Dynamic population monitoring method and system based on real-time public information platform

Abstract

The invention provides a population dynamic monitoring method and system based on a real-time public information platform. The method comprises the following steps: acquiring people flow sequence data of a preset area through a real-time public information platform, analyzing and processing the people flow sequence data in different time periods to obtain population scale data and population flow data of the preset area, and performing data correction and accuracy evaluation to obtain population flow data of the preset area; analyzing and processing the people flow sequence data, the population scale data and the population flow data through a preset population dynamic monitoring model to obtain a population change index, and comparing and evaluating a population dynamic monitoring state through a threshold value; according to the method, the human traffic sequence data, the population scale data and the population flow data of the preset region in different scenes and different time dimensions are obtained by relying on the real-time public information platform, and data optimization and accuracy verification are performed, so that comprehensive, accurate and multi-dimensional monitoring of regional population dynamics is realized.

Description

Technical Field

[0001] This application relates to the field of population monitoring technology, and more specifically, to a method and system for dynamic population monitoring based on a real-time public information platform. Background Technology

[0002] Accurate monitoring of regional population dynamics is crucial for urban management, public resource allocation, and emergency response. Traditional population monitoring methods suffer from problems such as data lag, limited scenario coverage, and limited statistical dimensions. For example, relying on manual statistics or single data sources makes it difficult to reflect real-time population dynamics in different areas (such as streets, communities, scenic spots, and enterprises), failing to meet the needs of refined management across multiple scenarios. Some monitoring systems only count pedestrian flow in specific scenarios (such as transportation hubs), lacking integrated analysis of data on resident population size and cross-regional population movement. Furthermore, their data processing methods are simplistic, lacking a robust accuracy verification mechanism, resulting in insufficient data reliability. Therefore, there is an urgent need for a unified platform-based method for multi-scenario, multi-dimensional, and highly accurate population dynamic monitoring.

[0003] Effective technical solutions are urgently needed to address the above problems. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for monitoring population dynamics based on a real-time public information platform. By relying on the real-time public information platform, it can obtain population flow sequence data, population size data, and population movement data of a preset area under different scenarios and time dimensions, and perform data optimization and accuracy verification, thereby achieving comprehensive, accurate, and multi-dimensional monitoring of regional population dynamics.

[0005] Firstly, this application provides a method for monitoring population dynamics based on a real-time public information platform, including the following steps: By acquiring personnel identification data and location data within a preset area through a real-time public information platform, and processing the data in conjunction with the data collection time, the population flow sequence data of the preset area is obtained. The population size data of the preset area is obtained by analyzing and processing the population flow sequence data within the first preset time period. The population flow sequence data within the second preset time period is analyzed and processed to obtain population flow data for the preset area. Based on the aforementioned population flow sequence data, population size data, and population mobility data, a population change index is obtained by analyzing and processing the data using a preset population dynamic monitoring model. The population change index is compared with a preset population change early warning threshold to obtain the population dynamic monitoring status. If the population change index is less than or equal to the preset population change early warning threshold, the population dynamic monitoring status is determined to be normal. If the population change index is greater than the preset population change early warning threshold, the population dynamic monitoring status is determined to be abnormal.

[0006] Optionally, in the population dynamic monitoring method based on a real-time public information platform described in this application, the step of obtaining personnel identification data and location data within a preset area through the real-time public information platform, and processing the data in conjunction with the data collection time to obtain the population flow sequence data of the preset area, includes: Obtain personnel identification data and location data within a preset area through a real-time public information platform; The personnel identification data and location data are fused together with the data collection time to obtain personnel flow sequence data. The pedestrian flow sequence data is aggregated to obtain pedestrian flow sequence data for a preset area.

[0007] Optionally, the population dynamic monitoring method based on a real-time public information platform described in this application further includes: Based on the location data, the location of the pedestrian flow sequence data is extracted to obtain real-time pedestrian flow data for a preset area; The real-time pedestrian flow data includes total real-time pedestrian flow data, pedestrian flow data for long-term stays, and on-duty personnel data.

[0008] Optionally, in the population dynamic monitoring method based on a real-time public information platform described in this application, the step of analyzing and processing the population flow sequence data within a first preset time period to obtain population size data for a preset area includes: The initial population size data is obtained by filtering the population flow sequence data within the first preset time period. Multi-SIM card users are removed from the initial population size data to obtain optimized population size data; Obtain the percentage of mobile phone-free samples in the preset area and compare it with the preset mobile phone-free compensation coefficient of the real-time public information platform to obtain the mobile phone-free correction deviation rate. If the mobile phone-free correction deviation rate is less than or equal to the preset mobile phone-free correction deviation threshold, then the population size optimization data is corrected according to the preset mobile phone-free compensation coefficient to obtain the population size data of the preset area. If the mobile phone-free correction deviation rate is greater than the preset mobile phone-free correction deviation threshold, the preset mobile phone-free compensation coefficient is corrected to the mobile phone-free sampling ratio, and then the population size optimization data is corrected to obtain the population size data of the preset area.

[0009] Optionally, in the population dynamic monitoring method based on a real-time public information platform described in this application, the step of analyzing and processing the population flow sequence data within a second preset time period to obtain population flow data for a preset area includes: The population flow sequence data within the second preset time period is compared and analyzed with the historical population flow sequence data to obtain population flow data for the preset area; The population flow data includes population inflow data and population outflow data.

[0010] Optionally, the population dynamic monitoring method based on a real-time public information platform described in this application further includes: Based on the pedestrian flow sequence data, extract the preset scene type feature data and the corresponding scene pedestrian flow sequence data, and perform time dimension verification; If the verification passes, the total number of people in the same scene within the preset area during the third preset time period will be obtained. Data is extracted based on the scene traffic flow sequence data to obtain the total scene traffic flow data for the third preset time period. The total pedestrian flow monitoring data of the scene is compared with the total pedestrian flow data of the scene to obtain the total pedestrian flow deviation rate of the scene; Based on the preset scene type feature data, query the preset weight value list to obtain the scene weight value, and perform weighted average processing with the scene total pedestrian flow deviation rate to obtain the regional total pedestrian flow deviation rate of the preset area. If the deviation rate of the total pedestrian flow in the area is greater than the preset deviation threshold of the total pedestrian flow in the area, the real-time pedestrian flow data is determined to be inaccurate, and an early warning response is output. If the deviation rate of the total number of people in the area is less than or equal to the preset deviation threshold of the total number of people in the area, then the real-time total number of people data is determined to be accurate.

[0011] Optionally, the population dynamic monitoring method based on a real-time public information platform described in this application further includes: Obtain household registration record data within the preset area; The household registration data is compared with the population size data to obtain the population size deviation rate; The population size deviation rate is compared with a preset population size deviation threshold to obtain the population size deviation status, including normal status or abnormal status. Obtain population ledger data for a preset number of communities within a preset area; Data is extracted based on the population size data to obtain the community population size data corresponding to the same community. The correlation coefficient between the community and the platform is calculated using a preset Pearson correlation coefficient calculation method based on the population ledger data and the community population size data. The data correlation coefficient is compared with a preset data correlation threshold to obtain the data correlation status, including normal or abnormal status. Perform an AND operation between the population size deviation state and the data correlation state; If the situation is normal, then the population size data is considered accurate. If the situation is abnormal, the population size data is determined to be inaccurate.

[0012] Optionally, the population dynamic monitoring method based on a real-time public information platform described in this application further includes: Data is extracted based on the population inflow and outflow data to obtain urban population inflow data, out-of-city population inflow data, urban population outflow data, and out-of-city population outflow data. The inflow data of the city's population is compared with the outflow data of the city's population to obtain the deviation value of the city's population flow. The deviation value of the urban population flow is compared with the preset urban population flow deviation threshold to obtain the urban population flow deviation status, including normal status or abnormal status. Acquire traffic record data, including inflow traffic record data and outflow traffic record data; The out-of-city population inflow data is compared with the inflow traffic record data to obtain the out-of-city population inflow deviation value; The outflow data of the population from outside the city is compared with the outflow traffic record data to obtain the outflow population deviation value. The deviation values ​​of the inflow population from outside the city and the outflow population from outside the city are compared with the preset inter-city population flow deviation threshold to obtain the deviation status of the inflow population from outside the city and the deviation status of the outflow population from outside the city, which include normal status or abnormal status respectively. Perform an AND operation on the deviation status of the urban population flow, the deviation status of the inflow of population from outside the city, and the deviation status of the outflow of population from outside the city; If the situation is normal, then the population flow data is considered accurate. If the situation is abnormal, the population flow data is determined to be inaccurate.

[0013] Optionally, in the population dynamic monitoring method based on a real-time public information platform described in this application, the step of analyzing and processing the population flow sequence data, population size data, and population mobility data through a preset population dynamic monitoring model to obtain a population change index includes: The population size data is compared with the historical average population size data for the same period to obtain the population size change rate; The population flow data is compared with the historical average population flow data for the same period to obtain the population flow change rate. The real-time total pedestrian flow data, long-term dwell pedestrian flow data, and on-duty personnel data are compared with the historical average total pedestrian flow data, historical average long-term dwell pedestrian flow data, and historical average on-duty personnel data for the same period to obtain the total pedestrian flow change rate, long-term dwell change rate, and on-duty personnel change rate. The changes in total population flow, long-term stay, and on-the-job personnel, along with the changes in population size and population mobility, are input into a preset population dynamic monitoring model for analysis and processing to obtain the population change index.

[0014] Secondly, this application provides a population dynamic monitoring system based on a real-time public information platform. The system includes a memory and a processor. The memory includes a program for a population dynamic monitoring method based on a real-time public information platform. When the program for the population dynamic monitoring method based on a real-time public information platform is executed by the processor, it performs the following steps: By acquiring personnel identification data and location data within a preset area through a real-time public information platform, and processing the data in conjunction with the data collection time, the population flow sequence data of the preset area is obtained. The population size data of the preset area is obtained by analyzing and processing the population flow sequence data within the first preset time period. The population flow sequence data within the second preset time period is analyzed and processed to obtain population flow data for the preset area. Based on the aforementioned population flow sequence data, population size data, and population mobility data, a population change index is obtained by analyzing and processing the data using a preset population dynamic monitoring model. The population change index is compared with a preset population change early warning threshold to obtain the population dynamic monitoring status. If the population change index is less than or equal to the preset population change early warning threshold, the population dynamic monitoring status is determined to be normal. If the population change index is greater than the preset population change early warning threshold, the population dynamic monitoring status is determined to be abnormal.

[0015] As can be seen from the above, the population dynamic monitoring method and system based on the real-time public information platform provided in this application obtains population flow sequence data, population size data and population flow data of a preset area under different scenarios and time dimensions by relying on the real-time public information platform, and optimizes the data and verifies its accuracy, thereby realizing comprehensive, accurate and multi-dimensional monitoring of regional population dynamics.

[0016] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a population dynamics monitoring method based on a real-time public information platform, provided for embodiments of this application; Figure 2 A flowchart illustrating the method for obtaining population size data of a preset area using a population dynamic monitoring method based on a real-time public information platform, as provided in this application embodiment. Figure 3 This is a flowchart illustrating the method for obtaining the population change index based on a real-time public information platform, as provided in the embodiments of this application. Detailed Implementation

[0019] 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 a part of the embodiments of this application, and not all of the 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. 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 represents selected embodiments of this 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.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1This is a flowchart of a population dynamics monitoring method based on a real-time public information platform according to some embodiments of this application. This population dynamics monitoring method based on a real-time public information platform is used in terminal devices, such as computers and mobile phones. The population dynamics monitoring method based on a real-time public information platform includes the following steps: S11. Obtain personnel identification data and location data within the preset area through the real-time public information platform, and process the data in conjunction with the data collection time to obtain the population flow sequence data of the preset area. S12. Analyze and process the population flow sequence data within the first preset time period to obtain population size data of the preset area; S13. Analyze and process the population flow sequence data within the second preset time period to obtain population flow data for the preset area; S14. Based on the population flow sequence data, population size data, and population mobility data, the population dynamic monitoring model is used to analyze and process the data to obtain the population change index. S15. Compare the population change index with the preset population change early warning threshold to obtain the population dynamic monitoring status. S151. If the population change index is less than or equal to the preset population change early warning threshold, the population dynamic monitoring status is determined to be normal. S152. If the population change index is greater than the preset population change early warning threshold, the population dynamic monitoring status is determined to be abnormal.

[0022] It should be noted that, in order to achieve accurate dynamic monitoring of the population, firstly, personnel identification data and location data within a preset area are obtained from the real-time public information platform. The preset area refers to a district-level area of ​​a prefecture-level city or municipality directly under the central government, such as Haidian District in Beijing. Based on the data collection time, a population flow sequence data of the preset area is constructed. Then, analysis is performed based on different time dimensions to obtain corresponding population size data and population flow data. After data optimization and accuracy verification, the pre-trained preset population dynamic monitoring model is used for analysis and processing to obtain a population change index. Finally, the population dynamic monitoring status is determined by threshold comparison. Different monitoring strategies are implemented according to the normal or abnormal status. If the status is normal, continuous monitoring is sufficient. If the status is abnormal, the abnormal situation needs to be uploaded to the platform operation and maintenance terminal for intervention by operation and maintenance personnel.

[0023] According to an embodiment of the present invention, the step of obtaining personnel identification data and location data within a preset area through a real-time public information platform, and processing the data in conjunction with the data collection time to obtain pedestrian flow sequence data for the preset area, includes: Obtain personnel identification data and location data within a preset area through a real-time public information platform; The personnel identification data and location data are fused together with the data collection time to obtain personnel flow sequence data. The pedestrian flow sequence data is aggregated to obtain pedestrian flow sequence data for a preset area.

[0024] It should be noted that the basic data obtained from the real-time public information platform is processed and integrated to obtain population flow sequence data reflecting the flow patterns of people within a preset area. This provides a standardized data foundation for subsequent population dynamic monitoring, trend analysis, and scenario-based applications. In this embodiment, the personnel identification data is the mobile phone MAC address, but it can also be other information that can identify an individual. Through association matching, the binding and fusion of "personnel, location, and time" is achieved, forming the flow trajectory data of a single individual at different times (collected once every 5 minutes), i.e., population flow sequence data, such as "Mobile phone MAC address A, 09:05 located in area A, business district A". The sequence data of all individuals in the preset area are summarized and statistically analyzed according to the region and time dimensions to obtain the total flow change sequence of the area within a continuous time interval. The data is summarized every 10 minutes. For example, from 10:00 to 10:10, the total flow of people in area A is 195 people (including 45 people in shopping mall A, 50 people in shopping mall B, and 105 people in shopping mall C).

[0025] According to an embodiment of the present invention, it further includes: Based on the location data, the location of the pedestrian flow sequence data is extracted to obtain real-time pedestrian flow data for a preset area; The real-time pedestrian flow data includes total real-time pedestrian flow data, pedestrian flow data for long-term stays, and on-duty personnel data.

[0026] It should be noted that, firstly, the location data of an individual determines which area the individual belongs to. Then, after deduplication (the same identity identifier is only counted once in the same time period), the total number of all identity identifier data in that area is the real-time total pedestrian flow data. At the same time, the total number of individuals who stay in a certain scene (such as shopping mall A) for more than 30 minutes is counted as long-term dwelling pedestrian flow data. Then, based on the identity identifier data, a preset employee identity identifier database (pre-built by different scenes based on identity identifier data and dynamically updated) is queried to determine whether the individual is a staff member of that scene. If so, the individual is marked as an on-duty staff member and the total number is counted as the on-duty staff data.

[0027] Please refer to Figure 2 , Figure 2This is a flowchart illustrating the population size data of a preset area obtained by a population dynamic monitoring method based on a real-time public information platform, as described in some embodiments of this application. According to an embodiment of the present invention, the step of analyzing and processing the pedestrian flow sequence data within a first preset time period to obtain the population size data of the preset area includes: S21. Filter the population flow sequence data within the first preset time period to obtain initial population size data; S22. Remove multi-SIM users from the initial population size data to obtain optimized population size data; S23. Obtain the sampling percentage of no mobile phones in the preset area and compare it with the preset compensation coefficient of no mobile phones in the real-time public information platform to obtain the correction deviation rate of no mobile phones. S231. If the mobile phone-free correction deviation rate is less than or equal to the preset mobile phone-free correction deviation threshold, the population size optimization data is corrected according to the preset mobile phone-free compensation coefficient to obtain the population size data of the preset area. S232. If the mobile phone-free correction deviation rate is greater than the preset mobile phone-free correction deviation threshold, then the preset mobile phone-free compensation coefficient is corrected to the mobile phone-free sampling ratio, and then the population size optimization data is corrected to obtain the population size data of the preset area.

[0028] It should be noted that the population flow sequence data within the first preset time period is filtered. In this embodiment, the first preset time period is the past 12 months. The filtering rule is to count all individuals who have lived in the preset area for more than 6 months in a year and whose location data during more than 80% of the nighttime hours (23:00-04:00) is in a residential area or living area. This is determined as the initial population size data. Considering that some individuals use multiple mobile phones at the same time, the deduplication algorithm for users with multiple mobile / Unicom / Telecom mobile phones is used to deduplicate users who have multiple mobile phones, thus obtaining optimized population size data. Considering that some elderly people and children do not have mobile phones, it is necessary to conduct population sampling periodically to determine the sampling percentage without mobile phones, such as 3.1%. The real-time public information platform will preset a compensation coefficient for no mobile phones, such as 3.2%. The two are compared, and the correction deviation rate for no mobile phones is (3.2-3.1) / 3.2≈0.03. This is then compared with the preset correction deviation threshold for no mobile phones. Based on the comparison, it is determined whether the preset compensation coefficient for no mobile phones needs to be corrected. Finally, the population size data of the preset area is obtained, that is, (1+preset compensation coefficient for no mobile phones)x optimized population size data.

[0029] According to an embodiment of the present invention, the step of analyzing and processing the population flow sequence data within a second preset time period to obtain population flow data for a preset area includes: The population flow sequence data within the second preset time period is compared and analyzed with the historical population flow sequence data to obtain population flow data for the preset area; The population flow data includes population inflow data and population outflow data.

[0030] It should be noted that in this embodiment, the second preset time period is a natural month. Data is determined as population inflow if the person stayed in the preset area for less than 10 hours in the previous month and lived in the preset area for more than 183 hours in the current month. Data is determined as population outflow if the person lived in the preset area for more than 183 hours in the previous month and stayed in the preset area for less than 10 hours in the current month.

[0031] According to an embodiment of the present invention, it further includes: Based on the pedestrian flow sequence data, extract the preset scene type feature data and the corresponding scene pedestrian flow sequence data, and perform time dimension verification; If the verification passes, the total number of people in the same scene within the preset area during the third preset time period will be obtained. Data is extracted based on the scene traffic flow sequence data to obtain the total scene traffic flow data for a third preset time period. The total pedestrian flow monitoring data of the scene is compared with the total pedestrian flow data of the scene to obtain the total pedestrian flow deviation rate of the scene; Based on the preset scene type feature data, query the preset weight value list to obtain the scene weight value, and perform weighted average processing with the scene total pedestrian flow deviation rate to obtain the regional total pedestrian flow deviation rate of the preset area. If the deviation rate of the total pedestrian flow in the area is greater than the preset deviation threshold of the total pedestrian flow in the area, the real-time pedestrian flow data is determined to be inaccurate, and an early warning response is output. If the deviation rate of the total number of people in the area is less than or equal to the preset deviation threshold of the total number of people in the area, then the real-time total number of people data is determined to be accurate.

[0032] It should be noted that preset scenario types, such as shopping mall A, office building A, and residential community A, use unique identifiers to represent the characteristic data of these scenario types. The time-dimensional verification is performed on the pedestrian flow sequence data of any two adjacent time periods within a scenario. For example, "09:00-09:10, shopping mall A, total number of people: 50", "09:10-09:20, shopping mall A, total number of people: 60", the rate of change between the two is (60-50) / 50=0.2. If this does not exceed a preset threshold, the time-dimensional verification is considered successful; otherwise, it is considered unsuccessful and requires manual review by maintenance personnel. After successful verification, the third preset time obtained from the real-time public information platform will be used. The total pedestrian flow data of a segment (in this embodiment, it is set to the past 1 hour) is compared with the total pedestrian flow monitoring data of the same segment (such as monitoring by infrared counters, video analysis terminals, and WiFi probes) to obtain the total pedestrian flow deviation rate of the segment. That is, the absolute value of the difference between the total pedestrian flow data of the segment and the total pedestrian flow monitoring data of the segment is divided into the total pedestrian flow data of the segment. Different segments are assigned different weight values. The total pedestrian flow deviation rates of all segments in the preset area are weighted and averaged to determine the total pedestrian flow deviation rate of the preset area. Then, the accuracy of the real-time pedestrian flow data of the real-time public information platform is evaluated by comparing the threshold.

[0033] According to an embodiment of the present invention, it further includes: Obtain household registration record data within the preset area; The household registration data is compared with the population size data to obtain the population size deviation rate; The population size deviation rate is compared with a preset population size deviation threshold to obtain the population size deviation status, including normal status or abnormal status. Obtain population ledger data for a preset number of communities within a preset area; Data is extracted based on the population size data to obtain the community population size data corresponding to the same community. The correlation coefficient between the community and the platform is calculated using a preset Pearson correlation coefficient calculation method based on the population ledger data and the community population size data. The data correlation coefficient is compared with a preset data correlation threshold to obtain the data correlation status, including normal or abnormal status. Perform an AND operation between the population size deviation state and the data correlation state; If the situation is normal, then the population size data is considered accurate. If the situation is abnormal, the population size data is determined to be inaccurate.

[0034] It should be noted that, in order to assess the accuracy of the population size data obtained by the real-time public information platform, the household registration record data within a preset area is compared with the population size data to obtain the population size deviation rate, which is the ratio of the absolute value of the difference between the household registration record data and the population size data to the household registration record data. Then, a threshold comparison is performed to determine the population size deviation status. A population size deviation rate less than or equal to a preset population size deviation threshold is considered normal, while the opposite is considered abnormal. Simultaneously, population ledger data of a preset number of communities (e.g., 4 communities) within the preset area is obtained. The real-time public information platform obtains the community population size data corresponding to the same community based on location data, calculates the Pearson correlation coefficient to obtain the data correlation coefficient between the community and the platform, and then performs a threshold comparison. A data correlation coefficient greater than or equal to a preset data correlation threshold is considered normal, while the opposite is considered abnormal. Finally, the population size deviation status and the data correlation status are ANDed. Only when both are normal is the population size data determined to be accurate; otherwise, it is considered inaccurate.

[0035] According to an embodiment of the present invention, it further includes: Data is extracted based on the population inflow and outflow data to obtain urban population inflow data, out-of-city population inflow data, urban population outflow data, and out-of-city population outflow data. The inflow data of the city's population is compared with the outflow data of the city's population to obtain the deviation value of the city's population flow. The deviation value of the urban population flow is compared with the preset urban population flow deviation threshold to obtain the urban population flow deviation status, including normal status or abnormal status. Acquire traffic record data, including inflow traffic record data and outflow traffic record data; The out-of-city population inflow data is compared with the inflow traffic record data to obtain the out-of-city population inflow deviation value; The outflow data of the population from outside the city is compared with the outflow traffic record data to obtain the outflow population deviation value. The deviation values ​​of the inflow population from outside the city and the outflow population from outside the city are compared with the preset inter-city population flow deviation threshold to obtain the deviation status of the inflow population from outside the city and the deviation status of the outflow population from outside the city, which include normal status or abnormal status respectively. Perform an AND operation on the deviation status of the urban population flow, the deviation status of the inflow of population from outside the city, and the deviation status of the outflow of population from outside the city; If the situation is normal, then the population flow data is considered accurate. If the situation is abnormal, the population flow data is determined to be inaccurate.

[0036] It should be noted that in this embodiment, the city level refers to a prefecture-level city or a municipality directly under the central government, such as Beijing. The deviation values ​​of intra-city population flow, inflow of out-of-city population, and outflow of out-of-city population are compared with their corresponding thresholds. If the deviation values ​​are less than or equal to the corresponding thresholds, it is determined to be a normal state; otherwise, it is an abnormal state. Intra-city population flow refers to the flow of people between preset areas within the city (such as from Haidian District to Dongcheng District of Beijing). Inflow of out-of-city population refers to people entering the city from outside the city. Outflow of out-of-city population refers to people going from the city to other cities. The deviation values ​​are all absolute values ​​of the differences. Inflow traffic record data and outflow traffic record data include airplane, car, train, and self-driving. Finally, the intra-city population flow deviation status, inflow of out-of-city population deviation status, and outflow of out-of-city population deviation status are ANDed. Only when all three are in a normal state is the population flow data determined to be accurate; otherwise, it is inaccurate.

[0037] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating the method for obtaining a population change index based on a real-time public information platform in some embodiments of this application. According to an embodiment of the present invention, the step of analyzing and processing the population flow sequence data, population size data, and population mobility data through a preset population dynamic monitoring model to obtain the population change index includes: S311. Compare the population size data with the historical average population size data for the same period in history to obtain the population size change rate; S312. Compare the population flow data with the historical average population flow data for the same period in history to obtain the population flow change rate. S313. The real-time total pedestrian flow data, long-term dwelling pedestrian flow data, and on-duty personnel data are compared with the historical average total pedestrian flow data, historical average long-term dwelling pedestrian flow data, and historical average on-duty personnel data for the same period in history, respectively, to obtain the total pedestrian flow change rate, long-term dwelling change rate, and on-duty personnel change rate. S32. Input the total population change rate, long-term stay change rate, and on-the-job personnel change rate, as well as the population size change rate and population mobility change rate, into a preset population dynamic monitoring model for analysis and processing to obtain the population change index.

[0038] It should be noted that the population size change rate refers to the ratio of the absolute value of the difference between the population size data and the historical average population size data for the same period to the historical average population size data. Similarly, the population mobility change rate, total population flow change rate, long-term stay change rate, and on-the-job personnel change rate can be obtained. These can be input into a preset population dynamic monitoring model for analysis and processing to obtain a population change index, which is used to intuitively reflect the current population change situation relative to the same period in history. The preset population dynamic monitoring model is obtained by pre-training with a large number of historical samples of total population flow change rate, long-term stay change rate, on-the-job personnel change rate, population size change rate, and population mobility change rate, as well as the corresponding population change index.

[0039] It is worth mentioning that, according to embodiments of the present invention, it further includes: By comparing the on-duty personnel data at different time periods, the on-duty personnel change rate within the preset area is obtained; The on-the-job personnel change rate and the on-the-job personnel change rate are weighted and summed to obtain the on-the-job personnel change index; The employee attendance change index is compared with the preset employee attendance change threshold. If the on-duty personnel change index is less than or equal to the preset on-duty personnel change threshold, the on-duty status is determined to be normal. If the on-duty personnel change index is greater than the preset on-duty personnel change threshold, the on-duty situation is determined to be abnormal.

[0040] It should be noted that, in order to assess the staff turnover within the preset area and thus indirectly reflect the company's development, the on-duty staff data from different time periods are compared to obtain the on-duty staff turnover rate within the preset area. The on-duty staff turnover rate is the ratio of the absolute value of the difference between the on-duty staff data of the previous day and the on-duty staff data of today to the on-duty staff data of the previous day. The on-duty staff turnover rates assessed in conjunction with historical data from the same period are weighted and summed to obtain the on-duty staff turnover index. The index is then determined to be normal by comparing it with a threshold. The weight values ​​and thresholds are preset by those skilled in the art and can be dynamically adjusted.

[0041] This invention also discloses a population dynamic monitoring system based on a real-time public information platform, including a memory and a processor. The memory includes a population dynamic monitoring method program based on the real-time public information platform. When the processor executes the population dynamic monitoring method program based on the real-time public information platform, it performs the following steps: By acquiring personnel identification data and location data within a preset area through a real-time public information platform, and processing the data in conjunction with the data collection time, the population flow sequence data of the preset area is obtained. The population size data of the preset area is obtained by analyzing and processing the population flow sequence data within the first preset time period. The population flow sequence data within the second preset time period is analyzed and processed to obtain population flow data for the preset area. Based on the aforementioned population flow sequence data, population size data, and population mobility data, a population change index is obtained by analyzing and processing the data using a preset population dynamic monitoring model. The population change index is compared with a preset population change early warning threshold to obtain the population dynamic monitoring status. If the population change index is less than or equal to the preset population change early warning threshold, the population dynamic monitoring status is determined to be normal. If the population change index is greater than the preset population change early warning threshold, the population dynamic monitoring status is determined to be abnormal.

[0042] It should be noted that, in order to achieve accurate dynamic monitoring of the population, firstly, personnel identification data and location data within a preset area are obtained from the real-time public information platform. The preset area refers to a district-level area of ​​a prefecture-level city or municipality directly under the central government, such as Haidian District in Beijing. Based on the data collection time, a population flow sequence data of the preset area is constructed. Then, analysis is performed based on different time dimensions to obtain corresponding population size data and population flow data. After data optimization and accuracy verification, the pre-trained preset population dynamic monitoring model is used for analysis and processing to obtain a population change index. Finally, the population dynamic monitoring status is determined by threshold comparison. Different monitoring strategies are implemented according to the normal or abnormal status. If the status is normal, continuous monitoring is sufficient. If the status is abnormal, the abnormal situation needs to be uploaded to the platform operation and maintenance terminal for intervention by operation and maintenance personnel.

[0043] According to an embodiment of the present invention, the step of obtaining personnel identification data and location data within a preset area through a real-time public information platform, and processing the data in conjunction with the data collection time to obtain pedestrian flow sequence data for the preset area, includes: Obtain personnel identification data and location data within a preset area through a real-time public information platform; The personnel identification data and location data are fused together with the data collection time to obtain personnel flow sequence data. The pedestrian flow sequence data is aggregated to obtain pedestrian flow sequence data for a preset area.

[0044] It should be noted that the basic data obtained from the real-time public information platform is processed and integrated to obtain population flow sequence data reflecting the flow patterns of people within a preset area. This provides a standardized data foundation for subsequent population dynamic monitoring, trend analysis, and scenario-based applications. In this embodiment, the personnel identification data is the mobile phone MAC address, but it can also be other information that can identify an individual. Through association matching, the binding and fusion of "personnel, location, and time" is achieved, forming the flow trajectory data of a single individual at different times (collected once every 5 minutes), i.e., population flow sequence data, such as "Mobile phone MAC address A, 09:05 located in area A, business district A". The sequence data of all individuals in the preset area are summarized and statistically analyzed according to the region and time dimensions to obtain the total flow change sequence of the area within a continuous time interval. The data is summarized every 10 minutes. For example, from 10:00 to 10:10, the total flow of people in area A is 195 people (including 45 people in shopping mall A, 50 people in shopping mall B, and 105 people in shopping mall C).

[0045] According to an embodiment of the present invention, it further includes: Based on the location data, the location of the pedestrian flow sequence data is extracted to obtain real-time pedestrian flow data for a preset area; The real-time pedestrian flow data includes total real-time pedestrian flow data, pedestrian flow data for long-term stays, and on-duty personnel data.

[0046] It should be noted that, firstly, the location data of an individual determines which area the individual belongs to. Then, after deduplication (the same identity identifier is only counted once in the same time period), the total number of all identity identifier data in that area is the real-time total pedestrian flow data. At the same time, the total number of individuals who stay in a certain scene (such as shopping mall A) for more than 30 minutes is counted as long-term dwelling pedestrian flow data. Then, based on the identity identifier data, a preset employee identity identifier database (pre-built by different scenes based on identity identifier data and dynamically updated) is queried to determine whether the individual is a staff member of that scene. If so, the individual is marked as an on-duty staff member and the total number is counted as the on-duty staff data.

[0047] According to an embodiment of the present invention, the step of analyzing and processing the population flow sequence data within a first preset time period to obtain population size data of a preset area includes: The initial population size data is obtained by filtering the population flow sequence data within the first preset time period. Multi-SIM card users are removed from the initial population size data to obtain optimized population size data; Obtain the percentage of mobile phone-free samples in the preset area and compare it with the preset mobile phone-free compensation coefficient of the real-time public information platform to obtain the mobile phone-free correction deviation rate. If the mobile phone-free correction deviation rate is less than or equal to the preset mobile phone-free correction deviation threshold, then the population size optimization data is corrected according to the preset mobile phone-free compensation coefficient to obtain the population size data of the preset area. If the mobile phone-free correction deviation rate is greater than the preset mobile phone-free correction deviation threshold, the preset mobile phone-free compensation coefficient is corrected to the mobile phone-free sampling ratio, and then the population size optimization data is corrected to obtain the population size data of the preset area.

[0048] It should be noted that the population flow sequence data within the first preset time period is filtered. In this embodiment, the first preset time period is the past 12 months. The filtering rule is to count all individuals who have lived in the preset area for more than 6 months in a year and whose location data during more than 80% of the nighttime hours (23:00-04:00) is in a residential area or living area. This is determined as the initial population size data. Considering that some individuals use multiple mobile phones at the same time, the deduplication algorithm for users with multiple mobile / Unicom / Telecom mobile phones is used to deduplicate users who have multiple mobile phones, thus obtaining optimized population size data. Considering that some elderly people and children do not have mobile phones, it is necessary to conduct population sampling periodically to determine the sampling percentage without mobile phones, such as 3.1%. The real-time public information platform will preset a compensation coefficient for no mobile phones, such as 3.2%. The two are compared, and the correction deviation rate for no mobile phones is (3.2-3.1) / 3.2≈0.03. This is then compared with the preset correction deviation threshold for no mobile phones. Based on the comparison, it is determined whether the preset compensation coefficient for no mobile phones needs to be corrected. Finally, the population size data of the preset area is obtained, that is, (1+preset compensation coefficient for no mobile phones)x optimized population size data.

[0049] According to an embodiment of the present invention, the step of analyzing and processing the population flow sequence data within a second preset time period to obtain population flow data for a preset area includes: The population flow sequence data within the second preset time period is compared and analyzed with the historical population flow sequence data to obtain population flow data for the preset area; The population flow data includes population inflow data and population outflow data.

[0050] It should be noted that in this embodiment, the second preset time period is a natural month. Data is determined as population inflow if the person stayed in the preset area for less than 10 hours in the previous month and lived in the preset area for more than 183 hours in the current month. Data is determined as population outflow if the person lived in the preset area for more than 183 hours in the previous month and stayed in the preset area for less than 10 hours in the current month.

[0051] According to an embodiment of the present invention, it further includes: Based on the pedestrian flow sequence data, extract the preset scene type feature data and the corresponding scene pedestrian flow sequence data, and perform time dimension verification; If the verification passes, the total number of people in the same scene within the preset area during the third preset time period will be obtained. Data is extracted based on the scene traffic flow sequence data to obtain the total scene traffic flow data for the third preset time period. The total pedestrian flow monitoring data of the scene is compared with the total pedestrian flow data of the scene to obtain the total pedestrian flow deviation rate of the scene; Based on the preset scene type feature data, query the preset weight value list to obtain the scene weight value, and perform weighted average processing with the scene total pedestrian flow deviation rate to obtain the regional total pedestrian flow deviation rate of the preset area. If the deviation rate of the total pedestrian flow in the area is greater than the preset deviation threshold of the total pedestrian flow in the area, the real-time pedestrian flow data is determined to be inaccurate, and an early warning response is output. If the deviation rate of the total number of people in the area is less than or equal to the preset deviation threshold of the total number of people in the area, then the real-time total number of people data is determined to be accurate.

[0052] It should be noted that preset scenario types, such as shopping mall A, office building A, and residential community A, use unique identifiers to represent the characteristic data of these scenario types. The time-dimensional verification is performed on the pedestrian flow sequence data of any two adjacent time periods within a scenario. For example, "09:00-09:10, shopping mall A, total number of people: 50", "09:10-09:20, shopping mall A, total number of people: 60", the rate of change between the two is (60-50) / 50=0.2. If this does not exceed a preset threshold, the time-dimensional verification is considered successful; otherwise, it is considered unsuccessful and requires manual review by maintenance personnel. After successful verification, the third preset time obtained from the real-time public information platform will be used. The total pedestrian flow data of a segment (in this embodiment, it is set to the past 1 hour) is compared with the total pedestrian flow monitoring data of the same segment (such as monitoring by infrared counters, video analysis terminals, and WiFi probes) to obtain the total pedestrian flow deviation rate of the segment. That is, the absolute value of the difference between the total pedestrian flow data of the segment and the total pedestrian flow monitoring data of the segment is divided into the total pedestrian flow data of the segment. Different segments are assigned different weight values. The total pedestrian flow deviation rates of all segments in the preset area are weighted and averaged to determine the total pedestrian flow deviation rate of the preset area. Then, the accuracy of the real-time pedestrian flow data of the real-time public information platform is evaluated by comparing the threshold.

[0053] According to an embodiment of the present invention, it further includes: Obtain household registration record data within the preset area; The household registration data is compared with the population size data to obtain the population size deviation rate; The population size deviation rate is compared with a preset population size deviation threshold to obtain the population size deviation status, including normal status or abnormal status. Obtain population ledger data for a preset number of communities within a preset area; Data is extracted based on the population size data to obtain the community population size data corresponding to the same community. The correlation coefficient between the community and the platform is calculated using a preset Pearson correlation coefficient calculation method based on the population ledger data and the community population size data. The data correlation coefficient is compared with a preset data correlation threshold to obtain the data correlation status, including normal or abnormal status. Perform an AND operation between the population size deviation state and the data correlation state; If the situation is normal, then the population size data is considered accurate. If the situation is abnormal, the population size data is determined to be inaccurate.

[0054] It should be noted that, in order to assess the accuracy of the population size data obtained by the real-time public information platform, the household registration record data within a preset area is compared with the population size data to obtain the population size deviation rate, which is the ratio of the absolute value of the difference between the household registration record data and the population size data to the household registration record data. Then, a threshold comparison is performed to determine the population size deviation status. A population size deviation rate less than or equal to a preset population size deviation threshold is considered normal, while the opposite is considered abnormal. Simultaneously, population ledger data of a preset number of communities (e.g., 4 communities) within the preset area is obtained. The real-time public information platform obtains the community population size data corresponding to the same community based on location data, calculates the Pearson correlation coefficient to obtain the data correlation coefficient between the community and the platform, and then performs a threshold comparison. A data correlation coefficient greater than or equal to a preset data correlation threshold is considered normal, while the opposite is considered abnormal. Finally, the population size deviation status and the data correlation status are ANDed. Only when both are normal is the population size data determined to be accurate; otherwise, it is considered inaccurate.

[0055] According to an embodiment of the present invention, it further includes: Data is extracted based on the population inflow and outflow data to obtain urban population inflow data, out-of-city population inflow data, urban population outflow data, and out-of-city population outflow data. The inflow data of the city's population is compared with the outflow data of the city's population to obtain the deviation value of the city's population flow. The deviation value of the urban population flow is compared with the preset urban population flow deviation threshold to obtain the urban population flow deviation status, including normal status or abnormal status. Acquire traffic record data, including inflow traffic record data and outflow traffic record data; The out-of-city population inflow data is compared with the inflow traffic record data to obtain the out-of-city population inflow deviation value; The outflow data of the population from outside the city is compared with the outflow traffic record data to obtain the outflow population deviation value. The deviation values ​​of the inflow population from outside the city and the outflow population from outside the city are compared with the preset inter-city population flow deviation threshold to obtain the deviation status of the inflow population from outside the city and the deviation status of the outflow population from outside the city, which include normal status or abnormal status respectively. Perform an AND operation on the deviation status of the urban population flow, the deviation status of the inflow of population from outside the city, and the deviation status of the outflow of population from outside the city; If the situation is normal, then the population flow data is considered accurate. If the situation is abnormal, the population flow data is determined to be inaccurate.

[0056] It should be noted that in this embodiment, the city level refers to a prefecture-level city or a municipality directly under the central government, such as Beijing. The deviation values ​​of intra-city population flow, inflow of out-of-city population, and outflow of out-of-city population are compared with their corresponding thresholds. If the deviation values ​​are less than or equal to the corresponding thresholds, it is determined to be a normal state; otherwise, it is an abnormal state. Intra-city population flow refers to the flow of people between preset areas within the city (such as from Haidian District to Dongcheng District of Beijing). Inflow of out-of-city population refers to people entering the city from outside the city. Outflow of out-of-city population refers to people going from the city to other cities. The deviation values ​​are all absolute values ​​of the differences. Inflow traffic record data and outflow traffic record data include airplane, car, train, and self-driving. Finally, the intra-city population flow deviation status, inflow of out-of-city population deviation status, and outflow of out-of-city population deviation status are ANDed. Only when all three are in a normal state is the population flow data determined to be accurate; otherwise, it is inaccurate.

[0057] According to an embodiment of the present invention, the step of analyzing and processing the population flow sequence data, population size data, and population mobility data through a preset population dynamic monitoring model to obtain a population change index includes: The population size data is compared with the historical average population size data for the same period to obtain the population size change rate; The population flow data is compared with the historical average population flow data for the same period to obtain the population flow change rate. The real-time total pedestrian flow data, long-term dwell pedestrian flow data, and on-duty personnel data are compared with the historical average total pedestrian flow data, historical average long-term dwell pedestrian flow data, and historical average on-duty personnel data for the same period to obtain the total pedestrian flow change rate, long-term dwell change rate, and on-duty personnel change rate. The changes in total population flow, long-term stay, and on-the-job personnel, along with the changes in population size and population mobility, are input into a preset population dynamic monitoring model for analysis and processing to obtain the population change index.

[0058] It should be noted that the population size change rate refers to the ratio of the absolute value of the difference between the population size data and the historical average population size data for the same period to the historical average population size data. Similarly, the population mobility change rate, total population flow change rate, long-term stay change rate, and on-the-job personnel change rate can be obtained. These can be input into a preset population dynamic monitoring model for analysis and processing to obtain a population change index, which is used to intuitively reflect the current population change situation relative to the same period in history. The preset population dynamic monitoring model is obtained by pre-training with a large number of historical samples of total population flow change rate, long-term stay change rate, on-the-job personnel change rate, population size change rate, and population mobility change rate, as well as the corresponding population change index.

[0059] It is worth mentioning that, according to embodiments of the present invention, it further includes: By comparing the on-duty personnel data at different time periods, the on-duty personnel change rate within the preset area is obtained; The on-the-job personnel change rate and the on-the-job personnel change rate are weighted and summed to obtain the on-the-job personnel change index; The employee attendance change index is compared with the preset employee attendance change threshold. If the on-duty personnel change index is less than or equal to the preset on-duty personnel change threshold, the on-duty status is determined to be normal. If the on-duty personnel change index is greater than the preset on-duty personnel change threshold, the on-duty situation is determined to be abnormal.

[0060] It should be noted that, in order to assess the staff turnover within the preset area and thus indirectly reflect the company's development, the on-duty staff data from different time periods are compared to obtain the on-duty staff turnover rate within the preset area. The on-duty staff turnover rate is the ratio of the absolute value of the difference between the on-duty staff data of the previous day and the on-duty staff data of today to the on-duty staff data of the previous day. The on-duty staff turnover rates assessed in conjunction with historical data from the same period are weighted and summed to obtain the on-duty staff turnover index. The index is then determined to be normal by comparing it with a threshold. The weight values ​​and thresholds are preset by those skilled in the art and can be dynamically adjusted.

[0061] The present invention discloses a population dynamic monitoring method and system based on a real-time public information platform. By relying on the real-time public information platform to obtain population flow sequence data, population size data and population flow data of a preset area under different scenarios and time dimensions, and by optimizing the data and verifying its accuracy, the method and system achieve comprehensive, accurate and multi-dimensional monitoring of regional population dynamics.

[0062] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0063] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0064] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0065] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0066] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A population dynamic monitoring method based on a real-time public information platform, characterized in that, Includes the following steps: By acquiring personnel identification data and location data within a preset area through a real-time public information platform, and processing the data in conjunction with the data collection time, the population flow sequence data of the preset area is obtained. The population size data of the preset area is obtained by analyzing and processing the population flow sequence data within the first preset time period. The population flow sequence data within the second preset time period is analyzed and processed to obtain population flow data for the preset area. Based on the aforementioned population flow sequence data, population size data, and population mobility data, a population change index is obtained by analyzing and processing the data using a preset population dynamic monitoring model. The population change index is compared with a preset population change early warning threshold to obtain the population dynamic monitoring status. If the population change index is less than or equal to the preset population change early warning threshold, the population dynamic monitoring status is determined to be normal. If the population change index is greater than the preset population change early warning threshold, the population dynamic monitoring status is determined to be abnormal.

2. The population dynamic monitoring method based on a real-time public information platform according to claim 1, characterized in that, The process involves acquiring personnel identification data and location data within a preset area through a real-time public information platform, and processing this data in conjunction with the data collection time to obtain pedestrian flow sequence data for the preset area, including: Obtain personnel identification data and location data within a preset area through a real-time public information platform; The personnel identification data and location data are fused together with the data collection time to obtain personnel flow sequence data. The pedestrian flow sequence data is aggregated to obtain pedestrian flow sequence data for a preset area.

3. The population dynamic monitoring method based on a real-time public information platform according to claim 2, characterized in that, Also includes: Based on the location data, the location of the pedestrian flow sequence data is extracted to obtain real-time pedestrian flow data for a preset area; The real-time pedestrian flow data includes total real-time pedestrian flow data, pedestrian flow data for long-term stays, and on-duty personnel data.

4. The population dynamic monitoring method based on a real-time public information platform according to claim 3, characterized in that, The step of analyzing and processing the population flow sequence data within a first preset time period to obtain population size data for a preset area includes: The initial population size data is obtained by filtering the population flow sequence data within the first preset time period. Multi-SIM card users are removed from the initial population size data to obtain optimized population size data; Obtain the percentage of mobile phone-free samples in the preset area and compare it with the preset mobile phone-free compensation coefficient of the real-time public information platform to obtain the mobile phone-free correction deviation rate. If the mobile phone-free correction deviation rate is less than or equal to the preset mobile phone-free correction deviation threshold, then the population size optimization data is corrected according to the preset mobile phone-free compensation coefficient to obtain the population size data of the preset area. If the mobile phone-free correction deviation rate is greater than the preset mobile phone-free correction deviation threshold, the preset mobile phone-free compensation coefficient is corrected to the mobile phone-free sampling ratio, and then the population size optimization data is corrected to obtain the population size data of the preset area.

5. The population dynamic monitoring method based on a real-time public information platform according to claim 4, characterized in that, The step of analyzing and processing the population flow sequence data within the second preset time period to obtain population flow data for a preset area includes: The population flow sequence data within the second preset time period is compared and analyzed with the historical population flow sequence data to obtain population flow data for the preset area; The population flow data includes population inflow data and population outflow data.

6. The population dynamic monitoring method based on a real-time public information platform according to claim 5, characterized in that, Also includes: Based on the pedestrian flow sequence data, extract the preset scene type feature data and the corresponding scene pedestrian flow sequence data, and perform time dimension verification; If the verification passes, the total number of people in the same scene within the preset area during the third preset time period will be obtained. Data is extracted based on the scene traffic flow sequence data to obtain the total scene traffic flow data for the third preset time period. The total pedestrian flow monitoring data of the scene is compared with the total pedestrian flow data of the scene to obtain the total pedestrian flow deviation rate of the scene; Based on the preset scene type feature data, query the preset weight value list to obtain the scene weight value, and perform weighted average processing with the scene total pedestrian flow deviation rate to obtain the regional total pedestrian flow deviation rate of the preset area. If the deviation rate of the total pedestrian flow in the area is greater than the preset deviation threshold of the total pedestrian flow in the area, the real-time pedestrian flow data is determined to be inaccurate, and an early warning response is output. If the deviation rate of the total number of people in the area is less than or equal to the preset deviation threshold of the total number of people in the area, then the real-time total number of people data is determined to be accurate.

7. The population dynamic monitoring method based on a real-time public information platform according to claim 6, characterized in that, Also includes: Obtain household registration record data within the preset area; The household registration data is compared with the population size data to obtain the population size deviation rate; The population size deviation rate is compared with a preset population size deviation threshold to obtain the population size deviation status, including normal status or abnormal status. Obtain population ledger data for a preset number of communities within a preset area; Data is extracted based on the population size data to obtain the community population size data corresponding to the same community. The correlation coefficient between the community and the platform is calculated using a preset Pearson correlation coefficient calculation method based on the population ledger data and the community population size data. The data correlation coefficient is compared with a preset data correlation threshold to obtain the data correlation status, including normal or abnormal status. Perform an AND operation between the population size deviation state and the data correlation state; If the situation is normal, then the population size data is considered accurate. If the situation is abnormal, the population size data is determined to be inaccurate.

8. The population dynamic monitoring method based on a real-time public information platform according to claim 7, characterized in that, Also includes: Data is extracted based on the population inflow and outflow data to obtain urban population inflow data, out-of-city population inflow data, urban population outflow data, and out-of-city population outflow data. The inflow data of the city's population is compared with the outflow data of the city's population to obtain the deviation value of the city's population flow. The deviation value of the urban population flow is compared with the preset urban population flow deviation threshold to obtain the urban population flow deviation status, including normal status or abnormal status. Acquire traffic record data, including inflow traffic record data and outflow traffic record data; The out-of-city population inflow data is compared with the inflow traffic record data to obtain the out-of-city population inflow deviation value; The outflow data of the population from outside the city is compared with the outflow traffic record data to obtain the outflow population deviation value. The deviation values ​​of the inflow population from outside the city and the outflow population from outside the city are compared with the preset inter-city population flow deviation threshold to obtain the deviation status of the inflow population from outside the city and the deviation status of the outflow population from outside the city, which include normal status or abnormal status respectively. Perform an AND operation on the deviation status of the urban population flow, the deviation status of the inflow of population from outside the city, and the deviation status of the outflow of population from outside the city; If the situation is normal, then the population flow data is considered accurate. If the situation is abnormal, the population flow data is determined to be inaccurate.

9. The population dynamic monitoring method based on a real-time public information platform according to claim 8, characterized in that, The step of analyzing and processing the population flow sequence data, population size data, and population mobility data using a preset population dynamic monitoring model to obtain a population change index includes: The population size data is compared with the historical average population size data for the same period to obtain the population size change rate; The population flow data is compared with the historical average population flow data for the same period to obtain the population flow change rate. The real-time total pedestrian flow data, long-term dwell pedestrian flow data, and on-duty personnel data are compared with the historical average total pedestrian flow data, historical average long-term dwell pedestrian flow data, and historical average on-duty personnel data for the same period to obtain the total pedestrian flow change rate, long-term dwell change rate, and on-duty personnel change rate. The changes in total population flow, long-term stay, and on-the-job personnel, along with the changes in population size and population mobility, are input into a preset population dynamic monitoring model for analysis and processing to obtain the population change index.

10. A population dynamic monitoring system based on a real-time public information platform, characterized in that, The system includes a memory and a processor. The memory contains a program for a population dynamics monitoring method based on a real-time public information platform. When the processor executes the program for the population dynamics monitoring method based on the real-time public information platform, it performs the following steps: By acquiring personnel identification data and location data within a preset area through a real-time public information platform, and processing the data in conjunction with the data collection time, the population flow sequence data of the preset area is obtained. The population size data of the preset area is obtained by analyzing and processing the population flow sequence data within the first preset time period. The population flow sequence data within the second preset time period is analyzed and processed to obtain population flow data for the preset area. Based on the aforementioned population flow sequence data, population size data, and population mobility data, a population change index is obtained by analyzing and processing the data using a preset population dynamic monitoring model. The population change index is compared with a preset population change early warning threshold to obtain the population dynamic monitoring status. If the population change index is less than or equal to the preset population change early warning threshold, the population dynamic monitoring status is determined to be normal. If the population change index is greater than the preset population change early warning threshold, the population dynamic monitoring status is determined to be abnormal.

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