Intelligent path planning and jam avoiding system based on traffic data

Abstract

The invention belongs to the technical field of driving planning management, and particularly relates to an intelligent path planning and congestion avoidance system based on traffic data, which comprises an intelligent path planning module, a forward area monitoring and transmission module, a congestion detection and identification module, a congestion avoidance necessity decision module and a vehicle-mounted terminal, according to the invention, all feasible paths are analyzed through the intelligent path planning module to generate the path sorting table and determine the optimal path, so that the path planning can be automatically and reasonably performed and pushed to the user, the travel efficiency is improved, and the driving safety is ensured; the congestion detection and identification module is used for carrying out identification and early warning on a front congested road based on the traffic monitoring information, and after the front congested road is identified, the congestion avoidance necessity decision-making module is used for carrying out congestion avoidance necessity analysis to generate a congestion avoidance feasible signal or a congestion avoidance unnecessary signal, so that a user can conveniently carry out forward planning in advance; the driving safety of the user is further ensured, and the intelligent degree is high.

Description

Technical Field

[0001] The present invention relates to the technical field of driving planning and management, in particular to an intelligent path planning and congestion avoidance system based on traffic data. Background Art

[0002] Traffic congestion refers to the phenomenon that the total traffic volume on a road section or intersection is greater than the traffic capacity of the road due to the increase in traffic demand over a period of time, resulting in the inability of traffic flow on the road to flow smoothly. With the acceleration of urbanization, traffic congestion has become a severe challenge faced by major cities around the world.

[0003] At present, before users drive out, it is often difficult to automatically and reasonably plan a route based on the user's destination and push it to the user, which is not conducive to improving travel efficiency and ensuring driving safety. It is also impossible to identify the congested road ahead of the user and accurately determine the necessity of avoiding the congestion after identifying the congested road ahead, which is inconvenient for users to plan ahead in advance, and the degree of intelligence is low;

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent route planning and congestion avoidance system based on traffic data, which solves the problems that the prior art is difficult to automatically and reasonably plan a route based on the user's destination and push it to the user, and is unable to identify the congested road ahead of the user and accurately determine the necessity of avoiding congestion after identifying the congested road ahead, making it inconvenient for the user to plan ahead in advance and having a low level of intelligence.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] Intelligent path planning and congestion avoidance system based on traffic data, including intelligent path planning module, forward area monitoring and transmission module, congestion detection and recognition module, congestion avoidance necessity decision module and vehicle terminal;

[0008] The intelligent path planning module collects the user's destination and current location, obtains several feasible paths based on the user's destination and current location, analyzes all feasible paths to generate a path ranking table and determine the optimal path, and sends the path ranking table and the optimal path to the vehicle terminal;

[0009] The user follows the optimal route or selects the desired route from the route ranking table to go to the destination. During the forward process, the forward area monitoring and transmission module marks the area within the L1 distance ahead of the user as the monitoring area, collects the traffic monitoring information of the monitoring area and transmits it to the congestion detection and identification module;

[0010] The congestion detection and identification module identifies road congestion in the monitoring area based on traffic monitoring information, generates a congestion signal if there is road congestion in the monitoring area, and sends the congestion signal of the monitoring area to the congestion avoidance necessity decision module;

[0011] When the congestion avoidance necessity decision module receives a congestion signal, it generates a congestion avoidance non-essential signal or a congestion avoidance feasible signal through congestion avoidance necessity analysis, and sends the congestion avoidance non-essential signal or the congestion avoidance feasible signal to the vehicle terminal. When the congestion avoidance feasible signal is generated, the route that can avoid the congested road ahead is sent to the vehicle terminal.

[0012] Furthermore, the specific analysis process of the intelligent path planning module is as follows:

[0013] The path length of the corresponding feasible path is collected and marked as the path extension coefficient, the number of traffic lights in the corresponding feasible path is collected and marked as the forward stop coefficient, and the path length of the corresponding feasible path where the passage width does not exceed the preset passage width threshold is marked as the forward narrow detection coefficient, and the dangerous road surface coefficient is obtained through analysis;

[0014] And obtain a number of traffic monitoring points arranged in the corresponding feasible path, mark the average vehicle speed of the corresponding traffic monitoring point in unit time as the vehicle speed value, and calculate the average of the vehicle speed values ​​of all traffic monitoring points on the corresponding feasible path to obtain the traffic smoothness coefficient;

[0015] The path advantage negative influence coefficient is obtained by numerically calculating the path extension coefficient, forward stop coefficient, forward narrow inspection coefficient, dangerous road surface coefficient and smooth traffic coefficient. All feasible paths are sorted in order from small to large according to the values ​​of the path advantage negative influence coefficient, and the feasible path ranked first is marked as the optimal path.

[0016] Furthermore, the specific analysis process of the dangerous road surface coefficient is as follows:

[0017] Acquire all the curves, uphill and downhill sections existing in the corresponding feasible path, collect the turning radius of the corresponding curve section, and mark the curve section with a turning radius less than a preset turning radius threshold as a sharp turn; collect the uphill inclination angle of the corresponding uphill section, and mark the uphill section with an uphill inclination angle exceeding a preset uphill inclination angle threshold as a high-inclination uphill; and collect the downhill inclination angle of the corresponding downhill section, and mark the downhill section with a downhill inclination angle exceeding a preset downhill inclination angle threshold as a high-inclination downhill;

[0018] The lengths of all sharp turns, all high-inclined uphills and all high-inclined downhills are obtained, and the lengths of all sharp turns, all high-inclined uphills and all high-inclined downhills are summed up to calculate the dangerous road surface coefficient.

[0019] Furthermore, the specific analysis process of the necessity of avoiding congestion includes:

[0020] Obtaining other smooth routes that can avoid the congested road ahead, and if there are no other smooth routes that can avoid the congested road ahead, generating a non-essential signal for avoiding congestion;

[0021] If there are other smooth routes that can avoid the congested road ahead, the vehicle congestion length and the average vehicle moving speed in the area where the road congestion occurs are collected and marked as the congestion performance value and the vehicle moving speed value respectively, and the minimum increase in the forward path length after changing to other smooth routes is marked as the path increase value;

[0022] The congestion avoidance decision value is obtained by numerically calculating the congestion performance value, the vehicle speed value and the path increase value, and the congestion avoidance decision value is numerically compared with the preset congestion avoidance decision threshold. If the congestion avoidance decision value does not exceed the preset congestion avoidance decision threshold, a congestion avoidance non-essential signal is generated; if the congestion avoidance decision value exceeds the preset congestion avoidance decision threshold, a congestion avoidance feasible signal is generated.

[0023] Furthermore, the traffic jam avoidance necessity decision module is communicatively connected to the forward movement risk decision module, and the traffic jam avoidance necessity decision module sends a traffic jam avoidance non-essential signal to the forward movement risk decision module. When the forward movement risk decision module receives the traffic jam avoidance non-essential signal, it analyzes the user's current forward movement risk, generates a high-risk signal or a low-risk signal through the analysis, and sends the high-risk signal or the low-risk signal to the vehicle-mounted terminal.

[0024] Furthermore, the forward risk decision module is communicatively connected to the vehicle operation detection module and the user monitoring and analysis module. The vehicle operation detection module analyzes the vehicle operation risk, assigns the vehicle operation symbol LP-1 or LP-2 through the analysis, and sends the vehicle operation symbol LP-1 or LP-2 to the forward risk decision module.

[0025] The user monitoring and analysis module monitors the user, assigns the user monitoring symbol YP-1 or YP-2 through analysis, and sends the user monitoring symbol YP-1 or YP-2 to the forward risk decision module; when the forward risk decision module receives LP-2∩YP-2, it generates a low-risk signal, and in other cases it generates a high-risk signal.

[0026] Furthermore, the specific analysis process of the vehicle operation detection module is as follows:

[0027] The ambient wind speed and ambient visibility of the vehicle's environment are collected and marked as the ambient wind detection value and the visibility detection value respectively, and the distance of the vehicle from the congested road ahead is marked as the risk distance detection value, and the shaking amplitude of the vehicle is collected and marked as the vehicle shaking detection value;

[0028] The vehicle operation evaluation value is obtained by numerically calculating the environmental wind detection value, the visibility detection value, the risk distance detection value and the vehicle sway detection value, and a plurality of groups of preset vehicle operation evaluation value ranges are set in advance, and each group of preset vehicle operation evaluation value ranges corresponds to a group of preset vehicle speed upper limit thresholds; the vehicle operation evaluation value is numerically compared with all preset vehicle operation evaluation value ranges, and the preset vehicle operation evaluation value range containing the corresponding vehicle operation evaluation value is marked as a target range, and the preset vehicle speed upper limit threshold corresponding to the target range is marked as a vehicle speed judgment value;

[0029] The current running speed of the vehicle is collected, and the current running speed of the vehicle is compared with the speed judgment value. If the current running speed of the vehicle exceeds the speed judgment value, the vehicle running symbol LP-1 is assigned; if the current running speed of the vehicle does not exceed the speed judgment value, the vehicle running symbol LP-2 is assigned.

[0030] Furthermore, the specific analysis process of the user monitoring and analysis module is as follows:

[0031] The user's blinking frequency, heart rate data, body temperature data and breathing frequency are collected, and the deviation value of the blinking frequency compared to the preset blinking frequency standard value is marked as a blink coefficient, the deviation value of the heart rate data compared to the preset heart rate data standard value is marked as a heart rate coefficient, the deviation value of the body temperature data compared to the preset body temperature data standard value is marked as a body temperature coefficient, and the deviation value of the breathing frequency compared to the preset breathing frequency standard value is marked as a breathing coefficient;

[0032] The pressure applied by the user to the vehicle steering wheel is collected and marked as the steering wheel pressure value, and the operation impact ratio value is obtained through analysis. The operation impact ratio value, blink coefficient, heart rate coefficient, body temperature coefficient, breathing coefficient and steering wheel pressure value are numerically calculated to obtain the user monitoring coefficient, and the user monitoring coefficient is numerically compared with the preset user monitoring coefficient threshold. If the user monitoring coefficient exceeds the preset user monitoring coefficient threshold, the user monitoring symbol YP-1 is assigned; if the user monitoring coefficient does not exceed the preset user monitoring coefficient threshold, the user monitoring symbol YP-2 is assigned.

[0033] Furthermore, the analysis and acquisition method of the operation impact ratio value is as follows:

[0034] The temperature and humidity in the vehicle are collected, the temperature is calculated by difference with the median of the preset suitable driving temperature range and the absolute value is taken to obtain a driving temperature test value, the humidity is calculated by difference with the median of the preset suitable driving humidity range and the absolute value is taken to obtain a driving humidity test value, and the carbon dioxide concentration and oxygen concentration in the cab are calculated by ratio to obtain a driving air test value, and the dust particle concentration in the cab is marked as a driving dust test value;

[0035] A driving analysis value is obtained by numerically calculating a driving temperature check value, a driving humidity check value, a driving air check value and a driving dust check value, and a plurality of groups of preset driving analysis value ranges are set in advance, and each group of driving analysis value ranges corresponds to a group of preset driving influence coefficients; the driving analysis value is compared with all preset driving analysis value ranges one by one, and the preset driving influence coefficient corresponding to the preset driving analysis value range containing the corresponding driving analysis value is marked as an operation influence proportion value.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. In the present invention, the intelligent path planning module analyzes all feasible paths to generate a path ranking table and determine the optimal path. The path planning can be automatically and reasonably performed and pushed to the user, which is conducive to improving travel efficiency and ensuring driving safety. The congestion detection and recognition module identifies and warns the congested road ahead based on traffic monitoring information. After identifying the congested road ahead, the congestion avoidance necessity decision module performs a congestion avoidance necessity analysis to generate a congestion avoidance feasible signal or a congestion avoidance non-essential signal, which is convenient for users to plan ahead and further ensure the user's driving safety. The intelligent level is high;

[0038] 2. In the present invention, the traffic jam avoidance non-essential signal is sent to the forward risk decision module through the traffic jam avoidance necessity decision module. When the forward risk decision module receives the traffic jam avoidance non-essential signal, it analyzes the user's current forward risk. When a high-risk signal is generated, the vehicle-mounted terminal issues an early warning to remind the user to drive carefully and take corresponding countermeasures as needed, so as to further ensure the forward safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0040] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0041] Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Embodiment 1: Figure 1As shown, the intelligent path planning and congestion avoidance system based on traffic data proposed by the present invention includes an intelligent path planning module, a forward area monitoring and transmission module, a congestion detection and identification module, a congestion avoidance necessity decision module and a vehicle-mounted terminal;

[0044] The intelligent path planning module collects the user's destination and current location, obtains several feasible paths based on the user's destination and current location, analyzes all feasible paths to generate a path ranking table and determine the optimal path, and sends the path ranking table and the optimal path to the vehicle terminal. It can automatically and reasonably plan the path and push it to the user. It has a high degree of intelligence, which is conducive to improving travel efficiency and ensuring driving safety. The specific analysis process of the intelligent path planning module is as follows:

[0045] The path length of the corresponding feasible path is collected and marked as the path extension coefficient, the number of traffic lights in the corresponding feasible path is collected and marked as the forward stop coefficient, and the path length in which the passage width does not exceed the preset passage width threshold in the corresponding feasible path is marked as the forward narrow detection coefficient;

[0046] Acquire all the curves, uphill and downhill sections existing in the corresponding feasible path, collect the turning radius of the corresponding curve section, and mark the curve section with a turning radius less than a preset turning radius threshold as a sharp turn; collect the uphill inclination angle of the corresponding uphill section, and mark the uphill section with an uphill inclination angle exceeding a preset uphill inclination angle threshold as a high-inclination uphill; and collect the downhill inclination angle of the corresponding downhill section, and mark the downhill section with a downhill inclination angle exceeding a preset downhill inclination angle threshold as a high-inclination downhill;

[0047] The lengths of all sharp turns, all high-inclined uphills and all high-inclined downhills are obtained, and the lengths of all sharp turns, all high-inclined uphills and all high-inclined downhills are summed up to calculate a dangerous road surface coefficient;

[0048] And obtain a number of traffic monitoring points arranged in the corresponding feasible path, mark the average vehicle speed of the corresponding traffic monitoring point in unit time as the vehicle speed value, and calculate the average of the vehicle speed values ​​of all traffic monitoring points on the corresponding feasible path to obtain the traffic smoothness coefficient;

[0049] By formula The path extension coefficient YW, the forward stop coefficient YM, the forward narrow detection coefficient YS, the dangerous road surface coefficient YF and the smooth traffic coefficient YL are numerically calculated to obtain the path advantage negative influence coefficient YP; wherein, a1, a2, a3, a4, a5 are preset proportional coefficients, a5>a2>a4>a3>a1>0.45; and, the larger the value of the path advantage negative influence coefficient YP, the less suitable the corresponding feasible path is;

[0050] All feasible paths are sorted in order from small to large according to the value of the negative impact coefficient of path advantage, and the feasible path ranked first is marked as the optimal path.

[0051] The user follows the optimal route or selects the desired route from the route sorting table to go to the destination. During the forward process, the forward area monitoring and transmission module marks the area within the L1 distance ahead of the user as the monitoring area. Preferably, L1 is five kilometers, and collects the traffic monitoring information of the monitoring area and transmits it to the congestion detection and identification module;

[0052] The congestion detection and identification module identifies road congestion in the monitoring area based on traffic monitoring information. If there is road congestion in the monitoring area, a congestion signal is generated and the congestion signal of the monitoring area is sent to the congestion avoidance necessity decision module to achieve early warning of congested roads ahead, which is conducive to users changing routes in time to avoid congested roads ahead.

[0053] When the congestion avoidance necessity decision module receives a congestion signal, it generates a non-essential congestion avoidance signal or a feasible congestion avoidance signal through congestion avoidance necessity analysis, and sends the non-essential congestion avoidance signal or the feasible congestion avoidance signal to the vehicle terminal. When the feasible congestion avoidance signal is generated, the route that can avoid the congested road ahead is sent to the vehicle terminal, and when the non-essential congestion avoidance signal is generated, the user is reminded to slow down in advance to avoid safety accidents, so as to facilitate the user to plan ahead in advance and further ensure the user's driving safety. The specific analysis process of the congestion avoidance necessity analysis is as follows:

[0054] Obtaining other smooth routes that can avoid the congested road ahead. If there are no other smooth routes that can avoid the congested road ahead, indicating that the congested road ahead cannot be avoided and the user can only move forward along the established route, a non-essential signal for avoiding congestion is generated;

[0055] If there are other smooth routes that can avoid the congested road ahead, the vehicle congestion length and the average vehicle moving speed in the area where the road congestion occurs are collected and marked as the congestion performance value and the vehicle moving speed value respectively, and the minimum increase in the forward path length after changing to other smooth routes is marked as the path increase value;

[0056] By formula The congestion performance value LP, the vehicle speed value LS and the path increase value LZ are numerically calculated to obtain the congestion avoidance decision value LX; wherein rg1, rg2 and rg3 are preset proportional coefficients with values ​​greater than zero, and the smaller the value of the congestion avoidance decision value LX, the less necessary it is to avoid the congested road ahead;

[0057] The congestion avoidance decision value LX is numerically compared with the preset congestion avoidance decision threshold. If the congestion avoidance decision value LX does not exceed the preset congestion avoidance decision threshold, it indicates that there is no need to avoid the congested road ahead, and you can choose to continue along the current route, then a congestion avoidance non-essential signal is generated; if the congestion avoidance decision value LX exceeds the preset congestion avoidance decision threshold, it indicates that it is necessary to avoid the congested road ahead, then a congestion avoidance feasible signal is generated.

[0058] Embodiment 2: The difference between this embodiment and embodiment 1 is that the traffic jam avoidance necessity decision module is communicatively connected to the forward risk decision module, and the traffic jam avoidance necessity decision module sends a traffic jam avoidance non-essential signal to the forward risk decision module. When the forward risk decision module receives the traffic jam avoidance non-essential signal, it analyzes the user's current forward risk, generates a high-risk signal or a low-risk signal through the analysis, and sends the high-risk signal or the low-risk signal to the vehicle-mounted terminal. When the vehicle-mounted terminal receives the high-risk signal, it issues a warning to remind the user to drive carefully and take corresponding countermeasures as needed, thereby ensuring forward safety.

[0059] It should be noted that the forward risk decision module is connected to the vehicle operation detection module and the user monitoring and analysis module in communication. The vehicle operation detection module analyzes the vehicle operation risk, assigns the vehicle operation symbol LP-1 or LP-2 through analysis, and sends the vehicle operation symbol LP-1 or LP-2 to the forward risk decision module, providing information support for the analysis process of the forward risk decision module;

[0060] The user monitoring and analysis module monitors the user, assigns the user monitoring symbol YP-1 or YP-2 through analysis, and sends the user monitoring symbol YP-1 or YP-2 to the forward risk decision module to provide information support for the analysis process of the forward risk decision module; when the forward risk decision module receives LP-2∩YP-2, it generates a low-risk signal, and in other cases (LP-1∩YP-1, LP-1∩YP-2 or LP-2∩YP-1), it generates a high-risk signal.

[0061] Specifically, the specific analysis process of the vehicle operation detection module is as follows: the environmental wind speed and environmental visibility of the vehicle's environment are collected and marked as environmental wind detection value and visibility detection value respectively, and the distance between the vehicle and the congested road ahead is marked as risk distance detection value, and the shaking amplitude of the vehicle is collected and marked as vehicle shaking detection value;

[0062] By formula The environmental wind detection value CY, the visibility detection value CM, the risk distance detection value CS and the vehicle shaking detection value CP are numerically calculated to obtain the vehicle operation evaluation value CX; wherein sy1, sy2, sy3, sy4 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the vehicle operation evaluation value CX is, the greater the current operation risk of the vehicle is;

[0063] A plurality of preset vehicle operation evaluation value ranges are set in advance, and each preset vehicle operation evaluation value range corresponds to a set of preset vehicle speed upper limit thresholds; wherein, the larger the value of the preset vehicle operation evaluation value range is, the smaller the value of the preset vehicle speed upper limit threshold adapted thereto is;

[0064] Compare the vehicle operation evaluation value CX with all preset vehicle operation evaluation value ranges, mark the preset vehicle operation evaluation value range containing the corresponding vehicle operation evaluation value CX as the target range, and mark the preset vehicle speed upper limit threshold corresponding to the target range as the vehicle speed judgment value;

[0065] The current running speed of the vehicle is collected, and the current running speed of the vehicle is compared with the speed judgment value. If the current running speed of the vehicle exceeds the speed judgment value, it indicates that the current running speed of the vehicle is too fast, and the vehicle running symbol LP-1 is assigned; if the current running speed of the vehicle does not exceed the speed judgment value, it indicates that the current running speed of the vehicle is relatively safe, and the vehicle running symbol LP-2 is assigned.

[0066] Further, the specific analysis process of the user monitoring and analysis module is as follows: collecting the blinking frequency, heart rate data, body temperature data and breathing frequency of the user, marking the deviation value of the blinking frequency compared with the preset blinking frequency standard value as the blinking coefficient, marking the deviation value of the heart rate data compared with the preset heart rate data standard value as the heart rate coefficient, marking the deviation value of the body temperature data compared with the preset body temperature data standard value as the body temperature coefficient, and marking the deviation value of the breathing frequency compared with the preset breathing frequency standard value as the breathing coefficient, and collecting the pressure applied by the user to the vehicle steering wheel and marking it as the steering wheel pressure value;

[0067] The temperature and humidity in the vehicle are collected, the temperature is calculated by difference with the median of the preset suitable driving temperature range and the absolute value is taken to obtain a driving temperature test value, the humidity is calculated by difference with the median of the preset suitable driving humidity range and the absolute value is taken to obtain a driving humidity test value, and the carbon dioxide concentration and oxygen concentration in the cab are calculated by ratio to obtain a driving air test value, and the dust particle concentration in the cab is marked as a driving dust test value;

[0068] The driving temperature detection value WS, the driving humidity detection value WK, the driving air detection value WR and the driving dust detection value WN are numerically calculated by the formula WY=(hu1*WS+hu2*WK+hu3*WR+hu4*WN) / 4 to obtain the driving analysis value WY; wherein hu1, hu2, hu3 and hu4 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the driving analysis value WY is, the worse the driving environment is, and the less conducive it is to ensure the driving operation safety of the user;

[0069] A plurality of preset driving analysis value ranges are set in advance, and each group of driving analysis value ranges corresponds to a group of preset driving influence coefficients; it should be noted that the larger the value of the preset driving analysis value range is, the larger the value of the preset driving influence coefficient adapted thereto is;

[0070] The driving analysis value WY is compared with all preset driving analysis value ranges one by one, and the preset driving influence coefficient corresponding to the preset driving analysis value range containing the corresponding driving analysis value WY is marked as the operation influence ratio value β;

[0071] The user monitoring coefficient ZK is obtained by numerically calculating the operation influence ratio value β, the blink coefficient RY, the heart rate coefficient NP, the body temperature coefficient TN, the breathing coefficient HY and the steering wheel pressure value SF through the formula ZK=β*(eq1*RY+eq2*NP+eq3*TN+eq4*HY+eq5*SF); wherein eq1, eq2, eq3, eq4 and eq5 are preset ratio coefficients with values ​​greater than zero, and the larger the value of the user monitoring coefficient ZK is, the greater the driving operation risk of the user is in general;

[0072] The user monitoring coefficient ZK is numerically compared with the preset user monitoring coefficient threshold. If the user monitoring coefficient ZK exceeds the preset user monitoring coefficient threshold, indicating that the overall risk of the user's driving operation is relatively large, the user monitoring symbol YP-1 is assigned; if the user monitoring coefficient ZK does not exceed the preset user monitoring coefficient threshold, indicating that the overall risk of the user's driving operation is relatively small, the user monitoring symbol YP-2 is assigned.

[0073] The working principle of the present invention is as follows: when in use, the intelligent path planning module obtains several feasible paths based on the user's destination and current location, analyzes all feasible paths to generate a path ranking table and determines the optimal path, can automatically and reasonably plan the path and push it to the user, which is beneficial to improving travel efficiency and ensuring driving safety. In the process of the user moving forward, the forward area monitoring and transmission module collects the traffic monitoring information in front of the user and transmits it to the congestion detection and identification module. The congestion detection and identification module identifies and warns of the congested road ahead based on the traffic monitoring information. After identifying the congested road ahead, the necessity of avoiding congestion is analyzed through the necessity decision module for avoiding congestion. When a feasible signal for avoiding congestion is generated, the route that can avoid the congested road ahead is sent to the vehicle-mounted terminal. When a non-essential signal for avoiding congestion is generated, the user is reminded to slow down in advance, which is convenient for the user to plan ahead, further ensuring the user's driving safety, and has a high degree of intelligence.

[0074] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. Intelligent path planning and congestion avoidance system based on traffic data, characterized by: It includes intelligent path planning module, forward area monitoring and transmission module, congestion detection and identification module, congestion avoidance necessity decision module and vehicle terminal; The intelligent path planning module collects the user's destination and current location, obtains several feasible paths based on the user's destination and current location, analyzes all feasible paths to generate a path ranking table and determine the optimal path, and sends the path ranking table and the optimal path to the vehicle terminal; The user follows the optimal route or selects the desired route from the route ranking table to go to the destination. During the forward process, the forward area monitoring and transmission module marks the area within the L1 distance ahead of the user as the monitoring area, collects the traffic monitoring information of the monitoring area and transmits it to the congestion detection and identification module; The congestion detection and identification module identifies road congestion in the monitoring area based on traffic monitoring information, generates a congestion signal if there is road congestion in the monitoring area, and sends the congestion signal of the monitoring area to the congestion avoidance necessity decision module; When the congestion avoidance necessity decision module receives a congestion signal, it generates a congestion avoidance non-essential signal or a congestion avoidance feasible signal through congestion avoidance necessity analysis, and sends the congestion avoidance non-essential signal or the congestion avoidance feasible signal to the vehicle terminal. When the congestion avoidance feasible signal is generated, the route that can avoid the congested road ahead is sent to the vehicle terminal.

2. The intelligent path planning and congestion avoidance system based on traffic data according to claim 1, characterized in that: The specific analysis process of the intelligent path planning module is as follows: The path length of the corresponding feasible path is collected and marked as the path extension coefficient, the number of traffic lights in the corresponding feasible path is collected and marked as the forward stop coefficient, and the path length of the corresponding feasible path where the passage width does not exceed the preset passage width threshold is marked as the forward narrow detection coefficient, and the dangerous road surface coefficient is obtained through analysis; And obtain a number of traffic monitoring points arranged in the corresponding feasible path, mark the average vehicle speed of the corresponding traffic monitoring point in unit time as the vehicle speed value, and calculate the average of the vehicle speed values ​​of all traffic monitoring points on the corresponding feasible path to obtain the traffic smoothness coefficient; The path advantage negative influence coefficient is obtained by numerically calculating the path extension coefficient, forward stop coefficient, forward narrow inspection coefficient, dangerous road surface coefficient and smooth traffic coefficient. All feasible paths are sorted in order from small to large according to the values ​​of the path advantage negative influence coefficient, and the feasible path ranked first is marked as the optimal path.

3. The intelligent path planning and congestion avoidance system based on traffic data according to claim 2, characterized in that: The specific analysis process of the dangerous road surface coefficient is as follows: All the curves, uphill and downhill sections existing in the corresponding feasible path are obtained, and the curve sections with a turning radius less than a preset turning radius threshold are marked as sharp turns, the uphill sections with an uphill inclination angle exceeding a preset uphill inclination angle threshold are marked as high-inclination uphills, and the downhill sections with a downhill inclination angle exceeding a preset downhill inclination angle threshold are marked as high-inclination downhills; the lengths of all sharp turns, all high-inclination uphills and all high-inclination downhills are summed up to calculate the dangerous road surface coefficient.

4. The intelligent path planning and congestion avoidance system based on traffic data according to claim 1, characterized in that: The specific analysis process of the necessity of avoiding congestion includes: Obtaining other smooth routes that can avoid the congested road ahead, and if there are no other smooth routes that can avoid the congested road ahead, generating a non-essential signal for avoiding congestion; If there are other smooth routes that can avoid the congested road ahead, the vehicle congestion length and the average vehicle moving speed in the area where the road congestion occurs are collected and marked as the congestion performance value and the vehicle moving speed value respectively, and the minimum increase in the forward path length after changing to other smooth routes is marked as the path increase value; The congestion avoidance decision value is obtained by numerically calculating the congestion performance value, the vehicle speed value and the path increase value, and the congestion avoidance decision value is numerically compared with the preset congestion avoidance decision threshold. If the congestion avoidance decision value does not exceed the preset congestion avoidance decision threshold, a congestion avoidance non-essential signal is generated; if the congestion avoidance decision value exceeds the preset congestion avoidance decision threshold, a congestion avoidance feasible signal is generated.

5. The intelligent path planning and congestion avoidance system based on traffic data according to claim 1, characterized in that: The traffic jam avoidance necessity decision module is communicatively connected to the forward travel risk decision module. The traffic jam avoidance necessity decision module sends a traffic jam avoidance non-essential signal to the forward travel risk decision module. When the forward travel risk decision module receives the traffic jam avoidance non-essential signal, it analyzes the user's current forward travel risk, generates a high-risk signal or a low-risk signal through the analysis, and sends the high-risk signal or the low-risk signal to the vehicle-mounted terminal.

6. The intelligent path planning and congestion avoidance system based on traffic data according to claim 5, characterized in that: The forward risk decision module is communicatively connected to the vehicle operation detection module and the user monitoring and analysis module. The vehicle operation detection module analyzes the vehicle operation risk, assigns the vehicle operation symbol LP-1 or LP-2 through the analysis, and sends the vehicle operation symbol LP-1 or LP-2 to the forward risk decision module. The user monitoring and analysis module monitors the user, assigns the user monitoring symbol YP-1 or YP-2 through analysis, and sends the user monitoring symbol YP-1 or YP-2 to the forward risk decision module; when the forward risk decision module receives LP-2∩YP-2, it generates a low-risk signal, and in other cases it generates a high-risk signal.

7. The intelligent path planning and congestion avoidance system based on traffic data according to claim 6, characterized in that: The specific analysis process of the vehicle operation detection module is as follows: The ambient wind speed and ambient visibility of the vehicle's environment are collected and marked as the ambient wind detection value and the visibility detection value respectively, and the distance of the vehicle from the congested road ahead is marked as the risk distance detection value, and the shaking amplitude of the vehicle is collected and marked as the vehicle shaking detection value; The vehicle operation evaluation value is obtained by numerically calculating the environmental wind detection value, the visibility detection value, the risk distance detection value and the vehicle sway detection value, and a plurality of groups of preset vehicle operation evaluation value ranges are set in advance, and each group of preset vehicle operation evaluation value ranges corresponds to a group of preset vehicle speed upper limit thresholds; the preset vehicle operation evaluation value ranges containing the corresponding vehicle operation evaluation values ​​are marked as target ranges, and the preset vehicle speed upper limit thresholds corresponding to the target ranges are marked as vehicle speed judgment values; The current running speed of the vehicle is collected. If the current running speed of the vehicle exceeds the speed judgment value, the vehicle running symbol LP-1 is assigned; If the current running speed of the vehicle does not exceed the vehicle speed judgment value, the vehicle running symbol LP-2 is assigned.

8. The intelligent path planning and congestion avoidance system based on traffic data according to claim 6, characterized in that: The specific analysis process of the user monitoring and analysis module is as follows: The user's blinking frequency, heart rate data, body temperature data and breathing frequency are collected, and the deviation value of the blinking frequency compared to the preset blinking frequency standard value is marked as a blink coefficient, the deviation value of the heart rate data compared to the preset heart rate data standard value is marked as a heart rate coefficient, the deviation value of the body temperature data compared to the preset body temperature data standard value is marked as a body temperature coefficient, and the deviation value of the breathing frequency compared to the preset breathing frequency standard value is marked as a breathing coefficient; The pressure applied by the user to the vehicle steering wheel is collected and marked as the steering wheel pressure value, and the operation influence ratio value is obtained through analysis. The operation influence ratio value, the blink coefficient, the heart rate coefficient, the body temperature coefficient, the breathing coefficient and the steering wheel pressure value are numerically calculated to obtain the user monitoring coefficient. If the user monitoring coefficient exceeds the preset user monitoring coefficient threshold, the user monitoring symbol YP-1 is assigned; If the user monitoring coefficient does not exceed the preset user monitoring coefficient threshold, the user monitoring symbol YP-2 is assigned.

9. The intelligent path planning and congestion avoidance system based on traffic data according to claim 8, characterized in that: The analysis and acquisition method of the operation impact ratio value is as follows: The driving analysis value is obtained by numerically calculating the driving temperature test value, the driving humidity test value, the driving air test value and the driving dust test value, and a plurality of groups of preset driving analysis value ranges are set in advance, and each group of driving analysis value ranges corresponds to a group of preset driving influence coefficients; A preset driving influence coefficient corresponding to a preset driving analysis value range including the corresponding driving analysis value is marked as an operation influence proportion value.

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