CN111506382B - Progress bar curve determination method and device, storage medium and electronic equipment - Google Patents
Progress bar curve determination method and device, storage medium and electronic equipment Download PDFInfo
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- CN111506382B CN111506382B CN202010337946.2A CN202010337946A CN111506382B CN 111506382 B CN111506382 B CN 111506382B CN 202010337946 A CN202010337946 A CN 202010337946A CN 111506382 B CN111506382 B CN 111506382B
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Abstract
The disclosure provides a progress bar curve determining method, a progress bar curve determining device, a computer readable storage medium and electronic equipment, and relates to the technical field of computers. The progress bar curve determining method comprises the following steps: acquiring progress bar curve slope values corresponding to a plurality of time points in a preset time period; determining a plurality of key points of a slope curve corresponding to the progress bar curve according to the plurality of time points and a plurality of progress bar curve slope values respectively corresponding to the plurality of time points; generating a slope curve based on the key points; and performing integration processing on the slope curve to determine a corresponding progress bar curve. The method and the device can realize the determination of the nonlinear progress bar curve, so that the progress bar curve is more flexible to apply.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a progress bar curve determining method, a progress bar curve determining apparatus, a computer-readable storage medium, and an electronic device.
Background
In various computer applications, progress bars are often used to show the execution speed of a certain task, the required execution time, how many tasks remain unexecuted, and the like.
For example, in some particular plays of a game, progress bars are used to express the progress of a task. The existing progress bar determination scheme includes two types: the first solution is to first determine the size of the loaded game file, then calculate the current percentage of the loaded game file size to the total game file size, and finally determine the progress bar curve. The second scheme is to obtain a progress bar curve with uniform growth by setting the starting and stopping time and the linear growth coefficient of the progress bar. However, the existing technical solution cannot express the task progress conditions of slow initial growth, fast middle growth and slow growth before completion by using the progress bar curve, and cannot determine the non-linear progress bar curve.
It is noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure and therefore may include information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
An object of the present disclosure is to provide a progress bar curve determining method, a progress bar curve determining apparatus, a computer-readable storage medium, and an electronic device, which overcome, at least to some extent, problems that determination of a non-linear progress bar curve cannot be achieved and an applicable scenario of the progress bar curve is limited due to limitations and disadvantages of related art.
According to a first aspect of the present disclosure, there is provided a progress bar curve determining method, including: acquiring a progress bar curve slope value corresponding to a plurality of time points in a preset time period; determining a plurality of key points of a slope curve corresponding to the progress bar curve according to the plurality of time points and a plurality of progress bar curve slope values respectively corresponding to the plurality of time points; generating a slope curve based on the key points; and performing integration processing on the slope curve to determine a corresponding progress bar curve.
According to a second aspect of the present disclosure, there is provided a progress bar curve determining apparatus including: the device comprises a slope value acquisition module, a progress bar curve slope value acquisition module and a progress bar curve slope value acquisition module, wherein the slope value acquisition module is used for acquiring the slope values of progress bars corresponding to a plurality of time points in a preset time period; the key point determining module is used for determining a plurality of key points of a slope curve corresponding to the progress bar curve according to the plurality of time points and a plurality of progress bar curve slope values respectively corresponding to the plurality of time points; the slope curve generating module is used for generating a slope curve based on the key points; and the progress bar curve determining module is used for performing integral processing on the slope curve and determining a corresponding progress bar curve.
Optionally, the progress bar curve determination module may be configured to perform: and carrying out normalization processing on the slope curve, and carrying out integral processing on the slope curve after the normalization processing to determine a corresponding progress bar curve.
Optionally, the slope value obtaining module may be configured to perform: and acquiring the slope values of the progress bar curves corresponding to a plurality of time points at fixed time intervals in a preset time period.
Optionally, the slope curve generating module may further include: a coefficient determining unit for determining a plurality of curve shape control coefficients corresponding to the key points; a discrete sequence determining unit, configured to determine a discrete slope sequence based on the multiple time points and the corresponding key points; and the slope curve determining unit is used for determining a slope curve according to the curve shape control coefficients and the discrete slope sequence.
Optionally, the progress bar curve determining device further includes: the time point determining module is used for determining a plurality of time points at intervals of preset duration in a preset time period as sampling time points; and the slope value determining module is used for determining the slope value of the progress bar curve corresponding to the sampling time point based on the slope curve.
Optionally, the progress bar curve determining apparatus further includes: a feature detection module may be configured to perform: detecting the monotonous characteristic of the progress bar curve according to the slope curve; an information alert module that may be configured to perform: if the monotonic characteristics and the slope curve do not have a direct proportion relation, reminding information is sent out.
Optionally, the progress bar curve determining apparatus further includes: a progress bar curve generation module that may be configured to perform: and generating a plurality of target progress bar curves according to the preset error range and the progress bar curves.
According to a third aspect of the present disclosure, there is provided a computer-readable storage medium, on which a computer program is stored, which program, when executed by a processor, implements the progress bar curve determining method as described above.
According to a fourth aspect of the present disclosure, there is provided an electronic device comprising: one or more processors; a storage device for storing one or more programs which, when executed by one or more processors, cause the one or more processors to implement the progress bar curve determining method as described above.
Exemplary embodiments of the present disclosure have the following advantageous effects:
in the technical solutions provided in some embodiments of the present disclosure, first, rate values of progress bar curves corresponding to a plurality of time points in a preset time period are obtained; then, determining a plurality of key points of a slope curve corresponding to the progress bar curve according to the plurality of time points and a plurality of progress bar curve slope values respectively corresponding to the plurality of time points; then, generating a slope curve based on the key points; subsequently, an integration process is performed on the slope curve to determine a corresponding progress bar curve. On the one hand, the progress bar curve slope values corresponding to the multiple time points in the preset time period are obtained, the problem that the amplification form of the progress bar curve is fixed and the application is single due to the fact that the fixed progress bar curve slope values are set is avoided, and the application flexibility of the progress bar curve is improved. On the one hand, according to the method and the device, a plurality of key points of a slope curve corresponding to the progress bar curve are determined according to a plurality of time points and a plurality of progress bar curve slope values corresponding to the time points respectively, the slope curve is generated based on the key points, integral processing is performed on the slope curve, the corresponding progress bar curve is determined, determination of the nonlinear progress bar curve is achieved, meanwhile, the determination process of the progress bar curve is avoided being achieved through a large amount of data, and system resource consumption is reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty. In the drawings:
fig. 1 schematically illustrates a flow chart of a progress bar curve determination method according to an exemplary embodiment of the present disclosure;
fig. 2 schematically shows a schematic diagram of a slope curve corresponding to a progress bar curve according to an exemplary embodiment of the present disclosure;
FIG. 3 schematically illustrates a graph of progress percentage versus time point according to an exemplary embodiment of the present disclosure;
fig. 4 schematically shows a block diagram of a progress bar curve determining apparatus according to an exemplary embodiment of the present disclosure;
FIG. 5 schematically illustrates a block diagram of a slope curve generation module according to an exemplary embodiment of the present disclosure;
fig. 6 schematically shows a block diagram of a progress bar curve determining apparatus according to another exemplary embodiment of the present disclosure;
fig. 7 schematically shows a block diagram of an electronic device in an exemplary embodiment according to the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the embodiments of the disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
It should be noted that, in the present disclosure, the terms "comprises" and "comprising" are used in an open-ended manner, and mean that there may be additional elements/components/etc. other than the listed elements/components/etc.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the steps. For example, some steps may be decomposed, some steps may be combined or partially combined, and thus the actual execution order may be changed according to the actual situation.
In some play systems for network games, progress bars are often used to express the progress of a task.
At present, a scheme for determining a progress bar curve adopted in a game playing system includes: firstly, selecting a slow-motion function related to the task progress condition, such as a polynomial trigonometric function, an exponential function and the like; then, the time parameter is input into the selected slow-motion function system to obtain the converted current progress value, and then a progress bar curve is determined. However, the existing progress bar curve determination scheme cannot show the task situations of slow initial growth, fast middle growth and slow growth before finishing, so that the progress bar curve is not flexible to apply, and meanwhile, the progress value is rebounded, and the progress value is not increased but reduced.
To address this problem, the present disclosure provides a method for determining a progress bar curve.
It should be noted that, in the exemplary embodiment of the present disclosure, the progress bar curve determining method described below may be generally implemented by a server, that is, the steps of the progress bar curve determining method may be performed by the server, in which case, the progress bar curve determining apparatus may be configured in the server.
In addition, the progress bar curve determining method may be implemented by a terminal device (e.g., a mobile phone, a tablet, a personal computer, etc.), that is, the respective steps of the progress bar curve determining method may be performed by the terminal device, in which case the progress bar curve determining means may be configured in the terminal device.
Hereinafter, each step of the progress bar curve determining method in the present exemplary embodiment will be described in more detail with reference to the drawings and examples.
Fig. 1 schematically shows a flowchart of a progress bar curve determination method of an exemplary embodiment of the present disclosure. In the following description, a server is taken as an execution subject. Referring to fig. 1, the method for determining a progress bar curve may specifically include the following steps:
s102, obtaining the slope values of the progress bar curves corresponding to a plurality of time points in a preset time period.
In an exemplary embodiment of the present disclosure, the preset time period may refer to a time period between an initial time and an end time of calculating the progress bar curve. The plurality of time points may refer to a plurality of time points randomly determined within a preset time period, or may refer to a plurality of time points determined at fixed intervals within a preset time period.
The slope value of the progress bar curve may refer to how fast the progress bar curve changes at a time point.
This is disclosed can avoid setting up fixed progress strip curve slope value and lead to progress strip curvilinear increase amplitude fixed through obtaining the corresponding progress strip curve slope value of a plurality of time points, and the environment of use is single, improves the curved application flexibility of progress strip.
For example, referring to fig. 2, the preset time period is one day, that is, 24 hours, the server may obtain a corresponding progress bar curve slope value 6M at a point 0, a corresponding progress bar curve slope value 8M at a point 10, and the like, where M is a unit of the progress bar curve slope value and may be represented in millions.
It should be noted that the number of the slope values of the progress bar curve obtained by the server may be greater than or equal to a preset number, so that the determined progress bar slope curve is more accurate.
According to the exemplary embodiment of the disclosure, the server may obtain the slope values of the progress bar curves corresponding to the multiple time points at fixed time intervals within a preset time period.
For example, the preset time period is one day, namely 24 hours, and the server acquires the slope value of the progress bar curve corresponding to the time point every 5 hours.
S104, determining a plurality of key points of a slope curve corresponding to the progress bar curve according to the plurality of time points and a plurality of progress bar curve slope values corresponding to the plurality of time points respectively.
The key points may be composed of 2-dimensional values and may include time points and a slope value of the progress bar curve corresponding to the time points.
After obtaining the slope values of the progress bar curves corresponding to the multiple time points in the preset time period, the server may determine the key points corresponding to the time points according to the time points, or obtain the slope values of the progress bar curves corresponding to the time points according to the time points.
For example, referring to fig. 2, the preset time period is one day, that is, 24 hours, and the server acquires a corresponding progress bar curve slope value 8M at 10 points in the preset time period. The server may determine that at 10 points the corresponding keypoint is (10, 8).
And S106, generating a slope curve based on the key points.
The slope curve may be derived from a discrete slope sequence containing the keypoints. Fig. 2 shows a slope curve of the progress bar over a preset time period, i.e. 24 hours.
According to an exemplary embodiment of the disclosure, after the server obtains the key points, first, curve shape control coefficients corresponding to the key points may be determined; then, determining a discrete slope sequence based on a plurality of time points and corresponding key points; a slope curve is then determined from the plurality of curve shape control coefficients and the discrete slope sequence.
Wherein, the curve shape control coefficient can be used to control the slope curve to pass through the first key point and the last key point in the discrete slope sequence, and a plurality of curve shape control coefficients can be stored in the form of an array. The array length of the curve shape control coefficient can be calculated by the order of the curve and the number of key points. The value of the order of the curve can be determined according to the number of the key points, and the value range can be [1, the number of the key points-1 ].
For example, the server determines 6 key points, which are: [0,0], [1,1], [2,2], [3,1], [4,0], [ 5-1 ], and the order of the curve is 2. In the curve shape control coefficient array, the first 3 curve shape control coefficients (curve order + 1) are 0, the last 2 curve shape control coefficients (curve order) are 1, and the middle 4 curve shape control coefficients (number of key points — curve order) are equally divided by 1.
The server can calculate that the length of the array of the curve shape control coefficient is 9, and the array of the curve shape control coefficient is as follows: [0,0,0,0.25,0.5,0.75,1,1,1].
The discrete slope sequence may be a sequence obtained by arranging the key points corresponding to the multiple time points according to the sequence of the multiple time points.
The slope curve is generated based on the plurality of key points, so that the slope curve is prevented from being calculated by using a large amount of data, the calculation resources of the system are reduced, and the energy consumption is reduced.
In the exemplary embodiment of the present disclosure, the server determines the slope curve through the plurality of curve shape control coefficients and the discrete slope sequence, and the server may also calculate the slope curve by using the related formula (1) and formula (2) of the b-spline algorithm. The specific formula is as follows:
wherein u is i Representing an i-th curve shape control coefficient of the plurality of curve shape control coefficients; u. of i+n Representing the i + n-th curve shape control coefficient in the plurality of curve shape control coefficients; u. of i+n+1 Represents the i + n + 1-th curve shape control coefficient in the plurality of curve shape control coefficients. x represents the number of keypoints. When n =0, i =1,
the first keypoint in the discrete slope sequence may be represented. When n is>At the time of 0, the number of the first,
the key point of the ith discrete slope sequence in the nth layer of recursive computation can be represented;
can represent the key point of the ith in the discrete slope sequence in the n-1 th layer recursive computation;
the keypoint of the (i + 1) th in the discrete slope sequence in the (n-1) th layer of recursive computation can be represented.
In the b-spline curve calculation, the plurality of key points are control points in the b-spline curve, the server inputs the plurality of key points into a correlation formula of the b-spline curve, and the obtained curve is the slope curve of the disclosure.
And S108, performing integral processing on the slope curve to determine a corresponding progress bar curve.
The integration process may integrate the slope curve with a pointer within a preset time period. The initial time during the integration process may be an initial time point of the slope curve within a preset time period, and the end time during the integration process may be an end time point of the slope curve within the preset time period. The server may perform an integration process on the slope curve using a stepped curve integration algorithm to determine a corresponding progress bar curve.
According to an exemplary embodiment of the present disclosure, the server may first perform normalization processing on the slope curve, and then perform integration processing on the slope curve after the normalization processing to determine a corresponding progress bar curve. The present disclosure expresses complex non-linear growth application scenarios by enabling the determination of non-linear progress bar curves.
For example, the slope curve calculated by the server is shown in fig. 2, and when the horizontal axis represents a preset time period from 0 to 24, the vertical axis represents the slope value of the progress bar curve corresponding to each time point, which is referred to as the slope value in fig. 2 for short. That is, fig. 2 illustrates a slope curve in a preset time period from 0 to 24, in which the range of the slope value of the progress bar curve belongs to (0, 15M).
The slope curve in fig. 2 is integrated to determine a progress bar curve, as shown in fig. 3. The horizontal axis represents the preset time period 0 to 24, and the vertical axis represents the percentage of the progress bar corresponding to each time point.
From fig. 2 and 3, it can be seen that: the more gentle the progress bar curve, the lower the slope value of the progress bar curve, and the slope curve corresponding to the progress bar curve may form a valley. The steeper the progress bar curve is, the higher the slope value of the progress bar curve is, and the corresponding slope curve of the progress bar curve forms a peak. In addition, the slope values of the progress bar curves are all larger than 0, namely, the progress bar curves present increasing relations.
The method and the device can avoid the phenomenon that the progress bar curve rebounds due to the slow motion function, and improve the application flexibility of the progress bar curve.
After the server calculates the slope curve, a plurality of time points at intervals of preset duration can be determined in a preset time period and serve as sampling time points; and determining the slope value of the progress bar curve corresponding to the sampling time point based on a slope curve.
The preset duration can be smaller than the fixed duration when the server acquires the multiple progress bar curve slope values, and the preset duration can be larger than the fixed duration when the server acquires the multiple progress bar curve slope values. The sampling point time point may be different from a time point when the server acquires the plurality of progress bar curve slope values.
For example, in a preset time period from 0 to 24 hours, the server acquires the slope value of the progress bar curve corresponding to the time point at intervals of 10 minutes for the slope curve. Wherein, sampling the time point may include: 1 point 10 points, 1 point 20 points, 2 points 30 points, etc. The server can also randomly select a time point and determine a corresponding progress bar curve slope value.
It should be noted that the slope curve includes a slope value of the progress bar curve corresponding to each second time point in the preset time period. The plurality of progress bar curve slope values obtained by the server through the slope curve and the sampling time point can be different from the progress bar curve slope values for determining the key point.
According to an exemplary embodiment of the present disclosure, the server may detect a monotonic characteristic of the progress bar curve according to the slope curve; if the monotonic characteristics and the slope curve do not have a direct proportion relation, reminding information is sent out.
The monotonous characteristic of the progress bar curve may refer to a variation trend of the progress bar curve.
The direct proportion relation between the monotonic characteristic of the progress bar curve and the slope curve can mean that the rate of slope of the progress bar curve of the slope curve is smaller, and the rate of monotonic increase of the progress bar curve is slow; the larger the slope value of the progress bar curve of the slope curve is, the fast the monotonous increase of the progress bar curve is.
The server detects that the monotonic characteristics and the slope curve do not have a direct proportion relation, namely, the slope value of the progress bar curve is large, and the increasing speed of the progress bar curve is slow. It can be shown that, when the integral processing process of the slope curve is wrong, the server can send a reminding message to facilitate the correction of a designer. The reminder information may be a corresponding reminder identifier indicating that the monotonic feature and the slope curve do not have a direct proportion relationship, and may be, for example, a red icon or the like.
According to an exemplary embodiment of the present disclosure, the server may generate a plurality of target progress bar curves according to a preset error range and the progress bar curves. According to the method, the preset error range is designed to obtain a plurality of target progress bar curves which accord with the same slope curve, and the usability of the progress bar curves is improved.
Wherein the preset error range may be, for example, between (-1, 1). The server can also obtain a plurality of target progress bar curves by adopting a random disturbance method on the basis of the progress bar curves.
After the server obtains the progress bar curve, the corresponding progress percentage can be determined according to any time point.
In an exemplary embodiment of the present disclosure, the server may also first obtain a progress percentage corresponding to a plurality of time points within a preset time period; then, determining a plurality of key points of a progress bar curve according to the plurality of time points and a plurality of progress bar percentages respectively corresponding to the time points; then, a progress bar curve is generated based on the plurality of curve shape control coefficients and the plurality of key points of the progress bar curve.
The key points corresponding to the progress bar curve can be two dimensions, and include time points and the percentages of the progress bars corresponding to the time points. The key points corresponding to the progress bar curve may be, for example, (1, 10%), (24, 100%), etc.
Before generating the progress bar curve based on the curve shape control coefficients and the key points of the progress bar curve, the server may determine preset monotonic characteristics and curve types of the progress bar curve. The preset monotone characteristic may be monotone increasing or monotone decreasing. The curve type may indicate whether the progress bar curve is adjacent to or contains a key point.
The server generates the progress bar curve according to the curve shape control coefficients and the key points of the progress bar curve, and the server can also input the key points of the progress bar curve into a related formula of a b-spline curve algorithm to calculate the progress bar curve.
It should be noted that the key point corresponding to the first time point of the multiple key points corresponding to the progress bar curve is the same as the initial point of the progress bar curve, and the key point corresponding to the last time point of the multiple key points corresponding to the progress bar curve is the same as the end point of the progress bar curve.
According to the progress bar curve detection method and device, the progress bar curve slope value can be detected by executing the integral processing on the determined slope curve, a plurality of key points of the progress bar curve are avoided being detected, and the determination process of the progress bar curve is easy to debug.
It should be noted that although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order or that all of the depicted steps must be performed to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken into multiple step executions, etc.
Further, in an exemplary embodiment of the present disclosure, a progress bar curve determining apparatus is also provided.
Fig. 4 schematically shows a block diagram of a progress bar curve determining apparatus according to an exemplary embodiment of the present disclosure. Referring to fig. 4, the progress bar curve determining apparatus 400 according to an exemplary embodiment of the present disclosure may include: a slope value obtaining module 401, a key point determining module 403, a slope curve generating module 405, and a progress bar curve determining module 407.
The slope value obtaining module 401 is configured to obtain slope values of progress bar curves corresponding to multiple time points in a preset time period; a key point determining module 403, configured to determine a plurality of key points of a slope curve corresponding to the progress bar curve according to the plurality of time points and a plurality of slope values of the progress bar curve corresponding to the plurality of time points, respectively; a slope curve generation module 405, configured to generate a slope curve based on the key point; the progress bar curve determining module 407 is configured to perform integration processing on the slope curve to determine a corresponding progress bar curve.
According to another embodiment of the present disclosure, the progress bar curve determining module 407 may be configured to perform: and carrying out normalization processing on the slope curve, and carrying out integral processing on the slope curve after the normalization processing to determine a corresponding progress bar curve.
According to another embodiment of the present disclosure, the slope value obtaining module 401 may be configured to perform: and acquiring the slope values of the progress bar curves corresponding to the multiple time points at fixed time intervals in a preset time period.
According to another embodiment of the present disclosure, referring to fig. 5, the slope curve generating module 405 may further include: a coefficient determination unit 502, a discrete sequence determination unit 504, and a slope curve determination unit 506.
Wherein, the coefficient determining unit 502 is configured to determine a plurality of curve shape control coefficients corresponding to the key points; a discrete sequence determining unit 504, configured to determine a discrete slope sequence based on a plurality of time points and corresponding key points; a slope curve determining unit 506 for determining a slope curve based on the plurality of curve shape control coefficients and the discrete slope sequence.
According to another embodiment of the present disclosure, referring to fig. 6, the progress bar curve determining apparatus 600 may further include, in comparison with the progress bar curve determining apparatus 400: a time point determination module 601 and a slope value determination module 603.
The time point determining module 601 is configured to determine, within a preset time period, a plurality of time points at intervals of a preset duration as sampling time points; a slope value determining module 603, configured to determine, based on a slope curve, a slope value of the progress bar curve corresponding to the sampling time point.
According to another embodiment of the present disclosure, the progress bar curve determining apparatus 400 may further include: a feature detection module may be configured to perform: detecting the monotonous characteristic of the progress bar curve according to the slope curve; an information alert module that may be configured to perform: if the monotonic characteristics and the slope curve do not have a direct proportion relation, reminding information is sent out.
According to another embodiment of the present disclosure, the progress bar curve determining apparatus 400 may further include: a progress bar curve generation module that may be configured to perform: and generating a plurality of target progress bar curves according to the preset error range and the progress bar curves.
The details of each module/unit in the above-mentioned apparatus have been described in detail in the embodiments of the method section, and thus are not described again.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above-mentioned "exemplary methods" section of the present description, when the program product is run on the terminal device.
In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 700 according to this embodiment of the invention is described below with reference to fig. 7. The electronic device 700 shown in fig. 7 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, electronic device 700 is embodied in the form of a general purpose computing device. The components of the electronic device 700 may include, but are not limited to: the at least one processing unit 710, the at least one memory unit 720, a bus 730 connecting different system components (including the memory unit 720 and the processing unit 710), and a display unit 740.
Wherein the memory unit stores program code that is executable by the processing unit 710 to cause the processing unit 710 to perform steps according to various exemplary embodiments of the present invention as described in the above section "exemplary method" of the present specification. For example, the processing unit 710 may perform steps S102 to S108 as shown in fig. 1.
The storage unit 720 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM) 7201 and/or a cache memory unit 7202, and may further include a read only memory unit (ROM) 7203.
The storage unit 720 may also include a program/utility 7204 having a set (at least one) of program modules 7205, such program modules 7205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 700 may also communicate with one or more external devices 800 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to communicate with the electronic device 700, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 700 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 750. Also, the electronic device 700 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 760. As shown, the network adapter 760 communicates with the other modules of the electronic device 700 via the bus 730. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, to name a few.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Furthermore, the above-described drawings are only schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is to be limited only by the terms of the appended claims.
Claims (9)
1. A progress bar curve determination method is characterized by comprising the following steps:
acquiring progress bar curve slope values corresponding to a plurality of time points in a preset time period;
determining a plurality of key points of a slope curve corresponding to the progress bar curve according to the plurality of time points and a plurality of progress bar curve slope values respectively corresponding to the plurality of time points;
determining a plurality of curve shape control coefficients corresponding to the key points;
determining a discrete slope sequence based on the plurality of time points and the corresponding key points;
generating a slope curve according to the curve shape control coefficients and the discrete slope sequence;
and performing integration processing on the slope curve to determine a corresponding progress bar curve.
2. The method of claim 1, wherein the performing an integration process on the slope curve and determining a corresponding progress bar curve comprises:
and carrying out normalization processing on the slope curve, and carrying out integral processing on the slope curve after the normalization processing to determine a corresponding progress bar curve.
3. The method for determining the progress bar curve according to claim 1 or 2, wherein the obtaining of the slope values of the progress bar curve corresponding to a plurality of time points within a preset time period comprises:
and acquiring the slope values of the progress bar curves corresponding to the multiple time points at fixed time intervals in a preset time period.
4. The progress bar curve determining method according to claim 1 or 2, further comprising:
determining a plurality of time points at intervals of preset duration in the preset time period as sampling time points;
and determining the slope value of the progress bar curve corresponding to the sampling time point based on the slope curve.
5. The progress bar curve determining method according to claim 1 or 2, further comprising:
detecting the monotonous characteristic of the progress bar curve according to the slope curve;
and if the monotonic characteristics and the slope curve do not have a direct proportion relation, sending out reminding information.
6. The progress bar curve determining method according to claim 1 or 2, further comprising:
and generating a plurality of target progress bar curves according to a preset error range and the progress bar curves.
7. A progress bar curve determining apparatus, comprising:
the device comprises a slope value acquisition module, a progress bar display module and a progress bar display module, wherein the slope value acquisition module is used for acquiring the slope values of progress bars corresponding to a plurality of time points in a preset time period;
a key point determining module, configured to determine, according to the multiple time points and multiple slope values of the progress bar curve corresponding to the multiple time points, multiple key points of a slope curve corresponding to the progress bar curve;
a slope curve generating module, configured to determine a plurality of curve shape control coefficients corresponding to the key points, determine a discrete slope sequence based on the plurality of time points and corresponding key points, and generate a slope curve according to the plurality of curve shape control coefficients and the discrete slope sequence;
and the progress bar curve determining module is used for executing integral processing on the slope curve and determining a corresponding progress bar curve.
8. A computer-readable storage medium on which a computer program is stored, the program, when executed by a processor, implementing the progress bar curve determining method according to any one of claims 1 to 6.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the progress bar curve determination method according to any one of claims 1 to 6.
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| CN107705069B (en) * | 2017-09-27 | 2021-06-29 | 北京小米移动软件有限公司 | Progress display method and device |
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