CN116864076A - Motion load optimization method and system - Google Patents
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Abstract
The application discloses a motion load optimization method, which comprises the following steps: moving object & moving item setting, generating a matrix to be optimized, optimizing condition definition (limiting condition, forcing condition), and performing optimization on the basis of matrix diagonalization. The application also correspondingly discloses a motion load optimizing system. The application can rapidly, intelligently, adjustably and accurately optimize and promote the motion load of multiple motion projects and multiple motion targets, and can simultaneously consider the absolute value of the motion effect and the optimal balance of the multiple motion targets.
Description
Technical Field
The application relates to the technical field of kinematics, in particular to a motion optimization method based on load management.
Background
The exercise load refers to physiological stimulus to which the human body is subjected during exercise activities. According to the nature of the exercise load to stimulate the human body, we divide the exercise load into two aspects of load intensity and load quantity. The significance of this division is: on the one hand, it is convenient for us to know, recognize and study the exercise load, and more importantly, to arrange and adjust the exercise load. According to the existing researches, the exercise load modes include standard mode, double-peak mode, front high-back low mode, front low-back high mode and the like. The standard type means that the exercise amount gradually rises to a corresponding level from small to large and gradually falls again for a certain time. It is generally thought that the physical, psychological and intellectual natural potential can be fully exerted only under the pressure that the exercise load of physical exercise reaches the limit, thereby achieving the purpose of enhancing physical ability. However, the exercise load is excessive, and fatigue and damage are easily caused.
Multidimensional exercise load management, generally comprising multidimensional exercise targets such as weight loss, regional muscle gain, abdominal fat loss, leg fat loss, regional modeling, joint strengthening, regional strength strengthening, and the like, while including multidimensional exercise items such as running, swimming, yoga, balancing appliances, strength appliances (dumbbell, barbell, regional muscle appliances, and the like), dead weight loads, athletic items, and the like; the method involves a plurality of different combination modes, and the different combination modes determine or generate quite obvious movement effect differences and are deeply related with a moving object of a moving body.
Therefore, optimizing and managing the exercise load, especially under the condition of orienting to a plurality of groups of exercise targets by means of a plurality of exercise projects, reasonably and effectively arranging the exercise load is a hot spot problem in research and a technical problem with high practical value in practice.
Disclosure of Invention
The application aims to solve the technical problem of providing a motion load optimizing method which takes absolute value and balance into account, can quickly, intelligently, adjustably and accurately optimize and improve motion load setting of multiple motion projects and multiple motion targets in a scene, and takes absolute value of motion effect and optimal balance of the multiple motion targets into account.
In order to solve the technical problems, the technical scheme adopted by the application is as follows.
The motion load optimizing method with both absolute value and balance includes the following steps:
1.1, setting a moving target; the method comprises the steps that an optional moving target range is set in a database of an intelligent moving load optimizing system, a multi-dimensional moving target is input on a screen keyboard of the optimizing system, and the moving targets are mutually orthogonal data sets, so that a multi-dimensional row vector a is formed T Storing data; for n groups of moving objects, row vector a T Is set to n corresponding to the dimension of (a), and the system controller calls the row vector a T Generating a row vector a T A peer-to-peer 1×n matrix a is stored as a moving target matrix;
1.2 setting sports items;
1.2.1 popping up a motion item input keyboard on a screen keyboard of an optimization system, calling a multi-dimensional motion item efficiency value for a multi-dimensional motion object stored in a database by the system after the motion item is input, and constructing the efficiency value for different dimension motion items of any dimension motion object into a column vector b for storage because different motion items have different effects on different motion objects;
1.2.2 for m groups of motion items, the dimension of the column vector b is correspondingly set as m for data storage; further, distinguishing n groups of vectors B constructed by n groups of moving targets through subscripts, wherein the sequence of the subscripts corresponds to that of the moving targets one by one, and n groups of column vectors with the subscripts are further constructed into an m multiplied by n-order matrix B according to the sequence of the subscripts for storage;
1.3 the background of the system controller constructs an unknown 1×m-order matrix as an optimization matrix C, which is set to satisfy the linear transformation a=c×b;
1.4, setting limiting conditions of an optimization matrix; the system controller automatically calls the stored data in the database to automatically set the Range-span Limiting parameter and the transmission-span Limiting parameter, or allows editing of the Range-span Limiting parameter and the transmission-span Limiting parameter in the automatic setting Limiting condition on the basis of the stored data in the system database;
1.4.1 wherein, the Range-span Limiting mode is: respectively setting the highest value and the lowest value which take the motion time length as a unit for m data of the optimized matrix, and recording as Range-span Limiting;
1.4.2 wherein the transmission-span Limiting is defined in the following manner: respectively setting interval values taking the movement time length as a unit for m data of the optimization matrix in the Range of Range-span Limiting, and recording as transmission-span Limiting; when the data in the optimization matrix changes, transition type change can only be carried out in a Range of Range-span Limiting by taking Transition-span Limiting as a unit, and continuous change cannot be carried out;
1.5 setting forcing conditions of an optimization matrix; the system controller automatically or in a mode allowing editing to automatically set, selecting 1-k groups of data in the optimization matrix as forced data, wherein k is less than or equal to m/2; setting a forcing condition for each forcing data, automatically calling the storage data in a database by a system controller to automatically set a mustRange-span Limiting parameter and a mustTransmit-span Limiting parameter, or allowing editing of the mustRange-span Limiting parameter and the mustTransmit-span Limiting parameter in the forcing condition on the basis of the storage data of the system database;
1.5.1 wherein the mandatory way of mustRange-span Limiting is: intercepting a small Range in the Range of Range-span Limiting as mustRange-span Limiting;
when the data is intercepted, data points divided by Transition-span Limiting in the Range of Range-span Limiting are taken as end values;
1.5.2 wherein the mandatory way of MustTransition-span Limiting is: respectively setting interval values taking the motion time length as a unit for m data of an optimization matrix in a Range of Range-span Limiting, wherein the interval values are not more than the transmission-span Limiting;
1.6 constructing an accompanying matrix X, diagonalizing n groups of data of the matrix A by a system, and storing the obtained n multiplied by n order matrix as the accompanying matrix X;
1.7, intelligent optimization of motion load and terminal presentation;
1.7.1 the controller firstly carries out the assignment in sequence on the forced data according to the interval value in the MustTransmit-span Limiting in the MustRange-span Limiting; then carrying out assignment of non-limiting data;
1.7.2, for any group of the forced data which are already assigned, sequentially assigning values to the rest data in Range-span Limiting according to interval values in transmission-span Limiting;
1.7.3 assigning values to each group of data, and obtaining a 1 Xn-order matrix A by linear transformation calculation in the step 1.3;
1.7.4 obtaining a companion matrix x for the 1×n-order matrix a by diagonal transformation in step 1.6;
1.7.5 for the accompanying matrix X, calculating the volume value of the accompanying matrix X, comparing the volume values of data assignments of different groups, and selecting the highest value of a specific proportion;
1.7.6 for the accompanying matrix X, calculating absolute values formed by the sum of absolute values on diagonal lines of the accompanying matrix X, comparing absolute values of data assignment of different groups, and selecting the highest value of a specific proportion;
1.7.7 the data sets corresponding to the values screened in the steps 1.7.5 and 1.7.6 are compared, common data are selected, and the motion items corresponding to the common data and the respective optimal motion durations thereof are used as the optimal motion loads to be presented at the terminal.
As a preferred embodiment of the present application, in step 1.7.7, the data sets corresponding to the values screened in step 1.7.5 and step 1.7.6 are compared, if there is no shared data; returning to steps 1.7.5 and 1.7.6, and expanding the selected ratio of the highest values of the two steps at the same ratio; until the shared data is obtained in step 1.7.7.
As a preferred embodiment of the present application, in step 1.7.7, the data sets corresponding to the values screened in step 1.7.5 and step 1.7.6 are compared, if there is no shared data; returning to the steps 1.7.5 and 1.7.6, and alternately widening the selected ratio of the highest value in the two steps in equal ratio or different ratio based on the different steps; until the shared data is obtained in step 1.7.7.
As a preferred embodiment of the present application, step 1.7.5 serves as the initial item to be adjusted when the selected ratio of the highest values of the two steps is alternately widened in sequence.
As a preferred embodiment of the present application, step 1.7.6 serves as the initial item to be adjusted when the selected ratio of the highest values of the two steps is alternately widened in sequence.
As a preferred embodiment of the present application, in step 1.7.7, the data sets corresponding to the values screened in step 1.7.5 and step 1.7.6 are compared, if there is no shared data; returning to step 1.7.5, widening the selected proportion of the highest value in this step; until the shared data is obtained in step 1.7.7.
As a preferred embodiment of the present application, in step 1.7.7, the data sets corresponding to the values screened in step 1.7.5 and step 1.7.6 are compared, if there is no shared data; returning to step 1.7.6, widening the selected proportion of the highest value in this step; until the shared data is obtained in step 1.7.7.
In step 1.1, the moving object is a numerical parameter after quantization treatment; the quantization processing method is to take the absolute value of the difference value between the effect value of the moving object and the corresponding existing value as the moving object value parameter; in step 1.3, the unit of any data in the optimization matrix is the motion duration; in step 1.4, the end value of the Range-span Limiting constitutes an integral multiple of the transmission-span Limiting interval value; in step 1.5, the MustTransmit-span Limiting is obtained by dividing the Transmit-span Limiting by an integer within 1-10.
As a preferred embodiment of the present application, in steps 1.4 and 1.5, the control of the motion load optimization complexity is performed by the following method: after a moving object and a moving item are selected, the limit condition range and the forced condition range of the matrix are optimized to enable the optimization complexity to exceed an upper limit, firstly, the moving object and the moving item are subjected to overrun alarm, then the limit condition and the forced condition are subjected to overrun alarm, and the complexity is reduced through adjustment of corresponding data; after the overrun alarm of the moving object and the moving item, if the complexity is reduced to be below the upper limit after the moving object and/or the moving item are reduced, setting is completed, and if the moving object and the moving item can not be reduced or the overrun alarm is still carried out after the reduction, the overrun alarm of the limiting condition and the forced condition is carried out; after the limit condition and the forced condition exceed the limit alarm, complexity is reduced by reducing the end value of Range-span Limiting and mustRange-span Limiting in the limit condition and the forced condition; or by increasing the interval value in transmission-span Limiting, musttransmission-span Limiting.
As a preferred technical scheme of the application, in the steps 1.7.5-1.7.6, the specific proportion is 1-50%; the determination and adjustment of the specific proportion can be performed by dividing the number of the reference presentation values by the total calculated amount; the number of the presentation values is 3-30; in step 1.7.7, the terminal presents as a screen display presentation, a voice broadcast, an electronic document report sent over a wireless network, a paper document report presented by printing.
The beneficial effects of adopting above-mentioned technical scheme to produce lie in: the motion load optimization method which is researched and developed by the application and takes absolute value and balance into consideration can realize motion load optimization and promotion under the conditions of various motion projects and multiple groups of motion targets, and especially can take absolute value of motion effect and optimal balance of multiple motion targets into consideration.
The application simultaneously calculates the sum of diagonal values of the accompanying matrixes as parallel indexes, interacts with the matrix volume, the former represents the total absolute value of the motion effect, the latter represents the balanced optimization of the motion, and the preferable results of the two are simultaneously presented (such as the top 5 optimal combinations are respectively presented) when the results are presented, and especially, the rectangle of the combined data which simultaneously appears in the two indexes is particularly recommended. The method can meet the requirements of multidimensional exercise load management and optimization, and for selected multidimensional exercise targets such as weight reduction, regional muscle increase, abdominal fat reduction, leg fat reduction, regional modeling, joint strengthening, regional strength strengthening and the like, the data matrix constructed by the multidimensional array and the linear transformation thereof are cooperatively associated with multidimensional exercise projects such as running, swimming, yoga, balance equipment, strength equipment, dumbbell, barbell, regional muscle equipment, dead weight load, competitive projects and the like, the collaborative optimization of exercise loads can be rapidly, intelligently, adjustably and accurately carried out, and the overall combined mode of the exercise loads with the optimal synergy of the exercise targets of the exercise body can be obtained from a plurality of different exercise load combined modes, so that the remarkable optimal exercise effect is achieved.
Drawings
Fig. 1 is a schematic diagram of the system method of the present application.
Fig. 2 is a schematic diagram of the motion load optimizing result of the present application, and the two optimizing overlapping areas give consideration to the absolute value of the motion effect and the motion balance optimization.
Detailed Description
The following examples illustrate the application in detail. In the following description of embodiments, for purposes of explanation and not limitation, specific details are set forth, such as particular system architectures, techniques, etc. in order to provide a thorough understanding of the embodiments of the application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in the present description and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
Example 1 moving object setting
Multi-dimensional exercise goals include weight loss, regional muscle gain, abdominal fat loss, leg fat loss, regional modeling, joint strengthening, regional strength enhancement, and the like. The method comprises the steps that an optional moving target range is set in a database of an intelligent moving load optimizing system, a multi-dimensional moving target is input on a screen keyboard of the optimizing system, and the moving targets are mutually orthogonal data sets, so that a multi-dimensional row vector a is formed T Storing data; for n groups of moving objects, row vector a T Is set to n corresponding to the dimension of (a), and the system controller calls the row vector a T Generating a row vector a T A peer-to-peer 1×n matrix a is stored as a moving target matrix; the moving target is a numerical parameter after quantization treatment; the quantization processing method is to take the absolute value of the difference between the effect value of the moving object and the corresponding existing value as the moving object value parameter.
Example 2 sports setting
Multi-dimensional sports include running, swimming, yoga, balancing appliances, strength appliances, dumbbell, barbell, regional muscle appliances, dead weight loads, athletic items, and the like.
1.2.1 popping up a motion item input keyboard on a screen keyboard of an optimization system, after the motion item is input, the system calls a multi-dimensional motion item efficiency value which is stored in a database and aims at a multi-dimensional motion object, and as different motion items have different effects on different motion objects, the efficiency value of different dimension motion items which aim at any dimension motion object is constructed into a column vector b to be stored;
1.2.2 for m groups of motion items, the dimension of the column vector b is correspondingly set as m for data storage;
further, for n groups of vectors B constructed by n groups of moving targets, distinguishing the n groups of vectors by using subscripts, wherein the sequence of the subscripts corresponds to that of the moving targets one by one, and n groups of column vectors with the subscripts are further constructed into an m multiplied by n order matrix B according to the sequence of the subscripts for storage.
Example 3 optimization matrix
The background of the system controller constructs an unknown 1×m-order matrix as an optimization matrix C, wherein the optimization matrix is set to meet the linear transformation A=C×B; the unit of any data in the optimization matrix is the motion duration.
EXAMPLE 4 limitation
Optimizing the Limiting condition setting of the matrix, automatically calling the storage data in the database by the system controller to automatically set the Range-span Limiting parameter and the transmission-span Limiting parameter, or allowing editing of the Range-span Limiting parameter and the transmission-span Limiting parameter in the automatic Limiting condition on the basis of the storage data of the system database; the Range-span Limiting mode is as follows: respectively setting the highest value and the lowest value which take the motion time length as a unit for m data of the optimized matrix, and recording as transmission-span Limiting; the transmission-span Limiting mode is as follows: respectively setting interval values taking the movement time length as a unit for m data of an optimization matrix in the Range of Range-span Limiting, and recording the interval values as Range-span Limiting; when the data in the optimization matrix changes, transition type change can only be carried out in a Range of Range-span Limiting by taking Transition-span Limiting as a unit, and continuous change cannot be carried out; wherein the end value of Range-span Limiting constitutes an integer multiple of the Transition-span Limiting interval value.
EXAMPLE 5 forced conditions
Setting forced conditions of the optimization matrix, and selecting 1-k groups of data in the optimization matrix as forced data by the system controller automatically or in a mode allowing editing of the automatic setting, wherein k is less than or equal to m/2; setting a forcing condition for each forcing data, automatically calling the storage data in a database by a system controller to automatically set a mustRange-span Limiting parameter and a mustTransmit-span Limiting parameter, or allowing editing of the mustRange-span Limiting parameter and the mustTransmit-span Limiting parameter in the forcing condition on the basis of the storage data of the system database; the mandatory mode of the MustRange-span Limiting is as follows: intercepting a small Range in the Range of Range-span Limiting as mustRange-span Limiting; when the data is intercepted, data points divided by Transition-span Limiting in the Range of Range-span Limiting are taken as end values; wherein, the mandatory mode of the MustTransmit-span Limiting is as follows: respectively setting interval values taking the motion time length as a unit for m data of the optimization matrix in a Range of Range-span Limiting, wherein the interval values are not more than transmission-span Limiting; wherein, mustTransmit-span Limiting is obtained by dividing Transmit-span Limiting by an integer within 1-10.
Meanwhile, the control of the motion load optimization complexity is performed by the following method: after the moving object and the moving item are selected, the limit condition range and the forced condition range of the optimization matrix enable the optimization complexity to exceed the upper limit, the moving object and the moving item are subjected to overrun alarm at first, then the limit condition and the forced condition are subjected to overrun alarm, and the complexity is reduced through adjustment of corresponding data. After the overrun alarm of the moving object and the moving item, if the complexity is reduced to be below the upper limit after the moving object and/or the moving item are reduced, setting is completed, and if the moving object and the moving item can not be reduced or the overrun alarm is still carried out after the reduction, the overrun alarm of the limiting condition and the forced condition is carried out; after the limit condition and the forced condition exceed the limit alarm, complexity is reduced by reducing the end value of Range-span Limiting and mustRange-span Limiting in the limit condition and the forced condition; or by increasing the interval value in transmission-span Limiting, musttransmission-span Limiting.
Example 6 companion matrix
Constructing an accompanying matrix X, diagonalizing n groups of data of the matrix A by the system, and storing the obtained n multiplied by n order matrix as the accompanying matrix X.
Example 7 Sport load optimization
The controller firstly carries out sequential assignment on the forced data in the MustRange-span Limiting according to interval values in the MustTransmit-span Limiting; then carrying out assignment of non-limiting data; for any group of the data which is assigned with the forced data, the rest data are assigned in sequence in Range-span Limiting according to the interval value in transmission-span Limiting; assigning a 1×n-order matrix a to each group of data by linear transformation calculation in embodiment 3; obtaining a companion matrix x for a 1×n-order matrix a by diagonal transformation in embodiment 6;
for the accompanying matrix X, on one hand, calculating the volume value of the accompanying matrix X, comparing the volume values of data assignments of different groups, and selecting the highest value of a specific proportion (the step is named as 1.7.5); on the other hand, calculating absolute values formed by the sum of absolute values on the diagonal of the accompanying matrix, comparing the absolute values of data assignment of different groups, and selecting the highest value of a specific proportion (the step is marked as 1.7.6); finally, comparing the data sets corresponding to the values screened in the steps 1.7.5 and 1.7.6, selecting common data, and presenting the motion items corresponding to the common data and the respective optimal motion durations thereof as optimized motion loads.
Example 8 alternate collaborative optimization expansion strategy
In example 7, the data sets corresponding to the values selected in step 1.7.5 and 1.7.6 are compared, if there is no shared data; returning to the steps 1.7.5 and 1.7.6, and alternately widening the selected ratio of the highest value in the two steps in equal ratio or different ratio based on the different steps; until the shared data is obtained in step 1.7.7;
when the selected proportion of the highest numerical value in the two steps is widened alternately in turn, if the balance of the movement is more emphasized, the step 1.7.6 is taken as an initial adjusted item; if the absolute value of the motion is more emphasized, step 1.7.5 is taken as the initial adjusted item.
Example 9 Co-entry collaborative optimization expansion strategy
In example 7, the data sets corresponding to the values selected in step 1.7.5 and 1.7.6 are compared, if there is no shared data; returning to steps 1.7.5 and 1.7.6, and expanding the selected ratio of the highest values of the two steps at the same ratio; until the shared data is obtained in step 1.7.7.
Example 10
The hardware implementation of the application can directly adopt the existing intelligent equipment, including but not limited to industrial computers, PCs, smart phones, handheld single machines, floor type single machines and the like. The input device preferably adopts a screen keyboard, the data storage and calculation module adopts an existing memory, a calculator and a controller, the internal communication module adopts an existing communication port and protocol, and the remote communication adopts an existing gprs network, an internet and the like.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (10)
1. A method of motion load optimization, the method comprising the steps of:
1.1 moving object setting:
according to the range of the selectable moving object, a multi-dimensional moving object is input, and the moving objects are mutually orthogonal numbersFrom the group, a multidimensional row vector a is formed T Storing data; for n groups of moving objects, row vector a T Corresponding to the dimension of n, invoking row vector a T Generating a row vector a T A peer-to-peer 1×n matrix a is stored as a moving target matrix;
the selectable range of motion goals may include weight loss, regional muscle gain, abdominal fat loss, leg fat loss, regional modeling, joint strengthening, and regional strength enhancement;
1.2 sports item setting:
1.2.1, after a motion item is input, invoking a multi-dimensional motion item efficiency value which is stored in a database and aims at a multi-dimensional motion object, and constructing the efficiency value of different dimension motion items aiming at any dimension motion object into a column vector b for storage; the athletic items may include running, swimming, yoga, balancing appliances, strength appliances, dumbbell, barbell, regional muscle appliances, dead weight loads, and athletic items;
1.2.2 for m groups of motion items, the dimension of the column vector b is correspondingly set as m for data storage; for n groups of vectors B constructed by n groups of moving targets, distinguishing the n groups of vectors by using subscripts, wherein the sequence of the subscripts corresponds to that of the moving targets one by one, and n groups of column vectors with the subscripts are constructed into an m multiplied by n order matrix B according to the sequence of the subscripts for storage;
1.3 constructing a 1×m-order matrix as an optimization matrix C, the optimization matrix being set to satisfy the linear transformation a=c×b;
1.4 constraint setting of optimization matrix:
automatically calling stored data in a database to automatically set a Range-span Limiting parameter and a transmission-span Limiting parameter, or allowing editing of the Range-span Limiting parameter and the transmission-span Limiting parameter in the automatically set Limiting conditions on the basis of the stored data in the database;
1.4.1 wherein, the Range-span Limiting mode is: respectively setting the highest value and the lowest value which take the motion time length as a unit for m data of the optimized matrix, and recording as Range-span Limiting;
1.4.2 wherein the transmission-span Limiting is defined in the following manner: respectively setting interval values taking the movement time length as a unit for m data of the optimization matrix in the Range of Range-span Limiting, and recording as transmission-span Limiting; when the data in the optimization matrix changes, transition type change can only be carried out in a Range of Range-span Limiting by taking Transition-span Limiting as a unit, and continuous change cannot be carried out;
1.5 mandatory condition setting for optimization matrix:
selecting 1-k groups of data in the optimization matrix as forced data, wherein k is less than or equal to m/2; setting a forcing condition for each forcing data, automatically calling the storage data in the database to automatically set a mustRange-span Limiting parameter and a mustTransmit-span Limiting parameter, or allowing editing of the mustRange-span Limiting parameter and the mustTransmit-span Limiting parameter in the automatic setting forcing condition on the basis of the storage data of the database;
1.5.1 wherein the mandatory way of mustRange-span Limiting is: intercepting a small Range in the Range of Range-span Limiting as mustRange-span Limiting; when the data is intercepted, data points divided by Transition-span Limiting in the Range of Range-span Limiting are taken as end values;
1.5.2 wherein the mandatory way of MustTransition-span Limiting is: respectively setting interval values taking the motion time length as a unit for m data of an optimization matrix in a Range of Range-span Limiting, wherein the interval values are not more than the transmission-span Limiting;
1.6 construction of the companion matrix X:
diagonalizing n groups of data of the matrix A, and storing the obtained n multiplied by n order matrix as an accompanying matrix X;
1.7 motion load optimization:
1.7.1 firstly, sequentially assigning the forced data in the MustRange-span Limiting according to interval values in the MustTransition-span Limiting; then carrying out assignment of non-limiting data;
1.7.2, for any group of the forced data which are already assigned, sequentially assigning values to the rest data in Range-span Limiting according to interval values in transmission-span Limiting;
1.7.3 assigning values to each group of data, and obtaining a 1 Xn-order matrix A by linear transformation calculation in the step 1.3;
1.7.4 obtaining a companion matrix x for the 1×n-order matrix a by diagonal transformation in step 1.6;
1.7.5 for the accompanying matrix X, calculating the volume value of the accompanying matrix X, comparing the volume values of data assignments of different groups, and selecting the highest value of a specific proportion;
1.7.6 for the accompanying matrix X, calculating absolute values formed by the sum of absolute values on diagonal lines of the accompanying matrix X, comparing absolute values of data assignment of different groups, and selecting the highest value of a specific proportion;
1.7.7 the data sets corresponding to the values screened in steps 1.7.5 and 1.7.6 are compared, common data are selected, the motion items corresponding to the common data and their respective optimal motion durations are used as optimized motion loads, and the optimized motion loads can be presented through the terminal.
2. The method as claimed in claim 1, wherein: in step 1.7.7, the data sets corresponding to the values selected in step 1.7.5 and step 1.7.6 are compared, if there is no shared data; returning to steps 1.7.5 and 1.7.6, and expanding the selected ratio of the highest values of the two steps at the same ratio; until the shared data is obtained in step 1.7.7.
3. The method as claimed in claim 1, wherein: in step 1.7.7, the data sets corresponding to the values selected in step 1.7.5 and step 1.7.6 are compared, if there is no shared data; returning to the steps 1.7.5 and 1.7.6, and alternately widening the selected ratio of the highest value in the two steps in equal ratio or different ratio based on the different steps; until the shared data is obtained in step 1.7.7.
4. A method as claimed in claim 3, wherein: step 1.7.5 or step 1.7.6 serve as the initial adjusted item when the selected proportion of the highest value of the two steps is alternately widened in turn.
5. The method as claimed in claim 1, wherein: in step 1.7.7, the data sets corresponding to the values selected in step 1.7.5 and step 1.7.6 are compared, if there is no shared data; returning to step 1.7.5 or step 1.7.6, widening the selected proportion of the highest value in this step; until the shared data is obtained in step 1.7.7.
The method as claimed in claim 1, wherein: in step 1.7.7, the data sets corresponding to the values selected in step 1.7.5 and step 1.7.6 are compared, if there is no shared data; returning to widen the selected proportion of the highest value in the step; until the shared data is obtained in step 1.7.7.
6. A method according to any preceding claim, characterized by: in step 1.1, the moving target is a numerical parameter after quantization processing; the quantization processing method may be to use the absolute value of the difference between the effect value of the moving object and the corresponding existing value as the moving object value parameter.
7. The method as claimed in claim 1, wherein: in step 1.3, the unit of any data in the optimization matrix is the motion duration; independently, in the step 1.4, the end value of the Range-span Limiting forms an integral multiple of the transmission-span Limiting interval value; independently, in step 1.5, the MustTransition-span Limiting is obtained by dividing the transformation-span Limiting by an integer within 1-10.
8. The method according to any one of claims 1-7, wherein in steps 1.4 and 1.5, the control of the motion load optimization complexity is performed by: after a moving object and a moving item are selected, the limit condition range and the forced condition range of the matrix are optimized to enable the optimization complexity to exceed an upper limit, firstly, the moving object and the moving item are subjected to overrun alarm, then the limit condition and the forced condition are subjected to overrun alarm, and the complexity is reduced through adjustment of corresponding data; after the overrun alarm of the moving object and the moving item, if the complexity is reduced to be below the upper limit after the moving object and/or the moving item are reduced, setting is completed, and if the moving object and the moving item can not be reduced or the overrun alarm is still carried out after the reduction, the overrun alarm of the limiting condition and the forced condition is carried out; after the limit condition and the forced condition exceed the limit alarm, complexity is reduced by reducing the end value of Range-span Limiting and mustRange-span Limiting in the limit condition and the forced condition; or by increasing the interval value in transmission-span Limiting, musttransmission-span Limiting.
9. The method of any one of claims 1-7, wherein: in steps 1.7.5 and 1.7.6, the specific ratio is 1% -50%, the determination and adjustment of the specific ratio can be performed by back-pushing by dividing the number of the presented values by the total calculated amount, and the number of the presented values can be 3-30; independently optionally, in step 1.7.7, the terminal presents as a screen display presentation, a voice broadcast, sending an electronic document report over a wireless network, or presenting a paper document report by printing.
10. A sports load optimizing system based on the method of any preceding claim.
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