CN115497320B - Smart city traffic management system and method based on big data platform - Google Patents
Smart city traffic management system and method based on big data platform Download PDFInfo
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- CN115497320B CN115497320B CN202211122442.4A CN202211122442A CN115497320B CN 115497320 B CN115497320 B CN 115497320B CN 202211122442 A CN202211122442 A CN 202211122442A CN 115497320 B CN115497320 B CN 115497320B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096833—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096855—Systems involving transmission of navigation instructions to the vehicle where the output is provided in a suitable form to the driver
- G08G1/096872—Systems involving transmission of navigation instructions to the vehicle where the output is provided in a suitable form to the driver where instructions are given per voice
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Abstract
The invention discloses a smart city traffic management system based on a big data platform, which comprises a navigation module, a scheduling module, a monitoring module and a data analysis module, wherein the distance between a current running vehicle and a destination is acquired in real time, and parking judgment is carried out on the current running vehicle based on the distance between the current running vehicle and the destination, so that the occurrence of inaccurate data of a finally selected parking lot caused by the too far distance between the current running vehicle and the destination is avoided, and the public parking lot in a city is monitored to obtain the optimal parking lot name of the current running vehicle reaching the destination and directly navigate to the parking lot, thereby avoiding time waste caused by the fact that the parking lot cannot be found after the current running vehicle and the like reach the destination; on the other hand, the occurrence of traffic congestion caused when the current running vehicle searches for the parking lot is avoided.
Description
Technical Field
The invention relates to the technical field of intelligent parking of intelligent urban traffic, in particular to an intelligent urban traffic management system and method based on a big data platform.
Background
Traffic is an artery for urban economic development, and intelligent traffic is an important component of smart city construction. The intelligent traffic is deeply fused with traffic engineering through new technologies such as big data, internet, artificial intelligence, blockchain, cloud computing, super computing, beidou satellite navigation system and the like, so that urban traffic efficiency is brought into full play, a traffic operation coordination system for coordinated operation of people, vehicles, roads and environments is established, traffic efficiency and safety are improved, and a vital role is played in development and construction of intelligent cities.
Because people's living standard improves, most families have the car, and the increase of vehicle must lead to the difficult problem of parking, when the user is going the destination that will arrive, the public parking stall of current destination parks more fully, the car owner can't observe the vehicle vacancy condition comparatively intuitively, consequently can only drive the slow short distance of going and patrol and look for empty parking stall, this leads to the traffic near the destination more complicated to a certain extent, and the public parking area of destination if stopping up, the car owner will have to find the spare parking stall by one near the public parking area, the spare parking stall that finally finds also probably is far away from the destination, cause the waste of car owner's very big time resource.
In order to solve the above-mentioned drawbacks, the present invention proposes a solution.
Disclosure of Invention
The invention aims to provide a smart city traffic management system and method based on a big data platform, and aims to solve the problems of waste of personal time resources caused by finding vacant parking spaces by a vehicle owner, more complex traffic caused by finding parking spaces and the like.
The aim of the invention can be achieved by the following technical scheme:
a big data platform based smart city traffic management system comprising:
the navigation module acquires position information of a running vehicle loaded with intelligent navigation software, the navigation module comprises a first processor, a navigation unit and a broadcasting unit, and the navigation unit acquires the linear distance from the current running vehicle to a destination in real time and transmits the linear distance to the scheduling module;
the scheduling module comprises a judging unit, a scheduling unit and an optional database, wherein the judging unit is used for judging the parking of the current traveling vehicle from the linear distance of the destination, and the specific judging steps are as follows:
s11: the judging unit receives the linear distance between the current running vehicle and the destination, which is transmitted by the scheduling module, marks the linear distance as S, and records the current time T1 of the S:
s12: comparing the S with a preset threshold S1 in size:
if S > S1, the judging unit generates a to-be-parked instruction and transmits the to-be-parked instruction to the navigation unit, and the navigation unit continuously acquires the linear distance from the current driving vehicle to the destination and transmits the linear distance to the dispatching module after receiving the to-be-parked instruction transmitted by the dispatching module;
if S is less than or equal to S1, the judging unit generates a pre-stopping instruction and transmits the pre-stopping instruction to the first processor; the first processor screens a parking space near a current vehicle destination according to a certain screening generation rule after receiving the pre-parking instruction transmitted by the scheduling module and generates optional data of the current running vehicle, and the first processor transmits the optional data of the current running vehicle to the scheduling module;
the scheduling module generates optimal data according to a certain planning step after receiving the selected data of the current running vehicle transmitted by the navigation module and transmits the optimal data to the broadcasting unit and the navigation unit respectively;
the broadcasting unit performs optimal parking lot data broadcasting on a driver of the current vehicle according to the optimal data transmitted currently;
and the navigation unit acquires the name of the parking lot in the optimal data after receiving the optimal data transmitted currently, and navigates according to the name of the parking lot in the optimal data.
Further, after receiving the pre-parking instruction transmitted by the scheduling module, the first processor generates a pause instruction and transmits the pause instruction to the navigation unit, and after receiving the pause instruction transmitted by the first processor, the navigation unit pauses to transmit the linear distance between the current running vehicle and the destination to the judging unit.
Further, in S12, the screening generation rule of the optional data is as follows:
s21: the first processor acquires the position of the current running vehicle destination, and creates a retrieval circle of the current running vehicle by taking the position of the current running vehicle destination as the center of the retrieval circle and taking 3 km as the radius of the retrieval circle;
s22: the first processor transmits a search circle of the current running vehicle to the navigation module;
s23: the navigation module receives the search circle of the current running vehicle transmitted by the first processor, and then obtains all parking lots covered in the search circle of the current running vehicle and marked as Q1, Q2, qp, p is more than or equal to 1 and less than or equal to 10;
s24: the navigation unit acquires names of Qp parking lots, and sequentially acquires walking times A1, A2, a..ap of the Qp parking lots, and travel times B1, B2, bp of the Qp parking lots; the walking time Ap of the parking lot is the time spent by the driver of the current running vehicle walking from the parking lot to the destination, and the running time Bp of the parking lot is the time spent by the current running vehicle arriving at the parking lot;
s25: the first processor generates optional data of the current running vehicle according to the names, walking time and running time of the Qp parking lots acquired by the navigation unit.
Further, the specific planning step of generating the optimal data by the scheduling module is as follows:
step one: the dispatching module searches and inquires in the optional database according to the Qp parking lot names in the optional data to obtain parking data tables of Qp parking lots in the optional database;
step two: taking a parking lot as an example, the scheduling module obtains the number Yz of the vacant parking spaces of the parking lot in the current time T1;
the scheduling module takes a monitoring end where the current time T1 is located as retrieval information to acquire a monitoring type corresponding to the parking lot in the current time T1;
if the monitoring type corresponding to the parking lot is an idle section, the scheduling module obtains the idle filling rate alpha z of the parking lot in the monitoring section corresponding to the current time T1, and the formula is utilizedCalculating and obtaining a scheduling value X1 of the parking lot at the current time T1;
if the monitoring type corresponding to the parking lot is a stable section, the scheduling module obtains the stable filling rate beta z of the parking lot in the monitoring section corresponding to the current time T1, and the formula is utilizedCalculating and obtaining a scheduling value X1 of the parking lot at the current time T1;
if the monitoring type corresponding to the parking lot is a redundancy, the scheduling module obtains the redundancy filling rate θz of the monitoring section corresponding to the current time T1 of the parking lot, and a formula is utilizedCalculating and obtaining a scheduling value X1 of the parking lot at the current time T1;
step three: calculating and obtaining scheduling values X1, X2, and XP of all parking lots at the current time T1 according to the second step;
step four: and obtaining the maximum value of all the dispatching values of the parking lots at the current time T1 by using a max function, and generating optimal data by the dispatching module according to the name of the parking lot corresponding to the maximum dispatching value in all the parking lots.
Further, the system comprises a monitoring module, wherein the monitoring module comprises a plurality of monitoring units, the monitoring units are used for monitoring the parking data of each public parking lot in the city and generating parking monitoring data, and the monitoring module transmits the parking data to the data analysis module for analysis to generate parking information tables of all public parking lots in the city.
Further, the specific steps of the data analysis module for analyzing the parking monitoring data of all public parking lots in the city are as follows:
s31: firstly, selecting a public parking lot in a city as a point to be sampled, and acquiring the total amount C of parking spaces of the point to be sampled;
s32: dividing the time of one monitoring period into n monitoring segments with equal duration, and marking the n monitoring segments of one monitoring period as L1, L2, ln;
the method comprises the steps of obtaining initial empty space numbers of points to be sampled in one monitoring section of t monitoring periods, marking the initial empty space numbers as D1, D2, D, and Dt, and marking final empty space numbers of points to be sampled as E1, E2, et; wherein t monitoring periods refer to t monitoring periods traced back to the past with the current monitoring period as a starting point; one monitoring period is 1 day, and one monitoring period is 1 hour;
s33: calculating and obtaining a variation difference Ft of the empty vehicle position of the point to be sampled of the monitoring section in t monitoring periods by using a formula Ft=Et-Dt;
s34: and judging the variation difference value Ft of the empty parking space of the point to be sampled in the monitoring section for t monitoring periods according to a certain judging step, wherein the judging step is as follows:
s341: the data analysis module creates variables N1 and N2 with initial values of 0, namely n1=0 and n2=0;
s342: taking F1 as an example, if F1 is greater than or equal to 0, then adding 1 to the value of N1, where n1=1; if F1<0, then the value of N2 is self-added to 1, where n2=1;
s343: sequentially comparing the variation differences Ft and 0 of the empty parking spaces of the points to be sampled in the monitoring section for t monitoring periods according to S342, and obtaining final N1 and N2;
s35: using the formulaCalculating and obtaining the discrete value of the number of empty spaces of the point to be sampled of t monitoring periods, comparing G with G1, if G>G1, deleting corresponding Eu values according to the sequence from large to small of the I Eu-E I, and correspondingly calculating discrete values of the number of the remaining empty parking spaces until G is less than or equal to G1; the G1 is a preset threshold value, and the E is the average value of the number of empty parking spaces of the point to be sampled of the monitoring section in t monitoring periods participating in the calculation of the residual discrete value;
s36: the type judgment is carried out on the monitoring segment of t monitoring periods of the point to be sampled, and the type judgment comprises the following steps:
s361: if N1-N2 is more than or equal to Max, determining t monitoring periods that the monitoring segment is an idle segment of a point to be sampled, and utilizing a formulaCalculating and obtaining an idle filling rate alpha 1 of an idle section of a point to be sampled, wherein Fi is a positive variation difference value in all idle vehicle position variation difference values Ft of the point to be sampled in the monitoring section in t monitoring periods;
s362: if Mmin<N1-N2<Mmax, then determining t monitoring periods that the monitoring segment is a stable segment of the point to be sampled, and using the formulaCalculating and obtaining a stable filling rate beta 1 of a stable section of a point to be sampled, wherein Fj is a variation difference value of all positive numbers in empty vehicle position variation difference values Ft of the point to be sampled in the monitoring section in t monitoring periods;
s363: if N1-N2 is less than or equal to M, determining t monitoring periods as the redundant period of the point to be sampled, and utilizing a formulaCalculating and obtaining a redundancy filling rate theta 1 of a redundancy section of a point to be sampled; fv is the variation difference value of negative numbers in the variation difference value Ft of the empty car position of the point to be sampled in the monitoring section in t monitoring periods; the Mmax and the Mmin are preset thresholds;
s37: according to steps S32 to S36, sequentially carrying out type judgment on n monitoring segments of t monitoring periods of a point to be sampled, and respectively calculating the idle filling rate of the idle segment, the stable filling rate of the stable segment and the tedious filling rate of the tedious segment after the type judgment;
s38: the data analysis module generates a parking information table of the point to be sampled according to all idle segments of the point to be sampled and idle filling rates corresponding to the idle segments, stable filling rates corresponding to the stable segments and tedious filling rates corresponding to the tedious segments;
s39: according to the steps from S31 to S38, the data analysis module sequentially generates and transmits the parking information tables of all public parking lots in the city to the alternative database for storage.
The invention has the beneficial effects that:
(1) According to the method, the distance between the current running vehicle and the destination is obtained in real time, and the parking judgment is carried out on the current running vehicle based on the distance between the current running vehicle and the destination, so that the occurrence of inaccurate data of a finally selected parking lot caused by the fact that the current running vehicle is too far away from the destination is avoided;
(2) According to the method, the public parking lots in the city are monitored, the types of the idle section, the stable section and the tedious section are divided according to the parking data of each parking lot in different time periods, the type filling rate of the corresponding type of the different time periods is correspondingly calculated, the optimal parking lot name of the current running vehicle reaching the destination is obtained and the current running vehicle is directly navigated to the parking lot based on the walking time and the running time of each different parking lot, and the resource waste in time caused by the fact that the parking lot cannot be found after the current running vehicle reaches the destination is avoided; on the other hand, the occurrence of traffic congestion caused when the current running vehicle reaches the destination and searches the parking lot is avoided, and the occurrence rate of traffic accidents is reduced.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a system block diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the above-mentioned method is performed by a large data platform-based smart city traffic management system including a navigation module, a scheduling module, a monitoring module, and a data analysis module.
The navigation module is used for acquiring position information of a running vehicle loaded with intelligent navigation software, and comprises a first processor, a navigation unit and a broadcasting unit, wherein the navigation unit acquires the linear distance from the current running vehicle to a destination in real time and transmits the linear distance to the scheduling module;
the dispatching module comprises a judging unit, a dispatching unit and an optional database, wherein the dispatching module receives the linear distance between the current running vehicle and the destination, which is transmitted by the navigation unit, in real time and transmits the linear distance to the judging unit, and the judging unit is used for judging the parking of the linear distance between the current running vehicle and the destination, and specifically comprises the following judging steps:
s11: the judging unit receives the linear distance between the current running vehicle and the destination, which is transmitted by the scheduling module, marks the linear distance as S, and records the current time T1:
s12: comparing the size of S with the preset S1:
if S > S1, the judging unit generates a to-be-parked instruction and transmits the to-be-parked instruction to the navigation unit, and the navigation unit continuously acquires the linear distance from the current driving vehicle to the destination and transmits the linear distance to the dispatching module after receiving the to-be-parked instruction transmitted by the dispatching module;
if S is less than or equal to S1, the judging unit generates a pre-stopping instruction and transmits the pre-stopping instruction to the first processor;
the first processor receives the pre-stopping instruction transmitted by the scheduling module, generates a pause instruction and transmits the pause instruction to the navigation unit, and the navigation unit pauses the transmission of the linear distance between the current running vehicle and the destination to the judging unit after receiving the pause instruction transmitted by the first processor; the first processor receives the pre-parking instruction transmitted by the scheduling module, and then screens and generates a parking space near the current vehicle destination and generates optional data of the current running vehicle according to a certain screening and generating rule, wherein the specific screening and generating rule is as follows:
s21: the first processor acquires the position of the current running vehicle destination, and creates a retrieval circle of the current running vehicle by taking the position of the current running vehicle destination as the circle center of the retrieval circle and r kilometers as the radius of the retrieval circle; in this embodiment, r has a value of 3;
s22: the first processor transmits a search circle of the current running vehicle to the navigation module;
s23: the navigation module receives the search circle of the current running vehicle transmitted by the first processor, and then obtains all parking lots which are covered in the search circle of the current running vehicle and marked as Q1, Q2, & gt, qp, wherein in the embodiment, the position range of p is [1, 10];
s24: the navigation unit acquires names of Qp parking lots, and sequentially acquires walking times A1, A2, a..ap of the Qp parking lots, and travel times B1, B2, bp of the Qp parking lots; the walking time of the parking lot is the time spent by a driver of the current running vehicle walking from the parking lot to the destination, and the running time of the parking lot is the time spent by the current running vehicle arriving at the parking lot;
s25: the first processor generates optional data of the current running vehicle according to the names, walking time and running time of the Qp parking lots acquired by the navigation unit and transmits the optional data to the scheduling module;
the scheduling module receives the optional data of the current running vehicle transmitted by the navigation module and then generates optimal data according to a certain planning step, wherein the specific planning step is as follows:
step one: the dispatching module searches and inquires in the optional database according to the Qp parking lot names in the optional data to obtain parking data tables of Qp parking lots in the optional database;
step two: taking a parking lot as an example, the scheduling module obtains the number Yz of the vacant parking spaces of the parking lot in the current time T1;
the scheduling module takes a monitoring end where the current time T1 is located as retrieval information to acquire a monitoring type corresponding to the parking lot in the current time T1;
if the monitoring type corresponding to the parking lot is an idle section, the scheduling module acquires the idle filling rate alpha z of the monitoring section corresponding to the current time T1 of the parking lot;
using the formulaCalculating and obtaining a scheduling value X1 of the parking lot at the current time T1;
if the monitoring type corresponding to the parking lot is a stable section, the scheduling module acquires the stable filling rate beta z of the monitoring section corresponding to the current time T1 of the parking lot;
using the formulaCalculating and obtaining a scheduling value X1 of the parking lot at the current time T1;
if the monitoring type corresponding to the parking lot is a redundancy, the scheduling module acquires the redundancy filling rate theta z of the monitoring section corresponding to the current time T1 of the parking lot;
using the formulaCalculating and obtaining a scheduling value X1 of the parking lot at the current time T1;
step three: calculating and obtaining scheduling values X1, X2, and XP of all parking lots at the current time T1 according to the second step;
step four: obtaining the maximum value of all the dispatching values of the parking lots at the current time T1 by using a max function, generating optimal data by the dispatching module according to the parking lot names corresponding to the maximum dispatching values in all the parking lots, and transmitting the optimal data to the navigation module by the dispatching module;
the navigation module receives the optimal data transmitted by the scheduling module and then transmits the optimal data to the navigation unit and the broadcasting unit respectively, and the broadcasting unit performs optimal parking lot data broadcasting on the driver of the current vehicle according to the current transmitted optimal data;
and the navigation unit acquires the name of the parking lot in the optimal data after receiving the optimal data transmitted currently, and navigates according to the name of the parking lot in the optimal data.
The monitoring module comprises a plurality of monitoring units, wherein the monitoring units are used for monitoring the parking data of each public parking lot in the city and generating parking monitoring data, and the monitoring units are used for transmitting the parking monitoring data of all the public parking lots in the city to the data analysis module;
the data analysis module is used for analyzing parking monitoring data of all public parking lots in the city, and the specific analysis steps are as follows:
s31: firstly, selecting a public parking lot in a city as a point to be sampled, and acquiring the total amount C of parking spaces of the point to be sampled;
s32: dividing the time of one monitoring period into n monitoring segments with equal duration, and marking the n monitoring segments of one monitoring period as L1, L2, ln;
the method comprises the steps of obtaining initial empty space numbers of points to be sampled in one monitoring section of t monitoring periods, marking the initial empty space numbers as D1, D2, D, and Dt, and marking final empty space numbers of points to be sampled as E1, E2, et; in this embodiment, t monitoring periods refer to t monitoring periods traced back to the past with the current monitoring period as a start point; in this embodiment, one monitoring period is 1 day, and one monitoring period is 1 hour;
s33: calculating and obtaining a variation difference Ft of the empty vehicle position of the point to be sampled of the monitoring section in t monitoring periods by using a formula Ft=Et-Dt;
s34: and judging the variation difference value Ft of the empty parking space of the point to be sampled in the monitoring section for t monitoring periods according to a certain judging step, wherein the judging step is as follows:
s341: the data analysis module creates variables N1, N2 and N3 with initial values of 0, namely n1=0 and n2=0;
s342: taking F1 as an example, if F1 is greater than or equal to 0, then adding 1 to the value of N1, where n1=1; if F1<0, then the value of N2 is self-added to 1, where n2=1;
s343: sequentially comparing the variation differences Ft and 0 of the empty parking spaces of the points to be sampled in the monitoring section for t monitoring periods according to S342, and obtaining final N1 and N2;
s35: using the formulaCalculating and obtaining the discrete value of the number of empty spaces of the point to be sampled of t monitoring periods, comparing G with G1, if G>G1, deleting corresponding Eu values according to the sequence from large to small of the I Eu-E I, and correspondingly calculating discrete values of the number of the remaining empty parking spaces until G is less than or equal to G1;
the G1 is a preset threshold value, and the E is the average value of the number of empty parking spaces of the point to be sampled of the monitoring section in t monitoring periods participating in the calculation of the residual discrete value;
s36: the type judgment is carried out on the monitoring segment of t monitoring periods of the point to be sampled, and the type judgment comprises the following steps:
s361: if N1-N2 is more than or equal to Max, judging that the monitoring section is an idle section of a point to be sampled in t monitoring periods;
using the formulaCalculating and obtaining to-be-sampled pointsThe idle filling rate alpha 1 of the idle section, wherein Fi is the variation difference value of all positive numbers in the idle vehicle position variation difference value Ft of the point to be sampled in the monitoring section in t monitoring periods;
s362: if Mmin is less than N1-N2 is less than Mmax, judging that the monitoring section is a stable section of the point to be sampled in t monitoring periods;
using the formulaCalculating and obtaining a stable filling rate beta 1 of a stable section of a point to be sampled, wherein Fj is a variation difference value of all positive numbers in empty vehicle position variation difference values Ft of the point to be sampled in the monitoring section in t monitoring periods;
s363: if N1-N2 is less than or equal to M, judging t monitoring periods that the monitoring section is a redundant section of a point to be sampled;
using the formulaCalculating and obtaining a redundancy filling rate theta 1 of a redundancy section of a point to be sampled;
fv is the variation difference value of negative numbers in the variation difference value Ft of the empty car position of the point to be sampled in the monitoring section in t monitoring periods;
the Mmax and the Mmin are preset thresholds;
s37: according to steps S32 to S36, sequentially carrying out type judgment on n monitoring segments of t monitoring periods of a point to be sampled, and respectively calculating the idle filling rate of the idle segment, the stable filling rate of the stable segment and the tedious filling rate of the tedious segment after the type judgment;
s38: the data analysis module generates a parking information table of the point to be sampled according to all idle segments of the point to be sampled and idle filling rates corresponding to the idle segments, stable filling rates corresponding to the stable segments and tedious filling rates corresponding to the tedious segments;
s39: sequentially generating parking information tables of all public parking lots in the city according to the steps from S31 to S38;
the data analysis module transmits the parking information tables of all public parking lots in the city to the alternative database, and the alternative database stores the parking information tables of all public parking lots in the current city transmitted by the data analysis module after receiving the parking information tables.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative and explanatory of the invention, as various modifications and additions may be made to the particular embodiments described, or in a similar manner, by those skilled in the art, without departing from the scope of the invention or exceeding the scope of the invention as defined in the claims.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (3)
1. Smart city traffic management system based on big data platform, characterized by comprising:
the navigation module acquires position information of a running vehicle loaded with intelligent navigation software, the navigation module comprises a first processor, a navigation unit and a broadcasting unit, and the navigation unit acquires the linear distance from the current running vehicle to a destination in real time and transmits the linear distance to the scheduling module;
the monitoring module comprises a plurality of monitoring units, wherein the monitoring units are used for monitoring the parking data of each public parking lot in the city and generating parking monitoring data, and the monitoring module transmits the parking data to the data analysis module for analysis to generate parking information tables of all public parking lots in the city;
the specific steps of the data analysis module for analyzing the parking monitoring data of all public parking lots in the city are as follows:
s31: firstly, selecting a public parking lot in a city as a point to be sampled, and acquiring the total amount C of parking spaces of the point to be sampled;
s32: dividing the time of one monitoring period into n monitoring segments with equal duration, and marking the n monitoring segments of one monitoring period as L1, L2, ln;
the method comprises the steps of obtaining initial empty space numbers of points to be sampled in one monitoring section of t monitoring periods, marking the initial empty space numbers as D1, D2, D, and Dt, and marking final empty space numbers of points to be sampled as E1, E2, et; wherein t monitoring periods refer to t monitoring periods traced back to the past with the current monitoring period as a starting point; one monitoring period is 1 day, and one monitoring period is 1 hour;
s33: calculating and obtaining a variation difference Ft of the empty vehicle position of the point to be sampled of the monitoring section in t monitoring periods by using a formula Ft=Et-Dt;
s34: and judging the variation difference value Ft of the empty parking space of the point to be sampled in the monitoring section for t monitoring periods according to a certain judging step, wherein the judging step is as follows:
s341: the data analysis module creates variables N1 and N2 with initial values of 0, namely n1=0 and n2=0;
s342: taking F1 as an example, if F1 is greater than or equal to 0, then adding 1 to the value of N1, where n1=1; if F1<0, then the value of N2 is self-added to 1, where n2=1;
s343: sequentially comparing the variation differences Ft and 0 of the empty parking spaces of the points to be sampled in the monitoring section for t monitoring periods according to S342, and obtaining final N1 and N2;
s35: using the formula1≤u<t calculating to obtain the discrete value of the number of empty spaces of the point to be sampled in t monitoring periods, comparing G with G1, if G>G1, according to |EuE| sequentially deleting corresponding Eu values from large to small and correspondingly calculating discrete values of the number of the remaining empty parking spaces until G is less than or equal to G1; the G1 is a preset threshold value, and the E is the average value of the number of empty parking spaces of the point to be sampled of the monitoring section in t monitoring periods participating in the calculation of the residual discrete value;
s36: the type judgment is carried out on the monitoring segment of t monitoring periods of the point to be sampled, and the type judgment comprises the following steps:
s361: if N1-N2 is more than or equal to Max, determining t monitoring periods that the monitoring segment is an idle segment of a point to be sampled, and utilizing a formula1<i is less than or equal to t, calculating and obtaining an idle filling rate alpha 1 of an idle section of a point to be sampled, wherein Fi is a positive variation difference value in idle vehicle position variation difference values Ft of the point to be sampled in t monitoring periods;
s362: if Mmin<N1-N2<Mmax, then determining t monitoring periods that the monitoring segment is a stable segment of the point to be sampled, and using the formula1<j is less than or equal to t, calculating and obtaining a stable filling rate beta 1 of a stable section of the point to be sampled, wherein Fj is a positive variation difference value in all blank space variation difference values Ft of the point to be sampled in the monitoring section in t monitoring periods;
s363: if N1-N2 is less than or equal to M, determining t monitoring periods as the redundant period of the point to be sampled, and utilizing a formula1<v is less than or equal to t, and calculating and obtaining the redundancy filling rate theta 1 of redundancy sections of the points to be sampled; fv is the variation difference value of negative numbers in the variation difference value Ft of the empty car position of the point to be sampled in the monitoring section in t monitoring periods; the Mmax and the Mmin are preset thresholds;
s37: according to steps S32 to S36, sequentially carrying out type judgment on n monitoring segments of t monitoring periods of a point to be sampled, and respectively calculating the idle filling rate of the idle segment, the stable filling rate of the stable segment and the tedious filling rate of the tedious segment after the type judgment;
s38: the data analysis module generates a parking information table of the point to be sampled according to all idle segments of the point to be sampled and idle filling rates corresponding to the idle segments, stable filling rates corresponding to the stable segments and tedious filling rates corresponding to the tedious segments;
s39: according to the steps S31 to S38, the data analysis module sequentially generates parking information tables of all public parking lots in the city and transmits the parking information tables to an optional database for storage;
the scheduling module comprises a judging unit, a scheduling unit and an optional database, wherein the judging unit is used for judging the parking of the current traveling vehicle from the linear distance of the destination, and the specific judging steps are as follows:
s11: the judging unit receives the linear distance between the current running vehicle and the destination, which is transmitted by the scheduling module, marks the linear distance as S, and records the current time T1 of the S:
s12: comparing the S with a preset threshold S1 in size:
if S > S1, the judging unit generates a to-be-parked instruction and transmits the to-be-parked instruction to the navigation unit, and the navigation unit continuously acquires the linear distance from the current driving vehicle to the destination and transmits the linear distance to the dispatching module after receiving the to-be-parked instruction transmitted by the dispatching module;
if S is less than or equal to S1, the judging unit generates a pre-stopping instruction and transmits the pre-stopping instruction to the first processor; the first processor screens a parking space near a current vehicle destination according to a certain screening generation rule after receiving the pre-parking instruction transmitted by the scheduling module and generates optional data of the current running vehicle, and the first processor transmits the optional data of the current running vehicle to the scheduling module;
the scheduling module receives the optional data of the current running vehicle transmitted by the navigation module, generates optimal data according to a certain planning step and transmits the optimal data to the broadcasting unit and the navigation unit respectively, wherein the specific planning step for generating the optimal data is as follows:
step one: the dispatching module searches and inquires in the optional database according to the Qp parking lot names in the optional data to obtain parking data tables of Qp parking lots in the optional database;
step two: the scheduling module acquires the number Yz of the vacant parking spaces of one parking lot in the current time T1;
the scheduling module takes a monitoring end where the current time T1 is located as retrieval information to acquire a monitoring type corresponding to the parking lot in the current time T1;
if the monitoring type corresponding to the parking lot is an idle section, the scheduling module obtains the idle filling rate alpha z of the parking lot in the monitoring section corresponding to the current time T1, and the formula is utilizedCalculating and obtaining a scheduling value X1 of the parking lot at the current time T1;
if the monitoring type corresponding to the parking lot is a stable section, the scheduling module obtains the stable filling rate beta z of the parking lot in the monitoring section corresponding to the current time T1, and the formula is utilizedCalculating and obtaining a scheduling value X1 of the parking lot at the current time T1;
if the monitoring type corresponding to the parking lot is a redundancy, the scheduling module obtains the redundancy filling rate θz of the monitoring section corresponding to the current time T1 of the parking lot, and a formula is utilizedCalculating and obtaining a scheduling value X1 of the parking lot at the current time T1;
step three: calculating and obtaining scheduling values X1, X2, and XP of all parking lots at the current time T1 according to the second step;
step four: obtaining the maximum value of all the dispatching values of the parking lots at the current time T1 by using a max function, and generating optimal data by the dispatching module according to the name of the parking lot corresponding to the maximum dispatching value in all the parking lots; the broadcasting unit performs optimal parking lot data broadcasting on a driver of the current vehicle according to the optimal data transmitted currently;
and the navigation unit acquires the name of the parking lot in the optimal data after receiving the optimal data transmitted currently, and navigates according to the name of the parking lot in the optimal data.
2. The large data platform based intelligent city traffic management system of claim 1, wherein the first processor generates a pause command after receiving the pre-stop command transmitted by the scheduling module and transmits the pause command to the navigation unit, and the navigation unit pauses transmitting the straight line distance of the current driving vehicle from the destination to the determination unit after receiving the pause command transmitted by the first processor.
3. The large data platform based intelligent urban traffic management system according to claim 1, wherein in S12, the screening generation rule of the optional data is as follows:
s21: the first processor acquires the position of the current running vehicle destination, and creates a retrieval circle of the current running vehicle by taking the position of the current running vehicle destination as the center of the retrieval circle and taking 3 km as the radius of the retrieval circle;
s22: the first processor transmits a search circle of the current running vehicle to the navigation module;
s23: the navigation module receives the search circle of the current running vehicle transmitted by the first processor, and then obtains all parking lots covered in the search circle of the current running vehicle and marked as Q1, Q2, qp, p is more than or equal to 1 and less than or equal to 10;
s24: the navigation unit acquires names of Qp parking lots, and sequentially acquires walking times A1, A2, a..ap of the Qp parking lots, and travel times B1, B2, bp of the Qp parking lots; the walking time Ap of the parking lot is the time spent by the driver of the current running vehicle walking from the parking lot to the destination, and the running time Bp of the parking lot is the time spent by the current running vehicle arriving at the parking lot;
s25: the first processor generates optional data of the current running vehicle according to the names, walking time and running time of the Qp parking lots acquired by the navigation unit.
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