Scenic area passenger flow monitoring method based on base station data
By combining trend analysis and growth rate analysis, and using base station data to identify effective base stations, the problem of data lag and accuracy in scenic area visitor flow monitoring has been solved, achieving high-precision and real-time visitor flow prediction and adapting to the characteristic changes of different scenic areas.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- LIAONING MOBILE COMM
- Filing Date
- 2026-01-14
- Publication Date
- 2026-05-19
AI Technical Summary
Existing methods for monitoring visitor flow in scenic areas rely on traditional sensors or fixed equipment, which suffer from problems such as data lag, insufficient monitoring accuracy, and poor flexibility. They are difficult to accurately monitor changes in visitor flow during special periods such as holidays and in complex situations.
By combining trend analysis and growth rate analysis, effective base stations are identified using base station data, correcting for passenger flow lag. The passenger flow at the current moment is obtained using effective base stations and verified in conjunction with the gate data of the scenic area, thereby improving the accuracy and timeliness of monitoring.
It has improved the accuracy and timeliness of visitor flow monitoring in scenic areas, especially during holidays and peak periods, and solved the monitoring blind spots in the event of emergencies and data lag, enabling real-time risk response and high-precision prediction.
Smart Images
Figure CN122064897A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of data processing technology, and in particular to a method for monitoring visitor flow in scenic areas based on base station data. Background Technology
[0002] Existing tourist flow monitoring systems in scenic areas mostly rely on traditional sensors or fixed equipment (such as turnstiles and cameras), which presents the following technical problems: Data lag: The lag effect of visitor flow data was not fully considered, resulting in untimely reflection of the actual situation in the scenic area. Especially during special periods such as holidays with drastic changes, the lag seriously affects the prediction and monitoring results.
[0003] Insufficient monitoring accuracy: Monitoring methods based on sensors or fixed equipment are not capable of accurately identifying and predicting visitor flow in scenic areas, making it difficult to effectively cope with fluctuations in visitor numbers and flow.
[0004] Poor flexibility: It relies on a single data source and cannot comprehensively consider the synergistic effect of multiple data sources (such as base station data, gate data, etc.), resulting in an inability to make accurate judgments in complex situations. Summary of the Invention
[0005] In view of this, this application provides a method for monitoring tourist flow in scenic areas based on base station data. By correcting the lag in tourist flow, the method improves the accuracy, timeliness, and flexibility of tourist flow monitoring, thereby solving the aforementioned technical problems.
[0006] In a first aspect, embodiments of this application provide a method for monitoring visitor flow in scenic areas based on base station data, including: Obtain reported passenger flow, base station passenger flow, and scenic area gate passenger flow for the non-lag period and lag period prior to the current day, as well as base station passenger flow for several weeks prior to the current day; Based on the reported passenger flow and the passenger flow at the scenic area gates with both non-lag and lag periods, the non-lag trend difference and lag trend difference of the scenic area are determined. Determine whether the non-lag trend difference of the scenic area is less than the first threshold or whether the lag trend difference is less than the first threshold; If so, based on the reported passenger flow and base station passenger flow with both non-lag and lag periods, the trend method is used to identify the effective base stations in the scenic area. The current passenger flow and historical passenger flow obtained from the effective base stations are used to determine the predicted passenger flow value of the scenic area at the current moment. Otherwise, based on the base station passenger flow data from the previous week, calculate the passenger flow growth rate of the scenic area, and determine whether the passenger flow growth rate of the scenic area is greater than the second threshold. If so, use the growth rate method to identify valid base stations; use the passenger flow data obtained from the valid base stations at the current time to determine the predicted passenger flow value of the scenic area at the current time.
[0007] In one possible implementation, the reported visitor flow is the visitor flow reported by the scenic area; the base station visitor flow is the visitor flow reported by the base station, and the base station is located within a preset distance outside the boundary of the scenic area; the scenic area gate visitor flow is the visitor flow of the scenic area gate.
[0008] In one possible implementation, based on the reported passenger flow data with both non-lag and lag periods and the passenger flow data at the scenic area's gates, the non-lag trend difference and lag trend difference of the scenic area are determined; including: Calculate the non-lag trend difference of scenic areas :
[0009] in, Number the scenic area This is the start date of the non-lag period. For the number of days in the cycle, The number of turnstiles in the scenic area. For the first The first day Passenger flow at the scenic area's turnstiles , For the first Average passenger flow at Tianjing Scenic Area turnstiles ， For the first The scenic area reports its visitor volume daily; Calculate the lag trend difference of scenic spots :
[0010] in, Number the scenic area This is the start date of the lag period.
[0011] In one possible implementation, based on reported visitor flow and base station visitor flow with both non-lag and lag periods, a trend-based method is used to identify effective base stations in the scenic area, including: When the non-lag trend difference of the scenic area is less than the preset threshold, the non-lag trend difference of the base station is calculated based on the reported visitor flow and base station visitor flow of the non-lag period, and the effective base station is identified based on the non-lag trend difference of the base station. When the non-lag trend difference of the scenic area is less than the preset threshold, the lag trend difference of the base station is calculated based on the reported passenger flow and base station passenger flow based on the lag period, and the effective base station is identified based on the lag trend difference of the base station.
[0012] In one possible implementation, based on the reported passenger flow and base station passenger flow with no lag period, the non-lag trend difference degree of the base stations is calculated, and valid base stations are identified based on the non-lag trend difference degree of the base stations; including: Calculate the non-hysteresis trend difference of base stations :
[0013] in, Number the scenic area Number the base station. For the first Tian's base station Base station passenger flow For the first The scenic area reports its visitor volume daily; The base stations within the scenic area are sorted in ascending order based on the non-hysteresis trend difference to obtain the base station sequence. Before the base station sequence The sum of the base station passenger traffic within the non-hysteresis period of each base station is less than or equal to the threshold value, and the preceding If the sum of the base station passenger traffic within the non-hysteresis period of each base station is greater than the first threshold, then the first base station in the sequence will be... One base station is identified as a valid base station, and the rest are identified as invalid base stations; Wherein, the first threshold value is the sum of the base station passenger traffic of all base stations during the non-hysteresis period and the first adjustment coefficient. The quotient; the first adjustment coefficient The calculation formula is: .
[0014] In one possible implementation, based on the reported passenger flow and base station passenger flow with lag period, the lag trend difference degree of the base station is calculated, and effective base stations are identified based on the lag trend difference degree of the base station, including: Calculate the hysteresis trend difference of the base station :
[0015] in, Number the scenic area Number the base station. For the first Tian's base station Base station passenger flow For the first The scenic area reports its visitor volume daily; Based on the non-hysteresis trend difference, the base stations are sorted in ascending order to obtain the base station sequence; Before the base station sequence The sum of the passenger flow of the first base station within the non-hysteresis period of each base station is less than or equal to the threshold value, and the preceding If the sum of the passenger flow of the first base station within the non-hysteresis period of each base station is greater than the second threshold, then the base station sequence will be moved forward. One base station is identified as a valid base station, and the rest are identified as invalid base stations; Wherein, the second threshold value is the sum of the first base station passenger flow of all base stations within the non-hysteresis period and the second adjustment coefficient. The quotient; the second adjustment coefficient The calculation formula is: .
[0016] In one possible implementation, the tourist flow growth rate of the scenic area is calculated based on the base station passenger flow data from several weeks prior to the current day; including: The first The quotient of the sum of the base station visitor traffic of all base stations within the scenic area during the week-long holiday and the sum of the base station visitor traffic of all base stations within the scenic area during weekdays is determined as the [number of base stations]. A week's worth of base stations Passenger flow growth rate ; Computing base station Average passenger flow growth rate :
[0017] in, The number of weeks; Calculate the growth rate of visitor flow in the scenic area : .
[0018] In one possible implementation, an amplification method is used to identify valid base stations; including: Computing base station Standard deviation of the increase in weight :
[0019] When base station Average passenger flow growth rate Greater than 1.2 and the standard deviation of the increase rate If the value is less than 0.8, then the base station is determined. If it is a valid base station, then determine the base station. Invalid base station.
[0020] In one possible implementation, the current passenger flow and historical passenger flow obtained from effective base stations are used to determine the current passenger flow forecast value of the scenic area. include: Calculate the current time Tourist flow forecast for scenic spots :
[0021] in, Indicates base station Real-time reported passenger flow For the number of base stations, Indicates the previous day The daily passenger flow of the base station, when the base station When it is a valid base station, ,otherwise, .
[0022] In one possible implementation, the predicted visitor flow for the scenic area at the current moment is determined using the visitor flow data obtained from the effective base stations, including: Calculate the current time Tourist flow forecast for scenic spots :
[0023] in, Indicates base station Real-time reported passenger flow; The number of base stations; when base stations When it is a valid base station, ,otherwise, ; is a coefficient.
[0024] Secondly, embodiments of this application provide a scenic area visitor flow monitoring device based on base station data, comprising: The acquisition unit is used to acquire the reported passenger flow, base station passenger flow, and scenic area gate passenger flow for the non-lag period and lag period before the current day; as well as the base station passenger flow for several weeks before the current day; The determination unit is used to determine the non-lag trend difference and lag trend difference of the scenic area based on the reported passenger flow and the passenger flow of the scenic area gate based on the non-lag period and the lag period. The judgment unit is used to determine whether the non-lag trend difference degree of the scenic area is less than a preset threshold or whether the lag trend difference degree is less than a preset threshold. If yes, it enters the first processing unit; otherwise, it enters the second processing unit. The first processing unit is used to identify effective base stations in the scenic area based on the reported passenger flow and base station passenger flow based on the non-lag period and the lag period, and to determine the predicted passenger flow value of the scenic area at the current moment using the current passenger flow and historical passenger flow obtained from the effective base stations. The second processing unit is used to calculate the tourist flow growth rate of the scenic area based on the base station passenger flow of the previous week, and to determine whether the tourist flow growth rate of the scenic area is greater than the second threshold. If it is, the growth rate method is used to identify the effective base stations. The current tourist flow forecast value of the scenic area is determined by using the passenger flow obtained from the effective base stations.
[0025] Thirdly, embodiments of this application provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method of embodiments of this application.
[0026] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the methods of embodiments of this application.
[0027] Fifthly, embodiments of this application provide a computer program product, including a computer program / instructions, which, when executed by a processor, implement the method of embodiments of this application.
[0028] This embodiment improves the accuracy and timeliness of monitoring visitor flow in scenic areas by combining the trend method with the growth rate method, especially during holidays and peak periods; it also solves the technical problem of monitoring blind spots in existing methods under the circumstances of emergencies and data lag. Attached Figure Description
[0029] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0030] Figure 1 A flowchart illustrating the scenic area visitor flow monitoring method based on base station data provided in this application embodiment; Figure 2 Functional structure diagram of the scenic area visitor flow monitoring device based on base station data provided in the embodiments of this application; Figure 3 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation
[0031] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.
[0032] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.
[0033] First, a brief introduction to the design concept of the embodiments of this application will be given.
[0034] Most existing methods for monitoring visitor flow in scenic areas rely on data collected directly from traditional sensors or fixed equipment (such as turnstiles and cameras). These methods have significant limitations during peak periods, such as holidays. Furthermore, their accuracy and timeliness are poor, making it difficult to respond promptly to sudden changes in visitor flow.
[0035] To address this, this application proposes a method for monitoring visitor flow in scenic areas based on base station data. This method identifies effective base stations that highly correlate with actual visitor flow trends by calculating the trend differences between different visitor flow data within the scenic area. For scenic areas that do not meet the requirements of the trend method, the amplification method is applied. By comparing visitor flow changes between holidays and weekdays, the amplification rate of visitor flow is calculated. If the amplification rate exceeds a set threshold, the scenic area is considered to meet the conditions of the amplification method, thereby further identifying effective base stations. Furthermore, in practical applications, the scheme is verified by comparing with gate data, continuously adjusting and optimizing the algorithm to ensure high accuracy and reliability of the monitoring results. This comprehensive approach not only improves the accuracy of base station effectiveness identification but also adapts to the visitor flow characteristics of different scenic areas, exhibiting strong flexibility and adaptability, and providing more scientific and accurate data support for scenic area visitor flow management.
[0036] The technical solution of this application can achieve the following: 1. Improve data accuracy to drive industry decision-making. Data distortion correction: Traditional tourist flow monitoring in scenic areas relies on a single data source (scenic area base station), resulting in a generally high error rate. This solution reduces the average error rate and improves data reliability during peak holiday periods through lag correction and a dual-algorithm cross-validation mechanism, directly addressing the data credibility issue that scenic area managers are concerned about.
[0037] To dynamically adapt to complex scenarios and address the differences in characteristics among different types of scenic areas (such as large fluctuations in instantaneous visitor flow in urban parks and poor signal coverage in mountainous scenic areas), this solution intelligently switches between trend-based and amplification-based methods, enabling the monitoring system to adaptively adjust algorithm weights.
[0038] 2. Enable real-time risk response and improve the effectiveness of security management. Realize early warning of overcrowding and stampedes: By monitoring the density of base stations, thermal data, and sudden changes in the rate of increase, the risk of passenger overload can be predicted in advance (compared to traditional methods that result in delayed alarms).
[0039] Emergency resource dispatch: Based on the dynamic monitoring of the 100-meter extension area screened by the base station, the gathering status of tourists outside the scenic area can be tracked in real time, which shortens the response time of management manpower deployment.
[0040] 3. Realize business value creation and new business model operation By dynamically predicting the distribution of passenger flow in different areas using trend differences, the efficiency of personnel scheduling in scenic areas can be improved, the empty running rate of shuttle buses can be reduced, and energy consumption can be reduced (by controlling lighting / air conditioning zones).
[0041] 4. Industry standard upgrades drive digital transformation By creatively integrating operator base station data with the scenic area's existing systems, the problem of data silos from multiple sources was solved, and data access costs were reduced.
[0042] After introducing the application scenarios and design concepts of the embodiments of this application, the technical solutions provided by the embodiments of this application will be described below.
[0043] like Figure 1 As shown in the figure, this application provides a method for monitoring visitor flow in scenic areas based on base station data, including the following steps: Step 101: Obtain the reported passenger flow, base station passenger flow, and scenic area gate passenger flow for the non-lag period and lag period before the current day, as well as the base station passenger flow for several weeks before the current day; Step 102: Based on the reported passenger flow and the passenger flow at the scenic area gates, determine the non-lag trend difference and the lag trend difference of the scenic area. Step 103: Determine whether the non-lag trend difference of the scenic area is less than the first threshold or whether the lag trend difference is less than the first threshold; if yes, proceed to step 104; otherwise, proceed to step 105. For example, the first threshold is 0.1.
[0044] Step 104: Based on the reported passenger flow and base station passenger flow with non-lag period and lag period, the effective base stations of the scenic area are identified by the trend method. The current passenger flow and historical passenger flow obtained from the effective base stations are used to determine the predicted value of the scenic area passenger flow at the current moment. Step 105: Based on the base station passenger flow data from the previous weeks, calculate the passenger flow growth rate of the scenic area, and determine whether the passenger flow growth rate of the scenic area is greater than the second threshold. If so, use the growth rate method to identify valid base stations; use the passenger flow data obtained from the valid base stations at the current time to determine the predicted passenger flow value of the scenic area at the current time.
[0045] For example, the second threshold is 1.5.
[0046] This embodiment improves the accuracy and timeliness of monitoring visitor flow in scenic areas by combining the trend method with the growth rate method, especially during holidays and peak periods; it also solves the technical problem of monitoring blind spots in existing methods under the circumstances of emergencies and data lag.
[0047] In some embodiments, the reported visitor flow is the visitor flow reported by the scenic area; the base station visitor flow is the visitor flow reported by the base station, and the base station is located within a preset distance outside the boundary of the scenic area; the scenic area gate visitor flow is the visitor flow of the scenic area gate.
[0048] For example, the preset distance is 100 meters.
[0049] Specifically, the reported visitor flow is actually the visitor flow data observed the previous day (e.g., the reported visitor flow from October 3rd to October 7th is equal to the actual visitor flow data from October 2nd to October 6th). Therefore, before calculating the trend difference of the scenic area, this embodiment considers the lag in the reported visitor flow to clarify whether the scenic area meets the criteria for non-lag reported visitor flow or lag reported visitor flow. Non-lag reported visitor flow for a certain period is defined as the reported visitor flow for that period; lag reported visitor flow for a certain period is defined as the reported visitor flow one day after that period; for example, non-lag reported visitor flow from October 2nd to October 6th is the reported visitor flow from October 2nd to October 6th, and lag reported visitor flow from October 2nd to October 6th is the reported visitor flow from October 3rd to October 7th.
[0050] In some embodiments, based on the reported passenger flow data with both non-lag and lag periods and the passenger flow data at the scenic area's gates, the non-lag trend difference and lag trend difference of the scenic area are determined; including: Calculate the non-lag trend difference of scenic areas :
[0051] in, Number the scenic area This is the start date of the non-lag period. For the number of days in the cycle, The number of turnstiles in the scenic area. For the first The first day Passenger flow at the scenic area's turnstiles , For the first Average passenger flow at Tianjing Scenic Area turnstiles ， For the first The scenic area reports its visitor volume daily; Calculate the lag trend difference of scenic spots :
[0052] in, Number the scenic area This is the start date of the lag period.
[0053] In calculating the trend difference, this embodiment takes into account the lag in passenger flow data and optimizes the real-time performance of the data through a corresponding correction formula, thereby improving the accuracy of the monitoring results.
[0054] In some embodiments, based on the reported passenger flow and base station passenger flow with both non-lag and lag periods, a trend method is used to identify effective base stations in the scenic area, including: When the non-lag trend difference of the scenic area is less than the preset threshold, the non-lag trend difference of the base station is calculated based on the reported visitor flow and base station visitor flow of the non-lag period, and the effective base station is identified based on the non-lag trend difference of the base station. When the non-lag trend difference of the scenic area is less than the preset threshold, the lag trend difference of the base station is calculated based on the reported passenger flow and base station passenger flow based on the lag period, and the effective base station is identified based on the lag trend difference of the base station.
[0055] Preferably, the preset threshold is 0.1. In some embodiments, based on the reported passenger flow and base station passenger flow with no lag period, the non-lag trend difference degree of the base station is calculated, and effective base stations are identified based on the non-lag trend difference degree of the base station; including: Calculate the non-hysteresis trend difference of base stations :
[0056] in, Number the scenic area Number the base station. For the first Tian's base station Base station passenger flow For the first The scenic area reports its visitor volume daily; The base stations within the scenic area are sorted in ascending order based on the non-hysteresis trend difference to obtain the base station sequence. Before the base station sequence The sum of the base station passenger traffic within the non-hysteresis period of each base station is less than or equal to the threshold value, and the preceding If the sum of the base station passenger traffic within the non-hysteresis period of each base station is greater than the first threshold, then the first base station in the sequence will be... One base station is identified as a valid base station, and the rest are identified as invalid base stations; Wherein, the first threshold value is the sum of the base station passenger traffic of all base stations during the non-hysteresis period and the first adjustment coefficient. The quotient; the first adjustment coefficient The calculation formula is: .
[0057] In this embodiment, the adjustment coefficient is calculated using a comprehensive coefficient adjustment tool, where the benchmark data in the input sample data refers to the reported passenger flow. 0.65, which will convert reported passenger flow into operator market share.
[0058] This embodiment further improves the timeliness and accuracy of visitor flow monitoring in scenic areas by filtering base station data and predicting visitor flow trends.
[0059] In some embodiments, based on the reported passenger flow and base station passenger flow with lag period, the lag trend difference of the base station is calculated, and effective base stations are identified based on the lag trend difference of the base station, including: Calculate the hysteresis trend difference of the base station :
[0060] in, Number the scenic area Number the base station. For the first Tian's base station Base station passenger flow For the first The scenic area reports its visitor volume daily; Based on the non-hysteresis trend difference, the base stations are sorted in ascending order to obtain the base station sequence; Before the base station sequence The sum of the passenger flow of the first base station within the non-hysteresis period of each base station is less than or equal to the threshold value, and the preceding If the sum of the passenger flow of the first base station within the non-hysteresis period of each base station is greater than the second threshold, then the base station sequence will be moved forward. One base station is identified as a valid base station, and the rest are identified as invalid base stations; Wherein, the second threshold value is the sum of the first base station passenger flow of all base stations within the non-hysteresis period and the second adjustment coefficient. The quotient; the second adjustment coefficient The calculation formula is: .
[0061] In some embodiments, the tourist flow growth rate of the scenic area is calculated based on the base station passenger flow data from several weeks prior to the current day; including: The first The quotient of the sum of the base station visitor traffic of all base stations within the scenic area during the week-long holiday and the sum of the base station visitor traffic of all base stations within the scenic area during weekdays is determined as the [number of base stations]. A week's worth of base stations Passenger flow growth rate ; Computing base station Average passenger flow growth rate :
[0062] in, The number of weeks; Calculate the growth rate of visitor flow in the scenic area : .
[0063] This embodiment takes into full account the special characteristics of holiday passenger flow by using the amplification method, which greatly improves the accuracy of the monitoring system during peak periods and solves the problem of data deviation in existing technologies during peak periods.
[0064] In some embodiments, an amplification method is used to identify valid base stations; including: Computing base station Standard deviation of the increase in weight :
[0065] When base station Average passenger flow growth rate Greater than 1.2 and the standard deviation of the increase rate If the value is less than 0.8, then the base station is determined. If it is a valid base station, then determine the base station. Invalid base station.
[0066] This embodiment accurately identifies effective base stations and performs real-time monitoring by calculating the increase rate and standard deviation, further improving the effectiveness of passenger flow monitoring.
[0067] In some embodiments, the predicted visitor flow for the scenic area at the current moment is determined using the current visitor flow and historical visitor flow obtained from effective base stations; including: Calculate the current time Tourist flow forecast for scenic spots :
[0068] in, Indicates base station Real-time reported passenger flow For the number of base stations, Indicates the previous day The daily passenger flow of the base station, when the base station When it is a valid base station, ,otherwise, .
[0069] In some embodiments, the predicted visitor flow for the scenic area at the current moment is determined using the visitor flow data obtained from valid base stations, including: Calculate the current time Tourist flow forecast for scenic spots :
[0070] in, Indicates base station Real-time reported passenger flow; The number of base stations; when base stations When it is a valid base station, ,otherwise, ; is a coefficient.
[0071] To verify the rationality of the method in this embodiment, a sample of scenic spots was sampled in a practical application. The gate data of the scenic spots was compared with the monitored passenger flow for verification, and the error between the gate passenger flow and the monitored passenger flow was calculated: Error = (Monitored passenger flow - Gate passenger flow) / Gate passenger flow. The errors for each scenic spot on each date are shown in Table 1. Table 1
[0072] Of the 56 days, 2 days had an error greater than 0.2, while the remaining monitoring results were quite satisfactory. The algorithm was accurate for approximately 0.96 days, indicating good accuracy.
[0073] Based on the same inventive concept, this application provides a scenic area visitor flow monitoring device based on base station data, see reference. Figure 2 As shown, the scenic area visitor flow monitoring device 200 based on base station data provided in this application embodiment includes at least: The acquisition unit 201 is used to acquire the reported passenger flow, base station passenger flow, and scenic area gate passenger flow for the non-lag period and lag period before the current day; as well as the base station passenger flow for several weeks before the current day; Unit 202 is used to determine the non-lag trend difference and lag trend difference of the scenic area based on the reported passenger flow and the passenger flow of the scenic area gate based on the non-lag period and the lag period. Judgment unit 203 is used to determine whether the non-lag trend difference degree of the scenic area is less than a preset threshold or whether the lag trend difference degree is less than a preset threshold. If yes, it enters the first processing unit; otherwise, it enters the second processing unit. The first processing unit 204 is used to identify the effective base stations in the scenic area based on the reported passenger flow and base station passenger flow based on the non-lag period and the lag period, and to determine the predicted passenger flow value of the scenic area at the current moment by using the current passenger flow and historical passenger flow obtained from the effective base stations. The second processing unit 205 is used to calculate the tourist flow growth rate of the scenic area based on the base station passenger flow of the previous week, determine whether the tourist flow growth rate of the scenic area is greater than the preset second threshold, and if so, use the growth method to identify the effective base station; and use the current passenger flow obtained from the effective base station to determine the current tourist flow prediction value of the scenic area.
[0074] It should be noted that the principle of the scenic area visitor flow monitoring device 200 based on base station data provided in this application embodiment to solve the technical problem is similar to the method provided in this application embodiment. Therefore, the implementation of the scenic area visitor flow monitoring device 200 based on base station data provided in this application embodiment can refer to the implementation of the method provided in this application embodiment, and the repeated parts will not be described again.
[0075] Based on the same inventive concept, embodiments of this application also provide an electronic device, such as... Figure 3 As shown, it includes a memory and a processor. The memory stores an executable program, and the processor executes the executable program to implement the steps of the scenic area visitor flow monitoring method based on base station data provided in the above embodiments.
[0076] The aforementioned processor can be a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The aforementioned PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof. The general-purpose processor can be a microprocessor or any conventional processor, etc.
[0077] Since the electronic device described in this application embodiment is an electronic device equipped with a memory for implementing the scenic area visitor flow monitoring method based on base station data disclosed in this application embodiment, those skilled in the art can understand the structure and variations of the electronic device described in this application embodiment based on the scenic area visitor flow monitoring method based on base station data disclosed in this application embodiment, and therefore will not be described again here.
[0078] This application also provides a computer-readable storage medium storing a computer program thereon. When the computer program is run by a processor, it implements the steps of the scenic area visitor flow monitoring method based on base station data provided in the above embodiments.
[0079] The storage medium in this embodiment may be included in an electronic device; or it may exist independently and not be assembled into an electronic device. The storage medium carries one or more computer programs, which, when executed, implement the steps of the scenic area visitor flow monitoring method based on base station data provided in the above embodiment.
[0080] It should be understood that the various solutions in this embodiment have the same technical effects as those in the above method embodiments, and will not be repeated here.
[0081] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. Optionally, specific examples in this embodiment can refer to the examples described in any embodiment of this application, which will not be repeated here. Obviously, those skilled in the art should understand that the various modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular hardware and software combination.
[0082] This application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the data flow behavior identification method based on multi-source logs provided in the above embodiments.
[0083] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions targeted in the blocks may occur in a different order than those targeted in the drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0084] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. Multitasking and parallel processing may be advantageous in certain environments. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this application. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.
Claims
1. A method for monitoring visitor flow in scenic areas based on base station data, characterized in that, include: Obtain reported passenger flow, base station passenger flow, and scenic area gate passenger flow for the non-lag period and lag period prior to the current day, as well as base station passenger flow for several weeks prior to the current day; Based on the reported passenger flow and the passenger flow at the scenic area gates with both non-lag and lag periods, the non-lag trend difference and lag trend difference of the scenic area are determined. Determine whether the non-lag trend difference of the scenic area is less than the first threshold or whether the lag trend difference is less than the first threshold; If so, based on the reported passenger flow and base station passenger flow with both non-lag and lag periods, the trend method is used to identify the effective base stations in the scenic area. The current passenger flow and historical passenger flow obtained from the effective base stations are used to determine the predicted passenger flow value of the scenic area at the current moment. Otherwise, based on the base station passenger flow data from the previous week, calculate the passenger flow growth rate of the scenic area, and determine whether the passenger flow growth rate of the scenic area is greater than the second threshold. If so, use the growth rate method to identify valid base stations; use the passenger flow data obtained from the valid base stations at the current time to determine the predicted passenger flow value of the scenic area at the current time.
2. The method according to claim 1, characterized in that, The reported visitor flow refers to the visitor flow reported by the scenic area; the base station visitor flow refers to the visitor flow reported by the base station, which is located within a preset distance outside the boundary of the scenic area; the scenic area gate visitor flow refers to the visitor flow of the scenic area gate.
3. The method according to claim 1, characterized in that, Based on the reported passenger flow data with both non-lag and lag periods, and the passenger flow data at the scenic area's gates, the non-lag trend difference and lag trend difference of the scenic area are determined; including: Calculate the non-lag trend difference of scenic areas : in, Number the scenic area This is the start date of the non-lag period. For the number of days in a cycle, The number of turnstiles in the scenic area. For the first The first day Passenger flow at the scenic area's turnstiles , For the first Average passenger flow at Tianjing Scenic Area turnstiles ， For the first The scenic area reports its visitor volume daily; Calculate the lag trend difference of scenic spots : in, Number the scenic area This is the start date of the lag period.
4. The method according to claim 3, characterized in that, Based on reported visitor flow and base station visitor flow with both non-lag and lag periods, a trend-based method is used to identify effective base stations in the scenic area, including: When the non-lag trend difference of the scenic area is less than the preset threshold, the non-lag trend difference of the base station is calculated based on the reported visitor flow and base station visitor flow of the non-lag period, and the effective base station is identified based on the non-lag trend difference of the base station. When the non-lag trend difference of the scenic area is less than the preset threshold, the lag trend difference of the base station is calculated based on the reported passenger flow and base station passenger flow based on the lag period, and the effective base station is identified based on the lag trend difference of the base station.
5. The method according to claim 4, characterized in that, Based on the reported passenger flow and base station passenger flow with no lag period, calculate the no-lag trend difference degree of the base stations, and identify effective base stations based on the no-lag trend difference degree; including: Calculate the non-hysteresis trend difference of the base station : in, Number the scenic area Number the base station. For the first Tian's base station Base station passenger flow For the first The scenic area reports its visitor volume daily; The base stations within the scenic area are sorted in ascending order based on the non-hysteresis trend difference to obtain the base station sequence. Before the base station sequence The sum of the base station passenger traffic within the non-hysteresis period of each base station is less than or equal to the first threshold, and the preceding If the sum of the base station passenger traffic within the non-hysteresis period of each base station is greater than a threshold, then the first base station in the sequence will be... One base station is identified as a valid base station, and the rest are identified as invalid base stations; Wherein, the first threshold value is the sum of the base station passenger traffic of all base stations during the non-hysteresis period and the first adjustment coefficient. The quotient; the first adjustment coefficient The calculation formula is: 。 6. The method according to claim 4, characterized in that, Based on the reported passenger flow and base station passenger flow with lag period, the lag trend difference of base stations is calculated, and effective base stations are identified based on the lag trend difference, including: Calculate the hysteresis trend difference of the base station : in, Number the scenic area Number the base station. For the first Tian's base station Base station passenger flow For the first The scenic area reports its visitor volume daily; Based on the non-hysteresis trend difference, the base stations are sorted in ascending order to obtain the base station sequence; Before the base station sequence The sum of the passenger flow of the first base station within the non-hysteresis period of each base station is less than or equal to the threshold value, and the preceding If the sum of the passenger flow of the first base station within the non-hysteresis period of each base station is greater than the second threshold, then the base station sequence will be moved forward. One base station is identified as a valid base station, and the rest are identified as invalid base stations; Wherein, the second threshold value is the sum of the first base station passenger flow of all base stations within the non-hysteresis period and the second adjustment coefficient. The quotient; the second adjustment coefficient The calculation formula is: 。 7. The method according to claim 1, characterized in that, The tourist flow growth rate of the scenic area is calculated based on the base station passenger flow data from the previous week. include: The first The quotient of the sum of the base station visitor traffic of all base stations within the scenic area during the week-long holiday and the sum of the base station visitor traffic of all base stations within the scenic area during weekdays is determined as the [number of base stations]. A week's worth of base stations Passenger flow growth rate ; Computing base station Average passenger flow growth rate : in, The number of weeks; Calculate the growth rate of visitor flow in the scenic area : 。 8. The method according to claim 7, characterized in that, The method of amplification is used to identify valid base stations; including: Computing base station Standard deviation of the increase in weight : When base station Average passenger flow growth rate Greater than 1.2 and the standard deviation of the increase rate If the value is less than 0.8, then the base station is determined. If it is a valid base station, then determine the base station. Invalid base station.
9. The method according to claim 4, characterized in that, By using the current and historical visitor flow data obtained from valid base stations, the predicted visitor flow value for the scenic area at the current moment is determined. include: Calculate the current time Tourist flow forecast for scenic spots : in, Indicates base station Real-time reported passenger flow For the number of base stations, Indicates the previous day The daily passenger flow of the base station, when the base station When it is a valid base station, ,otherwise, .
10. The method according to claim 7, characterized in that, Using the current passenger flow data obtained from valid base stations, the predicted passenger flow value for the scenic area at the current moment is determined, including: Calculate the current time Tourist flow forecast for scenic spots : in, Indicates base station Real-time reported passenger flow; The number of base stations; when base stations When it is a valid base station, ,otherwise, ; is a coefficient.