CN117594192A - Outdoor fitness equipment service system combined with sports prescriptions - Google Patents
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Abstract
The invention discloses an outdoor fitness equipment service system combined with a sports prescription, which comprises a sports prescription acquisition module, a data cleaning module, an adaptive negative learning module and a sports prescription implementation efficiency evaluation module. The invention belongs to the technical field of data processing, and particularly relates to an outdoor fitness equipment service system combined with a sports prescription, which is used for judging the prediction confidence of a sample based on a confidence threshold value, reasonably evaluating the vulnerability rate of the sample based on noise rate estimation, accurately pruning the noise sample based on pruning rate, and improving model learning efficiency and prediction effect; based on design objective function, model complexity and generalization capability are considered, approximate gradient is calculated based on disturbance, efficiency and stability of the model are improved, parameter updating strategies are adjusted based on defined momentum and learning rate, and model training effect is optimized.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an outdoor fitness equipment service system combined with a sports prescription.
Background
The outdoor fitness equipment service system combined with the exercise prescription is based on the outdoor fitness equipment, aims at implementing the effect of the exercise prescription, and develops a set of service system to ensure good exercise effect of users; however, the problems of noise interference and poor accuracy of the prediction result of the follow-up model caused by improper data cleaning exist in general data; the general processing model has the problem that the model is over-fitted, and the gradient disappears or the gradient explodes to cause poor stability of the model.
Disclosure of Invention
Aiming at the situation, in order to overcome the defects of the prior art, the invention provides an outdoor fitness equipment service system combined with a sports prescription, aiming at the problems that noise interference exists in general data and the accuracy of a follow-up model prediction result is poor due to improper data cleaning, the scheme judges the prediction confidence of a sample based on a confidence threshold, reasonably evaluates the vulnerability rate of the sample based on noise rate estimation, accurately cuts out the noise sample based on pruning rate, and improves model learning efficiency and prediction effect; aiming at the problem that the model stability is poor due to model overfitting and gradient disappearance or gradient explosion of a general data processing model, the model training effect is optimized by considering the complexity and generalization capability of the model based on a design objective function, calculating an approximate gradient based on disturbance, improving the efficiency and stability of the model and adjusting a parameter updating strategy based on defined momentum and learning rate.
The technical scheme adopted by the invention is as follows: the invention provides an outdoor fitness equipment service system combined with a sports prescription, which comprises a sports prescription acquisition module, a data cleaning module, an adaptive negative learning module and a sports prescription implementation efficiency evaluation module, wherein the data acquisition module is used for acquiring the sports prescription;
the exercise prescription acquisition module acquires exercise prescription information, medical examination, exercise risk screening and system test results of users, prescribes frequency, intensity, time, mode, total exercise amount and step of exercise, forms an exercise guidance scheme with definite purposes, systemicity and individuation health promotion and disease prevention and treatment, and sends data to the data cleaning module;
the data acquisition module acquires exercise data of a user, personal body data and actual exercise prescription implementation efficiency after exercise based on outdoor exercise equipment, and sends the data to the data cleaning module;
the data cleaning module calculates noise rate estimation by defining a threshold value so as to obtain pruning rate, completes pruning based on the pruning rate, and sends data to the adaptive negative learning module;
the adaptive negative learning module calculates approximate gradients based on disturbance so as to update parameters of the neural network, completes the design of the neural network based on a defined loss function and an objective function, and sends data to the exercise prescription implementation efficiency evaluation module;
the athletic prescription performance evaluation module predicts an athletic prescription performance evaluation based on the trained model.
Further, in the data acquisition module, the user motion data comprises frequency, intensity, time, mode and total motion amount of motion; the personal body data includes height, weight, age, gender, body fat, and muscle content; taking the collected data as sample data and taking the actual exercise prescription implementation efficacy as a data real label.
Further, the data cleaning module specifically includes the following:
the threshold is defined using the formula:
；
；
in LB s=1 Is the confidence sample lower bound, UB, for tag 1 s=0 Is the upper bound of confidence sample with label 0, P s=1 Is the number of samples with a neural network predictive label of 1, N s=1 And N s=0 The number of samples with labels of 1 and 0 at the time of acquisition, and P () is a conditional probability; e (E) x∈P [g(x)]The expected value of the neural network prediction confidence is the average prediction confidence of the positive class samples for the samples x in the sample set P, P is the positive class sample set, and g (x) is the sample confidence; e (E) x∈N [g(x)]Is the expected value of the neural network prediction confidence, i.e., the average prediction confidence for the negative class samples, for sample x in sample set N, N is the negative class sample set,is an equivalent symbol;
the noise rate estimate is calculated using the following formula:
；
；
wherein ρ is 1 And ρ 0 Sample noise rates, P, of tag 1 and tag 0, respectively s=0 Is the number of samples for which the predictive label is 0;
the pruning rate is calculated by the following formula
；
；
Wherein beta is 1 And beta 0 Pruning rates, P, of label 1 and label 0, respectively y1 Is a number ofA percentage of samples in which the label y is 1 in the dataset;
pruning, based on pruning rate, deleting the sample with the smallest confidence in the positive sample set and the sample with the largest confidence in the negative sample set.
Further, the adaptive negative learning module is used for constructing a neural network, and specifically comprises the following contents:
the loss function is defined using the formula:
；
where L () is the loss function, f is the neural network map, y is the real label,is a predictive tag; c is the tag class, k is the class index, p k Is the prediction probability of the neural network to the category k, y k Is the value of the kth category in the true tag vector,is the value of the kth class in the predicted tag vector;
an objective function is defined using the following formula:
；
where λ is the superparameter of weight decay, ρ is the superparameter of sharpness perception,is the norm, ω is the neural network parameter, ε is the variable of the disturbance;
the approximate gradient is calculated based on the perturbation using the following formula:
；
in the method, in the process of the invention,is the gradient of the parameters of the neural network,is based on the original gradient to introduce disturbance at parameter omegaTime gradient;
updating parameters by using the following formula:
；
；
wherein omega is t+1 And omega t The parameters of the neural network at the t+1st training and the t training respectively,is the learning rate, s t Is momentum, T is the maximum number of exercises, beta is the momentum decay coefficient,is the rate of change of the parameter.
Further, the exercise prescription implementation efficiency evaluation module is used for cleaning data by the data cleaning module and training a model by the adaptive negative learning module based on the sample data; after model training is completed, the exercise prescriptions, exercise data and personal body data of the user are collected in real time, and predicted exercise prescriptions are output to implement efficiency evaluation, so that the user is helped to adapt gradually and obtain better exercise effects, and excessive monotonous exercise and stagnation are avoided.
By adopting the scheme, the beneficial effects obtained by the invention are as follows:
(1) Aiming at the problems that noise interference exists in general data and the accuracy of a follow-up model prediction result is poor due to improper data cleaning, the scheme judges the prediction confidence of the sample based on a confidence threshold, reasonably evaluates the vulnerability rate of the sample based on noise rate estimation, accurately cuts out the noise sample based on pruning rate, and improves model learning efficiency and prediction effect.
(2) Aiming at the problem that the model stability is poor due to model overfitting and gradient disappearance or gradient explosion of a general processing model, the model training effect is optimized by considering the complexity and generalization capability of the model based on a design objective function, calculating an approximate gradient based on disturbance, improving the efficiency and stability of the model and adjusting a parameter updating strategy based on defined momentum and learning rate.
Drawings
FIG. 1 is a schematic flow chart of an outdoor fitness equipment service system combined with a sports prescription according to the present invention;
FIG. 2 is a schematic flow chart of a data cleaning module;
FIG. 3 is a flow chart of the adaptive negative learning module.
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "top," "bottom," "inner," "outer," and the like indicate orientation or positional relationships based on those shown in the drawings, merely to facilitate description of the invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the invention.
Referring to fig. 1, the outdoor fitness equipment service system combined with a sports prescription provided by the invention comprises a sports prescription acquisition module, a data cleaning module, an adaptive negative learning module and a sports prescription implementation efficiency evaluation module;
the exercise prescription acquisition module acquires exercise prescription information, medical examination, exercise risk screening and system test results of users, prescribes frequency, intensity, time, mode, total exercise amount and step of exercise, forms an exercise guidance scheme with definite purposes, systemicity and individuation health promotion and disease prevention and treatment, and sends data to the data cleaning module;
the data acquisition module acquires exercise data of a user, personal body data and actual exercise prescription implementation efficiency after exercise based on outdoor exercise equipment, and sends the data to the data cleaning module;
the data cleaning module calculates noise rate estimation by defining a threshold value so as to obtain pruning rate, completes pruning based on the pruning rate, and sends data to the adaptive negative learning module;
the adaptive negative learning module calculates approximate gradients based on disturbance so as to update parameters of the neural network, completes the design of the neural network based on a defined loss function and an objective function, and sends data to the exercise prescription implementation efficiency evaluation module;
the athletic prescription performance evaluation module predicts an athletic prescription performance evaluation based on the trained model.
Second embodiment referring to fig. 1, the embodiment is based on the above embodiment, in the data acquisition module, the user motion data includes frequency, intensity, time, mode and total motion amount of the motion; the personal body data includes height, weight, age, gender, body fat, and muscle content; taking the collected data as sample data and taking the actual exercise prescription implementation efficacy as a data real label.
Referring to fig. 1 and 2, the data cleansing module according to the above embodiment specifically includes the following:
the threshold is defined using the formula:
；
；
in LB s=1 Is the confidence sample lower bound, UB, for tag 1 s=0 Is the upper bound of confidence sample with label 0, P s=1 Is the number of samples with a neural network predictive label of 1, N s=1 And N s=0 The number of samples with labels of 1 and 0 at the time of acquisition, and P () is a conditional probability; e (E) x∈P [g(x)]The expected value of the neural network prediction confidence is the average prediction confidence of the positive class samples for the samples x in the sample set P, P is the positive class sample set, and g (x) is the sample confidence; e (E) x∈N [g(x)]Is the expected value of the neural network prediction confidence, i.e., the average prediction confidence for the negative class samples, for sample x in sample set N, N is the negative class sample set,is an equivalent symbol;
the noise rate estimate is calculated using the following formula:
；
；
wherein ρ is 1 And ρ 0 Sample noise rates, P, of tag 1 and tag 0, respectively s=0 Is the number of samples for which the predictive label is 0;
the pruning rate is calculated by the following formula
；
；
Wherein beta is 1 And beta 0 Pruning rates, P, of label 1 and label 0, respectively y1 Is the percentage of samples in the dataset for which the label y is 1;
pruning, based on pruning rate, deleting the sample with the smallest confidence in the positive sample set and the sample with the largest confidence in the negative sample set.
By executing the operation, the method and the device for model prediction based on the confidence threshold judge the prediction confidence of the sample, reasonably evaluate the vulnerability rate of the sample based on noise rate estimation, accurately prune the noise sample based on pruning rate and improve model learning efficiency and prediction effect aiming at the problem that the accuracy of a follow-up model prediction result is poor due to noise interference and improper data cleaning of general data.
In a fourth embodiment, referring to fig. 1 and 3, the adaptive negative learning module is configured to construct a neural network based on the above embodiment, and specifically includes the following:
the loss function is defined using the formula:
；
where L () is the loss function, f is the neural network map, y is the real label,is a predictive tag; c is the tag class, k is the class index, p k Is the prediction probability of the neural network to the category k, y k Is the value of the kth category in the true tag vector,is the value of the kth class in the predicted tag vector;
an objective function is defined using the following formula:
；
where λ is the superparameter of weight decay, ρ is the superparameter of sharpness perception,is the norm, ω is the neural network parameter, ε is the variable of the disturbance;
the approximate gradient is calculated based on the perturbation using the following formula:
；
in the method, in the process of the invention,is the gradient of the parameters of the neural network,is based on the original gradient to introduce disturbance at parameter omegaTime gradient;
updating parameters by using the following formula:
；
；
wherein omega is t+1 And omega t The parameters of the neural network at the t+1st training and the t training respectively,is the learning rate, s t Is momentum, T is the maximum number of exercises, beta is the momentum decay coefficient,is the rate of change of the parameter.
By executing the operations, the problem that the model stability is poor due to model overfitting, gradient disappearance or gradient explosion of a general data processing model is solved.
Fifth, referring to fig. 1, the exercise prescription implementation performance evaluation module trains a model by using the adaptive negative learning module while cleaning data by using the data cleaning module based on sample data according to the above embodiment; after model training is completed, the exercise prescriptions, exercise data and personal body data of the user are collected in real time, and predicted exercise prescriptions are output to implement efficiency evaluation, so that the user is helped to adapt gradually and obtain better exercise effects, and excessive monotonous exercise and stagnation are avoided.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
The invention and its embodiments have been described above with no limitation, and the actual construction is not limited to the embodiments of the invention as shown in the drawings. In summary, if one of ordinary skill in the art is informed by this disclosure, a structural manner and an embodiment similar to the technical solution should not be creatively devised without departing from the gist of the present invention.
Claims (5)
1. An outdoor fitness equipment service system in combination with a sports prescription, characterized by: the system comprises a sport prescription acquisition module, a data cleaning module, an adaptive negative learning module and a sport prescription implementation efficiency evaluation module;
the exercise prescription acquisition module acquires exercise prescription information, medical examination, exercise risk screening and system test results of users, prescribes frequency, intensity, time, mode, total exercise amount and step of exercise, forms an exercise guidance scheme with definite purposes, systemicity and individuation health promotion and disease prevention and treatment, and sends data to the data cleaning module;
the data acquisition module acquires exercise data of a user, personal body data and actual exercise prescription implementation efficiency after exercise based on outdoor exercise equipment, and sends the data to the data cleaning module;
the data cleaning module calculates noise rate estimation by defining a threshold value so as to obtain pruning rate, completes pruning based on the pruning rate, and sends data to the adaptive negative learning module;
the adaptive negative learning module calculates approximate gradients based on disturbance so as to update parameters of the neural network, completes the design of the neural network based on a defined loss function and an objective function, and sends data to the exercise prescription implementation efficiency evaluation module;
the athletic prescription performance evaluation module predicts an athletic prescription performance evaluation based on the trained model.
2. An outdoor fitness equipment service system in combination with a sports prescription as claimed in claim 1 wherein: the adaptive negative learning module is used for constructing a neural network and specifically comprises the following contents:
the loss function is defined using the formula:
；
where L () is the loss function, f is the neural network map, y is the real label,is a predictive tag; c is the tag class, k is the class index, p k Is the prediction probability of the neural network to the category k, y k Is the value of the kth category in the true tag vector,/->Is the value of the kth class in the predicted tag vector;
an objective function is defined using the following formula:
；
where λ is the superparameter of weight decay, ρ is the superparameter of sharpness perception,is the norm, ω is the neural network parameter, ε is the variable of the disturbance;
the approximate gradient is calculated based on the perturbation using the following formula:
；
in the method, in the process of the invention,is the gradient of the neural network parameters, +.>Is based on the original gradient to introduce a disturbance at the parameter ω>Time gradient;
updating parameters by using the following formula:
；
；
wherein omega is t+1 And omega t The parameters of the neural network at the t+1st training and the t training respectively,is the learning rate, s t Is momentum, T is maximum training times, beta is momentum decay coefficient, +.>Is the rate of change of the parameter.
3. An outdoor fitness equipment service system in combination with a sports prescription as claimed in claim 1 wherein: the data cleaning module specifically comprises the following contents:
the threshold is defined using the formula:
；
；
in LB s=1 Is the confidence sample lower bound, UB, for tag 1 s=0 Is the upper bound of confidence sample with label 0, P s=1 Is the number of samples with a neural network predictive label of 1，N s=1 And N s=0 The number of samples with labels of 1 and 0 at the time of acquisition, and P () is a conditional probability; e (E) x∈P [g(x)]The expected value of the neural network prediction confidence is the average prediction confidence of the positive class samples for the samples x in the sample set P, P is the positive class sample set, and g (x) is the sample confidence; e (E) x∈N [g(x)]Is the expected value of the neural network prediction confidence, i.e., the average prediction confidence for the negative class samples, for sample x in sample set N, N is the negative class sample set,is an equivalent symbol;
the noise rate estimate is calculated using the following formula:
；
；
wherein ρ is 1 And ρ 0 Sample noise rates, P, of tag 1 and tag 0, respectively s=0 Is the number of samples for which the predictive label is 0;
the pruning rate is calculated by the following formula
；
；
Wherein beta is 1 And beta 0 Pruning rates, P, of label 1 and label 0, respectively y1 Is the percentage of samples in the dataset for which the label y is 1;
pruning, based on pruning rate, deleting the sample with the smallest confidence in the positive sample set and the sample with the largest confidence in the negative sample set.
4. An outdoor fitness equipment service system in combination with a sports prescription as claimed in claim 1 wherein: in the data acquisition module, the user motion data comprise the frequency, intensity, time, mode and total motion quantity of motion; the personal body data includes height, weight, age, gender, body fat, and muscle content; taking the collected data as sample data and taking the actual exercise prescription implementation efficacy as a data real label.
5. An outdoor fitness equipment service system in combination with a sports prescription as claimed in claim 1 wherein: the exercise prescription implementation efficiency evaluation module is used for training a model by utilizing the adaptive negative learning module while cleaning data by utilizing the data cleaning module based on sample data; after model training is completed, the exercise prescriptions, exercise data and personal body data of the user are collected in real time, and predicted exercise prescriptions are output to implement efficiency evaluation, so that the user is helped to adapt gradually and obtain better exercise effects, and excessive monotonous exercise and stagnation are avoided.
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