An intelligent supervisory control management system and method based on big data

Abstract

The present invention relates to the field of vehicle supervision and management technology, specifically to an intelligent supervision control management system and method based on big data, the system comprising a calibration correlation deviation analysis module, the calibration correlation deviation analysis module compares the differences between vehicle supervision feature data acquired at different times, calculates the calibration requirements of the current vehicle information, and combines the vehicle supervision feature data corresponding to the current time to obtain the vehicle calibration correlation deviation. The present invention can also jointly control the route planning of the vehicle with the monitoring status of the vehicle itself when monitoring its own status through the vehicle-mounted terminal, and after generating a new route, even if the owner has not exited the system and re-planned the route, if the subsequent road condition information changes, the route selected by the user can be adaptively calibrated and changed, thereby enhancing the user's driving experience.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle supervision management, and in particular to an intelligent supervision control management system and method based on big data. Background Art

[0002] With the diversified development of sensor functions, the supervisory control and management scenarios achieved by sensors are becoming more and more extensive. In the field of vehicle applications, the use of sensors includes the supervision of the vehicle itself and the supervision of traffic roads. The supervision of the vehicle itself mainly includes the installation of sensors on different parts of the vehicle to monitor the running status of the vehicle body, while the supervision of traffic roads mainly involves the monitoring of the corresponding road conditions by the road traffic network:

[0003] Existing vehicle terminals often have route planning and navigation functions when monitoring their own status. However, existing vehicle route planning methods have defects. When generating a new route, the system often plans it based on the road condition information when the route is screened. After the route is generated, if the owner does not exit the system and re-plan the route, even if the subsequent road condition information changes, the route screened by the user will remain unchanged. This situation may result in a congested route when the user plans the route. During the journey, the congested section may be unblocked, and the planned congested section may subsequently result in new congestion points due to an increase in the number of users selecting it. At the same time, the route planning cannot be jointly controlled with the vehicle's own monitoring status, thereby affecting the user's driving experience. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent supervisory control management system and method based on big data to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent supervisory control management method based on big data, the method comprising the following steps:

[0006] S1. Obtain vehicle information in real time through on-board sensors, obtain traffic network road condition data in real time through the traffic network, and generate vehicle supervision feature data;

[0007] S2. Compare the differences between the vehicle supervision feature data obtained at different times, calculate the adjustment requirements of the current vehicle information, and combine the vehicle supervision feature data corresponding to the current time to obtain the vehicle adjustment correlation deviation;

[0008] S3. Based on the current position of the vehicle, while meeting the calibration requirements of the current vehicle information, plan an alternative route for the current vehicle; and calculate the change impact values ​​corresponding to different alternative routes in combination with the calibration correlation deviation of the vehicle;

[0009] S4. Filter the best change route for the current vehicle according to the calculation results of the change impact values ​​corresponding to different alternative routes; generate adaptive change feedback information of the route, feed back the obtained adaptive change feedback information of the route to the display end of the vehicle terminal in real time, interact with the user, and after the user interaction is confirmed, adjust the current vehicle information according to the adaptive change feedback information of the route.

[0010] Preferably, the vehicle information includes the vehicle navigation route being used, the vehicle location, and the component operating status collected by the monitoring sensors of various components preset in the vehicle;

[0011] The traffic network road condition data includes a set of road condition monitoring information of the vehicle navigation route being used and each road within a preset radius around it, wherein the road condition monitoring information includes the vehicle traffic frequency on the road surface;

[0012] The vehicle supervision feature data is a collection of vehicle information and traffic network road condition data corresponding to the same time point.

[0013] Preferably, when calculating the adjustment requirements of the current vehicle information, the vehicle supervision feature data corresponding to the vehicle to be tested at different time points are obtained, and the actual average speed of the vehicle in the initial vehicle route is recorded as the first evaluation proportion Vc, and the initial vehicle route is the first planned travel route of the corresponding vehicle from the starting point to the destination; the actual average speed of the vehicle from the starting point to the current vehicle position is recorded as the second evaluation proportion Vs;

[0014] Obtain the vehicle traffic frequency in the road condition monitoring information corresponding to each road in the current vehicle information when planning or adjusting the vehicle navigation route, and record it as the first communication state information ZT1 corresponding to the jth road in the corresponding route j , obtain the vehicle passing frequency in the road condition monitoring information corresponding to each road when the vehicle actually passes through each road in the vehicle navigation route in the current vehicle information, and record it as the second communication state information ZT2 corresponding to each road in the corresponding route j ;

[0015] When Vc / Vs is greater than or equal to the first preset value or ZT2 j / ZT1 j When the value is greater than or equal to the second preset value or the component operation status collected by the monitoring sensors of the preset components in the vehicle is abnormal, it is determined that the current vehicle information needs to be adjusted; otherwise, it is determined that the current vehicle information does not need to be adjusted;

[0016] In the process of obtaining the vehicle calibration correlation deviation in S2, the vehicle calibration correlation deviation is recorded as P.

[0017] P = g1·Vc / Vs+g2·max{ZT2 j / ZT1 j}.

[0018] Preferably, when planning the alternative route for the current vehicle, a set of road condition monitoring information of the vehicle navigation route in use and each road within a preset radius around it is obtained from the traffic network road condition data, and the end point in the vehicle navigation route in use and the current vehicle position are extracted; different planned routes are generated with the current vehicle position as the starting point and the end point in the vehicle navigation route in use unchanged, and any position point in the generated planned routes belongs to each planned route of the vehicle navigation route in use and each road within a preset radius around it as a passable alternative route for the current vehicle.

[0019] Preferably, in the process of calculating the change impact values ​​corresponding to different passable alternative routes, the vehicle calibration correlation deviations corresponding to the passable alternative routes at the current time are calculated, and the vehicle calibration correlation deviation corresponding to the i-th passable alternative route at the current time is recorded as GPi.

[0020] GPi=g1·Vc / Vs+g2·max{ZT4 h / ZT3 h},

[0021] Wherein, g1 represents the preset first deviation conversion coefficient; g2 represents the preset second deviation conversion coefficient; ZT4 h ZT3 represents the vehicle traffic frequency of the hth road belonging to the i-th alternative route at the current time in the road condition monitoring information corresponding to the current time; h Indicates the vehicle traffic frequency in the road condition monitoring information corresponding to the h-th road belonging to the i-th passable alternative route at the current time when the vehicle navigation route in the vehicle information before adjustment is planned or adjusted;

[0022] Get the change impact value corresponding to the ith alternative route, denoted as BGi,

[0023]

[0024] Wherein, g3 represents the preset change interference coefficient;

[0025] XZ i Indicates the risk ratio of the navigation software obtained by the external port at the current time based on the i-th alternative route at the current time; XZ i=Egi / Egmax, Egi represents the number of planned routes in the navigation software connected at the current time that overlap with the i-th passable alternative route at the current time; Egmax represents the maximum number of planned routes in the navigation software connected at the current time that overlap with each passable alternative route at the current time;

[0026] Count represents the number of times the vehicle's route is adjusted and changed based on the initial vehicle route. When the vehicle's route is the initial vehicle route, Count = 0;

[0027] DB i represents the route difference ratio between the i-th passable alternative route at the current time and the vehicle navigation route before adjustment at the current time;

[0028] The DB i =CHC i / CH i , CH i Indicates the length of the vehicle navigation route before the current time adjustment; CHC i represents the length of the section of the vehicle navigation route before the current time adjustment, excluding the section that overlaps with the i-th passable alternative route at the current time;

[0029] ZBY i represents the total change difference ratio of the passable route based on the i-th passable alternative route at the current time;

[0030] ZBY i =ZHL i / CSL i ,

[0031] CSL i Indicates the length of the road segment of the initial vehicle route; ZHL i It represents the length of the section in the initial vehicle route except the section that overlaps with the integrated section of the i-th passable alternative route at the current time; the i-th passable alternative route integrated section at the current time is the concatenation result of the actual passable route of the vehicle from the starting point of the initial vehicle route to the current vehicle position and the i-th passable alternative route at the current time.

[0032] Preferably, if the operating status of each component collected by the monitoring sensors of each component pre-installed in the vehicle has an abnormality, the optimal change route of the current vehicle is the alternative route that is closest to the nearest vehicle maintenance point on the passing road; if the operating status of each component collected by the monitoring sensors of each component pre-installed in the vehicle has no abnormality, the optimal change route of the current vehicle is the alternative route that has the smallest corresponding change impact value;

[0033] The adaptive change feedback information of the passable route includes the number of changes, the navigation route before the change, the navigation route after the change, the route difference ratio before and after the change of the passable route, and the total change difference ratio of the passable route.

[0034] An intelligent supervisory control management system based on big data, the system comprises the following modules:

[0035] A vehicle monitoring data acquisition module, which acquires vehicle information in real time through vehicle-mounted sensors, acquires traffic network road condition data in real time through the traffic network, and generates vehicle monitoring feature data;

[0036] A calibration correlation deviation analysis module, wherein the calibration correlation deviation analysis module compares the differences between the vehicle supervision feature data acquired at different times, calculates the calibration requirements of the current vehicle information, and obtains the vehicle calibration correlation deviation in combination with the vehicle supervision feature data corresponding to the current time;

[0037] A route change impact analysis module, which plans a traffic alternative route for the current vehicle based on the current position of the vehicle while satisfying the calibration requirements of the current vehicle information; and calculates the change impact values ​​corresponding to different traffic alternative routes in combination with the calibration correlation deviation of the vehicle;

[0038] The adaptive adjustment data management module selects the best change route for the current vehicle according to the calculation results of the change impact values ​​corresponding to different alternative routes, generates adaptive change feedback information of the route, feeds back the obtained adaptive change feedback information of the route to the display end of the vehicle terminal in real time, interacts with the user, and after the user interaction is confirmed, adjusts the current vehicle information according to the adaptive change feedback information of the route.

[0039] Preferably, the route change impact analysis module includes an alternative route planning unit and a route change impact analysis unit.

[0040] The alternative route planning unit plans an alternative route for the current vehicle according to the current position of the vehicle while satisfying the calibration requirements of the current vehicle information;

[0041] The route change impact analysis unit calculates the change impact values ​​corresponding to different alternative travel routes in combination with the vehicle's calibration correlation deviation.

[0042] Preferably, the adaptive adjustment data management module includes a route change screening unit, an information feedback interaction unit and an information adjustment change unit;

[0043] The route change screening unit screens the best route change for the current vehicle according to the calculation results of the change impact values ​​corresponding to different alternative routes;

[0044] The information feedback interaction unit generates route adaptive change feedback information based on the selected optimal change route of the current vehicle, feeds back the obtained route adaptive change feedback information to the display end of the vehicle terminal in real time, and performs feedback interaction with the user;

[0045] The information adjustment and change unit adjusts the current vehicle information according to the route adaptive change feedback information after the user interactively confirms.

[0046] Compared with the prior art, the beneficial effects achieved by the present invention are: the present invention can jointly control the route planning of the vehicle with the monitoring status of the vehicle itself while monitoring its own status through the on-board terminal, and after generating a new route, even if the owner has not exited the system and re-planned the route, if the subsequent road condition information changes, the route selected by the user can be adaptively adjusted and changed, thereby enhancing the user's driving experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0048] Figure 1 It is a schematic diagram of an intelligent supervisory control management system based on big data of the present invention;

[0049] Figure 2 It is a schematic diagram of an intelligent supervisory control management method based on big data of the present invention. DETAILED DESCRIPTION

[0050] 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.

[0051] The present invention provides a technical solution: Figure 1 As shown, an intelligent supervisory control management system based on big data includes the following modules:

[0052] A vehicle monitoring data acquisition module, which acquires vehicle information in real time through vehicle-mounted sensors, acquires traffic network road condition data in real time through the traffic network, and generates vehicle monitoring feature data;

[0053] A calibration correlation deviation analysis module, wherein the calibration correlation deviation analysis module compares the differences between the vehicle supervision feature data acquired at different times, calculates the calibration requirements of the current vehicle information, and obtains the vehicle calibration correlation deviation in combination with the vehicle supervision feature data corresponding to the current time;

[0054] A route change impact analysis module, wherein the route change impact analysis module includes an alternative route planning unit and a route change impact analysis unit,

[0055] The alternative route planning unit plans an alternative route for the current vehicle according to the current position of the vehicle while satisfying the calibration requirements of the current vehicle information;

[0056] The route change impact analysis unit calculates the change impact values ​​corresponding to different alternative travel routes in combination with the vehicle's calibration correlation deviation;

[0057] An adaptive adjustment data management module, the adaptive adjustment data management module includes a route change screening unit, an information feedback interaction unit and an information adjustment change unit;

[0058] The route change screening unit screens the best route change for the current vehicle according to the calculation results of the change impact values ​​corresponding to different alternative routes;

[0059] The information feedback interaction unit generates route adaptive change feedback information based on the selected optimal change route of the current vehicle, feeds back the obtained route adaptive change feedback information to the display end of the vehicle terminal in real time, and performs feedback interaction with the user;

[0060] The information adjustment and change unit adjusts the current vehicle information according to the route adaptive change feedback information after the user interactively confirms.

[0061] like Figure 2 As shown, an intelligent supervisory control management method based on big data comprises the following steps:

[0062] S1. Obtain vehicle information in real time through on-board sensors, obtain traffic network road condition data in real time through the traffic network, and generate vehicle supervision feature data;

[0063] The vehicle information includes the vehicle navigation route being used, the vehicle location, and the component operating status collected by the monitoring sensors of various components preset in the vehicle;

[0064] The traffic network road condition data includes a set of road condition monitoring information of the vehicle navigation route being used and each road within a preset radius around it, wherein the road condition monitoring information includes the vehicle traffic frequency on the road surface;

[0065] The vehicle supervision feature data is a collection of vehicle information and traffic network road condition data corresponding to the same time point.

[0066] S2. Compare the differences between the vehicle supervision feature data obtained at different times, calculate the adjustment requirements of the current vehicle information, and combine the vehicle supervision feature data corresponding to the current time to obtain the vehicle adjustment correlation deviation;

[0067] When calculating the adjustment requirements of the current vehicle information, the vehicle supervision feature data corresponding to the vehicle to be tested at different time points are obtained, and the actual average speed of the vehicle in the initial vehicle route is recorded as the first evaluation proportion Vc, and the initial vehicle route is the first planned travel route of the corresponding vehicle from the starting point to the destination; the actual average speed of the vehicle from the starting point to the current vehicle position is recorded as the second evaluation proportion Vs;

[0068] Obtain the vehicle traffic frequency in the road condition monitoring information corresponding to each road in the current vehicle information when planning or adjusting the vehicle navigation route, and record it as the first communication state information ZT1 corresponding to the jth road in the corresponding route j , obtain the vehicle passing frequency in the road condition monitoring information corresponding to each road when the vehicle actually passes through each road in the vehicle navigation route in the current vehicle information, and record it as the second communication state information ZT2 corresponding to each road in the corresponding route j ;

[0069] When Vc / Vs is greater than or equal to the first preset value or ZT2 j / ZT1 j When the value is greater than or equal to the second preset value or the component operation status collected by the monitoring sensors of the preset components in the vehicle is abnormal, it is determined that the current vehicle information needs to be adjusted; otherwise, it is determined that the current vehicle information does not need to be adjusted;

[0070] In the process of obtaining the vehicle calibration correlation deviation in S2, the vehicle calibration correlation deviation is recorded as P.

[0071] P = g1·Vc / Vs+g2·max{ZT2 j / ZT1 j}.

[0072] S3. Based on the current position of the vehicle, while meeting the calibration requirements of the current vehicle information, plan an alternative route for the current vehicle; and calculate the change impact values ​​corresponding to different alternative routes in combination with the calibration correlation deviation of the vehicle;

[0073] When planning the alternative route for the current vehicle, a set of road condition monitoring information of the vehicle navigation route in use and each road within a preset radius around it is obtained from the traffic network road condition data, and the end point in the vehicle navigation route in use and the current vehicle position are extracted; different planned routes are generated with the current vehicle position as the starting point and the end point in the vehicle navigation route in use unchanged, and any position point in the generated planned routes belongs to each planned route of the vehicle navigation route in use and each road within a preset radius around it as the alternative route for the current vehicle.

[0074] In the process of calculating the change impact values ​​corresponding to different alternative routes, the vehicle calibration correlation deviations corresponding to each alternative route at the current time are calculated, and the vehicle calibration correlation deviation corresponding to the i-th alternative route at the current time is recorded as GPi.

[0075] GPi=g1·Vc / Vs+g2·max{ZT4 h / ZT3 h},

[0076] Wherein, g1 represents the preset first deviation conversion coefficient; g2 represents the preset second deviation conversion coefficient; ZT4 h ZT3 represents the vehicle traffic frequency of the hth road belonging to the i-th alternative route at the current time in the road condition monitoring information corresponding to the current time; h Indicates the vehicle traffic frequency in the road condition monitoring information corresponding to the h-th road belonging to the i-th passable alternative route at the current time when the vehicle navigation route in the vehicle information before adjustment is planned or adjusted;

[0077] Get the change impact value corresponding to the ith alternative route, denoted as BGi,

[0078]

[0079] Wherein, g3 represents the preset change interference coefficient;

[0080] XZ i Indicates the risk ratio of the navigation software obtained by the external port at the current time based on the i-th alternative route at the current time; XZ i =Egi / Egmax, Egi represents the number of planned routes in the navigation software connected at the current time that overlap with the i-th passable alternative route at the current time; Egmax represents the maximum number of planned routes in the navigation software connected at the current time that overlap with each passable alternative route at the current time;

[0081] Count represents the number of times the vehicle's route is adjusted and changed based on the initial vehicle route. When the vehicle's route is the initial vehicle route, Count = 0;

[0082] DB i represents the route difference ratio between the i-th passable alternative route at the current time and the vehicle navigation route before adjustment at the current time;

[0083] The DB i =CHC i / CH i , CH i Indicates the length of the vehicle navigation route before the current time adjustment; CHC i represents the length of the section of the vehicle navigation route before the current time adjustment, excluding the section that overlaps with the i-th passable alternative route at the current time;

[0084] ZBY i represents the total change difference ratio of the passable route based on the i-th passable alternative route at the current time;

[0085] ZBY i =ZHL i / CSL i ,

[0086] CSL i Indicates the length of the road segment of the initial vehicle route; ZHL i It represents the length of the section in the initial vehicle route except the section that overlaps with the integrated section of the i-th passable alternative route at the current time; the i-th passable alternative route integrated section at the current time is the concatenation result of the actual passable route of the vehicle from the starting point of the initial vehicle route to the current vehicle position and the i-th passable alternative route at the current time.

[0087] S4. Filter the best change route for the current vehicle according to the calculation results of the change impact values ​​corresponding to different alternative routes; generate adaptive change feedback information of the route, feed back the obtained adaptive change feedback information of the route to the display end of the vehicle terminal in real time, interact with the user, and after the user interaction is confirmed, adjust the current vehicle information according to the adaptive change feedback information of the route.

[0088] If the operating status of each component detected by the monitoring sensors of each component pre-installed in the vehicle is abnormal, the best route change for the current vehicle is the alternative route that is closest to the nearest vehicle maintenance point on the passing road; if the operating status of each component detected by the monitoring sensors of each component pre-installed in the vehicle is normal, the best route change for the current vehicle is the alternative route that has the smallest corresponding change impact value;

[0089] The adaptive change feedback information of the passable route includes the number of changes, the navigation route before the change, the navigation route after the change, the route difference ratio before and after the change of the passable route, and the total change difference ratio of the passable route.

[0090] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0091] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent supervisory control management method based on big data, characterized in that: The method comprises the following steps: S1. Obtain vehicle information in real time through on-board sensors, obtain traffic network road condition data in real time through the traffic network, and generate vehicle supervision feature data; S2. Compare the differences between the vehicle supervision feature data obtained at different times, calculate the adjustment requirements of the current vehicle information, and combine the vehicle supervision feature data corresponding to the current time to obtain the vehicle adjustment correlation deviation; S3. Based on the current position of the vehicle, while meeting the calibration requirements of the current vehicle information, plan an alternative route for the current vehicle; and calculate the change impact values ​​corresponding to different alternative routes in combination with the calibration correlation deviation of the vehicle; S4. Filter the best route change for the current vehicle according to the calculation results of the change impact values ​​corresponding to different alternative routes; generate route adaptive change feedback information, feed back the obtained route adaptive change feedback information to the display end of the vehicle terminal in real time, interact with the user, and after the user interaction is confirmed, adjust the current vehicle information according to the route adaptive change feedback information; The vehicle information includes the vehicle navigation route being used, the vehicle location, and the component operating status collected by the monitoring sensors of various components preset in the vehicle; The traffic network road condition data includes a set of road condition monitoring information of the vehicle navigation route being used and each road within a preset radius around it, wherein the road condition monitoring information includes the vehicle traffic frequency on the road surface; The vehicle supervision feature data is a collection of vehicle information and traffic network road condition data corresponding to the same time point; When calculating the adjustment requirements of the current vehicle information, the vehicle supervision feature data corresponding to the vehicle to be tested at different time points are obtained, and the actual average speed of the vehicle in the initial vehicle route is recorded as the first evaluation proportion Vc, and the initial vehicle route is the first planned travel route of the corresponding vehicle from the starting point to the destination; the actual average speed of the vehicle from the starting point to the current vehicle position is recorded as the second evaluation proportion Vs; Obtain the vehicle traffic frequency in the road condition monitoring information corresponding to each road in the current vehicle information when planning or adjusting the vehicle navigation route, and record it as the first communication state information ZT1 corresponding to the jth road in the corresponding route j , obtain the vehicle passing frequency in the road condition monitoring information corresponding to each road when the vehicle actually passes through each road in the vehicle navigation route in the current vehicle information, and record it as the second communication state information ZT2 corresponding to each road in the corresponding route j ; When Vc / Vs is greater than or equal to the first preset value or ZT2 j / ZT1 j When the value is greater than or equal to the second preset value or the component operation status collected by the monitoring sensors of the preset components in the vehicle is abnormal, it is determined that the current vehicle information needs to be adjusted; otherwise, it is determined that the current vehicle information does not need to be adjusted; In the process of obtaining the vehicle calibration correlation deviation in S2, the vehicle calibration correlation deviation is recorded as P. P = g1·Vc / Vs+g2·max{ZT2 j / ZT1 j }; In the process of calculating the change impact values ​​corresponding to different alternative routes, the vehicle calibration correlation deviations corresponding to each alternative route at the current time are calculated, and the vehicle calibration correlation deviation corresponding to the i-th alternative route at the current time is recorded as GPi. GPi=g1·Vc / Vs+g2·max{ZT4 h / ZT3 h }, Wherein, g1 represents the preset first deviation conversion coefficient; g2 represents the preset second deviation conversion coefficient; ZT4 h ZT3 represents the vehicle traffic frequency of the hth road belonging to the i-th alternative route at the current time in the road condition monitoring information corresponding to the current time; h Indicates the vehicle traffic frequency in the road condition monitoring information corresponding to the h-th road belonging to the i-th passable alternative route at the current time when the vehicle navigation route in the vehicle information before adjustment is planned or adjusted; Get the change impact value corresponding to the ith alternative route, denoted as BGi, Wherein, g3 represents the preset change interference coefficient; XZ i Indicates the risk ratio of the navigation software obtained by the external port at the current time based on the i-th alternative route at the current time; XZ i =Egi / Egmax, Egi represents the number of planned routes in the navigation software connected at the current time that overlap with the i-th passable alternative route at the current time; Egmax represents the maximum number of planned routes in the navigation software connected at the current time that overlap with each passable alternative route at the current time; Count represents the number of times the vehicle's route is adjusted and changed based on the initial vehicle route. When the vehicle's route is the initial vehicle route, Count = 0; DB i represents the route difference ratio between the i-th passable alternative route at the current time and the vehicle navigation route before adjustment at the current time; The DB i =CHC i / CH i , CH i Indicates the length of the vehicle navigation route before the current time adjustment; CHC i represents the length of the section of the vehicle navigation route before the current time adjustment, excluding the section that overlaps with the i-th passable alternative route at the current time; ZBY i represents the total change difference ratio of the passable route based on the i-th passable alternative route at the current time; <h2 style=";text-align:left;direction:ltr">ZBY<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> =ZHL<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> / CSL<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> , CSL i Indicates the length of the road segment of the initial vehicle route; ZHL i It represents the length of the section in the initial vehicle route except the section that overlaps with the integrated section of the i-th passable alternative route at the current time; the i-th passable alternative route integrated section at the current time is the concatenation result of the actual passable route of the vehicle from the starting point of the initial vehicle route to the current vehicle position and the i-th passable alternative route at the current time.

2. According to the big data-based intelligent supervisory control management method of claim 1, it is characterized by: When planning the alternative route for the current vehicle, a set of road condition monitoring information of the vehicle navigation route in use and each road within a preset radius around it is obtained from the traffic network road condition data, and the end point in the vehicle navigation route in use and the current vehicle position are extracted; different planned routes are generated with the current vehicle position as the starting point and the end point in the vehicle navigation route in use unchanged, and any position point in the generated planned routes belongs to each planned route of the vehicle navigation route in use and each road within a preset radius around it as the alternative route for the current vehicle.

3. The intelligent supervisory control management method based on big data according to claim 1 is characterized in that: If the operating status of each component detected by the monitoring sensors of each component pre-installed in the vehicle is abnormal, the best route change for the current vehicle is the alternative route that is closest to the nearest vehicle maintenance point on the passing road; if the operating status of each component detected by the monitoring sensors of each component pre-installed in the vehicle is normal, the best route change for the current vehicle is the alternative route that has the smallest corresponding change impact value; The adaptive change feedback information of the passable route includes the number of changes, the navigation route before the change, the navigation route after the change, the route difference ratio before and after the change of the passable route, and the total change difference ratio of the passable route.

4. An intelligent supervisory control management system based on big data, applying an intelligent supervisory control management method based on big data as described in any one of claims 1 to 3, characterized in that: The system includes the following modules: A vehicle monitoring data acquisition module, which acquires vehicle information in real time through vehicle-mounted sensors, acquires traffic network road condition data in real time through the traffic network, and generates vehicle monitoring feature data; A calibration correlation deviation analysis module, wherein the calibration correlation deviation analysis module compares the differences between the vehicle supervision feature data acquired at different times, calculates the calibration requirements of the current vehicle information, and obtains the vehicle calibration correlation deviation in combination with the vehicle supervision feature data corresponding to the current time; A route change impact analysis module, wherein the route change impact analysis module plans an alternative route for the current vehicle based on the current position of the vehicle while satisfying the adjustment requirements of the current vehicle information; Combined with the vehicle's calibration correlation deviation, the change impact values ​​corresponding to different alternative routes are calculated respectively; An adaptive calibration data management module, wherein the adaptive calibration data management module selects the best change route for the current vehicle according to calculation results of change impact values ​​corresponding to different alternative routes; And generate the adaptive change feedback information of the route, feed back the obtained adaptive change feedback information of the route to the display end of the vehicle terminal in real time, interact with the user for feedback, and after the user interaction is confirmed, adjust the current vehicle information according to the adaptive change feedback information of the route.

5. The intelligent supervisory control management system based on big data according to claim 4 is characterized in that: The route change impact analysis module includes an alternative route planning unit and a route change impact analysis unit. The alternative route planning unit plans an alternative route for the current vehicle according to the current position of the vehicle while satisfying the calibration requirements of the current vehicle information; The route change impact analysis unit calculates the change impact values ​​corresponding to different alternative travel routes in combination with the vehicle's calibration correlation deviation.

6. The intelligent supervisory control management system based on big data according to claim 4 is characterized in that: The adaptive adjustment data management module includes a route change screening unit, an information feedback interaction unit and an information adjustment change unit; The route change screening unit screens the best route change for the current vehicle according to the calculation results of the change impact values ​​corresponding to different alternative routes; The information feedback interaction unit generates route adaptive change feedback information based on the selected optimal change route of the current vehicle, feeds back the obtained route adaptive change feedback information to the display end of the vehicle terminal in real time, and performs feedback interaction with the user; The information adjustment and change unit adjusts the current vehicle information according to the route adaptive change feedback information after the user interactively confirms.

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