Intelligent bus shelter data processing method and system based on digital analysis

Abstract

The invention discloses an intelligent bus shelter data processing method and system based on digital analysis, and the method comprises the steps: collecting the use information of seats in a target vehicle and the use state of an air conditioner, calculating the temperature distribution information and congestion degree information of different seats, and transmitting the information to all bus shelters for data sharing; receiving a request instruction of a target passenger, identifying a historical riding record and current riding demand information of the target passenger, and predicting a riding preference behavior and an expected distance from a demand seat to a get-off gate; and building a seat recommendation model according to the temperature distribution information, the congestion degree information, the riding preference behavior and the expected distance, generating a target seat for recommendation, and when the target passenger determines to use the target seat, updating the data, synchronizing the data to all bus shelter, and continuing to perform seat recommendation for the next passenger. Personalized requirements of different passengers are considered, seats can be scientifically and reasonably recommended for the passengers, the problem that the passengers frequently replace the seats after selecting the seats blindly is avoided, and the riding experience of the passengers is improved.

Description

Technical Field

[0001] The present invention relates to the field of smart public transportation technology, and in particular to a smart bus shelter data processing method and system based on digital analysis. Background Art

[0002] With the acceleration of urbanization, public transportation systems play an increasingly important role in people's daily lives. Traditional bus shelters usually only provide basic waiting and information display functions, such as estimated arrival time, intervals and other information. Since passengers cannot obtain more detailed status information in the car and lack an effective seat recommendation mechanism, when the vehicle arrives at the station, passengers can only randomly select seats based on their feelings. This will result in the seats selected by customers not meeting the passengers' real riding needs. For example, passengers actually need a ventilated seat, but they choose a seat with a window that cannot be opened due to the fast seat filling. Or, passengers randomly sit in a seat with a lower temperature, but they happen to be unwell, so they need to change seats. At this time, passengers cannot predict whether the next seat is suitable, and when other seats are occupied, the change operation may not be smooth, and being forced to sit in the original seat will greatly reduce the passenger experience. In addition, this blind seat selection method by passengers may also cause some areas to be too crowded while other places are vacant, reducing space utilization. Summary of the invention

[0003] In order to solve at least one of the technical problems mentioned above, the present invention provides a smart bus shelter data processing method and system based on digital analysis.

[0004] In a first aspect, the present invention provides a method for processing data of a smart bus shelter based on digital analysis, the method comprising:

[0005] The bus departing from the previous bus shelter of the current smart bus shelter is taken as the target vehicle, and the seat usage information and air conditioning usage status in the target vehicle are collected;

[0006] Calculate the temperature distribution information of different seats based on the seat usage information and air conditioning usage status; calculate the crowding information around different seats based on the seat usage information; send the temperature distribution information and crowding information to the current smart bus shelter, and share the data with other shelters;

[0007] Based on the queue order of passengers in the current smart bus shelter, the first passenger in line is selected as the target passenger; the request instruction of the target passenger is received, and the historical riding record and current riding demand information of the target passenger are identified; the riding preference behavior of the target passenger is predicted based on the historical riding record, and the expected distance from the target passenger's desired seat to the exit is evaluated based on the current riding demand information;

[0008] A seat recommendation model is constructed based on temperature distribution information, congestion information, target passengers' riding preference behavior and expected distance, and the target seat recommended for the target passenger is output;

[0009] In response to the target passenger's confirmation result, when the target passenger decides to use the target seat, the congestion information and temperature distribution information of the remaining seats are updated and synchronized to all smart bus shelters, and the updated data is used to continue to recommend seats for the next passenger.

[0010] Preferably, the step of calculating the temperature distribution information of different seats according to the seat usage information and the air conditioner usage status includes:

[0011]

[0012] Where, T i represents the temperature of the i-th seat, is the average temperature in the car, ΔT a (d i ) represents the air conditioning influence factor, which is the distance d from seat i to the nearest air conditioning outlet ia Determine; ΔT hu (n j , d ij ) represents the heat dissipation increment of the human body, which is determined by the distance d between the i-th seat and the adjacent seat j. ij and the number of passengers n adjacent to seat j j To decide, N means there are N seats in total.

[0013] Preferably, the step of constructing a seat recommendation model based on the temperature distribution information and the congestion information, the target passenger's riding preference behavior and the expected distance, and outputting the target seat recommended for the target passenger includes:

[0014] Build a seat recommendation model:

[0015] G i =w1G(T i )+w2G(C i )+w3G(D i )+w4G(P i )+b

[0016] In the formula, G i represents the matching score between the i-th seat and the target passenger, w1, w2, w3, w4 are weight factors, b is a constant and satisfies 0 <b≤0.1;G(T i )、G(C i )、G(D i )、G(P i ) are the matching scores of the temperature, crowdedness, distance and riding preference between the i-th seat and the target passenger respectively;

[0017]

[0018]

[0019]

[0020]

[0021] Where, T i represents the temperature of the ith seat, T min , T max is the minimum and maximum value of the ideal temperature required by the target passengers, T avg T min With T max The middle value of M ij is the number of seats adjacent to the ith seat, and m is M ij The number of occupied seats; d io is the distance from the ith seat to the exit, d min d max is the minimum and maximum value of the expected distance of the target passenger;

[0022] The matching score between each seat and the target passenger is calculated according to the seat recommendation model, and the seat with the highest matching score is identified from the unoccupied seats and recommended to the target passenger as the target seat.

[0023] Preferably, after responding to the confirmation result of the target passenger, the method further comprises:

[0024] When the target passenger refuses to use the target seat, a continue matching instruction and an independent seat selection instruction are sent for the target passenger to choose;

[0025] When the target passenger responds to the instruction to continue matching, other seats are recommended to the target passenger in descending order of matching scores until the target passenger confirms to use a certain seat, and the seat usage status is updated;

[0026] When the target passenger responds to the autonomous seat selection instruction, the seat autonomously selected by the target passenger is allocated to the target passenger, and the seat usage status is updated.

[0027] In a second aspect, the present invention further provides a smart bus shelter data processing system based on digital analysis, the system comprising:

[0028] A data collection unit is used to take the bus departing from the previous bus shelter of the current smart bus shelter as the target vehicle, and collect the seat usage information and air conditioning usage status in the target vehicle;

[0029] A data sharing unit is used to calculate the temperature distribution information of different seats according to the seat usage information and the air conditioning usage status; calculate the crowding information around different seats according to the seat usage information; send the temperature distribution information and crowding information to the current smart bus shelter, and share the data with other shelters;

[0030] The demand identification unit is used to select the first passenger in the queue as the target passenger based on the current queue order of the passengers in the smart bus shelter; receive the request instruction of the target passenger, identify the historical riding record and current riding demand information of the target passenger; predict the riding preference behavior of the target passenger based on the historical riding record, and evaluate the expected distance from the target passenger's required seat to the exit based on the current riding demand information;

[0031] A seat recommendation unit is used to construct a seat recommendation model based on temperature distribution information and congestion information, the target passenger's riding preference behavior and expected distance, and output a target seat recommended for the target passenger;

[0032] The data update unit is used to respond to the confirmation result of the target passenger. When the target passenger decides to use the target seat, the congestion information and temperature distribution information of the remaining seats are updated and synchronized to all smart bus shelters, and the updated data is used to continue to recommend seats for the next passenger.

[0033] Preferably, the data sharing unit is used to calculate the temperature distribution information of different seats according to the seat usage information and the air conditioner usage status, including:

[0034]

[0035] Where, T i represents the temperature of the i-th seat, is the average temperature in the car, ΔT a (d i ) represents the air conditioning influence factor, which is the distance d from seat i to the nearest air conditioning outlet ia Determine; ΔT hu (n j , d ij ) represents the heat dissipation increment of the human body, which is determined by the distance d between the i-th seat and the adjacent seat j. ij and the number of passengers n adjacent to seat j j To decide, N means there are N seats in total.

[0036] Preferably, the seat recommendation unit is further used for:

[0037] Build a seat recommendation model:

[0038] G i =w1G(T i )+w2G(Ci )+w3G(D i )+w4G(P i )+b

[0039] In the formula, G i represents the matching score between the i-th seat and the target passenger, w1, w2, w3, w4 are weight factors, b is a constant and satisfies 0 <b≤0.1;G(T i )、G(C i )、G(D i )、G(P i ) are the matching scores of the temperature, crowdedness, distance and riding preference between the i-th seat and the target passenger respectively;

[0040]

[0041]

[0042]

[0043]

[0044] Where, T i represents the temperature of the ith seat, T min , T max is the minimum and maximum value of the ideal temperature required by the target passengers, T avg T min With T max The middle value of M ij is the number of seats adjacent to the ith seat, and m is M ij The number of occupied seats; d io is the distance from the ith seat to the exit, d min d max is the minimum and maximum value of the expected distance of the target passenger;

[0045] The matching score between each seat and the target passenger is calculated according to the seat recommendation model, and the seat with the highest matching score is identified from the unoccupied seats and recommended to the target passenger as the target seat.

[0046] Preferably, the data updating unit is used to, after responding to the confirmation result of the target passenger, further include:

[0047] When the target passenger refuses to use the target seat, a continue matching instruction and an independent seat selection instruction are sent for the target passenger to choose;

[0048] When the target passenger responds to the instruction to continue matching, other seats are recommended to the target passenger in descending order of matching scores until the target passenger confirms to use a certain seat, and the seat usage status is updated;

[0049] When the target passenger responds to the autonomous seat selection instruction, the seat autonomously selected by the target passenger is allocated to the target passenger, and the seat usage status is updated.

[0050] In a third aspect, the present invention further provides an electronic device comprising a processor and a memory, wherein the memory is used to store computer program code, and the computer program code comprises computer instructions. When the processor executes the computer instructions, the electronic device executes the method as described in the first aspect above and any possible implementation thereof.

[0051] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, wherein the computer program includes program instructions, and when the program instructions are executed by a processor of an electronic device, the processor executes the method as described in the first aspect above and any possible implementation thereof.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The data processing method of the smart bus shelter based on digital analysis provided by the present invention collects and analyzes the seat usage information and air conditioning status in the target vehicle, calculates the temperature distribution and congestion information of different seats, and sends this information to the current smart bus shelter for sharing with other shelters, thereby solving the information asymmetry problem in the above-mentioned prior art. At the same time, for each target passenger waiting in line, the present invention can identify his historical riding record and current demand, predict his preference behavior, and build a seat recommendation model in combination with temperature distribution and congestion information, so as to provide the target passenger with the best seat recommendation. This process not only improves passenger satisfaction, but also realizes more reasonable resource allocation and improves the efficiency of the use of space in the car. Finally, when the target passenger confirms to use a recommended seat, the system will immediately update the relevant data and synchronize it between all smart bus shelters to ensure that subsequent passengers can also enjoy accurate seat recommendation services. Therefore, the present invention effectively improves the intelligence level and service quality of the public transportation system and provides a new solution for urban traffic management.

[0054] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0056] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0057] Figure 1 A schematic diagram of a flow chart of a method for processing data of a smart bus shelter based on digital analysis provided by an embodiment of the present invention;

[0058] Figure 2 A schematic diagram of the structure of a smart bus shelter data processing system based on digital analysis provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0061] At present, when passengers take public buses, due to information asymmetry and lack of a reasonable seat recommendation mechanism, passengers can only choose seats blindly, which causes passengers to frequently change seats, low resource utilization, and poor passenger experience. To this end, the present invention aims to provide a method that can timely obtain information such as the temperature in the car, the seat usage status, the seat distribution and the distance from the door, so as to recommend seats that better meet the needs of passengers, greatly improve the passenger experience, optimize the resource allocation in the car and improve the utilization rate.

[0062] See also Figure 1 , Figure 1 The present invention provides a flow chart of a method for processing data of a smart bus shelter based on digital analysis. Figure 1 As shown, the method includes:

[0063] S10: Take the bus that departs from the previous bus stop of the current smart bus stop as the target vehicle, and collect the seat usage information and air conditioning usage status in the target vehicle.

[0064] The target vehicle refers to the bus that departs from the previous bus shelter of the current smart bus shelter. For example, there are two bus shelters, A and B, and the next stop of A is B. Therefore, all buses that stop from A to B can be regarded as target vehicles. Because once the vehicle starts from station A, the doors usually do not open and close during the period of reaching B, that is, there is no passenger flow. The usage status information of the carriage in this section is relatively stable, so the subsequent analysis process is also relatively effective. Once more stations are selected, such as starting the analysis from the stations before station A, the usage of several of these stations will change dynamically, and it is impossible to accurately collect data.

[0065] Usually, the seat usage information in the target vehicle is counted by the camera inside the vehicle, so that the degree of crowding in the vehicle can be understood. Preferably, the number of passengers in each row of seats can also be estimated by infrared sensors. The air conditioning usage status refers to the air conditioning switch status and the parameter size status when it is used. Since most buses are currently equipped with intelligent air conditioning systems, the most direct way is to read the status information from the air conditioning control system, such as the air conditioning's instant output power, temperature, wind speed and other information.

[0066] S20. Calculate the temperature distribution information of different seats according to the seat usage information and the air conditioning usage status; calculate the congestion information around different seats according to the seat usage information; send the temperature distribution information and congestion information to the current smart bus shelter, and share the data with other shelters.

[0067] The temperature is mainly displayed by the air conditioning controller in the car, but the temperature displayed by the air conditioner is usually the average temperature level in the car, and it cannot accurately predict the temperature of each location. For example, the temperature near the air outlet is different from the temperature at other locations, the temperature in crowded areas and scattered areas is different, and the temperature distribution of seats that can be ventilated and cannot be ventilated is also different. If temperature sensors are installed around each seat, although the temperature conditions of different seats can be accurately obtained, this method is too expensive and not conducive to large-scale promotion. In order to solve this problem, this embodiment aims to predict the temperature conditions of different seats through modeling to provide a more accurate prediction of passenger comfort.

[0068] In a preferred embodiment, calculating the temperature distribution information of different seats according to the seat usage information and the air conditioning usage status includes:

[0069]

[0070] Where, Ti represents the temperature of the i-th seat, is the average temperature in the car, ΔT a (d i ) represents the air conditioning influence factor, which is the distance d from seat i to the nearest air conditioning outlet ia Determine; ΔT hu (n j , d ij ) represents the heat dissipation increment of the human body, which is determined by the distance d between the i-th seat and the adjacent seat j. ij and the number of passengers n adjacent to seat j j To decide, N means there are N seats in total.

[0071] In the above formula, multiple temperature sensors installed in the vehicle cabin collect data and calculate the average value of these data as the average temperature in the vehicle cabin. Based on the distance d from each seat to the nearest air conditioning outlet ia , using pre-established empirical formulas or simulation results to determine the degree of impact of air conditioning on each seat. Generally speaking, the closer the distance, the more obvious the effect of air conditioning, and therefore the greater the temperature change. Air conditioning impact factor ΔT a (d i ) takes into account the air conditioner's operating mode (cooling / heating), wind speed and other factors to ensure that the model can reflect real physical phenomena. For each seat, analyze the situation of the surrounding seats, especially the number of occupied seats and the distance between them and the adjacent seats. ij Using heat conduction theory and ergonomics knowledge, combined with experimental data or simulation results, a function is constructed to describe the effect of human body heat dissipation on the ambient temperature. This function will gradually weaken as the distance increases, and when more people are close, the total heat contribution will increase accordingly. Combining the above three parts, the actual temperature T of each seat is calculated according to the given formula i This not only allows the temperature distribution in the entire car to be determined, but also allows for personalized adjustments and service optimization for specific locations.

[0072] In another preferred embodiment, the temperature distribution of different seats can be predicted by combining models of physical phenomena such as heat conduction, convection and radiation. The specific steps are as follows:

[0073] 1) Establish initial conditions and boundary conditions:

[0074] Carriage structure parameters: including car size, seat layout, window position and area, door position, etc.

[0075] Material properties: Different materials (e.g. metal frames, plastic seats, glass windows) have different thermal conductivities, and this information is critical for accurate modeling.

[0076] External environmental conditions: The vehicle location is obtained through GPS, and the external temperature, humidity, wind speed and other data are obtained in combination with the weather API.

[0077] 2) Define heat sources and heat dissipation points:

[0078] Human body heat source: Assuming that each passenger radiates a certain amount of heat per minute (e.g. 70-100 watts), the total human body heat is calculated based on the real-time monitored passenger density.

[0079] Air conditioning system: Determine the location, air volume and set temperature of the air conditioning outlet; also consider the role of the return air vent to ensure proper air circulation.

[0080] Solar radiation: If the vehicle has areas of direct sunlight, the effects of solar radiation need to be considered, especially during daylight hours.

[0081] 3) Apply thermodynamic principles to model:

[0082] Four equations, namely, steady-state heat conduction equation, transient heat conduction equation, natural convection and forced convection, and radiation heat transfer, are used to calculate the temperature effects in these situations respectively.

[0083] 4) Introducing dynamic factors:

[0084] Passenger movement: As the vehicle moves, passengers get on and off the vehicle, which causes changes in the distribution of people in the vehicle, thus affecting the local temperature field. Passenger positions can be tracked through sensors or cameras to update model inputs.

[0085] Air conditioning adjustment: Automatically adjust the air conditioning working mode (cooling / heating), wind speed and direction according to the temperature difference between inside and outside the car and the preset temperature to maintain a comfortable indoor environment.

[0086] 5) Numerical solution and simulation:

[0087] Discretization mesh: Divide the cabin space into finite element meshes to facilitate the numerical solution of partial differential equations.

[0088] Iterative algorithm: Use numerical methods such as finite difference method, finite volume method or finite element method to solve the above equations and obtain the temperature value at each node.

[0089] Software tools: Existing CFD (computational fluid dynamics) and FEA (finite element analysis) software can be used to perform efficient simulation calculations.

[0090] 6) Result visualization and interaction:

[0091] Temperature cloud map display: The calculated temperature distribution is intuitively presented on the 3D model in the form of color coding to help users understand the temperature conditions around each seat.

[0092] Real-time feedback mechanism: Combined with smart bus stops or mobile applications, it can provide waiting passengers with a preview of the seat temperature of the upcoming vehicle, helping them make better choices.

[0093] In this way, we only need to collect the temperature conditions at key locations and combine them with thermodynamic models to accurately predict the temperature distribution of different seats. Therefore, through detailed modeling of temperature distribution, we can better understand and improve the in-vehicle environment, making the passengers' experience more comfortable during the journey. In this way, we can understand which areas require more cooling or heating resources, so as to accurately control the air conditioning system and avoid unnecessary energy waste. Especially during the flu season or other public health events, reasonable ventilation and temperature management can help reduce the risk of disease transmission.

[0094] Furthermore, the degree of congestion can be determined based on the number of occupied seats and the number of seats. After the temperature distribution of different seats and the number of occupied seats are obtained, the temperature distribution information and congestion information are sent to the current smart bus shelter, and the data is shared with other shelters. Specifically, the processed temperature distribution information and congestion information can be sent directly to the display screen of the current smart bus shelter for waiting passengers to view. It is also possible to push notifications to users who have subscribed to the service through mobile phone applications to provide personalized recommendations. In order to interconnect the urban bus system, it is also necessary to build a bus information sharing network so that the latest vehicle status information can be exchanged between the shelters. In this way, passengers can understand the use environment of the carriage more intuitively and accurately, solve the problems of information asymmetry and information islands, and passengers can choose the most appropriate time and place to take the bus based on the information provided, reducing waiting time and discomfort, thereby improving the overall travel experience.

[0095] S30. Based on the queue order of passengers in the current smart bus shelter, the first passenger in line is selected as the target passenger; the request instruction of the target passenger is received, and the historical riding record and current riding demand information of the target passenger are identified; the riding preference behavior of the target passenger is predicted based on the historical riding record, and the expected distance from the target passenger's desired seat to the exit is evaluated based on the current riding demand information.

[0096] Usually, the entire bus system adopts the following architecture design:

[0097] Smart bus shelter: equipped with cameras, sensors, touch screens and other equipment to monitor the queue order of passengers and provide an interactive interface.

[0098] Mobile application: allows passengers to input personal preferences and ride demand information in advance, and supports data synchronization with smart bus stops.

[0099] Cloud server: responsible for processing data, running prediction models and seat recommendation algorithms, and feeding back results to the bus shelter or application.

[0100] In order to maintain order when boarding the bus, people need to queue up, so when recommending seats, they are also based on the order of first come, first served, with a certain priority. The camera installed in the bus shelter is combined with AI vision technology to automatically detect and record the position of the queued passengers. When the vehicle is about to arrive at the station, the system will confirm the first passenger in line as the target passenger based on the latest image analysis results. Through facial recognition or other biometric recognition technology, the identity of the target passenger is ensured to be accurate.

[0101] Further, the target passenger's request instruction is received, and the target passenger's historical riding record and current riding demand information are identified; through the touch screen or mobile application, the target passenger's specific requirements for this trip are received, such as whether to carry large luggage, whether there are special health conditions (such as fear of cold), and the expected alighting station. The target passenger's historical riding record is retrieved from the cloud server, including the routes, time, and seat type (window / corridor) that are often taken. Based on a large amount of existing riding data, a machine learning model that can predict passenger preferences is trained. The model can identify which types of seats passengers tend to choose (such as close to the door, away from noise sources) and their preferences for factors such as temperature and light. When evaluating the expected distance from the target passenger's desired seat to the alighting exit, the layout diagram inside the bus is considered, and combined with the target passenger's destination information, the optimal path length from different seats to the alighting exit is calculated. Prioritize those seats that allow passengers to quickly leave the bus when approaching their destination. Considering that the bus may be very crowded during peak hours, the system will also evaluate potential obstacles on each path (such as standing passengers) to ensure that the recommended seats not only meet preferences but also ensure convenience.

[0102] S40: construct a seat recommendation model based on the temperature distribution information and the congestion information, the target passenger's riding preference behavior and the expected distance, and output a target seat recommended for the target passenger.

[0103] In step S20, the temperature distribution information of different seats is obtained, and in step S30, the target passenger's riding preference behavior and the expected distance from the required seat to the exit are obtained. This embodiment aims to perform modeling and seat recommendation based on these data.

[0104] Specifically, the seat recommendation model is constructed according to the temperature distribution information and the congestion information, the target passenger's riding preference behavior and the expected distance, and the target seat recommended for the target passenger is output, including:

[0105] Build a seat recommendation model:

[0106] G i =w1G(T i )+w2G(C i )+w3G(D i )+w4G(P i )+b

[0107] In the formula, G i represents the matching score between the i-th seat and the target passenger, w1, w2, w3, w4 are weight factors, b is a constant and satisfies 0 <b≤0.1;G(T i )、G(C i )、G(D i )、G(P i ) are the matching scores of the temperature, crowdedness, distance and riding preference between the i-th seat and the target passenger respectively;

[0108]

[0109]

[0110]

[0111]

[0112] Where, T i represents the temperature of the ith seat, T min , T max is the minimum and maximum value of the ideal temperature required by the target passengers, T avg T min With T max The middle value of M ij is the number of seats adjacent to the ith seat, and m is M ij The number of occupied seats; d io is the distance from the ith seat to the exit, d min d max is the minimum and maximum value of the expected distance of the target passenger;

[0113] The matching score between each seat and the target passenger is calculated according to the seat recommendation model, and the seat with the highest matching score is identified from the unoccupied seats and recommended to the target passenger as the target seat.

[0114] This embodiment can help bus companies better understand passenger needs and optimize operational strategies and service quality by collecting and analyzing a large amount of riding behavior data. By comprehensively considering multiple factors such as temperature, crowding, distance, and personal preferences, the system can provide each passenger with the seat recommendations that best meet their needs, greatly improving passenger satisfaction. Finally, the final generated seat recommendations will be presented to the target passengers in an intuitive way, such as through the large screen of the bus shelter or the map annotation on the mobile phone APP.

[0115] Therefore, by matching the first passenger with a suitable seat in advance, the search time after boarding is reduced, especially during peak hours, which helps to speed up passenger flow and reduce congestion. Personalized seat recommendations fully consider the personal preferences and actual needs of passengers, making the journey more comfortable and pleasant; at the same time, clear guidance on the way to get off the bus also increases the sense of security. The system can reasonably arrange seats according to real-time conditions, avoiding the situation where some areas are over-concentrated while other places are vacant, thereby making more efficient use of the compartment space.

[0116] S50. In response to the confirmation result of the target passenger, when the target passenger determines to use the target seat, the congestion information and temperature distribution information of the remaining seats are updated and synchronized to all smart bus shelters, and the updated data is used to continue to recommend seats for the next passenger.

[0117] When the target passenger decides to use the target seat recommended by the system, the target passenger's confirmation of the recommended seat selection is received through the touch screen or mobile application. The seat selected by the target passenger is then marked as occupied, and the relevant information of the passenger (such as boarding time, destination, etc.) is recorded. Then the crowding information and temperature distribution information are updated and pushed from the cloud server to all relevant smart bus shelters. Ensure that each shelter can receive the latest data in real time to provide accurate seat recommendations for the next passenger.

[0118] In another embodiment, if the passenger is not satisfied with the target seat recommended by the system, the method further includes:

[0119] When the target passenger refuses to use the target seat, a continue matching instruction and an independent seat selection instruction are sent for the target passenger to choose;

[0120] When the target passenger responds to the instruction to continue matching, other seats are recommended to the target passenger in descending order of matching scores until the target passenger confirms to use a certain seat, and the seat usage status is updated;

[0121] When the target passenger responds to the autonomous seat selection instruction, the seat autonomously selected by the target passenger is allocated to the target passenger, and the seat usage status is updated.

[0122] This system combines recommendations with passenger confirmation to ensure that passengers are matched with suitable seats, greatly improving the user experience.

[0123] In summary, the data processing method for smart bus shelters based on digital analysis provided by the present invention collects and analyzes the seat usage information and air conditioning status in the target vehicle, calculates the temperature distribution and congestion information of different seats, and sends this information to the current smart bus shelter for sharing with other shelters, thereby solving the information asymmetry problem in the above-mentioned prior art. At the same time, for each target passenger waiting in line, the present invention can identify their historical riding records and current needs, predict their preference behavior, and build a seat recommendation model in combination with temperature distribution and congestion information, thereby providing the target passenger with the best seat recommendation. This process not only improves passenger satisfaction, but also achieves more reasonable resource allocation and improves the efficiency of the use of space in the car. Finally, when the target passenger confirms the use of a recommended seat, the system will immediately update the relevant data and synchronize it between all smart bus shelters to ensure that subsequent passengers can also enjoy accurate seat recommendation services. Therefore, the present invention effectively improves the intelligence level and service quality of the public transportation system and provides a new solution for urban traffic management.

[0124] See also Figure 2 In one embodiment, the present invention further provides a smart bus shelter data processing system based on digital analysis, the system comprising:

[0125] The data collection unit 100 is used to take the bus departing from the previous bus shelter of the current smart bus shelter as the target vehicle, and collect the seat usage information and air conditioning usage status in the target vehicle;

[0126] The data sharing unit 200 is used to calculate the temperature distribution information of different seats according to the seat usage information and the air conditioning usage status; calculate the crowding information around different seats according to the seat usage information; send the temperature distribution information and the crowding information to the current smart bus shelter, and share the data with other shelters;

[0127] The demand identification unit 300 is used to select the first passenger as the target passenger based on the queue order of the passengers in the current smart bus shelter; receive the request instruction of the target passenger, identify the historical riding record and current riding demand information of the target passenger; predict the riding preference behavior of the target passenger based on the historical riding record, and evaluate the expected distance from the target passenger's required seat to the exit based on the current riding demand information;

[0128] A seat recommendation unit 400 is used to construct a seat recommendation model based on the temperature distribution information and the congestion information, the target passenger's riding preference behavior and the expected distance, and output a target seat recommended for the target passenger;

[0129] The data updating unit 500 is used to respond to the confirmation result of the target passenger. When the target passenger determines to use the target seat, the congestion information and temperature distribution information of the remaining seats are updated and synchronized to all smart bus shelters, and the updated data is used to continue to recommend seats for the next passenger.

[0130] Preferably, the data sharing unit 200 is used to calculate the temperature distribution information of different seats according to the seat usage information and the air conditioner usage status, including:

[0131]

[0132] Where, T i represents the temperature of the i-th seat, is the average temperature in the car, ΔT a (d i ) represents the air conditioning influence factor, which is the distance d from seat i to the nearest air conditioning outlet ia Determine; ΔT hu (n j , d ij ) represents the heat dissipation increment of the human body, which is determined by the distance d between the i-th seat and the adjacent seat j. ij and the number of passengers n adjacent to seat j j To decide, N means there are N seats in total.

[0133] Preferably, the seat recommendation unit 400 is further used for:

[0134] Build a seat recommendation model:

[0135] G i =w1G(T i )+w2G(C i )+w3G(D i )+w4G(P i )+b

[0136] In the formula, G i represents the matching score between the i-th seat and the target passenger, w1, w2, w3, w4 are weight factors, b is a constant and satisfies 0 <b≤0.1;G(T i )、G(C i )、G(D i )、G(P i ) are the matching scores of the temperature, crowdedness, distance and riding preference between the i-th seat and the target passenger respectively;

[0137]

[0138]

[0139]

[0140]

[0141] Where, T i represents the temperature of the ith seat, T min , T max is the minimum and maximum value of the ideal temperature required by the target passengers, T avg f min With T max The middle value of M ij is the number of seats adjacent to the ith seat, and m is M ij The number of occupied seats; d io is the distance from the ith seat to the exit, d min d max is the minimum and maximum value of the expected distance of the target passenger;

[0142] The matching score between each seat and the target passenger is calculated according to the seat recommendation model, and the seat with the highest matching score is identified from the unoccupied seats and recommended to the target passenger as the target seat.

[0143] Preferably, the data updating unit 500, after responding to the confirmation result of the target passenger, further comprises:

[0144] When the target passenger refuses to use the target seat, a continue matching instruction and an independent seat selection instruction are sent for the target passenger to choose;

[0145] When the target passenger responds to the instruction to continue matching, other seats are recommended to the target passenger in descending order of matching scores until the target passenger confirms to use a certain seat, and the seat usage status is updated;

[0146] When the target passenger responds to the autonomous seat selection instruction, the seat autonomously selected by the target passenger is allocated to the target passenger, and the seat usage status is updated.

[0147] It can be understood that the functions or modules included in the system provided in this embodiment can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0148] The present invention also provides an electronic device, including a processor and a memory, wherein the memory is used to store computer program code, and the computer program code includes computer instructions. When the processor executes the computer instructions, the electronic device executes a method as described in any possible implementation manner.

[0149] The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a method as described in any possible implementation manner.

[0150] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0151] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those skilled in the art can also clearly understand that the descriptions of the various embodiments of the present invention have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in a certain embodiment, refer to the records of other embodiments.

Claims

1. A method for processing data of a smart bus shelter based on digital analysis, characterized in that: The method comprises: The bus departing from the previous bus shelter of the current smart bus shelter is taken as the target vehicle, and the seat usage information and air conditioning usage status in the target vehicle are collected; Calculate the temperature distribution information of different seats based on the seat usage information and air conditioning usage status; calculate the crowding information around different seats based on the seat usage information; send the temperature distribution information and crowding information to the current smart bus shelter, and share the data with other shelters; Based on the queue order of passengers in the current smart bus shelter, the first passenger in line is selected as the target passenger; the request instruction of the target passenger is received, and the historical riding record and current riding demand information of the target passenger are identified; the riding preference behavior of the target passenger is predicted based on the historical riding record, and the expected distance from the target passenger's desired seat to the exit is evaluated based on the current riding demand information; A seat recommendation model is constructed based on temperature distribution information, congestion information, target passengers' riding preference behavior and expected distance, and the target seat recommended for the target passenger is output; In response to the target passenger's confirmation result, when the target passenger decides to use the target seat, the congestion information and temperature distribution information of the remaining seats are updated and synchronized to all smart bus shelters, and the updated data is used to continue to recommend seats for the next passenger.

2. The method for processing data of a smart bus shelter based on digital analysis according to claim 1 is characterized in that: The calculating of temperature distribution information of different seats according to seat usage information and air conditioning usage status includes: Where, T i represents the temperature of the i-th seat, is the average temperature in the car, ΔT a (d i ) represents the air conditioning influence factor, which is the distance d from seat i to the nearest air conditioning outlet ia Determine; ΔT hu (n j ,d ij ) represents the heat dissipation increment of the human body, which is determined by the distance d between the i-th seat and the adjacent seat j. ij and the number of passengers n adjacent to seat j j To decide, N means there are N seats in total.

3. The method for processing data of a smart bus shelter based on digital analysis according to claim 2 is characterized in that: The method of constructing a seat recommendation model based on the temperature distribution information and the congestion information, the target passenger's riding preference behavior and the expected distance, and outputting a target seat recommended for the target passenger, includes: Build a seat recommendation model: G i =w1G(T i )+w2G(C i )+w3G(D i )+w4G(P i )+b In the formula, G i represents the matching score between the i-th seat and the target passenger, w1, w2, w3, w4 are weight factors, b is a constant and satisfies 0 <b≤0.1;G(T i )、G(C i )、G(D i )、G(P i ) are the matching scores of the temperature, crowdedness, distance and riding preference between the i-th seat and the target passenger respectively; Where, T i represents the temperature of the ith seat, T min 、T max is the minimum and maximum value of the ideal temperature required by the target passengers, T avg T min With T max The middle value of M ij is the number of seats adjacent to the ith seat, and m is M ij The number of occupied seats; d io is the distance from the ith seat to the exit, d min ,d max is the minimum and maximum value of the expected distance of the target passenger; The matching score between each seat and the target passenger is calculated according to the seat recommendation model, and the seat with the highest matching score is identified from the unoccupied seats and recommended to the target passenger as the target seat.

4. The method for processing data of a smart bus shelter based on digital analysis according to claim 3 is characterized in that: After the confirmation result of the responding target passenger, the method further includes: When the target passenger refuses to use the target seat, a continue matching instruction and an independent seat selection instruction are sent for the target passenger to choose; When the target passenger responds to the instruction to continue matching, other seats are recommended to the target passenger in descending order of matching scores until the target passenger confirms to use a certain seat, and the seat usage status is updated; When the target passenger responds to the autonomous seat selection instruction, the seat autonomously selected by the target passenger is allocated to the target passenger, and the seat usage status is updated.

5. A smart bus shelter data processing system based on digital analysis, characterized in that: The system comprises: A data collection unit is used to take the bus departing from the previous bus shelter of the current smart bus shelter as the target vehicle, and collect the seat usage information and air conditioning usage status in the target vehicle; A data sharing unit is used to calculate the temperature distribution information of different seats according to the seat usage information and the air conditioning usage status; calculate the crowding information around different seats according to the seat usage information; send the temperature distribution information and crowding information to the current smart bus shelter, and share the data with other shelters; The demand identification unit is used to select the first passenger in the queue as the target passenger based on the current queue order of the passengers in the smart bus shelter; receive the request instruction of the target passenger, identify the historical riding record and current riding demand information of the target passenger; predict the riding preference behavior of the target passenger based on the historical riding record, and evaluate the expected distance from the target passenger's required seat to the exit based on the current riding demand information; A seat recommendation unit is used to construct a seat recommendation model based on temperature distribution information and congestion information, the target passenger's riding preference behavior and expected distance, and output a target seat recommended for the target passenger; The data update unit is used to respond to the confirmation result of the target passenger. When the target passenger decides to use the target seat, the congestion information and temperature distribution information of the remaining seats are updated and synchronized to all smart bus shelters, and the updated data is used to continue to recommend seats for the next passenger.

6. The smart bus shelter data processing system based on digital analysis according to claim 5 is characterized in that: The data sharing unit is used to calculate the temperature distribution information of different seats according to the seat usage information and the air conditioner usage status, including: Where, T i represents the temperature of the i-th seat, is the average temperature in the car, ΔT a (d i ) represents the air conditioning influence factor, which is the distance d from seat i to the nearest air conditioning outlet ia Determine; ΔT hu (n j ,d ij ) represents the heat dissipation increment of the human body, which is determined by the distance d between the i-th seat and the adjacent seat j. ij and the number of passengers n adjacent to seat j j To decide, N means there are N seats in total.

7. The smart bus shelter data processing system based on digital analysis according to claim 6 is characterized in that: The seat recommendation unit is further used for: Build a seat recommendation model: G i =w1G(T i )+w2G(C i )+w3G(D i )+w4G(P i )+b In the formula, G i represents the matching score between the i-th seat and the target passenger, w1, w2, w3, w4 are weight factors, b is a constant and satisfies 0 <b≤0.1;G(T i )、G(C i )、G(D i )、G(P i ) are the matching scores of the temperature, crowdedness, distance and riding preference between the i-th seat and the target passenger respectively; Where, T i represents the temperature of the ith seat, T min 、T max is the minimum and maximum value of the ideal temperature required by the target passengers, T avg T min With T max The middle value of M ij is the number of seats adjacent to the ith seat, and m is M ij The number of occupied seats; d io is the distance from the ith seat to the exit, d min ,d max is the minimum and maximum value of the expected distance of the target passenger; The matching score between each seat and the target passenger is calculated according to the seat recommendation model, and the seat with the highest matching score is identified from the unoccupied seats and recommended to the target passenger as the target seat.

8. The smart bus shelter data processing system based on digital analysis according to claim 7 is characterized in that: The data updating unit is used to, after the confirmation result of the response target passenger, further include: When the target passenger refuses to use the target seat, a continue matching instruction and an independent seat selection instruction are sent for the target passenger to choose; When the target passenger responds to the instruction to continue matching, other seats are recommended to the target passenger in descending order of matching scores until the target passenger confirms to use a certain seat, and the seat usage status is updated; When the target passenger responds to the autonomous seat selection instruction, the seat autonomously selected by the target passenger is allocated to the target passenger, and the seat usage status is updated.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store computer program codes, wherein the computer program codes include computer instructions. When the processor executes the computer instructions, the electronic device executes the smart bus shelter data processing method based on digital analysis as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes the smart bus shelter data processing method based on digital analysis as described in any one of claims 1 to 4.

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