Method and device for determining object distribution state

Abstract

The present disclosure provides a method and device for determining the distribution state of an object, and the present disclosure relates to the field of computer technology, and in particular to data processing. The method includes: obtaining initial object distribution data of a target object in a preset area; dividing each of the multiple intervals into multiple sub-intervals according to the geographic location data of multiple intervals; for each of the multiple intervals, determining the number of objects in each of the multiple edge sub-intervals located at the edge of the interval and the position of the peak sub-interval and the number of objects therein according to the initial object distribution data; and determining the number of objects in each of the multiple sub-intervals in the interval according to the peak sub-interval and the number of objects therein according to the number of objects in each edge sub-interval using an interpolation algorithm.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, in particular to data processing, and specifically to a method and device for determining a distribution state of an object, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] In business, it is usually necessary to collect the number of active smart terminal devices in a certain area (hereinafter referred to as the number of devices) to evaluate the number of potential customers, so as to further decide which areas to include in the business district division to ensure the maximum business district profit. However, the relevant technology cannot obtain the precise location data of each object, but can only obtain very rough device distribution data, which can record the total number of objects in a large interval in a certain area.

[0003] The methods described in this section are not necessarily methods that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any method described in this section is considered to be prior art simply because it is included in this section. Similarly, unless otherwise indicated, the issues mentioned in this section should not be considered to have been recognized in any prior art. Summary of the invention

[0004] The present disclosure provides a method and device for determining a distribution state of an object, an electronic device, a computer-readable storage medium, and a computer program product.

[0005] According to one aspect of the present disclosure, a method for determining an object distribution state is provided, comprising: obtaining initial object distribution data of a target object in a preset area, wherein the preset area includes a plurality of intervals, and the initial object distribution data includes geographic location data of the plurality of intervals and data on the number of objects in each of the plurality of intervals; dividing each of the plurality of intervals into a plurality of sub-intervals according to the geographic location data of the plurality of intervals; for each of the plurality of intervals, determining, according to the initial object distribution data, the number of objects in each of a plurality of edge sub-intervals located at the edge of the interval and the position of a peak sub-interval and the number of objects therein, wherein the peak sub-interval is the sub-interval with the largest number of objects in the interval; and determining, according to the position of the peak sub-interval and the number of objects therein and the number of objects in each edge sub-interval, using an interpolation algorithm to determine the number of objects in each of the plurality of sub-intervals in the interval.

[0006] According to another aspect of the present disclosure, a device for determining an object distribution state is provided, comprising: an acquisition unit, configured to acquire initial object distribution data of a target object in a preset area, wherein the preset area comprises a plurality of intervals, and the initial object distribution data comprises geographic location data of the plurality of intervals and data on the number of objects in each of the plurality of intervals; a division unit, configured to divide each of the plurality of intervals into a plurality of sub-intervals according to the geographic location data of the plurality of intervals; a first determination unit, configured to determine the number of objects in each of a plurality of edge sub-intervals located at the edge of the interval and the position of a peak sub-interval and the number of objects therein, respectively, according to the initial object distribution data, wherein the peak sub-interval is a sub-interval with the largest number of objects in the interval; and a second determination unit, configured to determine the number of objects in each of the other sub-intervals in the plurality of sub-intervals in the interval by using an interpolation algorithm according to the position of the peak sub-interval and the number of objects therein and the number of objects in each edge sub-interval.

[0007] The method of the embodiment of the present disclosure performs interpolation calculation on the initial object distribution data. The method divides each interval into multiple sub-intervals, first calculates the number of objects in the sub-intervals at the edge of each interval, then calculates the peak sub-interval position and the number of objects therein, and uses an interpolation algorithm to calculate the number of objects in the remaining sub-intervals. Compared with traditional average interpolation or other single interpolation methods, the method of the embodiment of the present disclosure can more accurately simulate the distribution of objects in each interval, thereby improving the accuracy of the object distribution data.

[0008] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0010] Figure 1 A schematic diagram showing a method for determining a distribution state of an object according to an embodiment of the present disclosure is shown;

[0011] Figure 2 A schematic diagram showing a preset area according to an embodiment of the present disclosure;

[0012] Figure 3 A schematic diagram showing a method for determining a distribution state of an object according to another embodiment of the present disclosure is shown;

[0013] Figure 4 A schematic diagram showing a preset area according to another embodiment of the present disclosure;

[0014] Figure 5 A structural block diagram of a device for determining a distribution state of an object according to an embodiment of the present disclosure is shown;

[0015] Figure 6 A structural block diagram of a device for determining a distribution state of an object according to another embodiment of the present disclosure is shown;

[0016] Figure 7 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0017] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0018] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0019] The terms used in the description of various examples in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0020] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0021] Figure 1 A schematic diagram of a method 100 for determining a distribution state of an object according to an embodiment of the present disclosure is shown. Figure 1 As shown, the method 100 includes:

[0022] Step 101, obtaining initial object distribution data of target objects in a preset area, wherein the preset area includes a plurality of intervals, and the initial object distribution data includes geographic location data of the plurality of intervals and data on the number of objects in each of the plurality of intervals;

[0023] Step 102, dividing each of the multiple intervals into multiple sub-intervals according to the geographic location data of the multiple intervals;

[0024] Step 103, for each interval of the multiple intervals, determining the number of objects in each edge sub-interval of the multiple edge sub-intervals located at the edge of the interval and the position of the peak sub-interval and the number of objects therein, respectively, according to the initial object distribution data, wherein the peak sub-interval is the sub-interval with the largest number of objects in the interval; and

[0025] Step 104 , according to the position of the peak subinterval and the number of objects therein and the number of objects in each edge subinterval, use an interpolation algorithm to determine the number of objects in each of the other subintervals of the multiple subintervals within the interval.

[0026] The method of the embodiment of the present disclosure performs interpolation calculation on the initial object distribution data. The method first calculates the number of objects in the edge subinterval of each interval, and then calculates the peak subinterval position and the number of objects in the interval. Compared with traditional average interpolation or other single interpolation methods, the method of the embodiment of the present disclosure can more accurately simulate the distribution of objects in each subinterval, thereby improving the accuracy of the object distribution data.

[0027] In multiple embodiments of the present disclosure, a preset area refers to a physical area in the real world. The preset area may be, for example, a specific area such as a commercial street or a residential area in a city, or may be a larger area such as an administrative district in a city. An object in the preset area may refer to a device such as a mobile terminal that can collect its signal in order to obtain object distribution data of multiple objects in the preset area.

[0028] Figure 2 FIG. 2 shows an exemplary schematic diagram of a preset area 200 according to an embodiment of the present disclosure. Figure 2 As shown, the preset area includes 4 intervals, namely, a first interval located at the upper left, a second interval located at the upper right, a third interval located at the lower left, and a fourth interval located at the lower right. Figure 2The preset area shown is only exemplary. In other embodiments of the present disclosure, the preset area may also include more than or less than 4 intervals. In addition, although in the present embodiment, the preset area and its respective intervals are rectangular, in other embodiments, the preset area and its respective intervals may also be other shapes such as triangles, parallelograms, etc. In addition, the respective intervals of the preset area are not limited to being distributed in a grid, for example, they may also be distributed in a ring shape, and the preset area may be divided into a plurality of intervals with corresponding distribution patterns according to the actual application scenario.

[0029] Positioning technology such as the global positioning system (GPS) can be used to determine the number of objects in each interval in a preset area, and then generate initial object distribution data and store the distribution data in a related server. Due to the accuracy limitation of positioning technology, the prior art cannot accurately determine the specific location of each object, and can only obtain distribution data about the number of objects in each preset interval of the preset area. The method of this embodiment is used to further refine the object distribution data with poor accuracy to obtain more accurate object distribution data.

[0030] In step 101, initial object distribution data may be obtained from the above-mentioned related server. The initial object distribution data includes geographic location data of multiple intervals and data on the number of objects in each of the multiple intervals. Figure 2 As shown, for Figure 2 For a preset area, the initial object distribution data includes the geographical location data of each interval from the first interval to the fourth interval and the number of objects in each interval. The geographical location data of each interval may include, for example, the position coordinate data of each vertex.

[0031] Step 102: further divide each of the multiple intervals into multiple sub-intervals to generate more detailed object distribution data in subsequent steps. Each interval can be divided according to the geographic location data of the multiple intervals, for example, the interval can be divided into equal parts according to the latitude and longitude or spatial location coordinates of the interval. Figure 2 As shown, for each interval (taking the first interval as an example), the span of the latitude and longitude of the interval can be further divided into 6 sub-ranges, that is, each interval is further divided into 6×6 sub-ranges. It can be understood that, similar to the division of multiple intervals, the method of dividing each interval into multiple sub-ranges is not limited to Figure 2 In the manner illustrated in the figure, each interval can be divided into a plurality of sub-intervals with corresponding distribution patterns according to the actual application scenario, which is not limited here.

[0032] In step 103, the edge sub-intervals refer to a plurality of sub-intervals located at the edge positions of each interval. Figure 2As shown, the plurality of edge subintervals of the first interval are marked with the letter “E”. In some embodiments, step 103 may further include: determining the number of objects in each edge subinterval of the plurality of edge subintervals located at the edge of the interval based on the number of objects in the interval and the number of objects in at least one adjacent interval of the plurality of intervals adjacent to the interval; and determining a peak subinterval and the number of objects in the peak subinterval from the plurality of subintervals in the interval based at least on the number of objects in each edge subinterval.

[0033] For an interval, the number of objects in the adjacent edge subintervals of the interval adjacent to the adjacent interval can be determined based on the average number of objects in multiple subintervals in the interval and the average number of objects in the adjacent intervals adjacent to the interval. Figure 2 As shown, the edge sub-interval of the first interval adjacent to the second interval (i.e. Figure 2 The number of objects in the edge subinterval (in the edge subinterval marked by the letter E1) can be calculated based on the average number of objects in multiple subintervals in the first interval and the average number of objects in multiple subintervals in the second interval, which will be described in detail below and will not be repeated here.

[0034] The position of the peak subinterval in the interval and the number of objects in the peak subinterval can be calculated based on the number of objects in the at least one edge subinterval determined above and the total number of objects in the interval. The peak subinterval has the largest number of objects among the multiple subintervals in the interval, indicating that the subinterval in the interval has the most densely populated objects. Figure 2 As shown, the peak subinterval is marked by the letter P. It should be additionally noted that the peak subinterval may also be an edge subinterval.

[0035] In step 104, the number of objects in other intervals between the peak subinterval and the at least one edge subinterval is calculated based on the number of objects in the at least one edge subinterval and the number of objects in the peak subinterval. In some embodiments, the number of objects in multiple subintervals between the peak subinterval and the side edge subinterval can be calculated based on the number of objects in the peak subinterval and the side edge subinterval of the interval. Figure 2 As shown, the number of objects in the multiple subintervals M before the peak subinterval P and the number of objects in the multiple edge subintervals E1 on the right edge can be calculated, that is, an interpolation operation is performed between the peak subinterval P and the multiple edge subintervals E1. The number of objects in the subintervals between the peak subinterval P and the edge subintervals on other side edges can be obtained in the same way, and finally the number of objects in all subintervals in the interval can be obtained.

[0036] Combine the following Figure 3Details how to count the number of objects in each subrange of an interval. Figure 3 FIG. 3 is a schematic diagram of a method 300 for determining a distribution state of an object according to another embodiment of the present disclosure. Figure 3 The method shown is used for a preset area where multiple intervals are arranged along a one-dimensional direction. Of course, it can be understood that Figure 3 The method shown can also be extended to a preset area where multiple intervals are arranged in a two-dimensional manner.

[0037] Figure 4 A schematic diagram of a preset area in an embodiment of the present disclosure is shown. Figure 4 As shown, multiple intervals of the preset area are arranged along the one-dimensional direction shown in the figure. In order to simplify the figure, only two intervals of the preset area (the first interval on the left and the second interval adjacent to the first interval) are shown. The above method 300 includes:

[0038] Step 301, calculating a first average number of objects in multiple subintervals within the interval and a second average number of objects in multiple subintervals in adjacent intervals adjacent to the interval according to the initial object distribution data and the number of subintervals within each interval;

[0039] Step 302, determining the number of objects in an adjacent edge subinterval of the interval adjacent to the adjacent interval according to the first average number of objects and the second average number of objects;

[0040] Step 303, determining the number of objects in at least one non-adjacent edge subinterval of the interval according to the first average number of objects and the number of objects in at least one adjacent edge subinterval of the interval;

[0041] Step 304, determining the position of the peak subinterval in the plurality of subintervals according to the first average number of objects and the number of objects in each edge subinterval;

[0042] Step 305, determining the number of objects in the peak subinterval according to the position of the peak subinterval in the multiple subintervals and the number of objects in each edge subinterval;

[0043] Step 306, determining the number of objects in each subinterval located on the first side of the peak subinterval according to the number of objects in the first edge subinterval and the peak subinterval and its number of objects;

[0044] Step 307 : Determine the number of objects in each subinterval located on the second side of the peak subinterval according to the number of objects in the second edge subinterval and the peak subinterval and its number of objects.

[0045] In steps 301-303, the number of objects in the edge subintervals of all intervals is calculated. Since method 300 is used for the case of one-dimensionally arranged intervals, Figure 4 As shown in , each interval only includes two edge sub-intervals on the left and right. Figure 4 Take the first interval on the left side of the example, which includes the right edge subinterval marked as E1 and the left edge subinterval marked as E2. The above edge subintervals include adjacent edge subintervals and non-adjacent edge subintervals. The adjacent edge subintervals are edge subintervals adjacent to other intervals, and the non-adjacent edge subintervals are edge subintervals that are not adjacent to any other intervals. Figure 4 As shown, taking the first interval as an example, the left edge subinterval E2 is not adjacent to other intervals and is a non-adjacent edge subinterval, while the right edge subinterval E1 is adjacent to the second interval and is an adjacent edge subinterval. For the second interval, the edge subintervals on both sides are adjacent edge subintervals.

[0046] The step of calculating the number of objects in the edge subintervals of all intervals further includes the step of calculating adjacent edge subintervals and the step of calculating non-adjacent edge subintervals.

[0047] The step of calculating the adjacent edge subintervals includes: steps 301-302. Figure 4 For each interval in , the left edge subinterval and right edge subinterval of each interval belonging to the adjacent edge subinterval are calculated by the following formula:

[0048]

[0049]

[0050] Where i represents the number of the interval. represents the number of objects in the right edge subinterval of the i-th interval, It represents the number of objects in the left edge subinterval of the i+1th interval, n is the number of objects in the subinterval of each interval, and the boundary adjustment coefficient coff∈[0,0.5) is used to adjust the difference in the number of objects in the adjacent edge subintervals of adjacent intervals. When coff=0, the number of objects in adjacent edge subintervals is the same.

[0051] From the above formula, we can see that the number of objects in the edge subinterval and the first average number of objects in multiple subintervals within the interval and the second average number of objects in multiple subintervals within the adjacent interval In the calculation process, the first average number of objects and the second average number of objects can be calculated first, and then the number of objects in the adjacent edge subinterval can be determined. Figure 4 Taking the preset area shown as an example, the number of objects in the right edge subinterval E1 of the first interval and the number of objects in the left edge subinterval and the right edge subinterval of the second interval can be calculated by the above formula.

[0052] The step of calculating the non-adjacent edge subintervals includes: the above step 303. Figure 4 For each interval in , the left edge subinterval of the first interval and the right edge subinterval of the last interval, i.e., the non-adjacent edge subinterval, are calculated by the following formula:

[0053]

[0054]

[0055] Where i represents the index of the interval, N is the number of intervals, and n is the number of subintervals in each interval. Indicates the number of objects in the left edge subinterval of the first interval. Indicates the number of objects in the right edge subinterval of the Nth interval, Indicates the number of objects in the right edge subinterval of the first interval. Indicates the number of objects in the left edge subinterval of the Nth interval.

[0056] It can be seen from the above formula that the number of objects in a non-adjacent edge sub-interval is determined by the average number of objects in multiple sub-intervals in the interval and the number of objects in adjacent edge sub-intervals in the same interval.

[0057] In step 304, refer to Figure 4 As shown, for a preset area in which multiple intervals are arranged along a one-dimensional direction, the multiple edge sub-intervals include a first edge sub-interval located at a first side edge of the interval and a second edge sub-interval located at a second side edge of the interval that is different from the first side edge. In step 304, the position of the peak sub-interval can be determined based on the number of objects in the first edge sub-interval, the number of objects in the second edge sub-interval, and the first average number of objects.

[0058] Exemplarily, the second side edge may be, but is not limited to, opposite to the first side edge.

[0059] like Figure 4 As shown, for the first interval, the first edge subinterval of the first side edge may be the left edge subinterval E2, the second edge subinterval of the second side edge may be the right edge subinterval E1, and the position of the peak subinterval of the i-th interval may be calculated based on the following formula:

[0060]

[0061] Among them, p i is the subinterval position number of the peak subinterval, is the first average number of objects, is the number of objects in the left edge subinterval of the i-th interval, that is, the number of objects in the first edge subinterval, is the number of objects in the right edge subinterval of the i-th interval, that is, the number of objects in the second edge subinterval, and n is the number of subintervals in the interval.

[0062] In step 305, the number of objects in the peak subinterval may be determined according to the number of objects in the first edge subinterval, the number of objects in the second edge subinterval, and the position.

[0063] The number of objects in the peak subinterval of the i-th interval can be calculated based on the following formula:

[0064]

[0065] in, is the number of objects in the peak subinterval within the i-th interval, is the number of objects in the left edge subinterval of the i-th interval, that is, the number of objects in the first edge subinterval, is the number of objects in the right edge subinterval of the i-th interval, that is, the number of objects in the second edge subinterval, and n is the number of multiple subintervals in the interval.

[0066] In step 306, the number of objects in each subinterval between the left edge subinterval and the peak subinterval can be calculated first, and the number of objects in each subinterval can be calculated by arithmetic interpolation, that is, assuming that the number of objects in multiple subintervals between the left edge subinterval and the peak subinterval is arranged according to an arithmetic progression. Therefore, the number of objects in each subinterval can be calculated according to the following formula:

[0067]

[0068] Among them, m ij is the number of objects in the jth subinterval of the i-th interval. is the number of objects in the peak subinterval of this interval, is the number of objects in the left edge subinterval of the interval, p i It is the sub-interval position number where the peak sub-interval is located.

[0069] In step 307, the number of objects in each subinterval between the right edge subinterval and the peak subinterval can be calculated. The number of objects in each subinterval can be calculated using arithmetic interpolation, that is, assuming that the number of objects in multiple subintervals between the right edge subinterval and the peak subinterval is arranged according to an arithmetic progression. Therefore, the number of objects in each subinterval can be calculated according to the following formula:

[0070]

[0071] Among them, m ij is the number of objects in the jth subinterval of the i-th interval. is the number of objects in the peak subinterval of this interval, is the number of objects in the right edge subinterval of the interval, p i It is the sub-interval position number where the peak sub-interval is located.

[0072] The sum of the number of objects in the multiple sub-intervals within each interval calculated by the method 300 will be consistent with the number of multiple objects in the interval in the initial object distribution data, that is:

[0073]

[0074] That is, for the i-th interval, the sum of the number of objects in all sub-intervals after interpolation in the interval is equal to the number of objects in the interval in the initial object distribution data.

[0075] It should be noted that, although in this embodiment, the above method 300 is used for the case where multiple intervals are arranged in one dimension according to a preset area, the method 300 can also be extended to multiple intervals arranged in two dimensions. For the two-dimensional case, the multiple intervals arranged in two dimensions can be divided into multiple rows of intervals arranged in one dimension, and the method 300 can be applied to each row of intervals. After using the method 300 to interpolate each row of distribution data, multiple rows of data are combined to obtain the refined object distribution data of multiple intervals arranged in two dimensions.

[0076] According to another aspect of the present disclosure, a device for determining a distribution state of an object is also provided. Figure 5 FIG. 5 shows a structural block diagram of an apparatus 500 for determining an object distribution state according to an embodiment of the present disclosure. Figure 5 As shown, the device 500 includes: an acquisition unit 510, configured to acquire initial object distribution data of a target object in a preset area, wherein the preset area includes multiple intervals, and the initial object distribution data includes geographic location data of the multiple intervals and data on the number of objects in each of the multiple intervals; a division unit 520, configured to divide each of the multiple intervals into multiple sub-intervals according to the geographic location data of the multiple intervals; a first determination unit 530, configured to determine the number of objects in each edge sub-interval of multiple edge sub-intervals located at the edge of the interval and the position of a peak sub-interval and the number of objects therein according to the initial object distribution data, wherein the peak sub-interval is the sub-interval with the largest number of objects in the interval; and a second determination unit 540, which determines the number of objects in each of the other sub-intervals in the multiple sub-intervals in the interval by using an interpolation algorithm according to the peak sub-interval and the number of objects therein and the number of objects in each edge sub-interval.

[0077] Figure 6 FIG. 6 is a block diagram showing a structure of an apparatus 600 for determining an object distribution state according to another embodiment of the present disclosure. Figure 6 As shown, the first determination unit includes: a first sub-unit 631, configured to determine the number of objects in each edge sub-interval of multiple edge sub-intervals located at the edge position of the interval based on the number of objects in the interval and the number of objects in at least one adjacent interval adjacent to the interval in multiple intervals; and a second sub-unit 635, configured to determine a peak sub-interval and the number of objects in the peak sub-interval from multiple sub-intervals in the interval based on at least the number of objects in each edge sub-interval.

[0078] The plurality of edge subintervals include at least one adjacent edge subinterval of the interval that is adjacent to other intervals, such as Figure 6 As shown, the first subunit 631 includes: a calculation module 632, configured to calculate the first average object number of multiple subintervals within the interval and the second average object number of multiple subintervals in an adjacent interval adjacent to the interval based on the initial object distribution data and the number of subintervals in each interval; a first determination module 633, configured to determine the number of objects in the adjacent edge subinterval of the interval adjacent to the adjacent interval based on the first average object number and the second average object number.

[0079] In some embodiments, the multiple edge sub-intervals also include at least one non-adjacent edge sub-interval of the interval that is not adjacent to other intervals, wherein the first sub-unit 631 also includes: a second determination module 634, configured to determine the number of objects in at least one non-adjacent edge sub-interval of the interval based on the first average number of objects and the number of objects in at least one adjacent edge sub-interval of the interval.

[0080] In some embodiments, the second subunit 635 includes: a third determination module 636, configured to determine the position of the peak subinterval in multiple subintervals based on the first average number of objects and the number of objects in each edge subinterval; and a fourth determination module 637, configured to determine the number of objects in the peak subinterval based on the position of the peak subinterval in multiple subintervals and the number of objects in each edge subinterval.

[0081] In some embodiments, the plurality of edge sub-intervals include a first edge sub-interval located at a first side edge of the interval and a second edge sub-interval located at a second side edge of the interval, wherein the third determination module 636 is further configured to determine the position based on the number of objects in the first edge sub-interval, the number of objects in the second edge sub-interval, and the first average number of objects.

[0082] In some embodiments, the fourth determination module 637 is further configured to determine the number of objects in the peak subinterval according to the number of objects in the first edge subinterval, the number of objects in the second edge subinterval, and the position.

[0083] In some embodiments, the plurality of edge sub-intervals include a first edge sub-interval located at a first side edge of the interval and a second edge sub-interval located at a second side edge of the interval, wherein the second determination unit 640 includes: a fifth determination module 641, configured to determine the number of objects in each sub-interval located on a first side of the peak sub-interval based on the number of objects in the first edge sub-interval and the peak sub-interval and its number of objects; and a sixth determination module 642, configured to determine the number of objects in each sub-interval located on a second side of the peak sub-interval based on the number of objects in the second edge sub-interval and the peak sub-interval and its number of objects.

[0084] Here, the operations of each unit 510~540 of the object distribution state determination device 500 are similar to the operations of steps 101~104 in the method 100 described above, and the operations of the above modules of the object distribution state determination device 600 are similar to the operations of the corresponding steps in the method 300 described above, which will not be repeated here.

[0085] According to another aspect of the present disclosure, an electronic device is also provided, 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, and the instructions are executed by the at least one processor so that the at least one processor can execute the above method.

[0086] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is further provided, wherein the computer instructions are used to enable a computer to execute the above-mentioned method for determining the distribution state of objects.

[0087] According to another aspect of the present disclosure, a computer program product is also provided, including a computer program, wherein the computer program implements the above-mentioned method for determining the object distribution state when executed by a processor.

[0088] See also Figure 7, a block diagram of an electronic device 700 that can be used as the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device can be a computer device of different types, such as a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0089] Figure 7 1 shows a block diagram of an electronic device according to an embodiment of the present disclosure. Figure 7 As shown, the electronic device 700 may include at least one processor 701 , a working memory 702 , an I / O device 704 , a display device 705 , a storage device 706 , and a communication interface 707 that can communicate with each other via a system bus 703 .

[0090] The processor 701 may be a single processing unit or multiple processing units, all of which may include a single or multiple computing units or multiple cores. The processor 701 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and / or any device that manipulates signals based on operating instructions. The processor 701 may be configured to obtain and execute computer-readable instructions stored in a working memory 702, a storage device 706, or other computer-readable media, such as program codes of an operating system 702a, program codes of an application 702b, and the like.

[0091] The working memory 702 and the storage device 706 are examples of computer-readable storage media for storing instructions, which are executed by the processor 701 to implement the various functions described above. The working memory 702 may include both volatile memory and non-volatile memory (e.g., RAM, ROM, etc.). In addition, the storage device 706 may include a hard disk drive, a solid-state drive, a removable medium, including external and removable drives, a memory card, a flash memory, a floppy disk, an optical disk (e.g., CD, DVD), a storage array, a network attached storage, a storage area network, etc. The working memory 702 and the storage device 706 may all be collectively referred to herein as memory or computer-readable storage media, and may be a non-transitory medium capable of storing computer-readable, processor-executable program instructions as computer program code, which may be executed by the processor 701 as a specific machine configured to implement the operations and functions described in the examples herein.

[0092] The I / O device 704 may include an input device and / or an output device. The input device may be any type of device capable of inputting information to the electronic device 700, and may include but is not limited to a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and / or a remote controller. The output device may be any type of device capable of presenting information, and may include but is not limited to a video / audio output terminal, a vibrator, and / or a printer.

[0093] The communication interface 707 allows the electronic device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as a Bluetooth™ device, a 702.11 device, a WiFi device, a WiMax device, a cellular communication device and / or the like.

[0094] The application 702b in the working register 702 can be loaded to execute the various methods and processes described above, such as Figure 1 In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 700 via the storage device 706 and / or the communication interface 707. When the computer program is loaded and executed by the processor 701, one or more steps of the method for determining the object distribution state described above may be performed.

[0095] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0096] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0097] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0098] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0099] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0100] A computing system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0101] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0102] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but only by the claims after authorization and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, each step can be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples can be combined in various ways. It is important that with the evolution of technology, many elements described herein can be replaced by equivalent elements that appear after the present disclosure.

Claims

1. A method for determining a distribution state of an object, comprising: Acquire initial object distribution data of target objects in a preset area, wherein the preset area includes a plurality of intervals, and the initial object distribution data includes geographic location data of the plurality of intervals and data on the number of objects in each of the plurality of intervals; Dividing each of the plurality of intervals into a plurality of sub-intervals according to the geographic location data of the plurality of intervals; For each interval in the plurality of intervals, According to the initial object distribution data, respectively determine the number of objects in each edge subinterval of a plurality of edge subintervals located at the edge of the interval and the position of a peak subinterval and the number of objects therein, wherein the peak subinterval is a subinterval with the largest number of objects in the interval; and According to the position of the peak subinterval and the number of objects in it and the number of objects in each edge subinterval, the number of objects in each other subinterval of the multiple subintervals in the interval is determined by using an interpolation algorithm. Wherein, according to the initial object distribution data, respectively determining the number of objects in each edge subinterval of a plurality of edge subintervals located at the edge position of the interval and the position of the peak subinterval and the number of objects therein comprises: Determine the number of objects in each edge sub-interval of a plurality of edge sub-intervals located at edge positions of the interval based on the number of objects in the interval and the number of objects in at least one adjacent interval of the plurality of intervals; and At least according to the number of objects in each edge subinterval, determine a peak subinterval and the number of objects in the peak subinterval from multiple subintervals in the interval, Wherein, for each interval, the multiple edge subintervals include a first edge subinterval located at a first side edge of the interval and a second edge subinterval located at a second side edge of the interval, wherein, according to the peak subinterval and the number of objects thereof and the number of objects in each edge subinterval, the number of objects in each of the multiple subintervals in the interval is determined by using an interpolation algorithm, including: Determine the number of objects in each subinterval located on a first side of the peak subinterval according to the number of objects in the first edge subinterval and the peak subinterval and its number of objects; and The number of objects in each subinterval on the second side of the peak subinterval is determined according to the number of objects in the second edge subinterval and the peak subinterval and the number of objects therein, wherein the number of objects in each subinterval on the first side of the peak subinterval is determined by arithmetic interpolation according to the number of objects in the first edge subinterval and the peak subinterval and the number of objects therein, and / or According to the number of objects in the second edge subinterval and the peak subinterval and its number of objects, the number of objects in each subinterval located on the second side of the peak subinterval is determined by arithmetic interpolation. The preset area is a physical area in the real world, and the object includes a mobile terminal.

2. The determination method according to claim 1, wherein: For each interval, the plurality of edge subintervals include at least one adjacent edge subinterval of the interval that is adjacent to another interval, Wherein, based on the number of objects in the interval and the number of objects in at least one adjacent interval adjacent to the interval, determining the number of objects in each edge sub-interval of a plurality of edge sub-intervals located at the edge position of the interval includes: Calculate, based on the initial object distribution data and the number of subintervals in each interval, a first average number of objects in multiple subintervals in the interval and a second average number of objects in multiple subintervals in adjacent intervals adjacent to the interval; The number of objects in an adjacent edge sub-interval of the interval adjacent to the adjacent interval is determined according to the first average number of objects and the second average number of objects.

3. The determination method according to claim 2, wherein: in, For each interval, the plurality of edge subintervals further include at least one non-adjacent edge subinterval of the interval that is not adjacent to other intervals, Wherein, based on the number of objects in the interval and the number of objects in at least one adjacent interval adjacent to the interval, determining the number of objects in each edge sub-interval of a plurality of edge sub-intervals located at the edge position of the interval further comprises: The number of objects in at least one non-adjacent edge subinterval of the interval is determined according to the first average number of objects and the number of objects in at least one adjacent edge subinterval of the interval.

4. The determination method according to claim 3, wherein: At least according to the number of objects in each edge subinterval, determining a peak subinterval and the number of objects in the peak subinterval from a plurality of subintervals in the interval comprises: Determining the position of the peak subinterval in the plurality of subintervals according to the first average number of objects and the number of objects in each edge subinterval; and The number of objects in the peak subinterval is determined according to the position of the peak subinterval in the multiple subintervals and the number of objects in each edge subinterval.

5. The determination method according to claim 4, wherein: The plurality of edge sub-intervals include a first edge sub-interval located at a first side edge of the interval and a second edge sub-interval located at a second side edge of the interval different from the first side edge, Wherein, determining the position of the peak subinterval in the plurality of subintervals according to the first average number of objects and the number of objects in each edge subinterval comprises: The position is determined according to the number of objects in the first edge subinterval, the number of objects in the second edge subinterval, and the first average number of objects.

6. The determination method according to claim 5, wherein: Determining the number of objects in the peak subinterval according to the position of the peak subinterval in the multiple subintervals and the number of objects in each edge subinterval includes: The number of objects in the peak subinterval is determined according to the number of objects in the first edge subinterval, the number of objects in the second edge subinterval, and the position.

7. A device for determining a distribution state of an object, comprising: an acquisition unit configured to acquire initial object distribution data of target objects in a preset area, wherein the preset area includes a plurality of intervals, and the initial object distribution data includes geographic location data of the plurality of intervals and data on the number of objects in each of the plurality of intervals; a dividing unit configured to divide each of the plurality of intervals into a plurality of sub-intervals according to the geographical location data of the plurality of intervals; A first determining unit is configured to determine, according to the initial object distribution data, the number of objects in each edge subinterval of a plurality of edge subintervals located at the edge of the interval and the position of a peak subinterval and the number of objects therein, respectively, wherein the peak subinterval is a subinterval with the largest number of objects in the interval; and The second determining unit is configured to determine the number of objects in each of the other subintervals of the multiple subintervals within the interval by using an interpolation algorithm according to the position of the peak subinterval and the number of objects therein and the number of objects in each edge subinterval, Wherein, according to the initial object distribution data, respectively determining the number of objects in each edge subinterval of a plurality of edge subintervals located at the edge position of the interval and the position of the peak subinterval and the number of objects therein comprises: Determine the number of objects in each edge sub-interval of a plurality of edge sub-intervals located at edge positions of the interval based on the number of objects in the interval and the number of objects in at least one adjacent interval of the plurality of intervals; and At least according to the number of objects in each edge subinterval, determine a peak subinterval and the number of objects in the peak subinterval from multiple subintervals in the interval, For each interval, the plurality of edge subintervals include a first edge subinterval located at a first side edge of the interval and a second edge subinterval located at a second side edge of the interval. According to the position of the peak subinterval and the number of objects therein and the number of objects in each edge subinterval, using an interpolation algorithm to determine the number of objects in each of the other subintervals of the multiple subintervals within the interval includes: Determine the number of objects in each subinterval located on a first side of the peak subinterval according to the number of objects in the first edge subinterval and the peak subinterval and its number of objects; and According to the number of objects in the second edge subinterval and the peak subinterval and its number of objects, the number of objects in each subinterval located on the second side of the peak subinterval is determined, wherein, according to the number of objects in the first edge subinterval and the peak subinterval and its number of objects, the number of objects in each subinterval located on the first side of the peak subinterval is determined by arithmetic interpolation, and / or According to the number of objects in the second edge subinterval and the peak subinterval and its number of objects, the number of objects in each subinterval located on the second side of the peak subinterval is determined by arithmetic interpolation. The preset area is a physical area in the real world, and the object includes a mobile terminal.

8. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed 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 claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

10. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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