A dynamic and static combined traffic control method and system

Abstract

The application provides a dynamic and static combined traffic control method and system, and the method comprises the following steps: constructing a road network according to the current position of each target object at the current moment; calibrating target objects in the road network at different moments, and drawing trajectories of the target objects in the target range to obtain the moving trajectories of the target objects in the target range, to establish the dynamic probability, the static probability and the position intersection probability of each position point in the target range, and divide the target range according to different probabilities, to obtain the dynamic intersection sub-range, the static intersection sub-range, the first independent sub-range and the second independent sub-range according to the division result; setting a control label to the corresponding sub-range according to the range attribute of each sub-range, and performing label similarity analysis on all control labels, and performing traffic control on the control range corresponding to the similar control labels. The traffic control of the road can be effectively realized, and the control efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of traffic control, in particular to a dynamic and static combined traffic control method and system. BACKGROUND

[0002] Traffic control refers to control measures for vehicle and personnel passage of part or all traffic sections for certain safety reasons. Generally, it is a restriction on traffic behavior in the case of meetings, parades, large-scale sports meetings, road and bridge construction, disaster relief, and important security tasks, and is mainly a temporary provision.

[0003] Generally, due to the unknown traffic situation in some areas, or simply according to the vehicle flow located by the GPS of the area or the situation that the sky eye monitors that there is a traffic accident in the area, the control mode is obtained by human judgment, and since different personnel have different traffic control capabilities, the control efficiency is undoubtedly reduced.

[0004] Therefore, the present application provides a dynamic and static combined traffic control method and system. SUMMARY

[0005] The present application provides a dynamic and static combined traffic control method and system, which realizes multiple divisions of the range by depicting the moving track of the target object and determining the dynamic probability, static probability and position intersection probability, and effectively realizes the traffic control of the road and improves the control efficiency by comparing the results and analyzing the label similarity.

[0006] The present application provides a dynamic and static combined traffic control method, comprising:

[0007] Step 1: capturing target objects on the road, and constructing a road network according to the current position of each target object at the present moment;

[0008] Step 2: target object calibration is performed on the road network at different times, and the same target object in the target range is tracked according to the calibration results, to obtain the moving track of the same target object in the target range;

[0009] Step 3: according to all the moving tracks, the dynamic probability, static probability and position intersection probability of each position point in the target range are established, the target range is divided according to the dynamic probability, the target range is divided according to the static probability, and the target range is divided according to the position intersection probability;

[0010] Step 4: obtaining dynamic intersection sub-ranges and static intersection sub-ranges according to the first division result and the third division result, and the second division result and the third division result;

[0011] Meanwhile, a first independent sub-range based on the first division result and a second independent sub-range based on the second division result are obtained;

[0012] Step 5: setting a control label to each sub-range according to a range attribute of each sub-range, and performing label similarity analysis on all control labels, calling a control method from a control database, and performing traffic control on a control range corresponding to similar control labels.

[0013] Preferably, the target objects include a person object and a vehicle object.

[0014] Preferably, a road network is constructed according to a current position of each target object at a current time, including:

[0015] A target range of the target network is planned, and a position coordinate is set to the target range.

[0016] Each current position is placed on a coordinate matched with the target range, and a road network is obtained.

[0017] Preferably, a moving track of a same target object in the target range is obtained by trajectory drawing of the same target object according to the calibration result, including:

[0018] A position point of the same target object at each time is obtained.

[0019] A thickness calibration is set to a corresponding position point according to a number of occurrences of the same position point.

[0020] A moving track of the same target object is constructed according to all calibration results, wherein the moving track includes a stay time at different track points and point coordinates of different track points.

[0021] Preferably, a dynamic probability, a static probability and a position intersection probability of each position point in the target range are established according to all moving tracks, including:

[0022] A statistical array of each position point in the target range is constructed according to the moving track, wherein the statistical array includes a non-occupied time point, an occupied time point and an occupied object of the occupied time point.

[0023] A static probability J0 of a corresponding position point is determined according to the non-occupied time point.

[0024] Meanwhile, according to the continuous occupation time point of the same occupation object to the same position point, and based on the object type-time-influence database, a dynamic influence factor J of the corresponding position point is determined;

[0025] According to the dynamic occupation time point of different occupation objects on the same position point, a first dynamic probability D1 of the corresponding position point is determined;

[0026] According to 1-J0, a second dynamic probability D2 is obtained;

[0027] According to the first dynamic probability D1 and the second dynamic probability D2, a final dynamic probability D0 is obtained;

[0028]

[0029] Wherein, max represents the maximum value symbol; max(D1, D2) represents obtaining the larger one of D1 and D2; a1 represents the weight based on the first dynamic probability D1; (1-a1) represents the weight based on the second dynamic probability D2; δ(J) represents the fine tuning function of the dynamic probability;

[0030] The number of occupied occupation objects of the same position point at the same occupation time point is determined, and a position intersection probability W is obtained:

[0031]

[0032] Wherein, n1 represents the number of occupied occupation objects of the same position point at the same occupation time point; n2 represents the maximum number of occupation objects of the corresponding position point at the same occupation time point.

[0033] Preferably, the target range is first divided according to the dynamic probability, comprising:

[0034] The dynamic probability of each position point in the target range is obtained, and a dynamic probability graph is constructed;

[0035] According to the dynamic probability level, each position point in the dynamic probability graph is color labeled to obtain the same labeled color distribution, and the first division of the target range is realized.

[0036] Preferably, according to the first division result and the third division result, and the second division result and the third division result, a dynamic intersection sub-range and a static intersection sub-range are obtained, comprising:

[0037] The intersection range of the same color of the first division result and the third division result is obtained, and the dynamic intersection sub-range is obtained, and the intersection range of the same color of the second division result and the third division result is obtained, and the static intersection sub-range is obtained;

[0038] The color label corresponding to the second division result is determined according to a static probability level.

[0039] The color label corresponding to the third division result is determined according to a position intersection probability level.

[0040] Preferably, the label similarity analysis is performed on all control labels, and the control mode is called from the control database, including:

[0041] The color array is set to the corresponding sub-range according to the control label of each sub-range, wherein the color array includes the index type of the control index contained in the corresponding control label, the display color matched with the index type, the index value matched with the index type and the identification size matched with the index value.

[0042] The range classification is performed on all sub-ranges based on the color similarity analysis of all color arrays.

[0043] The centralized color similarity analysis result in the same classification result is determined, and the first range corresponding to the centralized color and the second range below the centralized color are locked, and the third range above the centralized color is locked.

[0044] The first control mode matched with the first range is called from the control database, and the same control is performed on the first range and the second range.

[0045] The second control mode matched with the third range is called from the control database, and the same control is performed on the third range.

[0046] The application provides a dynamic and static combined traffic control system, comprising:

[0047] The network construction module is used for capturing target objects on the road and constructing the road network according to the current position of each target object at the current time.

[0048] The trajectory determination module is used for target object calibration on the road network at different times, and trajectory drawing of the same target object in the target range according to the calibration result, so as to obtain the moving trajectory of the same target object in the target range.

[0049] The range division module is used for establishing the dynamic probability, the static probability and the position intersection probability of each position point in the target range according to all moving trajectories, performing the first division on the target range according to the dynamic probability, performing the second division on the target range according to the static probability, and performing the third division on the target range according to the position intersection probability.

[0050] The result intersection module is configured to obtain a dynamic intersection sub-range and a static intersection sub-range according to the first division result and the third division result, and the second division result and the third division result;

[0051] Meanwhile, a first independent sub-range based on the first division result and a second independent sub-range based on the second division result are obtained;

[0052] The traffic control module is configured to set a control label to each sub-range according to the range attribute of each sub-range, and perform label similarity analysis on all control labels, so as to obtain a control mode from a control database and control the traffic in a range corresponding to the similar control label.

[0053] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and achieved by means of the instrumentalities and combinations particularly pointed out in the written description and claims hereof.

[0054] The technical solutions of the present application will be further described in detail below with reference to the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0055] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the embodiments of the present application, and are used to explain the present application, and do not constitute a limitation on the present application. In the drawings:

[0056] Figure 1 A flow chart of a dynamic and static combined traffic control method in an embodiment of the present application;

[0057] Figure 2 A structural diagram of a dynamic and static combined traffic control system in an embodiment of the present application. DETAILED DESCRIPTION

[0058] The preferred embodiments of the present application will be described below with reference to the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and do not limit the present application.

[0059] The present application provides a dynamic and static combined traffic control method, as shown in Figure 1 The method comprises the following steps:

[0060] Step 1: capturing target objects on the road, and constructing a road network according to the current position of each target object at the current time;

[0061] Step 2: target object calibration is performed on the road network at different time points, and the same target object in the target range is tracked according to the calibration results, so as to obtain the moving track of the same target object in the target range;

[0062] Step 3: according to all moving tracks, dynamic probability, static probability and position intersection probability of each position point in the target range are established, the target range is divided according to the dynamic probability, the target range is divided according to the static probability, and the target range is divided according to the position intersection probability;

[0063] Step 4: according to the first division result and the third division result, and the second division result and the third division result, dynamic intersection sub-range and static intersection sub-range are obtained;

[0064] At the same time, the first independent sub-range based on the first division result and the second independent sub-range based on the second division result are obtained;

[0065] Step 5: according to the range attribute of each sub-range, control label is set to the corresponding sub-range, and label similarity analysis is performed on all control labels, the control method is called from the control database, and traffic control is performed on the control range corresponding to the similar control label.

[0066] Preferably, the target object includes a person object and a vehicle object.

[0067] In this embodiment, the current position of the vehicle is obtained based on GPS positioning, and the current position of the person is obtained based on terminal positioning of the smart terminal carried by the person.

[0068] In this embodiment, the road network is obtained by counting all position points that can be positioned on the road.

[0069] In this embodiment, the track drawing refers to the combination of multiple parameters such as the position point of the object movement, the moving time at each position point, the time of reaching each position point and the time of leaving each position point.

[0070] In this embodiment, the dynamic probability is obtained according to the dynamic moving position point, the static probability refers to the time point when the position point is not occupied, and the dynamic probability, the static probability and the position intersection probability are calculated in real time during the calculation process, mainly for real-time adjustment of traffic control.

[0071] In the embodiment, the dynamic probability, the static probability and the position intersection probability each has a corresponding probability level division database, that is, the levels corresponding to different probabilities can be the same or different, at this time, the target range is divided by color labeling the probabilities of the same type and the same level.

[0072] In the embodiment, the greater the position intersection probability, the greater the object density of the corresponding position, that is, the greater the need for regulation, the greater the dynamic probability, the greater the flow of the corresponding position, that is, the greater the need for control, and the greater the static probability, the smaller the flow of the corresponding position, the less the need for control.

[0073] In the embodiment, after the range is divided in three ways, the range intersection is determined, which can effectively determine the sub-range with large flow and large density as the dynamic intersection sub-range.

[0074] The sub-range with small flow and small density can also be effectively determined as the static intersection sub-range.

[0075] In the embodiment, the target range corresponding to the first division result, except for the dynamic intersection sub-range, is the first independent sub-range, and the target range corresponding to the second division result, except for the static intersection sub-range, is the second independent sub-range, wherein the first independent sub-range and the second independent sub-range can or can not have an overlapping range.

[0076] In the embodiment, the range attribute is related to the probability type and the probability value of the probability involved in the corresponding range, and the control label is obtained based on the identifier that can represent the probability type and the probability value.

[0077] In the embodiment, the label similarity analysis refers to the similarity analysis between the color and the symbol size of the identifier.

[0078] In the embodiment, the control database contains the identifier corresponding to different similarity analysis results and the control method matched with the identifier, and the control method is related to congestion and flow release, lane change, etc.

[0079] In the embodiment, the control range contains several sub-ranges, and the control method corresponding to each sub-range is related to the label identifier, and finally the corresponding control methods can be the same or different, mainly for simultaneous traffic control of a large area to improve the control efficiency.

[0080] The beneficial effects of the above technical solution are: by depicting the moving track of the target object and determining the dynamic probability, the static probability and the position intersection probability, the range is divided multiple times, and by comparing the results and analyzing the label similarity, the traffic control of the road can be effectively realized, and the control efficiency is improved.

[0081] The application provides a dynamic and static combined traffic control method, which comprises the following steps of:

[0082] planning a target range of the target network, and setting a position coordinate to the target range;

[0083] placing each current position on a coordinate matched with the target range to obtain a road network.

[0084] In this embodiment, the target range refers to a range of 10 meters on the left and right of a position 1 on a road A, and the position coordinate is set to the target range based on geographic information.

[0085] In this embodiment, the current position refers to the position of the object positioned.

[0086] The beneficial effects of the above technical solution are that the position coordinate of the target range is set, and the positioned position is matched with the set position, so that the road network is obtained, and the traffic condition of the road is reasonably analyzed, and an analysis basis is provided for subsequent traffic control.

[0087] The application provides a dynamic and static combined traffic control method, which comprises the following steps of:

[0088] acquiring a position point of the same target object at each moment;

[0089] setting a thickness mark to the corresponding position point according to the number of times of appearance of the same position point;

[0090] constructing a moving track of the same target object according to all the mark results, wherein the moving track comprises a stay time at different track points and point coordinates of different track points.

[0091] In this embodiment, the number of times of appearance of the same position point refers to the stay time of the same target object at the position point, the longer the stay time, the thicker the corresponding mark, and the shorter the stay time, the thinner the corresponding mark.

[0092] In this embodiment, the moving track is mainly constructed according to the position coordinates, and the thickness of each position coordinate point may be different.

[0093] The beneficial effects of the above technical solution are that the thickness mark is set according to the number of times of appearance of the same position point, the drawing of the track is realized, and a basis is provided for the calculation of the subsequent dynamic probability, static probability and position intersection probability.

[0094] The application provides a traffic control method combining dynamic and static, which establishes dynamic probability, static probability and position intersection probability of each position point in the target range according to all moving trajectories, comprising:

[0095] According to the moving trajectory, a statistical array of each position point in the target range is constructed, wherein the statistical array comprises non-occupied time points, occupied time points and occupied objects of the occupied time points.

[0096] According to the non-occupied time points, the static probability J0 of the corresponding position point is determined.

[0097] Meanwhile, according to the continuous occupied time points of the same occupied object to the same position point, and based on the object type-time-impact database, the dynamic impact factor J of the corresponding position point is determined.

[0098] According to the dynamic occupied time points of different occupied objects on the same position point, the first dynamic probability D1 of the corresponding position point is determined.

[0099] According to 1-J0, the second dynamic probability D2 is obtained.

[0100] According to the first dynamic probability D1 and the second dynamic probability D2, the final dynamic probability D0 is obtained.

[0101]

[0102] Wherein, max represents the maximum value symbol; max(D1, D2) represents the larger one of D1 and D2; a1 represents the weight based on the first dynamic probability D1; (1-a1) represents the weight based on the second dynamic probability D2; δ(J) represents the fine tuning function of the dynamic probability.

[0103] The number of occupied objects of the same position point at the same occupied time point is determined, and the position intersection probability W is obtained:

[0104]

[0105] Wherein, n1 represents the number of occupied objects of the same position point at the same occupied time point; n2 represents the maximum number of occupied objects of the corresponding position point at the same occupied time point.

[0106] In this embodiment, the calculation of the static probability J0 is as follows:

[0107] J0=t / t0

[0108] Wherein, t represents the cumulative time sum of the non-occupied time points; t0 represents the cumulative time sum of the non-occupied time points and the occupied time points.

[0109] In this embodiment, for example, the traffic control is updated once every 10 seconds, at which time 10 seconds is the total time, if there are 3 seconds of non-occupancy time points at the same location, at this time, the static probability is: 3 / 10.

[0110] In this embodiment, if the continuous occupancy time point for position 1 is 2, and the corresponding object type is a vehicle in a driving state, then the dynamic influence factor is obtained from the object type-time-influence database, wherein the object type-time-influence database contains different object types, continuous occupancy times, and matched dynamic influence situations to ensure the accuracy of subsequent control, and the dynamic influence factor is mainly used to determine the influence of the object on the dynamic probability in the case of moving but moving slowly, and the value is generally [0.0.1].

[0111] In this embodiment, the first dynamic probability D1 is calculated as follows:

[0112] D1=t1 / t0, wherein t1 represents a dynamic occupancy time point, and the dynamic occupancy time point can not include a continuous occupancy time point.

[0113] In this embodiment, the second dynamic probability D2=1-J0.

[0114] In this embodiment, the value range of δ(J) is [0, 0.05], and the smaller the dynamic influence factor, the smaller the value of δ(J).

[0115] In this embodiment, the dynamic probability is recalculated by combining the two ways of obtaining the dynamic probability to ensure the uniqueness of the obtained dynamic probability.

[0116] In this embodiment, the value of a1 corresponding to the peak period is 0.5, and the value corresponding to the low peak period is 0.8.

[0117] In this embodiment, the number of occupied objects refers to the occupancy of the same location at the same time, for example, the occupancy of position 1 is 3, and the maximum occupancy of this position 1 is 5, at which time it can be preliminarily determined that there is a congestion situation, which is represented by the position intersection probability.

[0118] In this embodiment, the calibration of each position point in the moving trajectory is not the same, so the statistical array of each position point can be effectively determined to facilitate the acquisition of various probabilities subsequently.

[0119] The beneficial effects of the above technical solutions are: by constructing the statistical array of each position point, and by continuously, dynamically analyzing the occupation and non-occupation of the position points at the time points, the various probabilities existing can be effectively determined, an effective basis for subsequent range division is provided, and the efficiency of subsequent traffic control is ensured.

[0120] The application provides a dynamic and static combined traffic control method, which performs first division on the target range according to the dynamic probability, and includes the following steps.

[0121] The dynamic probability of each position point in the target range is obtained, and a dynamic probability graph is constructed.

[0122] According to the dynamic probability level, each position point in the dynamic probability graph is color-labeled to obtain the same type of labeled color distribution, and the first division of the target range is realized.

[0123] In this embodiment, the dynamic probability graph refers to the fact that each position point in the graph has a matching dynamic probability.

[0124] In this embodiment, the dynamic probability level is pre-set, for example, 0-0.3 is a level, 0.31-0.5 is a level, 0.51-0.7 is a level, and 0.71-1 is a level.

[0125] The beneficial effects of the above technical solutions are: by constructing the dynamic probability graph and color-labeling each position point in the graph according to the probability level, the first division of the range can be realized, a basis for the subsequent simultaneous control of multiple sub-ranges is provided, and the control efficiency is further improved.

[0126] The application provides a dynamic and static combined traffic control method, which obtains dynamic intersection sub-ranges and static intersection sub-ranges according to the first division result and the third division result, and the second division result and the third division result, and includes the following steps.

[0127] The intersection range with the same color of the first division result and the third division result is obtained, and the dynamic intersection sub-range is obtained, and the intersection range with the same color of the second division result and the third division result is obtained, and the static intersection sub-range is obtained.

[0128] The color label corresponding to the second division result is determined according to the static probability level.

[0129] The color label corresponding to the third division result is determined according to the position intersection probability level.

[0130] In this embodiment, the setting mode of the static probability level and the position intersection probability level is similar to the setting mode of the dynamic probability level, and will not be repeated here.

[0131] In this embodiment, however, the dynamic and static probability levels are opposite when color calibration is performed, for example, as the dynamic probability level increases, the corresponding colors are white, pink, blue, and black in turn, but as the static probability level increases, the corresponding colors are black, blue, pink, and white in turn, that is, the current occupancy of the same position point to be represented should be consistent.

[0132] The beneficial effects of the above technical solution are: by intersecting the colors according to different first, second and third division results, the dynamic intersection sub-range and the static intersection sub-range can be effectively determined, the area with large flow and high density and the area with small flow and low density can be effectively determined, and a basis is provided for subsequent management and control.

[0133] The application provides a traffic management and control method combining dynamic and static, which performs label similarity analysis on all management and control labels, and retrieves management and control modes from a management and control database, including:

[0134] A color array is set for the corresponding sub-range according to the management and control label of each sub-range, wherein the color array includes: an index type of a management and control index contained in the corresponding management and control label, a display color matched with the index type, an index value matched with the index type, and an identification size matched with the index value.

[0135] Color similarity analysis is performed based on all color arrays to classify all sub-ranges by range;

[0136] The concentrated color similarity analysis result in the same classification result is determined, and the first range corresponding to the concentrated color and the second range below the concentrated color are locked, and the third range above the concentrated color is also locked.

[0137] A first management and control mode matched with the first range is retrieved from the management and control database, and the first range and the second range are subjected to corresponding management and control;

[0138] A second management and control mode matched with the third range is retrieved from the management and control database, and the third range is subjected to corresponding management and control.

[0139] In this embodiment, the management and control index refers to a congestion index corresponding to dynamic and static, so as to determine the corresponding management and control index, the greater the role of the management and control index, the deeper the corresponding display color, and the greater the value of the corresponding index, the larger the corresponding identification display.

[0140] In this embodiment, the control indicators include indicators 1, 2 and 3, wherein indicator 1 belongs to type 1, indicator 2 belongs to type 2, and indicator 3 belongs to type 3, the display color of indicator 1 is light gray, the display color of indicator 2 is medium gray, and the display color of indicator 3 is dark gray, wherein the indicator value of indicator 1 is 1, the original identification size is kept unchanged, the indicator value of indicator 2 is 0.5, and the original identification is reduced to half of the original, and the indicator value of indicator 3 is 2, and the original identification is enlarged to half of the original.

[0141] By determining the color and the identification size, the similarity between the arrays can be effectively determined.

[0142] For example, sub-range 1: indicator 1: light gray, original identification size, indicator 2: non-existent, and indicator 3: non-existent.

[0143] Sub-range 2: indicator 1: light gray, original identification size, indicator 2: medium gray, original identification size, and indicator 3: non-existent.

[0144] Sub-range 3: indicator 1: non-existent, indicator 2: non-existent, and indicator 3: dark gray, original identification size.

[0145] After the similarity analysis, the ranges are classified: sub-range 1 and sub-range 2 are one category, and sub-range 3 is one category.

[0146] Among them, the color similarity analysis result is concentrated, for example, light gray is the concentrated color, the corresponding first range is sub-range 1, the second range does not exist, and the third range is sub-range 2.

[0147] In this embodiment, the control database includes the combined color and the combined identification size of various different indicators and the control method matched with the combined result, for example, indicator 1 is for congestion, the more congested, the larger the corresponding identification, and then measures such as dredging are required.

[0148] In this embodiment, the second range and the first range are controlled in the same way because the traffic of the second range is definitely better than that of the first range, and in the case of the first range being reasonable, the second range is also reasonable, so the same control method can be used.

[0149] In this embodiment, the traffic control difficulty of the third range is greater than that of the second range, so the third range needs to be controlled specifically, that is, the control method of the first range and the second range is consistent, and the control method of each third range is inconsistent.

[0150] The beneficial effects of the above technical solutions are: classifying the sub-ranges according to colors, facilitating effective control of the sub-ranges, and ensuring high-level control of traffic.

[0151] The application provides a traffic control system combining dynamic and static, which comprises Figure 2 As shown in the figure, comprising:

[0152] A network construction module is used for capturing target objects on the road and constructing a road network according to the current position of each target object at the current time;

[0153] A trajectory determination module is used for calibrating target objects on the road network at different times and drawing trajectories of the target objects in the target range according to the calibration results, so as to obtain the moving trajectories of the target objects in the target range;

[0154] A range division module is used for establishing the dynamic probability, static probability and position intersection probability of each position point in the target range according to all the moving trajectories, and performing first division on the target range according to the dynamic probability, second division on the target range according to the static probability and third division on the target range according to the position intersection probability;

[0155] A result intersection module is used for obtaining dynamic intersection subranges and static intersection subranges according to the first division results and the third division results and the second division results and the third division results;

[0156] Meanwhile, a first independent sub-range based on the first division results and a second independent sub-range based on the second division results are obtained;

[0157] A traffic control module is used for setting control labels to the corresponding sub-ranges according to the range attributes of each sub-range, and performing label similarity analysis on all the control labels, so as to call control methods from a control database and control the traffic in the control range corresponding to the similar control labels.

[0158] The beneficial effects of the above technical solution are that the moving trajectories of the target objects are drawn and the dynamic probability, static probability and position intersection probability are determined, so as to realize multiple divisions of the range, and through the result comparison and label similarity analysis, the traffic control of the road can be effectively realized and the control efficiency is improved.

[0159] Obviously, those skilled in the art can make various modifications and variations to the application without departing from the spirit and scope of the application. Thus, if these modifications and variations of the application belong to the scope of the claims of the application and the equivalent technology thereof, the application also intends to include these modifications and variations.

Claims

1. A traffic control method combining dynamic and static approaches, characterized in that, include: Step 1: Capture target objects on the road and construct a road network based on the current position of each target object at the current moment; Step 2: Identify the target objects in the road network at different times, and draw the trajectory of the same target object within the target range according to the identification results to obtain the movement trajectory of the same target object within the target range; Step 3: Based on all movement trajectories, establish the dynamic probability, static probability, and position intersection probability of each location point in the target range, and divide the target range into a first division according to the dynamic probability, a second division according to the static probability, and a third division according to the position intersection probability; Step 4: Based on the first and third partitioning results, as well as the second and third partitioning results, obtain the dynamic and static intersecting subranges; Simultaneously, a first independent sub-range based on the first partitioning result and a second independent sub-range based on the second partitioning result are obtained; Step 5: Based on the range attributes of each sub-range, set control labels for the corresponding sub-range, perform label similarity analysis on all control labels, retrieve control methods from the control database, and implement traffic control on the control ranges corresponding to similar control labels; Based on all movement trajectories, establish the dynamic probability, static probability, and location intersection probability of each location point within the target range, including: Based on the movement trajectory, a statistical array is constructed for each location point within the target range, wherein the statistical array includes: non-occupied time points, occupied time points, and the occupied objects of the occupied time points; Based on the non-occupied time points, determine the static probability of the corresponding location points. ; Simultaneously, based on the consecutive occupancy times of the same object at the same location, and using the object type-time-influence database, the dynamic influence factor of the corresponding location is determined. ; Based on the dynamic occupancy time points of different occupant objects at the same location point, determine the first dynamic probability D1 of the corresponding location point; According to 1- The second dynamic probability D2 is obtained. Based on the first dynamic probability D1 and the second dynamic probability D2, the final dynamic probability D0 is obtained; in, Indicates the sign of the maximum value; Indicates obtaining The larger of the two; Indicates based on the first dynamic probability The weights; This represents the weights based on the second dynamic probability D2; This represents a fine-tuning function for dynamic probabilities; Determine the number of occupied objects at the same location point at the same time point, and obtain the probability W of location intersection: Where n1 represents the number of occupied objects at the same location point at the same time point; n2 represents the maximum number of occupied objects at the corresponding location point at the same time point.

2. The traffic control method combining dynamic and static elements as described in claim 1, characterized in that, The target objects include: human objects and vehicle objects.

3. The traffic control method combining dynamic and static elements as described in claim 1, characterized in that, Based on the current location of each target object at the current moment, construct a road network, including: Plan the target area of ​​the road network and set location coordinates within the target area; Each current location is placed on coordinates that match the target range to obtain the road network.

4. The traffic control method combining dynamic and static elements as described in claim 1, characterized in that, Based on the calibration results, the trajectory of the same target object within the target range is drawn to obtain the movement trajectory of the same target object within the target range, including: Obtain the position of the target object at each time step; Based on the frequency of occurrence of the same location point, set the coarseness of the calibration for the corresponding location point; Based on all calibration results, the movement trajectory of the target object is constructed, wherein the movement trajectory includes the dwell time at different trajectory points and the coordinates of different trajectory points.

5. The traffic control method combining dynamic and static elements as described in claim 1, characterized in that, The target range is first divided according to the dynamic probability, including: Obtain the dynamic probability of each location point within the target range and construct a dynamic probability graph; According to the dynamic probability level, each location point in the dynamic probability map is color-marked to obtain the color distribution of the same type of marking, thereby achieving the first division of the target range.

6. The traffic control method combining dynamic and static elements as described in claim 1, characterized in that, Based on the first and third partitioning results, and the second and third partitioning results, the dynamic and static intersecting subranges are obtained, including: Obtain the intersection range of the same color between the first partitioning result and the third partitioning result, and obtain the dynamic intersection sub-range. At the same time, obtain the intersection range of the same color between the second partitioning result and the third partitioning result, and obtain the static intersection sub-range. The color label corresponding to the second division result is determined according to the static probability level; The color labels corresponding to the third partitioning result are determined according to the probability level of positional intersection.

7. The traffic control method combining dynamic and static elements as described in claim 1, characterized in that, Perform tag similarity analysis on all control tags and retrieve control methods from the control database, including: A color array is set for the corresponding sub-range based on the control label of each sub-range, wherein the color array includes: the indicator type of the control indicator contained in the corresponding control label and the display color matching the indicator type, the indicator value matching the indicator type and the identifier size matching the indicator value; Color similarity analysis is performed on all color arrays to classify all sub-ranges. Identify the clustered color similarity analysis results within the same classification result, and lock the first range corresponding to the clustered color and the second range below the clustered color. At the same time, lock the third range based on the clustered color. Retrieve the first control method that matches the first scope from the control database, and perform corresponding same control on the first scope and the second scope; The second control method matching the third scope is retrieved from the control database, and the third scope is subject to corresponding control.

8. A traffic control system combining dynamic and static methods, applied in the method described in any one of claims 1-7, characterized in that, include: The network construction module is used to capture target objects on the road and construct a road network based on the current position of each target object at the current moment. The trajectory determination module is used to identify target objects in the road network at different times, and to draw the trajectory of the same target object within the target range according to the identification results, so as to obtain the movement trajectory of the same target object within the target range. The range division module is used to establish the dynamic probability, static probability, and position intersection probability of each location point in the target range based on all movement trajectories, and to perform a first division of the target range according to the dynamic probability, a second division of the target range according to the static probability, and a third division of the target range according to the position intersection probability. The result intersection module is used to obtain the dynamic intersection subrange and the static intersection subrange based on the first and third division results, as well as the second and third division results. Simultaneously, a first independent sub-range based on the first partitioning result and a second independent sub-range based on the second partitioning result are obtained; The traffic control module is used to set control labels for the corresponding sub-ranges based on the range attributes of each sub-range, perform label similarity analysis on all control labels, retrieve control methods from the control database, and perform traffic control on the control ranges corresponding to similar control labels.

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