CN116403670A - Intelligent monitoring management method and system for postoperative care training - Google Patents
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Abstract
The disclosure provides an intelligent monitoring management method and system for postoperative care training, and relates to the technical field of postoperative care training, the method comprises the following steps: acquiring a preset training data set of a first user; performing staged division and outputting a training data set of a preset stage; collecting a real-time training data set of the first user, wherein the real-time training data set comprises a real-time muscle strength data set for postoperative training and a real-time sensing data set of equipment; generating a stage monitoring model; inputting the real-time muscle force data set and the equipment real-time sensing data set into the stage monitoring model for analysis, and outputting real-time stage completion; when the real-time stage completion is greater than the preset stage completion, a stage change instruction is sent to the stage monitoring model, so that the technical problems of insufficient monitoring accuracy and poor monitoring management effect of postoperative care training of a user in the prior art are solved, and the technical effect of improving the monitoring management accuracy is achieved.
Description
Technical Field
The disclosure relates to the technical field of postoperative care training, in particular to an intelligent monitoring management method and system for postoperative care training.
Background
The patient can carry out corresponding postoperative training and resume after the operation treatment, and postoperative training resumes and has important influence to operation treatment effect and patient's health recovery situation, and traditional postoperative training often monitors through the manual work, along with the development of science and technology, intelligent monitoring also is applied to in the postoperative training.
At present, the technical problems of insufficient monitoring accuracy and poor monitoring management effect of postoperative care training of a user exist in the prior art.
Disclosure of Invention
The disclosure provides an intelligent monitoring management method and system for postoperative care training, which are used for solving the technical problems of insufficient monitoring accuracy and poor monitoring management effect of the postoperative care training of a user in the prior art.
According to a first aspect of the present disclosure, there is provided an intelligent monitoring management method for postoperative care training, including: acquiring a preset training data set of a first user; performing stepwise division according to the change characteristics of the preset training data set, and outputting the preset training data set; collecting a real-time training data set of the first user, wherein the real-time training data set comprises a real-time muscle strength data set for postoperative training and a real-time sensing data set of equipment of the first user using the first training equipment; generating a stage monitoring model according to the preset stage training data set; inputting the real-time muscle force data set and the equipment real-time sensing data set into the stage monitoring model for analysis, and outputting real-time stage completion; and when the real-time stage completion is greater than the preset stage completion, sending a stage change instruction to the stage monitoring model, wherein the stage change instruction is used for controlling the monitoring parameters of the stage monitoring model to be changed into the monitoring parameters of the next stage.
According to a second aspect of the present disclosure, there is provided an intelligent monitoring management system for post-operative care training, comprising: the system comprises a preset training data set acquisition module, a first user acquisition module and a second user acquisition module, wherein the preset training data set acquisition module is used for acquiring a preset training data set of the first user; the phase division module is used for carrying out phase division according to the change characteristics of the preset training data set and outputting the preset phase training data set; the real-time training data set acquisition module is used for acquiring a real-time training data set of the first user, wherein the real-time training data set comprises a real-time muscle strength data set for postoperative training and a real-time sensing data set of equipment of the first user using the first training equipment; the phase monitoring model generation module is used for generating a phase monitoring model according to the preset phase training data set; the stage completion degree output module is used for inputting the real-time muscle force data set and the equipment real-time sensing data set into the stage monitoring model for analysis and outputting real-time stage completion degree; and the monitoring parameter changing module is used for sending a stage changing instruction to the stage monitoring model when the real-time stage completion degree is larger than the preset stage completion degree, and controlling the monitoring parameter of the stage monitoring model to be changed into the monitoring parameter of the next stage.
According to the intelligent monitoring management method for postoperative care training, the method comprises the steps of firstly, carrying out stepwise division according to the change characteristics of a preset training data set, outputting the preset training data set, collecting a real-time muscle strength data set of a first user and a real-time sensing data set of equipment, inputting the real-time muscle strength data set and the real-time sensing data set into a stage monitoring model for analysis, outputting the real-time stage completion degree, judging whether to carry out training monitoring of the next stage according to the real-time stage completion degree, achieving the purpose of improving the monitoring management accuracy of postoperative care training, improving the monitoring management effect, and providing auxiliary technical effects for postoperative rehabilitation training.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
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For a clearer description of the present disclosure or of the prior art, the drawings that are required to be used in the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are merely illustrative and that other drawings may be obtained, without inventive effort, by a person skilled in the art from the drawings provided.
Fig. 1 is a schematic flow chart of an intelligent monitoring management method for postoperative care training according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of inputting a real-time muscle force dataset and a real-time device sensory dataset into a phase monitoring model in an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart of outputting training quality indicators in an embodiment of the disclosure;
fig. 4 is a schematic structural diagram of an intelligent monitoring management system for postoperative care training according to an embodiment of the present disclosure.
Reference numerals illustrate: the system comprises a preset training data set acquisition module 11, a stepwise dividing module 12, a real-time training data set acquisition module 13, a stage monitoring model generation module 14, a stage completion degree output module 15 and a monitoring parameter changing module 16.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In order to solve the technical problems of insufficient monitoring accuracy and poor monitoring management effect of postoperative care training of a user in the prior art, the inventor of the present disclosure obtains the intelligent monitoring management method and system of the postoperative care training through creative labor.
Example 1
Fig. 1 is a diagram of an intelligent monitoring management method for postoperative care training, which is provided by an embodiment of the present disclosure, where the method is applied to a training data monitoring system, and the system is communicatively connected to a first training device, as shown in fig. 1, and the method includes:
step S100: acquiring a preset training data set of a first user;
the training data monitoring system is a system platform for performing intelligent monitoring management on postoperative training of a user, the first training device is a sensor for assisting training to acquire postoperative muscle recovery conditions of the user, such as a hand holding sensor, a leg movement sensor and the like, the type of the first training device is determined according to the surgical site of the user and the type of postoperative training data in combination with actual conditions, no limitation is made here, and the training data monitoring system is in communication connection with the first training device, so that interactive transmission of information can be realized.
Specifically, the first user refers to any user who needs to perform postoperative care training, the preset training data set is a training data set pre-designated according to the operation condition of the first user, in short, after the first user completes the operation, medical staff formulates organism training recovery data under different periods for the first user, for example, the muscle strength data of the first user is far lower than a normal level in the early stage of postoperative care training, the muscle strength data slowly rises until the normal level is reached along with the change of training time, and the preset training data set comprises requirements of different periods on the organism training recovery data.
Step S200: performing stepwise division according to the change characteristics of the preset training data set, and outputting the preset training data set;
specifically, the preset training data set includes requirements of different periods on the body training recovery data, that is, the body training recovery data dynamically changes along with the migration of training nursing time until the user is completely recovered, the training data can reach a normal level, based on the requirements, the data in the preset training data set is subjected to change feature analysis, the training data in the same level in the preset training data set are divided together to form a preset stage training data set, and the preset stage training data set comprises training data sets of a plurality of different training stages.
Step S300: collecting a real-time training data set of the first user, wherein the real-time training data set comprises a real-time muscle strength data set for postoperative training and a real-time sensing data set of equipment of the first user using the first training equipment;
specifically, the real-time training data set refers to current training data of a user, and comprises a real-time muscle strength data set for postoperative training and a real-time sensing data set of first user equipment using first training equipment, wherein the real-time muscle strength data set refers to strength information of muscle contraction when limbs of the user do random exercises, and is generally obtained by checking the first user by a nursing staff, and can also be obtained by a muscle strength test instrument. The device real-time sensing data set refers to data, such as grip strength conditions, leg movement conditions and the like, of a first user, which are automatically collected by the first training device when the first user uses the first training device.
Step S400: generating a stage monitoring model according to the preset stage training data set;
specifically, the stage monitoring model is a functional model for monitoring the first user in stages according to a preset stage training data set, in short, the preset stage training data set includes training data of a plurality of different training stages, and the training data in the preset stage training data set can be used as target data of each training stage, that is, only real-time training data of each training stage reaches the training data in the preset stage training data set, which indicates that the stage training is completed. According to the preset stage training data set, a stage monitoring model is generated, and the stage monitoring model is used for monitoring each training stage of the first user, namely, training data of each stage in the preset stage training data set is used as monitoring parameters for monitoring comparison, real-time monitoring data and training data of each stage in the preset stage training data set are compared and identified through inputting the real-time training data into the stage monitoring model, real-time training conditions of each stage are monitored, and postoperative care training conditions under different stages are obtained.
Step S500: inputting the real-time muscle force data set and the equipment real-time sensing data set into the stage monitoring model for analysis, and outputting real-time stage completion;
as shown in fig. 2, step S500 of the embodiment of the disclosure further includes:
step S510: performing curve drawing on the real-time muscle force data set and the real-time sensing data set of the equipment to obtain a muscle force change curve and a sensing change curve;
step S520: performing stability analysis on the muscle strength change curve and the sensing change curve, and outputting a stability index of the muscle strength change curve and a stability index of the sensing change curve;
step S530: and inputting the real-time muscle force data set and the real-time equipment sensing data set into the stage monitoring model when the stability index of the muscle force change curve and the stability index of the sensing change curve are both larger than a preset stability index.
Specifically, firstly, according to the training stage where the first user is currently located, preset training data of a corresponding training stage can be extracted from training data of each stage in a preset stage training data set, the training data is used as monitoring parameters of a stage monitoring model, a real-time muscle force data set and a real-time equipment sensing data set are input into the stage monitoring model, the monitoring parameters and the real-time muscle force data set and the real-time equipment sensing data set are compared and analyzed through the stage monitoring model, and the recovery state of the current training stage of the first user is judged according to comparison and analysis results, namely, the smaller the difference between the monitoring parameters and the real-time muscle force data set and the real-time equipment sensing data set is, the better the recovery state is, namely, the higher the corresponding real-time stage completion degree is, and the real-time stage completion degree is between 0% and 100%.
Specifically, before the real-time muscle force data set and the real-time sensing data set of the device are input into the stage monitoring model for analysis, the real-time muscle force data set and the real-time sensing data set need to be analyzed, and the analysis process is as follows: the time is taken as the abscissa, the real-time muscle force data and the real-time sensing data of the equipment are taken as the ordinate, so that the real-time muscle force data set and the real-time sensing data set are subjected to curve drawing to obtain a muscle force change curve and a sensing change curve, stability analysis is further carried out on the muscle force change curve and the sensing change curve respectively, the stability is not that the real-time muscle force data set and the real-time sensing data set of the equipment are required to maintain a state value unchanged, the real-time muscle force data set and the real-time sensing data set of the equipment are required to be changed in real time in the postoperative training process of a user, the stability analysis is used for analyzing whether the change of the real-time muscle force data set and the real-time sensing data set of the equipment is stable or not, for example, the muscle force change curve and the sensing change curve are steadily increased according to a certain rule, the stability is higher, the corresponding stability index is also higher, the stability index is used for reflecting the change stability of real-time muscle force data and real-time sensing data of equipment in a certain time, a plurality of points can be found on a muscle force change curve and a sensing change curve, the change rate between any two adjacent points is calculated, so that a plurality of change rates are obtained, standard deviations of the change rates are further calculated to be respectively used as the stability indexes of the muscle force change curve and the sensing change curve, the stability indexes of the muscle force change curve and the stability indexes of the sensing change curve are output, a preset stability index is further set by a nursing staff according to actual conditions, the stability indexes of the muscle force change curve and the sensing change curve are further compared with the preset stability indexes, and when the stability indexes of the muscle force change curve and the sensing change curve are both larger than the preset stability indexes, and inputting the real-time muscle force data set and the real-time equipment sensing data set into a stage monitoring model for postoperative training monitoring. The stability analysis is carried out on the real-time muscle force data set and the real-time equipment sensing data set, so that the initial judgment of the postoperative training condition of the first user is facilitated, if the stability indexes of the real-time muscle force data set and the real-time equipment sensing data set cannot reach the preset stability index, the fact that the first user has problems in postoperative training, unstable recovery conditions, the continuous postoperative training monitoring is meaningless, the unpredictable consequences can be caused, and the judgment of the stability index is facilitated, so that the follow-up postoperative training monitoring is carried out under the condition that the stability index reaches the preset stability index; under the condition that the stability index does not reach the preset stability index, the first user is assisted by a nursing staff to check in time, so that more serious consequences are prevented.
Step S600: and when the real-time stage completion is greater than the preset stage completion, sending a stage change instruction to the stage monitoring model, wherein the stage change instruction is used for controlling the monitoring parameters of the stage monitoring model to be changed into the monitoring parameters of the next stage.
Specifically, a preset stage completion degree is set according to actual conditions, the preset stage completion degree is a reference index for judging whether the postoperative care training of the current stage is qualified or not, the real-time stage completion degree and the preset stage completion degree are further compared, if the real-time stage completion degree is larger than the preset stage completion degree, the postoperative care training of the current stage is qualified, a stage change instruction is further sent to a stage monitoring model, the monitoring parameters of the stage monitoring model are controlled to be changed into monitoring parameters of the next stage through the stage change instruction, the monitoring parameters are preset training data of different stages in a preset stage training data set, further, the postoperative training of the next stage is continuously monitored, for example, the preset stage completion degree is set to be 95%, the postoperative recovery state of the current stage of a first user is good, the real-time stage completion degree reaches 98%, the current stage training is indicated to be completed, the next stage training can be carried out, therefore, real-time monitoring of the postoperative care training of the user is achieved, the monitoring accuracy is improved, and meanwhile the monitoring management effect is improved.
As shown in fig. 3, step S700 of the embodiment of the disclosure further includes:
step S710: generating a plurality of data storage sub-modules according to the training data set of the preset stage;
step S720: each data storage sub-module is used for storing a training record data set of the first user in a corresponding stage, wherein the training record data set comprises training completion degree record data, training period record data and training window period record data;
step S730: acquiring a plurality of stage completion degrees according to the plurality of data storage sub-modules;
step S740: and carrying out quality assessment on the multiple degrees of completion, and outputting a training quality index.
Wherein, step S740 of the embodiment of the present disclosure further includes:
step S741: according to the training completion degree, the training period and the training window period as quality evaluation indexes, outputting a completion quality index, a period quality index and a window quality index;
step S742: training the finishing quality index, the periodic quality index and the window quality index, and building a quality evaluation model, wherein the quality evaluation model is a three-layer fully-connected neural network model;
step S743: and outputting the training quality index according to the quality evaluation model.
Specifically, the training data set in the preset stage includes preset training data in a plurality of different training stages, one training stage corresponds to one data storage submodule, and each data storage submodule is used for storing a training record data set corresponding to the training stage, wherein the training record data set includes training completion degree record data, training period record data and training window period record data. Specifically, each training stage comprises multiple training, the training completion record data refers to multiple completions corresponding to multiple training in one training stage, one training stage comprises different training actions, the training frequency of each training action is different, for example, the hand grip is trained 50 times, the lower limb is stretched and trained 50 times, the training is repeated 5 times as one training, the training completion record data comprises the action completion and the time completion, the action completion refers to whether one training action meets the standard, for example, the depth of the grip is reached, whether the fingertips are contacted with the palm, and whether the duration of each grip is qualified; the training period is an integral training period, the training period can be divided into a plurality of training periods according to a plurality of times of training, the training period record data comprises training time corresponding to a plurality of times of training, the training window period record data refers to a time period when a user does not train in time in one training period, for example, the user needs to train for 4 times in one day, the user does not train in the 3 rd training time, and the missing training time period is recorded. According to the multiple data storage sub-modules, multiple stage completions are obtained, the multiple stage completions comprise training completions record data, training period record data and training window period record data corresponding to multiple training stages, quality evaluation is further carried out on the multiple completions, training quality indexes are output, the training quality indexes represent the overall training completions of all the training stages, and therefore monitoring management of postoperative care training is achieved.
Specifically, the process of quality assessment of the plurality of degrees of completion is as follows: firstly, a plurality of action completions and a plurality of time completions of multiple times of training corresponding to each training stage are obtained according to training completions record data, mean value calculation is carried out on the plurality of action completions and the plurality of time completions, and a mean value calculation result is used as a completion quality index. The method comprises the steps of setting a hidden layer to be a multi-layer neural network structure, carrying out complex nonlinear logic operation through the hidden layer, outputting a training quality index, specifically, carrying out training supervision on a quality evaluation model through obtaining a plurality of groups of sample completion quality indexes, sample period quality indexes and sample window quality indexes, carrying out training supervision on the quality evaluation model through sample data, and testing the accuracy of the quality evaluation model to obtain the quality evaluation model meeting expected requirements. Inputting the finishing quality index, the period quality index and the window quality index into a quality evaluation model, and outputting a training quality index. By analyzing and evaluating the postoperative care training data, the training quality index is obtained, and the monitoring management of postoperative training is realized.
Wherein, step S800 of the embodiment of the present disclosure further includes:
step S810: according to the training data set of the preset stage, monitoring granularity configuration is carried out on each stage, and granularity configuration parameters are output;
step S820: the granularity configuration parameters are used for distributing the monitoring granularity when the stage monitoring model is in different training stages, and the monitoring granularity corresponding to each stage is different.
Specifically, the monitoring granularity refers to data granularity, in short, the training data set in the preset stage includes preset training data in different training stages, the data change degree of the preset training data in the different training stages is different, if the same data granularity is set for the preset training data in the different training stages, when the training data in the different training stages are analyzed, if the change degree of the corresponding preset training data in one stage is smaller, the change of the training data in the stage may be insignificant, and even the change of the data is not seen, for example, when the quality evaluation is performed, the training completion degree record data, the training period record data and the training window period record data may not be significantly changed, and thus the training quality index may not be accurate
Therefore, different monitoring granularity configurations are required to be carried out on different stages, granularity configuration parameters are obtained, the granularity configuration parameters are also data granularity and are used for distributing the monitoring granularity when the stage monitoring model is in different training stages, and the monitoring granularity corresponding to each stage is different, namely the frequency and the data quantity of data acquisition of each stage are different.
The granularity configuration parameters at least comprise one of total muscle force data, total muscle force data acquisition frequency, total equipment sensing data and total equipment sensing acquisition frequency, that is, for a training stage with small data change degree, the total muscle force data acquisition frequency, the total equipment sensing data and the total equipment sensing acquisition frequency can be increased, and the monitoring precision is improved, so that the monitoring accuracy is improved. When the number of the granularity configuration parameters is greater than or equal to 2, that is, the granularity configuration parameters at least comprise two data of muscle force data total amount, muscle force data acquisition frequency, equipment sensing data total amount and equipment sensing acquisition frequency, granularity triggering probability calculation is further carried out on each group of parameters in the granularity configuration parameters, the granularity triggering probability is determined according to the data change degree corresponding to each group of parameters in the granularity configuration parameters, if the data change degree is large, the corresponding granularity triggering probability is large, weight calculation is further carried out on the configuration parameters in the granularity configuration parameters according to the granularity triggering probability calculation result, in a simple way, the granularity configuration parameters comprise two or more parameters, the data change degrees corresponding to different parameters are different, the granularity triggering probability corresponding to the parameters with large change degrees is large, and the set monitoring granularity value of the set distance can be set to be larger for the parameters with large granularity triggering probability. By carrying out monitoring granularity configuration according to the change degree of the data, the monitoring precision and the monitoring management accuracy can be effectively improved.
Wherein, step S900 of the embodiment of the present disclosure further includes:
step S910: obtaining training deviation by comparing deviation of the real-time muscle force data set and the real-time sensing data set of the equipment;
step S920: when the training deviation is larger than the preset training deviation, generating early warning information, and sending the early warning information to the first user through first electronic equipment, wherein the early warning information is used for reminding the error of the training data record.
Specifically, the real-time muscle force data set and the equipment real-time sensing data set both represent postoperative training recovery conditions of the first user, under normal conditions, the two conditions are consistent, due to equipment or individual differences, minor deviations can exist, the existing deviations are training deviations, as long as the deviations are within a deviation allowable range, the deviations belong to normal conditions, the preset training deviations are the deviation allowable range, the deviation comparison can be carried out on the training deviations and the preset training deviations according to actual conditions, if the training deviations are larger than the preset training deviations, errors of the training data records are indicated, early warning information is generated, the early warning information comprises the training deviations, the early warning information is sent to the first user through first electronic equipment, the first electronic equipment is electronic equipment such as a flat plate, a mobile phone and a computer connected with a training data monitoring system and is used for reminding the first user of errors in training data records, the correction of the training data records is assisted, and the accuracy of monitoring management is guaranteed.
Based on the above analysis, the disclosure provides an intelligent monitoring management method for postoperative care training, in this embodiment, firstly, stage division is performed according to the change characteristics of a preset training data set, the preset stage training data set is output, a real-time muscle force data set of a first user and a real-time sensing data set of equipment are collected, the real-time muscle force data set and the real-time sensing data set are input into a stage monitoring model for analysis, the real-time stage completion degree is output, whether the next stage of training monitoring is performed is judged according to the real-time stage completion degree, so that the monitoring accuracy of postoperative care training is improved, the monitoring management effect is improved, and the auxiliary technical effect is provided for postoperative rehabilitation training.
Example two
Based on the same inventive concept as the intelligent monitoring management method for post-operation nursing training in the foregoing embodiment, as shown in fig. 4, the present disclosure further provides an intelligent monitoring management system for post-operation nursing training, where the system is communicatively connected to a first training device, and the system includes:
the preset training data set acquisition module 11 is configured to acquire a preset training data set of a first user;
the stage division module 12 is configured to perform stage division according to the change characteristics of the preset training data set, and output the preset stage training data set;
a real-time training data set acquisition module 13, where the real-time training data set acquisition module 13 is configured to acquire a real-time training data set of the first user, where the real-time training data set includes a real-time muscle strength data set for postoperative training and a real-time sensing data set of a device of the first user using the first training device;
the phase monitoring model generation module 14, wherein the phase monitoring model generation module 14 is configured to generate a phase monitoring model according to the preset phase training data set;
the stage completion degree output module 15 is used for inputting the real-time muscle force data set and the equipment real-time sensing data set into the stage monitoring model for analysis, and outputting real-time stage completion degree;
and the monitoring parameter changing module 16 is configured to send a stage changing instruction to the stage monitoring model when the real-time stage completion is greater than a preset stage completion, and the monitoring parameter changing module 16 is configured to control the monitoring parameter of the stage monitoring model to be changed to the monitoring parameter of the next stage.
Further, the system further comprises:
the data storage module is used for generating a plurality of data storage sub-modules according to the training data set of the preset stage, wherein each data storage sub-module is used for storing a training record data set of the first user in a corresponding stage, and the training record data set comprises training completion degree record data, training period record data and training window period record data;
the system comprises a plurality of stage completion acquiring modules, a plurality of data storage sub-modules and a plurality of data storage sub-modules, wherein the plurality of stage completion acquiring modules are used for acquiring a plurality of stage completion according to the plurality of data storage sub-modules;
the quality evaluation module is used for performing quality evaluation on the plurality of completion degrees and outputting training quality indexes.
Further, the system further comprises:
the quality evaluation index acquisition module is used for outputting a completion quality index, a period quality index and a window quality index according to the training completion degree, the training period and the training window period as quality evaluation indexes;
the quality evaluation model building module is used for training the finishing quality index, the periodic quality index and the window quality index and building a quality evaluation model, wherein the quality evaluation model is a three-layer fully-connected neural network model;
and the training quality index output module is used for outputting the training quality index according to the quality evaluation model.
Further, the system further comprises:
the curve drawing module is used for drawing curves of the real-time muscle force data set and the real-time sensing data set of the equipment to obtain a muscle force change curve and a sensing change curve;
the stability analysis module is used for carrying out stability analysis on the muscle strength change curve and the sensing change curve and outputting a stability index of the muscle strength change curve and a stability index of the sensing change curve;
and the stability index judging module is used for inputting the real-time muscle force data set and the real-time equipment sensing data set into the stage monitoring model when the stability index of the muscle force change curve and the stability index of the sensing change curve are both larger than preset stability indexes.
Further, the system further comprises:
the monitoring granularity configuration module is used for carrying out monitoring granularity configuration on each stage according to the preset stage training data set and outputting granularity configuration parameters, wherein the granularity configuration parameters are used for distributing the monitoring granularity when the stage monitoring model is in different training stages, and the monitoring granularity corresponding to each stage is different.
Further, the system further comprises:
the granularity configuration parameter analysis module is used for determining that the granularity configuration parameter at least comprises one of muscle force data total amount, muscle force data acquisition frequency, equipment sensing data total amount and equipment sensing acquisition frequency;
and the weight calculation module is used for carrying out granularity triggering probability calculation on each group of parameters in the granularity configuration parameters when the number of the granularity configuration parameters is more than or equal to 2, and carrying out weight calculation on the configuration parameters in the granularity configuration parameters according to a granularity triggering probability calculation result.
Further, the system further comprises:
the deviation comparison module is used for obtaining training deviation by comparing the deviation of the real-time muscle force data set and the real-time sensing data set of the equipment;
the early warning information sending module is used for generating early warning information when the training deviation is larger than a preset training deviation, sending the early warning information to the first user through first electronic equipment and reminding the first user of errors in training data recording.
The specific example of the intelligent monitoring management method for post-operative care training in the first embodiment is also applicable to the intelligent monitoring management system for post-operative care training in the present embodiment, and by the foregoing detailed description of the intelligent monitoring management method for post-operative care training, those skilled in the art can clearly know the intelligent monitoring management system for post-operative care training in the present embodiment, so that details of the description are not described herein for brevity. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (8)
1. An intelligent monitoring management method for postoperative care training, wherein the method is applied to a training data monitoring system, the system is in communication connection with a first training device, and the method comprises:
acquiring a preset training data set of a first user;
performing stepwise division according to the change characteristics of the preset training data set, and outputting the preset training data set;
collecting a real-time training data set of the first user, wherein the real-time training data set comprises a real-time muscle strength data set for postoperative training and a real-time sensing data set of equipment of the first user using the first training equipment;
generating a stage monitoring model according to the preset stage training data set;
inputting the real-time muscle force data set and the equipment real-time sensing data set into the stage monitoring model for analysis, and outputting real-time stage completion;
and when the real-time stage completion is greater than the preset stage completion, sending a stage change instruction to the stage monitoring model, wherein the stage change instruction is used for controlling the monitoring parameters of the stage monitoring model to be changed into the monitoring parameters of the next stage.
2. The method of claim 1, wherein the method further comprises:
generating a plurality of data storage sub-modules according to the training data set of the preset stage;
each data storage sub-module is used for storing a training record data set of the first user in a corresponding stage, wherein the training record data set comprises training completion degree record data, training period record data and training window period record data;
acquiring a plurality of stage completion degrees according to the plurality of data storage sub-modules;
and carrying out quality assessment on the multiple degrees of completion, and outputting a training quality index.
3. The method of claim 2, wherein the plurality of degrees of completion are evaluated for quality, a training quality indicator is output, the method comprising:
according to the training completion degree, the training period and the training window period as quality evaluation indexes, outputting a completion quality index, a period quality index and a window quality index;
training the finishing quality index, the periodic quality index and the window quality index, and building a quality evaluation model, wherein the quality evaluation model is a three-layer fully-connected neural network model;
and outputting the training quality index according to the quality evaluation model.
4. The method of claim 1, wherein prior to inputting the real-time muscle force dataset and the device real-time sensory dataset into the phase monitoring model for analysis, the method comprises:
performing curve drawing on the real-time muscle force data set and the real-time sensing data set of the equipment to obtain a muscle force change curve and a sensing change curve;
performing stability analysis on the muscle strength change curve and the sensing change curve, and outputting a stability index of the muscle strength change curve and a stability index of the sensing change curve;
and inputting the real-time muscle force data set and the real-time equipment sensing data set into the stage monitoring model when the stability index of the muscle force change curve and the stability index of the sensing change curve are both larger than a preset stability index.
5. The method of claim 1, wherein the method further comprises:
according to the training data set of the preset stage, monitoring granularity configuration is carried out on each stage, and granularity configuration parameters are output;
the granularity configuration parameters are used for distributing the monitoring granularity when the stage monitoring model is in different training stages, and the monitoring granularity corresponding to each stage is different.
6. The method of claim 5, wherein the granularity configuration parameter comprises at least one of a total amount of muscle force data, a total amount of muscle force data acquisition frequency, a total amount of device sensing data, and a device sensing acquisition frequency;
when the number of the granularity configuration parameters is greater than or equal to 2, performing granularity trigger probability calculation on each group of parameters in the granularity configuration parameters, and performing weight calculation on the configuration parameters in the granularity configuration parameters according to the granularity trigger probability calculation result.
7. The method of claim 1, wherein the method further comprises:
obtaining training deviation by comparing deviation of the real-time muscle force data set and the real-time sensing data set of the equipment;
when the training deviation is larger than the preset training deviation, generating early warning information, and sending the early warning information to the first user through first electronic equipment, wherein the early warning information is used for reminding the error of the training data record.
8. An intelligent monitoring management system for post-operative care training, the system being in communication with a first training device, the system comprising:
the system comprises a preset training data set acquisition module, a first user acquisition module and a second user acquisition module, wherein the preset training data set acquisition module is used for acquiring a preset training data set of the first user;
the phase division module is used for carrying out phase division according to the change characteristics of the preset training data set and outputting the preset phase training data set;
the real-time training data set acquisition module is used for acquiring a real-time training data set of the first user, wherein the real-time training data set comprises a real-time muscle strength data set for postoperative training and a real-time sensing data set of equipment of the first user using the first training equipment;
the phase monitoring model generation module is used for generating a phase monitoring model according to the preset phase training data set;
the stage completion degree output module is used for inputting the real-time muscle force data set and the equipment real-time sensing data set into the stage monitoring model for analysis and outputting real-time stage completion degree;
and the monitoring parameter changing module is used for sending a stage changing instruction to the stage monitoring model when the real-time stage completion degree is larger than the preset stage completion degree, and controlling the monitoring parameter of the stage monitoring model to be changed into the monitoring parameter of the next stage.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117633625A (en) * | 2023-11-30 | 2024-03-01 | 成都市成华区妇幼保健院 | Gynaecology and obstetrics postoperative care data analysis method and system based on big data |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114090377A (en) * | 2021-11-18 | 2022-02-25 | 中国银联股份有限公司 | Data monitoring method and device |
| CN114652311A (en) * | 2022-03-22 | 2022-06-24 | 南通市第二人民医院 | Fine nursing method and system based on stepped nursing intervention |
| CN114690890A (en) * | 2020-12-31 | 2022-07-01 | 浙江凡聚科技有限公司 | Virtual reality-based rotator cuff injury postoperative rehabilitation training system and method |
| CN114881941A (en) * | 2022-04-21 | 2022-08-09 | 赵治洋 | Spine health change prediction method and system based on big data |
-
2023
- 2023-04-04 CN CN202310354952.2A patent/CN116403670A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114690890A (en) * | 2020-12-31 | 2022-07-01 | 浙江凡聚科技有限公司 | Virtual reality-based rotator cuff injury postoperative rehabilitation training system and method |
| CN114090377A (en) * | 2021-11-18 | 2022-02-25 | 中国银联股份有限公司 | Data monitoring method and device |
| CN114652311A (en) * | 2022-03-22 | 2022-06-24 | 南通市第二人民医院 | Fine nursing method and system based on stepped nursing intervention |
| CN114881941A (en) * | 2022-04-21 | 2022-08-09 | 赵治洋 | Spine health change prediction method and system based on big data |
Non-Patent Citations (1)
| Title |
|---|
| 张莹莹 等: "多功能肌力康复训练系统的设计与分析", 《医药卫生科技;信息科技》, pages 18 - 23 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117633625A (en) * | 2023-11-30 | 2024-03-01 | 成都市成华区妇幼保健院 | Gynaecology and obstetrics postoperative care data analysis method and system based on big data |
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