Statistical methods, devices, equipment, and computer program products for dynamic objects
By constructing a statistical method for dynamic objects in big data processing, and decoupling the dependency between the loop and the dataset using assignment nodes and loop counts, the inefficiency problem of static loop mode is solved, achieving efficient dynamic data processing and scalability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING BAIJU YIXING TECH CO LTD
- Filing Date
- 2026-01-19
- Publication Date
- 2026-06-02
AI Technical Summary
Existing technologies struggle to meet real-time business demands in big data processing. Static loop patterns require pre-determining iteration objects, leading to low data processing efficiency and poor system scalability.
By determining the assignment nodes and the number of iterations, a statistical method for dynamic objects is constructed. The second dataset is dynamically updated using authorization information. A loop algorithm is established based on the assignment nodes and the number of iterations to decouple the dependency between the loop and the dataset, thereby achieving dynamic loop updates.
It enables efficient determination of statistical features in the case of dynamic dataset changes, flexibly handles sets of data objects that are not predefined, and improves data processing efficiency and system scalability.
Smart Images
Figure CN122132456A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of data processing technology, and in particular to a statistical method, apparatus, device, and computer program product for dynamic objects. Background Technology
[0002] In the field of big data, some application scenarios require statistical analysis of dynamically changing data.
[0003] The relevant technologies use a static loop model for statistical analysis, but this approach requires pre-determining the iteration objects and determining statistical characteristics based on the static iteration objects, making it difficult to cope with real-time changing business needs. Summary of the Invention
[0004] To overcome the problems existing in related technologies, this disclosure provides a statistical method, apparatus, device, and computer program product for dynamic objects, which can solve the above-mentioned problems.
[0005] According to a first aspect of the present disclosure, a statistical method for dynamic objects is provided. The method includes: receiving authorization information; determining driver behavior data as a first dataset, and determining a driver identifier for a portion of the behavior data corresponding to the authorization information in the first dataset as a second dataset; determining an assignment node and determining a loop count, wherein the assignment node is a parameter of the behavior data in the first dataset; and determining statistical features in the first dataset corresponding to the second dataset based on the loop count and the assignment node.
[0006] According to a second aspect of the present disclosure, a statistical apparatus for dynamic objects is provided. The apparatus includes: a receiving unit configured to receive authorization information; a processing unit configured to determine driver behavior data as a first dataset and determine a driver identifier of a portion of the behavior data corresponding to the authorization information from the first dataset as a second dataset; determine an assignment node and determine a number of iterations, wherein the assignment node is a parameter of the behavior data in the first dataset; and a looping unit configured to determine statistical features in the second dataset corresponding to the first dataset based on the number of iterations and the assignment node.
[0007] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor and a memory; the memory being used to store a computer program; and the processor being used to execute a statistical method for dynamic objects as described in the first aspect by invoking the computer program.
[0008] According to a fourth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method as described in the first aspect.
[0009] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: This disclosure accepts authorization information and then determines a second dataset based on that information, ensuring that the second dataset can dynamically change as the authorization information changes in real time. The first dataset consists of actual behavioral data, which can be dynamically updated in real time. When it is necessary to statistically analyze the features in the first dataset, a loop can be established based on the assignment nodes and the number of iterations. This decouples the loop establishment from the first and second datasets, ensuring that the loop itself is not affected by the dynamically changing first and second datasets. Since the assignment nodes are parameters of the behavioral data in the first dataset, the established loop can be matched with the cyclical update of these parameters. After the loop is established, statistical features can be determined based on the first and second datasets, targeting the dynamically updated parameters and the dynamic authorization information.
[0010] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description
[0011] The accompanying drawings, which are incorporated in and form part of this disclosure, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.
[0012] Figure 1 This is a schematic flowchart illustrating a statistical method for dynamic objects according to an exemplary embodiment of the present disclosure.
[0013] Figure 2 This disclosure is a block diagram illustrating a statistical device for dynamic objects according to an exemplary embodiment.
[0014] Figure 3 This disclosure is a schematic block diagram illustrating a statistical device for dynamic objects according to an exemplary embodiment. Detailed Implementation
[0015] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.
[0016] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.
[0017] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."
[0018] The static loop model used in related technologies requires pre-determining the iteration objects in big data processing scenarios, and then setting loop statistical parameters for the iteration objects to perform loop statistics. This approach is difficult to adapt to the loop traversal and statistical needs of uncertain objects, affecting data processing efficiency and system scalability.
[0019] To address the aforementioned technical problems, this disclosure proposes a statistical method for dynamic objects.
[0020] Figure 1 This is a schematic flowchart illustrating a statistical method for dynamic objects according to an embodiment of the present disclosure. The statistical method for dynamic objects can be executed by an electronic device.
[0021] like Figure 1 As shown, the statistical methods for dynamic objects include: In step S101, the authorization information is accepted; In step S102, the driver's behavior data is determined as the first dataset, and the driver identifier of the partial behavior data corresponding to the authorization information is determined from the first dataset as the second dataset; In step S103, the assignment node is determined and the number of iterations is determined. The assignment node is a parameter of the behavior data in the first dataset. In step S104, the statistical characteristics of the second dataset corresponding to the first dataset are determined based on the number of iterations and the assignment node.
[0022] In some embodiments, authorization information is accepted.
[0023] It can accept authorization information input by the user or obtain authorization information from an interface. The authorization information is used to distinguish authorized behavioral data in the first dataset, so that the statistics in this disclosure are only performed on the authorized portion of the dataset.
[0024] Authorization information can change dynamically. For example, previously authorized data may have included data from Company A and Company B. Authorization information can be updated to match the actual authorization status in a timely manner.
[0025] In some embodiments, driver behavior data is determined as a first dataset, and driver identifiers of the portion of behavior data corresponding to the authorization information are determined from the first dataset as a second dataset.
[0026] Driver behavior data can be obtained and used as the first dataset. The first dataset is the total set; however, authorization to access all behavior data is not always obtained for statistical analysis. In some technical scenarios, based on authorization information, only a portion of the first dataset can be statistically analyzed. Therefore, the second dataset can be determined.
[0027] The second dataset can be a set of driver identifiers, or a set of driver identifiers corresponding to partial behavioral data related to authorization information. Since driver behavioral data can contain many parameters, the second dataset can contain only driver identifiers. This allows for more efficient determination of the data objects to be analyzed in subsequent iterative statistical analyses based on the second dataset, before statistical analysis is performed based on the first dataset.
[0028] For example, in the context of ride-hailing technology, the first dataset is the driver's behavior data, and the authorization information can be the authorization from Company A and Company B. Then, the second dataset determined from the first dataset can be the identifiers of the drivers belonging to Company A and Company B.
[0029] In some embodiments, an assignment node is determined and the number of iterations is determined, wherein the assignment node is a parameter of the behavioral data in the first dataset.
[0030] Assignment nodes can be components in data processing platforms (such as DataWorks) used to declare global variables, and cross-node parameter passing can be achieved through syntax. In this embodiment, the assignment node is a parameter of the behavioral data in the first dataset.
[0031] When performing loop iterations, it is difficult to use dynamically changing datasets as global variables. On the one hand, the dataset is large, and directly substituting it into a global variable will make the loop algorithm slow to respond and inefficient in data processing. On the other hand, when the dataset changes dynamically, the global variable as a whole needs to be changed, resulting in poor system scalability.
[0032] Therefore, parameters of the behavioral data in the first dataset can be selected as assignment nodes. For example, if one parameter in the behavioral data is the departure date, the assignment node can be the date. In the loop algorithm, the global variable is only the date, and the date will not change due to changes in the first dataset (such as addition, deletion, etc.). In addition, the data volume of this variable is small, the processing efficiency is high, and it is easy to expand.
[0033] Looping algorithms require limiting the number of loops for statistics, so the number of loops can be determined and used to limit the looping algorithm.
[0034] In some embodiments, global variables in a loop algorithm can be determined based on the assignment node and the number of iterations.
[0035] The assignment node and the number of iterations can be used to determine global variables, and then the loop algorithm can be run based on the global variables to perform loop statistics on the behavioral data in the first dataset.
[0036] For example, if the assignment node can be a date and the loop count is n, then the determined global variable can be a series of n consecutive dates. The loop algorithm can then use these n dates to perform statistical analysis on the behavioral data within those n dates.
[0037] For example, the assignment node can be a mileage interval, and the loop count is m times, so the behavioral data within the m adjacent mileage intervals can be statistically analyzed.
[0038] In some embodiments, the statistical characteristics of the second dataset corresponding to the first dataset are determined based on the number of iterations and the assignment node.
[0039] The loop algorithm can be determined based on the number of loops and the assignment nodes. Then, based on the authorized second dataset, the behavioral data in the first dataset is statistically analyzed in a loop to determine the statistical characteristics.
[0040] The number of iterations for the loop statistics is the loop count, the global variable for the loop statistics is the assignment node, and the object of the loop statistics is a portion of the behavioral data in the first dataset corresponding to the second dataset. Based on this portion of the behavioral data, statistical features are determined. For example, it can determine the sum of a parameter in the behavioral data across the assignment node dimension, and / or the distribution characteristics of a parameter across the assignment node dimension, etc.
[0041] It should be noted that the driver behavior data in this disclosure can also be other types of data, and the driver identifier can also be other data identifiers. The method disclosed herein is not limited to statistical analysis of driver data in ride-hailing scenarios.
[0042] The method disclosed herein allows for dynamic changes in both the authorization information and the first dataset. Based on the dynamically changing first dataset, a second dataset can be determined using the authorization information. A loop algorithm is then constructed based on assignment nodes and the number of iterations. Finally, the statistical characteristics of the dynamically changing first dataset are determined according to the loop algorithm. This disclosure utilizes assignment nodes to dynamically pass context parameters within the loop algorithm, supporting flexible adjustments to statistical dimensions at runtime. This approach can address real-time business requirements and flexibly handle statistical analysis scenarios involving dynamically generated sets of data objects that are not predefined.
[0043] In some embodiments, determining the statistical features in the first dataset corresponding to the second dataset based on the number of iterations and the assignment node includes: determining the assignment node as a loop parameter; substituting the loop parameter into a preset loop node, and calculating the behavioral data in the first dataset through the loop node; updating the loop parameter based on the number of iterations, and accumulating the calculation results after each update of the loop parameter to determine the statistical features.
[0044] Assignment nodes can serve as loop parameters in multiple loop nodes, which together form a loop algorithm. Loop parameters can be updated based on the number of loop iterations. For each loop parameter, the behavioral data in the first dataset can be calculated through the loop node. After reaching the required number of loop iterations, these calculation results are accumulated to determine statistical characteristics.
[0045] The assignment node determines the dimension for statistical analysis of the behavioral data in the first dataset. The loop parameter, determined based on the assignment node and the number of iterations, can be an array. The number of variables in the array corresponds to the number of iterations. This loop parameter constitutes a global variable, which can be substituted into each loop node of the loop algorithm to determine the calculation result of the loop node under each loop parameter.
[0046] The calculation of each loop node is based on the first dataset. The first dataset is used to provide the data objects to be processed in the calculation of the loop node. The actual content of the first dataset does not affect the implementation of the loop function, nor does it affect the operation and termination of the loop node and the loop algorithm. Therefore, the first dataset can be dynamically changed. Thus, this embodiment is more efficient in processing dynamic datasets.
[0047] In some embodiments, determining the driver identifier of the partial behavioral data corresponding to the authorization information from the first dataset as the second dataset includes: determining the authorized driver category based on the authorization information; determining the partial behavioral data corresponding to the authorized driver category from the first dataset, and determining the driver identifier of the partial behavioral data as the second dataset.
[0048] The driver's behavioral data includes parameters in multiple dimensions. The authorization information can determine the category of the authorized driver. The driver category can be the operating company to which the driver belongs, the driver's gender, or the driver's age. This disclosure does not impose any restrictions on this.
[0049] For the first dataset, it is neither necessary nor feasible to statistically analyze all behavioral data. Therefore, authorization information can be used to identify a portion of the behavioral data in the first dataset for statistical analysis. The second dataset is used to distinguish this portion of behavioral data from the first dataset.
[0050] The second dataset may contain driver identifiers, which correspond to the driver categories in the authorization information.
[0051] Based on this embodiment, the authorization information can be authorized for a specific driver category, which improves the efficiency of authorization and determination of the second dataset when faced with massive amounts of data.
[0052] In some embodiments, the method further includes: updating the first dataset based on updated driver behavior data; and determining the statistical features based on the updated first dataset.
[0053] Because driver behavior data changes frequently, the first dataset can also change accordingly. And when the first dataset changes, the statistical features determined by the iterative algorithm can also change.
[0054] Since the loop algorithm is based on the assignment nodes and the number of iterations, changes to the first dataset do not affect the loop algorithm, and there is no need to update the loop algorithm because the first dataset changes. Therefore, the method in this embodiment can handle the first dataset, which may change dynamically.
[0055] In some embodiments, the method further includes: updating a second dataset based on the changed authorization information when the authorization information changes; and determining the statistical features based on the updated second dataset.
[0056] The authorization information can also change. When the authorization information changes, the second dataset can be updated accordingly, which in turn updates the statistical features determined by the iterative algorithm.
[0057] In this disclosed solution, the second dataset is used to determine a portion of the behavioral data in the first dataset. This portion of behavioral data is the object to be statistically analyzed in the cyclic algorithm. Therefore, updating the second dataset will not affect the implementation of the cyclic algorithm, but only the calculation result, i.e., the statistical features. Based on this, when the authorization of some drivers expires, the statistical features can be updated accordingly without adjusting the cyclic algorithm, avoiding privacy issues caused by untimely synchronization of authorization information.
[0058] In some embodiments, determining the driver's behavior data as a first dataset includes: acquiring the driver's behavior data; partitioning the driver's behavior data according to the dimension of the assignment node; and cleaning the partitioned driver's behavior data to form the first dataset.
[0059] The behavioral data is partitioned along the dimension of the assignment node, and the number of partitions can at least correspond to the number of loops. When using a loop algorithm for statistics, each loop corresponds to one partition of the behavioral data, and the number of loops corresponds to the number of times statistics need to be performed on different partitions.
[0060] For example, if the assignment node can be a date, then the behavioral data can be divided into multiple partitions based on different dates, with each partition containing behavioral data for the corresponding date. For instance, the behavioral data of each driver on month X day corresponds to one partition, while the behavioral data on month X-1 corresponds to another partition.
[0061] In addition to partitioning behavioral data, it can also be cleaned. For example, business logic can be used to preprocess this data to remove duplicate, invalid, or abnormal data, forming a more accurate primary dataset.
[0062] In some embodiments, the driver's behavior data includes driver identification, driving duration, and driving date; the assignment node includes a specified date; and the statistical features include statistical features. Determining the statistical features in the first dataset corresponding to the second dataset based on the number of cycles and the assignment node includes: determining a portion of the behavior data in the first dataset corresponding to the driver identification in the second dataset; accumulating the driving duration of the portion of the behavior data on the driving date corresponding to the assignment node, and updating the assignment node, until the accumulated number of driving durations reaches the number of cycles.
[0063] Driver behavior data can include driver identification, driving duration, and driving date.
[0064] The first dataset obtained may include the first driver, the actual departure date, and the corresponding departure duration for each departure date.
[0065] If the authorization information includes drivers belonging to Company A, a second dataset can be identified. The second dataset is a collection of driver identifiers, all of which belong to Company A.
[0066] After determining the second dataset based on the authorization information, we can determine some behavioral data in the first dataset based on the second dataset, that is, some behavioral data corresponding to the driver identifier in the second dataset (behavioral data of all drivers belonging to Company A).
[0067] When the assignment node includes a specified date (e.g., the first date), the driving hours of these drivers can be accumulated. The specified date is then updated (e.g., decremented by 1 each time), and the driving hours for that date are accumulated again until the required number of cycles is reached. This allows for the collection of statistical characteristics related to the specified number of cycles.
[0068] In some embodiments, the loop algorithm can be a for-each algorithm, and the loop node is a for-each node.
[0069] A for-each node is a dynamic loop executor that can automatically expand a parallel stream of subtasks based on upstream input. The method disclosed herein does not impose any restrictions on specific loop algorithms or statistical content.
[0070] The following specific example will further illustrate the solution disclosed herein.
[0071] In ride-hailing technology scenarios, it is necessary to continuously update the cumulative driving time of the dynamic driver object set within the ride-hailing company over the past 30 days.
[0072] Step S1: Obtain heterogeneous datasets through metadata API or dynamic rules, and load the dynamic data set to be processed.
[0073] It can integrate all actual behavioral data within an enterprise and form a first dataset with driver identifiers as the smallest granularity. This first dataset can be partitioned based on assigned nodes, such as by date, and each partition stores the daily driving time of all driver identifiers.
[0074] After determining the first dataset, it can be preprocessed according to business logic to remove duplicate, invalid, or abnormal drivers. Since the statistical object does not need to be all drivers in the first dataset, and some drivers in the first dataset have not obtained authorization and their behavioral data cannot be statistically analyzed, the determined dataset of objects to be statistically analyzed will change dynamically. This dynamically changing dataset of statistical objects is called the second dataset, which may contain only the driver identifier field.
[0075] Step S2: Using the assignment node, the loop condition is dynamically generated through the parameter passing mechanism.
[0076] You can choose a suitable assignment language, such as ODPS SQL, PYTHON, SHELL, etc.
[0077] The number of partitions that need to be iterated over is determined as the loop count. For example, as an instance, the loop count can be determined to be 30, indicating that the partitions that need to be iterated over 30 times are partitioned based on the dimensions of the assigned nodes during the determination of the first dataset.
[0078] For example, using Python to create an assignment node, `arr = [str(i) for i in range(30)]`. The output parameter is an array: ["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29"] The output of the assigned node is passed as a parameter to the nested child nodes within the for-each loop.
[0079] In step S3, the for-each node iterates based on the parameters output by the predecessor assignment node, and automatically extracts the current element and updates the context variables in each loop.
[0080] Create a for-each loop node to construct the loop algorithm.
[0081] The for-each loop node mainly consists of four parts: loop start, parameter reception, parameter execution, and loop end. The loop start and loop end are built-in parts when creating the for-each loop node, and will not be elaborated on in this publication.
[0082] In the parameter receiving stage, using the Python language, print '${dag.foreach.current}'. The input parameters are: ["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29"] The output parameter sql_input is: "0","1","2","3",…"29" Then, using ODPS SQL, SELECT TO_CHAR(DATEADD(TO_DATE('${ds}','yyyymmdd'),-${sql_input},'dd'),'yyyymmdd') The input parameter is sql_input, and the output parameter sql_output is: (ds-0), (ds-1), ... (ds-29).
[0083] Where ds is the specified date, which can be the current date for which statistics are performed, or any specified date, and the date format is yyyymmdd.
[0084] In step S4, nested child nodes execute preset statistical logic in each iteration, and the output parameters are passed in a chain.
[0085] The parameter execution node is a nested child node within the for-each loop. This nested child node iterates through the previous step's calculation result, `sql_output`, using it as a parameter. In each iteration, it executes predefined statistical logic (e.g., summing the driving time). This predefined statistical logic can associate a first dataset storing the daily driving times of all drivers with a second dataset containing the corresponding dynamic statistical objects. This yields statistical data on the driving time of each driver identifier in the second dataset across historical 30-day partitions. This data could include the distribution of daily driving times over the past 30 days, the total cumulative driving time, or the average driving time over the past 30 days, among other statistical features.
[0086] These statistical features can form a third dataset (statistical feature dataset) that is updated in real time. This third dataset can dynamically change based on the first and second datasets, thereby cyclically updating the statistical data of the historical 30-day partition.
[0087] Corresponding to the embodiments of the statistical method for dynamic objects of this disclosure, this disclosure also provides embodiments of a corresponding statistical apparatus for dynamic objects.
[0088] Please see Figure 2 , Figure 2 This is a block diagram of a statistical device for dynamic objects in one embodiment of this disclosure. Figure 2 As shown, the statistical device for dynamic objects includes: The receiving unit 210 is configured to receive authorization information; The processing unit 220 is configured to determine the driver's behavior data as a first dataset, and determine the driver identifier of the partial behavior data corresponding to the authorization information from the first dataset as a second dataset; determine the assignment node, and determine the number of loops, wherein the assignment node is a parameter of the behavior data in the first dataset; The loop unit 230 is configured to determine the statistical features in the first dataset corresponding to the second dataset based on the number of loops and the assignment node.
[0089] In some embodiments, determining the statistical features in the first dataset corresponding to the second dataset based on the number of iterations and the assignment node includes: determining the assignment node as a loop parameter; substituting the loop parameter into a preset loop node, and calculating the behavioral data in the first dataset through the loop node; updating the loop parameter based on the number of iterations, and accumulating the calculation results after each update of the loop parameter to determine the statistical features.
[0090] In some embodiments, determining the driver identifier of the partial behavioral data corresponding to the authorization information from the first dataset as the second dataset includes: determining the authorized driver category based on the authorization information; determining the partial behavioral data corresponding to the authorized driver category from the first dataset, and determining the driver identifier of the partial behavioral data as the second dataset.
[0091] In some embodiments, the apparatus is further configured to: update the first dataset based on updated driver behavior data; and determine the statistical features based on the updated first dataset.
[0092] In some embodiments, the apparatus is further configured to: update a second dataset based on the changed authorization information when the authorization information changes; and determine the statistical features based on the updated second dataset.
[0093] In some embodiments, determining the driver's behavior data as a first dataset includes: acquiring the driver's behavior data; partitioning the driver's behavior data according to the dimension of the assignment node; and cleaning the partitioned driver's behavior data to form the first dataset.
[0094] In some embodiments, the driver's behavior data includes driver identification, driving duration, and driving date; the assignment node includes a specified date; and the statistical features include statistical features. Determining the statistical features in the first dataset corresponding to the second dataset based on the number of cycles and the assignment node includes: determining a portion of the behavior data in the first dataset corresponding to the driver identification in the second dataset; accumulating the driving duration of the portion of the behavior data on the driving date corresponding to the assignment node, and updating the assignment node, until the accumulated number of driving durations reaches the number of cycles.
[0095] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.
[0096] Embodiments of this disclosure also provide an electronic device, including: a processor and a memory; the memory for storing a computer program; and the processor for executing a statistical method for a dynamic object as described in any of the above embodiments by invoking the computer program.
[0097] Embodiments of this disclosure also propose a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the statistical method for dynamic objects as described in any of the foregoing embodiments.
[0098] Embodiments of this disclosure also provide a computer program product, including a computer program that, when executed by a processor, implements the methods described in any of the foregoing embodiments.
[0099] Figure 3 This is a schematic block diagram illustrating a statistical device 300 for dynamic objects according to embodiments of the present disclosure. For example, device 300 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.
[0100] Reference Figure 3 The device 300 may include one or more of the following components: processing component 302, memory 304, power supply component 306, multimedia component 308, audio component 310, input / output (I / O) interface 312, sensor component 314, and communication component 316.
[0101] Processing component 302 typically controls the overall operation of device 300, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 302 may include one or more processors 320 to execute instructions to complete all or part of the steps of the statistical method for dynamic objects described above. Furthermore, processing component 302 may include one or more modules to facilitate interaction between processing component 302 and other components. For example, processing component 302 may include a multimedia module to facilitate interaction between multimedia component 308 and processing component 302.
[0102] Memory 304 is configured to store various types of data to support operation of device 300. Examples of such data include instructions for any application or method operating on device 300, contact data, phonebook data, messages, pictures, videos, etc. Memory 304 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.
[0103] Power supply component 306 provides power to various components of device 300. Power supply component 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to device 300.
[0104] Multimedia component 308 includes a screen that provides an output interface between the device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 308 includes a front-facing camera and / or a rear-facing camera. When the device 300 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
[0105] Audio component 310 is configured to output and / or input audio signals. For example, audio component 310 includes a microphone (MIC) configured to receive external audio signals when device 300 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 304 or transmitted via communication component 316. In some embodiments, audio component 310 also includes a speaker for outputting audio signals.
[0106] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, start buttons, and lock buttons.
[0107] Sensor assembly 314 includes one or more sensors for providing status assessments of various aspects of device 300. For example, sensor assembly 314 may detect the on / off state of device 300, the relative positioning of components such as the display and keypad of device 300, changes in the position of device 300 or a component of device 300, the presence or absence of user contact with device 300, the orientation or acceleration / deceleration of device 300, and temperature changes of device 300. Sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 314 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 314 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.
[0108] Communication component 316 is configured to facilitate wired or wireless communication between device 300 and other devices. Device 300 can access wireless networks based on communication standards, such as WiFi, 2G, 3G, 4G LTE, 5G NR, or combinations thereof. In one exemplary embodiment, communication component 316 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 316 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
[0109] In an exemplary embodiment, the apparatus 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the statistical methods for the dynamic objects described above.
[0110] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 304 including instructions, which can be executed by a processor 320 of the device 300 to complete the statistical method for the dynamic object described above. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.
[0111] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.
[0112] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.
[0113] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0114] The methods and apparatus provided in the embodiments of this disclosure have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this disclosure. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this disclosure. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this disclosure. Therefore, the content of this specification should not be construed as a limitation of this disclosure.
Claims
1. A statistical method for dynamic objects, characterized in that, The method includes: Accept authorization information; The driver's behavior data is identified as the first dataset, and the driver identifier corresponding to a portion of the behavior data with the authorization information is identified as the second dataset from the first dataset; Determine the assignment node and the number of iterations, wherein the assignment node is a parameter of the behavior data in the first dataset; The statistical characteristics of the second dataset corresponding to the first dataset are determined based on the number of iterations and the assignment nodes.
2. The method according to claim 1, characterized in that, The step of determining the statistical features of the second dataset corresponding to the first dataset based on the number of iterations and the assignment nodes includes: The assignment node is defined as a loop parameter; The loop parameters are substituted into the preset loop node, and the behavioral data in the first dataset is calculated through the loop node; The loop parameters are updated based on the number of loop iterations, and the calculation results after each loop parameter update are accumulated to determine the statistical characteristics.
3. The method according to claim 1, characterized in that, The step of determining the driver identifier of the partial behavioral data corresponding to the authorization information from the first dataset as the second dataset includes: The authorized driver category is determined based on the authorization information; Determine partial behavioral data corresponding to the authorized driver category from the first dataset, and use the driver identifiers of the partial behavioral data as the second dataset.
4. The method according to claim 1, characterized in that, The method further includes: The first dataset is updated based on the updated driver behavior data; The statistical features are determined based on the updated first dataset.
5. The method according to claim 1, characterized in that, The method further includes: If the authorization information changes, the second dataset is updated based on the changed authorization information. The statistical features are determined based on the updated second dataset.
6. The method according to claim 1, characterized in that, The data used to determine the driver's behavior is the first dataset, which includes: Obtain driver behavior data; The driver's behavior data is partitioned along the dimension of the assignment node; The driver behavior data after partitioning is cleaned to form the first dataset.
7. The method according to claim 1, characterized in that, The driver's behavioral data includes driver identification, driving duration, and driving date; the assignment node includes a specified date; and the statistical features include statistical characteristics. The step of determining the statistical features of the second dataset corresponding to the first dataset based on the number of iterations and the assignment nodes includes: Identify the portion of behavioral data in the first dataset that corresponds to the driver identifier in the second dataset; The vehicle departure duration for the specified behavioral data on the departure date corresponding to the assignment node is accumulated, and the assignment node is updated until the number of accumulated departure durations reaches the specified number of cycles.
8. A statistical device for dynamic objects, characterized in that, The device includes: The receiving unit is configured to accept authorization information; The processing unit is configured to determine the driver's behavior data as a first dataset, and to determine the driver identifier of the partial behavior data corresponding to the authorization information from the first dataset as a second dataset; to determine the assignment node and the number of loops, wherein the assignment node is a parameter of the behavior data in the first dataset; The loop unit is configured to determine the statistical features in the first dataset corresponding to the second dataset based on the number of loops and the assignment node.
9. An electronic device, characterized in that, include: Processor, memory; The memory is used to store computer programs; The processor is configured to execute a statistical method for dynamic objects as described in any one of claims 1-7 by invoking the computer program.
10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements a statistical method for dynamic objects as described in any one of claims 1 to 7.