CN112863177A - Navigation duration prediction method based on data analysis - Google Patents
Navigation duration prediction method based on data analysis Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
Abstract
The invention discloses a navigation duration prediction method based on data analysis, which relates to the technical field of navigation duration prediction and solves the technical problem that the influence of a vehicle owner on the navigation duration cannot be judged in the prior art, so that the accuracy performance of prediction is reduced, a vehicle owner analysis unit is used for analyzing the vehicle owner data, so that a current driver is detected, the vehicle owner data is obtained, a vehicle owner analysis coefficient Xo is obtained through a formula, and the vehicle owner analysis coefficient Xo is compared with a vehicle owner analysis coefficient threshold value: if the vehicle owner analysis coefficient Xo is larger than or equal to the vehicle owner analysis coefficient threshold value, judging that the vehicle owner has influence, generating a vehicle owner influence signal and sending the vehicle owner influence signal and the corresponding vehicle owner analysis coefficient to the data management platform; the influence of the car owner on the navigation time is judged, the accuracy of prediction is further improved, the working efficiency is improved, and the use quality of user navigation is improved.
Description
Technical Field
The invention relates to the technical field of navigation duration prediction, in particular to a navigation duration prediction method based on data analysis.
Background
Navigation is a method of guiding a device to move from one point of a given course to another. Navigation is divided into two categories: (1) autonomous navigation, namely, navigation by equipment on an aircraft or a ship, such as inertial navigation, Doppler navigation, astronomical navigation and the like; (2) the non-autonomous navigation is used for the cooperation navigation of traffic equipment such as aircrafts, ships, automobiles and the like and related ground or air equipment, and comprises radio navigation and satellite navigation. In military affairs, the system is also required to be matched with the system to complete the tasks of weapon projection, reconnaissance, patrol, anti-submergence, rescue and the like.
However, in the prior art, the influence of the vehicle owner on the navigation time cannot be judged during the driving process of the driver, so that the accuracy of prediction is reduced.
Disclosure of Invention
The invention aims to provide a navigation duration prediction method based on data analysis, which comprises the following steps of analyzing environment data at different moments through an environment analysis unit, detecting a driving environment, obtaining the environment data, obtaining an environment analysis coefficient Xi through a formula, and comparing the environment analysis coefficient Xi with an environment analysis coefficient threshold value: if the environmental analysis coefficient Xi is larger than or equal to the environmental analysis coefficient threshold value, judging that the environment has influence, generating an environmental influence signal and sending the environmental influence signal and the environmental analysis coefficient to the data management platform together; if the environment analysis coefficient Xi is smaller than the environment analysis coefficient threshold value, judging that the environment has no influence, marking the environment analysis coefficient as 1, generating an environment non-influence signal, and sending the environment non-influence signal and the environment analysis coefficient to the data management platform; the driving environment is detected in real time, the influence of the environment on the navigation time is judged, the accuracy of time prediction is improved, and the safety performance of an owner is also improved.
The purpose of the invention can be realized by the following technical scheme:
a navigation duration prediction method based on data analysis comprises the following steps:
t1, logging in a data management platform by a user and a manager through a registration login unit, and acquiring analysis data through the data management platform;
step T2, analyzing the environment data through the environment analysis unit, obtaining an environment analysis coefficient and sending the environment analysis coefficient to the data management platform, analyzing the vehicle owner data through the vehicle owner analysis unit, obtaining a vehicle owner analysis coefficient and sending the vehicle owner analysis coefficient to the data management platform, analyzing the road condition data through the road condition analysis unit, obtaining a road condition analysis coefficient and sending the road condition analysis coefficient to the data management platform;
and T3, after receiving the analysis coefficient, the data management platform obtains a total analysis coefficient value through calculation and sends the total analysis coefficient value to the duration prediction unit, and the duration prediction unit predicts the navigation duration through the total analysis coefficient value.
Further, the environment analysis unit is configured to analyze environment data at different times, so as to detect a driving environment, where the environment data includes temperature data, visibility data, and rainfall data, the temperature data is a difference between a temperature of a surrounding environment at the current time when the vehicle owner is driving and a temperature of the ground, the visibility data is a maximum distance at which the vehicle owner can see a target profile clearly under a weather condition at the current time, the rainfall data is an average rainfall per hour at the current time when the vehicle owner is driving, and the time is marked as i, i is 1, 2, … …, n, and n is a positive integer, and a specific analysis and detection process is as follows:
acquiring the difference between the temperature of the surrounding environment at the current time when the owner drives and the temperature of the ground, and marking the difference between the temperature of the surrounding environment at the current time when the owner drives and the temperature of the ground as Wi;
acquiring the maximum distance which can be used by the vehicle owner to clearly see the target contour under the weather condition of the current moment, and marking the maximum distance which can be used by the vehicle owner to clearly see the target contour under the weather condition of the current moment as Ji;
acquiring the average rainfall per hour at the current time when the vehicle owner runs, and marking the average rainfall per hour at the current time when the vehicle owner runs as Yi;
step four, passing through a formula
Obtaining an environment analysis coefficient Xi, wherein a1, a2 and a3 are all proportional coefficients, and a1 is more than a2 is more than a3 is more than 0;
step five, comparing the environment analysis coefficient Xi with an environment analysis coefficient threshold value:
if the environmental analysis coefficient Xi is larger than or equal to the environmental analysis coefficient threshold value, judging that the environment has influence, generating an environmental influence signal and sending the environmental influence signal and the environmental analysis coefficient to the data management platform together;
and if the environment analysis coefficient Xi is less than the environment analysis coefficient threshold value, judging that the environment has no influence, marking the environment analysis coefficient as 1, generating an environment non-influence signal, and sending the environment non-influence signal and the environment analysis coefficient to the data management platform together.
Further, the vehicle owner analysis unit is configured to analyze vehicle owner data so as to detect a current driver, where the vehicle owner data is driving age data, time data and duration data, the driving age data is a difference between a license receiving time of a current driver license and a current system time, the time data is a total number of times that the current driver drives the vehicle for one week, the duration data is a single average duration that the current driver drives the vehicle, and the vehicle owner is marked as o, o is 1, 2, … …, m, and m is a positive integer, and a specific analysis and detection process is as follows:
step S1: acquiring a difference value between the license acquiring time of the current driver license and the current system time, and marking the difference value between the license acquiring time of the current driver license and the current system time as Co;
step S2: acquiring the total times of driving the vehicle by the current driver for one week, and marking the total times of driving the vehicle by the current driver for one week as So;
step S3: acquiring the single average time length of the current driver for driving the vehicle, and marking the single average time length of the current driver for driving the vehicle as Po;
step S4: by the formula Xo ═ e (Co × c1+ So × c2+ Po × c3)c1+c2+c3Acquiring an owner analysis coefficient Xo, wherein c1, c2 and c3 are proportional coefficients, c1 is larger than c2 and is larger than c3 is larger than 0, and e is a natural constant;
step S5: comparing the vehicle owner analysis coefficient Xo with a vehicle owner analysis coefficient threshold value:
if the vehicle owner analysis coefficient Xo is larger than or equal to the vehicle owner analysis coefficient threshold value, judging that the vehicle owner has influence, generating a vehicle owner influence signal and sending the vehicle owner influence signal and the corresponding vehicle owner analysis coefficient to the data management platform;
if the car owner analysis coefficient Xo is smaller than the car owner analysis coefficient threshold value, judging that no influence exists in a car owner, marking the corresponding car owner analysis coefficient as 1, generating a car owner influence signal, and sending the car owner influence signal and the corresponding car owner analysis coefficient to the data management platform together.
Further, the road condition analysis unit is used for analyzing the road condition data so as to detect the road condition, the road condition data are vehicle speed data, traffic flow data and indicator light data, the vehicle speed data are the average speed of vehicles on the current driving road, the traffic flow data are the increment of the number of vehicles per minute on the current driving road, the indicator light data are the total number of traffic lights on the current driving road, and the specific analysis and detection process is as follows:
step SS 1: acquiring the average speed of the vehicles on the current running road, and marking the average speed of the vehicles on the current running road as SD;
step SS 2: acquiring the increment of the number of vehicles per minute on the current driving road, and marking the increment of the number of vehicles per minute on the current driving road as SL;
step SS 3: acquiring the total number of traffic lights on the current driving road, and marking the total number of the traffic lights on the current driving road as LD;
step SS 4: acquiring a road condition analysis coefficient FX by a formula FX ═ beta (SD × b1+ SL × b2+ LD × b3), wherein b1, b2 and b3 are proportional coefficients, b1 > b2 > b3 > 0, and beta is an error correction factor and is 2.3698546;
step SS 5: comparing the road condition analysis coefficient FX with a road condition analysis coefficient threshold value:
if the road condition analysis coefficient FX is larger than or equal to the road condition analysis coefficient threshold value, judging that the road condition has influence, generating a road condition influence signal and sending the road condition influence signal and the corresponding road condition analysis coefficient to the data management platform;
and if the road condition analysis coefficient FX is smaller than the road condition analysis coefficient threshold value, judging that the road condition has no influence, marking the corresponding road condition analysis coefficient as 1, generating a road condition non-influence signal, and sending the road condition non-influence signal and the corresponding road condition analysis coefficient to the data management platform.
Further, after receiving the environment analysis coefficient, the vehicle owner analysis coefficient and the road condition analysis coefficient, the data management platform sends the environment analysis coefficient, the vehicle owner analysis coefficient and the road condition analysis coefficient to the duration prediction unit, the duration prediction unit is used for receiving the environment analysis coefficient, the vehicle owner analysis coefficient and the road condition analysis coefficient, and then obtains a total value of the analysis coefficients through calculation, and the specific calculation prediction process is as follows:
step L1: acquiring an environment analysis coefficient Xi, an owner analysis coefficient Xo and a road condition analysis coefficient FX, and acquiring a total analysis coefficient ZZ by a formula ZZ ═ alpha (Xi x v1+ Xo x v2+ FX x v3), wherein v1, v2 and v3 are all proportionality coefficients, v1 is greater than v2 is greater than v3 is greater than 0, and alpha is an error correction factor and is 2.369512302;
step L2: acquiring the current vehicle speed and the remaining total distance, and respectively marking the current vehicle speed and the remaining total distance as VFront sideAnd LThe residue is leftThe total value of the analysis coefficient is substituted into a calculation formula to obtain the predicted time length, and the calculation formula is
Step L3: the predicted time length is sent to a mobile phone terminal of a vehicle owner, and after the travel is finished, if the difference value between the predicted time length and the actual time length is less than a time length threshold value, the prediction is normal, a predicted normal signal is generated, and the predicted normal signal is sent to a mobile phone terminal of a manager; and if the difference value between the predicted time length and the actual time length is larger than or equal to the time length threshold value, judging that the prediction is abnormal, generating a predicted abnormal signal and sending the predicted abnormal signal to the mobile phone terminal of the manager.
Further, the registration login unit is used for the owner and the manager to submit owner information and manager information for registration through the mobile phone terminal, and send the owner information and the manager information which are successfully registered to the database for storage, wherein the owner information comprises the name, the age and the driving age of the owner and the mobile phone number of the real name authentication of the owner, and the manager information comprises the name, the age, the time of entry and the mobile phone number of the real name authentication of the manager.
Compared with the prior art, the invention has the beneficial effects that:
1. in the invention, the environment analysis unit is used for analyzing the environment data at different moments so as to detect the driving environment, obtain the environment data, obtain the environment analysis coefficient Xi through a formula, and compare the environment analysis coefficient Xi with the environment analysis coefficient threshold value: if the environmental analysis coefficient Xi is larger than or equal to the environmental analysis coefficient threshold value, judging that the environment has influence, generating an environmental influence signal and sending the environmental influence signal and the environmental analysis coefficient to the data management platform together; if the environment analysis coefficient Xi is smaller than the environment analysis coefficient threshold value, judging that the environment has no influence, marking the environment analysis coefficient as 1, generating an environment non-influence signal, and sending the environment non-influence signal and the environment analysis coefficient to the data management platform; the driving environment is detected in real time, the influence of the environment on the navigation time is judged, the accuracy of time prediction is improved, and the safety performance of an owner is also improved;
2. in the invention, the vehicle owner analysis unit is used for analyzing vehicle owner data so as to detect the current driver and obtain the vehicle owner data, the vehicle owner analysis coefficient Xo is obtained through a formula, and the vehicle owner analysis coefficient Xo is compared with a vehicle owner analysis coefficient threshold value: if the vehicle owner analysis coefficient Xo is larger than or equal to the vehicle owner analysis coefficient threshold value, judging that the vehicle owner has influence, generating a vehicle owner influence signal and sending the vehicle owner influence signal and the corresponding vehicle owner analysis coefficient to the data management platform; if the car owner analysis coefficient Xo is smaller than the car owner analysis coefficient threshold value, judging that no influence exists in a car owner, marking the corresponding car owner analysis coefficient as 1, generating a car owner influence signal, and sending the car owner influence signal and the corresponding car owner analysis coefficient to the data management platform together; the influence of the car owner on the navigation time is judged, the accuracy of prediction is further improved, the working efficiency is improved, and the use quality of user navigation is improved;
3. according to the method, an environment analysis coefficient Xi, an automobile owner analysis coefficient Xo and a road condition analysis coefficient FX are obtained through a time length prediction unit, an analysis coefficient total value ZZ is obtained through a formula, a current vehicle speed and a remaining total distance are obtained, and the analysis coefficient total value is substituted into a calculation formula to obtain a predicted time length; the predicted time length is sent to a mobile phone terminal of a vehicle owner, and after the travel is finished, if the difference value between the predicted time length and the actual time length is less than a time length threshold value, the prediction is normal, a predicted normal signal is generated, and the predicted normal signal is sent to a mobile phone terminal of a manager; if the difference value between the predicted time length and the actual time length is larger than or equal to the time length threshold value, judging that the prediction is abnormal, generating a predicted abnormal signal and sending the predicted abnormal signal to a mobile phone terminal of a manager; the prediction time is calculated, and the result is checked, so that the accuracy of time prediction is improved, the error rate is reduced, and the working efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a navigation duration prediction method based on data analysis includes the following steps:
t1, logging in a data management platform by a user and a manager through a registration login unit, and acquiring analysis data through the data management platform;
step T2, analyzing the environment data through the environment analysis unit, obtaining an environment analysis coefficient and sending the environment analysis coefficient to the data management platform, analyzing the vehicle owner data through the vehicle owner analysis unit, obtaining a vehicle owner analysis coefficient and sending the vehicle owner analysis coefficient to the data management platform, analyzing the road condition data through the road condition analysis unit, obtaining a road condition analysis coefficient and sending the road condition analysis coefficient to the data management platform;
step T3, after receiving the analysis coefficient, the data management platform obtains a total value of the analysis coefficient through calculation and sends the total value of the analysis coefficient to a duration prediction unit, and the duration prediction unit predicts the navigation duration through the total value of the analysis coefficient;
the registration login unit is used for submitting owner information and manager information for registration by a vehicle owner and a manager through a mobile phone terminal, and sending the owner information and the manager information which are successfully registered to a database for storage, wherein the owner information comprises the name, the age, the driving age of a vehicle owner and the mobile phone number of real name authentication of a person, and the manager information comprises the name, the age, the time of entry and the mobile phone number of real name authentication of the person of the manager;
the environment analysis unit is used for analyzing the environment data at different moments so as to detect the driving environment, the environment data comprises temperature data, visibility data and rainfall data, the temperature data is the difference between the temperature of the surrounding environment at the current moment and the temperature of the ground when a vehicle owner drives, the visibility data is the maximum distance at which the vehicle owner can see the target contour clearly under the weather condition at the current moment, the rainfall data is the average rainfall of the vehicle owner at the current moment during driving, the moment is marked as i, i is 1, 2, … …, n and n is a positive integer, and the specific analysis and detection process is as follows:
acquiring the difference between the temperature of the surrounding environment at the current time when the owner drives and the temperature of the ground, and marking the difference between the temperature of the surrounding environment at the current time when the owner drives and the temperature of the ground as Wi;
acquiring the maximum distance which can be used by the vehicle owner to clearly see the target contour under the weather condition of the current moment, and marking the maximum distance which can be used by the vehicle owner to clearly see the target contour under the weather condition of the current moment as Ji;
acquiring the average rainfall per hour at the current time when the vehicle owner runs, and marking the average rainfall per hour at the current time when the vehicle owner runs as Yi;
step four, passing through a formula
Obtaining an environment analysis coefficient Xi, wherein a1, a2 and a3 are all proportional coefficients, and a1 is more than a2 is more than a3 is more than 0;
step five, comparing the environment analysis coefficient Xi with an environment analysis coefficient threshold value:
if the environmental analysis coefficient Xi is larger than or equal to the environmental analysis coefficient threshold value, judging that the environment has influence, generating an environmental influence signal and sending the environmental influence signal and the environmental analysis coefficient to the data management platform together;
if the environment analysis coefficient Xi is smaller than the environment analysis coefficient threshold value, judging that the environment has no influence, marking the environment analysis coefficient as 1, generating an environment non-influence signal, and sending the environment non-influence signal and the environment analysis coefficient to the data management platform;
the vehicle owner analysis unit is used for analyzing vehicle owner data so as to detect a current driver, the vehicle owner data are driving age data, frequency data and duration data, the driving age data are difference values of the license receiving time of a current driver license and the current system time, the frequency data are the total times of driving the vehicle for one week of the current driver, the duration data are single average duration of driving the vehicle for the current driver, the vehicle owner is marked as o, o is 1, 2, … …, m and m is a positive integer, and the specific analysis and detection process is as follows:
step S1: acquiring a difference value between the license acquiring time of the current driver license and the current system time, and marking the difference value between the license acquiring time of the current driver license and the current system time as Co;
step S2: acquiring the total times of driving the vehicle by the current driver for one week, and marking the total times of driving the vehicle by the current driver for one week as So;
step S3: acquiring the single average time length of the current driver for driving the vehicle, and marking the single average time length of the current driver for driving the vehicle as Po;
step S4: by the formula Xo ═ e (Co × c1+ So × c2+ Po × c3)c1+c2+c3Acquiring an owner analysis coefficient Xo, wherein c1, c2 and c3 are proportional coefficients, c1 is larger than c2 and is larger than c3 is larger than 0, and e is a natural constant;
step S5: comparing the vehicle owner analysis coefficient Xo with a vehicle owner analysis coefficient threshold value:
if the vehicle owner analysis coefficient Xo is larger than or equal to the vehicle owner analysis coefficient threshold value, judging that the vehicle owner has influence, generating a vehicle owner influence signal and sending the vehicle owner influence signal and the corresponding vehicle owner analysis coefficient to the data management platform;
if the car owner analysis coefficient Xo is smaller than the car owner analysis coefficient threshold value, judging that no influence exists in a car owner, marking the corresponding car owner analysis coefficient as 1, generating a car owner influence signal, and sending the car owner influence signal and the corresponding car owner analysis coefficient to the data management platform together;
the road condition analysis unit is used for analyzing the road condition data, thereby detecting the road condition, the road condition data are vehicle speed data, traffic flow data and indicator light data, the vehicle speed data are the average speed of the vehicles on the current driving road, the traffic flow data are the increment of the number of the vehicles per minute on the current driving road, the indicator light data are the total number of the traffic lights on the current driving road, and the specific analysis and detection process is as follows:
step SS 1: acquiring the average speed of the vehicles on the current running road, and marking the average speed of the vehicles on the current running road as SD;
step SS 2: acquiring the increment of the number of vehicles per minute on the current driving road, and marking the increment of the number of vehicles per minute on the current driving road as SL;
step SS 3: acquiring the total number of traffic lights on the current driving road, and marking the total number of the traffic lights on the current driving road as LD;
step SS 4: acquiring a road condition analysis coefficient FX by a formula FX ═ beta (SD × b1+ SL × b2+ LD × b3), wherein b1, b2 and b3 are proportional coefficients, b1 > b2 > b3 > 0, and beta is an error correction factor and is 2.3698546;
step SS 5: comparing the road condition analysis coefficient FX with a road condition analysis coefficient threshold value:
if the road condition analysis coefficient FX is larger than or equal to the road condition analysis coefficient threshold value, judging that the road condition has influence, generating a road condition influence signal and sending the road condition influence signal and the corresponding road condition analysis coefficient to the data management platform;
if the road condition analysis coefficient FX is smaller than the road condition analysis coefficient threshold value, judging that the road condition has no influence, marking the corresponding road condition analysis coefficient as 1, generating a road condition non-influence signal, and sending the road condition non-influence signal and the corresponding road condition analysis coefficient to the data management platform;
after receiving the environment analysis coefficient, the vehicle owner analysis coefficient and the road condition analysis coefficient, the data management platform sends the environment analysis coefficient, the vehicle owner analysis coefficient and the road condition analysis coefficient to the time length prediction unit, the time length prediction unit is used for receiving the environment analysis coefficient, the vehicle owner analysis coefficient and the road condition analysis coefficient, then a total analysis coefficient value is obtained through calculation, and the specific calculation prediction process is as follows:
step L1: acquiring an environment analysis coefficient Xi, an owner analysis coefficient Xo and a road condition analysis coefficient FX, and acquiring a total analysis coefficient ZZ by a formula ZZ ═ alpha (Xi x v1+ Xo x v2+ FX x v3), wherein v1, v2 and v3 are all proportionality coefficients, v1 is greater than v2 is greater than v3 is greater than 0, and alpha is an error correction factor and is 2.369512302;
step L2: acquiring the current vehicle speed and the remaining total distance, and respectively marking the current vehicle speed and the remaining total distance as VFront sideAnd LThe residue is leftThe total value of the analysis coefficient is substituted into a calculation formulaTaking the predicted time length and calculating the formula as
Step L3: the predicted time length is sent to a mobile phone terminal of a vehicle owner, and after the travel is finished, if the difference value between the predicted time length and the actual time length is less than a time length threshold value, the prediction is normal, a predicted normal signal is generated, and the predicted normal signal is sent to a mobile phone terminal of a manager; and if the difference value between the predicted time length and the actual time length is larger than or equal to the time length threshold value, judging that the prediction is abnormal, generating a predicted abnormal signal and sending the predicted abnormal signal to the mobile phone terminal of the manager.
The working principle of the invention is as follows:
a navigation duration prediction method based on data analysis comprises the steps that when in work, a user and a manager log in a data management platform through a registration login unit, and analysis data are obtained through the data management platform; analyzing the environmental data through an environmental analysis unit, acquiring an environmental analysis coefficient and sending the environmental analysis coefficient to a data management platform, analyzing the vehicle owner data through a vehicle owner analysis unit, acquiring a vehicle owner analysis coefficient and sending the vehicle owner analysis coefficient to the data management platform, analyzing the road condition data through a road condition analysis unit, acquiring a road condition analysis coefficient and sending the road condition analysis coefficient to the data management platform; and after receiving the analysis coefficient, the data management platform obtains a total analysis coefficient value through calculation and sends the total analysis coefficient value to the duration prediction unit, and the duration prediction unit predicts the navigation duration through the total analysis coefficient value.
The above formulas are all calculated by taking the numerical value of the dimension, the formula is a formula which obtains the latest real situation by acquiring a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (6)
1. A navigation duration prediction method based on data analysis is characterized in that the navigation duration prediction method specifically comprises the following steps:
t1, logging in a data management platform by a user and a manager through a registration login unit, and acquiring analysis data through the data management platform;
step T2, analyzing the environment data through the environment analysis unit, obtaining an environment analysis coefficient and sending the environment analysis coefficient to the data management platform, analyzing the vehicle owner data through the vehicle owner analysis unit, obtaining a vehicle owner analysis coefficient and sending the vehicle owner analysis coefficient to the data management platform, analyzing the road condition data through the road condition analysis unit, obtaining a road condition analysis coefficient and sending the road condition analysis coefficient to the data management platform;
and T3, after receiving the analysis coefficient, the data management platform obtains a total analysis coefficient value through calculation and sends the total analysis coefficient value to the duration prediction unit, and the duration prediction unit predicts the navigation duration through the total analysis coefficient value.
2. The method for predicting navigation duration based on data analysis according to claim 1, wherein the environment analysis unit is configured to analyze environment data at different times to detect a driving environment, the environment data includes temperature data, visibility data, and rainfall data, the temperature data is a difference between a temperature of a surrounding environment at a current time when the vehicle owner is driving and a temperature of the ground, the visibility data is a maximum distance that the vehicle owner can see a target profile clearly under a weather condition at the current time, the rainfall data is an average hourly rainfall at the current time when the vehicle owner is driving, and the time is marked as i, i is 1, 2, … …, n, n is a positive integer, and the specific analysis and detection process is as follows:
acquiring the difference between the temperature of the surrounding environment at the current time when the owner drives and the temperature of the ground, and marking the difference between the temperature of the surrounding environment at the current time when the owner drives and the temperature of the ground as Wi;
acquiring the maximum distance which can be used by the vehicle owner to clearly see the target contour under the weather condition of the current moment, and marking the maximum distance which can be used by the vehicle owner to clearly see the target contour under the weather condition of the current moment as Ji;
acquiring the average rainfall per hour at the current time when the vehicle owner runs, and marking the average rainfall per hour at the current time when the vehicle owner runs as Yi;
step four, passing through a formula
Obtaining an environment analysis coefficient Xi, wherein a1, a2 and a3 are all proportional coefficients, and a1 is more than a2 is more than a3 is more than 0;
step five, comparing the environment analysis coefficient Xi with an environment analysis coefficient threshold value:
if the environmental analysis coefficient Xi is larger than or equal to the environmental analysis coefficient threshold value, judging that the environment has influence, generating an environmental influence signal and sending the environmental influence signal and the environmental analysis coefficient to the data management platform together;
and if the environment analysis coefficient Xi is less than the environment analysis coefficient threshold value, judging that the environment has no influence, marking the environment analysis coefficient as 1, generating an environment non-influence signal, and sending the environment non-influence signal and the environment analysis coefficient to the data management platform together.
3. The navigation duration prediction method based on data analysis according to claim 1, wherein the vehicle owner analysis unit is configured to analyze vehicle owner data to detect a current driver, the vehicle owner data includes driving age data, time data, and duration data, the driving age data is a difference between a license acquiring time of a current driver license and a current system time, the time data is a total number of times that the current driver drives the vehicle in one week, the duration data is an average duration of a single time that the current driver drives the vehicle, and the vehicle owner is marked as o, o is 1, 2, … …, m, and m is a positive integer, and the specific analysis and detection process is as follows:
step S1: acquiring a difference value between the license acquiring time of the current driver license and the current system time, and marking the difference value between the license acquiring time of the current driver license and the current system time as Co;
step S2: acquiring the total times of driving the vehicle by the current driver for one week, and marking the total times of driving the vehicle by the current driver for one week as So;
step S3: acquiring the single average time length of the current driver for driving the vehicle, and marking the single average time length of the current driver for driving the vehicle as Po;
step S4: by the formula Xo ═ e (Co × c1+ So × c2+ Po × c3)c1+c2+c3Acquiring an owner analysis coefficient Xo, wherein c1, c2 and c3 are proportional coefficients, c1 is larger than c2 and is larger than c3 is larger than 0, and e is a natural constant;
step S5: comparing the vehicle owner analysis coefficient Xo with a vehicle owner analysis coefficient threshold value:
if the vehicle owner analysis coefficient Xo is larger than or equal to the vehicle owner analysis coefficient threshold value, judging that the vehicle owner has influence, generating a vehicle owner influence signal and sending the vehicle owner influence signal and the corresponding vehicle owner analysis coefficient to the data management platform;
if the car owner analysis coefficient Xo is smaller than the car owner analysis coefficient threshold value, judging that no influence exists in a car owner, marking the corresponding car owner analysis coefficient as 1, generating a car owner influence signal, and sending the car owner influence signal and the corresponding car owner analysis coefficient to the data management platform together.
4. The navigation duration prediction method based on data analysis as claimed in claim 1, wherein the road condition analysis unit is configured to analyze road condition data to detect a road condition, the road condition data are vehicle speed data, traffic flow data and indicator light data, the vehicle speed data are an average speed of vehicles on a current driving road, the traffic flow data are an increment of the number of vehicles per minute on the current driving road, and the indicator light data are a total number of traffic lights on the current driving road, and the specific analysis and detection process is as follows:
step SS 1: acquiring the average speed of the vehicles on the current running road, and marking the average speed of the vehicles on the current running road as SD;
step SS 2: acquiring the increment of the number of vehicles per minute on the current driving road, and marking the increment of the number of vehicles per minute on the current driving road as SL;
step SS 3: acquiring the total number of traffic lights on the current driving road, and marking the total number of the traffic lights on the current driving road as LD;
step SS 4: acquiring a road condition analysis coefficient FX by a formula FX ═ beta (SD × b1+ SL × b2+ LD × b3), wherein b1, b2 and b3 are proportional coefficients, b1 > b2 > b3 > 0, and beta is an error correction factor and is 2.3698546;
step SS 5: comparing the road condition analysis coefficient FX with a road condition analysis coefficient threshold value:
if the road condition analysis coefficient FX is larger than or equal to the road condition analysis coefficient threshold value, judging that the road condition has influence, generating a road condition influence signal and sending the road condition influence signal and the corresponding road condition analysis coefficient to the data management platform;
and if the road condition analysis coefficient FX is smaller than the road condition analysis coefficient threshold value, judging that the road condition has no influence, marking the corresponding road condition analysis coefficient as 1, generating a road condition non-influence signal, and sending the road condition non-influence signal and the corresponding road condition analysis coefficient to the data management platform.
5. The navigation duration prediction method based on data analysis of claim 1, wherein after receiving the environment analysis coefficient, the vehicle owner analysis coefficient and the road condition analysis coefficient, the data management platform sends the environment analysis coefficient, the vehicle owner analysis coefficient and the road condition analysis coefficient to a duration prediction unit, the duration prediction unit is configured to receive the environment analysis coefficient, the vehicle owner analysis coefficient and the road condition analysis coefficient, and then obtains a total analysis coefficient value through calculation, and the specific calculation prediction process is as follows:
step L1: acquiring an environment analysis coefficient Xi, an owner analysis coefficient Xo and a road condition analysis coefficient FX, and acquiring a total analysis coefficient ZZ by a formula ZZ ═ alpha (Xi x v1+ Xo x v2+ FX x v3), wherein v1, v2 and v3 are all proportionality coefficients, v1 is greater than v2 is greater than v3 is greater than 0, and alpha is an error correction factor and is 2.369512302;
step L2: acquiring the current vehicle speed and the remaining total distance, and respectively marking the current vehicle speed and the remaining total distance as VFront sideAnd LThe residue is leftThe total value of the analysis coefficient is substituted into a calculation formula to obtain the predicted time length, and the calculation formula is
Step L3: the predicted time length is sent to a mobile phone terminal of a vehicle owner, and after the travel is finished, if the difference value between the predicted time length and the actual time length is less than a time length threshold value, the prediction is normal, a predicted normal signal is generated, and the predicted normal signal is sent to a mobile phone terminal of a manager; and if the difference value between the predicted time length and the actual time length is larger than or equal to the time length threshold value, judging that the prediction is abnormal, generating a predicted abnormal signal and sending the predicted abnormal signal to the mobile phone terminal of the manager.
6. The data analysis-based navigation duration prediction method according to claim 1, wherein the registration login unit is configured to enable a vehicle owner and a manager to submit vehicle owner information and manager information for registration through a mobile phone terminal, and send the vehicle owner information and the manager information that are successfully registered to the database for storage, the vehicle owner information includes a name, an age, a driving age of the vehicle owner and a mobile phone number for personal real name authentication, and the manager information includes a name, an age, an enrollment time of the manager and a mobile phone number for personal real name authentication.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113421170A (en) * | 2021-06-16 | 2021-09-21 | 广东诚誉工程咨询监理有限公司 | Comprehensive optimization management system and method for power engineering quality |
| CN114724355A (en) * | 2021-12-23 | 2022-07-08 | 韶关学院 | Road traffic intelligent analysis device and analysis system based on internet |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113421170A (en) * | 2021-06-16 | 2021-09-21 | 广东诚誉工程咨询监理有限公司 | Comprehensive optimization management system and method for power engineering quality |
| CN114724355A (en) * | 2021-12-23 | 2022-07-08 | 韶关学院 | Road traffic intelligent analysis device and analysis system based on internet |
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