Data processing method, device, electronic device and storage medium

Abstract

The present invention provides a data processing method, comprising: obtaining the average reported data volume AB of the APP corresponding to the set area i corresponding to the current time i Average number of AR devices associated with APP i ; AB i Compare with the corresponding reference range to obtain the corresponding first comparison result k i1 , and AR i Compare with the corresponding reference range to obtain the corresponding second comparison result k i2 Based on k i1 and k i2 Get f i1 ; Get the population difference value DR corresponding to the set area i in the current target time window corresponding to the current time i =C i / AQ i ; Based on the population difference value set DR, the resident population observation value set C and the resident population quantity set AQ, the fitting coefficient set g is obtained; based on g, f is obtained i2 ; Based on f i1 and f i2 The active population restoration value corresponding to the set area i within the current target time window is obtained. The present invention can improve the accuracy of the prediction.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a data processing method, device, electronic equipment and storage medium. Background Art

[0002] Many application scenarios require predicting the number of targets within a geographic area, such as obtaining the foot traffic of a store over a period of time or the number of active residents in a city over a period of time. With the rapid development of mobile devices and information technology, data such as time and location information from mobile devices has become readily available. For example, mobile apps can report time and location information, enabling predictions of many of these targets and foot traffic. However, for a given geographic area, numerical variations can be easily influenced by factors such as app reporting, resulting in different "dimensionalities" for the number of mobile devices in that area at different time points when compared vertically. Furthermore, numerical variations across different geographic areas, influenced by factors such as target structure, population structure, and collection differences, can also lead to different "dimensionalities" for the number of mobile device IDs in different geographic areas when compared horizontally.

[0003] Therefore, how to reduce these numerical differences as much as possible to improve the accuracy of predicting target data based on position data and make the predicted data closer to the actual target data has become a technical problem that needs to be solved urgently. Summary of the Invention

[0004] In view of the above technical problems, the technical solution adopted by the present invention is:

[0005] An embodiment of the present invention provides a data processing method, comprising the following steps:

[0006] S100, obtain the average reported data volume AB of the APP corresponding to the set area i in the current target time window corresponding to the current time i =N i / RP i Average number of AR devices associated with APP i =RE i / RP i ; N i RP is the total number of APP report data belonging to the set area i obtained within the current target time window, i is the number of apps belonging to the set area i obtained within the current target time window; RE iThe number of sample devices belonging to the set area i obtained within the current target time window; the APP reporting data includes at least the ID of the sample device corresponding to the APP, the reporting time, the reporting location information, and the ID of the APP; the value of i ranges from 1 to n, where n is the number of set areas;

[0007] S200, AB i Compare with the corresponding reference range, and obtain the corresponding first comparison result k based on the comparison result i1 , and AR i Compare with the corresponding reference range, and obtain the corresponding second comparison result k based on the comparison result i2 ;

[0008] S300, based on k i1 and k i2 Get the first correction coefficient corresponding to the set area i in the current target time window

[0009] S400: Obtaining the population difference value DR corresponding to the set area i within the current target time window corresponding to the current time. i =C i / AQ i , C i is the observed value of the permanent population of the set area i in the set time period, C i Based on the reporting location information corresponding to the set area i obtained within the set time period; AQ i is the number of permanent residents in the set area i during the set time period;

[0010] S500, obtaining a fitting coefficient set g based on a population difference value set DR, a resident population observation value set C, and a resident population quantity set AQ, wherein DR = {DR1, DR2, ..., DR i ,…,DR n}, C={C1, C2, ..., C i ,…,C n},AQ={AQ1,AQ2,…,AQ i ,…,AQ n}, g={g1, g2, ..., g i ,…,g n}, g i is the fitting coefficient corresponding to the set area i;

[0011] S600: Obtain the second correction coefficient f corresponding to the set area i i2 =1 / g ci ;

[0012] S700: Obtain the active population restoration value corresponding to the set area i within the current target time window Among them, w i To set the population weight of region i, b1 is a first setting coefficient, b2 is a second setting coefficient, and b1+b2=1.

[0013] Another embodiment of the present invention provides a data processing device, which is applied to a server and includes:

[0014] The first acquisition module is used to obtain the average reported data volume AB of the APP corresponding to the set area i in the current target time window corresponding to the current time i =N i / RP i Average number of AR devices associated with APP i =RE i / RP i ; N i RP is the total number of APP report data belonging to the set area i obtained within the current target time window, i is the number of apps belonging to the set area i obtained within the current target time window; RE i The number of sample devices belonging to the set area i obtained within the current target time window; the APP reporting data includes at least the ID of the sample device corresponding to the APP, the reporting time, the reporting location information, and the ID of the APP; the value of i ranges from 1 to n, where n is the number of set areas;

[0015] The second acquisition module is used to obtain the population difference value DR corresponding to the set area i in the current target time window corresponding to the current time i =C i / AQ i , C i is the observed value of the permanent population of the set area i in the set time period, AQ i is the number of permanent residents in the set area i during the set time period;

[0016] The first determination module is used to determine AB i Compare with the corresponding reference range, and obtain the corresponding first comparison result k based on the comparison result i1 , and AR i Compare with the corresponding reference range, and obtain the corresponding second comparison result k based on the comparison result i2 ; and based on k i1 and k i2 Determine the first correction coefficient corresponding to the set area i in the current target time window

[0017]

[0018] The second determination module is used to determine the i ,…,C n Get the corresponding fitting coefficients g1, g2, ..., g i ,…,g n , g i is the fitting coefficient corresponding to the set area i; and is used to determine the second correction coefficient f corresponding to the set area i i2 =1 / g ci ;

[0019] The calculation module is used to calculate the active population restoration value corresponding to the set area i within the current target time window Among them, w i To set the population weight of region i, b1 is a first setting coefficient, b2 is a second setting coefficient, and b1+b2=1.

[0020] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which stores at least one instruction or at least one program, and is characterized in that the at least one instruction or the at least one program is loaded and executed by a processor to implement the aforementioned method.

[0021] An embodiment of the present invention further provides an electronic device, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0022] The present invention has at least the following beneficial effects:

[0023] The data processing method provided by an embodiment of the present invention first obtains the average APP reported data volume and the average APP associated device volume based on the APP reported data volume and the number of sample devices in each target time window corresponding to each set area; then, obtains a first correction coefficient based on the average APP reported data volume and the average APP associated device volume; then, the set areas are grouped based on the population difference values of each set area, and the corresponding fitting coefficients are obtained based on the permanent population observation values and the number of permanent residents corresponding to each group; then, the second correction coefficient is obtained based on the fitting coefficient; finally, the active population restoration value corresponding to each set area is obtained based on the first correction coefficient, the second coefficient, the number of sample devices, and the number of permanent residents corresponding to each set area. Since the reporting differences of the area itself and the reporting differences between regions are taken into account, the impact caused by collection fluctuations can be minimized as much as possible, so that the predicted number of active people in each set area can be as close as possible to the actual number of active people. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 This is a flow chart of a data processing method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specified order or sequence. It should be understood that the numbers used in this way are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] The data processing method provided in the embodiment of the present invention can be applied to any device with data processing capabilities, which can be a terminal or a server. When the device executes the index table establishment method for the video library provided in the embodiment of the present invention, it can be executed independently or in a cluster collaborative manner.

[0029] This embodiment provides a data processing method. Figure 1 It is a flowchart of a data processing method provided by this embodiment. This specification provides the method operation steps as described in the embodiment or flowchart, but may include more or fewer operation steps based on conventional or non-creative labor. The order of steps listed in the embodiment is only one way of executing the steps among many steps and does not represent the only execution order. When the actual system or server product is executed, it can be executed in sequence or in parallel according to the method shown in the embodiment or the accompanying drawings (for example, in a parallel processor or multi-threaded processing environment).

[0030] The implementation environment provided by this embodiment may include at least a client and a server.

[0031] Furthermore, the client may be one or more smartphones, tablet computers, laptop computers, desktop computers, and the like. The user terminal may be an application provided by a service provider to a user, or a webpage provided by a service provider to a user. The server may be a standalone server, a server cluster consisting of multiple servers, or a cloud computing service center. The server may include a network communication unit, a processor, a database, and a memory. The memory may store a computer program. The server may establish a communication connection with the user terminal via a wireless or wired network.

[0032] Specifically, if Figure 1 As shown, the data processing method provided in the embodiment of the present invention can be implemented by a processor executing a computer program, and may include the following steps:

[0033] S100, obtain the average reported data volume AB of the APP corresponding to the set area i in the current target time window corresponding to the current time i =N i / RP i Average number of AR devices associated with APP i =RE i / RP i ; N i RP is the total number of APP report data belonging to the set area i obtained within the current target time window, i is the number of apps belonging to the set area i obtained within the current target time window; RE i is the number of sample devices belonging to the set area i obtained within the current target time window; the APP reporting data at least includes the ID of the sample device corresponding to the APP, the reporting time, the reporting location point information and the ID of the APP; the value of i is 1 to n, and n is the number of set areas.

[0034] In this embodiment of the present invention, the current time belongs to the current target time window and is the time for executing data processing. For example, it can be the end time of the current target time window. The duration of each target time window is fixed. The target time window can be divided into hours or days, which is not specifically limited in this embodiment of the present invention. Preferably, the target time window can be divided into days, and the duration of each target time window is one day, for example, from 0:00 to 24:00.

[0035] In this embodiment of the present invention, the set region i can be an administrative region, such as Beijing, Hangzhou, or Haidian District, Beijing. For administrative regions, a mapping relationship between administrative regions and grid sets is stored in the memory, such as the grid set corresponding to Hangzhou. The number of set regions can be set based on actual needs, for example, all cities in China or a specified number of cities in China, and is not specifically limited in the present invention.

[0036] In the embodiment of the present invention, the device may be a mobile communication device.

[0037] In an embodiment of the present invention, the processor in the implementation environment will communicate with all devices in the set area through the network communication unit, and each device will report the corresponding data to the processor through the APP installed on it. The processor will obtain the corresponding report data information table after pre-processing the report data received in each target time window and store it in the database. Each row of data in the report data information table corresponding to each target time window may include the ID of the sample device corresponding to the row of data, the reporting time, the reporting location point information and the APP ID. The ID of the device can be a unique code to distinguish different mobile terminals. For example, the terminal ID can be any one or a combination of the IMEI, IMSI, MAC address, and SIM card number of the mobile terminal, or any one or a combination of the IMEI, IMSI, MAC address, and SIM card number of the mobile terminal that has been encrypted (for example, MD5 encryption processing). The embodiment of the present invention does not make specific limitations. The ID of the APP can be a unique identifier of the APP, such as a similar unique identifier such as UDID, UUID, IDFA, IDFV, etc.

[0038] In the embodiment of the present invention, N i It can be obtained by following the steps below:

[0039] (1) Based on the current target time window, obtain the corresponding report data information table from the database;

[0040] (2) Based on the keywords corresponding to the set area i, the amount of report data belonging to the set area i is retrieved from the obtained report data information table, and the retrieved report data amount is used as N i .

[0041] In the embodiment of the present invention, RP i This is the number of apps after deduplication, that is, multiple apps with the same ID are counted as one app. i is the number of sample devices after deduplication, that is, multiple sample devices with the same ID are regarded as one sample device.

[0042] In an embodiment of the present invention, the reported location point information may be longitude and latitude coordinates.

[0043] In addition, in the embodiment of the present invention, the average amount of APP reported data and the average amount of APP associated devices in each set area within each target time window will be stored in the database.

[0044] In an embodiment of the present invention, since the amount of APP reporting data and the number of sampling devices in each target time window will change dynamically and are insufficient to reflect the reporting capability, the use of the average amount of APP reporting data and the average amount of APP associated devices can better reflect the reporting capability. When not affected by APP reporting factors, this capability is relatively stable.

[0045] S200, AB i Compare with the corresponding reference range, and obtain the corresponding first comparison result k based on the comparison result i1 , and AR i Compare with the corresponding reference range, and obtain the corresponding second comparison result k based on the comparison result i2 .

[0046] Furthermore, in the embodiment of the present invention, S200 may specifically include:

[0047] S210, if D i1 ≤AB i ≤D i2 , then set the first correction coefficient k corresponding to the set area i in the current target time window i1 =1, if AB i >D i2 , then set k1=AB i / D i2 , if AB i <D i1 , then set k1=AB i / D i1 ;D i1 AB i The corresponding lower reference value in the reference range, D i2 AB i The upper reference value in the corresponding reference range.

[0048] S220, if H i1 ≤AR i ≤H i2 , then set the second correction coefficient k corresponding to the set area i in the current target time window i2 =1, if AR i >H i2 , then set k i2 =AR i / H i2 , if AR i <H i1, then set k i2 =AR i / H i1 ;H i1 For AR i The corresponding lower reference value in the reference range, H i2 For AR i The upper reference value in the corresponding reference range.

[0049] Furthermore, in an embodiment of the present invention, the AB i The corresponding benchmark range can be obtained by following the steps below:

[0050] S10, obtaining the average reported data volume of the APP corresponding to the set historical time period.

[0051] In the embodiment of the present invention, the historical time period may be set based on actual needs, for example, it may be set to 12 months or longer, and the present invention does not make any special limitation.

[0052] S12, taking the set unit time unit as the sliding window and a target time window as the sliding step, obtains the average APP reported data volume corresponding to the target time period that meets the following preset conditions from the average APP reported data volume corresponding to the set historical time period as the target APP average reported data volume.

[0053] The preset condition is: the average reported data volume of the APP corresponding to the target time period is between [AvgAB i -c*SAB i , AvgAB i +c*SAB i ] h≥q*g, where q is the number of data in the average reported data volume of the APP corresponding to the target time period, and g is the preset coefficient AvgAB i SAB is the average amount of data reported by the target APP. i The deviation of the average reported data volume for the target APP, ABS ir The rth data in the average reported data volume of the APP corresponding to the target time period, r is 1 to q; c is 2 or 3. The set unit time unit includes at least one target time window.

[0054] S14, get D i1 =AvgAB i -c*SAB i , D i2 =AvgAB i +c*SAB i .

[0055] Furthermore, the AR iThe corresponding benchmark range is obtained through the following steps:

[0056] S20: Obtain the average number of APP-associated devices corresponding to a set historical time period.

[0057] S22, using a set unit time unit as a sliding window and a target time window as a sliding step, obtain the average number of APP-associated devices corresponding to the target time period that meets the following preset conditions from the average number of APP-associated devices corresponding to the set historical time period as the target average number of APP-associated devices; the preset conditions are: the average number of APP-associated devices corresponding to the target time period is between [AvgAR i -c*SAR i , AvgAR i +c*SAR i ], where p is the number of data in the average number of APP-associated devices corresponding to the target time period, and g is the preset coefficient. i SAR is the average number of devices associated with the target APP. i is the deviation of the average number of associated devices of the APP, ARS is The sth data in the AAP average associated device quantity corresponding to the target time period, where s is 1 to p, and c is 2 or 3; the set unit time unit includes at least one target time window;

[0058] S24, get H i1 =AvgAR i -c*SAR i , H i2 =AvgAR i +c*SAR i .

[0059] In the embodiment of the present invention, g may be an empirical value, for example, g is 0.9 to 0.98, preferably g=0.9, which can make the calculation result as accurate as possible while tolerating a certain fluctuation range.

[0060] In the embodiment of the present invention, c is preferably 2. The inventors of the present invention have found through experiments that when c=2, the calculation result can be made more accurate.

[0061] In an embodiment of the present invention, the set unit time unit includes at least one target time window. In an embodiment of the present invention, the set unit time unit can be set based on actual needs, for example, it can include 30 target time windows. Those skilled in the art know that any method using the set unit time unit as a sliding window, a target time window as a sliding step, and obtaining the average amount of APP reported data corresponding to the target time period that meets the following preset conditions from the average amount of APP reported data corresponding to the set historical time period as the target APP average reported data amount, and using the set unit time unit as a sliding window, a target time window as a sliding step, and obtaining the average amount of APP associated devices corresponding to the target time period that meets the following preset conditions from the average amount of APP associated devices corresponding to the set historical time period as the target APP average associated device amount all fall within the scope of protection of the present invention.

[0062] S300, based on k i1 and k i2 Get the first correction coefficient corresponding to the set area i in the current target time window

[0063] In the embodiment of the present invention, the first correction coefficient is used to reduce the reporting difference in the time dimension as much as possible, that is, the reporting difference of the setting area itself.

[0064] S400: Obtaining the population difference value DR corresponding to the set area i within the current target time window corresponding to the current time. i =C i / AQ i , C i is the observed value of the permanent population of the set area i in the set time period, AQ i is the number of permanent residents in the set area i during the set time period.

[0065] In the embodiments of the present invention, the population difference value can, to a certain extent, reflect the broad characteristics of urban groups, including but not limited to the proportion of data collection devices, age structure, gender ratio, and activity frequency. The inventors of the present invention have found that regions with closer population difference values exhibit more similar reporting differences.

[0066] In the embodiment of the present invention, the set time period can be set based on actual needs and can be a period that can indicate that the number of permanent residents in most set areas is basically stable during the period. For example, it can be 1 week, 1 month, 2 months, 3 months, or 6 months. The observed value of the permanent resident population of each set area in the set time period can be obtained based on the reporting location information obtained during the set time period. Specifically, it can include:

[0067] Step 1: query the reporting location point information belonging to the set area i within the set time period from the database as the target reporting location point information of the set area i.

[0068] Step 2: Based on the target reported location point information, obtain location information of a set type and use the device ID corresponding to the obtained location information of the set type as the target device ID. The set type of location information may be, for example, location information of a residential address.

[0069] Those skilled in the art know that existing methods can be used to obtain location information of a set type based on target reporting location point information.

[0070] Step 3, get MP i The number of target device IDs in the set area i within the set time period.

[0071] In an embodiment of the present invention, the number of permanent residents in each set area during a set time period may be officially published population data, such as population data obtained from the most recent census.

[0072] S500, obtaining a fitting coefficient set g based on the population difference value set DR, the resident population observation value set C and the resident population quantity set AQ, wherein DR = {DR1, DR2, ..., DR i ,…,DR n}, C={C1, C2, ..., C i ,…,C n},AQ={AQ1,AQ2,…,AQ i ,…,AQ n}, g={g1, g2, ..., g i ,…,g n}, g i is the fitting coefficient corresponding to the set area i.

[0073] Furthermore, S500 may specifically include:

[0074] S501, DR1, DR2, ..., DR i ,…,DR n Arrange in order, for example, from large to small or from small to large, to form an ordered sequence.

[0075] S502, based on the ordered sequence, respectively obtain the first target group to the mth target group, wherein the first target group includes the first target group in the ordered sequence. The jth target group includes the first population difference values in the ordered sequence except the first target group to the j-1th target group. population difference values, the mth target group includes the population difference values in the ordered sequence except the 1st target group to the m-1th target group, the value of j is 2 to m-1; m is the number of target groups, 0<a<b<1, and 2*a+(m-2)*b=1. Indicates rounding down.

[0076] The number of target groups can be set based on actual needs. In an exemplary embodiment, m=6, a=0.1, and b=0.2.

[0077] S503, based on the 1st target group to the mth target group, respectively obtain the 1st fitting coefficient to the mth fitting coefficient, wherein each fitting coefficient is obtained by linear fitting based on the permanent population observation value and the permanent population number in the corresponding target group.

[0078] Specifically, each fitting coefficient can be obtained based on the following steps:

[0079] S5031, set a corresponding rectangular coordinate system for each target group, where the X-axis of the rectangular coordinate system can be the resident population observation value, and the Y-axis can be the resident population number;

[0080] S5032, obtaining corresponding coordinate points on the corresponding rectangular coordinate system based on the observed values of the permanent population and the number of permanent residents corresponding to each target group;

[0081] S5033: Perform linear fitting on the coordinate points corresponding to each target group to obtain the corresponding fitting slope, i.e., fitting coefficient. The specific fitting method may be the existing technology.

[0082] S600: Obtain the second correction coefficient f corresponding to the set area i i2 =1 / g ci .

[0083] In the embodiment of the present invention, the second correction coefficient is used to minimize the reporting differences in the spatial dimension, that is, to set the reporting differences between regions.

[0084] S700: Obtain the active population restoration value corresponding to the set area i within the current target time window Among them, w i To set the population weight of region i, b1 is a first setting coefficient, b2 is a second setting coefficient, and b1+b2=1.

[0085] In the embodiment of the present invention, b1=b2=0.5. In this way, the difference in reporting in the spatial dimension can be taken into account without causing too much impact on the difference in reporting in the spatial dimension.

[0086] In another embodiment of the present invention, S400 is replaced by:

[0087] S410: Obtain the population difference value DR corresponding to the set area i within the current target time window corresponding to the current time. i =H i / AQ i , H i is the restored value of the permanent population of the set area i in the set time period, C i is the observed value of the permanent population of the set area i in the set time period, C i Based on the reporting location information corresponding to the set area i obtained within the set time period; AQ i is the number of permanent residents in the set area i during the set time period.

[0088] The S500 is replaced by:

[0089] S510, obtaining a fitting coefficient set g based on the population difference value set DR, the resident population restoration value set H and the resident population quantity set AQ, wherein DR = {DR1, DR2, ..., DR i ,…,DR n}, H={H1, H2, ..., H i ,…,H n},AQ={AQ1,AQ2,…,AQ i ,…,AQ n}, g={g1, g2, ..., g i ,…,g n}, g i is the fitting coefficient corresponding to the set area i.

[0090] The specific implementation of S510 is basically the same as S500, except that each fitting coefficient is obtained by linear fitting based on the restored value of the permanent population and the number of permanent residents in the corresponding target group.

[0091] In this embodiment, the population difference value is obtained based on the restored value of the permanent population and the number of permanent residents. Compared with the solution based on the observed value of the permanent population and the number of permanent residents in the previous embodiment, the result can be more accurate.

[0092] The data processing method provided by an embodiment of the present invention first obtains the average APP reported data volume and the average APP associated device volume based on the APP reported data volume and the number of sample devices in each target time window corresponding to each set area; then, obtains a first correction coefficient based on the average APP reported data volume and the average APP associated device volume; then, the set areas are grouped based on the population difference values of each set area, and the corresponding fitting coefficients are obtained based on the permanent population observation values and the number of permanent residents corresponding to each group; then, the second correction coefficient is obtained based on the fitting coefficient; finally, the active population restoration value corresponding to each set area is obtained based on the first correction coefficient, the second coefficient, the number of sample devices, and the number of permanent residents corresponding to each set area. Since the reporting differences of the area itself and the reporting differences between regions are taken into account, the impact caused by collection fluctuations can be minimized as much as possible, and the active population restoration value of each set area in the target time window can be as close as possible to the actual number of active people.

[0093] Another embodiment of the present invention further provides a data processing device, which is applied to a server and includes:

[0094] The first acquisition module is used to obtain the average reported data volume AB of the APP corresponding to the set area i in the current target time window corresponding to the current time i =N i / RP i Average number of AR devices associated with APP i =RE i / RP i ; N i RP is the total number of APP report data belonging to the set area i obtained within the current target time window, i is the number of apps belonging to the set area i obtained within the current target time window; RE i The number of sample devices belonging to the set area i obtained within the current target time window; the APP reporting data includes at least the ID of the sample device corresponding to the APP, the reporting time, the reporting location information, and the ID of the APP; the value of i ranges from 1 to n, where n is the number of set areas;

[0095] The second acquisition module is used to obtain the population difference value DR corresponding to the set area i in the current target time window corresponding to the current time i =C i / AQ i , C i is the observed value of the permanent population of the set area i in the set time period, AQ i is the number of permanent residents in the set area i during the set time period;

[0096] The first determination module is used to determine AB i Compare with the corresponding reference range, and obtain the corresponding first comparison result k based on the comparison result i1 , and AR i Compare with the corresponding reference range, and obtain the corresponding second comparison result k based on the comparison result i2 ; and based on k i1 and k i2 Determine the first correction coefficient corresponding to the set area i in the current target time window

[0097]

[0098] The second determination module is used to determine the i ,…,C n Get the corresponding fitting coefficients g1, g2, ..., g i ,…,g n , g i is the fitting coefficient corresponding to the set area i; and is used to determine the second correction coefficient f corresponding to the set area i i2 =1 / g ci ;

[0099] The calculation module is used to calculate the active population restoration value corresponding to the set area i within the current target time window Among them, w i To set the population weight of region i, b1 is a first setting coefficient, b2 is a second setting coefficient, and b1+b2=1.

[0100] For the specific definition of the data processing device, please refer to the definition of the data processing method above and will not be repeated here. Each module in the above-mentioned device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software so that the processor can call and execute the operations corresponding to each of the above modules.

[0101] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which can be set in an electronic device to store at least one instruction or at least one program related to implementing a method in a method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiment.

[0102] An embodiment of the present invention further provides an electronic device including a processor and the aforementioned non-transitory computer-readable storage medium.

[0103] An embodiment of the present invention further provides a computer program product comprising program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present invention described above in this specification.

[0104] Although some specific embodiments of the present invention have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A data processing method, characterized in that: The method comprises the following steps: S100, obtain the average reported data volume AB of the APP corresponding to the set area i in the current target time window corresponding to the current time i =N i / RP i Average number of AR devices associated with APP i =RE i / RP i ; N i RP is the total number of APP report data belonging to the set area i obtained within the current target time window, i is the number of apps belonging to the set area i obtained within the current target time window; RE i The number of sample devices belonging to the set area i obtained within the current target time window; the APP reporting data includes at least the ID of the sample device corresponding to the APP, the reporting time, the reporting location information, and the ID of the APP; the value of i ranges from 1 to n, where n is the number of set areas; S200, AB i Compare with the corresponding reference range, and obtain the corresponding first comparison result k based on the comparison result i1 , and AR i Compare with the corresponding reference range, and obtain the corresponding second comparison result k based on the comparison result i2 ; S300, based on k i1 and k i2 Get the first correction coefficient corresponding to the set area i in the current target time window S400: Obtaining the population difference value DR corresponding to the set area i within the current target time window corresponding to the current time. i =C i / AQ i , C i is the observed value of the permanent population of the set area i in the set time period, C i Based on the reporting location point information corresponding to the set area i obtained within the set time period; AQ i is the number of permanent residents in the set area i during the set time period; S500, obtaining a fitting coefficient set g based on a population difference value set DR, a resident population observation value set C, and a resident population quantity set AQ, wherein DR = {DR1, DR2, ..., DR i ,…,DR n }, C={C1, C2, ..., C i ,…,C n },AQ={AQ1,AQ2,…,AQ i ,…,AQ n }, g={g1, g2, ..., g i ,…,g n }, g i is the fitting coefficient corresponding to the set area i; S600: Obtain the second correction coefficient f corresponding to the set area i i2 =1 / g ci ; S700: Obtain the active population restoration value corresponding to the set area i within the current target time window Among them, w i To set the population weight of region i, b1 is a first setting coefficient, b2 is a second setting coefficient, and b1+b2=1.

2. The method according to claim 1, characterized in that S200 specifically includes: S210, if D i1 ≤AB i ≤D i2 , then set the first correction coefficient k corresponding to the set area i in the current target time window i1 =1, if AB i >D i2 , then set k1=AB i / D i2 , if AB i <D i1 , then set k1=AB i / D i1 ;D i1 AB i The corresponding lower reference value in the reference range, D i2 AB i The upper reference value in the corresponding reference range; S220, if H i1 ≤AR i ≤H i2 , then set the second correction coefficient k corresponding to the set area i in the current target time window i2 =1, if AR i >H i2 , then set k i2 =AR i / H i2 , if AR i <H i1 , then set k i2 =AR i / H i1 ;H i1 For AR i The corresponding lower reference value in the reference range, H i2 For AR i The upper reference value in the corresponding reference range.

3. The method according to claim 1, characterized in that S500 specifically includes: S501, DR1, DR2, ..., DR i ,…,DR n Arrange them in order to form an ordered sequence; S502, based on the ordered sequence, respectively obtain the first target group to the mth target group, wherein the first target group includes the first target group in the ordered sequence. The jth target group includes the first population difference values in the ordered sequence except the first target group to the j-1th target group. population difference values, the mth target group includes the population difference values in the ordered sequence except the 1st target group to the m-1th target group, j ranges from 2 to m-1; m is the number of target groups, 0<a<b<1, and 2*a+(m-2)*b=1; S503, based on the 1st target group to the mth target group, respectively obtain the 1st fitting coefficient to the mth fitting coefficient, wherein each fitting coefficient is obtained by linear fitting based on the permanent population observation value and the permanent population number in the corresponding target group.

4. The method according to claim 1, wherein The S400 is replaced by: S410: Obtain the population difference value DR corresponding to the set area i within the current target time window corresponding to the current time. i =H i / AQ i , H i The restored value of the permanent population of the set area i in the set time period, C i is the observed value of the permanent population of the set area i in the set time period, C i Based on the reporting location information corresponding to the set area i obtained within the set time period; AQ i is the number of permanent residents in the set area i during the set time period; The S500 is replaced by: S510, obtaining a fitting coefficient set g based on the population difference value set DR, the resident population restoration value set H and the resident population quantity set AQ, wherein DR = {DR1, DR2, ..., DR i ,…,DR n }, H={H1, H2, ..., H i ,…,H n },AQ={AQ1,AQ2,…,AQ i ,…,AQ n }, g={g1, g2, ..., g i ,…,g n }, g i is the fitting coefficient corresponding to the set area i.

5. The method according to claim 4, characterized in that S510 specifically includes: S511, DR1, DR2, ..., DR i ,…,DR n Arrange them in order to form an ordered sequence; S512, based on the ordered sequence, respectively obtain the first target group to the mth target group, wherein the first target group includes the first target group in the ordered sequence. The jth target group includes the first population difference values in the ordered sequence except the first target group to the j-1th target group. population difference values, the mth target group includes the population difference values in the ordered sequence except the 1st target group to the m-1th target group, j ranges from 2 to m-1; m is the number of target groups, 0<a<b<1, and 2*a+(m-2)*b=1; S513, based on the 1st target group to the mth target group, respectively obtain the 1st fitting coefficient to the mth fitting coefficient, wherein each fitting coefficient is obtained by linear fitting based on the permanent population restoration value and the permanent population number in the corresponding target group.

6. The method according to claim 2, characterized in that The AB i The corresponding benchmark range is obtained through the following steps: S10, obtaining the average amount of APP reported data corresponding to the set historical time period; S12, using the set unit time unit as a sliding window and a target time window as a sliding step, obtain the average APP reporting data volume corresponding to the target time period that meets the following preset conditions from the average APP reporting data volume corresponding to the set historical time period as the target APP average reporting data volume; the preset conditions are: the average APP reporting data volume corresponding to the target time period is between [AvgAB i -c*SAB i , AvgAB i +c*SAB i ] h≥q*g, where q is the number of data in the average reported data volume of the APP corresponding to the target time period, and g is the preset coefficient, AvgAB i SAB is the average amount of data reported by the target APP. i The deviation of the average reported data volume for the target APP, ABS ir The rth data in the average reported data volume of the APP corresponding to the target time period, where r is 1 to q; c is 2 or 3; the set unit time unit includes at least one target time window; S14, get D i1 =AvgAB i -c*SAB i , D i2 =AvgAB i +c*SAB i .

7. The method according to claim 2, characterized in that The AR i The corresponding benchmark range is obtained through the following steps: S20, obtaining the average number of APP-associated devices corresponding to a set historical time period; S22, using a set unit time unit as a sliding window and a target time window as a sliding step, obtain the average number of APP-associated devices corresponding to the target time period that meets the following preset conditions from the average number of APP-associated devices corresponding to the set historical time period as the target average number of APP-associated devices; the preset conditions are: the average number of APP-associated devices corresponding to the target time period is between [AvgAR i -c*SAR i , AvgAR i +c*SAR i ], where p is the number of data in the average number of APP-associated devices corresponding to the target time period, and g is the preset coefficient. i SAR is the average number of devices associated with the target APP. i is the deviation of the average number of associated devices of the APP, ARS is The sth data in the AAP average associated device quantity corresponding to the target time period, where s is 1 to p, and c is 2 or 3; the set unit time unit includes at least one target time window; S24, get H i1 =AvgAR i -c*SAR i , H i2 =AvgAR i +c*SAR i .

8. A data processing device, characterized in that: The device is applied to a server, and includes: The first acquisition module is used to obtain the average reported data volume AB of the APP corresponding to the set area i in the current target time window corresponding to the current time i =N i / RP i Average number of AR devices associated with APP i =RE i / RP i ; N i RP is the total number of APP report data belonging to the set area i obtained within the current target time window, i is the number of apps belonging to the set area i obtained within the current target time window; RE i The number of sample devices belonging to the set area i obtained within the current target time window; the APP reporting data includes at least the ID of the sample device corresponding to the APP, the reporting time, the reporting location information, and the ID of the APP; the value of i ranges from 1 to n, where n is the number of set areas; The second acquisition module is used to obtain the population difference value DR corresponding to the set area i in the current target time window corresponding to the current time i =C i / AQ i , C i is the observed value of the permanent population of the set area i in the set time period, AQ i is the number of permanent residents in the set area i during the set time period; The first determination module is used to determine AB i Compare with the corresponding reference range, and obtain the corresponding first comparison result k based on the comparison result i1 , and AR i Compare with the corresponding reference range, and obtain the corresponding second comparison result k based on the comparison result i2 ; and based on k i1 and k i2 Determine the first correction coefficient corresponding to the set area i in the current target time window The second determination module is used to determine the i ,…,C n Get the corresponding fitting coefficients g1, g2, ..., g i ,…,g n , g i is the fitting coefficient corresponding to the set area i; and is used to determine the second correction coefficient f corresponding to the set area i i2 =1 / g ci ; The calculation module is used to calculate the active population restoration value corresponding to the set area i within the current target time window Among them, w i To set the population weight of region i, b1 is a first setting coefficient, b2 is a second setting coefficient, and b1+b2=1.

9. A non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, characterized in that: The at least one instruction or the at least one program is loaded and executed by a processor to implement the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The device comprises a processor and the non-transitory computer-readable storage medium as claimed in claim 9.

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