Road network passenger flow detection method, device, equipment, medium and prediction system

Abstract

The invention discloses a road network passenger flow detection method, device and equipment, a medium and a prediction system. The method comprises the following steps: acquiring passenger flow information of each station in a train road network in a first time period; the passenger flow information comprises ticket checking passenger flow and transfer passenger flow; acquiring real-time public information in a second time period, wherein the real-time public information comprises public activity information; determining a passenger flow prediction result of the second time period according to the passenger flow information of the first time period and the real-time public information of the second time period; wherein the first time period is prior to the second time period. According to the embodiment of the invention, the efficiency and accuracy of road network passenger flow detection can be improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a road network passenger flow detection method, device, equipment, medium and prediction system. Background Art

[0002] With the development of rail transit, the number of passengers has increased significantly. In order to ensure the efficient operation of the dispatching and commanding system, it is necessary to obtain passenger flow information. The dispatching and commanding system adjusts the train plan based on the passenger flow information.

[0003] Currently, passenger flow information is obtained through ticket sales and inspection systems or manual ticket inspection.

[0004] However, the passenger flow information obtained by the ticket sales and inspection system or manual ticket inspection has low detection efficiency and cannot be obtained in real time. Summary of the invention

[0005] The present invention provides a road network passenger flow detection method, device, equipment, medium and prediction system. The embodiments of the present invention can improve the efficiency and accuracy of road network passenger flow detection.

[0006] In a first aspect, an embodiment of the present invention provides a road network passenger flow detection method, the method comprising:

[0007] Obtaining passenger flow information of each station in the train network in the first time period; the passenger flow information includes ticket checking passenger flow and transfer passenger flow;

[0008] Acquire real-time public information in a second time period, where the real-time public information includes public activity information;

[0009] The passenger flow prediction result for the second time period is determined according to the passenger flow information for the first time period and the real-time public information for the second time period; wherein the first time period is prior to the second time period.

[0010] In a second aspect, an embodiment of the present invention further provides a road network passenger flow detection device, the device comprising:

[0011] A historical passenger flow acquisition module is used to acquire passenger flow information of each station in the train network in the first time period; the passenger flow information includes ticket checking passenger flow and transfer passenger flow;

[0012] A public information acquisition module, used to acquire real-time public information in a second time period, where the real-time public information includes public activity information;

[0013] The passenger flow prediction module is used to determine the passenger flow prediction result of the second time period according to the passenger flow information of the first time period and the real-time public information of the second time period; wherein the first time period is prior to the second time period.

[0014] In a third aspect, an embodiment of the present invention further provides a road network passenger flow detection device, and the train track tracking device includes:

[0015] at least one processor; and

[0016] a memory communicatively connected to at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the road network passenger flow detection method of any embodiment of the present invention.

[0018] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a processor to implement the road network passenger flow detection method of any embodiment of the present invention when executed.

[0019] In a fifth aspect, an embodiment of the present invention further provides a road network passenger flow prediction system, comprising:

[0020] Passenger dispatching equipment and road network passenger flow detection equipment;

[0021] The passenger dispatching equipment is connected to the road network passenger flow detection equipment in communication;

[0022] Road network passenger flow detection equipment is used to predict passenger flow information;

[0023] Passenger transport dispatching equipment is used to determine driving adjustment information based on passenger flow information.

[0024] The technical solution of the embodiment of the present invention can obtain accurate passenger flow information of each station in the first time period by acquiring the passenger flow information of each station in the road network in the first time period, and provide the actual passenger flow information of each station; the ticket checking passenger flow and transfer passenger flow can accurately reflect the total number of passengers entering the road network, and provide reliable historical data for subsequent analysis and prediction; passenger flow prediction is performed by combining the passenger flow information of the first time period and the real-time public information of the second time period, so that the prediction result can refer to historical data and can also be combined with the specific circumstances of the current or future time period. Comprehensive analysis helps to improve the accuracy of passenger flow prediction and reduce the error caused by relying solely on historical passenger flow information for analysis.

[0025] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 A flow chart of a road network passenger flow detection method provided by an embodiment of the present invention;

[0028] Figure 2 A flow chart of a road network passenger flow detection method provided by an embodiment of the present invention;

[0029] Figure 3 A scene diagram of a road network passenger flow detection method provided by an embodiment of the present invention;

[0030] Figure 4 A schematic diagram of the structure of a road network passenger flow detection device provided by an embodiment of the present invention;

[0031] Figure 5 A schematic diagram of the structure of a road network passenger flow detection device provided by an embodiment of the present invention;

[0032] Figure 6 A schematic diagram of the structure of a road network passenger flow prediction system provided by an embodiment of the present invention;

[0033] Figure 7 A scene diagram of a road network passenger flow prediction system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] 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 should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0036] In the technical solution of the embodiment of the present invention, the acquisition, storage and application of the passenger flow information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0037] Figure 1 A flow chart of a road network passenger flow detection method provided by an embodiment of the present invention. The embodiment of the present invention is applicable to the situation of road network passenger flow detection, and the method can be executed by a road network passenger flow detection device, and the road network passenger flow detection device can be implemented in the form of hardware and / or software.

[0038] See also Figure 1 The road network passenger flow detection method shown includes:

[0039] S101. Obtain passenger flow information of each station in the train network in a first time period; the passenger flow information includes ticket checking passenger flow and transfer passenger flow.

[0040] The first time period may refer to a historical time interval. The first time period is used to obtain passenger flow information within the first time period.

[0041] The train network can refer to the network consisting of all stations, lines and interconnected lines in the railway or urban rail transit system. The train network includes the connection relationship between stations and transfer channels. The train network limits the spatial scope of passenger flow information. Passenger flow refers to the passenger flow behavior between stations in the train network.

[0042] The passenger flow information may refer to the number of passengers in a specific time period. The passenger flow information is used to reflect the flow of passengers in the train network and provide basic data for passenger flow prediction.

[0043] The ticket checking passenger flow may refer to the passenger flow entering through the station ticket gate. Each ticket checking action may be represented as a passenger. The ticket checking passenger flow is used to record the number of passengers entering the station.

[0044] Among them, transfer passenger flow can refer to the passenger flow entering the station through the transfer channel within the station.

[0045] In a specific example, the passenger flow information obtained from each station in the train network shows that the total number of passengers at each station is XXXX, the total number of ticket checking passengers is YYYY, and the total number of transfer passengers is ZZZZ, where the total number of passengers at each station is equal to the sum of the total number of ticket checking passengers and the total number of transfer passengers.

[0046] S102: Acquire real-time public information in a second time period, where the real-time public information includes public activity information.

[0047] The second time period may refer to a target time interval for passenger flow prediction. The second time period may refer to a certain time interval at present or in the future, and the time interval of the second time period is later than the time interval of the first time period. The second time period is used to calculate the passenger flow prediction result of the second time period through the passenger flow information of the first time period and the real-time public information of the second time period. The first time period may include a historical time period adjacent to the second time period, a historical time period of the same period, and / or a historical time period of public activity information.

[0048] In a specific example, in the scenario of passenger flow prediction within the second time period, if the second time period is the time period of a certain day, the first time period may refer to the time period of the previous day adjacent to the day; if the second time period is the time period of a certain holiday in a certain year, the first time period may refer to the time period of the holiday in the previous year; if the second time period is the time period of a certain day in a certain month, the first time period may refer to the time period of the day in the previous month; if the second time period is the time period when a star holds a concert at the same location, the first time period may refer to the time period when the star last held a concert at the same location.

[0049] The real-time public information may refer to the real-time related information related to the train passenger flow occurring in the second time period. The real-time public information may include various public time information or environmental factor information that affects the passenger flow. The real-time public information is used to provide external environmental conditions related to the passenger flow prediction, which helps to adjust the passenger flow prediction results according to the external environmental conditions.

[0050] S103. Determine a passenger flow prediction result for the second time period based on the passenger flow information for the first time period and the real-time public information for the second time period; wherein the first time period is prior to the second time period.

[0051] Among them, the passenger flow prediction result may refer to the predicted passenger flow information within a certain time period. The passenger flow prediction result can be predicted based on the passenger flow information of the first time period and the real-time public information of the second time period. The passenger flow prediction result is used to help the dispatching command system to understand the passenger flow situation in a certain time period in the future in advance, optimize train scheduling, site management and resource allocation, and ensure the stability of services and the convenience of passengers. In an example, in the scenario of predicting the passenger flow information of a holiday, the passenger flow information of the holiday can be predicted based on the passenger flow information of the holiday last year and the real-time public information of the holiday, such as public activities or weather forecasts occurring on the holiday.

[0052] The technical solution of the embodiment of the present invention can obtain accurate passenger flow information of each station in the first time period by acquiring passenger flow information of each station in the train network in the first time period, and provide actual passenger flow information of each station; ticket checking passenger flow and transfer passenger flow can accurately reflect the total number of passengers entering the train network, and provide reliable historical data for subsequent analysis and prediction; passenger flow prediction is performed by combining the passenger flow information of the first time period and the real-time public information of the second time period, so that the prediction result can refer to historical data and can also be combined with real-time public information of the current or future time period. Comprehensive analysis helps to improve the accuracy of passenger flow prediction and reduce the error caused by relying solely on historical passenger flow information for analysis.

[0053] Figure 2 The present invention provides a flow chart of a method for detecting passenger flow on a road network. The present invention optimizes and improves the operation of detecting passenger flow on a road network based on the above-mentioned embodiments.

[0054] The "obtaining passenger flow information of each station in the train network in the first time period" is refined into "obtaining the ticket checking data sent by the ticket checking system in the first time period, one ticket checking data corresponds to one passenger; determining the ticket checking passenger flow based on the obtained ticket checking data; obtaining the image of the transfer channel in the first time period; performing user recognition on the image of the transfer channel to obtain the number of transfer passengers and determine the transfer passenger flow; determining the ticket checking passenger flow and transfer passenger flow as the passenger flow information of each station in the train network in the first time period." to improve the operation of network passenger flow detection.

[0055] It should be noted that for the parts not described in detail in the embodiments of the present invention, reference may be made to the descriptions of other embodiments.

[0056] See also Figure 2 The road network passenger flow detection method shown includes:

[0057] S201. Obtain the ticket checking data sent by the ticket checking system within the first time period, where one piece of ticket checking data corresponds to one passenger.

[0058] The ticket checking system may refer to a device system used to record the entry and exit behavior of passengers. The ticket checking system may include an automatic ticket checking machine and / or a manual ticket checking system. The ticket checking system identifies the identity information of each passenger by swiping a ticket, scanning a code, swiping an ID card, or face recognition, and records the passenger's entry and exit time, boarding station, alighting station, and riding interval. In one example, at a subway station or railway station, when a passenger enters the ticket gate by swiping a ticket, scanning a code, swiping an ID card, or face recognition, the ticket checking system will record the passenger's entry time, boarding station, and riding interval.

[0059] Among them, the ticket checking data may refer to the data collected by the ticket checking system on the entry and exit of passengers. The ticket checking data may include data such as the passenger's entry time, exit time, boarding section, ticket gate, ticket type information and passenger identification. One ticket checking data corresponds to one passenger. All ticket checking data are counted to calculate the passenger capacity of the train, the passenger's travel path and the passenger's travel time in the first time period. In one example, in the obtained ticket checking data, the passenger enters the station at Station X at X in the morning and exits the station at Station B at X in the afternoon. The ticket checking data records the passenger's travel information.

[0060] S202: Determine the ticket checking passenger flow based on the obtained ticket checking data.

[0061] Specifically, the ticket checking passenger flow is determined by acquiring the ticket checking data sent by the ticket checking system in the first time period.

[0062] S203: Acquire an image of the transfer channel in the first time period.

[0063] The image of the transfer channel may refer to image data obtained by photographing the transfer area. The image of the transfer channel records the behavior of passengers transferring through the transfer channel in the first time period. The image of the transfer channel may be continuously acquired through a surveillance camera and / or other imaging equipment, and the image in the first time period may be extracted. The image of the transfer channel is used to record and analyze the transfer behavior of passengers.

[0064] S204: Perform user recognition on the image of the transfer channel, obtain the number of transfer passengers, and determine the transfer passenger flow.

[0065] Among them, user identification can refer to target identification of passengers in the transfer channel, determining the passengers included in the image, counting the number of transfer passengers, and obtaining the number of transfer passengers. Exemplarily, the user's clothing in the transfer channel can be used to determine whether the user is a passenger or staff member, and the passengers can be screened out. Through user identification, the degree of automation of counting the number of transfer passengers can be improved. In large-scale transportation networks, user identification can reduce manual intervention and increase the speed of counting the number of transfer passengers.

[0066] In one example, the ratio between the number of transfer passengers and the length of the first time period is determined as the transfer passenger flow, which represents the number of passengers passing through the transfer channel in a unit time. For example, within a one-hour time period, 200 passengers pass through the transfer channel, and the ratio of the transfer passenger flow is 200 people / 1 hour, which reflects the flow rate of the transfer passenger flow.

[0067] S205: Determine the ticket checking passenger flow and the transfer passenger flow as the passenger flow information of each station in the train network in the first time period.

[0068] It can be seen that in this embodiment, the ticket checking data obtained by the ticket checking system can be used to obtain the ticket checking passenger flow information, and the specific number of passengers entering and leaving the station can be determined. By obtaining the image of the transfer channel and performing user identification, the transfer passenger flow information can be supplemented. By combining the statistical method of ticket checking passenger flow and transfer passenger flow, more accurate passenger flow information can be obtained; by performing user identification on the image of the transfer channel and obtaining the number of transfer passengers, the degree of automation of target identification of passengers in the transfer channel can be improved, data errors caused by human factors can be reduced, and detection accuracy can be improved; the ticket checking passenger flow and transfer passenger flow are combined to form complete passenger flow information, providing more detailed historical data for subsequent passenger flow predictions, and in the subsequent prediction of passenger flow information, the accuracy and reliability of passenger flow predictions can be improved.

[0069] S206: Acquire real-time public information in a second time period, where the real-time public information includes public activity information.

[0070] S207. Determine a passenger flow prediction result for the second time period based on the passenger flow information for the first time period and the real-time public information for the second time period; wherein the first time period is prior to the second time period.

[0071] In some embodiments, the first time period includes at least one of the following: an adjacent historical time period of the second time period, a contemporaneous historical time period of the second time period, and a historical time period associated with public activity information of the second time period.

[0072] Among them, the adjacent historical time period of the second time period may refer to a historical time period that is immediately adjacent to or close to the second time period, and the time of the adjacent historical time period of the second time period is earlier than the second time period. The passenger flow data of the adjacent time period of the second time period can be used as a reference for predicting the passenger flow data of the second time period. By analyzing the adjacent historical time period of the second time period, the passenger flow information of the second time period can be inferred, thereby improving the accuracy of the prediction. In an example, the second time period is from X o'clock to Y o'clock in the afternoon of a certain day, and the adjacent historical time period of the second time period may refer to the time period from X o'clock to Y o'clock in the afternoon of the previous day.

[0073] Among them, the historical time period of the same period of the second time period may refer to a historical time period with the same or similar date as the second time period, and the time of the historical time period of the same period of the second time period is earlier than the second time period. The historical time period of the same period of the second time period may include the same date of the previous week of the second time period, the same date of the previous month of the second time period, and / or the same date of the previous year of the second time period. The historical time period of the same period of the second time period is used to analyze the passenger flow changes of the historical time period of the same period. Analyzing the historical time period of the same period is helpful to predict the passenger flow changes under the conditions of the same time period. In an example, the second time period is October 1st of this year, and the historical time period of the same period of the second time period may include October 1st of last year and / or the first day of last month; the second time period is the first week of the second quarter of this year, and the historical time period of the same period of the second time period may include the first week of the first quarter of this year and / or the first week of the second quarter of last year.

[0074] Among them, the historical time period associated with the public activity information of the second time period may refer to a time period in which a public activity has occurred in history. The historical time period associated with the public activity information of the second time period helps to predict the impact of the public activity information on passenger flow and make a more accurate passenger flow prediction for the passenger flow of the public activity in the second time period. In an example, the second time period corresponds to a music festival in a certain city, then the historical time period associated with the public activity information of the second time period may refer to the time period during the same music festival last year, and relevant predictions can be made for the passenger flow information of the second time period based on the passenger flow information during the music festival last year.

[0075] It can be seen that in this embodiment, by analyzing the adjacent historical time periods of the second time period as passenger flow information, historical passenger flow data close to the current time period can be captured, and adjacent time periods can reflect similar passenger flow trends; by referring to the historical time periods of the same period, the passenger flow data of the current time period can be inferred based on the historical time periods of the same period; by utilizing the historical time period data associated with public activity information, the direct impact of public activities on passenger flow can be predicted; combining different types of historical time period passenger flow information can improve the accuracy of passenger flow prediction.

[0076] In some embodiments, the first time period includes a historical time period associated with the public activity information of the second time period; before obtaining the passenger flow information of each station in the train network of the first time period, it also includes: obtaining the activity type of the public activity information of the second time period; obtaining the historical time period in which the historical activity corresponding to the activity type is located, and determining it as the first time period; wherein the length of the historical time period is the same as the length of the second time period.

[0077] The activity type may refer to various types of public activity information. Activity types may include festivals, sports events, large conferences, exhibitions, and other types of activities. Different activity types may have different effects on passenger flow. According to the impact of similar activities in historical data, passenger flow forecasts can be adjusted and optimized.

[0078] It can be seen that in this embodiment, by obtaining the activity type of the public activity information in the second time period and associating it with the corresponding historical activity time period, the passenger flow situation under similar circumstances to the current activity can be predicted; by ensuring that the length of the historical time period is the same as the length of the second time period, the prediction deviation caused by inconsistent time periods can be avoided to improve the accuracy of passenger flow prediction.

[0079] In some embodiments, based on the passenger flow information of the first time period and the real-time public information of the second time period, the passenger flow prediction result for the second time period is determined, including: inputting the passenger flow information of the first time period and the real-time public information of the second time period into the large model for processing, and outputting the passenger flow prediction result of the train network for the second time period. The passenger flow prediction result for the second time period includes: passenger flow distribution data of the train network, inbound and outbound passenger flow data of each station, cross-section passenger flow data and transfer passenger flow data.

[0080] The large model may refer to a computational model with large parameters and complex structure, and the large model may be constructed by machine learning or deep learning. The large model may output the passenger flow forecast result of the train network in the second time period based on the input passenger flow information in the first time period and the real-time public information in the second time period. The large model is used to combine historical passenger flow information and real-time public information to automatically analyze the correlation between data, thereby generating reasonable passenger flow forecast results.

[0081] Among them, passenger flow distribution data can refer to the passenger flow distribution of different areas or stations in the train network. Passenger flow distribution data can show stations or route sections with heavy passenger flow pressure. Passenger flow distribution data is used to understand the load of the train network, so that the dispatching and command system can reasonably dispatch resources and adjust services according to the passenger flow load to cope with peak passenger flow in specific areas or at times and avoid excessive congestion.

[0082] Among them, the inbound and outbound passenger flow data can refer to the number of inbound and outbound passengers at each station within a specific time period. The inbound and outbound passenger flow data reflects the passenger throughput of each station. The inbound and outbound passenger flow data is used to monitor the load of each station and help the dispatching command system determine whether to increase passenger volume.

[0083] Among them, cross-sectional passenger flow data can refer to the passenger flow passing through a specific section within a specific time. Cross-sectional passenger flow data is used to describe the density of passenger flow in the carriage and help analyze the load conditions of different sections of the route.

[0084] It can be seen that in this embodiment, by processing the passenger flow distribution, inbound and outbound passenger flow data, cross-sectional passenger flow data and transfer passenger flow data obtained through the big model, the dispatching and command system can dispatch traffic resources more reasonably; by inputting historical passenger flow data and real-time public data into the big model for processing, the correlation and trend of passenger flow data can be discovered from multiple dimensions, thereby improving the accuracy of passenger flow prediction.

[0085] In some embodiments, the real-time public information also includes holiday information and environmental information.

[0086] Holiday information refers to officially recognized holidays within a specific time period. Holiday information can significantly affect the passenger flow of public transportation. By analyzing holiday information, transportation capacity and service resources can be reasonably allocated to meet potential high passenger flow demand. In one example, during the National Day holiday or the Spring Festival, the passenger flow will increase significantly. The dispatching and command system can increase the number and frequency of trains according to the passenger flow demand during the holidays.

[0087] Environmental information may refer to data related to the natural environment and social environment. Environmental information may include information such as climate conditions, air quality, or traffic conditions. Environmental information affects passengers' travel decisions and passenger flow. In one example, for example, bad weather may affect passengers' travel decisions and reduce passenger flow. The dispatching and commanding system may adjust train dispatching arrangements according to weather conditions.

[0088] It can be seen that in this embodiment, real-time public information includes holiday information and environmental information, which helps to comprehensively optimize passenger flow prediction, resource scheduling and emergency response capabilities, and improve the accuracy of passenger flow prediction. In a specific example, Figure 3 A scene diagram of a road network passenger flow detection method provided by an embodiment of the present invention. The specific working scene is described as follows:

[0089] The road network passenger flow detection equipment obtains passenger flow information through the ticket sales and inspection system and the transfer channel camera, where the passenger flow information includes ticket inspection passenger flow and transfer passenger flow. The road network passenger flow detection equipment cleans and processes the passenger flow information. Cleaning can refer to deleting duplicate passenger flow information, correcting errors in passenger flow information, and checking the consistency of passenger flow information; processing can refer to converting the format of passenger flow information, classifying passenger flow information, and storing it in a distributed database. The transfer channel camera transmits the video to the road network passenger flow detection equipment. The road network passenger flow detection equipment has a video recognition function, which uses video recognition to identify the number of transfer passengers within a certain time interval.

[0090] Real-time transportation demand data can refer to the ticket checking passenger flow and transfer passenger flow within a certain period of time after processing. The real-time transportation demand data is stored in the database of the data platform. The date, time, weather and public event information corresponding to the real-time transportation demand data are also stored in the database. Real-time transportation demand data is used as historical passenger flow information.

[0091] The road network passenger flow detection equipment includes a large model, which uses historical data stored in the database for adaptive learning. The large model can adjust its own parameters according to the input of new data to adapt to changes in passenger flow. The large model uses real-time transportation demand data and the corresponding date, time, weather information, and public activity information as the basic data for passenger flow prediction, performs passenger flow prediction, and obtains passenger flow prediction results. The passenger flow prediction results may refer to the passenger flow distribution, cross-sectional passenger flow, inbound and outbound passenger flow of the train network within a certain period of time in the future, and the passenger flow of transfer passengers at a single station. Exemplarily, the passenger flow prediction results may refer to the passenger flow distribution, cross-sectional passenger flow, inbound and outbound passenger flow of the entire road network every 5 minutes in the next 24 hours.

[0092] The road network passenger flow detection equipment sends the passenger flow prediction results to the passenger dispatching equipment, which determines whether to adjust the driving plan based on the passenger flow prediction results and generates driving adjustment information. The driving adjustment information includes whether it is necessary to add temporary trains and / or adjust the driving interval.

[0093] The passenger service system obtains the corresponding passenger organization plan based on the driving adjustment information. The passenger organization plan includes the opening status of the station entry and exit gates, the opening status of the automatic ticket machines, the number of personnel, the duty position, the station broadcast plan, the station information display screen plan and the passenger flow guidance plan.

[0094] The passenger service system sends the passenger organization plan to the duty stationmaster and gives prompts for the execution steps. The duty stationmaster implements the passenger organization according to the passenger organization plan. Passengers form a new passenger flow situation based on the passenger organization implemented by the duty stationmaster. The new entry and exit behaviors are fed back to the road network passenger flow detection equipment through the ticket sales and inspection system, thus forming new passenger demand, thus forming a complete closed-loop management of transportation supply.

[0095] Figure 4 The present invention provides a schematic diagram of the structure of a road network passenger flow detection device. The present invention can be applied to the situation of road network passenger flow detection. The device can execute the road network passenger flow detection method. The device can be implemented in the form of hardware and / or software.

[0096] See also Figure 4 The road network passenger flow detection device shown includes: a historical passenger flow acquisition module 401, a public information acquisition module 402 and a passenger flow prediction module 403, wherein:

[0097] The historical passenger flow acquisition module 401 is used to acquire the passenger flow information of each station in the train network in the first time period; the passenger flow information includes the ticket checking passenger flow and the transfer passenger flow;

[0098] The public information acquisition module 402 is used to acquire real-time public information in a second time period, where the real-time public information includes public activity information;

[0099] The passenger flow prediction module 403 is used to determine the passenger flow prediction result of the second time period according to the passenger flow information of the first time period and the real-time public information of the second time period; wherein the first time period is prior to the second time period.

[0100] The technical solution of the embodiment of the present invention can obtain accurate passenger flow information of each station in the first time period by acquiring the passenger flow information of each station in the road network in the first time period, and provide the actual passenger flow information of each station; the ticket checking passenger flow and transfer passenger flow can accurately reflect the total number of passengers entering the road network, and provide reliable historical data for subsequent analysis and prediction; passenger flow prediction is performed by combining the passenger flow information of the first time period and the real-time public information of the second time period, so that the prediction result can refer to historical data and can also be combined with the specific circumstances of the current or future time period. Comprehensive analysis helps to improve the accuracy of passenger flow prediction and reduce the error caused by relying solely on historical passenger flow information for analysis.

[0101] In some embodiments, in terms of obtaining passenger flow information of each station in the train network in the first time period, the historical passenger flow acquisition module 401 is specifically used to:

[0102] Obtain the ticket checking data sent by the ticket checking system in the first time period, where one ticket checking data corresponds to one passenger;

[0103] Determine the ticket checking passenger flow based on the obtained ticket checking data;

[0104] Acquire an image of the transfer channel in a first time period;

[0105] Perform user recognition on the image of the transfer channel, obtain the number of transfer passengers, and determine the transfer passenger flow;

[0106] The ticket checking passenger flow and the transfer passenger flow are determined as the passenger flow information of each station in the train network in the first time period.

[0107] In some embodiments, the first time period includes at least one of the following: an adjacent historical time period of the second time period, a contemporaneous historical time period of the second time period, and a historical time period associated with public activity information of the second time period.

[0108] In some embodiments, in the aspect that the first time period includes the historical time period associated with the public activity information of the second time period; and before obtaining the passenger flow information of each station in the train network in the first time period, the historical passenger flow acquisition module 401 is specifically used to:

[0109] Acquire the activity type of the public activity information in the second time period;

[0110] A historical time period in which the historical activity corresponding to the activity type is located is obtained and determined as a first time period; wherein the length of the historical time period is the same as the length of the second time period.

[0111] In some embodiments, in determining the passenger flow prediction result of the second time period according to the passenger flow information of the first time period and the real-time public information of the second time period, the passenger flow prediction module 403 is specifically used to:

[0112] The passenger flow information of the first time period and the real-time public information of the second time period are input into the large model for processing, and the passenger flow prediction result of the train network in the second time period is output. The passenger flow prediction result of the second time period includes: passenger flow distribution data of the train network, inbound and outbound passenger flow data of each station, section passenger flow data and transfer passenger flow data.

[0113] In some embodiments, the real-time public information also includes holiday information and environmental information.

[0114] The road network passenger flow detection device provided in the embodiment of the present invention can execute the road network passenger flow detection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the road network passenger flow detection method.

[0115] Figure 5 A schematic diagram of the structure of a road network passenger flow detection device provided in an embodiment of the present invention.

[0116] like Figure 5 As shown, the road network passenger flow detection device 500 includes at least one processor 501, and a memory connected to the at least one processor 501, such as a read-only memory (ROM) 502, a random access memory (RAM) 503, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 501 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 502 or the computer program loaded from the storage unit 508 to the random access memory (RAM) 503. In RAM503, various programs and data required for the operation of the road network passenger flow detection device 500 can also be stored. The processor 501, ROM502 and RAM503 are connected to each other through a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0117] Multiple components in the road network passenger flow detection device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the road network passenger flow detection device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0118] The processor 501 may be any general and / or dedicated processing component with processing and computing capabilities. Some examples of the processor 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 501 executes the various methods and processes described above, such as a road network passenger flow detection method.

[0119] In some embodiments, the road network passenger flow detection method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on the road network passenger flow detection device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the processor 501, one or more steps of the road network passenger flow detection method described above can be performed. Alternatively, in other embodiments, the processor 501 can be configured to execute the road network passenger flow detection method in any other appropriate manner (for example, by means of firmware).

[0120] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0121] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0122] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0123] In order to provide interaction with the user, the systems and techniques described herein can be implemented on an operation detection device, which has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball), through which the user can provide input to the road network passenger flow detection device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0124] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0125] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS (Virtual Private Server) services.

[0126] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0127] Figure 6 A schematic diagram of the structure of a road network passenger flow prediction system provided in an embodiment of the present invention.

[0128] It should be noted that for the parts not described in detail in the embodiments of the present invention, reference may be made to the descriptions of other embodiments.

[0129] See also Figure 6 The road network passenger flow prediction system 600 shown includes: a passenger dispatching device 601 and a road network passenger flow detection device 602 as described in an embodiment of the present invention; the passenger dispatching device is communicatively connected to the road network passenger flow detection device; the road network passenger flow detection device is used to predict passenger flow information; and the passenger dispatching device is used to determine driving adjustment information based on passenger flow information.

[0130] Road network detection equipment is used to predict passenger flow information and provide a basis for decision-making for passenger dispatch equipment. Road network detection equipment can obtain real-time passenger flow information from multiple equipment sources. Road network passenger flow detection equipment uses deep learning big models to analyze and process real-time data and historical data to predict passenger flow in future time periods. After completing the passenger flow prediction, the road network detection equipment transmits the generated passenger flow prediction information to the passenger dispatch equipment.

[0131] The passenger dispatching device receives the passenger flow prediction information from the road network passenger flow detection device through the communication connection. According to the received passenger flow prediction information, the passenger dispatching device detects whether the current driving plan needs to be adjusted.

[0132] In a specific example, Figure 7 A scene diagram of a road network passenger flow prediction system provided by an embodiment of the present invention. The road network passenger flow prediction system obtains the ticket inspection passenger flow and transfer passenger flow in the first time period through the camera of the ticket sales and inspection system and the transfer channel, and cleans, processes and performs video recognition on the passenger flow information of the first time period. The road network passenger flow detection device obtains the passenger flow prediction result of the second time period through the passenger flow information of the first time period and the public information of the second time period. The passenger dispatching device generates the corresponding driving adjustment information for the second time period based on the passenger flow prediction result of the second time period.

[0133] The technical solution of the embodiment of the present invention can obtain accurate passenger flow information of each station in the first time period by acquiring passenger flow information of each station in the road network in the first time period, and provide actual passenger flow information of each station; ticket checking passenger flow and transfer passenger flow can accurately reflect the total number of passengers entering the road network, and provide reliable historical data for subsequent analysis and prediction; passenger flow prediction is performed by combining the passenger flow information of the first time period and the real-time public information of the second time period, so that the prediction result can refer to historical data and can also be combined with the specific circumstances of the current or future time period. Comprehensive analysis helps to improve the accuracy of passenger flow prediction and reduce the error caused by relying solely on historical passenger flow information for analysis; through the combination of passenger flow prediction and driving adjustment, the road network passenger flow prediction system can reasonably arrange train resources according to the predicted passenger flow situation, avoid resource waste due to excessive scheduling, or passenger congestion due to insufficient scheduling.

[0134] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A road network passenger flow detection method, characterized in that: The method comprises: Obtaining passenger flow information of each station in the train network in the first time period; the passenger flow information includes ticket checking passenger flow and transfer passenger flow; Acquiring real-time public information in a second time period, wherein the real-time public information includes public activity information; The passenger flow prediction result for the second time period is determined according to the passenger flow information for the first time period and the real-time public information for the second time period; wherein the first time period is prior to the second time period.

2. The method according to claim 1, characterized in that: The obtaining of passenger flow information of each station in the train network in the first time period includes: Acquire ticket checking data sent by the ticket checking system within the first time period, wherein one piece of ticket checking data corresponds to one passenger; Determine the ticket checking passenger flow based on the obtained ticket checking data; Acquire an image of the transfer channel in the first time period; Performing user recognition on the image of the transfer channel to obtain the number of transfer passengers and determine the transfer passenger flow; The ticket checking passenger flow and the transfer passenger flow are determined as the passenger flow information of each station in the train network in the first time period.

3. The method according to claim 1, characterized in that: The first time period includes at least one of the following: an adjacent historical time period of the second time period, a concurrent historical time period of the second time period, and a historical time period associated with public activity information of the second time period.

4. The method according to claim 3, characterized in that The first time period includes a historical time period associated with the public activity information of the second time period; Before obtaining the passenger flow information of each station in the train network in the first time period, the method further includes: Acquire the activity type of the public activity information in the second time period; A historical time period in which the historical activity corresponding to the activity type is located is obtained, and is determined as a first time period; wherein the duration of the historical time period is the same as the duration of the second time period.

5. The method according to claim 1, characterized in that Determining a passenger flow prediction result for the second time period according to the passenger flow information for the first time period and the real-time public information for the second time period includes: The passenger flow information of the first time period and the real-time public information of the second time period are input into the large model for processing, and the passenger flow prediction result of the train network in the second time period is output. The passenger flow prediction result of the second time period includes: the passenger flow distribution data of the train network, the inbound and outbound passenger flow data of each station, the cross-section passenger flow data and the transfer passenger flow data.

6. The method according to claim 1, characterized in that The real-time public information also includes holiday information and environmental information.

7. A road network passenger flow detection device, characterized in that: The device comprises: A historical passenger flow acquisition module is used to acquire passenger flow information of each station in the train network in the first time period; the passenger flow information includes ticket checking passenger flow and transfer passenger flow; A public information acquisition module, used to acquire real-time public information in a second time period, wherein the real-time public information includes public activity information; A passenger flow prediction module is used to determine the passenger flow prediction result of the second time period based on the passenger flow information of the first time period and the real-time public information of the second time period; wherein the first time period is prior to the second time period.

8. A road network passenger flow detection device, characterized in that: The train track tracking device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the road network passenger flow detection method described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the road network passenger flow detection method according to any one of claims 1 to 6 when executed.

10. A road network passenger flow prediction system, characterized in that: include: Passenger transport dispatching equipment and road network passenger flow detection equipment as claimed in claim 8; The passenger transport dispatching device is communicatively connected with the road network passenger flow detection device; The road network passenger flow detection device is used to predict passenger flow information; The passenger transport dispatching device is used to determine the driving adjustment information according to the passenger flow information.

Images