Intelligent guiding and real-time information pushing method for bus transfer station

Abstract

The application discloses a bus transfer station intelligent guiding and real-time information pushing method, and relates to the technical field of intelligent public transportation service.The application collects bus travel planning information input by a target user based on a mobile device, generates a bus travel guiding route of the target user according to the bus travel planning information, collects position information of the target user in each walking route in real time according to a plurality of walking routes determined for the current travel, updates a link mode between the mobile device of the target user and a server according to the position information, and links and adapts the guiding route and the link mode in this way, thereby improving the accuracy and flexibility of the guiding service, effectively solving the problem of long connection continuously occupying resources, reducing the load pressure of the mobile device and the server, reducing the operation burden of the server, and improving the resource utilization rate of the terminal device and the server.

Description

Technical Field

[0001] This invention relates to the field of intelligent public transportation services, specifically to a method for intelligent passenger guidance and real-time information push at public transportation transfer stations. Background Technology

[0002] With the continuous improvement of the networking and intelligence of urban public transportation, public transport hubs have become important nodes in the urban transportation system. To improve passenger transfer efficiency and travel experience, existing technologies have developed intelligent bus guidance and real-time information push systems based on mobile terminals. Passengers can use mobile apps, mini-programs, and other methods to check bus routes, real-time vehicle locations, estimated arrival times, and other information, and receive real-time push notifications during the transfer process. Most existing real-time information push solutions use long-connection communication mechanisms such as WebSocket and MQTT to achieve low-latency push of information such as vehicle arrival, route delay, and transfer suggestions. However, once a long connection is established, it will continuously occupy the network and system resources of the mobile terminal, regardless of the distance between the user and the bus transfer station or whether the user is in a transfer state, and maintain a constant long connection maintenance mechanism. For users who are far from public transportation hubs, they usually do not need high-frequency, second-level accurate real-time information, nor do they frequently check the bus information interface. Maintaining a long connection at this time will cause unnecessary consumption of mobile terminal battery and waste of network traffic, while increasing the server connection load and reducing the overall system resource utilization. To address the above problems, this invention proposes a solution. Summary of the Invention

[0003] The purpose of this invention is to provide a method for intelligent passenger guidance and real-time information push at public transport interchange stations, in order to solve the problems mentioned in the background art.

[0004] This invention provides a method for intelligent passenger guidance and real-time information push at public transport interchange stations, comprising the following steps: S1: Based on the target user's mobile device, collect the public transportation travel planning information input by the target user, and simultaneously obtain the target user's connection matching information. The connection matching information includes the connection mode between the target user's terminal device and the server in short-distance, medium-distance, and long-distance scenarios. The connection mode is divided into two types: long connection and short connection. S2: Generate a public transport route for the target user based on the public transport travel planning information and display it to the target user. The target user will then take the trip according to the public transport route. Simultaneously, configure the connection mode between the target user's terminal device and the server to be a short connection. S3: Determine several walking routes for the target user's current trip based on the target user's public transportation route, and collect the target user's location information in real time on each walking route. Update the connection mode between the target user's mobile device and the server based on the location information. The server then pushes real-time public transportation information for this trip to the target user's mobile device based on the connection mode. The real-time public transportation information includes the real-time location of the bus, the remaining arrival time, sudden delays in the route, and changes in transfer stations.

[0005] Furthermore, the public transportation travel planning information includes the latitude and longitude coordinates of the origin and destination input by the target user through the navigation software on their mobile device.

[0006] Furthermore, the connection matching information of the target user is generated according to preset generation rules by collecting historical operation data of the target user's past trips; The historical operation data for any trip includes the public transportation route for the target user's trip, the travel time and walking distance to each transfer station, the number of times the navigation software was opened during the process of reaching each transfer station, the viewing time of the navigation software each time it was opened, and the frequency of information interaction operations.

[0007] Furthermore, in step S1, the specific details of the rules for generating the connection matching information for the target user are as follows: S11: Mark all the acquired historical operation data as A1, A2, ..., Aa, where a is the historical operation data collection volume preset by the administrator; S12: Mark all the transfer stations included in the historical operation data A1 in ascending order of their station numbers as B1, B2, ..., Bb, b≥1; S13: Utilize the formula Calculate the location progress coefficient D1 of the target user to reach the transfer station B1. In the formula, Cmax and Cmin are the farthest and shortest straight-line distances of the target user to the transfer station B1, respectively. Cmin is set to 0.01km by default to avoid the denominator being 0. C1 is the walking distance of the target user to the transfer station B1. S14: Calculate and obtain the location progress coefficients D2, D3, ..., Db of the target user arriving at transfer stations B2, B3, ..., Bb in sequence according to S13. Use the discrete point filtering algorithm to process the location progress coefficients D1, D2, ..., Db, and calculate the average value of all remaining location progress coefficients after data processing. Define the average value as the location matching degree E1 of the target user in this instance. S15: Using the formula Calculate the activity level G1 of the target user's operation behavior during the trip. In the formula, F1 is the average of the number of times the target user opened the navigation software during the process of reaching all transfer stations, F2 is the maximum viewing time of each time the target user opened the navigation software during the process of reaching each transfer station, and F3 is the maximum information interaction operation frequency of the target user during the process of reaching each transfer station. F1max is the average of the total viewing time of the navigation software opened each time the target user arrives at all transfer stations; F2max is the average of the total viewing time of the navigation software opened each time the target user arrives at all transfer stations; and F3max is the maximum of the total frequency of information interaction operations of the target user during the process of arriving at all transfer stations. S16: Based on historical operation data A2, A3, ..., Aa, calculate and obtain the target user's position matching degree E2, E3, ..., Ea and operation behavior activity G2, G3, ..., Ga in the corresponding times according to S11 to S15; S17: Calculate and obtain the target user's interaction activity H1, H2, and H3 in short-distance, medium-distance, and long-distance scenarios based on the location matching degree E1, E2, ..., Ea respectively; S18: Determine the connection mode between the terminal device and the server in the corresponding scenario based on the target user's interactive activity in short-distance, medium-distance, and long-distance scenarios. S19: Generate connection matching information for the target user based on the connection mode between the terminal device and the server in short-distance, medium-distance, and long-distance scenarios.

[0008] Furthermore, in S17, the calculation of the target user's interaction activity levels H1, H2, and H3 in short-distance, medium-distance, and long-distance scenarios is as follows: Obtain the activity level of the trips corresponding to all location matching degrees less than P1 in E1, E2, ..., Ea, and calculate their mean using the summation and averaging formula. Define the mean as the interaction activity level H1 of the target user in the short-distance scenario, where P1 is a preset short-distance threshold. The average value of the operation behavior of each trip corresponding to the location matching degree E1, E2, ..., Ea, which is greater than or equal to P1 and less than or equal to P2, is calculated using the summation and averaging formula. The average value is defined as the interaction activity H2 of the target user in the medium distance scenario, where P2 is a preset medium-to-long distance threshold. The activity level of the trips corresponding to all location matching degrees greater than P2 in E1, E2, ..., Ea is calculated using the summation and averaging formula to obtain their mean value, which is then calibrated as the target user's interaction activity level H3 in long-distance scenarios.

[0009] Furthermore, in S18, the determination of the connection mode between the terminal device and the server in the corresponding scenario is as follows: If the interaction activity H1 ≥ P3, then the connection mode between the target user's terminal device and the server in a short-distance scenario is determined to be a long connection; otherwise, it is a short connection. If the interaction activity H2 ≥ P3, then the connection mode between the target user's terminal device and the server in a medium-distance scenario is determined to be a long connection; otherwise, it is a short connection. If the interaction activity H3 ≥ P3, then the connection mode between the target user's terminal device and the server in a long-distance scenario is determined to be a long connection; otherwise, it is a short connection. P3 is a preset mode determination threshold.

[0010] Furthermore, in step S2, the specific content of generating the public transportation travel guidance route for the target user is as follows: Extract the latitude and longitude coordinates of the origin and destination from the public transportation travel planning information; based on the origin and destination latitude and longitude coordinates, use the bus best travel route optimization algorithm to generate the optimal public transportation route for the target user; The optimal bus route includes several bus routes and their corresponding route numbers; the route numbers are numbered sequentially starting from the number 1, and the smaller the number, the more likely the target user should take the bus route corresponding to that route number. Each bus route contains several bus stops and stop numbers. The stop numbers are numbered sequentially starting from the number 1. The smaller the number, the more likely the target user should take that bus stop.

[0011] Further, step S3 specifically includes: S31: Based on the target user's public transportation travel guidance route, determine several walking routes for the target user's current trip and label them as I1, I2, ..., Ii in the order of walking. Among them, walking route I1 is the target user's walking route from the latitude and longitude coordinates of the input public transportation travel planning information to the first boarding station, and the other walking routes are deduced in the same way. The first boarding station refers to the bus stop with station number 1 in the optimal bus route. The bus stop with station number 1 in any other bus route other than the bus route with station number 1 refers to the transfer station. S32: Collect the latitude and longitude coordinates of the target user in real time. When the target user starts walking on any walking route, execute the terminal configuration of the target user on the walking route.

[0012] Furthermore, in S32, the terminal configuration of the target user on the walking route is executed as follows: The latitude and longitude coordinates of the target user on the walking route are collected in real time. According to S13, the location progress coefficient of the target user to reach the transfer station on the walking route is calculated in real time. The location progress coefficient is compared with P1 and P2 respectively. If the position progress coefficient is less than P1, then retrieve the connection mode between the terminal device and the server in the corresponding short-distance scenario from the target user connection matching information, and reconfigure the connection mode between the target user terminal device and the server based on the connection mode. If the position progress coefficient is greater than or equal to P1 and less than or equal to P2, then retrieve the connection mode between the terminal device and the server in the corresponding medium-distance scenario from the target user connection matching information, and reconfigure the connection mode between the target user terminal device and the server based on the connection mode. If the location progress coefficient is greater than P2, then retrieve the connection mode between the terminal device and the server in the corresponding long-distance scenario from the target user connection matching information, and reconfigure the connection mode between the target user terminal device and the server based on the connection mode.

[0013] Compared with existing technologies, it has the following advantages: This invention, based on the target user's mobile device, collects public transportation travel planning information input by the target user, simultaneously obtains the target user's connection matching information, generates a public transportation travel guidance route for the target user based on the public transportation travel planning information, and allows the target user to make this trip. During the trip, several walking routes are determined for this trip, and the target user's location information is collected in real time on each walking route. The connection mode between the target user's mobile device and the server is updated and configured based on the location information. In this way, the guidance route and the connection mode are linked and adapted, improving the accuracy and flexibility of the guidance service, effectively solving the problem of continuous resource occupation by existing long connections, reducing the load pressure on mobile devices and servers, alleviating the server's operating burden, and improving the resource utilization rate of terminal devices and servers. In this invention, the connection matching information of the target user is generated based on the historical operation data of the target user's past trips. During the generation process, the connection mode between the target user's terminal device and the server is determined by analyzing the target user's attention to public transportation information during the walking journey from the target user's departure point to each transfer station in the past trips. In this way, the switching of connection mode is more in line with the user's personal habits, realizing personalized intelligent ride guidance and further improving user satisfaction. Attached Figure Description

[0014] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figure 1 This application provides a method for intelligent passenger guidance and real-time information push at public transport interchange stations, including the following steps: S1: Based on the target user's mobile device, collect the public transportation travel planning information input by the target user, and simultaneously obtain the target user's connection matching information; In step S1, the public transportation travel planning information includes the latitude and longitude coordinates of the departure point and the destination point, which are input by the target user through the navigation software on their mobile device. The connection matching information includes the connection patterns between the target user's terminal device and the server in short-distance, medium-distance, and long-distance scenarios, respectively. In step S1, the connection matching information of the target user is generated by collecting historical operation data of the target user's past trips according to preset generation rules. The historical operation data for any trip includes the public transportation route for the target user's trip, the travel time and walking distance to each transfer station, the number of times the navigation software was opened during the process of reaching each transfer station, the viewing time of the navigation software each time it was opened, and the frequency of information interaction operations. The average frequency of opening the navigation software refers to the number of times a target user opens the navigation software per minute on average while traveling to a certain bus stop, which is used to characterize the user's historical frequency of attention to public transportation information. Average viewing time per session refers to the average duration for a target user to view public transport guidance and real-time information each time they open the navigation software, and is used to characterize the depth of user attention to public transport information. Information interaction frequency refers to the average number of times per minute that a target user performs interactive operations such as clicking, querying, and switching on public transportation information in navigation software during the travel time, which is used to characterize the degree of user's active demand for public transportation information. Step S1, the specific details of the rules for generating connection matching information for the target user are as follows: S11: Mark all the acquired historical operation data as A1, A2, ..., Aa, where a is the historical operation data collection quantity preset by the administrator. It is a fixed value, and its value is set by the administrator in combination with data validity and analysis accuracy to limit the total sample size of historical operation data to be analyzed. S12: Mark all the transfer stations included in the historical operation data A1 in ascending order of their station numbers as B1, B2, ..., Bb, b≥1; S13: Utilize the formula Calculate the location progress coefficient D1 of the target user to reach the transfer station B1. In the formula, Cmax and Cmin are the farthest and shortest straight-line distances of the target user to the transfer station B1, respectively. Cmin is set to 0.01km by default to avoid the denominator being 0. C1 is the walking distance of the target user to the transfer station B1. The farthest and shortest straight-line distances from the target user to transfer station B1 are determined based on the latitude and longitude coordinates of the origin and destination in the public transportation travel planning information entered by the target user, among all feasible routes. It should be noted that the closer the value of D1 is to 1, the closer the target user is to the transfer station, and the higher the location progress. S14: Calculate and obtain the location progress coefficients D2, D3, ..., Db of the target user arriving at transfer stations B2, B3, ..., Bb in sequence according to S13. Use the discrete point filtering algorithm to process the location progress coefficients D1, D2, ..., Db, and calculate the average value of all remaining location progress coefficients after data processing. Define the average value as the location matching degree E1 of the target user in this instance. In this application, the discrete point filtering algorithm is the H-score filtering algorithm; It should be noted here that in calculating the location progress coefficient D2 for the target user to reach transfer station B2, the farthest and shortest straight-line distances are the farthest and shortest straight-line distances determined by the target user from transfer station B1 to transfer station B2 among all feasible routes. The farthest and shortest straight-line distances in location progress coefficients D3, D4, ..., Dd are calculated in the same way. S15: Using the formula Calculate the activity level G1 of the target user's operation behavior for this trip. In the formula, F1 is the maximum number of times the target user opens the navigation software during the process of arriving at each transfer station, F2 is the maximum viewing time of each time the target user opens the navigation software during the process of arriving at each transfer station, and F3 is the maximum information interaction frequency of the target user during the process of arriving at each transfer station. F1max is the average of the number of times the target user opens the navigation software during the process of reaching all transfer stations; F2max is the average of the viewing time of the target user each time the navigation software is opened during the process of reaching all transfer stations; and F3max is the maximum of the sum of the frequency of information interaction operations of the target user during the process of reaching all transfer stations. It should be noted that the closer the G1 value is to 1, the higher the target user's attention to public transportation information and the stronger their proactive demand. S16: Based on historical operation data A2, A3, ..., Aa, calculate and obtain the target user's position matching degree E2, E3, ..., Ea and operation behavior activity G2, G3, ..., Ga in the corresponding times according to S11 to S15; S17: Calculate the target user's interaction activity levels H1, H2, and H3 in short-distance, medium-distance, and long-distance scenarios based on location matching degrees E1, E2, ..., Ea, respectively. The calculation details are as follows: Obtain the activity level of the trips corresponding to all location matching degrees less than P1 in E1, E2, ..., Ea, and calculate their mean using the summation and averaging formula. Define the mean as the interaction activity level H1 of the target user in the short-distance scenario, where P1 is a preset short-distance threshold. The average value of the operation behavior of each trip corresponding to the location matching degree E1, E2, ..., Ea, which is greater than or equal to P1 and less than or equal to P2, is calculated using the summation and averaging formula. The average value is defined as the interaction activity H2 of the target user in the medium distance scenario, where P2 is a preset medium-to-long distance threshold. The activity level of the trips corresponding to all location matching degrees greater than P2 in E1, E2, ..., Ea is calculated using the summation and averaging formula to obtain their mean value, which is then calibrated as the target user's interaction activity level H3 in long-distance scenarios. S18: Determine the connection mode between the terminal device and the server in short-distance, medium-distance, and long-distance scenarios based on the target user's interaction activity. The determination content is as follows: If the interaction activity H1 ≥ P3, then the connection mode between the target user's terminal device and the server in a short-distance scenario is determined to be a long connection; otherwise, it is a short connection. If the interaction activity H2 ≥ P3, the connection mode between the target user's terminal device and the server in the medium distance scenario is determined to be a long connection; otherwise, it is a short connection. If the interaction activity H3 ≥ P3, the connection mode between the target user's terminal device and the server in the long distance scenario is determined to be a long connection; otherwise, it is a short connection. P3 is a preset mode determination threshold. S19: Generate connection matching information for the target user based on the connection pattern between the terminal device and the server in short-distance, medium-distance, and long-distance scenarios. S2: Generate a bus travel guidance route for the target user based on the bus travel planning information and display it to the target user. The target user will then take the trip according to the bus travel guidance route. Simultaneously configure the connection mode between the target user's terminal device and the server. At this time, the default connection mode is short connection. Step S2, generating the public transportation route guidance content for the target user, specifically includes: Extract the latitude and longitude coordinates of the origin and destination from the public transportation travel planning information; based on the origin and destination latitude and longitude coordinates, use the bus best travel route optimization algorithm to generate the optimal public transportation route for the target user; The optimal bus route includes several bus routes and their corresponding route numbers; the route numbers are numbered sequentially starting from the number 1, and the smaller the number, the more likely the target user should take the bus route corresponding to that route number. Each bus route contains several bus stops and stop numbers. The stop numbers are numbered sequentially starting from the number 1. The smaller the number, the more likely the target user should take that bus stop. It should be noted that the optimal bus route is used to specify the bus routes that the target user should take from the origin to the destination, the corresponding bus stops for each bus route, and the transfer logic. S3: Determine several walking routes for the target user's current trip based on the target user's public transportation route, and collect the target user's location information in real time in each walking route, and update the connection mode between the target user's mobile device and the server based on the location information; Step S3 is as follows: S31: Based on the target user's public transport route, determine several walking routes for the target user's current trip and label them I1, I2, ..., Ii in the order of walking. Walking route I1 is the target user's walk from the latitude and longitude coordinates of the input public transport travel planning information to the first boarding station. Walking route I2 is the target user's walk to the transfer station of the bus route with route number 2. Walking routes I3, I4, ..., Ii are deduced in turn. Walking route Ii is the target user's walk to the transfer station of the bus route with route number n-1. n is the total number of bus routes included in the optimal public transport route. The first boarding station refers to the bus stop with station number 1 in the optimal bus route. The bus stop with station number 1 in any other bus route other than the bus route with station number 1 refers to the transfer station. S32: Collect the latitude and longitude coordinates of the target user in real time. When the target user starts walking on any walking route, execute the terminal configuration of the target user on the walking route. The latitude and longitude coordinates of the target user on the walking route are collected in real time. According to S13, the location progress coefficient of the target user to reach the transfer station on the walking route is calculated in real time. The location progress coefficient is compared with P1 and P2 respectively. If the location progress coefficient is less than P1, the connection mode between the terminal device and the server in the short-distance scenario is retrieved from the connection matching information of the target user, and the connection mode between the target user terminal device and the server is reconfigured based on the connection mode. The server pushes the real-time bus information for this trip to the target user's mobile device based on the connection mode. The real-time bus information includes the real-time location of the bus, the remaining arrival time, sudden delays in the route, changes in transfer stations, etc. If the location progress coefficient is greater than or equal to P1 and less than or equal to P2, the connection mode between the terminal device and the server in the corresponding medium-distance scenario is retrieved from the connection matching information of the target user, and the connection mode between the target user terminal device and the server is reconfigured based on the connection mode. The server then pushes the real-time bus information for this trip to the target user's mobile device based on the connection mode. The real-time bus information includes the real-time location of the bus, the remaining arrival time, sudden delays in the route, changes in transfer stations, etc. If the location progress coefficient is greater than P2, the connection mode between the terminal device and the server in the corresponding long-distance scenario is retrieved from the connection matching information of the target user, and the connection mode between the target user terminal device and the server is reconfigured based on the connection mode. The server pushes the real-time bus information for this trip to the target user's mobile device based on the connection mode. The real-time bus information includes the real-time location of the bus, the remaining arrival time, sudden delays in the route, changes in transfer stations, etc. In this application, the long connection mode refers to establishing a WebSocket / MQTT long connection between the mobile device and the server, with the information push frequency being on the order of seconds. In the long connection mode, the server pushes real-time bus information for this trip to the mobile device in real time. Short connection mode refers to establishing a short HTTP connection between the mobile device and the server, using a periodic polling method, with information push frequency at the minute level. In short connection mode, the mobile device polls the server to obtain real-time bus information for this trip. It should be noted that when the target user is detected to be on any bus route, the connection mode between the target user's terminal device and the server is automatically configured to a short connection. Here, "any bus route" means that the target user is riding on a public bus.

[0017] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0018] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for intelligent passenger guidance and real-time information push at public transport transfer stations, characterized in that: Includes the following steps: S1: Based on the target user's mobile device, collect the public transportation travel planning information input by the target user, and simultaneously obtain the target user's connection matching information. The connection matching information includes the connection mode between the target user's terminal device and the server in short-distance, medium-distance, and long-distance scenarios. The connection mode is divided into two types: long connection and short connection. S2: Generate a public transport route for the target user based on the public transport travel planning information and display it to the target user. The target user will then take the trip according to the public transport route. Simultaneously, configure the connection mode between the target user's terminal device and the server to be a short connection. S3: Determine several walking routes for the target user's current trip based on the target user's public transportation route, and collect the target user's location information in real time on each walking route. Update the connection mode between the target user's mobile device and the server based on the location information. The server then pushes real-time public transportation information for this trip to the target user's mobile device based on the connection mode. The real-time public transportation information includes the real-time location of the bus, the remaining arrival time, sudden delays in the route, and changes in transfer stations.

2. The intelligent passenger guidance and real-time information push method for public transport transfer stations according to claim 1, characterized in that, In step S1, the public transportation travel planning information includes the latitude and longitude coordinates of the origin and destination input by the target user through the navigation software of the mobile device.

3. The intelligent passenger guidance and real-time information push method for public transport transfer stations according to claim 1, characterized in that, In step S1, the connection matching information of the target user is generated by collecting historical operation data of the target user's past trips according to preset generation rules. The historical operation data for any trip includes the public transportation route for the target user's trip, the travel time and walking distance to each transfer station, the number of times the navigation software was opened during the process of reaching each transfer station, the viewing time of the navigation software each time it was opened, and the frequency of information interaction operations.

4. The intelligent passenger guidance and real-time information push method for public transport transfer stations according to claim 3, characterized in that, Step S1, the specific details of the rules for generating connection matching information for the target user are as follows: S11: Mark all the acquired historical operation data as A1, A2, ..., Aa, where a is the historical operation data collection volume preset by the administrator; S12: Mark all the transfer stations included in the historical operation data A1 in ascending order of their station numbers as B1, B2, ..., Bb, where b≥1; S13: Utilize the formula Calculate the location progress coefficient D1 of the target user to reach the transfer station B1. In the formula, Cmax and Cmin are the farthest and shortest straight-line distances of the target user to reach the transfer station B1, respectively. Cmin is set to 0.01km by default to avoid the denominator being 0. C1 is the walking distance of the target user to the transfer station B1. S14: Calculate and obtain the location progress coefficients D2, D3, ..., Db of the target user arriving at transfer stations B2, B3, ..., Bb in sequence according to S13. Use the discrete point filtering algorithm to process the location progress coefficients D1, D2, ..., Db, and calculate the average value of all remaining location progress coefficients after data processing. Define the average value as the location matching degree E1 of the target user in this instance. S15: Using the formula Calculate the activity level G1 of the target user's operation behavior for this trip. In the formula, F1 is the maximum number of times the target user opens the navigation software during the process of arriving at each transfer station, F2 is the maximum viewing time of each time the target user opens the navigation software during the process of arriving at each transfer station, and F3 is the maximum information interaction frequency of the target user during the process of arriving at each transfer station. F1max is the average of the number of times the target user opens the navigation software during the process of reaching all transfer stations; F2max is the average of the viewing time of the target user each time the navigation software is opened during the process of reaching all transfer stations; and F3max is the maximum of the sum of the frequency of information interaction operations of the target user during the process of reaching all transfer stations. S16: Based on historical operation data A2, A3, ..., Aa, calculate and obtain the target user's position matching degree E2, E3, ..., Ea and operation behavior activity G2, G3, ..., Ga in the corresponding times according to S11 to S15; S17: Calculate and obtain the target user's interaction activity H1, H2, and H3 in short-distance, medium-distance, and long-distance scenarios based on the location matching degree E1, E2, ..., Ea respectively; S18: Determine the connection mode between the terminal device and the server in the corresponding scenario based on the target user's interactive activity in short-distance, medium-distance, and long-distance scenarios. S19: Generate connection matching information for the target user based on the connection mode between the terminal device and the server in short-distance, medium-distance, and long-distance scenarios.

5. The intelligent passenger guidance and real-time information push method for public transport transfer stations according to claim 4, characterized in that, S17, The calculation of the target user's interaction activity H1, H2, and H3 in short-distance, medium-distance, and long-distance scenarios is as follows: Obtain the activity level of the trips corresponding to all location matching degrees less than P1 in E1, E2, ..., Ea, and calculate their mean using the summation and averaging formula. Define the mean as the interaction activity level H1 of the target user in the short-distance scenario, where P1 is a preset short-distance threshold. Get the activity level of the trip corresponding to the location matching degree E1, E2, ..., Ea, which is greater than or equal to P1 and less than or equal to P2, and calculate the mean value using the summation and averaging formula. The mean value is defined as the interaction activity level H2 of the target user in the medium distance scenario, where P2 is the preset medium and long distance threshold. Obtain the activity level of the trips corresponding to all location matching degrees greater than P2 in E1, E2, ..., Ea, and calculate their mean using the summation and averaging formula. Define the mean as the interaction activity level H3 of the target user in long-distance scenarios.

6. The intelligent passenger guidance and real-time information push method for public transport transfer stations according to claim 4, characterized in that, S18: The determination of the connection mode between the terminal device and the server in the corresponding scenario is as follows: If the interaction activity H1 ≥ P3, then the connection mode between the target user's terminal device and the server in a short-distance scenario is determined to be a long connection; otherwise, it is a short connection. If the interaction activity H2 ≥ P3, then the connection mode between the target user's terminal device and the server in a medium-distance scenario is determined to be a long connection; otherwise, it is a short connection. If the interaction activity H3 ≥ P3, then the connection mode between the target user's terminal device and the server in a long-distance scenario is determined to be a long connection; otherwise, it is a short connection. P3 is a preset mode determination threshold.

7. The intelligent passenger guidance and real-time information push method for public transport transfer stations according to claim 1, characterized in that, Step S2, generating the public transportation route guidance content for the target user, specifically includes: Extract the latitude and longitude coordinates of the origin and destination from the public transportation travel planning information; based on the origin and destination latitude and longitude coordinates, use the bus best travel route optimization algorithm to generate the optimal public transportation route for the target user; The optimal bus route includes several bus routes and their corresponding route numbers; the route numbers are numbered sequentially starting from the number 1, and the smaller the number, the more likely the target user should take the bus route corresponding to that route number. Each bus route contains several bus stops and stop numbers. The stop numbers are numbered sequentially starting from the number 1. The smaller the number, the more likely the target user should take that bus stop.

8. The intelligent passenger guidance and real-time information push method for public transport transfer stations according to claim 1, characterized in that, Step S3 is as follows: S31: Based on the target user's public transportation travel guidance route, determine several walking routes for the target user's current trip and mark them sequentially as I1, I2, ..., Ii according to the order of walking. Among them, walking route I1 is the target user's walking from the latitude and longitude coordinates of the input public transportation travel planning information to the first boarding station, and the other walking routes are deduced in the same way. The first boarding station refers to the bus stop with station number 1 in the optimal bus route. The bus stop with station number 1 in any other bus route other than the bus route with station number 1 refers to the transfer station. S32: Collect the latitude and longitude coordinates of the target user in real time. When the target user starts walking on any walking route, execute the terminal configuration of the target user on the walking route.

9. The intelligent passenger guidance and real-time information push method for public transport transfer stations according to claim 8, characterized in that, In S32, the terminal configuration of the target user on the walking route is executed as follows: The latitude and longitude coordinates of the target user on the walking route are collected in real time. According to S13, the location progress coefficient of the target user to the transfer station on the walking route is calculated in real time. The location progress coefficient is compared with P1 and P2 respectively. If the position progress coefficient is less than P1, then retrieve the connection mode between the terminal device and the server in the corresponding short-distance scenario from the target user connection matching information, and reconfigure the connection mode between the target user terminal device and the server based on the connection mode. If the position progress coefficient is greater than or equal to P1 and less than or equal to P2, then retrieve the connection mode between the terminal device and the server in the corresponding medium-distance scenario from the target user connection matching information, and reconfigure the connection mode between the target user terminal device and the server based on the connection mode. If the position progress coefficient is greater than P2, then retrieve the connection mode between the terminal device and the server in the corresponding long-distance scenario from the target user connection matching information, and reconfigure the connection mode between the target user terminal device and the server based on the connection mode.

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