A low-carbon intelligent city traffic guidance method, system and storage medium

Abstract

The present application relates to the technical field of low-carbon transportation construction, and in particular to a low-carbon intelligent urban traffic guidance method, system and storage medium, which include: obtaining static information of urban roads and equivalently generating a pipeline network model, and setting ideal load data based on the static information of urban roads; collecting dynamic information of urban roads including traffic flow and equivalently adding liquid flow to the pipeline network model and setting real-time flow data of liquid flow; setting time parameters so that the pipeline network model simulates flow tasks within the time range corresponding to the time parameters, and monitoring the dynamic changes of real-time flow data of each pipeline in the flow task; comparing the dynamic changes with the ideal load data, and generating a liquid flow guidance plan based on the comparison results; verifying the liquid flow guidance plan so that the liquid flow guidance plan is equivalent to a vehicle flow guidance plan. The present application has the effect of realizing a streamlined prediction of carbon emission impact scenarios of complex traffic flows and low-carbon guidance of traffic flows.

Description

Technical Field

[0001] The present application relates to the technical field of low-carbon transportation construction, and in particular to a low-carbon intelligent city traffic guidance method, system and storage medium. Background Art

[0002] At present, the construction of low-carbon cities is an important part of energy-saving and green development. Among them, vehicles are an important source of carbon emissions in cities. Good traffic guidance and unblocking on urban roads are important measures to reduce vehicle carbon emissions. Often, undesirable traffic conditions are accompanied by large traffic volume, low speed, high congestion, etc., which leads to higher carbon emissions from vehicles at low speeds and idling.

[0003] At present, for traffic guidance in low-carbon construction, it is generally through monitoring the traffic flow conditions, vehicle speed conditions and other road information of each section in the city, combining with the corresponding navigation map software algorithm to predict the future road conditions of the road, and adjusting the duration of traffic lights, recommending different driving routes, etc. to guide and divert traffic, so as to alleviate the congestion that is happening or predicted to happen on the road, and also enable subsequent vehicles on the road to be allocated and guided to better roads to reduce carbon emissions. However, the methods in related technologies require strict and real-time monitoring of traffic on several roads. As a random and unstable monitoring object, traffic flow will cause highly random changes in road conditions according to the real-time changes in road conditions, the technical level of the driver, driving habits and other random variables. This requires the system to spend a large amount of calculation to monitor and calculate the changes in road conditions of each road and give the correct guidance method in time when predicting and analyzing future road conditions. The calculation is large and relatively complex. Summary of the invention

[0004] In order to achieve streamlined prediction of carbon emission impact scenarios of complex traffic flows and low-carbon guidance of traffic flows, the present application provides a low-carbon smart city traffic guidance method, system and storage medium.

[0005] In the first aspect, the present application provides a low-carbon intelligent city traffic guidance method, which adopts the following technical solutions:

[0006] A low-carbon intelligent city traffic guidance method comprises the following steps:

[0007] Obtaining static information of urban roads and equivalently generating a pipeline network model, and setting ideal load data based on the static information of urban roads, wherein the pipeline network model is composed of a plurality of pipelines;

[0008] Collecting urban road dynamic information including traffic flow and adding liquid flow equivalently in the pipeline network model, and setting real-time flow data of the liquid flow based on vehicle condition data in the urban road dynamic information;

[0009] Setting time parameters so that the pipeline network model simulates the flow task within the time range corresponding to the time parameters, and monitoring the dynamic changes of the real-time flow data of each pipeline in the flow task;

[0010] Comparing the dynamic change with the ideal load data, and generating a liquid flow guiding scheme according to the comparison result;

[0011] The liquid flow guidance scheme is verified to make the liquid flow guidance scheme equivalent to the vehicle flow guidance scheme and then issued.

[0012] In some embodiments, obtaining urban road static information and equivalently generating a pipeline network model, and setting ideal load data based on the urban road static information, includes the following steps:

[0013] Equivalently generate a number of pipelines based on the number of roads in the urban road static information;

[0014] The length, direction and connectivity information of each road are used to generate the length, direction and connectivity information of a number of pipelines, and the traffic lights on the roads are equivalent to timed valves at corresponding positions of the pipelines;

[0015] The diameters of the pipelines are equivalently generated based on the number of lanes of the road, and the pressures corresponding to the pipelines are equivalently generated based on the speed limit information and traffic control information on the road;

[0016] Acquire the ideal smooth traffic flow information of each of the roads in the city static information, and add a standard liquid flow environment in each of the pipelines to monitor the standard flow information;

[0017] Based on the ideal smooth traffic flow information, the monitoring standard flow information is subjected to parameter equivalent transformation to obtain the ideal load data.

[0018] In some embodiments, collecting urban road dynamic information including traffic flow and equivalently adding liquid flow to the pipeline network model, and setting real-time flow data of the liquid flow based on vehicle condition data in the urban road dynamic information, comprises the following steps:

[0019] Obtaining the vehicle flow of the traffic flow on each of the roads to generate a corresponding number of liquid factors, wherein the liquid factors together constitute the liquid flow, and the liquid flow corresponds to an outlet flow, an inlet flow, and an intermediate flow;

[0020] acquiring the overall vehicle speed of the traffic flow to set the flow rate of the liquid flow;

[0021] acquiring a congestion state of the traffic flow to set the viscosity of the liquid flow;

[0022] Obtaining a vehicle type ratio of each of the vehicles in the traffic flow to set the density of the liquid flow, wherein the vehicle types include fuel vehicles and new energy vehicles;

[0023] The liquid flow with set parameters is placed in each of the corresponding pipelines to flow.

[0024] In some embodiments, setting a time parameter so that the pipeline network model simulates a flow task within a time range corresponding to the time parameter, and monitoring the dynamic changes of the real-time flow data of each pipeline in the flow task, includes the following steps:

[0025] Get the current time and generate corresponding time parameters based on the time period of the current time;

[0026] Based on the time parameter, the parameter of the liquid flow is weighted or deweighted, wherein the weighting is characterized by reducing the outlet flow rate and / or the inlet flow rate, increasing the intermediate flow rate, reducing the flow rate, and increasing the viscosity, and the deweighting is characterized by increasing the outlet flow rate and / or the inlet flow rate, reducing the intermediate flow rate, increasing the flow rate, and reducing the viscosity, wherein the weighting or deweighting is linear;

[0027] The flow of the liquid flow in the pipeline network model after weighting or weighting is simulated, and the real-time flow rate, real-time flow velocity, real-time viscosity, and real-time density corresponding to a number of monitoring points on each pipeline are monitored after the time range corresponding to the time parameter as the real-time flow data, and the difference between the real-time flow data and the parameters before the time range is used as the dynamic change.

[0028] In some embodiments, comparing the dynamic change with the ideal load data and generating a liquid flow guiding scheme according to the comparison result includes the following steps:

[0029] If there is a parameter in the dynamic change that exceeds the floating threshold of the ideal load data, it is considered that there is pipeline blockage;

[0030] The cause of the pipeline blockage is analyzed to generate items to be dredged and item scores, and the items to be dredged and the item scores are used as the liquid flow guiding plan.

[0031] In some embodiments, analyzing the cause of the pipeline blockage to generate items to be dredged and item scores, and using the items to be dredged and the item scores as the liquid flow guiding scheme includes the following steps:

[0032] If the item to be dredged is any one of large flow difference, low flow velocity, and high viscosity, then a corresponding first score is generated based on the volume and type of the item to be dredged;

[0033] If the item to be dredged is a plurality of items with large flow difference, low flow velocity and high viscosity, a corresponding second score is generated based on the volume size and type of the item to be dredged;

[0034] The corresponding liquid flow guiding scheme is selected based on the score of the first score or the second score, and the liquid flow guiding method includes increasing the pipeline pressure, increasing the difference in the flow rate of the timed valve and the difference in the opening and closing time between the pipeline inlet and outlet, increasing the flow rate and start time of the timed valve at the inlet of other pipelines connected to the congested pipeline, and restricting specific liquid factors from entering the pipeline.

[0035] In some embodiments, verifying the liquid flow guidance scheme so that the liquid flow guidance scheme is equivalent to a vehicle flow guidance scheme and issuing the scheme comprises the following steps:

[0036] Generate a simulation test task based on the liquid flow guiding scheme and send it to the pipeline network model, and monitor the test changes of the real-time flow data of each pipeline in the test task;

[0037] When the item score corresponding to the pipeline with pipeline congestion in the test task decreases and is lower than a preset value, it is considered that the test task is passed;

[0038] The liquid flow guidance scheme that passes the test task is equivalent to the vehicle flow guidance scheme, which includes increasing the intensity of traffic control, increasing the difference in vehicle entry and exit volume and the difference in signal light time between road entrances and exits, increasing the vehicle entry volume and signal light passing time at other road entrances connected to congested roads, and restricting specific types of vehicles from entering the road.

[0039] In some of the embodiments, after the traffic flow guidance plan is issued, the following steps are also included:

[0040] Calculating a first carbon emission corresponding to the target road after adjustment of the traffic flow guidance scheme;

[0041] Obtaining a second carbon emission corresponding to the target road before adjustment through the traffic flow guidance scheme;

[0042] The difference between the first carbon emission and the second carbon emission is calculated, and an adjustment optimization coefficient is generated based on the difference, and the liquid flow guiding scheme is optimized and adjusted based on the adjustment optimization coefficient.

[0043] In the second aspect, the present application provides a low-carbon intelligent urban traffic guidance system, which adopts the following technical solutions:

[0044] A low-carbon intelligent urban traffic guidance system, comprising:

[0045] An equivalent model generation module, used to obtain static information of urban roads and equivalently generate a pipeline network model, and set ideal load data based on the static information of urban roads, wherein the pipeline network model is composed of a plurality of pipelines;

[0046] A liquid flow generation module, used for collecting urban road dynamic information including traffic flow and adding liquid flow equivalently in the pipeline network model;

[0047] A flow data generation module, used for setting the real-time flow data of the liquid flow based on the vehicle condition data in the urban road dynamic information;

[0048] A flow simulation module, used to set time parameters so that the pipeline network model simulates the flow task within the time range corresponding to the time parameters, and monitor the dynamic changes of the real-time flow data of each pipeline in the flow task;

[0049] A liquid flow guiding scheme generating module, used for comparing the dynamic change with the ideal load data, and generating a liquid flow guiding scheme according to the comparison result;

[0050] The vehicle flow guidance scheme generation module is used to verify the liquid flow guidance scheme so that the liquid flow guidance scheme is equivalent to the vehicle flow guidance scheme and issue it. In some embodiments,

[0051] In a third aspect, the present application provides a computer storage medium, which adopts the following technical solution:

[0052] A computer storage medium stores at least one instruction, at least one program, a code set or an instruction set, wherein the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the method as described above.

[0053] The technical solution provided by the embodiments of the present application has the following technical effects:

[0054] The road network is converted into a pipeline network, and the dynamic traffic flow is equivalent to the dynamic liquid flow through the conversion of dynamic data, and the liquid flow is placed in the pipeline network for flow. In this way, a large number of individual vehicles can be integrated into liquid flow objects that are easy to observe as a whole, and the complex, random, and individually less directional traffic flow prediction is converted into a simple, clear, and highly directional liquid flow prediction. The smoothness of the liquid flow is monitored based on the real-time flow of the liquid flow in the pipeline and the dynamic changes of the flow. When a blockage occurs, a liquid flow guidance plan is generated based on the flow data, and the flow of the liquid flow is optimized and verified again through the liquid flow guidance plan. After verification, the liquid flow guidance plan is equivalently converted into a vehicle flow guidance plan and the guidance is issued, so as to realize an efficient and extremely low-computation traffic guidance method, and to optimize possible or existing traffic anomalies through traffic guidance to reduce carbon emissions and achieve low-carbon city construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the steps of a low-carbon smart city traffic guidance method provided in an embodiment of the present application.

[0056] Figure 2 This is a module connection diagram of a low-carbon smart city traffic guidance system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] To more clearly understand the purpose, technical solutions and advantages of the present application, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. However, it should be understood by those of ordinary skill in the art that the present application can be implemented without these details. In some cases, in order to avoid unnecessary descriptions that make various aspects of the present application obscure, well-known methods, processes, systems, components and / or circuits that have been described at a higher level will not be described in detail. For those of ordinary skill in the art, it is obvious that various changes can be made to the embodiments disclosed in the present application, and without departing from the principles and scope of the present application, the general principles defined in the present application can be applied to other embodiments and application scenarios. Therefore, the present application is not limited to the embodiments shown, but conforms to the broadest scope consistent with the scope claimed for protection of the present application.

[0058] It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in each embodiment of the present invention described below can be combined with each other as long as there is no conflict between them.

[0059] In the description of this application, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed", etc. are understood to exclude the number itself, and "above", "below", "within", etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used to distinguish the technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0060] In the description of the present application, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples.

[0061] The embodiment of the present application discloses a low-carbon intelligent city traffic guidance method.

[0062] like Figure 1 As shown, a low-carbon intelligent city traffic guidance method comprises the following steps:

[0063] S100, acquiring static information of urban roads and equivalently generating a pipeline network model, and setting ideal load data based on the static information of urban roads, the pipeline network model is composed of a plurality of pipelines.

[0064] Urban road static information is characterized by basic road information that does not include traffic flow, such as road length, width, location, connection relationship, etc.

[0065] With these data, a pipeline network model equivalent to the urban road network is established through modeling, digital twins, and other methods. The model is composed of multiple pipelines equivalent to roads.

[0066] At the same time, the ideal load data corresponding to the pipeline network model is set through the static information of urban roads. The data is represented by the liquid flow load pressure that the road network model based on the equivalent of the urban road network ideally bears based on the current basic data, such as the ideal flow velocity, ideal flow rate and other data corresponding to the ideal liquid flow.

[0067] On the one hand, the ideal load data is equivalent to various types of data that each road in the urban road network can bear under ideal traffic conditions.

[0068] S200, collecting urban road dynamic information including traffic flow and adding liquid flow equivalently in the pipeline network model, and setting real-time flow data of the liquid flow based on the vehicle condition data in the urban road dynamic information.

[0069] Secondly, data is collected on the urban road network. This time, dynamic information on each road is collected under the current environment. The dynamic information is represented by data information of each traffic flow on the road, such as current traffic volume, overall vehicle speed and other information.

[0070] First, the traffic flow is equivalently converted into the liquid flow that needs to flow in the pipeline. Secondly, the real-time flow data corresponding to the liquid flow in each pipeline is equivalently set through the specific urban road dynamic information, such as the flow rate at which the liquid flow needs to flow in the pipeline and other information.

[0071] S300, setting time parameters so that the pipeline network model simulates the flow task within the time range corresponding to the time parameters, and monitors the dynamic changes of the real-time flow data of each pipeline in the flow task.

[0072] The time parameter is characterized by the time increment corresponding to the length of time that needs to be predicted. When the traffic status and carbon emission status of the road network need to be predicted, the corresponding time parameters are generated based on the current time and the predicted duration, and the liquid flow in the pipeline network model starts to simulate the flow and flows within the time range corresponding to the time parameters to perform the simulated flow task.

[0073] In the flow simulation task, the real-time flow data in each pipeline network and the changes in the real-time flow data are collected and monitored to obtain dynamic changes.

[0074] S400, comparing the dynamic change with the ideal load data, and generating a liquid flow guiding plan according to the comparison result.

[0075] The dynamic changes of the data of the pipeline network model in the simulated flow task are compared with the ideal load data of the pipeline network model calculated in advance, and it is determined based on the comparison result whether the data after the dynamic changes do not meet the ideal load pressure corresponding to the ideal load data.

[0076] A corresponding liquid flow guidance scheme is generated based on the comparison results. The scheme is used to guide the liquid flow in the pipeline at the specific location where the abnormal blockage occurs in the pipeline network to adjust certain parameters to solve the blockage problem.

[0077] S500, verifying the liquid flow guidance scheme to make the liquid flow guidance scheme equivalent to the vehicle flow guidance scheme and issuing it.

[0078] After obtaining the liquid flow guidance plan, it is necessary to verify the liquid flow guidance plan first. Once the verification is passed, the liquid flow guidance plan is equivalently converted into the corresponding vehicle flow guidance plan and sent to the corresponding traffic pipeline network system end to realize the guidance of road traffic.

[0079] Through the above steps, the road network is converted into a pipeline network, the dynamic traffic flow is equivalently obtained into a dynamic liquid flow through the conversion of dynamic data, and the liquid flow is placed in the pipeline network for flow, so that a large number of individual vehicles can be integrated into liquid flow objects that are easy to observe as a whole, and the complex, random, and individually less directional traffic flow prediction is converted into a simple, clear, and highly directional liquid flow prediction. The smoothness of the liquid flow is monitored based on the real-time flow of the liquid flow in the pipeline and the dynamic changes of the flow, and a liquid flow guidance plan is generated based on the flow data when a blockage occurs. The flow of the liquid flow is optimized and verified again through the liquid flow guidance plan. After the verification, the liquid flow guidance plan is equivalently converted into a vehicle flow guidance plan and the guidance is issued, so as to realize an efficient and extremely low-computation traffic guidance method, and to optimize possible or existing traffic anomalies through traffic guidance to reduce carbon emissions and achieve low-carbon city construction.

[0080] In other embodiments, obtaining the static information of urban roads and equivalently generating a pipeline network model, and setting the ideal load data based on the static information of urban roads include the following steps:

[0081] S110, generating a plurality of pipelines based on the number of roads in the urban road static information.

[0082] A pipeline is generated for each road, wherein the roads in the embodiment of the present application are characterized as continuous roads without turns, wherein all straight roads without turns can be mapped to a pipeline, and roads with interruption signs such as intersections and traffic lights can also be mapped to a pipeline.

[0083] S120, the length, direction, and connectivity information of each road are used to generate the length, direction, and connectivity information of a number of pipelines, and the traffic lights on the roads are equivalent to timeable valves at corresponding positions of the pipelines.

[0084] Obtain the length of each road and use the corresponding scale to equivalently convert the length of each pipeline in the pipeline network model.

[0085] The direction corresponding to each pipeline is set by obtaining the direction between each road, where the direction includes a plane direction and a three-dimensional direction. The plane direction includes the driving direction of a single road and the horizontal and vertical directions between multiple roads. The three-dimensional direction includes the up and down directions between multiple roads, such as elevated roads and overpasses.

[0086] The connectivity information between multiple roads is obtained to set the connectivity information between corresponding pipelines. The connectivity information indicates that a vehicle enters another road by going straight, turning, going up, going down, etc., and also indicates that the liquid flow in a pipeline enters another pipeline by going straight, turning, going up, going down, etc.

[0087] At the same time, the traffic lights on the road are obtained and timed valves are added to the pipeline equivalently. The timing and adjustment logic of the valves are the same as those of the traffic lights. When the traffic light is red, the valve is closed and the liquid flow in the pipeline stops flowing. When the traffic light is green, the valve is opened and the liquid flow in the pipeline starts flowing.

[0088] S130, generating diameters of a plurality of pipelines based on the number of lanes of the road, and generating pressures corresponding to the plurality of pipelines based on speed limit information and traffic control information on the road.

[0089] The number of lanes on a road is equivalent to the diameter of the pipeline. The more lanes a road has, the larger the diameter of the corresponding pipeline. The more lanes a road has, the greater the vehicle throughput, that is, the greater the traffic volume it can bear, and the larger the diameter of the pipeline, the greater the flow rate of the liquid flow that can flow through it.

[0090] The speed limit information and traffic control information on the road are equivalent to the pressure of the pipeline. The higher the speed limit and the greater the traffic control amount, the faster the corresponding vehicle speed will be, the greater the corresponding pipeline pressure will be, and the higher the liquid flow rate in the pipeline will be.

[0091] S140, obtaining ideal smooth traffic flow information of each road in the city static information, and adding a standard liquid flow environment in each pipeline to monitor the standard flow information.

[0092] The ideal traffic flow information of the city's road network in a non-congested state with smooth vehicles is obtained, such as ideal traffic volume, ideal overall vehicle speed, ideal proportion of fuel-new energy vehicles, etc., to generate the ideal smooth traffic flow information of the city's road network, and based on the data, the corresponding standard liquid flow environment of the pipeline network is equivalently generated, which is characterized by the ideal flow information of the liquid flow obtained in the pipeline network under the standard data of the corresponding road, such as ideal flow rate, ideal flow velocity, etc.

[0093] S150, performing parameter equivalent conversion on the monitoring standard flow information based on the ideal smooth traffic flow information to obtain ideal load data.

[0094] By converting the vehicle flow data into corresponding liquid flow data through a preset equivalent conversion algorithm, the ideal load data of the pipeline network model can be obtained.

[0095] In other embodiments, collecting urban road dynamic information including traffic flow and adding liquid flow equivalently in the pipeline network model, and setting the real-time flow data of the liquid flow based on the vehicle condition data in the urban road dynamic information, includes the following steps:

[0096] S210, obtaining the current traffic volume of each road to generate a corresponding number of liquid factors, wherein the liquid factors together constitute a liquid flow.

[0097] First, the current traffic flow on each road is obtained, and each vehicle in the traffic flow is taken as an individual to obtain a liquid factor under standard data. A traffic flow can be converted into a liquid flow composed of multiple liquid factors.

[0098] The conversion process is specifically as follows: obtain the maximum instantaneous vehicle flow x of a road, obtain the maximum instantaneous liquid flow y of the pipeline corresponding to the road, and then obtain the conversion ratio x / y between the two, so that based on the conversion ratio, the specific parameters of a liquid factor corresponding to a vehicle can be obtained.

[0099] Among them, the liquid flow corresponds to the outlet flow, inlet flow and intermediate flow. The outlet flow is characterized by the liquid flow rate at the outlet of the pipeline, the inlet flow is characterized by the liquid flow rate at the inlet of the pipeline, and the intermediate flow is characterized by the liquid flow rate at the middle position of the pipeline.

[0100] S220: Obtain the overall vehicle speed of the traffic flow to set the flow rate of the liquid flow.

[0101] The flow rate of the liquid flow is set based on the overall speed of the vehicles on the road. Since the speed of vehicles at different locations on a long road is different, and the flow rate of the liquid in the pipeline is the same or nearly the same at different locations, the average speed of vehicles on a road can be obtained to set the flow rate in the pipeline. When the flow rate is equivalently converted, it is characterized as a vehicle speed that allows a vehicle to travel from one end of the road to the other end in time t, and the corresponding flow rate is characterized as the liquid flow flowing from one end of the pipeline to the other end in time t.

[0102] S230: Obtain the congestion status of the traffic flow to set the viscosity of the liquid flow.

[0103] Obtain the degree of traffic congestion on the road. The degree of congestion can be obtained through images or analyzed through the speed uploaded by each connected vehicle on the road. When the road is congested, the viscosity of the liquid flow in the corresponding pipeline is large. Conversely, when the road is smooth, the viscosity of the liquid flow in the pipeline is small.

[0104] Viscosity is a measure of the internal friction of a liquid. When a high-viscosity liquid flows, the internal friction is large and the resistance is also large, so the flow speed will slow down. On the contrary, a low-viscosity liquid has less flow resistance and flows faster.

[0105] S240: Obtain the vehicle type ratio of each vehicle in the traffic flow to set the density of the liquid flow.

[0106] Vehicle types include fuel vehicles and new energy vehicles.

[0107] The density of the liquid flow is obtained by judging the proportion of fuel vehicles and new energy vehicles in the traffic flow. The density of the liquid flow itself will not affect the flow rate and flow rate of the liquid flow in the pipeline. This is equivalent to the proportion of fuel vehicles and new energy vehicles not affecting the flow rate or flow rate corresponding to the traffic flow.

[0108] The density of the liquid flow is used in the embodiment of the present application to display the overall carbon emissions corresponding to the traffic flow in the liquid flow. The more fuel vehicles there are in the traffic flow, the greater the density of the liquid flow. Conversely, the more new energy vehicles there are, the smaller the density of the liquid flow. The carbon emissions of vehicles on the road can be intuitively detected through changes in the density of the liquid flow.

[0109] S250, placing the liquid flow with set parameters in the corresponding pipelines for flow.

[0110] After all the dynamic data of the traffic flow are collected and equivalently converted into liquid flow data, the set flow parameters are sent to the liquid flow, and the liquid flow is placed in the corresponding pipeline for flow.

[0111] In other embodiments, setting the time parameter so that the pipeline network model simulates the flow task within the time range corresponding to the time parameter and monitoring the dynamic changes of the real-time flow data of each pipeline in the flow task includes the following steps:

[0112] S310, obtaining the current time, and generating corresponding time parameters based on the time period of the current time.

[0113] The data size of the corresponding time parameter is different depending on the time period of the current time.

[0114] S320, weighting or subtracting the parameters of the liquid flow based on the time parameter.

[0115] The weighting is characterized by reducing the outlet flow and / or inlet flow, increasing the intermediate flow, reducing the flow rate, and increasing the viscosity, and the weighting is characterized by increasing the outlet flow and / or inlet flow, reducing the intermediate flow, increasing the flow rate, and reducing the viscosity, wherein the weighting or weighting is linear.

[0116] The time parameter is used to adjust the parameters of the liquid flow by weighting or reducing weights. If the current time is during rush hour or the current time on the roads leading to important scenic spots is during the peak travel time on weekends, the traffic pressure on the corresponding roads will continue to increase. As time goes by, the speed of traffic on these roads will continue to decrease, and the traffic volume will be high in the middle and low at both ends, with serious congestion. However, in some unpopular areas and remote areas at all times, or in some key roads during non-peak hours, the traffic pressure on their corresponding roads is relatively small. As time goes by, the speed of traffic on these roads will slowly increase, and the traffic volume will be high at both ends and low in the middle.

[0117] Then, the parameters of the liquid flow equivalently converted from the traffic flow are dynamically and linearly weighted or reduced by using the time parameter to simulate the flow state of the liquid flow in each pipeline during the period that needs to be monitored.

[0118] S330, simulates the flow of liquid flow in the pipeline network model after weighting or weight reduction, and monitors the real-time flow rate, real-time flow velocity, real-time viscosity, and real-time density corresponding to several monitoring points on each pipeline after the time range corresponding to the time parameter as real-time flow data, and uses the difference between the real-time flow data and the parameters before the time range as dynamic changes.

[0119] After calculating the liquid flow parameters in the pipeline network model by weighting or subtracting, the new flow parameters are used to simulate the flow of the liquid in the entire pipeline network model. The flow data changes and the values ​​after the changes corresponding to each detection point on the pipeline in the simulated flow task are monitored in real time, such as the current flow rate, the current flow velocity, the current viscosity, etc., and the current data is calculated by difference with the parameters before the simulation to obtain dynamic changes, such as the change of the liquid flow velocity in the pipeline after a period of simulation, the change of the liquid flow rate at different positions, etc.

[0120] In other embodiments, the dynamic change is compared with the ideal load data, and a liquid flow guiding scheme is generated according to the comparison result, including the following steps:

[0121] S410: If there is a parameter in the dynamic change that exceeds the floating threshold of the ideal load data, it is considered that there is pipeline blockage.

[0122] Each pipeline has a floating threshold under its corresponding ideal load data. When the floating threshold is exceeded, it means that the flow pressure in the pipeline is abnormal, and the abnormality is characterized by blockage of the pipeline.

[0123] When a pipeline is clogged, it manifests itself in the following ways: the liquid flow velocity in the pipeline is extremely low, the flow rate in the middle of the pipeline is much higher than the flow rate at the pipeline outlet, the flow rate at the pipeline inlet is much higher than the flow rate in the middle of the pipeline, and the viscosity of the liquid in the pipeline is extremely high.

[0124] When a pipeline is blocked, it indicates that the corresponding road has traffic abnormalities, such as extremely low vehicle speeds, much higher traffic flow in the middle of the road than at the road exit, much higher traffic flow entering the road than in the middle of the road, and abnormal and severe congestion in the pipeline.

[0125] S420, analyzing the cause of pipeline blockage to generate items to be dredged and item scores, and using the items to be dredged and item scores as a liquid flow guiding solution.

[0126] Based on various real-time flow data collected at the monitoring points of the pipeline, the cause of the blockage in the pipeline is analyzed, and the corresponding items to be dredged and the item scores are generated based on the causes.

[0127] The items to be dredged are characterized by specific reasons for data abnormalities that require guidance and resolution, such as low flow rate, high viscosity, large flow difference, etc.

[0128] The item score represents the severity of the abnormality of the pipeline. The larger the item score, the more serious the abnormality of the pipeline, the more severe the guidance method required, and the greater the difficulty of guidance; the smaller the item score, the less serious the abnormality of the pipeline, the more gentle the guidance method required, and the less difficult the guidance.

[0129] In other embodiments, analyzing the cause of pipeline blockage to generate items to be dredged and item scores, and using the items to be dredged and item scores as a liquid flow guidance plan includes the following steps:

[0130] S421: If the item to be dredged is any one of large flow difference, low flow velocity, and high viscosity, a corresponding first score is generated based on the volume size and type of the item to be dredged.

[0131] S422: If the item to be dredged is a plurality of items with large flow difference, low flow velocity, and high viscosity, a corresponding second score is generated based on the volume size and type of the item to be dredged.

[0132] First of all, the reason corresponding to the item to be cleared is a single item or a combination of multiple items. Different single items or different combinations of items, as well as the numerical size of each reason, all have an impact on the size of the item score.

[0133] The specific scores of different sizes are determined by selecting various guiding methods to guide the liquid flow. There are different optimal guiding methods for blockages caused by different reasons and different degrees of blockage.

[0134] It should be noted that the flow difference is characterized by the difference in flow rates at the inlet, outlet, and middle position in the pipeline.

[0135] S423: Select a corresponding liquid flow guiding scheme based on the score of the first score or the second score.

[0136] First, a guidance scheme table corresponding to different single or combined reasons and corresponding scores is set up, which is used to store the optimal guidance scheme corresponding to different items to be dredged and item scores. The above optimal guidance scheme is the preferred scheme obtained in advance through simulation testing, and the content of the preferred scheme can be updated according to the actual guidance situation when the system is constantly running.

[0137] Preferably, when setting the guidance plan table, the plan can also be refined based on the actual type or scenario of the road. For example, the same items to be dredged and item scores are used in different types of pipelines corresponding to urban roads, viaducts, tunnels, etc., and the corresponding optimal liquid flow guidance plans can also be independently refined accordingly. In order to implement this method, when the pipeline network model is equivalently generated through the static information of urban roads, it is also necessary to obtain the specific type of road to generate different types of pipelines. Different types of pipelines do not affect the dynamic flow process of the liquid flow, but are distinguished by different colors, surface patterns, etc., so as to subsequently match more refined liquid flow guidance plans of different types.

[0138] After calculating the first score or the second score, the optimal liquid flow guidance scheme is matched with the corresponding item to be dredged.

[0139] The liquid flow guiding method includes increasing the pipeline pressure, increasing the difference in the flow rate and the opening and closing time of the timed valve between the pipeline inlet and outlet, increasing the flow rate and start time of the timed valve at the inlet of other pipelines connected to the congested pipeline, and restricting specific liquid factors from entering the pipeline.

[0140] Specifically, if the flow difference between the pipeline outlet and the middle position of the pipeline is large (the outlet flow is small), appropriately increase the flow rate and opening time of the timeable valve at the pipeline outlet, temporarily close the timeable valve at the inlet, and pressurize the pipeline;

[0141] If the viscosity in the pipeline is high, limit the larger liquid factor to enter the pipeline (large vehicles, such as buses, transport vehicles, trucks), pressurize the pipeline, and appropriately increase the opening time of the timeable valve at the pipeline outlet.

[0142] In other embodiments, verifying the liquid flow guidance scheme so that the liquid flow guidance scheme is equivalent to the vehicle flow guidance scheme and issuing it includes the following steps:

[0143] S510, generating a simulation test task based on the liquid flow guidance scheme and sending it to the pipeline network model, and monitoring the test changes of the real-time flow data of each pipeline in the test task.

[0144] After the liquid flow guidance plan is obtained, a simulation test task is generated and sent to the pipeline network model. The pipeline network model adjusts the specific flow parameters of the liquid flow based on the changes in the corresponding liquid flow dynamic data in the simulation test task, and observes the changes in real-time flow data within a certain time range.

[0145] S520, when the item score corresponding to the pipeline with pipeline congestion in the test task decreases and is lower than the preset value, it is considered that the test task is passed.

[0146] When, after a certain period of time, the score of a pipeline that was originally congested in the test task is lower than the preset value, it means that the blockage of the pipeline has improved and improved to a state close to smooth flow, which means that the liquid flow guidance plan corresponding to the test task has passed the verification.

[0147] Among them, if the item score changes to 0, it is considered that the item to be dredged does not exist.

[0148] S530, equating the liquid flow guidance scheme that passes the test task to a vehicle flow guidance scheme.

[0149] The verified liquid flow guidance scheme is equivalently converted into a vehicle flow guidance scheme corresponding to the traffic flow. The equivalent conversion method is to convert the traffic flow data into the reverse operation of the liquid flow.

[0150] Specifically, increasing the pressure in the pipeline is equivalent to increasing the intensity of traffic control, increasing the difference in the flow rate of the timed valves and the difference in opening and closing time between the pipeline entrance and exit is equivalent to increasing the difference in the vehicle entry and exit volume and the signal light time difference between the road entrance and exit, increasing the flow rate and start time of the timed valves at the entrances of other pipelines connected to the congested pipeline is characterized by increasing the vehicle entry volume and signal light passing time at the entrances of other roads connected to the congested roads, and restricting specific liquid factors from entering the pipeline is equivalent to restricting specific types of vehicles from entering the road.

[0151] In this way, for example, when the difference between the traffic flow at the exit of the pipeline in the straight direction and the traffic flow in the middle of the road is large, the corresponding traffic flow guidance plan includes: appropriately increasing the green light duration of the signal light in the straight direction, changing the tidal lanes or variable lanes in other directions to the straight direction, guiding some vehicles to change their current straight driving behavior and recommending a new navigation map after passing the intersection in other directions, temporarily restricting vehicles from entering the congested road, and dispatching traffic police in the congested road.

[0152] In some other embodiments, after the traffic flow guidance plan is issued, the following steps are also included:

[0153] S600: Calculate a first carbon emission corresponding to the target road after adjustment of the traffic flow guidance plan.

[0154] S610: Obtain a second carbon emission corresponding to the target road before adjustment of the traffic guidance solution.

[0155] S620, calculating the difference between the first carbon emission and the second carbon emission, generating an adjustment optimization coefficient based on the difference, and optimizing and adjusting the liquid flow guiding scheme based on the adjustment optimization coefficient.

[0156] After the traffic guidance plan is issued, it is necessary to determine whether the carbon emissions are reduced after the guidance, so as to effectively determine whether the guidance plan is effective.

[0157] Therefore, the first carbon emission corresponding to the road adjusted by the traffic guidance plan is calculated first.

[0158] Secondly, obtain the second carbon emission calculated by the road detection without adjustment of the traffic guidance plan.

[0159] The difference in carbon emission changes before and after guidance is calculated, and an adjustment optimization coefficient is generated based on the difference. The larger the difference, the greater the improvement in carbon emissions, and the more effective the guidance scheme is. The lower the degree of adjustment optimization required, the smaller the adjustment optimization parameter is; conversely, the smaller the difference, the larger the adjustment optimization parameter is.

[0160] There is a linear inverse relationship between the difference and the adjustment optimization parameter.

[0161] The size of the adjustment optimization parameter represents the strength of adjustment of the guidance scheme in different scenarios. If the adjustment optimization parameter is greater than the preset value, it means that the overall comprehensive adjustment of the guidance method is required. If the adjustment optimization parameter is less than the preset value, it means that the part of the guidance scheme that needs to be adjusted is small or no adjustment is required.

[0162] like Figure 2 As shown, the present application also discloses a low-carbon intelligent urban traffic guidance system, including:

[0163] The equivalent model generation module is used to obtain the static information of urban roads and generate a pipeline network model by equivalent means, and set ideal load data based on the static information of urban roads. The pipeline network model is composed of a number of pipelines.

[0164] The liquid flow generation module is used to collect dynamic information of urban roads including traffic flow and add liquid flow equivalently in the pipeline network model.

[0165] The flow data generation module is used to set the real-time flow data of the liquid flow based on the vehicle condition data in the urban road dynamic information.

[0166] The flow simulation module is used to set the time parameters so that the pipeline network model simulates the flow task within the time range corresponding to the time parameters, and monitors the dynamic changes of the real-time flow data of each pipeline in the flow task.

[0167] The liquid flow guiding scheme generating module is used to compare the dynamic changes with the ideal load data and generate the liquid flow guiding scheme according to the comparison results.

[0168] The vehicle flow guidance scheme generation module is used to verify the liquid flow guidance scheme so that the liquid flow guidance scheme is equivalent to the vehicle flow guidance scheme and is issued.

[0169] The present application also discloses a computer storage medium storing at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the above method.

[0170] The implementation principle is:

[0171] The road network is converted into a pipeline network, and the dynamic traffic flow is equivalent to the dynamic liquid flow through the conversion of dynamic data, and the liquid flow is placed in the pipeline network for flow. In this way, a large number of individual vehicles can be integrated into liquid flow objects that are easy to observe as a whole, and the complex, random, and individually less directional traffic flow prediction is converted into a simple, clear, and highly directional liquid flow prediction. The smoothness of the liquid flow is monitored based on the real-time flow of the liquid flow in the pipeline and the dynamic changes of the flow. When a blockage occurs, a liquid flow guidance plan is generated based on the flow data, and the flow of the liquid flow is optimized and verified again through the liquid flow guidance plan. After verification, the liquid flow guidance plan is equivalently converted into a vehicle flow guidance plan and the guidance is issued, so as to realize an efficient and extremely low-computation traffic guidance method, and to optimize possible or existing traffic anomalies through traffic guidance to reduce carbon emissions and achieve low-carbon city construction.

[0172] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the instructions of the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise clearly stated in this document, the execution of these steps is not strictly limited in order and can be performed in other orders.

[0173] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A low-carbon intelligent city traffic guidance method, characterized in that: The following steps are involved: Obtaining urban road static information and equivalently generating a pipeline network model, and setting ideal load data based on the urban road static information, wherein the pipeline network model is composed of a number of pipelines, specifically, Equivalently generate a number of pipelines based on the number of roads in the urban road static information; The length, direction and connectivity information of each road are used to generate the length, direction and connectivity information of a number of pipelines, and the traffic lights on the roads are equivalent to timed valves at corresponding positions of the pipelines; The diameters of the pipelines are equivalently generated based on the number of lanes of the road, and the pressures corresponding to the pipelines are equivalently generated based on the speed limit information and traffic control information on the road; Acquire the ideal smooth traffic flow information of each of the roads in the static information of the urban roads, and add a standard liquid flow environment in each of the pipelines to monitor the standard flow information; Based on the ideal smooth traffic flow information, the monitoring standard flow information is subjected to parameter equivalent transformation to obtain the ideal load data; Collecting urban road dynamic information including traffic flow and adding liquid flow equivalently in the pipeline network model, and setting real-time flow data of the liquid flow based on vehicle condition data in the urban road dynamic information, specifically, Obtaining the vehicle flow of the traffic flow on each of the roads to generate a corresponding number of liquid factors, wherein the liquid factors together constitute the liquid flow, and the liquid flow corresponds to an outlet flow, an inlet flow, and an intermediate flow; acquiring the overall vehicle speed of the traffic flow to set the flow rate of the liquid flow; acquiring a congestion state of the traffic flow to set the viscosity of the liquid flow; Obtaining a vehicle type ratio of each vehicle in the traffic flow to set the density of the liquid flow, wherein the vehicle types include fuel vehicles and new energy vehicles; Placing the liquid flow with set parameters in the corresponding pipelines to flow; The time parameters are set so that the pipeline network model simulates the flow task within the time range corresponding to the time parameters, and monitors the dynamic changes of the real-time flow data of each pipeline in the flow task. Specifically, Get the current time and generate corresponding time parameters based on the time period of the current time; Based on the time parameter, the parameter of the liquid flow is weighted or deweighted, wherein the weighting is characterized by reducing the outlet flow rate and / or the inlet flow rate, increasing the intermediate flow rate, reducing the flow rate, and increasing the viscosity, and the deweighting is characterized by increasing the outlet flow rate and / or the inlet flow rate, reducing the intermediate flow rate, increasing the flow rate, and reducing the viscosity, wherein the weighting or deweighting is linear; Simulate the flow of the liquid flow in the pipeline network model after weighting or weight reduction, and monitor the real-time flow rate, real-time flow velocity, real-time viscosity, and real-time density corresponding to a number of monitoring points on each pipeline after the time range corresponding to the time parameter as the real-time flow data, and use the difference between the real-time flow data and the parameters before the time range as the dynamic change; Comparing the dynamic change with the ideal load data, and generating a liquid flow guiding scheme according to the comparison result; The liquid flow guidance scheme is verified to make the liquid flow guidance scheme equivalent to the vehicle flow guidance scheme and then issued.

2. The low-carbon intelligent city traffic guidance method according to claim 1 is characterized in that: The dynamic change is compared with the ideal load data, and a liquid flow guiding scheme is generated according to the comparison result, including the following steps: If there is a parameter in the dynamic change that exceeds the floating threshold of the ideal load data, it is considered that there is pipeline blockage; The cause of the pipeline blockage is analyzed to generate items to be dredged and item scores, and the items to be dredged and the item scores are used as the liquid flow guiding plan.

3. The low-carbon intelligent city traffic guidance method according to claim 2 is characterized in that: Analyzing the cause of the pipeline blockage to generate items to be dredged and item scores, and using the items to be dredged and the item scores as the liquid flow guiding scheme, includes the following steps: If the item to be dredged is any one of large flow difference, low flow velocity, and high viscosity, then a corresponding first score is generated based on the volume and type of the item to be dredged; If the item to be dredged is a plurality of items with large flow difference, low flow velocity and high viscosity, a corresponding second score is generated based on the volume size and type of the item to be dredged; The corresponding liquid flow guiding scheme is selected based on the score of the first score or the second score, and the liquid flow guiding method includes increasing the pipeline pressure, increasing the difference in the flow rate of the timed valve and the difference in the opening and closing time between the pipeline inlet and outlet, increasing the flow rate and start time of the timed valve at the inlet of other pipelines connected to the congested pipeline, and restricting specific liquid factors from entering the pipeline.

4. The low-carbon intelligent city traffic guidance method according to claim 3 is characterized in that: Verifying the liquid flow guidance scheme so that the liquid flow guidance scheme is equivalent to a vehicle flow guidance scheme and issuing the scheme comprises the following steps: Generate a simulation test task based on the liquid flow guiding scheme and send it to the pipeline network model, and monitor the test changes of the real-time flow data of each pipeline in the test task; When the item score corresponding to the pipeline with pipeline congestion in the test task decreases and is lower than a preset value, it is considered that the test task is passed; The liquid flow guidance scheme that passes the test task is equivalent to the vehicle flow guidance scheme, which includes increasing the intensity of traffic control, increasing the difference in vehicle entry and exit volume and the difference in signal light time between road entrances and exits, increasing the vehicle entry volume and signal light passing time at other road entrances connected to congested roads, and restricting specific types of vehicles from entering the road.

5. The low-carbon intelligent city traffic guidance method according to claim 1 is characterized in that: After the traffic flow guidance plan is issued, the following steps are also included: Calculating a first carbon emission corresponding to the target road after adjustment of the traffic flow guidance scheme; Obtaining a second carbon emission corresponding to the target road before adjustment through the traffic flow guidance scheme; The difference between the first carbon emission and the second carbon emission is calculated, and an adjustment optimization coefficient is generated based on the difference, and the liquid flow guiding scheme is optimized and adjusted based on the adjustment optimization coefficient.

6. A low-carbon intelligent urban traffic guidance system, characterized in that: include: The equivalent model generation module is used to obtain the static information of the urban road and generate the pipeline network model equivalently, and set the ideal load data based on the static information of the urban road. The pipeline network model is composed of a number of pipelines. Specifically, Equivalently generate a number of pipelines based on the number of roads in the urban road static information; The length, direction and connectivity information of each road are used to generate the length, direction and connectivity information of a number of pipelines, and the traffic lights on the roads are equivalent to timed valves at corresponding positions of the pipelines; The diameters of the pipelines are equivalently generated based on the number of lanes of the road, and the pressures corresponding to the pipelines are equivalently generated based on the speed limit information and traffic control information on the road; Acquire the ideal smooth traffic flow information of each of the roads in the static information of the urban roads, and add a standard liquid flow environment in each of the pipelines to monitor the standard flow information; Based on the ideal smooth traffic flow information, the monitoring standard flow information is subjected to parameter equivalent transformation to obtain the ideal load data; A liquid flow generation module is used to collect dynamic information of urban roads including traffic flow and to add liquid flow equivalently in the pipeline network model. Specifically, the vehicle flow of the traffic flow on each of the roads is obtained to generate a corresponding number of liquid factors, and the liquid factors together constitute the liquid flow. The liquid flow with set parameters is placed in the corresponding pipelines for flow, and the liquid flow corresponds to the outlet flow, the inlet flow and the intermediate flow; The flow data generation module is used to set the real-time flow data of the liquid flow based on the vehicle condition data in the urban road dynamic information. Specifically, acquiring the overall vehicle speed of the traffic flow to set the flow rate of the liquid flow; acquiring a congestion state of the traffic flow to set the viscosity of the liquid flow; Obtaining a vehicle type ratio of each vehicle in the traffic flow to set the density of the liquid flow, wherein the vehicle types include fuel vehicles and new energy vehicles; The flow simulation module is used to set the time parameters so that the pipeline network model simulates the flow task within the time range corresponding to the time parameters, and monitor the dynamic changes of the real-time flow data of each pipeline in the flow task. Specifically, Get the current time and generate corresponding time parameters based on the time period of the current time; Based on the time parameter, the parameter of the liquid flow is weighted or deweighted, wherein the weighting is characterized by reducing the outlet flow rate and / or the inlet flow rate, increasing the intermediate flow rate, reducing the flow rate, and increasing the viscosity, and the deweighting is characterized by increasing the outlet flow rate and / or the inlet flow rate, reducing the intermediate flow rate, increasing the flow rate, and reducing the viscosity, wherein the weighting or deweighting is linear; Simulate the flow of the liquid flow in the pipeline network model after weighting or weight reduction, and monitor the real-time flow rate, real-time flow velocity, real-time viscosity, and real-time density corresponding to a number of monitoring points on each pipeline after the time range corresponding to the time parameter as the real-time flow data, and use the difference between the real-time flow data and the parameters before the time range as the dynamic change; A liquid flow guiding scheme generating module, used for comparing the dynamic change with the ideal load data, and generating a liquid flow guiding scheme according to the comparison result; The vehicle flow guidance scheme generating module is used to verify the liquid flow guidance scheme so that the liquid flow guidance scheme is equivalent to the vehicle flow guidance scheme and issue it.

7. A computer storage medium, characterized in that: At least one instruction, at least one program, code set or instruction set is stored, and the at least one instruction, the at least one program, the code set or instruction set is loaded and executed by a processor to implement the method according to any one of claims 1 to 5.

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