A carbon emission management system for the transportation industry based on the Internet of Things

Abstract

The present invention relates to the technical field of carbon emission management, and specifically discloses a carbon emission management system for the transportation industry based on the Internet of Things (IoT). The system comprises: a collection module that divides roads into road segments and continuously collects violation data of non-motor vehicles on the road segments through IoT sensing devices; an aggregation module that dynamically aggregates the violation data of the road segments to generate a comprehensive violation intensity index for the road segments within continuous time periods; a classification module that divides the road segments into different risk levels based on the comprehensive violation intensity index and current traffic light phase data; and a recommendation module that recommends the route with the lowest total risk value based on real-time motor vehicle navigation requests and dynamically receives real-time non-motor vehicle movement trajectories uploaded from IoT sensing devices during navigation, triggering a reassessment of the route risk level and real-time adjustment of the navigation route. The present invention reduces carbon emissions during road transportation.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission management, and in particular to a carbon emission management system for the transportation industry based on the Internet of Things. Background Art

[0002] Transportation refers to the business activity of using transportation to transport goods or passengers to their destination, thereby transferring their location. Currently, transportation can be divided into five types based on the type of transportation: road transport, rail transport, water transport, air transport, and pipeline transport. Road transport is the most common mode of transportation, characterized by its adaptability and direct delivery.

[0003] Vehicles emit carbon emissions while driving. Compared to constant speed driving, frequent starts and stops, and sudden acceleration and deceleration significantly increase carbon emissions per mile. On urban roads, some non-motorized vehicles, such as electric bicycles and tricycles, disobey traffic lights, drive against traffic, change lanes arbitrarily, or cross traffic, forcing vehicles behind them to brake suddenly, stop briefly, and then restart. The moment the engine is brought from idle to loaded, the fuel injection rate increases, and combustion temperatures fluctuate, leading to cumulative peaks in greenhouse gas emissions such as carbon dioxide and nitrous oxide. Furthermore, the vehicle's inertia is repeatedly interrupted, increasing overall fuel consumption. Summary of the Invention

[0004] The purpose of the present invention is to provide a carbon emission management system for the transportation industry based on the Internet of Things to solve the above technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A carbon emission management system for the transportation industry based on the Internet of Things, comprising:

[0007] Collection module: Divides the road into road segments and continuously collects violation data of non-motor vehicles on the road segments through IoT sensing devices. The violation data includes the number of red light running, wrong-way driving, and lane change;

[0008] Aggregation module: dynamically aggregates the violation data of a road segment to generate a comprehensive violation intensity index for the road segment over a continuous time period;

[0009] Classification module: based on the comprehensive violation intensity index and combined with the current traffic light phase data, divide the road section into different risk levels;

[0010] Recommendation module: Based on real-time motor vehicle navigation requests, it recommends the path with the lowest total risk value, and dynamically receives the real-time motion trajectory of non-motor vehicles uploaded by IoT sensing devices during the navigation process, triggering the re-evaluation of the path risk level and real-time adjustment of the navigation path.

[0011] As a further solution of the present invention, collecting data on violations of non-motor vehicles on a road segment includes:

[0012] Multi-view monitoring equipment is installed at a preset location. Image recognition technology is used to capture the displacement distance and duration of non-motor vehicles crossing the stop line during red light periods. If the displacement distance exceeds the preset displacement distance threshold and continues before the green light comes on without returning, it will be recorded as running a red light.

[0013] Millimeter-wave radar is installed on the central divider of the road to monitor the relative speed difference and lateral displacement changes between non-motor vehicles and motor vehicles in adjacent lanes in a single-direction traffic flow. If a non-motor vehicle is detected to cross two or more lane lines continuously within a preset length range, it will be recorded as a lane change;

[0014] By using a pressure sensor array buried at the edge of the non-motorized vehicle lane, the continuous pressure signal sequence generated when the non-motorized vehicle travels along a path opposite to the prescribed driving direction is identified. The time interval and spatial distribution characteristics of the pressure signal are combined to determine whether the vehicle is traveling in the wrong direction.

[0015] As a further embodiment of the present invention, generating a comprehensive violation intensity index for a road segment within a continuous time period includes:

[0016] The violation data of a single road section is divided into multiple time windows according to the preset duration, and the number of red light running, lane changing and wrong-way driving in each time window is counted;

[0017] The three types of violation data within the same time window are assigned different weight coefficients and weighted summed to obtain the initial intensity value. The weight of red light running is the highest, the weight of wrong-way driving is the second highest, and the weight of lane changing and weaving is the lowest.

[0018] A moving average is calculated based on the initial intensity values of the same time window of the road segment within the past preset number of days, and the ratio of the initial intensity value of the current time window to the moving average is used as the comprehensive violation intensity index.

[0019] As a further solution of the present invention, dividing road segments into different risk levels includes:

[0020] Said risk levels include high risk, medium risk and low risk;

[0021] When the number of time windows in which the comprehensive violation intensity index continuously exceeds the preset threshold reaches a preset first number, and the traffic light is at the end of the red light or the beginning of the green light at the current moment, the corresponding risk level is high risk;

[0022] When the comprehensive violation intensity index exceeds the preset threshold within a single time window but does not meet the continuous condition and / or the traffic light is in the yellow light switching stage, the corresponding risk level is medium risk;

[0023] When the comprehensive violation intensity index does not exceed the preset threshold and the signal light is in the middle of the green light, the corresponding risk level is low risk.

[0024] As a further embodiment of the present invention, generating a comprehensive violation intensity index for a road segment within a continuous time period includes:

[0025] Establish a communication connection with the traffic signal controller through the roadside IoT gateway to read the current phase status and remaining duration of the traffic light in real time;

[0026] When the traffic light is in the red light state, the accumulated time from the time the red light is on to the current time and the preset total red light time are obtained synchronously;

[0027] When the traffic light is in the green light state, the cumulative time from the time the green light is turned on to the current time and the preset remaining time of the green light are obtained synchronously.

[0028] As a further solution of the present invention: the recommended path with the lowest total risk value includes:

[0029] Upon receiving a motor vehicle navigation request, extracting a set of all feasible paths from a departure point to a destination;

[0030] Eliminate routes from the route set that contain high-risk road segments and have no alternative detour solutions;

[0031] For the remaining paths, the total risk value of the path is calculated based on the risk level of each road segment, where the high-risk road segment is assigned the maximum risk coefficient, the medium-risk road segment is assigned the middle risk coefficient, and the low-risk road segment is assigned the minimum risk coefficient.

[0032] After arranging the total risk values of the paths in ascending order, the path with the lowest total risk value is recommended, and the risk level distribution on the path is marked with different colors in the electronic map interface.

[0033] As a further solution of the present invention, triggering the re-evaluation of the path risk level includes:

[0034] The IoT sensing device detects that the density of non-motor vehicle real-time movement trajectories on a certain road section of the current recommended route exceeds the historical level for the same period;

[0035] Or it is detected that a sudden traffic control occurs at the upstream intersection of the road segment, resulting in abnormal convergence of non-motor vehicle traffic;

[0036] Or a group of non-motor vehicles is detected to be continuously changing lanes and weaving in and out of the road;

[0037] When any trigger condition is met, the violation data within the most recently set time period is recalled for aggregate calculation, the comprehensive violation intensity index and risk level of the road section are updated, and new route recommendation instructions are generated based on the updated results.

[0038] As a further solution of the present invention: the real-time adjustment of the navigation path includes:

[0039] When a vehicle is driving along a recommended route, if the IoT sensing device detects an increased risk level on the road ahead, it triggers a dynamic route switching mechanism.

[0040] Recalculate a set of alternative routes based on the vehicle's current location. The set of alternative routes must meet the following conditions: exclude all newly promoted high-risk road segments, and the total length of the detour route does not exceed a set proportion of the original route length;

[0041] Select a new path with the lowest total risk value from the set of alternative paths and push a path change request to the driver via the vehicle terminal;

[0042] If the driver confirms the change, the navigation guidance trajectory is updated and a steering instruction sequence is sent to the vehicle control system via the Internet of Vehicles. The steering instruction sequence includes lane change prompts and steering angle recommendations required before each intersection.

[0043] If the driver does not respond to the request, a warning signal will be broadcast to the vehicle behind through the Internet of Vehicles communication, triggering the vehicle behind to automatically maintain a safe distance. Based on the predicted motion trajectory of the non-motor vehicle in front, speed control instructions will be dynamically generated to guide the vehicle through high-risk road sections with a gentle acceleration and deceleration curve.

[0044] The beneficial effects of the present invention are as follows:

[0045] This invention deploys IoT sensing devices along road sections to collect real-time data on violations such as non-motor vehicle violations such as running red lights, driving against traffic, and lane-changing. Combined with traffic signal phases, it dynamically calculates a comprehensive violation intensity index and categorizes road sections into high, medium, and low risk levels. The navigation system eliminates or deemphasizes high-risk road sections during route planning and automatically reassesses risk and adjusts routes based on real-time violation activity during driving. This allows vehicles to proactively avoid sections with high levels of violations, maintain a more stable speed profile, and significantly reduce the additional power output and fuel consumption caused by emergency braking, frequent starts and stops, and sudden acceleration and deceleration. This reduces instantaneous greenhouse gas emission peaks and overall carbon emissions, while also improving road traffic efficiency and driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described below with reference to the accompanying drawings.

[0047] Figure 1 This is a flow chart of a carbon emission management system for the transportation industry based on the Internet of Things according to the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0049] See also Figure 1 As shown, the present invention is a carbon emission management system for the transportation industry based on the Internet of Things, comprising:

[0050] Collection module: Divides the road into road segments and continuously collects violation data of non-motor vehicles on the road segments through IoT sensing devices. The violation data includes the number of red light running, wrong-way driving, and lane change;

[0051] In a preferred embodiment of the present invention, collecting data on violations of non-motor vehicles on a road segment includes:

[0052] Two to three multi-view HD cameras are positioned approximately 3 to 5 meters above the stop line at the intersection. The stop line and the area 0.5 meters in front and behind it are designated as regions of interest (ROIs) through perspective correction. The system uses a deep learning object detection network to segment and track the trajectories of non-motorized vehicles (NMVs) such as electric bicycles and tricycles that enter this area, calculating the shortest distance from their center of mass to the stop line in real time. A red light violation record is recorded in the database when the cumulative displacement of the center of mass exceeds a preset displacement threshold (which can be 0.30 to 0.50 meters) and the duration of this state from the time the red light turns red to the time the green light turns green exceeds a preset duration threshold (which can be 2 to 3 seconds), and the vehicle does not return behind the stop line.

[0053] A narrow-beam millimeter-wave radar is installed on a fixed bracket approximately 1.2 meters above the road's central divider. The radar operates at a frequency of 77 GHz. Its detection sector covers two to three lanes traveling in the same direction as motor vehicles and outputs the lateral displacement Δy and relative velocity Δv of non-motorized vehicles in real time. The system calculates the vehicle's instantaneous lane number based on the lane line equations calibrated in the electronic map. When a target crosses two or more lane lines during continuous lateral movement, and the total length of the crossing exceeds a preset length threshold (which can be 8 to 12 meters), it is registered as a lane change.

[0054] Single-point piezoelectric pressure sensors are embedded in an array at 0.5m intervals along the right edge of the non-motorized vehicle lane. Sensor numbers are incremented according to the vehicle's intended direction of travel. The system compares the pressure trigger sequence numbers in chronological order. When at least five consecutive numbers showing a decreasing trend are detected, and the interval between adjacent triggers is less than a preset time threshold (the time threshold can be 0.4s to 0.6s), the system identifies the incident as a wrong-way behavior and records it, confirming that the incident belongs to the same target based on the vehicle size model.

[0055] It should be noted that by using multi-view cameras, millimeter-wave radars, and pressure sensors to conduct complementary detection of non-motor vehicle violations at different spatial scales and dimensions, it is possible to maintain high recognition accuracy in complex scenarios such as changing lighting, rain and fog obstructions, or traffic congestion, thus avoiding missed or false detections by a single sensing method. Violations are stored in real time according to clear and unified event rules, enabling the back-end aggregation module to promptly generate a comprehensive violation intensity index and feed it back to the navigation decision-making, thereby helping the system to quickly identify high-risk road sections, guiding motor vehicles to detour or slow down in advance, and reducing the probability of sudden braking, idling, and secondary acceleration caused by sudden non-motor vehicle behavior. Ultimately, this helps to achieve stable vehicle driving conditions and refined control of carbon emissions from transportation.

[0056] Aggregation module: dynamically aggregates the violation data of a road segment to generate a comprehensive violation intensity index for the road segment over a continuous time period;

[0057] In another preferred embodiment of the present invention, generating a comprehensive violation intensity index for a road segment within a continuous time period includes:

[0058] An independent buffer zone is established for each road section. The red light running, wrong-way driving, and lane changing events reported by the sensing terminal in real time are written according to the time of occurrence and automatically attributed to a time window with a preset length. The time window can be 5 minutes to 10 minutes and supports sliding updates of 0.5 times the window length. After counting the number of times N1, N2, and N3 of the three types of events in a single window, the weight coefficients preset in the configuration file are called for weighting, where the red light running weight w1 can be 0.5-0.6, the wrong-way weight w2 can be 0.25-0.35, and the lane changing weight w 3 can be 0.15 to 0.25, satisfying w1+w2+w3=1, and the initial intensity value is obtained by the formula S0=w1N1+w2N2+w3N3; then the historical initial intensity sequence of the road segment in the same time window in the past preset number of days is retrieved from the database, and the preset number of days can be 7d to 14d. The benchmark mean S1 is obtained by using the simple moving average algorithm, and the ratio R of the current window intensity to the benchmark mean is calculated. R is written into the time series database as the comprehensive violation intensity index, while S0 and the original event count are retained for visualization and model backtracking;

[0059] It is understandable that fixed time windows can capture the instantaneous aggregation of violations in a fine-grained manner and avoid the information dilution caused by long-term statistics. The weight coefficient highlights high-risk factors such as running red lights, which can increase the sensitivity of the index to key factors that cause sudden braking and fuel consumption fluctuations of motor vehicles. The construction of a dynamic benchmark based on the historical same-window moving average can automatically offset the background changes caused by weather, holidays and traffic fluctuations during different time periods, making the index anomaly assessment more objective. Finally, the scales of different road sections are unified in the form of ratios to facilitate horizontal comparison and threshold judgment, thereby providing real-time, stable and interpretable input for risk level classification and route recommendation, helping the system to promptly identify potential congestion and high emission factors and guide vehicles to make energy-efficient driving decisions.

[0060] Classification module: based on the comprehensive violation intensity index and combined with the current traffic light phase data, divide the road section into different risk levels;

[0061] In another preferred embodiment of the present invention, dividing road segments into different risk levels includes:

[0062] Said risk levels include high risk, medium risk and low risk;

[0063] A real-time updated comprehensive violation intensity index sequence is maintained for each road segment in the edge computing server. The system first reads the threshold R0 from the configuration file, which can be 1.3 to 1.5, and then reads the number of consecutive threshold exceeding windows N0, which can be 3 to 4. The algorithm scrolls through the latest N0 windows with a step size of 1 time window. When it is detected that the indexes of these N0 windows are all greater than or equal to R0, it enters the light color judgment process: if the roadside signal controller returns that the current light color is red and the remaining red light time T_r ≤ 20% of the total red light time, it is defined as the end of the red light period; if the light color is green and the on time T_g ≤ green light, the system will automatically judge the light color. If the index exceeds the threshold continuously and the light color is at the end of the red light or the beginning of the green light, the road section risk level is written as high risk. If the index ≥ R0 in any single window but the number of consecutive windows is less than N0, or the light color is yellow and the yellow light on time T_y ≤ 50% of the total yellow light time, it is written as medium risk. If the current window index is less than R0 and the light color is green and the green light has been on for between 30% and 70% of the total green light time, it is defined as the middle green light period and written as low risk. The system then encapsulates the risk level and timestamp into a JSON message and pushes it to the route recommendation engine.

[0064] It should be noted that the continuous exceeding of the comprehensive violation intensity index threshold represents that there are high-density and high-risk violations on the road section for a period of time, and it needs to be superimposed with the signal phase to accurately assess the immediate impact on the passage of motor vehicles; the end of the red light and the beginning of the green light are the stages when the behavior of people and vehicles changes most drastically. Non-motor vehicles often make sudden crossings to rush or accelerate through the intersection, and motor vehicles are about to start or have just started. The superposition of the two is most likely to trigger sudden braking and acceleration, so it is defined as high risk; if the index exceeds the threshold only in a single window or the light color is in the yellow light switch (that is, the light color is yellow and the yellow light is on for a period of time T_y ≤ 50% of the total yellow light time), the violation is then If the signal change has not yet formed a sustained impact, the risk, although apparent, can be controlled by deceleration and concentration, and is therefore classified as medium risk. A low index and the mid-term green light mean that the flow of non-motor vehicles and motor vehicles has stabilized, making sudden braking less likely to be triggered, and is therefore classified as low risk. This dual judgment based on intensity continuity and light color phase makes the risk classification both sensitive and stable, and can provide targeted decision-making basis for dynamic navigation detours and vehicle adaptive speed control, thereby helping the system to remind the driver or the autonomous driving system to prevent non-motor vehicle interference in the most critical time window, reduce sudden stops and starts, and support the realization of refined management goals for traffic carbon emissions.

[0065] It should be noted that for ordinary roads without traffic lights, the risk level is directly divided according to the absolute value range of the comprehensive violation intensity index. The specific division range can be set based on experience and is not restricted here.

[0066] In a preferred embodiment of this invention, dividing the road segments into different risk levels further includes:

[0067] The roadside IoT gateway establishes a two-way long connection with the traffic signal controller via Gigabit Ethernet or RS-485 bus, uses NTCIP or JT / T794 protocol to send status query messages with a polling period of 0.5s to 1s and parses the phase number, light color identification and countdown information in the returned frame. When the current light color is red, it immediately reads the built-in phase table of the controller, extracts the timestamp T0 of the red light on, and uses the local high-precision clock to calculate the cumulative time Δt1 from T0 to the query time T1, and at the same time calls the timing plan The total red light duration L_r is preset for this phase. If the parsed result is a green light, the green light on timestamp T2 is recorded and the cumulative duration Δt2 is calculated. This is compared with the remaining green light duration L_g_r in the timing plan. If the countdown is found to differ from the local calculation by more than the preset deviation threshold (the deviation threshold can be 0.5s to 1s), secondary synchronization with the control machine is triggered and the clock offset is updated. All acquired phase states, cumulative durations, and remaining durations are encapsulated into a JSON object and published to the edge computing server via MQTT for subsequent calls.

[0068] It is important to note that the use of real-time high-frequency communication ensures that the system has the most accurate information on the phase and remaining time of traffic lights. This allows for rapid identification of risk scenarios at key points where non-motor vehicle violations connect to signal phases. Local clock difference correction and countdown comparison are used to improve data consistency, avoiding phase misjudgments caused by controller clock drift. Preset thresholds for the total duration of red lights and the remaining duration of green lights provide a unified scale, facilitating the normalization of indicators at different intersections and the triggering of rules. This allows for a more accurate linkage between the comprehensive violation intensity index and signal phase, providing a reliable timing basis for back-end risk level classification and dynamic navigation adjustments. This ultimately helps vehicles foresee changing traffic conditions in advance, reduces sudden stops and starts, and supports the achievement of precise carbon emission control targets.

[0069] Recommendation module: Based on real-time motor vehicle navigation requests, it recommends the route with the lowest total risk value. During the navigation process, it dynamically receives the real-time motion trajectory of non-motor vehicles uploaded by IoT sensing devices, triggering the reassessment of the route risk level and real-time adjustment of the navigation route.

[0070] In another preferred embodiment of the present invention, the path with the lowest total risk value is recommended to include:

[0071] After receiving a navigation request, the on-board terminal or cloud-based path planning module first calls the map engine to search for K feasible paths from the start point to the end point using a "road segment-topology" model. K can be 5 to 8 paths, and each path consists of several road segment numbers and attributes such as length and speed limit. Each path is then checked one by one to see if it contains a node marked as high risk by the risk classification module and has no available alternative road segments within a 500m lateral search radius. If so, the path is directly removed from the candidate set. For the remaining paths, each road segment is assigned a risk factor: a high risk factor can be 2.5 to 3.0, a medium risk factor can be 1.5 to 2.0, and a low risk factor can be 1.0 to 1.2. A weighted sum is then taken to obtain the total risk value of the path. The total risk values of all candidate paths are sorted in ascending order, and the path with the smallest value is recommended. A GIS rendering service is used on the HMI interface to color low-risk segments green, high-risk segments yellow, and high-risk segments red. A transparent dot is superimposed at each intersection to indicate risk changes within the next 50m.

[0072] It should be noted that eliminating high-risk paths with no room for detour can prevent navigation from leading vehicles into potential bottlenecks and preventing them from escaping in time. Multiplying the risk coefficient by the length of the road segment and accumulating them can normalize different driving distances and risk levels to the same evaluation scale, reflecting both the risk intensity and the driving cost. Limiting the amount of calculation by setting K candidate paths can ensure real-time performance, and setting a specific risk coefficient range allows for flexible parameter adjustment based on urban management strategies in the later stage. Finally, using color to intuitively mark the risk distribution can help drivers or autonomous driving systems perceive dangerous sections in advance and smoothly adjust driving strategies, thereby reducing the probability of sudden lane changes and sudden braking, and providing an operational navigation basis for reducing carbon emissions and improving traffic safety.

[0073] In a preferred embodiment of this invention, triggering the re-evaluation of the path risk level includes:

[0074] The edge computing server aggregates the number of non-motor vehicle trajectories continuously reported by the millimeter-wave radar in a five-second sliding window and writes it to the time series database. The average number of trajectories in the same window during the same period over the past fourteen days is retrieved. If the real-time number is greater than 1.5 times the average value and this condition is met for three consecutive windows, the trajectory density of the road section is determined to be abnormal.

[0075] Through the event interface of the traffic management cloud platform, the traffic light status of the intersection within 100 meters upstream of the target road section is subscribed to in real time. If the intersection is found to have a temporary red light that is always on or flashing red for more than 30 seconds, it is considered to be a sudden traffic control causing traffic congestion.

[0076] The target trajectories captured by millimeter-wave radar and high-definition cameras within the road section are spatially clustered. When no fewer than five non-motor vehicles are detected simultaneously within a 20-meter length with a cumulative lateral displacement exceeding one meter and crossing two lane lines continuously within three seconds, a group lane change is determined to have occurred.

[0077] When any of these conditions are triggered, the system immediately aggregates and recalculates the comprehensive violation intensity index based on the red light running, wrong-way lane changing, and other incident data from the past five minutes. It then updates the road segment risk level and issues a command containing the new level to the route recommendation engine. The vehicle terminal then pushes a route adjustment prompt.

[0078] It is understandable that by real-time monitoring of abnormal trajectory density and sudden changes in upstream flow direction, as well as capturing group lateral movement, the system can identify early signals of increased risk before a large number of violations have accumulated. The combination of sliding window statistics and multiple thresholds ensures the detection sensitivity to instantaneous fluctuations while suppressing short-term noise. The five-minute data recalculation limit avoids slow response caused by historical lags. Once the risk level is refreshed, the navigation system is driven to quickly detour or remind to slow down, reducing sudden braking, frequent starting and stopping, and sudden acceleration at the source, providing timely and reliable decision-making basis for smooth driving and precise carbon emission control goals.

[0079] In another preferred embodiment of the present invention, the real-time adjustment of the navigation path includes:

[0080] Subscribe to the latest risk level information of the road section within 200 meters ahead at a one-second cycle. When it is detected that the road section has increased from medium risk to high risk or directly increased from low risk to high risk, the dynamic path switching mechanism will be triggered immediately;

[0081] The navigation engine re-searches a set of candidate routes from the departure point to the destination, starting from the vehicle's current location. During the search, the map data is weighted in real time and all road sections marked as high-risk and unlikely to be downgraded within ten minutes are eliminated. The total length of the newly generated detour route is required to be no more than 1.15 to 1.3 times the length of the original route.

[0082] For each candidate path that meets the requirements, the risk weight calculation function is called to obtain the total risk value of the path. The path with the lowest total risk value is selected and a path change request is pushed through the vehicle terminal via voice and central control screen dual channels;

[0083] If the driver confirms the request by clicking or speaking within five seconds, the connected car will immediately update the navigation trajectory and continuously send a sequence of steering instructions to the vehicle control system. Each instruction is issued 50 meters from the intersection and includes the lane number, recommended steering angle, and advance speed change prompts.

[0084] If no response is received from the driver within five seconds, the vehicle network broadcasts a warning message to connected vehicles within a radius of 100 meters behind via dedicated short-range communication technology, triggering the following vehicles to automatically maintain a safe distance of at least 30 meters. At the same time, based on the sensor's prediction of the non-motor vehicle's movement trajectory in front for the next five seconds, a longitudinal speed control curve is generated and the acceleration change rate is limited to no more than 0.2 gravity acceleration to guide the vehicle through the high-risk road section in a smooth manner;

[0085] It should be noted that by immediately recalculating alternative routes and limiting detour lengths when risk levels suddenly increase, high-risk areas can be quickly avoided while ensuring acceptable travel distance and time costs. Sending clear route and control instructions to the driver in real time can reduce manual decision-making delays. Automatically issuing warnings and coordinating with following vehicles to maintain a safe distance and smooth speed curves when the driver fails to provide timely feedback ensures more stable longitudinal power output, reduces sudden lane changes and sudden braking, and overall helps maintain a uniform driving speed, reducing additional fuel consumption and greenhouse gas emissions caused by traffic shocks, further improving driving safety and carbon emission management.

[0086] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0087] The above is a detailed description of an embodiment of the present invention. However, the content is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A carbon emission management system for the transportation industry based on the Internet of Things, characterized by: include: Collection module: Divides the road into road segments and continuously collects violation data of non-motor vehicles on the road segments through IoT sensing devices. The violation data includes the number of red light running, wrong-way driving, and lane change; Aggregation module: dynamically aggregates the violation data of a road segment to generate a comprehensive violation intensity index for the road segment over a continuous time period; Classification module: based on the comprehensive violation intensity index and combined with the current traffic light phase data, divide the road section into different risk levels; Recommendation module: Based on real-time motor vehicle navigation requests, it recommends the route with the lowest total risk value. During the navigation process, it dynamically receives the real-time motion trajectory of non-motor vehicles uploaded by IoT sensing devices, triggering the reassessment of the route risk level and real-time adjustment of the navigation route. Generating a comprehensive violation intensity index for a road segment over a continuous time period includes: The violation data of a single road section is divided into multiple time windows according to the preset duration, and the number of red light running, lane changing and wrong-way driving in each time window is counted; The three types of violation data within the same time window are assigned different weight coefficients and weighted summed to obtain the initial intensity value. The weight of red light running is the highest, the weight of wrong-way driving is the second highest, and the weight of lane changing and weaving is the lowest. Calculating a moving average value based on the initial intensity values of the same time window of the road segment within the past preset number of days, and taking the ratio of the initial intensity value of the current time window to the moving average value as the comprehensive violation intensity index; The classification of road segments into different risk levels includes: Said risk levels include high risk, medium risk and low risk; When the number of time windows in which the comprehensive violation intensity index continuously exceeds the preset threshold reaches a preset first number, and the traffic light is at the end of the red light or the beginning of the green light at the current moment, the corresponding risk level is high risk; When the comprehensive violation intensity index exceeds the preset threshold within a single time window but does not meet the continuous condition and / or the traffic light is in the yellow light switching stage, the corresponding risk level is medium risk; When the comprehensive violation intensity index does not exceed the preset threshold and the signal light is in the middle of the green light, the corresponding risk level is low risk.

2. The carbon emission management system for the transportation industry based on the Internet of Things according to claim 1 is characterized in that: The data collected on violations of non-motor vehicles on road sections include: Multi-view monitoring equipment is installed at a preset location. Image recognition technology is used to capture the displacement distance and duration of non-motor vehicles crossing the stop line during red light periods. If the displacement distance exceeds the preset displacement distance threshold and continues before the green light comes on without returning, it will be recorded as running a red light. Millimeter-wave radar is installed on the central divider of the road to monitor the relative speed difference and lateral displacement changes between non-motor vehicles and motor vehicles in adjacent lanes in a single-direction traffic flow. If a non-motor vehicle is detected to cross two or more lane lines continuously within a preset length range, it will be recorded as a lane change; By using a pressure sensor array buried at the edge of the non-motorized vehicle lane, the continuous pressure signal sequence generated when the non-motorized vehicle travels along a path opposite to the prescribed driving direction is identified. The time interval and spatial distribution characteristics of the pressure signal are combined to determine whether the vehicle is traveling in the wrong direction.

3. The carbon emission management system for the transportation industry based on the Internet of Things according to claim 1 is characterized in that: The classification of road segments into different risk levels also includes: Establish a communication connection with the traffic signal controller through the roadside IoT gateway to read the current phase status and remaining duration of the traffic light in real time; When the traffic light is in the red light state, the accumulated time from the time the red light is on to the current time and the preset total red light time are obtained synchronously; When the traffic light is in the green light state, the cumulative time from the time the green light is turned on to the current time and the preset remaining time of the green light are obtained synchronously.

4. The carbon emission management system for the transportation industry based on the Internet of Things according to claim 1 is characterized in that: The recommended paths with the lowest total risk value include: Upon receiving a motor vehicle navigation request, extracting a set of all feasible paths from a departure point to a destination; Eliminate routes from the route set that contain high-risk road segments and have no alternative detour solutions; For the remaining paths, the total risk value of the path is calculated based on the risk level of each road segment, where the high-risk road segment is assigned the maximum risk coefficient, the medium-risk road segment is assigned the middle risk coefficient, and the low-risk road segment is assigned the minimum risk coefficient. After arranging the total risk values of the paths in ascending order, the path with the lowest total risk value is recommended, and the risk level distribution on the path is marked with different colors in the electronic map interface.

5. The carbon emission management system for the transportation industry based on the Internet of Things according to claim 4 is characterized in that: Triggering reassessment of the path risk level includes: The IoT sensing device detects that the density of non-motor vehicle real-time movement trajectories on a certain road section of the current recommended route exceeds the historical level for the same period; Or it is detected that a sudden traffic control occurs at the upstream intersection of the road segment, resulting in abnormal convergence of non-motor vehicle traffic; Or a group of non-motor vehicles is detected to be continuously changing lanes and weaving in and out of the road; When any trigger condition is met, the violation data within the most recently set time period is recalled for aggregate calculation, the comprehensive violation intensity index and risk level of the road section are updated, and new route recommendation instructions are generated based on the updated results.

6. The carbon emission management system for the transportation industry based on the Internet of Things according to claim 4 is characterized in that: Real-time adjustments to navigation paths include: When a vehicle is driving along a recommended route, if the IoT sensing device detects an increased risk level on the road ahead, it triggers a dynamic route switching mechanism. Recalculate a set of alternative routes based on the vehicle's current location. The set of alternative routes must meet the following conditions: exclude all newly promoted high-risk road segments, and the total length of the detour route does not exceed a set proportion of the original route length; Select a new path with the lowest total risk value from the set of alternative paths and push a path change request to the driver via the vehicle terminal; If the driver confirms the change, the navigation guidance trajectory is updated and a steering instruction sequence is sent to the vehicle control system via the Internet of Vehicles. The steering instruction sequence includes lane change prompts and steering angle recommendations required before each intersection. If the driver does not respond to the request, a warning signal will be broadcast to the vehicle behind through the Internet of Vehicles communication, triggering the vehicle behind to automatically maintain a safe distance. Based on the predicted motion trajectory of the non-motor vehicle in front, speed control instructions will be dynamically generated to guide the vehicle through high-risk road sections with a gentle acceleration and deceleration curve.

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