A region-based statistical method, apparatus, device and storage medium

Abstract

The present disclosure provides a region-based statistical method, device, equipment and storage medium, relates to the technical field of computers, in particular to the technical field of big data processing. The specific implementation scheme is: performing region division processing on a statistical region to obtain at least two sub-regions; determining at least one sub-region set which is orthogonal to a to-be-counted object according to to-be-counted data of the to-be-counted object, and a statistical value corresponding to each sub-region set in the at least one sub-region set; and determining a statistical value corresponding to a specified region in the statistical region according to the statistical value corresponding to each sub-region set. Since the sub-region sets are orthogonal to the to-be-counted object, there is no repeated to-be-counted object, so when the statistical value corresponding to the specified region is determined according to the statistical values corresponding to different sub-region sets, it is not necessary to remove the statistical data of the same to-be-counted object. In this way, the statistical difficulty is greatly simplified.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, and particularly relates to the technical field of big data processing. BACKGROUND

[0002] In many application scenarios, it is necessary to count the number of a certain movable target appearing in a certain specified area within a specified time period.

[0003] Since the movable target is movable within the specified time period, it may appear at different positions in a certain specified area, and thus is often counted multiple times, requiring a deduplication operation.

[0004] In a commonly used deduplication scheme, all movable target information is usually collected first, and then deduplication operation is performed on the collected all movable target information according to individual information of the movable target (for example, license plate number, ID information, face recognition information).

[0005] However, if the specified area range is large, the movable target flows and changes positions quickly, or the shooting gap is small, a large amount of data will be accumulated, and the complexity of deduplication operation using individual information will increase exponentially, making it difficult to quickly return the statistical result.

[0006] In addition, if it is in a continuous line, the deduplication operation using individual information may also have the risk of leaking individual information. SUMMARY

[0007] The present disclosure provides a region-based statistical method, device, equipment and storage medium.

[0008] According to an aspect of the present disclosure, a region-based statistical method is provided, comprising: performing region division processing on a statistical region to obtain at least two sub-regions; determining at least one sub-region set orthogonal to a to-be-counted object according to to-be-counted data of the to-be-counted object and sub-regions involved by the to-be-counted data, and a statistical value corresponding to each sub-region set in the at least one sub-region set; and determining a statistical value corresponding to a specified region in the statistical region according to the statistical value corresponding to each sub-region set.

[0009] According to another aspect of the present disclosure, a region-based statistical device is provided, comprising: a sub-region division module configured to perform region division processing on a statistical region to obtain at least two sub-regions; a sub-region set acquisition module configured to determine at least one sub-region set orthogonal to a to-be-counted object according to to-be-counted data of the to-be-counted object and sub-regions involved by the to-be-counted data, and a statistical value corresponding to each sub-region set in the at least one sub-region set; and a statistical value determination module configured to determine a statistical value corresponding to a specified region in the statistical region according to the statistical value corresponding to each sub-region set.

[0010] 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 any of the above-mentioned region-based statistical methods.

[0011] 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 cause a computer to perform any of the above-mentioned region-based statistical methods.

[0012] 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 any of the above-mentioned region-based statistical methods.

[0013] The present disclosure provides a region-based statistical method, device, equipment and storage medium. The method determines the statistical value corresponding to a specified region in a statistical region by determining at least one sub-region set of the objects to be counted which are orthogonal to each other, and the statistical value corresponding to each sub-region set in the at least one sub-region set.

[0014] Since the sub-region sets are orthogonal to each other, there is no duplication of the objects to be counted, and therefore, when determining the statistical value corresponding to the specified region according to the statistical values corresponding to different sub-region sets, it is not necessary to remove the statistical data of the same object to be counted. In this way, the statistical difficulty of counting the objects to be counted and their statistical data in the specified region in the statistical region is greatly simplified.

[0015] In addition, since the process of determining at least one sub-region set of the objects to be counted is not dependent on the online environment, it also provides the possibility of transferring most of the operations to the offline environment.

[0016] It should be understood that the content described in this part is not intended to identify 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

[0017] The accompanying drawings are used to better understand the present scheme and do not constitute a limitation on the present disclosure. Among them:

[0018] Figure 1 is a flowchart of a region-based statistical method implemented by the first embodiment of the present disclosure;

[0019] Figure 2 is a flowchart of a region-based statistical method implemented by the second embodiment of the present disclosure;

[0020] Figure 3 is a schematic diagram of the first scale grid division of the statistical region according to the second embodiment of the present disclosure;

[0021] Figure 4 is a schematic diagram of the second scale grid division of the statistical region according to the second embodiment of the present disclosure;

[0022] Figure 5 is a schematic diagram of the mapping of the data to be counted to the grid according to the second embodiment of the present disclosure;

[0023] Figure 6 is a schematic diagram of the mapping of the grid to the grid sequence and its statistical value according to the second embodiment of the present disclosure;

[0024] Figure 7 is a structural schematic diagram of the region-based counting device according to the embodiment of the present disclosure;

[0025] Figure 8 is a block diagram of an electronic device for implementing the region-based counting method according to the embodiment of the present disclosure. DETAILED DESCRIPTION

[0026] 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 assist in 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, descriptions of well-known functions and structures are omitted in the following description.

[0027] Figure 1 The main process of implementing the region-based counting method according to an embodiment of the present disclosure is shown, as shown in Figure 1 the method comprises: operation S110, region division processing is performed on a statistical region to obtain at least two sub-regions; operation S120, according to the data to be counted of the object to be counted and the sub-regions involved, at least one sub-region set orthogonal to the object to be counted is determined, and a statistical value corresponding to each sub-region set in the at least one sub-region set is determined; operation S130, according to the statistical value corresponding to each sub-region set, a statistical value corresponding to a specified region in the statistical region is determined.

[0028] In the present disclosure, the region mentioned can be a physical space region represented by a spatial coordinate, or a statistical sample interval defined by a coordinate axis representing different index values; it can be two-dimensional or multi-dimensional.

[0029] In operation S110, the statistical region is a preset region, which usually contains all the objects to be counted or data to be counted this time, and is a region with a relatively large and relatively fixed range.

[0030] In the region division processing of the statistical region, the region can be divided according to the region coordinates, or can be divided according to specific standards (for example, administrative region division) according to statistical needs.

[0031] In operation S120, the to-be-counted object refers to the object counted in the current statistics, or the object to which the statistical data is directed.

[0032] The to-be-counted data refers to the basic data according to which the current statistics is performed, and is data obtained in advance.

[0033] The to-be-counted data and the sub-regions involved therein refer to the sub-regions into which the to-be-counted data falls, or the sub-regions involved in the content of the to-be-counted data.

[0034] The at least one sub-region set orthogonal to the to-be-counted object refers to a plurality of sub-region sets in which the corresponding to-be-counted objects in the sub-region set do not overlap. For example, sub-region set A, sub-region set B, and sub-region set C, etc. The to-be-counted data of the to-be-counted object corresponding to the sub-region set A only involves the sub-region in the sub-region set A, and does not involve the sub-region in any other sub-region set.

[0035] The statistical value corresponding to each sub-region set refers to the value obtained by counting the to-be-counted object or the to-be-counted data involved in each sub-region in the sub-region set. It can be the statistical value corresponding to each sub-region in the sub-region set, or the statistical value corresponding to all sub-regions in the sub-region set.

[0036] For example, the current statistics is to output the floating population and the registered population of a certain region in a certain period of time, then the personnel are the to-be-counted objects of the current statistics; the population data collected by entering the households is the to-be-counted data of the current statistics; the sub-region where the personnel actually reside and the sub-region where the personnel are registered are the sub-regions involved in the population data of the personnel.

[0037] Suppose the region is divided into d1, d2, …, dn sub-regions. According to the actual residence and the registered residence of the personnel in the region, the sub-regions are divided into m sub-region sets, for example, sub-region set Sub_Area1{d1, d2}; sub-region set Sub_Area2{d1, dn}; sub-region set Sub_Area3{d2, dn}, etc.

[0038] The personnel corresponding to the sub-area set Sub_Area1 are either "residing in d1 and registered in d2" or "residing in d2 and registered in d1", that is, the personnel residing in or registered in the sub-area set Sub_Area1 are registered in or reside in the sub-areas in the sub-area set A and are not counted in the sub-areas in the sub-area set Sub_Area2 or the sub-area set Sub_Area3.

[0039] When the floating population is counted, the statistical value of the sub-area set Sub_Area1 is the total number of personnel corresponding to the set, which is the total number of floating population in the sub-area d1 and the total number of floating population in the sub-area d2.

[0040] When the registered population is counted, the statistical value corresponding to the sub-area set Sub_Area1 includes two values: the total number of personnel registered in d1 and the total number of personnel registered in d2.

[0041] In operation S130, the specified area is a region specified by the user in the statistical region, and the user can obtain the statistical value corresponding to the different regions by specifying different regions.

[0042] The specified area often includes one or more sub-areas.

[0043] In determining the statistical value corresponding to the specified area in the statistical region according to the statistical value corresponding to each sub-area set, the following method can be used: determining the sub-area set having intersection with the specified area in the statistical region to obtain the to-be-counted sub-area set, and then adding the statistical value corresponding to each sub-area set in the to-be-counted sub-area set according to the statistical value corresponding to each sub-area set to obtain the statistical value corresponding to the specified area in the statistical region.

[0044] The statistical value of the relevant to-be-counted object can be found through the sub-area set having intersection, so that all the relevant to-be-counted objects can be found by finding all the sub-area sets having intersection, and then the statistical values corresponding to the intersecting sub-areas in the corresponding sub-area set are added to obtain the statistical value corresponding to the specified area. Since there is no duplicate personnel among the sub-area sets, it is not necessary to perform the de-duplication operation on the personnel data, which is very convenient and can greatly simplify the complexity of the statistical operation.

[0045] Continuing the previous example, if the user wishes to obtain the floating population of sub-regions d1 and d2, it is only necessary to add the floating population corresponding to the sub-region set Sub_Area1, the floating population corresponding to the sub-region set Sub_Area2, and the floating population corresponding to the sub-region set Sub_Area3; if the user wishes to obtain the registered population of sub-region d1, it is only necessary to add the registered population corresponding to d1 in the sub-region set Sub_Area1 and the registered population corresponding to d1 in the sub-region set Sub_Area2, without worrying about inaccurate data due to duplication of personnel.

[0046] In the embodiments of the present disclosure, the statistical region is divided into smaller units by operation S110 to provide sub-regions, so that the statistical operation of a large region can be decomposed into the statistical operation of a small region, thereby providing a basic condition for simplifying subsequent statistical operations. Operation S120 classifies and reorganizes the sub-regions according to the to-be-counted data of the to-be-counted object and the sub-regions involved, to obtain at least one sub-region set of the to-be-counted object, so that the to-be-counted objects in each sub-region set do not overlap with the to-be-counted objects in other sub-region sets. In this way, when the values corresponding to different sub-region sets are added, there is no need to remove duplicates. This makes operation 130 only need to perform simple addition when determining the statistical value corresponding to a specified region in a statistical region according to the statistical value corresponding to each sub-region set, without the need to remove duplicates of the data of the same to-be-counted object, thereby greatly simplifying the difficulty of statistics.

[0047] In addition, since the process of determining at least one sub-region set of the to-be-counted object is not dependent on an online environment, it also provides the possibility of transferring most of the operations to an offline environment. In this way, only the statistical value corresponding to each sub-region set is used when performing online statistics, which not only greatly reduces the computational load of online statistics, but also does not need to expose the individual information of the to-be-counted object, thereby greatly reducing the risk of leaking the individual information of the to-be-counted object.

[0048] Figure 2 The main flow of the region-based statistical method according to another embodiment of the present disclosure is shown.

[0049] The embodiments of the present disclosure periodically (for example, every 1 minute) obtain the position of a vehicle in a certain geographical region, and output the number of vehicles appearing in a specified region in a specified period. The specified region is a free region, that is, an arbitrary region or combination of regions that can be freely selected in the statistical region without being restricted by pre-given region options, which can cross administrative regions, can not be adjacent, and can be any polygonal shape. In this way, various statistical needs can be met, rather than being limited to using a certain region division.

[0050] As Figure 2As shown, the main process of the embodiment of the present disclosure for counting how many vehicles appear in the free area includes:

[0051] In operation S2010, the map is processed by equidistant grid according to the specified unit length, and at least two grids are obtained.

[0052] Suppose the statistical area is Figure 3 A block of area represented by the left map can be processed by equidistant grid with 100m as the unit length of a grid, and Figure 3 The grid shown by the dashed line on the right is obtained.

[0053] In addition, different unit lengths can be used for division according to different statistical accuracy requirements, Figure 4 The grid shown is the grid obtained by processing the map by equidistant grid with 200m as the unit length of a grid.

[0054] Processing the area by equidistant grid is easy to meet various statistical accuracy and match various specified area queries.

[0055] After obtaining the grid, a unique identifier (shown in the figure) can be assigned to each grid to facilitate positioning and distinguishing each grid.

[0056] In operation S2020, the vehicle data is mapped to the grid processed by equidistant grid.

[0057] Suppose the vehicle data obtained in a certain period of time is as follows Figure 5 As shown by the left map:

[0058] The grid corresponding to the position sequence of the red car is (A, B, C);

[0059] The grid corresponding to the position sequence of the black car is (A, B, C);

[0060] The grid corresponding to the position sequence of the blue car is (D, E, F).

[0061] The above data is mapped to the grid corresponding to the map, and different fillings are used to represent different vehicles, gray filling represents blue car, thick line filling represents red car, and thin line filling represents black car, that is, the grid data shown on the right is obtained. Figure 5

[0062] A: (red car, black car);

[0063] B: (red car, black car);

[0064] C: (red car, black car);

[0065] D: (blue car);

[0066] ​E: (blue car);

[0067] F: (blue car).

[0068] Operation S2030, obtaining all grids where each vehicle appears, obtaining grid sequence corresponding to each vehicle;

[0069] With the data shown in Figure 5 , the following grid sequences can be obtained: (

[0071] (red car, (A, B, C)),

[0072] (black car, (A, B, C)),

[0073] (blue car, (D, E, F))

[0074] ).

[0075] Operation S2040, merging vehicles with the same corresponding grid sequence, obtaining at least one grid sequence and the vehicle corresponding to each grid sequence;

[0076] With the data shown in Figure 5 , the following grid sequences and the vehicle corresponding to each grid sequence can be obtained: (

[0078] ((A, B, C), (red car, black car)),

[0079] ((D, E, F), (blue car))

[0080] ).

[0081] Since each grid sequence contains all positions of vehicles, each vehicle can only appear in one grid sequence, and each grid sequence is naturally called a vehicle-orthogonal sub-region set.

[0082] The vehicle corresponding to the grid sequence usually does not repeat, so when obtaining the vehicle corresponding to the grid sequence, it is basically only necessary to perform simple merging.

[0083] Operation S2050, determining the number of vehicles corresponding to each grid sequence according to the vehicle corresponding to each grid sequence; (

[0085] ((A, B, C), 2),

[0086] ((D, E, F), 1) )

[0088] Operation S2060, determining the grid involved in the free area;

[0089] Suppose the free area is (B, C, D), the free area involves the grids: B, C and D.

[0090] Operation S2070, mapping relationship between each grid and each grid sequence is established;

[0091] According to the data obtained by operation S2040 and operation S2050, the mapping relationship from the grid to the grid sequence is established between the grid and the sequence containing the grid, as shown in Figure 6 The mapping relationship can also be used as an index for subsequent queries, thereby further accelerating the acquisition of grid sequences and their statistical values.

[0092] Operation S2080, according to the mapping relationship between each grid and each grid sequence, determine the grid sequence to be counted;

[0093] According to the above free area (B, C, D), it can be determined that the grid sequence to be counted is (A, B, C) and (D, E, F) respectively.

[0094] Operation S2090, the number of vehicles corresponding to the grid sequence to be counted is added to obtain the number of vehicles that have appeared in the free area.

[0095] Thus, the number of vehicles that have appeared in the free area (B, C, D) is the sum of the statistical value corresponding to the grid sequence (A, B, C): 2 and the statistical value corresponding to the grid sequence (D, E, F): 1, that is, 3.

[0096] Similarly, if the free area is (B), the number of vehicles that have appeared is the statistical value corresponding to the grid sequence (A, B, C): 2;

[0097] If the free area is (D), the number of vehicles that have appeared is the statistical value corresponding to the grid sequence (A, B, C): 1;

[0098] If the free area is (A, B), the number of vehicles that have appeared is the statistical value corresponding to the grid sequence (A, B, C): 2, etc.

[0099] In the embodiments of the present disclosure, the operations of operation S2030 to operation S2050 are all performed in an offline environment, and then the grid sequence and the corresponding statistical value determined in the offline environment are passed to online statistics, and the online statistics process only needs to perform a simple addition operation on the grid sequence and the corresponding statistical value, so that the statistical value corresponding to the specified area can be obtained. In this way, the operation amount of online statistics can be greatly reduced, and the individual information (such as vehicle ID or vehicle category) of the object to be counted does not need to be exposed.

[0100] Furthermore, in practical applications, the above statistical methods are particularly suitable for scenarios where the grid sequences of the objects to be analyzed are relatively similar. Since the grid sequences are relatively similar, many objects to be analyzed with the same grid sequence can be merged. Therefore, the scale of the data to be analyzed can be greatly reduced (from the number of individuals to the number of groups), which further reduces the dimensionality and burden of online computing.

[0101] It should be noted that the above embodiments are merely illustrative examples. Implementers can further refine and expand them according to specific implementation needs and conditions, or combine various implementation methods in the above embodiments to obtain new embodiments.

[0102] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0103] According to embodiments of this disclosure, this disclosure also provides a region-based statistical device, such as... Figure 7 As shown, the device 70 includes: a sub-region division module 701, used to perform region division processing on the statistical region to obtain at least two sub-regions; a sub-region set acquisition module 702, used to determine at least one set of sub-regions orthogonal to the object to be statistically analyzed and the sub-regions involved, and the statistical value corresponding to each sub-region set in the at least one set of sub-regions; and a statistical value determination module 703, used to determine the statistical value corresponding to a specified region within the statistical region based on the statistical value corresponding to each sub-region set.

[0104] According to another embodiment of this disclosure, the sub-region set acquisition module 702 is specifically used to determine, in an offline environment, at least one sub-region set orthogonal to the object to be counted and the statistical value corresponding to each sub-region set in the at least one sub-region set; correspondingly, the statistical value determination module 703 is specifically used to determine the statistical value corresponding to a specified region within the statistical region based on the statistical value corresponding to each sub-region set determined in the offline environment.

[0105] According to another embodiment of the present disclosure, the sub-region set obtaining module 702 comprises: a sub-region set corresponding to a to-be-counted object obtaining unit, configured to obtain all sub-regions involved by the to-be-counted data of each to-be-counted object, and obtain a sub-region set corresponding to each to-be-counted object; a sub-region set corresponding to a to-be-counted object orthogonal obtaining unit, configured to merge to-be-counted objects corresponding to the same sub-region set, and obtain at least one sub-region set corresponding to a to-be-counted object orthogonal, and a to-be-counted object set corresponding to each sub-region set; and a sub-region set corresponding statistical value obtaining unit, configured to determine a statistical value corresponding to each sub-region set according to the to-be-counted object set corresponding to each sub-region set and the to-be-counted data of each to-be-counted object.

[0106] According to another embodiment of the present disclosure, the sub-region dividing module 701 is specifically configured to perform equidistant grid processing on the statistical region according to a specified unit length, and obtain at least two grids.

[0107] According to another embodiment of the present disclosure, the statistical value determining module 704 comprises: a to-be-counted sub-region set determining unit, configured to determine a to-be-counted sub-region set intersecting with a specified region in the statistical region, and obtain the to-be-counted sub-region set;

[0108] a specified region corresponding statistical value determining unit, configured to add up the statistical value corresponding to each sub-region set in the to-be-counted sub-region set according to the statistical value corresponding to each sub-region set, and obtain a statistical value corresponding to the specified region in the statistical region.

[0109] According to another embodiment of the present disclosure, the to-be-counted sub-region set determining unit is specifically configured to determine a to-be-counted sub-region set intersecting with a specified region in the statistical region according to the mapping relationship between each sub-region and each sub-region set, and obtain the to-be-counted sub-region set.

[0110] According to another embodiment of the present disclosure, the apparatus 70 further comprises: a sub-region set mapping relationship establishing module, configured to establish a mapping relationship between each sub-region and each sub-region set.

[0111] According to an embodiment of the present disclosure, the present disclosure further provides an electronic device, a readable storage medium and a computer program product.

[0112] Figure 8A schematic block diagram of an example electronic device 800 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 laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0113] like Figure 8 As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0114] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0115] The computing unit 801 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 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, etc. The computing unit 801 performs various methods and processes described above, such as the region-based statistical methods of the present disclosure. For example, in some embodiments, the region-based statistical methods of the present disclosure can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded onto the RAM 803 and executed by the computing unit 801, one or more steps of the region-based statistical methods described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the region-based statistical methods of the present disclosure by any other suitable means, such as by means of firmware.

[0116] 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 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.

[0117] 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.

[0118] 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 would include an electrical connection based on one or more wires, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0119] 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.

[0120] 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.

[0121] The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0122] It should be understood that the various forms of flow shown above can be re-ordered, steps added or removed, etc. For example, the steps described in the present disclosure can be performed in parallel, in series, in a different order, etc., as long as the desired results of the techniques described in the present disclosure are achieved, and are not limited herein.

[0123] The specific implementation described above 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 region-based statistical method, comprising: performing region division processing on a statistical region to obtain at least two sub-regions; determining at least one sub-region set orthogonal to a to-be-counted object and a statistical value corresponding to each of the at least one sub-region set according to to-be-counted data of the to-be-counted object and sub-regions involved by the to-be-counted data; determining a statistical value corresponding to a specified region in the statistical region according to the statistical value corresponding to each of the at least one sub-region set; the specified region comprises one or more sub-regions; the determining of the statistical value corresponding to the specified region in the statistical region according to the statistical value corresponding to each of the at least one sub-region set comprises: determining a to-be-counted sub-region set according to a mapping relationship between each sub-region and each sub-region set, the to-be-counted sub-region set having an intersection with the specified region in the statistical region; performing summation on the statistical value corresponding to each of the to-be-counted sub-region set according to the statistical value corresponding to each of the at least one sub-region set to obtain the statistical value corresponding to the specified region in the statistical region.

2. The method of claim 1, wherein, the determining of the at least one sub-region set orthogonal to the to-be-counted object and the statistical value corresponding to each of the at least one sub-region set according to the to-be-counted data of the to-be-counted object and the sub-regions involved by the to-be-counted data comprises: determining the at least one sub-region set orthogonal to the to-be-counted object and the statistical value corresponding to each of the at least one sub-region set according to the to-be-counted data of the to-be-counted object and the sub-regions involved by the to-be-counted data in an offline environment; correspondingly, the determining of the statistical value corresponding to the specified region in the statistical region according to the statistical value corresponding to each of the at least one sub-region set comprises: determining the statistical value corresponding to the specified region in the statistical region according to the statistical value corresponding to each of the at least one sub-region set determined in the offline environment.

3. The method of claim 1 or 2, wherein, the determining of the at least one sub-region set orthogonal to the to-be-counted object and the statistical value corresponding to each of the at least one sub-region set according to the to-be-counted data of the to-be-counted object and the sub-regions involved by the to-be-counted data comprises: obtaining all sub-regions involved by the to-be-counted data of each to-be-counted object to obtain a sub-region set corresponding to each to-be-counted object; performing merging processing on to-be-counted objects having the same corresponding sub-region set to obtain at least one sub-region set orthogonal to a statistical object and a to-be-counted object set corresponding to each of the at least one sub-region set; determining a statistical value corresponding to each of the at least one sub-region set according to the to-be-counted object set corresponding to each of the at least one sub-region set and the to-be-counted data of each to-be-counted object.

4. The method of claim 1, wherein, the performing of the region division processing on the statistical region to obtain at least two sub-regions comprises: performing equidistance grid processing on the statistical region according to a specified unit length to obtain at least two grids. 5.The method of claim 1, further comprising: establishing a mapping relationship between each sub-region and each sub-region set.

6. The method according to any of claims 1 and 5, wherein, the specified region is a free region. 7.A region-based statistical device, comprising: a sub-region division module configured to perform region division processing on a statistical region to obtain at least two sub-regions. The sub-region set obtaining module is configured to determine at least one sub-region set orthogonal to the to-be-counted object according to the to-be-counted data of the to-be-counted object and the sub-regions involved in the to-be-counted data, and determine a statistical value corresponding to each of the at least one sub-region set; The statistical value determining module is configured to determine a statistical value corresponding to a specified region in the statistical region according to the statistical value corresponding to each of the sub-region sets; The specified region includes one or more sub-regions; The statistical value determining module includes: The to-be-counted sub-region set determining unit is configured to determine a to-be-counted sub-region set according to a mapping relationship between each sub-region and each sub-region set, and obtain the to-be-counted sub-region set according to the intersection between the to-be-counted sub-region set and the specified region in the statistical region; The statistical value corresponding to the specified region determining unit is configured to add up the statistical values corresponding to each of the to-be-counted sub-region sets to obtain the statistical value corresponding to the specified region in the statistical region.

8. The apparatus of claim 7, wherein, The sub-region set obtaining module is specifically configured to determine at least one sub-region set orthogonal to the to-be-counted object according to the to-be-counted data of the to-be-counted object and the sub-regions involved in the to-be-counted data, and determine a statistical value corresponding to each of the at least one sub-region set in an offline environment; Correspondingly, the statistical value determining module is specifically configured to determine a statistical value corresponding to a specified region in the statistical region according to the statistical value corresponding to each of the sub-region sets determined in the offline environment.

9. The apparatus of claim 7 or 8, wherein, The sub-region set obtaining module includes: The sub-region set corresponding to the to-be-counted object obtaining unit is configured to obtain all sub-regions involved in the to-be-counted data of each to-be-counted object to obtain a sub-region set corresponding to each to-be-counted object; The sub-region set corresponding to the to-be-counted object obtaining unit is configured to obtain at least one sub-region set orthogonal to the to-be-counted object by merging the to-be-counted objects corresponding to the same sub-region set, and obtain a to-be-counted object set corresponding to each of the at least one sub-region set; The statistical value corresponding to the sub-region set obtaining unit is configured to determine a statistical value corresponding to each of the at least one sub-region set according to the to-be-counted object set corresponding to each of the at least one sub-region set and the to-be-counted data of each to-be-counted object.

10. The apparatus of claim 7, wherein, The sub-region dividing module is specifically configured to perform equidistance grid processing on the statistical region according to a specified unit length to obtain at least two grids.

11. The apparatus of claim 7, further comprising: The sub-region set mapping relationship establishing module is configured to establish a mapping relationship between each sub-region and each sub-region set.

12. An electronic device, comprising: at least one processor; and a memory connected with the at least one processor in communication; 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 of any one of claims 1-6.

13. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to perform the method of any one of claims 1-6.

14. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.

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