Data processing method and apparatus, device, and storage medium

Abstract

The present disclosure provides a data processing method, device and equipment and storage medium, relates to the technical field of computers, in particular to the technical field of big data, cloud computing, smart city, intelligent transportation and the like. The data processing method comprises: acquiring spatio-temporal data generated by a target object using a smart terminal; if the target object is located in a target area, based on the spatio-temporal data, counting the number of the target object in the target area; and based on the number, determining spatio-temporal distribution information of the target object. The present disclosure can improve the data processing effect.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, in particular to the technical field of big data, cloud computing, smart city, intelligent transportation and the like, and especially relates to a data processing method and device, equipment and storage medium. BACKGROUND

[0002] Real-time spatial distribution of people (which can be referred to as real-time people flow) is a key problem of city digital management, and in many scenarios such as smart government affairs, public security emergency, scenic management, real-time people flow is the primary index to be obtained.

[0003] In the related art, special hardware devices such as cameras or infrared sensors can be used to obtain real-time people flow. SUMMARY

[0004] The present disclosure provides a data processing method, device, equipment and storage medium.

[0005] According to an aspect of the present disclosure, a data processing method is provided, comprising: obtaining spatio-temporal data generated by a target object using a smart terminal; if the target object is located in a target area, counting a number of the target object in the target area based on the spatio-temporal data; and determining spatio-temporal distribution information of the target object based on the number.

[0006] According to another aspect of the present disclosure, a data processing device is provided, comprising: an acquisition module configured to acquire spatio-temporal data generated by a target object using a smart terminal; a counting module configured to count a number of the target object in a target area based on the spatio-temporal data if the target object is located in the target area; and a determination module configured to determine spatio-temporal distribution information of the target object in the target area based on the number.

[0007] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of the aspects described above.

[0008] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to perform the method according to any one of the aspects described above.

[0009] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method according to any one of the aspects described above.

[0010] According to the technical solution of the present disclosure, the data processing effect can be improved.

[0011] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0012] The accompanying drawings are used to better understand the present solution and do not constitute a limitation on the present disclosure. Among them:

[0013] Figure 1 is a schematic diagram according to the first embodiment of the present disclosure;

[0014] Figure 2 is a schematic diagram according to the second embodiment of the present disclosure;

[0015] Figure 3 is a schematic diagram according to the third embodiment of the present disclosure;

[0016] Figure 4 is a schematic diagram according to the fourth embodiment of the present disclosure;

[0017] Figure 5 is a schematic diagram according to the fifth embodiment of the present disclosure;

[0018] Figure 6 is a schematic diagram according to the sixth embodiment of the present disclosure;

[0019] Figure 7 is a schematic diagram according to the seventh embodiment of the present disclosure;

[0020] Figure 8 is a schematic diagram according to the eighth embodiment of the present disclosure;

[0021] Figure 9 is a schematic diagram according to the ninth embodiment of the present disclosure;

[0022] Figure 10 is a schematic diagram of an electronic device for implementing any of the data processing methods according to the embodiments of the present disclosure. DETAILED DESCRIPTION

[0023] Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to help understanding, and should be considered as merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Also, in order to be clear and concise, the description below omits the description of well-known functions and structures.

[0024] In the related art, a special hardware device such as a camera or an infrared sensor can be used to obtain real-time people flow. However, this method needs to deploy a special hardware device such as a camera or an infrared sensor, which is costly and limited in application scenarios, and thus the processing effect is not ideal.

[0025] Figure 1 is a schematic diagram according to the first embodiment of the present disclosure, and the embodiment provides a data processing method, comprising:

[0026] 101. Obtain spatio-temporal data generated by a target object using a smart terminal.

[0027] 102. If the target object is located in a target area, based on the spatio-temporal data, count the number of the target object in the target area.

[0028] 103. Based on the number, determine the spatio-temporal distribution information of the target object in the target area.

[0029] The execution subject of the embodiment can be a data processing device, which is not limited in specific form and can be hardware, software, or a combination of software and hardware. The device can be located in an electronic device, which can be a user terminal or a server. The server can be a local server or a cloud server, and the user terminal can include a mobile device (such as a mobile phone or a tablet computer), a wearable device (such as a smart watch or a smart bracelet), a vehicle-mounted device (such as a car machine), etc.

[0030] In the embodiment, the spatio-temporal distribution information of the target object is determined based on the spatio-temporal data generated by the target object using the smart terminal, which can reduce the cost and improve the applicability without the need for special hardware devices, thereby improving the data processing effect.

[0031] The target object can be a person or other object that needs to obtain spatio-temporal distribution information.

[0032] Unless otherwise specified, the target object in the embodiments of the present disclosure is a person.

[0033] The smart terminal is a smart device used by a person (or user), such as a mobile phone or a wearable device. Generally, the positioning information of such a device can be used as the positioning information of the person.

[0034] The spatio-temporal data can include time data and / or space data, such as the geographic location information of a person at a certain time.

[0035] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information comply with relevant laws and regulations and do not violate public order and good customs.

[0036] For example, refer to Figure 2 For example, in the case of a server-side data processing method, when a user uses a smart terminal (such as a mobile phone) 201, the smart terminal can send the current spatiotemporal data to the server 202, so that the server can process based on the spatiotemporal data of the smart terminal.

[0037] Specifically, when the user uses the smart terminal, the spatiotemporal data can be generated by the operating system of the smart terminal or the APP installed on the smart terminal.

[0038] In some embodiments, the method can further include integrity verification of the spatiotemporal data; wherein, if the target object is located in the target area, the number of the target objects in the target area is counted based on the spatiotemporal data, including: if the spatiotemporal data passes the integrity verification and the target object is located in the target area, the number of the target objects in the target area is counted based on the spatiotemporal data.

[0039] That is, the spatiotemporal data that passes the integrity verification is processed subsequently, and the spatiotemporal data that does not pass the integrity verification can be discarded.

[0040] Specifically, the spatiotemporal data can include at least the information shown in Table 1:

[0041] Table 1

[0042] Name Meaning User ID A field that (tries to) uniquely identify a user, device encoding is a common scheme Position time The time at which the spatiotemporal data was generated, can be accurate to the second Position coordinates Describes the specific latitude and longitude information

[0043] Wherein, the user ID is used to identify each user, the positioning time is the time data in the spatiotemporal data, and the position coordinates are the spatial data in the spatiotemporal data.

[0044] Specifically, the integrity verification means that for each spatiotemporal data, it is verified whether the spatiotemporal data contains the three types of information shown in Table 1, if all of them are contained (generally refers to containing the corresponding field and the value in the corresponding field, and the value is reasonable), it passes the integrity verification, if at least one of the above three types of information is missing, it does not pass the integrity verification.

[0045] By performing integrity verification on the spatiotemporal data, the effectiveness of the spatiotemporal data can be guaranteed, and the accuracy of the spatiotemporal distribution information can be improved.

[0046] In some embodiments, the integrity verification of the spatiotemporal data includes: determining the computing resources for processing the spatiotemporal data, the computing resources are determined in at least one of the following ways: based on the pre-configured correspondence between the smart terminal and the computing resources, sequentially traversing each computing resource, and determining based on a preset recommendation strategy; using the computing resources, the integrity verification of the spatiotemporal data is performed.

[0047] The computing resource can be referred to as an access operator, and the access operator can perform integrity verification on the spatio-temporal data.

[0048] As shown in Figure 3 The intelligent terminal and the access operator can be in a many-to-many relationship, that is, one intelligent terminal can be processed by multiple access operators, or one access operator can access multiple intelligent terminals.

[0049] Further, the access operator can be in a distributed manner, such as different access operators located in different servers, etc.

[0050] The relationship between the access operator and the intelligent terminal can be determined in the following manner:

[0051] First, a fixed configuration manner. For example, the intelligent terminal-1 corresponds to the access operator-1, and the intelligent terminal-1 is processed by the access operator-1. Further, a fault handling strategy can be configured to ensure that the intelligent terminal originally corresponding to the access operator can be re-routed to a new available access operator when the access operator fails.

[0052] Second, a sequential traversal manner. For example, for a certain intelligent terminal, the first time is processed by the access operator-1, the second time is processed by the access operator-2, and so on. Further, a fault handling strategy can be configured to "skip" the access operator when the access operator fails.

[0053] Third, a manner based on a recommendation strategy. For example, the system "recommends" an access operator as the access operator corresponding to the intelligent terminal, and the recommendation standard can be that the current load of the access operator is the smallest. This manner can ensure the load balance among the access operators.

[0054] By determining the computing resource for processing the spatio-temporal data, the computing resource can be reasonably determined to improve the verification efficiency.

[0055] In some embodiments, the spatio-temporal data includes spatial data of a location point where the target object is located, and the method further includes: determining a spatial unit where the location point is located based on the spatial data; if all the spatial units where the location point is located are located within the target region, determining that the target object is located in the target region; or if some of the spatial units where the location point is located are located within the target region, generating a ray in a preset direction with the location point as an endpoint, and if the number of intersection points of the ray and the target region is odd, determining that the target object is located in the target region.

[0056] The target object is located in the target region or not can be determined based on whether the location point where the target object is located is located within the target region. For example, referring to Figure 4Fig. 2 shows a case where the target object is located in the target region.

[0057] The position point where the target object is located can also be referred to as a positioning point, and the spatial data of the positioning point can also be referred to as position data, which can be expressed by position coordinates. Assuming that the coordinates of the positioning point are (x, y), and the boundary coordinates of the target region (referred to as a plane for short) are {(x1, y1), (x2, y2), (x3, y3)…(xn, yn)}, to determine whether the positioning point is located in a certain region, a basic determination algorithm (referred to as a PIP algorithm) is as follows: a ray is generated in a specified direction (for example, the positive direction of the x-axis in a two-dimensional coordinate system) with the positioning point as an end point, and the intersection relationship between the ray and the polygon corresponding to the target region is calculated. If the number of intersection points is odd, the positioning point is located inside the target region, otherwise, the positioning point is located outside the target region. The complexity of this algorithm is O(n), and when the boundary of the target region is very complex and the number of coordinate points is very large, the efficiency of this algorithm will be severely reduced.

[0058] The efficiency of point-plane relationship determination can be improved by constructing a spatial index. The essence of the spatial index is to divide the entire space into units (similar to a grid), and to determine the relationship between the target region and each spatial unit in advance. As shown in Fig. 3, the relationship between a single spatial unit and the target region has only three possibilities (the relationship between the spatial unit and the target region is represented by numbers in Fig. 3), which are as follows: Figure 5 Figure 5 1. The spatial unit is completely located in the target region

[0059] 2. The spatial unit is partially located in the target region

[0060] 3. The spatial unit is completely located outside the target region

[0061] Generally, determining the spatial unit where the positioning point is located only needs simple calculation, and the complexity is O(1). For example, the size of the spatial unit is 500*500, and the coordinates of a certain positioning point are (x, y) = (5000, 10000). The positioning point is located in the 10th spatial unit in the x direction and the 20th spatial unit in the y direction.

[0062] The relationship between the positioning point and the target region can be efficiently determined through the spatial unit, which is as follows:

[0063] If the spatial unit is completely located in the target region, the positioning point is definitely located in the target region, and there is no need to perform the PIP algorithm.

[0064] If the spatial unit is partially located in the target region, the PIP algorithm needs to be used to further determine whether the positioning point is located in the target region.

[0065] If the spatial unit is partially located in the target region, the PIP algorithm needs to be used to further determine whether the positioning point is located in the target region.

[0066] ​If the space unit is completely outside the target area, the positioning point must be outside the target area, and the PIP algorithm does not need to be executed.

[0067] From the above process, it can be seen that the PIP algorithm is executed only in a few cases, and the point-face relationship determination efficiency is greatly improved.

[0068] In some embodiments, the number of target objects in the target area is a fitting number, and the number of target objects in the target area based on the spatiotemporal data comprises: determining a statistical duration; determining the number of positioning of the target objects in the statistical duration based on the spatiotemporal data; and fitting the number of positioning to obtain the fitting number of the target objects.

[0069] Taking people as an example, the real-time flow of people can be obtained by counting the positioning information of people. Assuming that each person generates positioning information at each moment, the real-time flow of people in the region at a certain moment is equal to the number of people positioned at that moment. However, in fact, positioning information is generated along with the use of smart terminals by users, and is not generated at every moment. Therefore, the number of people positioned at that moment is only a part of the current real-time flow of people, and therefore an algorithm needs to be designed to fit the real flow of people.

[0070] By fitting the number of positioning, the real number of target objects in the target area can be obtained.

[0071] Further, the determination of the statistical duration comprises: if the travel activity of the target object in the target area is regular, a preset duration is determined as the statistical duration; and if the travel activity of the target object in the target area is irregular, the statistical duration is determined based on the stay duration of the target object in the target area.

[0072] For example, the target object is a person, and the target area can be divided into: a residential area, a working area, and a general area. The general area refers to an area other than the residential area and the working area.

[0073] The attribute of the area can be obtained based on map data. For example, the attribute of a certain area can be recorded in the map data. Assuming that the attribute is a residential attribute, the corresponding area is a residential area. Similarly, if the attribute is a working attribute, the corresponding area is a working area.

[0074] The residential area and the working area can be considered as areas with regular travel activities, and the general area is an area with irregular travel activities.

[0075] For the working area and the residential area, the statistical duration can be a preset fixed duration, such as 1 hour.

[0076] Corresponding to the general area, the statistical duration can be non-fixed, and can be determined according to the residence duration.

[0077] Specifically, the real people flow of the residential area is less in the daytime (people flow out of the area in the morning), more in the evening (people flow back to the area), reaches the peak in the late night, that is, the residential population of the area, and maintains this data in the early morning period; and the positioning number also generally shows less in the daytime, more in the evening, and reaches the peak in the late night, but the positioning number gradually decreases in the early morning period because people use smart terminals less in the early morning.

[0078] The real people flow of the working area shows more in the daytime (people flow into the area in the morning), less in the evening (people flow out of the area), and the least in the early morning period; and the positioning number also shows the same rule.

[0079] Therefore, the travel activities of people in the residential area and the working area are relatively regular.

[0080] The real people flow of the general area shows more in the active period and less in the non-active period, and there is a simultaneous people flow in and out in the active period, and the positioning number also shows a similar rule.

[0081] It is a difficulty to define the real-time positioning number of such an area. People in the residential area or the working area show mobility only in a few moments in the morning and evening, so in most moments, 1 hour is selected as an observation window, and most people will have a positioning, thereby serving as an effective sample; and such an area has people flowing in or out in most moments, and 1 hour as an observation window is not necessarily suitable, for example, a vegetable market, the average residence time of people is about 20 minutes, if the number of people in a 1-hour window is counted as the real-time people flow, it will be higher than the actual value; and for example, a large-scale main amusement park, the average residence time of people can be more than 3 hours, if only 1 hour is taken as a counting window, it can be lower than the actual value. It can be seen that the real-time positioning number needs to be calculated according to the average residence time of people, and the specific steps are as follows:

[0082] For each person who has a positioning in the area, the time T1 of the first positioning in the area and the time Tn of the last positioning are determined, and Tn-T1 is the residence duration of the user in the area;

[0083] The median mid(T) of the residence durations of all users (for example, all users in 24 hours) is calculated as the average residence duration S of the crowd;

[0084] The average residence duration S is taken as the statistical duration, so that the positioning number in the recent S window can be the real-time positioning number at the current moment.

[0085] By determining the statistical time length based on different manners, more accurate statistical time lengths in different scenarios can be obtained.

[0086] In some embodiments, the target object is a human, and the target area is a residential area. The fitting of the positioning quantity to obtain the fitted quantity of the target object comprises:

[0087] determining a night peak value of the positioning quantity in a predetermined night period (e.g., 20:00 to 24:00) and a first time, which is the time corresponding to the night peak value;

[0088] determining a scaling ratio based on the night peak value and a residential population quantity of the residential area;

[0089] multiplying the scaling ratio by the positioning quantity to obtain an initial fitted quantity;

[0090] determining an early morning peak value of the initial fitted quantity in a predetermined early morning period (e.g., 4:00 to 10:00) and a second time, which is the time corresponding to the early morning peak value;

[0091] determining the residential population quantity as the fitted quantity between the first time and a preset early morning time point (e.g., 5:00);

[0092] performing an equal-ratio reduction on the initial fitted quantity based on the residential population quantity and the early morning peak value to obtain a fitted quantity between the preset early morning time point and the second time;

[0093] using the initial fitted quantity as the fitted quantity between the second time and the first time.

[0094] Specifically, for a residential area, the following can be performed:

[0095] determining the positioning quantity of a human in a statistical time length (e.g., 1 hour) before each sampling point (the sampling points are spaced apart by a preset period, e.g., 1 minute) at each sampling point;

[0096] calculating the positioning quantity at each sampling point in a predetermined night period (e.g., 20:00 to 24:00) and determining a night peak value and a corresponding time therefrom, which can be referred to as a first time. The night peak value and the first time are denoted by Pn and Tn, respectively;

[0097] determining a scaling ratio f as the ratio of a residential population quantity (denoted by N) to the night peak value Pn, and multiplying the positioning quantity at each sampling point by the scaling ratio f to obtain an initial fitted quantity at the corresponding sampling point. At this time, the initial fitted quantity corresponding to the night peak value is N;

[0098] Calculate the initial number of fitted samples for each sampling point within a predetermined morning period (e.g., 4 AM to 10 AM), and determine the morning peak value and its corresponding time, which can be referred to as the second time. Assume the morning peak value and the first time are represented by Pm and Tm, respectively.

[0099] The number of fitted values ​​between the first time Tn and the preset morning time (e.g., 5 am) is fitted to the number of residents N;

[0100] The number of fitted samples between a preset morning time (e.g., 5 AM) and a second time point Tm is gradually reduced from the number of residents N to the morning peak Pm. A uniform rate reduction strategy can be adopted, that is, the number of fitted samples Px corresponding to any sampling point Tx between the preset morning time (e.g., 5 AM) and the second time point Tm is N - (Tx - T5) * (N - Pm) / (Tm - T5), where T5 represents the time corresponding to 5 AM.

[0101] The number of fits for other sampling points is the same as the initial number of fits for each of the above sampling points.

[0102] The aforementioned number of residents N can be obtained based on long-term (e.g., 3 months) location data, recording the location of users from 18:00 to 8:00 the next day every day within 3 months, and taking the total number of users with the above location records for more than 10 days as the resident population of the area.

[0103] like Figure 6 As shown, this figure illustrates the changing trends of the number of people located in a residential area over a day, as well as the changing trends of the fitted values ​​of real-time pedestrian flow. The horizontal axis represents time, and the vertical axis represents either the number of people located (real-time location data) or the fitted values ​​(real-time pedestrian flow fitted values).

[0104] Through the above processing, the fitted number of residential areas can be obtained, and this fitted number can be used as the real-time pedestrian flow of the residential areas.

[0105] In some embodiments, the target object is a person, the target area is a work area, and fitting the number of locations to obtain the fitted number of the target object includes:

[0106] The scaling ratio is determined based on the peak number of locations and the number of working people during the predetermined working hours (e.g., 8:00 to 17:00) in the work area.

[0107] The product of the number of positions and the scaling ratio is used as the fitting number.

[0108] Specifically, for the work area, the following can be performed:

[0109] At each sampling point (with a preset interval between sampling points, such as 1 minute), determine the number of people located within the statistical time period (such as 1 hour) prior to that sampling point.

[0110] Calculate the number of locations for each sampling point within the predetermined working period (e.g., from 8:00 to 17:00), and determine the peak working time and its corresponding time, denoted by Pd and Td respectively.

[0111] The ratio of the number of working population (denoted as E) to the peak working population Pd is determined as the scaling factor, i.e., f = E / Pd. The number of locations of each sampling point is multiplied by the scaling factor f to obtain the number of fitting points for the corresponding sampling points. In this case, the number of fitting points corresponding to the peak working population is E.

[0112] That is, the number of fitted samples for each sampling point is equal to the number of localizations for each sampling point multiplied by f. Each sampling point includes sampling points from weekdays and sampling points from non-weekdays.

[0113] The aforementioned working population E can be obtained based on long-term (e.g., 3 months) location data, recording the location of users between 8:00 and 17:00 every day within 3 months, and taking the total number of users with the above location records for more than 10 days as the working population of the area.

[0114] like Figure 7 As shown, this figure illustrates the changing trends of the number of people located in a work area in real time and the changing trends of the fitted values ​​of real-time pedestrian flow over a day. The horizontal axis represents time, and the vertical axis represents either the number of people located (real-time location data) or the fitted values ​​(real-time pedestrian flow fitted values).

[0115] Through the above processing, the number of fitted data for the work area can be obtained, and this number can be used as the real-time pedestrian flow in the work area.

[0116] In some embodiments, the target object is a person, the target region is a general region, and fitting the number of locations to obtain the fitted number of the target object includes:

[0117] Obtain the true value of pedestrian traffic and the time of the true value in the general area;

[0118] The scaling ratio is determined based on the true value of the pedestrian flow and the number of locations at the true value time.

[0119] The product of the number of positions and the scaling ratio is used as the fitting number.

[0120] Specifically, taking a scenic spot as an example, the real-time passenger flow of the scenic spot can be crawled from the official website by using a web crawler, and generally such data is in an hour or minute granularity (the following is described taking an hour granularity as an example), and the real-time passenger flow is taken as the passenger flow true value.

[0121] For example, for any moment in [h, h+1) (such as 14:00-15:00), the scaling ratio f = the real-time passenger flow at the h moment / the real-time positioning number at the h moment; and the fitting number at any moment x in [h, h+1) = f*(the positioning number at the x moment).

[0122] In the case of lacking real-time passenger flow true value, f can be set as an empirical constant.

[0123] Through the above processing, the fitting number of the general area can be obtained, and the fitting number is taken as the real-time passenger flow of the general area.

[0124] In some embodiments, the determining the spatio-temporal distribution information of the target object in the target area based on the number comprises: generating a relationship curve between the number in the target area and time information based on the number and the time information corresponding to the number; and taking the relationship curve in the target area as the spatio-temporal distribution information.

[0125] The fitting number is obtained based on the positioning number, and the positioning number is obtained based on the spatio-temporal data, so the time information in the spatio-temporal data corresponding to the positioning number can be taken as the time information corresponding to the fitting number, and then the relationship curve between the fitting number and the time information can be generated, and the relationship curve corresponding to different areas can be generated, and the relationship curve is taken as the spatio-temporal distribution information of the corresponding area. For example, Figure 6 Or Figure 7 The relationship curve shown is the spatio-temporal distribution information of the corresponding area (residential area or working area).

[0126] The spatio-temporal distribution information of the corresponding area can be generated based on the number and the time information, so that the distribution rule of the target object can be provided in more detail.

[0127] Figure 8 is a schematic diagram according to an eighth embodiment of the present disclosure, and the present embodiment provides a data processing method, which comprises:

[0128] 801. Obtain spatio-temporal data generated by a target object using a smart terminal.

[0129] 802. Determine whether the spatio-temporal data is complete, if yes, execute 803, otherwise discard the spatio-temporal data.

[0130] 803, determining whether the spatio-temporal data belongs to a target region, if yes, executing 804, otherwise discarding the spatio-temporal data.

[0131] 804, determining a statistical time length.

[0132] 805, determining a number of positions of the target object in the statistical time length based on the spatio-temporal data.

[0133] 806, fitting the number of positions to obtain a fitted number of the target object.

[0134] 807, taking a relationship curve between the fitted number and time information corresponding to the fitted number as spatio-temporal distribution information in the target region, and displaying the spatio-temporal distribution information.

[0135] The specific implementation of each step of the embodiment can be referred to the related description in the above embodiment.

[0136] In the embodiment of the disclosure, the spatio-temporal distribution information of the target object is determined based on the spatio-temporal data generated by the target object using the intelligent terminal, which can reduce the cost and improve the applicability without the need for special hardware devices, thereby improving the data processing effect.

[0137] Figure 9 is a schematic diagram according to the ninth embodiment of the disclosure, and the embodiment provides a data processing apparatus. As shown in Figure 9 The data processing apparatus 900 includes an acquisition module 901, a statistical module 902, and a determination module 903.

[0138] The acquisition module 901 is configured to acquire spatio-temporal data generated by a target object using an intelligent terminal; the statistical module 902 is configured to, if the target object is located in a target region, statistically determine a number of the target object in the target region based on the spatio-temporal data; and the determination module 903 is configured to determine spatio-temporal distribution information of the target object in the target region based on the number.

[0139] In some embodiments, the spatio-temporal data includes spatial data of a position point where the target object is located, and the apparatus 900 further includes:

[0140] a positioning module configured to determine a spatial unit where the position point is located based on the spatial data; and a determination module configured to, if all the spatial units where the position point is located are located in the target region, determine that the target object is located in the target region, or if part of the spatial units where the position point is located are located in the target region, generate a ray in a preset direction with the position point as an endpoint, and if the number of intersection points between the ray and the target region is odd, determine that the spatio-temporal data belongs to the target region.

[0141] In some embodiments, the apparatus 900 further comprises a verification module configured to verify integrity of the spatio-temporal data; and the statistical module 902 is specifically configured to, if the spatio-temporal data passes the integrity verification and the target object is located in a target area, count a quantity of the target object in the target area based on the spatio-temporal data.

[0142] In some embodiments, the verification module is specifically configured to determine a computing resource to be used to process the spatio-temporal data, and the computing resource is determined in at least one of the following manners: based on a pre-configured correspondence between the intelligent terminal and the computing resource, sequentially traversing each computing resource, and determining based on a preset recommendation strategy; and the verification module is configured to use the computing resource to verify integrity of the spatio-temporal data.

[0143] In some embodiments, the quantity of the target object in the target area is a fitting quantity, and the statistical module 902 is specifically configured to determine a statistical duration, determine a positioning quantity of the target object in the statistical duration based on the spatio-temporal data, and fit the positioning quantity to obtain the fitting quantity of the target object.

[0144] In some embodiments, the statistical module 902 is further specifically configured to, if a travel activity of the target object in the target area is regular, determine a preset duration as the statistical duration; and if the travel activity of the target object in the target area is irregular, determine the statistical duration based on a stay duration of the target object in the target area.

[0145] In some embodiments, the target object is a human, and the target area is a residential area, and the statistical module 902 is further specifically configured to determine a night peak value of the positioning quantity in a predetermined night period and a first time, the first time being a time corresponding to the night peak value, determine a scaling ratio based on the night peak value and a residential population quantity of the residential area, take a product of the scaling ratio and the positioning quantity as an initial fitting quantity, determine an early morning peak value of the initial fitting quantity in a predetermined early morning period and a second time, the second time being a time corresponding to the early morning peak value, determine a fitting quantity between the first time and a preset early morning time point, and determine the residential population quantity based on the fitting quantity and the early morning peak value, perform an equal-ratio reduction on the initial fitting quantity based on the residential population quantity and the early morning peak value to obtain a fitting quantity between the preset early morning time point and the second time, and take the initial fitting quantity as a fitting quantity between the second time and the first time.

[0146] In some embodiments, the target object is a human, and the target area is a working area. The statistical module 902 is further configured to: determine a scaling ratio based on a peak value of the quantity of positions in a predetermined working period of the working area and a working population; and determine the fitting quantity as a product of the quantity of positions and the scaling ratio.

[0147] In some embodiments, the target object is a human, and the target area is a general area. The general area is an area other than a living area and a working area. The statistical module 902 is further configured to: obtain a true value of a human flow in the general area and a true value time; determine a scaling ratio based on a quantity of positions in the true value of the human flow and the true value time; and determine the fitting quantity as a product of the quantity of positions and the scaling ratio.

[0148] In some embodiments, the determining module 903 is configured to: generate a relationship curve between a quantity in the target area and time information based on the quantity and the time information corresponding to the quantity; and determine the relationship curve in the target area as the spatio-temporal distribution information.

[0149] In the embodiments of the present disclosure, the spatio-temporal distribution information of the target object is determined based on the spatio-temporal data generated by the target object using the intelligent terminal, which can reduce the cost and improve the applicability and the data processing effect without the need for special hardware devices.

[0150] It can be understood that the same or similar contents in different embodiments of the present disclosure can be referred to each other.

[0151] It can be understood that the "first", "second", and the like in the embodiments of the present disclosure are only used for distinction, and do not represent the importance level, time sequence, and the like.

[0152] According to the embodiments of the present disclosure, the present disclosure further provides an electronic device, a readable storage medium, and a computer program product.

[0153] In the technical solutions of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information comply with relevant laws and regulations and do not violate public order and good customs.

[0154] Figure 10A schematic block diagram of an example electronic device 1000 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, servers, blades, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present disclosure described and / or claimed in this document.

[0155] As shown in Figure 10 The electronic device 1000 includes a computing unit 1001 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 1002 or a computer program loaded into a random access memory (RAM) 1003 from a storage unit 1008. Various programs and data required for the operation of the electronic device 1000 can also be stored in the RAM 1003. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other through a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0156] Various components in the electronic device 1000 are connected to the I / O interface 1005, including an input unit 1006, such as a keyboard, a mouse, etc.; an output unit 1007, such as various types of displays, speakers, etc.; a storage unit 1008, such as a magnetic disk, an optical disk, etc.; and a communication unit 1009, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1009 allows the electronic device 1000 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0157] The computing unit 1001 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, and the like. The computing unit 1001 performs various methods and processes described above, such as the data processing method. For example, in some embodiments, the data processing method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 1008. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 1000 via the ROM 1002 and / or the communication unit 1009. When the computer program is loaded onto the RAM 1003 and executed by the computing unit 1001, one or more steps of the data processing method described above can be performed. Alternatively, in other embodiments, the computing unit 1001 can be configured to perform the data processing method by any other suitable means, such as by means of firmware.

[0158] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0159] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces a means for implementing the functions / acts specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0160] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0161] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0162] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0163] The computer system can include clients and servers. This relationship can be. The servers are generally remote from the users and can be accessed via the Internet using a communication network. The relationship can be a client-server relationship over a communications network, and as such, the servers can be accessed by the clients using computer programs. The servers can be cloud servers, also known as cloud computing servers or cloud hosts, which are mainframe products in the cloud computing service system, and solve the defects of large management difficulty and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or simply "VPS").

[0164] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in the present disclosure can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present disclosure can be achieved, and the present disclosure is not limited herein.

[0165] The above detailed description does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A data processing method, comprising: obtaining spatio-temporal data generated by a target object using a smart terminal; if the target object is located in a target area, counting a number of the target object in the target area based on the spatio-temporal data, comprising: determining a counting duration; determining a positioning number of the target object in the counting duration based on the spatio-temporal data; and fitting the positioning number to obtain a fitted number of the target object as the number of the target object in the target area; determining spatio-temporal distribution information of the target object in the target area based on the number; wherein, if the target area is an area where travel activities are irregular, the counting duration is determined based on a stay duration of the target object in the target area; and for any time between a current true value time and a next true value time, the fitted number is a product of the positioning number and a scaling ratio, and the scaling ratio is a ratio of a traffic true value at the current true value time to the positioning number at the current true value time.

2. The method of claim 1, wherein, The spatio-temporal data comprises spatial data of a location point where the target object is located, and the method further comprises: determining a spatial unit where the location point is located based on the spatial data; if all spatial units where the location point is located are located in the target area, determining that the target object is located in the target area; or if some spatial units where the location point is located are located in the target area, generating a ray in a preset direction with the location point as an endpoint, and if a number of intersection points of the ray and the target area is odd, determining that the target object is located in the target area. 3.The method of claim 1, further comprising: performing integrity verification on the spatio-temporal data; wherein, if the target object is located in a target area, counting a number of the target object in the target area based on the spatio-temporal data, comprises: if the spatio-temporal data passes the integrity verification and the target object is located in a target area, counting a number of the target object in the target area based on the spatio-temporal data.

4. The method of claim 3, wherein, The integrity verification on the spatio-temporal data comprises: determining a computing resource to be used to process the spatio-temporal data, wherein the computing resource is determined in at least one of the following manners: based on a pre-configured correspondence between a smart terminal and a computing resource, sequentially traversing each computing resource, and determining based on a pre-set recommendation strategy; using the computing resource to perform integrity verification on the spatio-temporal data.

5. The method of claim 1, wherein, The determination of the counting duration further comprises: if travel activities of the target object in the target area are regular, determining a preset duration as the counting duration.

6. The method of claim 1, wherein, The target object is a person, and the target area is a residential area, and the fitting of the positioning number to obtain the fitted number of the target object further comprises: determining a night peak value of the positioning number in a predetermined night period and a first time, wherein the first time is a time corresponding to the night peak value; determining a scaling ratio based on the night peak value and a residential population number of the residential area. multiplying the positioning quantity by the scaling ratio as an initial fitting quantity; determining an early morning peak value of the initial fitting quantity in a predetermined early morning period and a second time corresponding to the early morning peak value; determining a fitting quantity between the first time and a preset early morning time point as the resident population quantity; performing equal-ratio reduction processing on the initial fitting quantity based on the resident population quantity and the early morning peak value to obtain a fitting quantity between the preset early morning time point and the second time; taking the initial fitting quantity as a fitting quantity between the second time and the first time.

7. The method of claim 1, wherein, The target object is a person, and the target area is a working area. The fitting of the positioning quantity is further used to obtain the fitting quantity of the target object, and the method further includes: determining a scaling ratio based on a peak value of the positioning quantity in a predetermined working period of the working area and a working population quantity; multiplying the positioning quantity by the scaling ratio as the fitting quantity.

8. The method according to any one of claims 1 to 7, wherein, The method of determining the spatiotemporal distribution information of the target object in the target area based on the quantity includes: generating a relationship curve of the quantity and time information in the target area based on the quantity and the time information corresponding to the quantity; and taking the relationship curve in the target area as the spatiotemporal distribution information.

9. A data processing apparatus, comprising: an acquisition module configured to acquire spatiotemporal data generated by a target object using a smart terminal; a statistical module configured to, if the target object is located in a target area, count a quantity of the target object in the target area based on the spatiotemporal data; a determination module configured to determine spatiotemporal distribution information of the target object in the target area based on the quantity; The quantity of the target object in the target area is a fitting quantity, and the statistical module is specifically configured to: determine a statistical duration; determine a positioning quantity of the target object in the statistical duration based on the spatiotemporal data; fit the positioning quantity to obtain the fitting quantity of the target object; If the target area is an area in which travel activities are irregular, the statistical duration is determined based on a stay duration of the target object in the target area. For any time between a current true value time and a next true value time, the fitting quantity is a product of the positioning quantity and a scaling ratio, and the scaling ratio is a ratio of a traffic true value of the current true value time to the positioning quantity of the current true value time.

10. The apparatus of claim 9, wherein, The spatiotemporal data includes spatial data of a location point of the target object, and the apparatus further includes: a positioning module configured to determine a spatial unit in which the location point is located based on the spatial data. The determining module is configured to determine that the target object is located in the target region if all space units where the position points are located are located in the target region, or generate a ray in a preset direction with the position point as an end point if the space units where the position points are located are partially located in the target region, and determine that the target object is located in the target region if the number of intersection points of the ray and the target region is odd.

11. The apparatus of claim 9, further comprising: The verifying module is configured to perform integrity verification on the spatio-temporal data. The statistical module is specifically configured to, if the spatio-temporal data passes the integrity verification and the target object is located in a target region, count the number of the target objects in the target region based on the spatio-temporal data.

12. The apparatus of claim 11, wherein, The verifying module is specifically configured to: determine a computing resource to be used to process the spatio-temporal data, and determine the computing resource in at least one of the following manners: sequentially traversing each computing resource based on a pre-configured correspondence between intelligent terminals and computing resources, and determining based on a preset recommendation strategy; and perform integrity verification on the spatio-temporal data using the computing resource. The verifying module is specifically configured to:

13. The apparatus of claim 9, wherein, if the travel activity of the target object in the target region is regular, determine a preset time length as a statistical time length; if the travel activity of the target object in the target region is irregular, determine the statistical time length based on a stay time length of the target object in the target region. The target object is a person, and the target region is a residential region. The statistical module is further specifically configured to:

14. The apparatus of claim 9, wherein, determine a night peak value of the positioning quantity and a first time in a predetermined night period, the first time being a time corresponding to the night peak value; determine a scaling ratio based on the night peak value and a residential population quantity of the residential region; multiply the scaling ratio and the positioning quantity to obtain an initial fitting quantity; determine an early morning peak value of the initial fitting quantity and a second time in a predetermined early morning period, the second time being a time corresponding to the early morning peak value; determine a fitting quantity between the first time and a preset early morning time point as the residential population quantity; perform an equal-ratio reduction on the initial fitting quantity based on the residential population quantity and the early morning peak value to obtain a fitting quantity between the preset early morning time point and the second time; determine the initial fitting quantity as a fitting quantity between the second time and the first time. The target object is a person, and the target region is a work region. The statistical module is further specifically configured to:

15. The apparatus of claim 9, wherein, determine a scaling ratio based on a peak value of the positioning quantity in a predetermined work period of the work region and a work population quantity of the work region; multiply the positioning quantity and the scaling ratio to obtain the fitting quantity. The determining module is specifically configured to:

16. The apparatus of any one of claims 9-15, wherein, generate a relationship curve between the quantity in the target region and time information based on the quantity and the time information corresponding to the quantity; and determine the relationship curve in the target region as the spatio-temporal distribution information.

17. An electronic device, comprising: ​ at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

18. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, the computer instructions are for causing the computer to perform the method of any one of claims 1-8.

19. A computer program product comprising a computer program which, when executed by a processor, implements the method of any one of claims 1-8.

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