Intelligent electronic bus stop data management system and method based on big data

Abstract

The application discloses a kind of intelligent electronic station board data management system and method based on big data, it is related to electronic station board data management technical field, the present application includes: S10: the connection coefficient between target vehicle of prediction stopping at adjacent two travel nodes is predicted;S20: planning the emergency travel plan and normal travel plan of target passenger;S30: the degree of adaptation between target passenger and each type travel plan is analyzed, and target travel plan of target passenger is obtained by adjusting;S40: the intelligent terminal of target passenger according to target travel plan carries out call on target station board data on each target intelligent electronic station board.The present application can meet the emergency travel demand of passenger, and can also plan the most comfortable travel demand for passenger by obtaining intelligent electronic station board data before passenger reaches the station, improve the management effect of intelligent electronic station board data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic station board data management, and particularly relates to an intelligent electronic station board data management system and method based on big data. BACKGROUND

[0002] The intelligent electronic station board is a public transportation facility integrated with modern information technology, usually equipped with a high-definition display screen, which can display the estimated arrival time of the bus, line information and vehicle operating status in real time. Through networking with the bus dispatching system, the intelligent electronic station board provides accurate and timely bus travel information for passengers, helping passengers to better plan their journey and reduce anxiety.

[0003] Currently, the electronic display station board in the bus dispatching system can display real-time information of the vehicle, which requires the passenger to arrive at the bus stop to understand the bus information displayed by the electronic station board. For passengers with urgent travel needs, it is impossible to obtain real-time information of the vehicle in advance, so as to ensure that the passenger can take the nearest target vehicle, and the passenger cannot know whether the target vehicle can meet the passenger's travel needs. SUMMARY

[0004] The present application aims to provide an intelligent electronic station board data management system and method based on big data to solve the problems in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: an intelligent electronic station board data management method based on big data, the method comprising:

[0006] S10: According to the preset travel trajectory of the passenger, the real-time driving information and the real-time number of people waiting to get on the target vehicle displayed on each target intelligent electronic station board are obtained, the real-time information of the target vehicle is integrated, and the connection coefficient between the target vehicles stopping at adjacent two travel nodes is predicted;

[0007] S20: Plan the emergency travel plan and the normal travel plan of the target passenger;

[0008] S30: Analyze the adaptation degree between the target passenger and each type of travel plan, and adjust to obtain the target travel plan of the target passenger;

[0009] S40: The target passenger's intelligent terminal retrieves the target station board data on each target intelligent electronic station board according to the target travel plan, and the target passenger completes the travel plan according to the retrieved information.

[0010] Further, the S10 comprises:

[0011] S101: According to the travel trajectory preset by the target passenger, the travel nodes in the travel trajectory are identified, and each travel node in the travel trajectory is sequentially numbered according to the moving order of the target passenger on the travel trajectory, and the numbering processing result is: i = 1, 2, …, m; m represents the total number of travel nodes, and the real-time running information and the real-time number of passengers to be boarded of the target vehicle displayed on the intelligent electronic station board installed at each travel node are obtained, and the intelligent electronic station board installed at the travel node i is denoted as the target intelligent electronic station board i, and the target vehicle running to the target intelligent electronic station board i is denoted as the target vehicle i;

[0012] S102: The running information includes the real-time accommodation capacity value of the target vehicle and the estimated arrival time of the target vehicle to the target intelligent electronic station board, and at time u, the difference t u(i+1) between the estimated arrival time T ui of the target vehicle i+1 which stops at the travel node i+1 to the target intelligent electronic station board i+1 and the estimated arrival time T u(i+1→i) of the target vehicle i which stops at the travel node i to the target intelligent electronic station board i is calculated, and the average transfer time R u(i+1→i) of the passengers between the travel node i and the travel node i+1 is obtained, and the difference R' u(i+1→i) between t u(i+1→i) and R u(i+1→i) is calculated, and if R' u(i+1→i) ≥ 0, R' u(i+1→i) , -1) + 0.5 is calculated, and the first screening coefficient x u(i+1→i) of the target vehicle i+1 which stops at the travel node i+1 is obtained.

[0013] In the time period [u, T u(i+1) ], the number of passengers to be boarded displayed on the target intelligent electronic station board i+1 and the accommodation capacity value of the target vehicle i+1 stopping at the travel node i+1 are collected at a time interval y, and based on the linear model Y = k × X + b, the number of passengers to be boarded-time model D1 and the accommodation capacity value-time model D2 are constructed, and the slope constant k1 of the model D1 and the slope constant k2 of the model D2 are obtained.

[0014] At time u, the ratio W u(i+1) between the number of passengers to be boarded displayed on the target intelligent electronic station board i+1 and the maximum number of passengers G that can be accommodated by the target vehicle i+1 stopping at the travel node i+1 is calculated, and the sum P u(i+1) between W u(i+1) × (1 + k1) and [Q u(i+1) × (1 + k2) / G] is calculated, wherein Q u(i+1)represents the accommodative capacity value of the target vehicle i+1 stopping at the travel node i+1 at the time u, if (P u(i+1) > 1, -1, 1-P u(i+1) ) + 0.5, the second screening coefficient y u(i+1→i) of the target vehicle i+1 stopping at the travel node i+1 at the nearest time is calculated by taking the reciprocal of the above value.

[0015] S103: At the time u, according to K u(i+1) = g1x u(i+1→i) + g2y u(i+1→i) , the connection coefficient between the target vehicle i stopping at the travel node i and the target vehicle i+1 stopping at the travel node i+1 at the nearest time is predicted, wherein g1 and g2 both represent proportional coefficients and g1+g2=1.

[0016] Further, the S20 comprises:

[0017] S201: When 0.2 u(i+1) ≤1, the train number and the number i+1 of the target vehicle i+1 stopping at the travel node i+1 at the time u are put into the first target vehicle screening set M as a group of data;

[0018] When 0 u(i+1) ≤0.2, the train number and the number i+1 of the target vehicle i+1 stopping at the travel node i+1 at the time u are put into the second target vehicle screening set N as a group of data; the train number and the number i+1 of the target vehicle i+1 stopping at the travel node i+1 at the time T u(i+1) +V i+1 are put into the third target vehicle screening set C as a group of data, wherein V i+1 represents the interval departure time of the target vehicle stopping at the travel node i+1.

[0019] When K u(i+1) <0, the train number and the number i+1 of the target vehicle i+1 stopping at the travel node i+1 at the time T u(i+1) +V i+1 are put into the fourth target vehicle screening set Z as a group of data.

[0020] S202: The first target vehicle screening set M, the second target vehicle screening set N and the fourth target vehicle screening set Z are integrated in the order of the travel node number from small to large, to obtain the emergency travel plan of the target passenger.

[0021] S203: The first target vehicle screening set M, the third target vehicle screening set C and the fourth target vehicle screening set Z are integrated in the order of the travel node number from small to large, to obtain the normal travel plan of the target passenger.

[0022] Further, the S30 comprises:

[0023] S301: When the target vehicle 1 stops at the travel node 1, the emergency travel plan and the normal travel plan of the target passenger are determined, and the intersection travel node between the determined emergency travel plan and the normal travel plan is obtained;

[0024] S302: The abnormal reaction behavior of the target passenger when riding each travel tool is identified, the abnormal reaction behavior is identified by the smart device worn by the target passenger, and based on the identification result, the adaptation degree between the target passenger and the riding plan at each intersection travel node in each type of travel plan is analyzed;

[0025] S303: The riding plan at the intersection travel node corresponding to the maximum adaptation degree is reserved, and the emergency travel plan is adjusted based on the reserved riding plan to obtain the target travel plan of the target passenger.

[0026] Further, the specific method for the S302 to analyze the adaptation degree between the target passenger and the riding plan at each intersection travel node in each type of travel plan is:

[0027] The historical abnormal reaction behavior of the target passenger when riding the travel tool is obtained, the abnormal reaction behavior refers to the behavior inconsistent with the normal behavior of the target passenger, the probability value of the travel plan being adjusted in different adjustment directions is collected under each historical abnormal reaction behavior, and the adjustment directions include postponing the riding time at the next travel node and maintaining the riding time at the next travel node;

[0028] An intersection travel node is randomly selected, denoted as the number of the selected intersection travel node as τ, τ = 1, 2, …, m, the abnormal reaction behavior of the target passenger when riding the target vehicle τ is identified, and the identified abnormal reaction behavior is matched with the historical abnormal reaction behavior;

[0029] The probability value λ1 of the travel plan being adjusted in the adjustment direction of postponing the riding time at the next travel node and the probability value λ2 of the travel plan being adjusted in the adjustment direction of maintaining the riding time at the next travel node are obtained under the matched historical abnormal reaction behavior, λ1 is taken as the adaptation degree between the target passenger and the riding plan at the intersection travel node τ in the normal travel plan, and λ2 is taken as the adaptation degree between the target passenger and the riding plan at the intersection travel node τ in the emergency travel plan.

[0030] An intelligent electronic stop board data management system based on big data, the system comprises a stop board data analysis module, a travel plan planning module, a target travel plan determination module and a stop board data management module;

[0031] The bus stop data analysis module is configured to predict a connection coefficient between target vehicles stopping at two adjacent travel nodes.

[0032] The travel plan planning module is configured to plan an emergency travel plan and a normal travel plan of the target passenger.

[0033] The target travel plan determination module is configured to analyze an adaptation degree between the target passenger and each type of travel plan, and adjust to obtain a target travel plan of the target passenger.

[0034] The bus stop data management module is configured to call target bus stop data on each target intelligent electronic bus stop according to the target travel plan.

[0035] Further, the bus stop data analysis module comprises a travel node identification unit, a screening coefficient calculation unit and a connection coefficient prediction unit.

[0036] The travel node identification unit identifies travel nodes in a travel trajectory of the target passenger according to a preset travel trajectory of the target passenger, and obtains real-time driving information and real-time number of people waiting to get on a target vehicle displayed on an intelligent electronic bus stop installed at each travel node.

[0037] The screening coefficient calculation unit obtains a first screening coefficient and a second screening coefficient of a target vehicle stopping at each travel node according to the real-time driving information and the real-time number of people waiting to get on the target vehicle displayed on the intelligent electronic bus stop installed at each travel node.

[0038] The connection coefficient prediction unit predicts a connection coefficient between target vehicles stopping at adjacent travel nodes according to the first screening coefficient and the second screening coefficient.

[0039] Further, the travel plan planning module comprises a target vehicle screening set obtaining unit, an emergency travel plan planning unit and a normal travel plan planning unit.

[0040] The target vehicle screening set obtaining unit obtains a first, second, third and fourth target vehicle screening set according to the connection coefficient between target vehicles stopping at adjacent travel nodes.

[0041] The emergency travel plan planning unit integrates the first target vehicle screening set, the second target vehicle screening set and the fourth target vehicle screening set in ascending order of travel node number to obtain an emergency travel plan of the target passenger.

[0042] The normal travel plan planning unit integrates the first target vehicle screening set, the third target vehicle screening set and the fourth target vehicle screening set in ascending order of travel node number to obtain a normal travel plan of the target passenger.

[0043] Further, the target travel plan determination module comprises an intersection travel node acquisition unit, an adaptation degree calculation unit and a target travel plan determination unit.

[0044] The intersection travel node acquisition unit acquires intersection travel nodes between the determined emergency travel plan and the normal travel plan;

[0045] The adaptation degree calculation unit analyzes the adaptation degree between the target passenger and the boarding plan at each intersection travel node in each type of travel plan;

[0046] The target travel plan determination unit retains the boarding plan at the intersection travel node corresponding to the maximum adaptation degree, adjusts the emergency travel plan based on the retained boarding plan, and obtains the target travel plan of the target passenger.

[0047] Compared with the prior art, the present application has the following advantages:

[0048] 1. The present application analyzes the real-time driving information and the real-time number of people waiting to board displayed on the intelligent electronic stop board, analyzes the connection coefficient between the target vehicles recently stopped at adjacent two boarding platforms, and plans the emergency travel plan and the normal travel plan of the target passenger. Before the boarding passenger arrives at the boarding site, the emergency travel demand of the passenger can be met, and the most comfortable travel demand of the passenger can be planned, thereby improving the management effect of the intelligent electronic stop board data.

[0049] 2. The present application considers whether the vehicle carrying capacity can meet the boarding demand of the passenger when planning the travel plan, so as to ensure that the travel plan planned according to the intelligent electronic stop board data is practical.

[0050] 3. The present application analyzes the adjustment of the travel plan in different adjustment directions based on the historical abnormal reaction behavior of the target passenger when taking the travel tool, analyzes the adaptation degree between the target passenger and the boarding plan at each intersection travel node in each type of travel plan based on the analysis result, and obtains the target travel plan of the target passenger, thereby improving the adaptation degree between the target travel plan and the target passenger. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 It is a work flow diagram of the intelligent electronic stop board data management method based on big data. DETAILED DESCRIPTION

[0052] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0053] Embodiment: As shown in the figure, the present application provides a big data-based intelligent electronic bus stop data management system and method technical solution, a big data-based intelligent electronic bus stop data management method, the method comprising: Figure 1

[0054] S10: According to the preset travel trajectory of the passenger, the real-time driving information and the real-time number of people waiting to get on the target vehicle displayed on each target intelligent electronic bus stop are obtained, the real-time information of the target vehicle is integrated, and the connection coefficient between the target vehicles stopped at adjacent two travel nodes is predicted;

[0055] S10 comprises:

[0056] S101: According to the preset travel trajectory of the target passenger, the travel nodes in the travel trajectory are identified, the travel node refers to the passenger's boarding station, for example, the passenger wants to go from a place to b place, the passenger inputs the starting point and the ending point in the navigation system, the starting point is a place, and the ending point is b place, the route trajectory generated by the navigation system by default in the case that the passenger selects a bus as a travel tool is the preset travel trajectory of the passenger, the place where the passenger starts to ride the selected travel tool in the travel trajectory is the travel node, the travel nodes in the travel trajectory are sequentially numbered according to the moving order of the target passenger on the travel trajectory, the numbering result is: i = 1, 2, …, m; m represents the total number of travel nodes, the real-time driving information and the real-time number of people waiting to get on the target vehicle displayed on the intelligent electronic bus stop installed at each travel node are obtained, the intelligent electronic bus stop installed at the travel node i is recorded as the target intelligent electronic bus stop i, the target vehicle driving to the target intelligent electronic bus stop i is recorded as the target vehicle i, if the travel trajectory of the passenger between the travel node i and the travel node i+1 is consistent with the driving trajectory of the vehicle A between the travel node i and the travel node i+1, it is considered that the vehicle A is the target vehicle displayed on the intelligent electronic bus stop installed at the travel node i;

[0057] ​S102: The travel information includes the real-time capacity of the target vehicle and the estimated arrival time of the target vehicle at the target smart electronic bus stop. The real-time capacity = the total number of passengers the vehicle can accommodate in real time / the maximum number of passengers the vehicle can accommodate. The estimated arrival time of the target vehicle at the target smart electronic bus stop is calculated using real-time traffic conditions and historical data, which is existing technology. At time u, the estimated arrival time T of the target vehicle i+1, which is most recently stopped at travel node i+1, is calculated. u(i+1) The estimated arrival time T of the target vehicle i, which is most recently stopped at travel node i, at the target smart electronic bus stop i. ui The difference t between u(i+1→i) Calculate the average transfer time R for passengers between travel node i and travel node i+1. u(i+1→i) To obtain, for t u(i+1→i) With R u(i+1→i) The difference R′ between u(i+1→i) Perform calculations for if(R′) u(i+1→i) ≥0,R′ u(i+1→i) The first selection coefficient x of the target vehicle i+1 that is most recently stopped at the travel node i+1 is obtained by calculating the reciprocal of (-1)+0.5. u(i+1→i) The basic syntax of the if function is =if(condition, value if true, value if false). For example, the target vehicle i+1, which is most recently stopped at the departure node i+1, has not arrived at the departure node i+1 at time u.

[0058] In [u, T] u(i+1) During the time period, the number of passengers waiting to board displayed on the target smart electronic bus stop i+1 and the capacity value of the target vehicle i+1 stopped at the travel node i+1 are collected at time intervals y. Based on the linear model Y=k×X+b, a passenger waiting to board-time model D1 and a capacity value-time model D2 are constructed, where time is the independent variable and the passenger waiting to board and the capacity value are the dependent variables. The slope constant k1 of model D1 and the slope constant k2 of model D2 are obtained.

[0059] At time u, the ratio W between the number of passengers waiting to board displayed on the target smart electronic bus stop sign i+1 and the maximum number of passengers G that the target vehicle i+1 stopped at travel node i+1 can accommodate is... u(i+1) Perform calculations on W u(i+1) ×(1+k1) and [Q] u(i+1) The sum P between [×(1+k2) / G] u(i+1) The calculation assumes that the maximum number of passengers that target vehicles parked at the same travel node can accommodate is the same, where Q u(i+1) This represents the capacity value of target vehicle i+1 stopped at travel node i+1 at time u, for if(Pu(i+1) >1,-1,1-P u(i+1) ) the second screening coefficient y of the target vehicle i+1 which stops at the travel node i+1 at the nearest time is calculated by taking the reciprocal of 0.5 u(i+1→i) ;

[0060] S103: at the time u, according to K u(i+1) = g1 x x u(i+1→i) + g2 x y u(i+1→i) the connection coefficient between the target vehicle i which stops at the travel node i at the nearest time and the target vehicle i+1 which stops at the travel node i+1 at the nearest time is predicted, wherein g1 and g2 both represent proportional coefficients and g1+g2=1;

[0061] S20: planning the emergency travel plan and the normal travel plan of the target passenger;

[0062] S20 includes:

[0063] S201: when 0.2 u(i+1) ≤1, the train number and the number i+1 of the target vehicle i+1 which stops at the travel node i+1 at the nearest time at the time u are put into the first target vehicle screening set M as a group of data, and the representation format of the group of data is (train number, i+1);

[0064] when 0 u(i+1) ≤0.2, the train number and the number i+1 of the target vehicle i+1 which stops at the travel node i+1 at the nearest time at the time u are put into the second target vehicle screening set N as a group of data; the train number and the number i+1 of the target vehicle i+1 which stops at the travel node i+1 at the time T u(i+1) +V i+1 are put into the third target vehicle screening set C as a group of data, wherein V i+1 represents the interval departure time of the target vehicle which stops at the travel node i+1;

[0065] when K u(i+1) <0, the train number and the number i+1 of the target vehicle i+1 which stops at the travel node i+1 at the time T u(i+1) +V i+1 are put into the fourth target vehicle screening set Z as a group of data;

[0066] S202: according to the order from small to large of the travel node number, the first target vehicle screening set M, the second target vehicle screening set N and the fourth target vehicle screening set Z are integrated to obtain the emergency travel plan of the target passenger;

[0067] S203: integrating the first target vehicle screening set M, the third target vehicle screening set C and the fourth target vehicle screening set Z according to the order from small to large of the trip node number, to obtain the normal trip plan of the target passenger;

[0068] S30: analyzing the adaptation degree between the target passenger and each type of trip plan, and adjusting to obtain the target trip plan of the target passenger;

[0069] S30 includes:

[0070] S301: when the target passenger is on the target vehicle 1 parked at the trip node 1, determining the emergency trip plan and the normal trip plan of the target passenger, and obtaining the intersection trip node between the determined emergency trip plan and the normal trip plan;

[0071] S302: identifying the abnormal reaction behavior of the target passenger when riding each trip tool, the abnormal reaction behavior being identified by the smart device worn by the target passenger, the identification data being obtained with the authorization of the target passenger, based on the identification result, analyzing the adaptation degree between the target passenger and the ride plan in each intersection trip node in each type of trip plan, the specific method being:

[0072] obtaining the historical abnormal reaction behavior of the target passenger when riding the trip tool, the abnormal reaction behavior referring to the behavior inconsistent with the normal behavior of the target passenger, for example, the frequency of the target passenger checking the time on the target vehicle is S, while the normal frequency of the target passenger checking the time is s, when S>s(1+β), it is considered that the frequent checking of the time is the abnormal reaction behavior of the target passenger when the target passenger frequently checks the time and the checking frequency>s(1+β), wherein β represents an error coefficient, collecting the probability value of the target user adjusting the trip plan in different adjustment directions under each historical abnormal reaction behavior, the adjustment directions including postponing the ride time at the next trip node and maintaining the ride time at the next trip node;

[0073] randomly selecting an intersection trip node, denoted as τ, τ=1, 2, …, m, identifying the abnormal reaction behavior of the target passenger when riding the target vehicle τ, and matching the identified abnormal reaction behavior with the historical abnormal reaction behavior;

[0074] obtaining the probability value λ1 of the target user adjusting the trip plan in the adjustment direction of postponing the ride time at the next trip node under the matching successful historical abnormal reaction behavior, and the probability value λ2 of the target user adjusting the trip plan in the adjustment direction of maintaining the ride time at the next trip node, taking λ1 as the adaptation degree between the target passenger and the ride plan in the intersection trip node τ in the normal trip plan, and taking λ2 as the adaptation degree between the target passenger and the ride plan in the intersection trip node τ in the emergency trip plan;

[0075] S303: Reserving the boarding plan at the intersection node corresponding to the maximum value of the fitness degree, adjusting the emergency travel plan based on the reserved boarding plan, and the adjustment method is: randomly selecting an intersection node, replacing the boarding plan of the selected intersection node in the emergency travel plan with the reserved boarding plan at the selected intersection node, to obtain the target travel plan of the target passenger;

[0076] S40: The target passenger's intelligent terminal retrieves the target stop data on each target intelligent stop board according to the target travel plan, and the target passenger completes the travel plan according to the retrieved information, and the target stop data refers to the time when the target vehicle is estimated to arrive at the target intelligent stop board and the train number of the target vehicle.

[0077] An intelligent electronic stop board data management system based on big data, the system comprising a stop board data analysis module, a travel plan planning module, a target travel plan determination module, and a stop board data management module;

[0078] The stop board data analysis module is used to predict the connection coefficient between the target vehicles stopping at adjacent two travel nodes;

[0079] The stop board data analysis module comprises a travel node recognition unit, a screening coefficient calculation unit, and a connection coefficient prediction unit;

[0080] The travel node recognition unit recognizes the travel nodes in the travel trajectory according to the travel trajectory preset by the target passenger, and obtains the real-time running information and real-time number of people waiting to board of the target vehicle displayed on the intelligent electronic stop board installed at each travel node;

[0081] The screening coefficient calculation unit obtains the first screening coefficient and the second screening coefficient of the target vehicle stopping at each travel node according to the real-time running information and real-time number of people waiting to board of the target vehicle displayed on the intelligent electronic stop board installed at each travel node;

[0082] The connection coefficient prediction unit predicts the connection coefficient between the target vehicles stopping at adjacent travel nodes according to the first screening coefficient and the second screening coefficient;

[0083] The travel plan planning module is used to plan the emergency travel plan and the normal travel plan of the target passenger;

[0084] The travel plan planning module comprises a target vehicle screening set acquisition unit, an emergency travel plan planning unit, and a normal travel plan planning unit;

[0085] The target vehicle screening set acquisition unit obtains the first, second, third, and fourth target vehicle screening sets according to the connection coefficient between the target vehicles stopping at adjacent travel nodes;

[0086] The emergency travel plan planning unit integrates the first target vehicle screening set, the second target vehicle screening set and the fourth target vehicle screening set in ascending order of travel node number, to obtain the emergency travel plan of the target passenger;

[0087] The normal travel plan planning unit integrates the first target vehicle screening set, the third target vehicle screening set and the fourth target vehicle screening set in ascending order of travel node number, to obtain the normal travel plan of the target passenger;

[0088] The target travel plan determination module is configured to analyze the adaptation degree between the target passenger and each type of travel plan, and adjust to obtain the target travel plan of the target passenger;

[0089] The target travel plan determination module comprises an intersection travel node acquisition unit, an adaptation degree calculation unit and a target travel plan determination unit;

[0090] The intersection travel node acquisition unit acquires the intersection travel node between the determined emergency travel plan and the normal travel plan;

[0091] The adaptation degree calculation unit analyzes the adaptation degree between the target passenger and the boarding plan at each intersection travel node in each type of travel plan;

[0092] The target travel plan determination unit retains the boarding plan at the intersection travel node corresponding to the maximum adaptation degree, adjusts the emergency travel plan based on the retained boarding plan, and obtains the target travel plan of the target passenger;

[0093] The stop data management module is configured to retrieve the target stop data on each target intelligent electronic stop according to the target travel plan.

[0094] It is apparent to those skilled in the art that the present application is not limited to the details of the foregoing exemplary embodiments, and that the present application can be implemented in other concrete forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered in all respects as illustrative and not restrictive, the scope of the present application being defined by the appended claims rather than the foregoing description, and it is intended that all changes and modifications which come within the meaning and range of equivalency of the claims are resolvable thereunder. Any reference signs in the claims should not be construed as limiting the claims to the figures in which the reference signs are used.

Claims

1. A big data-based intelligent electronic signboard data management method, characterized in that: The method comprises: S10: According to the travel trajectory preset by the passenger, real-time driving information and real-time number of passengers to be picked up of the target vehicle displayed on each target intelligent bus stop are acquired, real-time information of the target vehicle is integrated, and a connection coefficient between the target vehicles stopping at adjacent two travel nodes is predicted; The S10 comprises: S101: According to the travel trajectory preset by the target passenger, travel nodes in the travel trajectory are identified, each travel node in the travel trajectory is sequentially numbered according to the moving order of the target passenger on the travel trajectory, the numbering result is i = 1, 2, …, m, m represents the total number of travel nodes, real-time driving information and real-time number of passengers to be picked up of the target vehicle displayed on the intelligent bus stop installed at each travel node are acquired, the intelligent bus stop installed at the travel node i is recorded as the target intelligent bus stop i, and the target vehicle running to the target intelligent bus stop i is recorded as the target vehicle i; S102: The travel information includes a real-time accommodative capacity value of the target vehicle and an estimated arrival time of the target vehicle to the target intelligent electronic stop, and at the u moment, an estimated arrival time T of the target vehicle i+1 closest to the travel node i+1 to the target intelligent electronic stop i+1 is calculated u(i+1) , and a difference t between the estimated arrival time T of the target vehicle i closest to the travel node i to the target intelligent electronic stop i ui is calculated u(i+1→i) , an average transfer time R of passengers between the travel node i and the travel node i+1 is calculated u(i+1→i) , a difference R' between t u(i+1→i) and R u(i+1→i) is obtained u(i+1→i) , and if R' u(i+1→i) ≥ 0, R' u(i+1→i) , -1) + 0.5 is calculated, and a first screening coefficient x of the target vehicle i+1 closest to the travel node i+1 is obtained u(i+1→i) ; In the [u, T u(i+1) ] time period, the number of people waiting to get on the target intelligent electronic stop board i+1 and the carrying capacity value of the target vehicle i+1 stopped at the travel node i+1 are collected at a time interval y, and based on the linear model Y=k×X+b, the waiting-to-get-on number-time model D1 and the carrying capacity value-time model D2 are constructed, and the slope constant k1 of the model D1 and the slope constant k2 of the model D2 are obtained; At the u moment, the ratio W between the number of passengers displayed on the target intelligent electronic station board i+1 and the maximum number of passengers G that the target vehicle i+1 parked at the travel node i+1 can accommodate u(i+1) The sum value P between W u(i+1) ×(1+k1) and [Q u(i+1) ×(1+k2) / G] is calculated u(i+1) , wherein Q u(i+1) represents the accommodable capacity value of the target vehicle i+1 parked at the travel node i+1 at the u moment, if(P u(i+1) >1,-1,1-P u(i+1) )+0.5 is calculated, and the second screening coefficient y u(i+1→i) of the target vehicle i+1 parked at the travel node i+1 is obtained S103: At u time, according to K u(i+1) = g1 x x u(i+1→i) + g2 x y u(i+1→i) The connection coefficient between the target vehicle i which stops at the travel node i and the target vehicle i+1 which stops at the travel node i+1 is predicted, wherein g1 and g2 both represent proportional coefficients and g1+g2=1; S20: An emergency travel plan and a normal travel plan of the target passenger are planned; The S20 comprises: S201: When 0.2 < K u(i+1) ≤1, the train number and the number i+1 of the target vehicle i+1 that stops at the travel node i+1 at the latest time u are put into the first target vehicle screening set M as a group of data; When 0≤K u(i+1) ≤0.2, the train number and the number i+1 of the target vehicle i+1 that stops at the travel node i+1 at the moment u are put into the second target vehicle screening set N as a group of data; the train number and the number i+1 of the target vehicle i+1 that stops at the travel node i+1 at the moment T u(i+1) +V i+1 are put into the third target vehicle screening set C as a group of data, where V i+1 represents the interval departure time of the target vehicle that stops at the travel node i+1. When K u(i+1) When <0, the target vehicle i+1 of the travel node i+1 at time T u(i+1) +V i+1 The train number and number i+1 of the target vehicle i+1 stopping at the travel node i+1 at time T S202: According to the order from small to large of the travel node number, the first target vehicle screening set M, the second target vehicle screening set N and the fourth target vehicle screening set Z are integrated to obtain the emergency travel plan of the target passenger; S203: According to the order from small to large of the travel node number, the first target vehicle screening set M, the third target vehicle screening set C and the fourth target vehicle screening set Z are integrated to obtain the normal travel plan of the target passenger; S30: The adaptation degree between the target passenger and each type of travel plan is analyzed, and a target travel plan of the target passenger is adjusted; S40: The target passenger's intelligent terminal calls the target bus stop data on each target intelligent bus stop according to the target travel plan, and the target passenger completes the travel plan according to the calling information. 2.The intelligent electronic billboard data management method based on big data according to claim 1, characterized in that: The S30 comprises: S301: When the target passenger gets on the target vehicle 1 stopping at the travel node 1, the emergency travel plan and the normal travel plan of the target passenger are determined, and the intersection travel nodes between the determined emergency travel plan and the normal travel plan are acquired; S302: Abnormal reaction behaviors of the target passenger when getting on each travel tool are identified, the abnormal reaction behaviors are identified by the intelligent device worn by the target passenger, based on the identification result, the adaptation degree between the target passenger and the boarding plan of each type of travel plan at each intersection travel node is analyzed; S303: The boarding plan at the intersection travel node corresponding to the maximum adaptation degree is reserved, the emergency travel plan is adjusted based on the reserved boarding plan, and a target travel plan of the target passenger is obtained. 3.The intelligent electronic billboard data management method based on big data according to claim 2, characterized in that: The specific method for the S302 to analyze the adaptation degree between the target passenger and the boarding plan of each type of travel plan at each intersection travel node is: Acquire historical abnormal reaction behaviors of the target passenger when taking the travel tool, the abnormal reaction behaviors refer to behaviors inconsistent with normal behaviors of the target passenger, collect probability values of the travel plan being adjusted in different adjustment directions under each historical abnormal reaction behavior, the adjustment directions include postponing the boarding time at the next travel node and maintaining the boarding time at the next travel node; Randomly select an intersection travel node, record the number of the selected intersection travel node as τ, τ=1, 2, …, m, identify the abnormal reaction behavior of the target passenger when taking the target vehicle τ, and match the identified abnormal reaction behavior with the historical abnormal reaction behavior; Acquire the probability value λ1 of the travel plan being adjusted in the adjustment direction of postponing the boarding time at the next travel node and the probability value λ2 of the travel plan being adjusted in the adjustment direction of maintaining the boarding time at the next travel node under the matched historical abnormal reaction behavior, take λ1 as the adaptation degree between the target passenger and the boarding plan at the intersection travel node τ in the normal travel plan, and take λ2 as the adaptation degree between the target passenger and the boarding plan at the intersection travel node τ in the emergency travel plan. 4.A big data based intelligent electronic stopboard data management system for implementing the big data based intelligent electronic stopboard data management method according to any one of claims 1-3, characterized in that: The system comprises a stop board data analysis module, a travel plan planning module, a target travel plan determination module, and a stop board data management module; The stop board data analysis module is configured to predict a connection coefficient between target vehicles stopping at adjacent two travel nodes; The travel plan planning module is configured to plan an emergency travel plan and a normal travel plan of the target passenger; The target travel plan determination module is configured to analyze the adaptation degrees between the target passenger and the travel plans of different types, and adjust to obtain a target travel plan of the target passenger; The stop board data management module is configured to call target stop board data on each target intelligent stop board according to the target travel plan.

5. The big data based intelligent electronic stopboard data management system according to claim 4, characterized in that: The stop board data analysis module comprises a travel node identification unit, a screening coefficient calculation unit, and a connection coefficient prediction unit; The travel node identification unit identifies travel nodes in a travel trajectory of the target passenger according to a preset travel trajectory of the target passenger, and acquires real-time running information and real-time number of passengers to be boarded of target vehicles displayed on intelligent stop boards installed at the travel nodes; The screening coefficient calculation unit obtains first and second screening coefficients of target vehicles stopping at the travel nodes according to the real-time running information and the real-time number of passengers to be boarded of the target vehicles displayed on the intelligent stop boards installed at the travel nodes; The connection coefficient prediction unit predicts a connection coefficient between target vehicles stopping at adjacent travel nodes according to the first and second screening coefficients.

6. The intelligent electronic display board data management system based on big data according to claim 5, characterized in that: The travel plan planning module comprises a target vehicle screening set acquisition unit, an emergency travel plan planning unit, and a normal travel plan planning unit; The target vehicle screening set acquisition unit obtains first, second, third, and fourth target vehicle screening sets according to the connection coefficients between the target vehicles stopping at the adjacent travel nodes; The emergency travel plan planning unit integrates the first target vehicle screening set, the second target vehicle screening set and the fourth target vehicle screening set in ascending order of travel node number, to obtain an emergency travel plan of the target passenger; The normal travel plan planning unit integrates the first target vehicle screening set, the third target vehicle screening set and the fourth target vehicle screening set in ascending order of travel node number, to obtain a normal travel plan of the target passenger.

7. The intelligent electronic display board data management system based on big data according to claim 6, characterized in that: The target travel plan determination module comprises an intersection travel node acquisition unit, an adaptation degree calculation unit and a target travel plan determination unit; The intersection travel node acquisition unit acquires intersection travel nodes between the determined emergency travel plan and the normal travel plan; The adaptation degree calculation unit analyzes adaptation degrees between the target passenger and travel plans at the intersection travel nodes in each type of travel plan; The target travel plan determination unit retains a travel plan at an intersection travel node corresponding to a maximum adaptation degree, adjusts the emergency travel plan based on the retained travel plan, and obtains a target travel plan of the target passenger.

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