CN107376246B - Training statistical method, device, storage medium and processor - Google Patents
Training statistical method, device, storage medium and processor Download PDFInfo
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- CN107376246B CN107376246B CN201710496173.0A CN201710496173A CN107376246B CN 107376246 B CN107376246 B CN 107376246B CN 201710496173 A CN201710496173 A CN 201710496173A CN 107376246 B CN107376246 B CN 107376246B
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
- A63B24/0003—Analysing the course of a movement or motion sequences during an exercise or trainings sequence, e.g. swing for golf or tennis
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B71/00—Games or sports accessories not covered in groups A63B1/00 - A63B69/00
- A63B71/06—Indicating or scoring devices for games or players, or for other sports activities
- A63B71/0619—Displays, user interfaces and indicating devices, specially adapted for sport equipment, e.g. display mounted on treadmills
- A63B71/0669—Score-keepers or score display devices
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B71/00—Games or sports accessories not covered in groups A63B1/00 - A63B69/00
- A63B71/06—Indicating or scoring devices for games or players, or for other sports activities
- A63B71/0686—Timers, rhythm indicators or pacing apparatus using electric or electronic means
Abstract
The invention discloses a training statistical method, a training statistical device, a storage medium and a processor. Wherein, the method comprises the following steps: acquiring uploading information, wherein the uploading information comprises at least one actual training time, and the actual training time represents the time for completing training of a user; sequentially judging whether the actual training time meets the preset requirements; determining that the actual training time corresponds to the first object under the condition that the actual training time meets the preset requirement; determining that the actual training time corresponds to the second object under the condition that the actual training time does not meet the preset requirement; and accumulating the first object and the second object corresponding to at least one actual training time. The invention solves the technical problem that the prior art can not determine the training adherence condition of the user according to the historical training data.
Description
Technical Field
The invention relates to the field of computers, in particular to a training statistical method, a training statistical device, a storage medium and a processor.
Background
With the improvement of living standard, people pay more and more attention to the health problem of the body. In order to be able to maintain physical fitness, more and more people start sports training.
The technical scheme provided by the prior art can judge the completion condition of single training. However, exercise training mainly aims at persisting, and requires a user to persist in training within a certain time range, and the existing judgment of the completion condition of a single training can only count the completion condition of each training before the current time, but cannot determine the persistence condition of the exercise training of the user in the training process.
Aiming at the problem that the prior art can not determine the training adherence condition of the user according to the historical training data, an effective solution is not provided at present.
Disclosure of Invention
The embodiment of the invention provides a training statistical method, a training statistical device, a storage medium and a processor, which are used for at least solving the technical problem that the training adherence condition of a user cannot be determined according to historical training data in the prior art.
According to an aspect of an embodiment of the present invention, there is provided a training statistic method, including: acquiring uploaded information after training is finished, wherein the uploaded information comprises at least one actual training time, and the actual training time represents the time of finishing training of a user; sequentially judging whether the actual training time meets the preset requirements; determining that the actual training time corresponds to a first object under the condition that the actual training time meets a preset requirement; determining that the actual training time corresponds to a second object under the condition that the actual training time does not meet the preset requirement; and accumulating the first object and the second object corresponding to at least one actual training time.
Further, acquiring the upload information includes: acquiring a basic score corresponding to each actual training time, wherein the uploading information comprises the basic score corresponding to the actual training time; determining a first predetermined adherence value corresponding to each of the actual training times; and determining an empirical value corresponding to each actual training time according to the first predetermined adherence value, wherein the empirical value is the product of the first predetermined adherence value and the base score.
Further, accumulating the first object and the second object corresponding to at least one of the actual training times comprises: determining a first numerical value corresponding to the first object and a second numerical value corresponding to the second object, wherein a second predetermined adherence value comprises the first numerical value and the second numerical value; and accumulating the second preset insistence value corresponding to at least one actual training time to obtain an accumulated value corresponding to at least one actual training time.
Further, sequentially judging whether the actual training time meets the predetermined requirement comprises at least one of the following steps: sequentially judging whether the actual training time corresponds to a plan training time, wherein the plan training time represents the time required by the user to train; sequentially judging whether the actual training time is continuous or not; and sequentially judging whether the time interval between the adjacent actual training times is smaller than a preset threshold value.
According to another aspect of the embodiments of the present invention, there is also provided a training statistic apparatus, including: the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring uploading information after training is finished, the uploading information comprises at least one actual training time, and the actual training time represents the time of finishing the training of a user; the judging unit is used for sequentially judging whether the actual training time meets the preset requirement; the first determining unit is used for determining that the actual training time corresponds to a first object under the condition that the actual training time meets a preset requirement; the second determining unit is used for determining that the actual training time corresponds to a second object under the condition that the actual training time does not meet the preset requirement; and the accumulation unit is used for accumulating the first object and the second object corresponding to at least one actual training time.
Further, the acquisition unit includes: a first obtaining module, configured to obtain a basic score corresponding to each actual training time, where the upload information includes the basic score corresponding to the actual training time; a first determining module, configured to determine a first predetermined adherence value corresponding to each of the actual training times; a second determining module, configured to determine an empirical value corresponding to each of the actual training times according to the first predetermined adherence value, where the empirical value is a product of the first predetermined adherence value and the base score.
Further, the accumulation unit includes: a third determining module, configured to determine a first numerical value corresponding to the first object and a second numerical value corresponding to the second object, where a second predetermined adherence value includes the first numerical value and the second numerical value; and the accumulation module is used for accumulating the second preset insistence value corresponding to at least one actual training time to obtain an accumulated value corresponding to at least one actual training time.
Further, the judging unit includes at least one of: the first judgment module is used for sequentially judging whether the actual training time corresponds to a planned training time, wherein the planned training time represents the time required by the user to train; the second judgment module is used for sequentially judging whether the actual training time is continuous or not; and the third judging module is used for sequentially judging whether the time interval between the adjacent actual training times is smaller than a preset threshold value.
According to yet another embodiment of the present invention, there is also provided a storage medium including a stored program, wherein the program performs any one of the above methods when executed.
According to yet another embodiment of the present invention, there is also provided a processor for executing a program, wherein the program executes to perform the method of any one of the above.
In the embodiment of the invention, the uploading information uploaded by a user after each training is finished is obtained, the actual training time representing the completion of the training of the user is extracted from the uploading information, whether the actual training time meets the preset requirement is judged, and under the condition that the actual training time meets the preset requirement, the actual training time is determined to correspond to the first object; under the condition that the actual training time is determined not to meet the preset requirement, the second object corresponding to the actual training time is determined, and then the first object meeting the preset requirement and the second object not meeting the preset requirement are accumulated, so that the completion condition of training in multiple times of training can be determined according to the first object and the second object corresponding to the actual training time of each training, the adherence condition of the user in multiple times of training can be further determined, and the technical problem that the training adherence condition of the user cannot be determined according to historical training data in the prior art is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a schematic diagram of an alternative training statistical method according to an embodiment of the present invention;
FIG. 2 is a diagram of an alternative training statistic device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present invention, there is provided a method of training statistics method embodiment, it is noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a schematic diagram of an alternative training statistical method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, obtaining uploaded information after training is completed, wherein the uploaded information comprises at least one actual training time, and the actual training time represents the time when a user completes training;
step S104, sequentially judging whether the actual training time meets the preset requirements;
step S106, determining that the actual training time corresponds to the first object under the condition that the actual training time meets the preset requirement;
step S108, determining that the actual training time corresponds to a second object under the condition that the actual training time does not meet the preset requirement;
step S110, accumulating the first object and the second object corresponding to at least one actual training time.
Through the steps, uploading information uploaded by a user after each training is finished is obtained, actual training time representing that the user finishes the training is extracted from the uploading information, whether the actual training time meets a preset requirement or not is judged, and under the condition that the actual training time meets the preset requirement, the actual training time is determined to correspond to a first object; under the condition that the actual training time is determined not to meet the preset requirement, the second object corresponding to the actual training time is determined, and then the first object meeting the preset requirement and the second object not meeting the preset requirement are accumulated, so that the completion condition of training in multiple times of training can be determined according to the first object and the second object corresponding to the actual training time of each training, the adherence condition of the user in multiple times of training can be further determined, and the technical problem that the training adherence condition of the user cannot be determined according to historical training data in the prior art is solved.
In the scheme provided in step S102, the uploaded information is an uploaded log of the user after each training is completed, and the type of subject of the training, the starting time of the training, the ending time of the training, the completion condition of the training, and the actual training time are recorded in the uploaded log, where the actual training time may be the date when the training is completed, for example, when 5000 meters of running training is completed on B-month and C-day of a year, the actual training time of the 5000 meters of running training is B-month and C-day of a year.
In the scheme provided in step S104, the predetermined requirement may be a preset judgment condition for judging the completion of each training by the judgment condition.
As an alternative embodiment, the sequentially determining whether the actual training time meets the predetermined requirement includes at least one of: sequentially judging whether the actual training time corresponds to the plan training time, wherein the plan training time represents the time required by the user to train; sequentially judging whether the actual training time is continuous or not; and sequentially judging whether the time interval between adjacent actual training times is smaller than a preset threshold value.
By adopting the embodiment of the invention, whether the actual training time corresponds to the plan training time or not is sequentially judged, and if the actual training time corresponds to the plan training time, the actual training time is determined to meet the preset requirement; and if the actual training time does not correspond to the planned training time, determining that the actual training time does not meet the preset requirement, so that the completion condition of the training can be judged, and the adherence condition of the training can be determined conveniently according to the completion condition of each training.
As an alternative example, if the planned training time includes day C of month B of a year, and the time training time also includes day C of month B of a year, it is determined that the actual training time corresponds to the planned training time, and the actual training time meets the predetermined requirement; if the planned training time includes day C of month B of a year and the time training time does not include day C of month B of a year, it is determined that the actual training time does not correspond to the planned training time, and the actual training time does not meet the predetermined requirement.
By adopting the embodiment of the invention, whether the actual training time is continuous or not is sequentially judged, and if the actual training time is continuous, the actual training time is determined to meet the preset requirement; and if the actual training time is not continuous, determining that the actual training time does not meet the preset requirement, so that the training completion condition can be judged, and the training adherence condition can be determined conveniently according to the training completion condition of each time.
As an optional example, if the actual training time includes 1 day and 2 days, it is determined that the actual training time is continuous, and the actual training time meets the predetermined requirement; and if the planned training time comprises 1 day and 3 days, determining that the actual training time is discontinuous, and the actual training time does not meet the preset requirement.
By adopting the embodiment of the invention, whether the time interval between the adjacent actual training times is smaller than the preset threshold value or not is sequentially judged, and if the time interval between the adjacent actual training times is smaller than the preset threshold value, the actual training time is determined to meet the preset requirement; and if the time interval between the adjacent actual training times is judged to be larger than the preset threshold value in sequence, the actual training time is determined to be not in accordance with the preset requirement, so that the training completion condition can be judged, and the training adherence condition can be determined conveniently according to the training completion condition of each time.
As an alternative example, the actual training time includes 1 day and 4 days, and if the predetermined threshold is 3 days, it is determined that the time interval between adjacent actual training times is smaller than the predetermined threshold, and the actual training time meets the predetermined requirement; and if the preset threshold value is 1 day, determining that the time interval between adjacent actual training times is greater than the preset threshold value, wherein the actual training times do not meet the preset requirement.
As an optional embodiment, the acquiring the upload information includes: acquiring a basic score corresponding to each actual training time, wherein the uploaded information comprises the basic score corresponding to the actual training time; determining a first predetermined adherence value corresponding to each actual training time; an empirical value corresponding to each actual training time is determined based on the first predetermined adherence value, wherein the empirical value is the product of the first predetermined adherence value and the base score.
By adopting the embodiment of the invention, the basic score corresponding to each actual training time and the first adherence value corresponding to each actual training time are determined from the uploaded information, and then the product of the first preset adherence value and the basic score is calculated to obtain the experience value corresponding to each actual training time, so that the training completion condition and the training adherence condition of the user can be intuitively reflected according to the experience value.
It should be noted that the adherence value is a quantitative index for measuring the adherence of the user, and is dynamically adjusted according to the user behavior.
As an alternative embodiment, the accumulating the first object and the second object corresponding to at least one actual training time comprises: determining a first numerical value corresponding to the first object and a second numerical value corresponding to the second object, wherein the second preset insistence value comprises the first numerical value and the second numerical value; and accumulating the second preset adherence value corresponding to the at least one actual training time to obtain an accumulated value corresponding to the at least one actual training time.
By adopting the above embodiment of the present invention, the second predetermined adherence value includes the first numerical value and the second numerical value, and when the actual training time meets the predetermined requirement, it is determined that the actual training time corresponds to the first numerical value, and when the actual training time does not meet the predetermined requirement, it is determined that the actual training time corresponds to the second numerical value, and further, the first numerical value and the second numerical value corresponding to the actual training time are accumulated to obtain the accumulated value corresponding to the actual training time, so that the completion condition of training and the adherence condition of the user for training are represented by the accumulated value.
Alternatively, the first value may be a positive number, indicating an increased second predetermined adherence value; the second value may be a negative number indicating a decreasing increase of the second predetermined adherence value, such that by accumulating the first value and the second value a trained accumulated value is obtained, such that the adherence to the training can be judged according to the magnitude of the accumulated value.
Alternatively, the larger the cumulative value, the longer the adherence time indicating training, and the smaller the cumulative value, the shorter the adherence time indicating training.
Alternatively, in the process of increasing the accumulated value, if the accumulated value reaches the second threshold value, the accumulated value stops increasing, and the second threshold value is called the accumulated value.
Alternatively, in the process of decreasing the accumulated value, when the accumulated value reaches the third threshold value, the decrease of the accumulated value is stopped, and the third threshold value is called the accumulated value.
Alternatively, the second predetermined adherence value may be the first predetermined adherence value.
As an optional embodiment, the acquiring the upload information includes: acquiring training subjects in the uploaded information, wherein the uploaded information comprises the training subjects used for indicating the training of the user; and determining the preset requirements corresponding to the training subjects.
By adopting the embodiment of the invention, the training subjects completed by the user can be obtained from the uploaded information uploaded by the user, and then the corresponding preset requirements are determined according to the training subjects, so that different preset requirements can be selected according to different subjects, and the preset requirements can meet the actual training situation.
As an alternative example, if it is determined that the training subject of the user is running outdoors at 3 km, the time and exercise intensity of running and the time and exercise intensity of an auxiliary exercise such as a lower limb exercise related to running are determined, a predetermined requirement corresponding to the running exercise or the auxiliary exercise is determined, and a score corresponding to the running exercise or the auxiliary exercise is determined.
As an alternative embodiment, after accumulating the first object and the second object corresponding to the actual training time, the embodiment may further include: and displaying an accumulated result corresponding to the accumulated actual training time.
By adopting the embodiment of the invention, the accumulated results of the first object and the second object corresponding to the accumulated actual training time can be displayed, so that other users can accurately know the training completion condition and the training persistence condition from the accumulated results.
The present invention also provides a preferred embodiment which provides an incentive method based on user monitoring behavior.
As an alternative example, in the case where the user selects 3 km outdoor running, training subjects may be scheduled for the user, and a training time corresponding to each training subject.
Optionally, training the subject may include: warming up before running, combining running and walking for 10 minutes, stretching after running, running knee prevention, running core training, running stability training, outdoor jogging for 1 kilometer, lower limb movement ability training and other training subjects.
Alternatively, in the case where the user selects a training program, a training schedule of the user and a training subject corresponding to each planned training time in the training schedule may be displayed. For example, in the case where the user selects 3 km outdoor running, a schedule of 3 km outdoor running is displayed, and displaying the subjects for the first day of training includes: warming up before running, combining running for 10 minutes, and stretching after running; the subjects trained the next day included: knee prevention in running; the subjects trained on the third day are rest; the subjects trained on the fourth day included: warming up before running, combining running for 10 minutes, and stretching after running; the subjects trained on the fifth day include: running core training; the subsequent daily training of the subjects is analogized in turn, and the details are not repeated here.
Optionally, after the user trains according to the planned training time, the training log may be filled in at a position of the planned training time corresponding to the training time in the training schedule according to the actual training time after the training is completed. For example, if the user completes the training subjects planned for the day in a year, B, C, the training log of the training is filled in the position corresponding to the day in a year, B, C in the training schedule.
Optionally, the actual training time and the actual training subject corresponding to the training log, and the base score and the adherence value corresponding to the training subject may be obtained from the training log filled by the user. For example, if the user completes three training subjects of "warm up before running", "run combined for 10 minutes", and "stretch after running" on B and C of a year, the training subjects, and the base scores and the adherence values corresponding to the training subjects can be obtained from the training logs of the day.
Optionally, the adherence value is a quantitative index used to measure the adherence of the user, and is dynamically adjusted according to the user behavior.
Optionally, the base score and adherence value for different training subjects are different. For example, the base score and adherence value for the "warm up before running" training subject may be lower than the base score and adherence value for the "run in conjunction with 10 minutes" training subject; the base score and adherence value corresponding to the "running stability training" training subject may be lower than the base score and adherence value corresponding to the "running combined with 10 minutes" training subject and higher than the base score and adherence value corresponding to the "warm up before running" training subject.
Alternatively, adherence values for training subjects that have completed by the current time may be accumulated. For example, three days of training, the adherence values corresponding to the training subjects of the user in the previous three days may be accumulated.
Optionally, the user deducts the adherence value corresponding to the item when the training subject corresponding to the plan training time is not completed within the plan training time.
Alternatively, if the user does not complete the planned training subjects within the planning time, the user may fill in a leave requisition at the location of the current time in the training schedule.
Optionally, if the user does not complete the planned training subjects within the planning time, determining whether the sum of the times of completing the planned training subjects within the planning time adjacent to the current time is lower than a threshold, and if the sum is lower than a predetermined threshold, not holding the value; and if the time is higher than the preset threshold value, deducting the adherence value of the training subject corresponding to the time.
Optionally, the accumulated adherence values are displayed at a personal interface of the user.
Optionally, the calculation method of the adherence value is as follows:
and calculating an increased adherence value, acquiring a training log uploaded by the user after each training is finished, and extracting training time from a training day. If the number of training days is continuous, the user's adherence value continues to increase until the highest threshold of adherence values is reached.
A reduced adherence value is calculated, the user is allowed to rest for one day during the training process, and if the interval time is longer than a predetermined date, the adherence value of the user will decrease every more days until the lowest threshold value of adherence value is reached.
Alternatively, the user's persistence value may expand the experience value increase caused by the user's behavior, which may be a non-linear increase, such as the experience value obtained by training the user once, i.e., the persistence value.
According to the embodiment of the invention, the adherence value of the user can be displayed on each page of the user following the user. For example, on a personal information page, a community dynamic page, a leader board, etc.
According to the embodiment of the invention, the user can compare a plurality of users according to the persistence value by displaying the persistence value to form a scene of mutual comparison of the users, so that the honor and the dazzling attributes of the users are met.
According to the embodiment of the invention, the adherence value can represent the adherence degree of the user in the exercise process, and the user can be stimulated through the adherence value under the condition that the training is difficult to adhere, so that the effect that the longer the adherence is, the more the stimulation is, is created, and the exercise habit of the user is helped to be established.
According to another aspect of the present invention, an embodiment of the present invention further provides a storage medium, where the storage medium includes a stored program, and when the program runs, the apparatus where the storage medium is located is controlled to execute the above training statistical method.
According to another aspect of the present invention, an embodiment of the present invention further provides a processor, where the processor is configured to execute a program, where the program executes the above training statistical method.
According to an embodiment of the present invention, an embodiment of a training statistical apparatus is further provided, and it should be noted that the training statistical apparatus may be used to execute a training statistical method in the embodiment of the present invention, and the training statistical method in the embodiment of the present invention may be executed in the training statistical apparatus.
Fig. 2 is a schematic diagram of an alternative training statistic device according to an embodiment of the present invention, as shown in fig. 2, the device may include: the acquisition unit 21 is configured to acquire the uploaded information after the training is completed, where the uploaded information includes at least one actual training time, and the actual training time represents a time when the user completes the training; a judging unit 23, configured to sequentially judge whether the actual training time meets a predetermined requirement; a first determining unit 25, configured to determine that the actual training time corresponds to the first object when the actual training time meets a predetermined requirement; a second determining unit 27, configured to determine that the actual training time corresponds to the second object when the actual training time does not meet the predetermined requirement; and an accumulating unit 29 for accumulating the first object and the second object corresponding to at least one actual training time.
According to the embodiment of the invention, the uploaded information uploaded by the user after each training is finished is obtained, the actual training time representing the completion of the training of the user is extracted from the uploaded information, whether the actual training time meets the preset requirement is judged, and under the condition that the actual training time meets the preset requirement, the actual training time is determined to correspond to the first object; under the condition that the actual training time is determined not to meet the preset requirement, the second object corresponding to the actual training time is determined, and then the first object meeting the preset requirement and the second object not meeting the preset requirement are accumulated, so that the completion condition of training in multiple times of training can be determined according to the first object and the second object corresponding to the actual training time of each training, the adherence condition of the user in multiple times of training can be further determined, and the technical problem that the training adherence condition of the user cannot be determined according to historical training data in the prior art is solved.
It should be noted that the obtaining unit 21 in this embodiment may be configured to execute step S102 in this embodiment, the determining unit 23 in this embodiment may be configured to execute step S104 in this embodiment, the first determining unit 25 in this embodiment may be configured to execute step S106 in this embodiment, the second determining unit 27 in this embodiment may be configured to execute step S108 in this embodiment, and the accumulating unit 29 in this embodiment may be configured to execute step S110 in this embodiment. The modules are the same as the corresponding steps in the realized examples and application scenarios, but are not limited to the disclosure of the above embodiments.
As an alternative embodiment, the obtaining unit includes: the first acquisition module is used for acquiring a basic score corresponding to each actual training time, wherein the uploaded information comprises the basic score corresponding to the actual training time; the first determining module is used for determining a first preset adherence value corresponding to each actual training time; and the second determination module is used for determining an empirical value corresponding to each actual training time according to the first preset adherence value, wherein the empirical value is the product of the first preset adherence value and the base score.
As an alternative embodiment, the accumulation unit includes: the third determining module is used for determining a first numerical value corresponding to the first object and a second numerical value corresponding to the second object, wherein the second preset insistence value comprises the first numerical value and the second numerical value; and the accumulation module is used for accumulating the second preset insistence value corresponding to the at least one actual training time to obtain an accumulated value corresponding to the at least one actual training time.
As an alternative embodiment, the judging unit includes at least one of: the first judgment module is used for sequentially judging whether the actual training time corresponds to the planned training time, wherein the planned training time represents the time required by the user to train; the second judgment module is used for sequentially judging whether the actual training time is continuous or not; and the third judging module is used for sequentially judging whether the time interval between the adjacent actual training times is smaller than a preset threshold value.
As an alternative embodiment, the obtaining unit includes: the second acquisition module is used for acquiring the training subjects in the uploaded information, wherein the uploaded information comprises the training subjects used for indicating the training of the user; and the fourth determining module is used for determining the preset requirements corresponding to the training subjects.
As an alternative embodiment, the apparatus further comprises: and the display unit is used for displaying an accumulated result corresponding to the accumulated actual training time after the first object and the second object corresponding to the actual training time are accumulated.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (6)
1. A method of training statistics, comprising:
acquiring uploaded information after training is finished, wherein the uploaded information comprises at least one actual training time, and the actual training time represents the time of finishing training of a user;
sequentially judging whether the actual training time meets the preset requirements;
determining that the actual training time corresponds to a first object under the condition that the actual training time meets a preset requirement;
determining that the actual training time corresponds to a second object under the condition that the actual training time does not meet the preset requirement;
accumulating the first object and the second object corresponding to at least one actual training time;
wherein accumulating the first object and the second object corresponding to at least one of the actual training times comprises:
determining a first numerical value corresponding to the first object and a second numerical value corresponding to the second object, wherein a second predetermined adherence value comprises the first numerical value and the second numerical value;
accumulating the second preset insistence value corresponding to at least one actual training time to obtain an accumulated value corresponding to at least one actual training time;
wherein, obtaining the upload information comprises:
acquiring training subjects in the uploaded information, wherein the uploaded information comprises the training subjects used for indicating the training of the user;
determining the predetermined requirements corresponding to the training subjects;
wherein, obtaining the upload information comprises:
acquiring a basic score corresponding to each actual training time, wherein the uploading information comprises the basic score corresponding to the actual training time;
determining a first predetermined adherence value corresponding to each of the actual training times;
and determining an empirical value corresponding to each actual training time according to the first predetermined adherence value, wherein the empirical value is the product of the first predetermined adherence value and the base score.
2. The method of claim 1, wherein sequentially determining whether the actual training time meets a predetermined requirement comprises at least one of:
sequentially judging whether the actual training time corresponds to a plan training time, wherein the plan training time represents the time required by the user to train;
sequentially judging whether the actual training time is continuous or not;
and sequentially judging whether the time interval between the adjacent actual training times is smaller than a preset threshold value.
3. A training statistic device, comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring uploading information after training is finished, the uploading information comprises at least one actual training time, and the actual training time represents the time of finishing the training of a user;
the judging unit is used for sequentially judging whether the actual training time meets the preset requirement;
the first determining unit is used for determining that the actual training time corresponds to a first object under the condition that the actual training time meets a preset requirement;
the second determining unit is used for determining that the actual training time corresponds to a second object under the condition that the actual training time does not meet the preset requirement;
an accumulation unit, configured to accumulate the first object and the second object corresponding to at least one of the actual training times;
wherein the accumulation unit includes:
a third determining module, configured to determine a first numerical value corresponding to the first object and a second numerical value corresponding to the second object, where a second predetermined adherence value includes the first numerical value and the second numerical value;
the accumulation module is used for accumulating the second preset insistence value corresponding to at least one actual training time to obtain an accumulated value corresponding to at least one actual training time;
wherein the acquisition unit includes:
the second acquisition module is used for acquiring training subjects in the uploaded information, wherein the uploaded information comprises the training subjects used for indicating the user to train;
a fourth determining module, configured to determine the predetermined requirement corresponding to the training subject;
wherein the acquisition unit includes:
a first obtaining module, configured to obtain a basic score corresponding to each actual training time, where the upload information includes the basic score corresponding to the actual training time;
a first determining module, configured to determine a first predetermined adherence value corresponding to each of the actual training times;
a second determining module, configured to determine an empirical value corresponding to each of the actual training times according to the first predetermined adherence value, where the empirical value is a product of the first predetermined adherence value and the base score.
4. The apparatus of claim 3, wherein the determining unit comprises at least one of:
the first judgment module is used for sequentially judging whether the actual training time corresponds to a planned training time, wherein the planned training time represents the time required by the user to train;
the second judgment module is used for sequentially judging whether the actual training time is continuous or not;
and the third judging module is used for sequentially judging whether the time interval between the adjacent actual training times is smaller than a preset threshold value.
5. A storage medium, characterized in that the storage medium comprises a stored program, wherein the program when executed performs the method of claim 1 or 2.
6. A processor, characterized in that the processor is configured to run a program, wherein the program when running performs the method of claim 1 or 2.
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| CN101504636A (en) * | 2008-02-06 | 2009-08-12 | 索尼株式会社 | Information processing apparatus, display data providing method and program |
| CN103544456A (en) * | 2013-09-30 | 2014-01-29 | 小米科技有限责任公司 | Information processing method, information processing device and mobile terminal |
| US8825445B2 (en) * | 2009-09-04 | 2014-09-02 | Nike, Inc. | Monitoring and tracking athletic activity |
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| CN101504636A (en) * | 2008-02-06 | 2009-08-12 | 索尼株式会社 | Information processing apparatus, display data providing method and program |
| US8825445B2 (en) * | 2009-09-04 | 2014-09-02 | Nike, Inc. | Monitoring and tracking athletic activity |
| CN103544456A (en) * | 2013-09-30 | 2014-01-29 | 小米科技有限责任公司 | Information processing method, information processing device and mobile terminal |
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