Passenger flow estimation method based on multi-mode and mobile signaling

Abstract

The invention relates to the technical field of smart tourism management, in particular to a multi-modal and mobile signaling-based passenger flow volume estimation method, which comprises the following steps of: acquiring multi-source data source passenger flow volume data of a target scenic area, and constructing a first passenger flow volume prediction model according to the multi-source data source passenger flow volume data; based on a preset mobile signaling collection strategy, obtaining effective mobile signaling data of the target scenic area, fusing the effective mobile signaling data with a prediction result of the first passenger flow prediction model, and constructing a second passenger flow prediction model; and estimating the passenger flow volume of the target scenic area based on a second passenger flow volume prediction model to obtain a passenger flow volume estimation result, and in a continuous monitoring period, if estimation results in any two adjacent time periods reach an early warning threshold value, triggering an early warning signal, and pushing early warning level information corresponding to the passenger flow volume estimation result to a management terminal. The problems that data deviation is large and actual passenger flow cannot be reflected in existing scenic spot passenger flow statistics are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart tourism management, and in particular to a passenger flow estimation method based on multimodality and mobile signaling. Background Art

[0002] With the rapid development of tourism, the passenger flow in tourist attractions has increased year by year. How to scientifically and rationally manage the flow of people (passengers) in tourist attractions has become an important issue. Traditional passenger flow monitoring methods mainly rely on a single data source (such as ticketing systems or manual statistics), which have problems such as data lag, low accuracy, and limited coverage. For example, ticketing data cannot reflect the flow of tourists who have not purchased tickets, video surveillance is limited by the density of equipment deployment and blind spots of viewing angles, and checkpoint gate data is difficult to capture the dynamic distribution of tourists in scenic spots. In addition, the existing technology lacks an effective fusion mechanism for multi-source data, resulting in insufficient robustness of the prediction model, making it difficult to cope with passenger flow fluctuations during holidays or emergencies.

[0003] As an emerging data source, mobile signaling data can provide users with real-time location information, but its use alone has problems such as signal drift and base station coverage mismatch. Therefore, how to deeply integrate multimodal data (such as video, ticketing, and card access data) with mobile signaling data, build a dynamic weight distribution model, and achieve accurate passenger flow estimation and early warning has become a technical problem that needs to be solved urgently.

[0004] Therefore, the present invention provides a passenger flow estimation method based on multi-modality and mobile signaling to solve the above problems. Summary of the invention

[0005] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a passenger flow estimation method based on multimodality and mobile signaling to solve the problem that the existing passenger flow estimation of scenic spots has large deviations and cannot reflect the actual passenger flow.

[0006] In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a passenger flow estimation method based on multi-modality and mobile signaling, the method comprising: obtaining passenger flow data from multiple sources of data of the target scenic spot, and constructing a first passenger flow prediction model according to the passenger flow data from the multiple sources of data, wherein the multiple sources of data include video data of the scenic spot parking lot, data of the scenic spot checkpoint gate, video surveillance data in the scenic spot, and ticketing data of the scenic spot; based on a preset mobile signaling adoption strategy, obtaining effective mobile signaling data of the target scenic spot, integrating the effective mobile signaling data with the prediction results of the first passenger flow prediction model, and constructing a second passenger flow prediction model; estimating the passenger flow of the target scenic spot based on the second passenger flow prediction model to obtain a passenger flow estimation result, and within a continuous monitoring cycle, if the estimation results in any two adjacent time periods reach the warning threshold, a warning signal is triggered, and the warning level information corresponding to the passenger flow estimation result is pushed to the management terminal.

[0007] A further improvement of the present application is that, before constructing the first passenger flow prediction model based on the passenger flow data from the multi-source data source, it also includes: performing spatiotemporal alignment and outlier cleaning on the multi-source data, unifying the time base of each data source through timestamps, and filling in missing data with linear interpolation; calculating the initial weight of each data source based on historical data, and the weight distribution rule is: the weight of checkpoint gate data is greater than the weight of video surveillance data, the weight of video surveillance data is greater than the weight of ticketing data, and the weight of ticketing data is greater than the weight of parking lot data.

[0008] A further improvement of the present application lies in the method of calculating the initial weight of each data source based on historical data, comprising: determining the accuracy of each data source based on the deviation between the predicted value of each data source in the historical data and the actual passenger flow, and determining the linear correlation between the historical data of each data source and the actual passenger flow through the Pearson correlation coefficient; allocating initial weights according to the weighted comprehensive score of the accuracy and correlation coefficient of each data source; dynamically adjusting the weights using the gradient descent method, and optimizing with minimizing the prediction error rate as the objective function, terminating the optimization when the error rate decreases by less than a preset threshold for several consecutive iterations, and outputting the final weight allocation result.

[0009] A further improvement of the present application is that the expression of the first passenger flow prediction model is: (1), In expression (1), represents the first predicted passenger flow, Indicates The contribution weight of each data source to passenger flow prediction is obtained by calculating the accuracy and correlation coefficient of each data source through historical data. Indicates Real-time data from multiple data sources, represents the influence factor of time on passenger flow, Represents the impact factor of weather on passenger flow.

[0010] A further improvement of the present application is that the preset mobile signaling adoption strategy includes daily mode, holiday mode and emergency mode; if the mobile signaling adoption strategy is the daily mode, the first radius of the coverage sector of the base station outside the target scenic area is used as the coverage range of the base station sector, and the second radius of the coverage sector of the base station within the target scenic area is used as the coverage range of the base station sector, and the second radius is greater than the first radius, and the number of active signaling IDs within the first radius and the second radius is collected as valid mobile signaling data; if the mobile signaling adoption strategy is the holiday mode, the first radius of the coverage sector of the base station outside the target scenic area is used as the center of the circle, and the number of active signaling IDs within the first radius and the second radius is collected as valid mobile signaling data. The coverage angle is expanded at the coverage sector angle to form an extended coverage range, and the extended coverage range and the second radius of the coverage sector of the base station within the target scenic area are collected as the number of active signaling IDs within the coverage range of the base station sector as valid mobile signaling data; if the mobile signaling adoption strategy is emergency mode, the first radius of the coverage sector of the base station outside the target scenic area is taken as the center of the circle, and the coverage angle is expanded to 360° at the original coverage sector angle to form a full coverage range, and the full coverage range and the second radius of the coverage sector of the base station within the target scenic area are collected as the number of active signaling IDs within the coverage range of the base station sector as valid mobile signaling data.

[0011] A further improvement of the present application is that the trigger condition for the preset mobile signaling adoption strategy conversion is: if the ratio between the number of active signaling IDs within the first radius and the second radius to the number of passengers collected by the scenic area checkpoint gate reaches a preset threshold, the daily mode is converted to the holiday mode; if the occupancy rate of the scenic area parking lot reaches the warning threshold or the day is a statutory holiday, the holiday mode is converted to the emergency mode.

[0012] A further improvement of the present application is that the expression of the second passenger flow prediction model is: (2), In expression (2), represents the final estimated value of passenger flow, represents the first predicted passenger flow, Indicates the number of active signaling IDs. Indicates the fusion coefficient, and its value range is 0.6-0.8.

[0013] A further improvement of the present application is that the warning level information includes yellow warning, red warning and overflow warning; if the final passenger flow estimation value reaches the first threshold of the full load design of the scenic area, the yellow warning information is sent to the management terminal; if the final passenger flow estimation value reaches the second threshold of the full load design of the scenic area, the red warning information is sent to the management terminal; if the final passenger flow estimation value reaches the third threshold of the full load design of the scenic area, the overflow warning information is sent to the management terminal.

[0014] The beneficial effects of the present invention are as follows: by integrating multimodal data (such as video, ticketing, and checkpoint data) with mobile signaling data, data complementarity and enhancement are achieved, effectively solving the problems of data lag, low precision, and limited coverage in traditional passenger flow monitoring methods. At the same time, by constructing a dynamic weight allocation model, the weight is dynamically adjusted according to the accuracy and linear correlation of the data source, thereby improving the robustness and adaptability of the prediction model, so that it can more accurately reflect the actual passenger flow of tourist attractions. In addition, the present invention also flexibly adjusts the collection scope of signaling data according to the needs of different scenarios through a preset mobile signaling adoption strategy, further improving the accuracy and practicality of passenger flow estimation. Finally, by realizing accurate estimation and early warning of passenger flow, it provides strong data support for the management and decision-making of tourist attractions, which helps to improve the operational efficiency and visitor experience of tourist attractions. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a schematic flow chart of a passenger flow estimation method based on multi-modality and mobile signaling according to the present invention; Figure 2 is a schematic flow chart of an embodiment of a passenger flow estimation method based on multi-modality and mobile signaling of the present invention; Figure 3 It is a schematic structural diagram of the passenger flow estimation system based on multi-modality and mobile signaling of the present invention. DETAILED DESCRIPTION

[0016] The following will describe various embodiments of the present invention in detail with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0017] Based on the above problems, the inventors provide the following solutions: First, the passenger flow data of the target scenic spot is obtained from multiple sources, including but not limited to the video data of the scenic spot parking lot, the data of the scenic spot checkpoint gate, the video surveillance data in the scenic spot, and the ticketing data of the scenic spot. These data provide a rich information basis for the subsequent passenger flow prediction. Then, these multi-source data are preprocessed, including spatiotemporal alignment and outlier cleaning, to ensure the consistency and accuracy of the data. The time base of each data source is unified by timestamp, and the missing data is filled by linear interpolation, thereby reducing the impact of data errors on the prediction results.

[0018] Next, the initial weight of each data source is calculated based on historical data. The weight distribution rule takes into account the accuracy and relevance of different data sources for passenger flow prediction, ensuring that the prediction model can fully utilize the advantages of each data source. Specifically, the checkpoint gate data is given the highest weight because it can directly reflect the entry and exit of tourists; although the video surveillance data is limited by equipment deployment and viewing angle, it can still provide a general situation of tourist flow, so the weight is second; ticketing data reflects the number of tourists who purchased tickets, but cannot cover all tourists (those who did not enter on the day of ticket purchase, or refunded tickets after purchasing tickets online), so the weight is the third; and parking lot data is used as supplementary information and has the smallest weight.

[0019] In order to improve the accuracy of the first passenger flow prediction model, the present invention further introduces mobile signaling data. According to the preset mobile signaling collection strategy, the effective mobile signaling data of the target scenic spot is obtained. These strategies include daily mode, holiday mode and emergency mode to meet the passenger flow estimation needs in different scenarios. By adjusting the collection range of signaling data, the present invention can more accurately reflect the actual flow of tourists.

[0020] Finally, the effective mobile signaling data is integrated with the prediction results of the first passenger flow prediction model to construct the second passenger flow prediction model. This model comprehensively considers the advantages of multimodal data and mobile signaling data to achieve accurate estimation of passenger flow. In a continuous monitoring cycle, if the estimation results in any two adjacent time periods reach the warning threshold, the warning signal is triggered, and the warning level information corresponding to the passenger flow estimation results is pushed to the management terminal. This function provides timely and accurate data support for the management and decision-making of tourist attractions.

[0021] The technical solution will be described in detail below in conjunction with specific embodiments. Example

[0022] refer to Figure 1 , a passenger flow estimation method based on multimodality and mobile signaling, the method comprising the following steps S100-S300: S100, obtaining passenger flow data from multiple sources of a target scenic spot, and constructing a first passenger flow prediction model based on the passenger flow data from multiple sources, wherein the multiple sources include video data from a scenic spot parking lot, data from a scenic spot checkpoint gate, video surveillance data within the scenic spot, and ticketing data from the scenic spot; S200, based on a preset mobile signaling adoption strategy, obtaining effective mobile signaling data of the target scenic spot, integrating the effective mobile signaling data with the prediction result of the first passenger flow prediction model, and constructing a second passenger flow prediction model; S300. Estimate the passenger flow of the target scenic spot based on the second passenger flow prediction model to obtain the passenger flow estimation result. During the continuous monitoring period, if the estimation results in any two adjacent time periods reach the warning threshold, the warning signal is triggered, and the warning level information corresponding to the passenger flow estimation result is pushed to the management terminal.

[0023] Specifically, in step S100, when the passenger flow is counted by the video data of the scenic spot parking lot, the estimation rule is: small cars are calculated as 2 people per car, small tourist cars (business cars, Jinbei, etc.) are calculated as 7 people per car, medium-sized tourist cars (vehicle length 6-9 meters) are calculated as 22 people per car, and large tourist cars (vehicle length more than 9 meters) are calculated as 40 people; when the passenger flow is counted by the data of the scenic spot checkpoint gate, the number of people inspected at the entrance (the number of scanned tickets) plus 5% of this base number (children, the elderly or other people who meet the conditions for free tickets) is used as the final data count number; when the passenger flow is counted by the video surveillance data in the scenic spot, the Mask R-CNN (segmentation) + density estimation + wandering detection algorithm combination is used to process the data of the dense channel / Internet celebrity area, and the lightweight YOLO + infrared thermal imaging + direction recognition algorithm combination is used for data processing in other areas to determine the estimated data of the passenger flow; when the passenger flow is counted by the ticketing data of the scenic spot, 98% of the ticket purchase amount (excluding some tickets purchased but not entered or refunded) is used as the estimation basis. It should be noted that the above quantity estimates are all calculated according to the same time period to ensure consistency.

[0024] In step S200, the preset mobile signaling collection strategy includes daily mode, holiday mode and emergency mode, and triangulation positioning technology is used to collect mobile signaling; If the mobile signaling collection strategy is the daily mode, the first radius (2.5km from the base station) of the coverage sector of the base station outside the target scenic area (such as the sector angle is ±30°) is used as the coverage range of the base station sector, and the second radius (5km from the base station) of the coverage sector of the base station within the target scenic area is used as the coverage range of the base station sector. The number of active signaling IDs within the first radius and the second radius is collected as valid mobile signaling data; If the mobile signaling adoption strategy is the holiday mode, the first radius of the coverage sector of the base station outside the target scenic area is taken as the center of the circle, and the coverage angle is expanded (such as expansion by ±15°) on the original coverage sector angle (such as the sector angle is ±30°) to form an extended coverage range, and the number of active signaling IDs within the extended coverage range and the second radius of the coverage sector of the base station within the target scenic area is collected as the coverage range of the base station sector as valid mobile signaling data; If the mobile signaling adoption strategy is emergency mode, the first radius of the coverage sector of the base station outside the target scenic area is taken as the center of the circle, and the coverage angle is expanded to 360° on the original coverage sector angle to form a full coverage range. The full coverage range and the second radius of the coverage sector of the base station within the target scenic area are collected as the number of active signaling IDs within the coverage range of the base station sector as valid mobile signaling data.

[0025] The triggering condition for switching the preset mobile signaling policy is: If the ratio between the number of active signaling IDs within the first radius and the second radius and the number of passengers collected by the scenic area checkpoint gate reaches a preset threshold (for example, the ratio between the number of active signaling IDs and the number of passengers collected by the scenic area checkpoint gate reaches 30%), the daily mode will be switched to the holiday mode; if the occupancy rate of the scenic area parking lot reaches the warning threshold (for example, the occupancy rate of the scenic area parking lot reaches 90%) or the day is a statutory holiday, the holiday mode will be switched to the emergency mode.

[0026] In step S300, a collection cycle is usually 5 minutes. If the estimation results in two consecutive 5-minute collection cycles reach the warning threshold, a warning signal is triggered. The warning level information includes yellow warning, red warning and over-limit warning. If the final passenger flow estimate reaches the first threshold (80%) of the scenic area's full load design, a yellow warning message is sent to the management terminal; If the final passenger flow estimate reaches the second threshold (90%) of the scenic spot's full load design, a red warning message is sent to the management terminal; If the final passenger flow estimate reaches the third threshold (100%) of the scenic spot's full load design, an overflow warning message will be sent to the management terminal.

[0027] In one embodiment of the present application, Figure 2 As shown, a passenger flow estimation method based on multi-modality and mobile signaling includes steps T100-T400: T100. Obtain passenger flow data from multiple sources of the target scenic spot, perform spatiotemporal alignment and outlier cleaning on the multi-source data, unify the time base of each data source through timestamps, and use linear interpolation to fill in missing data; calculate the initial weight of each data source based on historical data, and the weight distribution rule is: the weight of the checkpoint gate data is greater than the weight of the video surveillance data, the weight of the video surveillance data is greater than the weight of the ticketing data, and the weight of the ticketing data is greater than the weight of the parking lot data; T200, constructing a first passenger flow prediction model based on the passenger flow data from the multi-source data source, wherein the multi-source data source includes scenic spot parking lot video data, scenic spot checkpoint gate data, scenic spot video surveillance data, and scenic spot ticketing data; T300, based on a preset mobile signaling adoption strategy, obtaining effective mobile signaling data of the target scenic spot, integrating the effective mobile signaling data with the prediction result of the first passenger flow prediction model, and constructing a second passenger flow prediction model; T400, estimate the passenger flow of the target scenic spot based on the second passenger flow prediction model to obtain the passenger flow estimation result. During the continuous monitoring period, if the estimation results in any two adjacent time periods reach the warning threshold, the warning signal is triggered, and the warning level information corresponding to the passenger flow estimation result is pushed to the management terminal.

[0028] In step T100, in specific implementation, the time granularity of each data source is usually unified to 5 minutes through the timestamp, and the missing data is filled by linear interpolation. For example, if the video surveillance data is missing from 12:00 to 12:05 on May 1, 2024 due to equipment failure, it is filled by linear interpolation (taking the average of the 5 minutes before and after); The method for calculating the initial weight of each data source based on historical data includes steps T101-T103: T101. Determine the accuracy of each data source based on the deviation between the predicted value of each data source and the actual passenger flow in the historical data, and determine the linear correlation between the historical data of each data source and the actual passenger flow through the Pearson correlation coefficient ; Among them, based on the deviation between the predicted value of the data source in the historical data and the actual passenger flow, the accuracy is defined as , is the accuracy of the i-th data source, is the predicted value of the ith data source at time t, that is, the collected values ​​of each data source, such as the number of people collected by the checkpoint gate, is the actual passenger flow at time t; In the above steps, The data value within 5 minutes counted by each data source is preferred, but this data value may be affected by equipment limitations and human factors and may have data anomalies (for example, someone evading the ticket), so the accuracy of the data source is introduced.

[0029] Furthermore, the actual passenger flow at time t in the historical data is first counted The specific steps are as follows: 1: Select the checkpoint cameras at the main entrances and exits of the scenic area, obtain the data through the background cameras, and count the number of people entering and leaving (manually determine the area boundaries). Collect the data into the database every minute, including the name of the point camera, the name of the scenic area, the number of people entering, and the number of people leaving.

[0030] 2: Based on the number of parking spaces in parking lots near scenic spots, data is collected every minute, including the name of the parking lot, the name of the associated scenic spot, the total number of parking spaces, empty parking spaces, and the number of parking spaces.

[0031] 3: Using mobile base stations, according to the coverage of regional base stations, the selection rule of regional base stations is the standard coverage mode: A. For base stations outside the scenic area, the base station sector is used as the coverage area based on the base station coverage sector (with a radius of 3,000 meters); B. For base stations outside the scenic area, the base station sector coverage area (with a radius of 3,000 meters) is selected as the base station sector coverage area.

[0032] C. The collected data includes area number (mobile definition), scenic spot name, and 5-minute real-time traffic and cumulative traffic.

[0033] The actual passenger flow at time t in historical data , iterative optimization is performed through the daily sum (5 minutes * 5 minutes real-time traffic) / actual cumulative traffic of the day (the actual cumulative traffic of the day is obtained by comparing and adjusting the cumulative traffic of methods 1, 2, and 3, and manually analyzing and adjusting).

[0034] T102. Allocate initial weights based on the weighted comprehensive scores of the accuracy and correlation coefficients of each data source; Specifically, the accuracy weight is selected to account for 60%, the correlation coefficient weight is selected to account for 40%, and the initial weight is allocated according to the weighted comprehensive score of the accuracy and correlation coefficient. The calculation formula is: , is the initial weight of the ith data source, and n is the total number of data sources; T103. Use the gradient descent method to dynamically adjust the weights, and optimize with minimizing the prediction error rate as the objective function. When the error rate decreases by less than a preset threshold (1%) after several consecutive iterations (three times in this embodiment), the optimization is terminated and the final weight distribution result is output.

[0035] Specifically, the objective function is: , which is a loss function used to measure the difference between the model prediction results and the real data. Its goal is to minimize the sum of squared prediction errors for all time periods by adjusting the model parameters, where represents the predicted passenger flow at the tth time point, represents the actual passenger flow at time t.

[0036] In step T200, the expression of the first passenger flow prediction model is: (1), In expression (1), represents the first predicted passenger flow, Indicates The contribution weight of each data source to passenger flow prediction is obtained by calculating the accuracy and correlation coefficient of each data source through historical data. Indicates Real-time data from multiple data sources, Indicates the influence factor of time on passenger flow (e.g. 1.2 on holidays and 0.8 on non-holidays). It indicates the impact factor of weather on tourist flow (e.g. 1.0 for sunny days and 0.5 for rainy and snowy days). The specific value is determined according to the actual situation of the scenic spot.

[0037] In step T300, the expression of the second passenger flow prediction model is: (2), In expression (2), represents the final estimated value of passenger flow, represents the first predicted passenger flow, Indicates the number of active signaling IDs. Indicates the fusion coefficient, the value range is 0.6-0.8. When it is close to 0.8, it is more dependent on the prediction results of multi-source data. When it is close to 0.6, more emphasis is placed on the real-time dynamics of mobile signaling.

[0038] in, The number of active signaling IDs represented is the number of devices that have stayed within the base station range for more than 10 minutes.

[0039] Compared with the existing technology, by integrating multimodal data (such as video, ticketing, and card gate data) with mobile signaling data, the complementarity and enhancement between data are achieved, and the problems of data lag, low precision, and limited coverage in traditional passenger flow monitoring methods are effectively solved. At the same time, by constructing a dynamic weight allocation model, the weight is dynamically adjusted according to the accuracy and linear correlation of the data source, which improves the robustness and adaptability of the prediction model, so that it can more accurately reflect the actual passenger flow of tourist attractions. In addition, the present invention also flexibly adjusts the collection scope of signaling data according to the needs of different scenarios through a preset mobile signaling collection strategy, further improving the accuracy and practicality of passenger flow estimation. Finally, by realizing accurate estimation and early warning of passenger flow, it provides strong data support for the management and decision-making of tourist attractions, which helps to improve the operational efficiency and tourist experience of tourist attractions.

[0040] refer to Figure 3 , a schematic structural diagram that satisfies steps S100-S300 is provided, and various embodiments of the systems and techniques described above in this article 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), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs, which may be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, which may 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.

[0041] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code 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.

[0042] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. 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.

[0043] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: 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 pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. 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 input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0044] The systems and techniques described herein may be implemented in a computing system that includes back-end 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 front-end 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 back-end components, middleware components, or front-end 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), and the Internet.

[0045] A computer 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 relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0046] 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 recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0047] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. 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 disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A passenger flow estimation method based on multimodality and mobile signaling, characterized in that: The method comprises: Obtain passenger flow data from multiple sources of the target scenic spot, and construct a first passenger flow prediction model based on the passenger flow data from the multiple sources, wherein the multiple sources include video data from the scenic spot parking lot, data from the scenic spot checkpoint gate, video surveillance data within the scenic spot, and ticketing data from the scenic spot; Based on the preset mobile signaling adoption strategy, valid mobile signaling data of the target scenic spot is obtained, and the valid mobile signaling data is integrated with the prediction result of the first passenger flow prediction model to construct a second passenger flow prediction model; The passenger flow of the target scenic spot is estimated based on the second passenger flow prediction model to obtain the passenger flow estimation result. During the continuous monitoring period, if the estimation results in any two adjacent time periods reach the warning threshold, the warning signal is triggered and the warning level information corresponding to the passenger flow estimation result is pushed to the management terminal.

2. The passenger flow estimation method based on multimodality and mobile signaling according to claim 1 is characterized in that: Before constructing the first passenger flow prediction model according to the passenger flow data from the multi-source data source, it also includes: performing spatiotemporal alignment and outlier cleaning on the multi-source data, unifying the time base of each data source through a timestamp, and filling in the missing data with a linear interpolation method; calculating the initial weight of each data source based on historical data, and the weight distribution rule is: the weight of the checkpoint gate data is greater than the weight of the video surveillance data, the weight of the video surveillance data is greater than the weight of the ticketing data, and the weight of the ticketing data is greater than the weight of the parking lot data.

3. The passenger flow estimation method based on multimodality and mobile signaling according to claim 2 is characterized in that: The method for calculating the initial weight of each data source based on historical data includes: Based on the deviation between the predicted value of each data source and the actual passenger flow in the historical data, the accuracy of each data source is determined, and the linear correlation between the historical data of each data source and the actual passenger flow is determined by the Pearson correlation coefficient; Assign initial weights based on the weighted comprehensive scores of accuracy and correlation coefficients of each data source; The gradient descent method is used to dynamically adjust the weights, and the optimization is performed with minimizing the prediction error rate as the objective function. When the error rate decreases by less than the preset threshold after several consecutive iterations, the optimization is terminated and the final weight distribution result is output.

4. The passenger flow estimation method based on multimodality and mobile signaling according to claim 3 is characterized in that: The expression of the first passenger flow prediction model is: （1）， In expression (1), represents the first predicted passenger flow, Indicates The contribution weight of each data source to passenger flow prediction is obtained by calculating the accuracy and correlation coefficient of each data source through historical data. Indicates Real-time data from multiple data sources, represents the influence factor of time on passenger flow, Represents the impact factor of weather on passenger flow.

5. The passenger flow estimation method based on multimodality and mobile signaling according to claim 4 is characterized in that: The preset mobile signaling adoption strategy includes daily mode, holiday mode and emergency mode; If the mobile signaling adoption strategy is the daily mode, the first radius of the coverage sector of the base station outside the target scenic area is used as the coverage range of the base station sector, and the second radius of the coverage sector of the base station within the target scenic area is used as the coverage range of the base station sector, and the second radius is greater than the first radius. The number of active signaling IDs within the first radius and the second radius is collected as valid mobile signaling data; If the mobile signaling adoption strategy is the holiday mode, the first radius of the coverage sector of the base station outside the target scenic area is taken as the center of the circle, and the coverage angle is expanded on the original coverage sector angle to form an extended coverage range. The number of active signaling IDs within the extended coverage range and the second radius of the coverage sector of the base station within the target scenic area is collected as the coverage range of the base station sector as valid mobile signaling data; If the mobile signaling adoption strategy is emergency mode, the first radius of the coverage sector of the base station outside the target scenic area is taken as the center of the circle, and the coverage angle is expanded to 360° on the original coverage sector angle to form a full coverage range. The full coverage range and the second radius of the coverage sector of the base station within the target scenic area are collected as the number of active signaling IDs within the coverage range of the base station sector as valid mobile signaling data.

6. The passenger flow estimation method based on multimodality and mobile signaling according to claim 5 is characterized in that: The triggering condition for the preset mobile signaling adoption strategy conversion is: If the ratio between the number of active signaling IDs within the first radius and the second radius and the number of passengers collected by the scenic area checkpoint reaches the preset threshold, the daily mode will be switched to the holiday mode; if the occupancy rate of the scenic area parking lot reaches the warning threshold or the day is a statutory holiday, the holiday mode will be switched to the emergency mode.

7. The passenger flow estimation method based on multimodality and mobile signaling according to claim 6 is characterized in that: The expression of the second passenger flow prediction model is: （2）， In expression (2), represents the final estimated value of passenger flow, represents the first predicted passenger flow, Indicates the number of active signaling IDs. Indicates the fusion coefficient, and its value range is 0.6-0.

8.

8. The passenger flow estimation method based on multimodality and mobile signaling according to claim 7 is characterized in that: The warning level information includes yellow warning, red warning and explosion warning; If the final estimated passenger flow reaches the first threshold of the scenic spot's full load design, a yellow warning message is sent to the management terminal; If the final passenger flow estimate reaches the second threshold of the scenic spot's full load design, a red warning message is sent to the management terminal; If the final passenger flow estimate reaches the third threshold of the scenic spot's full-load design, an overflow warning message is sent to the management terminal.

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