed points to be controlled during the second tracking period. This information reflects the long-distance bus's speed characteristics during that period and provides a basis for subsequent control operations, such as determining whether the bus is within the safe speed range.

[0092] Taking the example of a truck traveling on a road with a specific slope, the speed control point is the safe speed for that slope (40-60 km / h). When the target speed prediction scheme is the second target speed prediction scheme, relevant data needs to be obtained from the cloud storage system for comprehensive calculation to determine the speed characteristic information.

[0093] The first speed data for the speed point to be controlled during the first tracking period is obtained from the cloud storage system, as is the second speed data for the speed point to be controlled at the current tracking node. Assume that during the first tracking period, the truck's speed data at each node on the sloped road section is statistically calculated, and the first speed data (average value) is 48 km / h. This data is accurately obtained from the cloud storage system and reflects the truck's speed at the speed point to be controlled during the first tracking period. At the current tracking node, the truck's speed is measured at 47 km / h. This is the second speed data, also recorded from the cloud storage system.

[0094] Based on the tracking trigger nodes of the first tracking period and the tracking trigger nodes of the second tracking period, a candidate tracking period is determined, and the third speed data of the vehicle speed knowledge point to be controlled in the candidate tracking period is determined. Assuming that the first tracking period starts from time point T1 and ends at T2, and the second tracking period starts from T2 and ends at T3, the candidate tracking period is from T1 to T3. During this candidate tracking period, after statistical analysis of the vehicle speed data of each node, it is assumed that the third speed data (average value) is 46 km / h. This data is accurately obtained from the records of the relevant nodes in the cloud storage system.

[0095] Finally, the first, second, and third speed data are combined to generate the speed signature information for the vehicle under control during the second tracking period. Using a pre-defined calculation method, such as a weighted average, assuming the first speed data has a weight of 0.3, the second speed data has a weight of 0.4, and the third speed data has a weight of 0.3, the calculation process is: (48 × 0.3 + 47 × 0.4 + 46 × 0.3) = 47 km / h. This 47 km / h represents the truck's speed signature information for the speed point under control during the second tracking period. This calculation method, which comprehensively considers speed data from different time periods, more accurately reflects the truck's speed during the second tracking period on the sloped road section. This helps determine whether the truck is complying with safe speed regulations and provides a precise basis for traffic control.

[0096] For small cars driving in tunnels, the speed knowledge point to be controlled is the safe speed in the tunnel (80-100 km / h).

[0097] When the target speed prediction scheme is the first target speed prediction scheme, information is obtained from the cloud storage system. Assume that in the second tracking period, the first period duration parameter corresponding to the node interval without speed data is the time period from node 8 to node 12, which is 4 minutes long. Then obtain the speed data of the speed knowledge points to be controlled for the second period duration parameter. In the second tracking period from node 3 to node 20, except for node 8 to node 12, there is no speed data. The speed data of other nodes are as follows: the speed data from node 3 to node 7 are 82, 85, 88, 90, and 92 kilometers per hour, respectively, and the speed data from node 13 to node 20 are 95, 93, 90, 88, 86, 85, 83, and 82 kilometers per hour, respectively. These speed data are aggregated and averaged. First, the sum is calculated: 82 + 85 + 88 + 90 + 92 + 95 + 93 + 90 + 88 + 86 + 85 + 83 + 82 = 1139 km / h. Dividing this by the number of data points (13) yields an average of approximately 87.62 km / h. This 87.62 km / h represents the speed signature of the small car during the second tracking period at the target speed control point. This can be used to assess whether the small car's speed in the tunnel meets safety requirements.

[0098] When the target speed prediction scheme is the second target speed prediction scheme, the first speed data of the small car during the first tracking period is obtained from the cloud storage system. Assume that the statistically calculated average is 88 km / h. The second speed data at the current tracking node is 86 km / h. Based on the tracking trigger node, a candidate tracking period is determined. Assume that the average third speed data within the candidate tracking period is 87 km / h. Using the weighted average method (assuming weights are 0.3, 0.4, and 0.3, respectively), the following is calculated: (88 × 0.3 + 86 × 0.4 + 87 × 0.3) = 87.1 km / h. This 87.1 km / h represents the speed characteristic information of the small car during the second tracking period for the target speed knowledge point, providing accurate speed information for traffic control.

[0099] In one possible implementation, the method further includes:

[0100] Step A110: determining the vehicle speed characteristic information of the vehicle to be controlled at each node time period within the second tracking time period.

[0101] Step A120: Obtain a period duration parameter of each node period in the second tracking period, where the period duration parameter includes an initial node and an end node.

[0102] Step A130: storing the time duration parameter of each node time period and the vehicle speed characteristic information of the vehicle to be controlled within each node time period in the cloud storage system.

[0103] In one possible implementation, the method further includes:

[0104] Step B110: Acquire the period characteristic parameters of the second tracking period, where the period characteristic parameters include an initial node and an end node.

[0105] Step B120: storing the time period characteristic parameters of the second tracking time period and the vehicle speed characteristic information of the vehicle to be controlled during the second tracking time period in the cloud storage system.

[0106] In this embodiment, taking a long-distance bus driving on a specific section of a highway as an example, the control unit of the section controls the speed knowledge point to be controlled (the normal driving safety speed range is 60-100 km / h).

[0107] First, determine the speed characteristics of the controlled vehicle (long-distance bus) at each node point within the second tracking period. For the second tracking period, assuming it runs from node 1 to node 20, analyze the speed characteristics for each node point. For example, for the node period from node 1 to node 3, the speed data captured by the speed measuring equipment is 70, 72, and 73 km / h, respectively. The average value is (70 + 72 + 73) ÷ 3 = 71.67 km / h, which is the speed characteristics for that node period. For the node period from node 4 to node 6, the speed data is 75, 78, and 80 km / h, with an average value of (75 + 78 + 80) ÷ 3 = 77.67 km / h, which is also the speed characteristics for that node period. In this manner, determine the speed characteristics for each node point.

[0108] Next, the duration parameters for each node segment within the second tracking period are obtained. The duration parameters include the initial node and the final node. For the node segment from node 1 to node 3, the initial node is 1 and the final node is 3. This information clearly defines the scope of the node segment. For the node segment from node 4 to node 6, the initial node is 4 and the final node is 6. The duration parameters for each node segment are obtained in this manner.

[0109] Then, the duration parameters of each node period and the speed characteristic information of the speed knowledge points of the vehicles to be controlled in each node period are stored in the cloud storage system. For the node period from node 1 to node 3, the initial node 1, the end node 3 and the speed characteristic information of 71.67 kilometers per hour are stored in the cloud storage system. The cloud storage system will store data according to a certain data structure for subsequent query and analysis. For the node period from node 4 to node 6, the initial node 4, the end node 6 and the speed characteristic information of 77.67 kilometers per hour are stored in the cloud storage system. According to this process, the relevant information of all node periods is stored in the cloud storage system, providing a detailed data basis for subsequent traffic control analysis.

[0110] Next, let's look at obtaining the time characteristic parameters for the second tracking period and storing the relevant information in the cloud storage system. Assuming the second tracking period starts at node 1 corresponding to time point T1 and ends at node 20 corresponding to time point T2, then the initial node is 1 and the ending node is 20. These are the time characteristic parameters for the second tracking period.

[0111] The time period characteristic parameters of the second tracking period and the speed characteristic information of the speed knowledge points of the vehicles to be controlled during the second tracking period are then stored in the cloud storage system. Assuming that the speed characteristic information of the long-distance bus in the second tracking period was previously calculated to be 78 kilometers per hour (obtained by aggregating and calculating the speed data of all nodes during the entire second tracking period), the initial node 1, the end node 20, and the speed characteristic information of 78 kilometers per hour are stored in the cloud storage system. In this way, the cloud storage system not only stores the detailed information of each node period, but also stores the summary information of the entire second tracking period. This information is very important for traffic control departments to analyze the speed of long-distance buses on this road section. For example, based on this information, it can be judged whether the speed of long-distance buses in different time periods meets safety regulations, whether there are abnormal speed fluctuations, etc., so as to take corresponding control measures.

[0112] Taking a truck driving on a road with a specific slope as an example, the speed control knowledge point is the safe speed for that slope (40-60 km / h).

[0113] To determine the speed characteristic information for the truck's speed control points at each node within the second tracking period, assume that the second tracking period runs from node 5 to node 15. For the node period from node 5 to node 7, the speed data captured by the speed measuring equipment is 45, 46, and 48 km / h. The average value (45 + 46 + 48) ÷ 3 = 46.33 km / h is the speed characteristic information for that node period. For the node period from node 8 to node 10, the speed data is 49, 50, and 52 km / h. The average value is (49 + 50 + 52) ÷ 3 = 50.33 km / h, which is the speed characteristic information for that node period. The speed characteristic information for each node period is determined in this manner.

[0114] Obtain the duration parameter of each node period in the second tracking period. For the node period from node 5 to node 7, the initial node is 5 and the ending node is 7. For the node period from node 8 to node 10, the initial node is 8 and the ending node is 10.

[0115] The duration parameters for each node period and the speed characteristic information of the truck's speed knowledge points to be controlled during each node period are stored in the cloud storage system. For the node period from node 5 to node 7, the initial node 5, the final node 7, and the speed characteristic information of 46.33 km / h are stored in the cloud storage system. For the node period from node 8 to node 10, the initial node 8, the final node 10, and the speed characteristic information of 50.33 km / h are stored in the cloud storage system.

[0116] Regarding obtaining the time period characteristic parameters and storing related information for the second tracking period, assume that the second tracking period begins at node 5 corresponding to time point T3 and ends at node 15 corresponding to time point T4, with the initial node being 5 and the final node being 15. These are the time period characteristic parameters for the second tracking period. Assuming that the truck's speed characteristic information throughout the second tracking period is 48 km / h (calculated by aggregating the speed data of all nodes throughout the second tracking period), the initial node 5, final node 15, and the speed characteristic information of 48 km / h are stored in the cloud storage system. This information helps traffic control departments analyze the truck's speed on the sloped road section, for example, determining whether the truck is traveling within a safe speed range, whether it is speeding, or whether its speed is unstable, so that timely control measures can be taken.

[0117] Taking a small car driving in a tunnel as an example, the speed knowledge point to be controlled is the safe speed in the tunnel (80-100 km / h).

[0118] When determining the speed characteristic information for the small sedan at each of the target speed control points during the second tracking period, assume that the second tracking period runs from node 3 to node 12. For the period from node 3 to node 5, the acquired speed data is 82, 85, and 83 km / h. The average value (82 + 85 + 83) ÷ 3 = 83.33 km / h is the speed characteristic information for this period. For the period from node 6 to node 8, the acquired speed data is 88, 90, and 92 km / h. The average value is (88 + 90 + 92) ÷ 3 = 90 km / h, which is the speed characteristic information for this period.

[0119] Obtain the duration parameter of each node period in the second tracking period. For the node period from node 3 to node 5, the initial node is 3 and the ending node is 5. For the node period from node 6 to node 8, the initial node is 6 and the ending node is 8.

[0120] The duration parameters for each node period and the speed characteristic information of the small car speed knowledge point to be controlled during each node period are stored in the cloud storage system. For the node period from node 3 to node 5, the initial node 3, the ending node 5, and the speed characteristic information of 83.33 km / h are stored in the cloud storage system. For the node period from node 6 to node 8, the initial node 6, the ending node 8, and the speed characteristic information of 90 km / h are stored in the cloud storage system.

[0121] To obtain the time characteristic parameters and store related information for the second tracking period, assume that the second tracking period begins at node 3 corresponding to time point T5 and ends at node 12 corresponding to time point T6, with the initial node being 3 and the final node being 12. These are the time characteristic parameters for the second tracking period. Assume that the speed characteristic information of the small car throughout the entire second tracking period is 86 km / h (calculated by aggregating the speed data of all nodes throughout the second tracking period). The initial node 3, final node 12, and the speed characteristic information of 86 km / h are stored in the cloud storage system. This information stored in the cloud storage system can provide traffic control departments with comprehensive information on the speed of the small car in the tunnel, facilitating speed control decisions, such as determining whether the small car is traveling at a safe speed in the tunnel and whether a speed adjustment prompt is necessary.

[0122] In a possible implementation, after step S140, the method further includes:

[0123] Step S150: generating a speed control plan for the vehicle to be controlled based on the vehicle speed feature information of the vehicle to be controlled at the vehicle speed knowledge point to be controlled during the second tracking period.

[0124] In this embodiment, taking a long-distance bus driving on a specific section of a highway as an example, the control unit of the section controls the speed knowledge point to be controlled (the normal driving safety speed range is 60-100 km / h).

[0125] After the speed characteristic information of the long-distance bus's speed knowledge point to be controlled during the second tracking period is determined based on the target speed prediction scheme, for example, the speed characteristic information is 75 kilometers per hour, a speed control scheme for the long-distance bus to be controlled is generated based on this speed characteristic information.

[0126] If the speed profile indicates that the long-distance bus's speed of 75 km / h is within the safe speed range (60-100 km / h) but close to the lower limit of 60 km / h, considering the overall traffic flow and efficiency of the expressway, a speed control plan may include the following. Because the long-distance bus's relatively low speed may impact the normal driving of vehicles behind it, the traffic control system can send a prompt to the long-distance bus, advising it to increase its speed appropriately while ensuring safety. This prompt can be sent to the long-distance bus driver via a roadside electronic display or an onboard terminal. The control system will also continuously monitor the bus's speed. If the speed does not increase to a reasonable range (such as 65-70 km / h) within a certain period of time (e.g., 5 minutes), the system will further analyze the traffic conditions around the bus. If a significant backlog of vehicles is detected behind the bus, the control system can adjust speed limit signs or traffic lights (if any) on nearby roads to prevent further congestion, and guide the following vehicles to appropriate diversions, such as directing some vehicles to switch to other lanes or wait in a nearby service area.

[0127] If the speed profile indicates that a long-distance bus is traveling at 75 km / h, approaching the upper limit of the safe speed range of 100 km / h, the speed control plan will be different. Because higher speeds pose certain safety risks, especially for large vehicles like long-distance buses, the control system will send a warning to the bus driver, urging them to check their speed and ensure they are within the safe range. This warning can also be sent via roadside electronic displays or onboard terminals. The control system will also increase the frequency of vehicle monitoring, collecting speed data every 5 minutes instead of every 10 minutes, to promptly detect any abnormal speed fluctuations. If the bus's speed remains close to the upper limit or continues to increase, the control system will contact the bus operating company, requesting that the company provide safe driving reminders and education to the driver. If necessary, a temporary speed limit sign will be set up in front of the bus or the vehicle will be directed to a slower lane to ensure traffic safety on the highway.

[0128] Consider a truck traveling on a specific slope. The target speed prediction solution determines the truck's speed characteristic at the target speed point during the second tracking period to be 55 km / h.

[0129] While the vehicle speed of 55 km / h is within the safe speed range, the special characteristics of the sloped road and the truck's load require multiple considerations for speed control. First, the control system assesses the truck's speed stability. If the speed is within the safe range but fluctuates significantly (for example, frequently fluctuating between 50 and 60 km / h), a reminder message is sent to the truck driver, advising them to maintain speed stability and avoid sudden acceleration or braking to prevent dangerous situations such as loss of control on the slope. This reminder message can be sent via the vehicle's terminal device. Furthermore, the control system will consider traffic flow conditions on the sloped road when formulating a control plan. If a large number of vehicles are detected behind the truck in the lane, the control system can adjust the lane speed limit sign on the sloped road to reduce the maximum speed limit for vehicles behind, thereby increasing the safe distance between vehicles. At the same time, the management and control system will continuously monitor the status of the truck's brake system (obtaining data by connecting to on-board sensors). If any abnormality is found in the brake system (such as excessive brake temperature or unstable brake pressure), even if the vehicle speed is within a safe range, an emergency warning message will be immediately sent to the truck driver, requiring him to stop and check as soon as possible, and guide the vehicle into the emergency parking lane if necessary.

[0130] If a truck's speed profile indicates a speed of 62 km / h, exceeding the safe speed limit of 60 km / h, the speed control solution will first send a severe warning to the truck driver, informing them of the speeding violation and requesting them to reduce their speed immediately. While the warning is being transmitted via the onboard terminal, roadside electronic displays will also display the vehicle's speeding status, alerting other vehicles. The control system will immediately increase its monitoring frequency for the vehicle, collecting speed data from every 15 minutes to every three minutes to closely monitor the vehicle's speed reduction. If the truck driver fails to respond to the warning promptly and fails to reduce its speed to a safe level within a specified timeframe (e.g., two minutes), the control system will contact law enforcement, who can then use highway enforcement equipment (such as speed cameras) to penalize the truck. The control system will also direct surrounding vehicles to avoid the danger posed by the speeding truck, for example by adjusting traffic lights or electronic guidance signs in nearby lanes to direct them to safer lanes.

[0131] For example, a small car traveling in a tunnel has a speed knowledge point to be controlled, which is the safe speed in the tunnel (80-100 km / h). Assume that the speed characteristic information for the small car during the second tracking period is 90 km / h.

[0132] Because 90 km / h is within the safe speed range, the speed control plan primarily focuses on maintaining safe driving within the tunnel. The control system analyzes the speed trends of small cars. If it detects a gradual increase in speed, even if it has not yet exceeded the safety limit, the system will send a warning to the driver, advising them to maintain a stable speed to avoid speeding hazards in the tunnel. This warning is sent via the vehicle's terminal device. The control system also adjusts its control strategy based on environmental factors such as tunnel lighting and ventilation. If tunnel ventilation is poor, which could affect the driver's vision and driving performance, the control system will lower the tunnel's maximum speed limit (for example, from 100 km / h to 95 km / h) and notify all vehicles in the tunnel to comply with the new speed limit. For small cars, the control system continuously monitors the distance between the car and the vehicle ahead. If the distance is too close (less than the safe distance standard), the driver will receive a warning message, urging them to maintain a safe distance.

[0133] If a small car's speed profile indicates a speed of 105 km / h, exceeding the safe speed range, the speed control solution will first send an emergency warning to the car driver, urging them to slow down immediately. Simultaneously with this warning, the electronic display within the tunnel will also display the car's speeding status, alerting other vehicles. The control system will rapidly increase its monitoring frequency for the car, acquiring speed data from every eight minutes to every minute, closely monitoring any decrease in speed. If the car driver fails to slow down promptly and remains above the safe speed range within a specified timeframe (e.g., one minute), the control system will take further action. It will contact tunnel management to install speed bumps or temporary speed limit signs to compel the car to slow down. Simultaneously, the control system will direct other vehicles in the tunnel to avoid the speeding car, for example by adjusting lane signals to direct them into safe lanes, thus preventing collisions.

[0134] Through the above process of generating speed control plans based on vehicle speed characteristic information for different vehicle types in their respective driving scenarios, it can be seen that this control plan based on vehicle speed characteristic information can effectively ensure the driving safety of vehicles on highways and improve the efficiency and accuracy of traffic management.

[0135] Figure 2 The hardware structure of the highway vehicle speed control system 100 based on the control unit for implementing the above-mentioned highway vehicle speed control method based on the control unit provided in an embodiment of the present invention is shown as follows: Figure 2As shown, the highway vehicle speed control system 100 based on the control unit may include a processor 110 , a machine-readable storage medium 120 , a bus 130 , and a communication unit 140 .

[0136] The machine-readable storage medium 120 can store data and / or instructions. In some embodiments, the machine-readable storage medium 120 can store data obtained from an external terminal. In some embodiments, the machine-readable storage medium 120 can store data and / or instructions that the control unit-based highway vehicle speed control system 100 executes or uses to implement the exemplary methods described herein.

[0137] During the specific implementation process, one or more processors 110 execute computer executable instructions stored in the machine-readable storage medium 120, so that the processor 110 can execute the highway vehicle speed control method based on the control unit in the above method embodiment. The processor 110, the machine-readable storage medium 120 and the communication unit 140 are connected through the bus 130, and the processor 110 can be used to control the sending and receiving actions of the communication unit 140.

[0138] The specific implementation process of the processor 110 can refer to the various method embodiments executed by the above-mentioned highway vehicle speed control system 100 based on the control unit. The implementation principles and technical effects are similar and will not be repeated here in this embodiment.

[0139] In addition, an embodiment of the present invention further provides a readable storage medium, in which computer executable instructions are preset. When a processor executes the computer executable instructions, the above-mentioned highway vehicle speed control method based on the control unit is implemented.

[0140] It should be noted that in order to simplify the description of the present invention and thus help understand one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, multiple features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A method for controlling the speed of vehicles on a highway based on a control unit, characterized in that: The method comprises: Obtaining pending control tasks of a target control unit of a target highway section, wherein the pending control tasks include pending control vehicles and pending control vehicle speed knowledge points; If speed data exists for the speed knowledge point to be controlled of the vehicle to be controlled at the current tracking node, determining a speed distribution characteristic parameter of the speed knowledge point to be controlled for the vehicle to be controlled within a second tracking period corresponding to the current tracking node based on the current tracking node, the end node of the first tracking period, and a tracking period duration parameter, where the first tracking period is a tracking period preceding the second tracking period; determining a target vehicle speed prediction scheme based on the vehicle speed distribution characteristic parameters of the to-be-controlled vehicle speed knowledge point during the second tracking period; Determining the vehicle speed characteristic information of the vehicle to be controlled at the vehicle speed knowledge point to be controlled within the second tracking period based on the target vehicle speed prediction scheme; The determining, based on the current tracking node, the termination node of the first tracking period, and the tracking period duration parameter, of the speed distribution characteristic parameter of the to-be-controlled vehicle speed knowledge point within the second tracking period corresponding to the current tracking node includes: Determining a node interval without vehicle speed data based on the current tracking node and the end node of the first tracking period; determining a vehicle speed distribution characteristic limit value based on the tracking period duration parameter and a predefined determination strategy; The speed distribution characteristic parameter of the to-be-controlled speed knowledge point of the to-be-controlled vehicle in the second tracking period corresponding to the current tracking node is determined based on the interval between nodes without speed data and the speed distribution characteristic limit value.

2. The highway vehicle speed control method based on the control unit according to claim 1 is characterized in that: The step of determining the vehicle speed distribution characteristic parameter of the to-be-controlled vehicle speed knowledge point within the second tracking period corresponding to the current tracking node based on the interval between nodes without vehicle speed data and the speed distribution characteristic limit value comprises: Determining the number of nodes in the node interval without vehicle speed data, the uniformity characteristics of the node distribution, and the proportion range of the node interval without vehicle speed data in the entire second tracking period, and generating a first analysis result; The first analysis result is preliminarily classified according to the vehicle speed distribution characteristic limit value, wherein if the number of nodes in the node interval without vehicle speed data is less than the node number threshold value set by the vehicle speed distribution characteristic limit value, the preliminary classification result is marked as the first category; if the uniformity characteristic of the node distribution in the node interval without vehicle speed data meets the uniformity standard set by the vehicle speed distribution characteristic limit value, the preliminary classification result is marked as the second category; if the proportion range of the node interval without vehicle speed data in the entire second tracking period is less than the proportion threshold value set by the vehicle speed distribution characteristic limit value, the preliminary classification result is marked as the third category, thereby outputting the preliminary classification result; Performing an integrated judgment on the preliminary classification results. If the preliminary classification results include the first, second, and third categories at the same time, performing a first integrated judgment logic. Under the first integrated judgment logic, further analyzing the relative position of the interval of nodes without speed data in the entire second tracking period, if the interval of nodes without speed data is located at the beginning of the second tracking period, outputting one integrated judgment result, labeled as Category A; if the interval of nodes without speed data is located in the middle of the second tracking period, outputting another integrated judgment result, labeled as Category B; if the interval of nodes without speed data is located at the end of the second tracking period, outputting a third integrated judgment result, labeled as Category C; and, if the preliminary classification results include only the first and second categories, performing a second integrated judgment logic, and outputting a fourth integrated judgment result, labeled as Category D or Category E, based on the specific feature combination of the first and second category results; and, if the preliminary classification results include only the second and third categories, performing a third integrated judgment logic, and outputting a corresponding integrated judgment result, labeled as Category F or Category G, based on the specific feature combination of the second and third categories, and outputting a fifth integrated judgment result. A preliminary framework of vehicle speed distribution characteristic parameters is determined based on various integrated judgment results. If the integrated judgment result is Class A, a vehicle speed distribution characteristic parameter framework is constructed with the initial part without vehicle speed data as the influencing factor. The specific framework content includes the estimated factors of the influence of the initial part without vehicle speed data on the trend of subsequent vehicle speed data, and the estimated factors of the fluctuation range of subsequent vehicle speed data under the influence of the initial part without vehicle speed data; and if the integrated judgment result is Class B, a vehicle speed distribution characteristic parameter framework is constructed with the middle part without vehicle speed data as the focus factor. The specific framework content includes the estimated factors of the middle part without vehicle speed data. The estimated factors of the impact of the speed data on the segmentation of the front and rear speed data, and the estimated factors of the correlation between the front and rear speed data when there is no speed data in the middle; and, if the integrated judgment result is Class C, then a speed distribution characteristic parameter framework is constructed with the lack of speed data at the end as the focus. The specific framework content includes the estimated factors of the impact of the lack of speed data at the end on the integrity of the overall speed data, and the estimated factors of the proportion of valid data of the overall speed data when there is no speed data at the end; and, if the integrated judgment result is Class D, then a speed distribution characteristic parameter based on the combined features of the first and second categories is constructed. The specific content of the framework covers the speed data change prediction factors corresponding to the feature combinations related to the first and second categories, and the interaction prediction factors between the speed data without speed data and the speed data under the feature combination; and if the integrated judgment result is E, then another speed distribution feature parameter framework based on the first and second category combined features is constructed, and the specific content of the framework includes the speed data fluctuation correlation prediction factors corresponding to other combined features of the first and second categories, and the influence prediction factors of the speed data continuity under a specific combination without speed data; and if the integrated judgment result is F, then a basic A speed distribution characteristic parameter framework based on the second and third category combined features is constructed. The specific framework content includes the speed data stability prediction factor corresponding to the feature combination related to the second and third categories, and the prediction factor of the impact of the absence of speed data on the overall speed data trend under this feature combination. And, if the integrated judgment result is Class G, another speed distribution characteristic parameter framework based on the second and third category combined features is constructed. The specific framework content includes the speed data dynamic change prediction factor corresponding to other second and third category combined features, and the prediction factor of the impact of the absence of speed data on the extreme value of the speed data under a specific combination. A further refinement operation is performed on each preliminary framework to generate a refined vehicle speed distribution characteristic parameter framework; wherein, if the framework is concerned with the initial portion having no vehicle speed data, the refinement operation includes determining the specific degree of influence of the length of time when the initial portion has no vehicle speed data on the estimation factor, and the specific functional relationship between the initial portion having no vehicle speed data and the subsequent vehicle speed data; and, if the framework is concerned with the middle portion having no vehicle speed data, the refinement operation includes determining the specific quantitative index of the influence of the middle portion having no vehicle speed data on the segmentation of the front and rear vehicle speed data, and the specific correlation function of the front and rear vehicle speed data under the segmentation of the middle portion having no vehicle speed data; and, if the framework is concerned with the ending portion having no vehicle speed data, the refinement operation includes determining the ending portion having no vehicle speed data. A quantitative assessment method for the impact of no speed data on the integrity of the overall speed data, and a specific method for calculating the proportion of valid data in the overall speed data when no speed data exists at the end; and, if the framework is based on the combined features of the first and second categories, the refinement operation includes determining the specific calculation logic of the speed data change prediction factor under the combination of the first and second related features, and a quantitative model for the interaction between the no speed data and the speed data under this feature combination; and, if the framework is based on the combined features of the second and third categories, the refinement operation includes determining the specific numerical range of the speed data stability prediction factor under the combination of the second and third related features, and a quantitative relationship between the impact of no speed data on the overall speed data trend under this feature combination; The final vehicle speed distribution characteristic parameters are determined based on the refined vehicle speed distribution characteristic parameter framework, wherein, for each refined vehicle speed distribution characteristic parameter framework, if it is a framework with no speed data related to the initial part, the average vehicle speed characteristic parameters under the influence of no speed data in the initial part and the vehicle speed fluctuation range characteristic parameters under the influence of no speed data in the initial part are determined according to the refined content; and if it is a framework with no speed data related to the middle part, the front and rear vehicle speed difference characteristic parameters under the segmentation without speed data in the middle part and the vehicle speed dispersion characteristic parameters under the segmentation without speed data in the middle part are determined. ; and, if it is a framework related to the end part without vehicle speed data, determine the overall vehicle speed deviation characteristic parameters under the end part without vehicle speed data, and the vehicle speed extreme value characteristic parameters under the end part without vehicle speed data; and, if it is a framework based on the first and second types of combined features, determine the comprehensive change rate characteristic parameters of the speed data under the combined features, and the vehicle speed data correction coefficient characteristic parameters under the combined features; and, if it is a framework based on the second and third types of combined features, determine the vehicle speed data stability adjustment parameters under the combined features, and the vehicle speed data trend correction characteristic parameters under the combined features.

3. The highway vehicle speed control method based on the control unit according to any one of claims 1-2, characterized in that: The determining of the target vehicle speed prediction scheme based on the vehicle speed distribution characteristic parameters of the to-be-controlled vehicle speed knowledge point during the second tracking period includes: If the speed distribution characteristic parameter of the to-be-controlled speed knowledge point in the second tracking period is discrete, outputting a first target speed prediction scheme from among the plurality of previously defined target speed prediction schemes as the target speed prediction scheme, the first target speed prediction scheme comprising: ignoring intervals between nodes without speed data, and summarizing and calculating the speed data of the to-be-controlled speed knowledge point in the current tracking period; If the speed distribution characteristic parameters of the speed knowledge point to be controlled in the second tracking period are concentrated, the second target speed prediction scheme among the multiple target speed prediction schemes defined previously will be output as the target speed prediction scheme, and the second target speed prediction scheme includes: comprehensively calculating the speed data of the speed knowledge point to be controlled in the previous tracking period, the speed data of the speed knowledge point to be controlled corresponding to the current tracking node, and the speed data of the speed knowledge point to be controlled corresponding to the candidate tracking period; the candidate tracking period is a tracking period composed of the tracking trigger node of the previous tracking period to the tracking trigger node of the current tracking period.

4. The highway vehicle speed control method based on the control unit according to claim 3 is characterized in that: The target vehicle speed prediction scheme is the first target vehicle speed prediction scheme, and determining the vehicle speed characteristic information of the vehicle to be controlled at the vehicle speed knowledge point to be controlled during the second tracking period based on the target vehicle speed prediction scheme includes: Obtaining from the cloud storage system a first time period duration parameter corresponding to the interval of nodes without speed data of the speed knowledge point to be controlled within the second tracking time period; Obtaining the vehicle speed data of the to-be-controlled vehicle speed knowledge point with a second period duration parameter from the cloud storage system, where the second period duration parameter is each period duration parameter within the second tracking period except the first period duration parameter; The speed data of the to-be-controlled speed knowledge point of the second time period duration parameter are summarized and calculated to generate speed feature information of the to-be-controlled vehicle at the to-be-controlled speed knowledge point during the second tracking period.

5. The highway vehicle speed control method based on the control unit according to claim 3 is characterized in that: The target vehicle speed prediction scheme is the second target vehicle speed prediction scheme, and determining the vehicle speed characteristic information of the vehicle to be controlled at the vehicle speed knowledge point to be controlled during the second tracking period based on the target vehicle speed prediction scheme includes: Acquire, from the cloud storage system, first vehicle speed data of the vehicle speed knowledge point to be controlled during the first tracking period, and acquire second vehicle speed data of the vehicle speed knowledge point to be controlled at the current tracking node; Determining a candidate tracking period based on the tracking trigger node of the first tracking period and the tracking trigger node of the second tracking period, and determining third vehicle speed data of the to-be-controlled vehicle speed knowledge point in the candidate tracking period; The first vehicle speed data, the second vehicle speed data, and the third vehicle speed data are comprehensively calculated to generate vehicle speed characteristic information of the vehicle to be controlled at the vehicle speed knowledge point to be controlled during the second tracking period.

6. The highway vehicle speed control method based on the control unit according to claim 4 or 5, characterized in that: The method further comprises: Determining vehicle speed characteristic information of the vehicle to be controlled at the vehicle speed knowledge point to be controlled at each node time period within the second tracking time period; Obtaining a period duration parameter of each node period in the second tracking period, wherein the period duration parameter includes an initial node and an end node; The time period duration parameter of each node time period and the speed characteristic information of the to-be-controlled vehicle speed knowledge point within each node time period are stored in the cloud storage system.

7. The highway vehicle speed control method based on the control unit according to claim 4 or 5, characterized in that: The method further comprises: Acquire a period characteristic parameter of the second tracking period, where the period characteristic parameter includes an initial node and an end node; The time period characteristic parameters of the second tracking time period and the speed characteristic information of the vehicle to be controlled at the speed knowledge point to be controlled during the second tracking time period are stored in the cloud storage system.

8. The highway vehicle speed control method based on the control unit according to claim 1, characterized in that: After the step of determining the vehicle speed characteristic information of the vehicle to be controlled at the vehicle speed knowledge point to be controlled within the second tracking period based on the target vehicle speed prediction scheme, the method further includes: A speed control plan for the vehicle to be controlled is generated based on the vehicle speed feature information of the vehicle to be controlled at the vehicle speed knowledge point to be controlled during the second tracking period.

9. A highway vehicle speed control system based on a control unit, characterized in that: The highway vehicle speed control system based on the control unit includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the highway vehicle speed control method based on the control unit as described in any one of claims 1 to 8 above.

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