CN116823803B - Biological compensation physiotherapy system - Google Patents
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Abstract
The invention relates to a biological compensation physiotherapy system, which belongs to the technical field of physiotherapy, and comprises the steps of obtaining sample data information in the physiotherapy process through a database, processing the sample data information through a Markov chain, obtaining a treatment state transition prediction model, obtaining physiotherapy image data information in the physiotherapy process within preset time, and generating compensation parameters of a physiotherapy instrument according to the physiotherapy image data information in the physiotherapy process within the preset time and the treatment state transition prediction model. The invention combines the image recognition technology and the Markov chain technology to recognize the treatment state condition, thereby simplifying and tracking the physiotherapy process of the user, rapidly recognizing the treatment state of the user according to the influence data in the treatment process of the user, adjusting the working parameters of the physiotherapy equipment according to the treatment state of the user and recommending a more reasonable treatment scheme.
Description
Technical Field
The invention relates to the technical field of physiotherapy, in particular to a biological compensation physiotherapy system.
Background
Physical therapy, also called physical factor treatment, refers to the application of natural or artificial physical factors to the human body to improve the health level and prevent and treat diseases. Sunlight, atmosphere, sea water, mineral spring and the like are all natural physical therapeutic factors, and physical factors commonly used in physiotherapy in hospitals include electricity, light, sound, magnetism, cold, heat, force and the like, and physiotherapy technologies with traditional characteristics, such as electric acupuncture, acupoint magnetotherapy, traditional Chinese medicine iontophoresis and the like. After acting on human body, physical factors can be absorbed by human body, and energy form transformation is generated, so that a series of physical and chemical changes are caused, and local or systemic physiological reaction is generated, thereby playing a role in treatment. For example, the intermediate frequency has the effects of promoting blood circulation, relieving pain and diminishing inflammation. The high frequency mainly utilizes the thermal effect and the non-thermal effect to treat diseases, the thermal effect has the effects of relieving pain and diminishing inflammation, and the non-thermal effect is mainly used for treating acute inflammation. Other physiotherapy factors have different categories, and corresponding indications are different. The image processing technology is a technology which is raised in recent years, the fusion image processing technology can identify the treatment condition of a user in the physiotherapy process, the physiotherapy process of tracking the user can reflect the whole physiotherapy condition of the user, at present, the treatment state is a state value describing the user in the rehabilitation physiotherapy process, such as a high abnormal state, a medium abnormal state and the like, and the treatment state cannot be identified by the fusion image identification technology in the prior art, so that the treatment parameters of the physiotherapy instrument cannot be compensated according to the actual treatment state.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a biological compensation physiotherapy system.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the first aspect of the present invention provides a bio-compensation physiotherapy method comprising the steps of:
acquiring historical image data information of a physiotherapy part in a treatment process, constructing a time stamp, and constructing sample data based on a time sequence by combining the time stamp and the historical image data information of the physiotherapy part in the treatment process;
constructing a database, introducing an attention mechanism to process the sample data, obtaining processed sample data, and inputting the processed sample data into the database for storage;
acquiring sample data information in the physiotherapy process through the database, and processing the sample data information through a Markov chain to acquire a treatment state transition prediction model;
and acquiring physiotherapy image data information in the physiotherapy process within the preset time, and generating compensation parameters of the physiotherapy instrument according to the physiotherapy image data information in the physiotherapy process within the preset time and the treatment state transition prediction model.
Further, in a preferred embodiment of the present invention, historical image data information of a physiotherapy site during a treatment process is obtained, a time stamp is constructed, and sample data is constructed by combining the time stamp and the historical image data information of the physiotherapy site during the treatment process, which specifically includes:
acquiring historical image data information of a physiotherapy part in a treatment process, cutting a non-treatment area of the historical image data information, and acquiring an interested area of the historical image data information after cutting;
smoothing the region of interest by means of an average filtering method, extracting image features of the physiotherapy parts from the image smoothing result by means of a canny operator, and obtaining image data information of the physiotherapy parts after treatment;
and constructing an image feature matrix of the physiotherapy part according to the processed image data information of the physiotherapy part, constructing a time stamp, and constructing sample data based on a time sequence by combining the time stamp and the image feature matrix of the physiotherapy part.
Further, in a preferred embodiment of the present invention, a database is constructed, the attention introducing mechanism processes the sample data, obtains processed sample data, and inputs the processed sample data into the database for storage, and specifically includes the following steps:
Constructing a database, introducing an attention mechanism, and calculating the attention score of each sample data through the attention mechanism to acquire the attention score information of each sample data;
randomly selecting one sample data as initial sample data, calculating Euclidean distance between the initial sample data and each other sample data, and sequencing each sample data based on the Euclidean distance value to generate sequencing results;
dividing the database into a plurality of storage spaces, and inputting each sample data into the storage spaces for storage according to the sorting result;
and meanwhile, merging the sample data with equal Euclidean distance, inquiring the address of each storage space configuration information, and updating the database periodically.
Further, in a preferred embodiment of the present invention, sample data information in a physiotherapy process is obtained through the database, and the sample data information is processed through a markov chain to obtain a treatment state transition prediction model, which specifically includes the following steps:
acquiring sample data information in a physiotherapy process through the database, carrying out state description on physiotherapy image data in the sample data information in the physiotherapy process through a Markov chain, and constructing a state transition matrix in the physiotherapy process;
Introducing an FCBF algorithm, carrying out symmetry measurement on sample data in the state transition matrix of the physiotherapy process through the FCBF algorithm, obtaining relevant redundant symmetry point sample data, and removing the relevant redundant symmetry point sample data from the state transition matrix of the physiotherapy process;
acquiring a physical therapy process state transition matrix from which relevant redundant symmetric point sample data are removed, introducing a singular value decomposition algorithm, and performing dimension reduction on the physical therapy process state transition matrix through the singular value decomposition algorithm to generate a covariance matrix;
and constructing a treatment state transition prediction model based on a convolutional neural network, inputting the covariance matrix into the treatment state transition prediction model for training, and outputting the treatment state transition prediction model after the treatment state transition prediction model meets the preset requirements.
Further, in a preferred embodiment of the present invention, physiotherapy image data information during physiotherapy within a preset time is obtained, and compensation parameters of the physiotherapy apparatus are generated according to the physiotherapy image data information during physiotherapy within the preset time and a treatment state transition prediction model, specifically including the following steps:
Acquiring physiotherapy image data information in a physiotherapy process within a preset time, inputting the physiotherapy image data information in the physiotherapy process within the preset time into the treatment state transition prediction model for prediction, and acquiring a treatment state transition prediction result;
presetting relevant treatment state transition probability value information, and acquiring a treatment state transition probability value in the treatment process of the physiotherapy instrument within the next preset time according to the treatment state transition prediction result;
judging whether the probability value of the treatment state transition is larger than a preset treatment state transition probability value, and if the probability value of the treatment state transition is not larger than the preset treatment state transition probability value, acquiring treatment parameter data information of the current physiotherapy instrument;
and acquiring a treatment state value in the current physiotherapy process through the treatment state transfer prediction result, acquiring treatment parameter data information of the physiotherapy instrument corresponding to the treatment state value in the current physiotherapy process through big data, and generating compensation parameters of the physiotherapy instrument according to the treatment parameter data information of the physiotherapy instrument corresponding to the treatment state value in the current physiotherapy process.
Further, in a preferred embodiment of the present invention, the bio-compensation physiotherapy method further comprises the following steps:
Acquiring treatment state information in a physiotherapy process within a preset time, and constructing treatment state change curve information according to the treatment state information in the physiotherapy process within the preset time;
inputting the treatment state change curve information into the database for data matching, obtaining sample data information with highest similarity, and obtaining physiotherapy instrument treatment parameter information in the physiotherapy treatment process of the sample data information with highest similarity;
and recommending treatment parameters of the treatment state information in the physiotherapy process within preset time according to the physiotherapy instrument treatment parameter information in the physiotherapy process of the sample data information with the highest similarity, and generating recommendation information.
The second aspect of the present invention provides a bio-compensation physiotherapy system, the system comprising a memory and a processor, the memory comprising a bio-compensation physiotherapy method program, the bio-compensation physiotherapy method program, when executed by the processor, implementing the steps of:
acquiring historical image data information of a physiotherapy part in a treatment process, constructing a time stamp, and constructing sample data based on a time sequence by combining the time stamp and the historical image data information of the physiotherapy part in the treatment process;
Constructing a database, introducing an attention mechanism to process the sample data, obtaining processed sample data, and inputting the processed sample data into the database for storage;
acquiring sample data information in the physiotherapy process through the database, and processing the sample data information through a Markov chain to acquire a treatment state transition prediction model;
and acquiring physiotherapy image data information in the physiotherapy process within the preset time, and generating compensation parameters of the physiotherapy instrument according to the physiotherapy image data information in the physiotherapy process within the preset time and the treatment state transition prediction model.
In the system, an attention introducing mechanism processes the sample data, acquires processed sample data, and inputs the processed sample data into the database for storage, and specifically comprises the following steps:
constructing a database, introducing an attention mechanism, and calculating the attention score of each sample data through the attention mechanism to acquire the attention score information of each sample data;
randomly selecting one sample data as initial sample data, calculating Euclidean distance between the initial sample data and each other sample data, and sequencing each sample data based on the Euclidean distance value to generate sequencing results;
Dividing the database into a plurality of storage spaces, and inputting each sample data into the storage spaces for storage according to the sorting result;
and meanwhile, merging the sample data with equal Euclidean distance, inquiring the address of each storage space configuration information, and updating the database periodically.
In the system, sample data information in the physiotherapy process is acquired through the database, and is processed through a Markov chain to acquire a treatment state transition prediction model, and the method specifically comprises the following steps of:
acquiring sample data information in a physiotherapy process through the database, carrying out state description on physiotherapy image data in the sample data information in the physiotherapy process through a Markov chain, and constructing a state transition matrix in the physiotherapy process;
introducing an FCBF algorithm, carrying out symmetry measurement on sample data in the state transition matrix of the physiotherapy process through the FCBF algorithm, obtaining relevant redundant symmetry point sample data, and removing the relevant redundant symmetry point sample data from the state transition matrix of the physiotherapy process;
acquiring a physical therapy process state transition matrix from which relevant redundant symmetric point sample data are removed, introducing a singular value decomposition algorithm, and performing dimension reduction on the physical therapy process state transition matrix through the singular value decomposition algorithm to generate a covariance matrix;
And constructing a treatment state transition prediction model based on a convolutional neural network, inputting the covariance matrix into the treatment state transition prediction model for training, and outputting the treatment state transition prediction model after the treatment state transition prediction model meets the preset requirements.
In the system, physiotherapy image data information in the physiotherapy process within the preset time is obtained, and compensation parameters of the physiotherapy instrument are generated according to the physiotherapy image data information in the physiotherapy process within the preset time and a treatment state transition prediction model, and the system specifically comprises the following steps:
acquiring physiotherapy image data information in a physiotherapy process within a preset time, inputting the physiotherapy image data information in the physiotherapy process within the preset time into the treatment state transition prediction model for prediction, and acquiring a treatment state transition prediction result;
presetting relevant treatment state transition probability value information, and acquiring a treatment state transition probability value in the treatment process of the physiotherapy instrument within the next preset time according to the treatment state transition prediction result;
judging whether the probability value of the treatment state transition is larger than a preset treatment state transition probability value, and if the probability value of the treatment state transition is not larger than the preset treatment state transition probability value, acquiring treatment parameter data information of the current physiotherapy instrument;
And acquiring a treatment state value in the current physiotherapy process through the treatment state transfer prediction result, acquiring treatment parameter data information of the physiotherapy instrument corresponding to the treatment state value in the current physiotherapy process through big data, and generating compensation parameters of the physiotherapy instrument according to the treatment parameter data information of the physiotherapy instrument corresponding to the treatment state value in the current physiotherapy process.
The invention solves the defects existing in the background technology, and has the following beneficial effects:
according to the invention, the historical image data information of the physiotherapy part in the treatment process is obtained, the time stamp is constructed, the time stamp and the historical image data information of the physiotherapy part in the treatment process are combined to construct sample data based on a time sequence, a database is further constructed, an attention mechanism is introduced to process the sample data, the processed sample data is obtained and is input into the database to be stored, the sample data information in the physiotherapy process is obtained through the database, the sample data information is processed through a Markov chain, a treatment state transition prediction model is obtained, the physiotherapy image data information in the physiotherapy process in the preset time is obtained, and compensation parameters of the physiotherapy instrument are generated according to the physiotherapy image data information in the physiotherapy process and the treatment state transition prediction model in the preset time. The invention combines the image recognition technology and the Markov chain technology to recognize the treatment state condition, thereby simplifying and tracking the physiotherapy process of the user, rapidly recognizing the treatment state of the user according to the influence data in the treatment process of the user, adjusting the working parameters of the physiotherapy equipment according to the treatment state of the user and recommending a more reasonable treatment scheme.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows an overall method flow diagram of a bio-compensation physiotherapy method;
FIG. 2 shows a first method flow diagram of a bio-compensation physiotherapy method;
FIG. 3 shows a second method flow diagram of a bio-compensation physiotherapy method;
fig. 4 shows a system block diagram of a bio-compensation physiotherapy system.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the first aspect of the present invention provides a bio-compensation physiotherapy method, comprising the following steps:
s102, acquiring historical image data information of a physiotherapy part in a treatment process, constructing a time stamp, and constructing sample data based on a time sequence by combining the time stamp and the historical image data information of the physiotherapy part in the treatment process;
s104, constructing a database, introducing an attention mechanism to process sample data, acquiring processed sample data, and inputting the processed sample data into the database for storage;
s106, acquiring sample data information in the physiotherapy process through a database, and processing the sample data information through a Markov chain to acquire a treatment state transition prediction model;
s108, acquiring physiotherapy image data information in the physiotherapy process within the preset time, and generating compensation parameters of the physiotherapy instrument according to the physiotherapy image data information in the physiotherapy process within the preset time and the treatment state transition prediction model.
It should be noted that the invention combines the image recognition technology and the Markov chain technology to recognize the treatment state condition, thereby simplifying and tracking the physiotherapy process of the user, rapidly recognizing the treatment state of the user according to the influence data in the treatment process of the user, adjusting the working parameters of the physiotherapy equipment according to the treatment state of the user, and recommending a more reasonable treatment scheme.
Further, in a preferred embodiment of the present invention, historical image data information of a physiotherapy site during a treatment process is obtained, a time stamp is constructed, and sample data is constructed by combining the time stamp and the historical image data information of the physiotherapy site during the treatment process, which specifically includes:
acquiring historical image data information of a physiotherapy part in a treatment process, cutting a non-treatment area of the historical image data information, and acquiring an interested area of the historical image data information after cutting;
smoothing the region of interest by means of a mean value filtering method, extracting image features of the physiotherapy parts from the image smoothing result by means of a canny operator, and obtaining image data information of the physiotherapy parts after treatment;
and constructing an image feature matrix of the physiotherapy part according to the processed image data information of the physiotherapy part, constructing a time stamp, and constructing sample data based on a time sequence by combining the time stamp and the image feature matrix of the physiotherapy part.
It should be noted that, by the method, the historical image data information of the user in the physiotherapy process can be tracked, so that sample data based on time series is constructed for reference.
As shown in fig. 2, in a preferred embodiment of the present invention, a database is constructed, a attention mechanism is introduced to process sample data, processed sample data is obtained, and the processed sample data is input into the database for storage, which specifically includes the following steps:
s202, constructing a database, introducing an attention mechanism, and calculating the attention score of each sample data through the attention mechanism to acquire the attention score information of each sample data;
s204, randomly selecting one sample data as initial sample data, calculating Euclidean distance between the initial sample data and each other sample data, and sequencing each sample data based on the value of the Euclidean distance to generate sequencing results;
s206, dividing the database into a plurality of storage spaces, and inputting each sample data into the storage spaces for storage according to the sequencing result;
and S208, merging the sample data with equal Euclidean distance, inquiring the address of each storage space configuration information, and updating the database periodically.
By the method, the sample data with equal Euclidean distance can be combined, so that each sample data is ordered according to the value of the Euclidean distance, and then the similar sample data is stored in adjacent space, so that the query and recognition speed of the sample data is improved.
As shown in fig. 3, in a preferred embodiment of the present invention, sample data information in a physiotherapy process is obtained through a database, and the sample data information is processed through a markov chain to obtain a treatment state transition prediction model, which specifically includes the following steps:
s302, acquiring sample data information in the physiotherapy process through a database, carrying out state description on physiotherapy image data in the sample data information in the physiotherapy process through a Markov chain, and constructing a state transition matrix in the physiotherapy process;
s304, introducing an FCBF algorithm, carrying out symmetry measurement on sample data in a state transition matrix of the physiotherapy process through the FCBF algorithm, obtaining relevant redundant symmetrical point sample data, and removing the relevant redundant symmetrical point sample data from the state transition matrix of the physiotherapy process;
s306, acquiring a physical therapy process state transition matrix after eliminating relevant redundant symmetric point sample data, introducing a singular value decomposition algorithm, and performing dimension reduction treatment on the physical therapy process state transition matrix through the singular value decomposition algorithm to generate a covariance matrix;
s308, constructing a treatment state transition prediction model based on a convolutional neural network, inputting a covariance matrix into the treatment state transition prediction model for training, and outputting the treatment state transition prediction model after the treatment state transition prediction model meets the preset requirements.
It should be noted that, the therapeutic image in the physiotherapy process can reflect the therapeutic state of the user, that is, the rehabilitation condition, for example, the intermediate frequency has the effects of promoting blood circulation, easing pain and diminishing inflammation, the corresponding image data is provided in the rehabilitation process, the state transition matrix in the physiotherapy process describes the state information in the treatment process, for example, from the high injury state, the moderate injury state, the low injury state, the normal state, etc., and the person skilled in the art can set according to the item of physiotherapy. The FCBF algorithm is an effective way for analyzing feature redundancy and is a rapid correlation filtering method. And carrying out symmetry measurement on sample data in the state transition matrix of the physiotherapy process through an FCBF algorithm, and obtaining relevant redundant symmetry point sample data, so that irrelevant data is removed, and the prediction speed of the treatment state transition prediction model is improved. Meanwhile, the fusion singular value decomposition algorithm can improve the calculated amount of the treatment state transition prediction model and improve the calculation complexity and calculation speed of the treatment state transition prediction model.
Further, in a preferred embodiment of the present invention, physiotherapy image data information during physiotherapy within a preset time is obtained, and compensation parameters of the physiotherapy apparatus are generated according to the physiotherapy image data information during physiotherapy within the preset time and a treatment state transition prediction model, specifically including the following steps:
Acquiring physiotherapy image data information in a physiotherapy process within a preset time, inputting the physiotherapy image data information in the physiotherapy process within the preset time into a treatment state transition prediction model for prediction, and acquiring a treatment state transition prediction result;
presetting relevant treatment state transition probability value information, and acquiring a treatment state transition probability value in the treatment process of the physiotherapy instrument within the next preset time according to a treatment state transition prediction result;
judging whether the probability value of the treatment state transition is larger than the preset treatment state transition probability value, and if the probability value of the treatment state transition is not larger than the preset treatment state transition probability value, acquiring treatment parameter data information of the current physiotherapy instrument;
and acquiring a treatment state value in the current physiotherapy process through a treatment state transfer prediction result, acquiring treatment parameter data information of the physiotherapy instrument corresponding to the treatment state value in the current physiotherapy process through big data, and generating compensation parameters of the physiotherapy instrument according to the treatment parameter data information of the physiotherapy instrument corresponding to the treatment state value in the current physiotherapy process.
It should be noted that, through the state information in the physiotherapy process of the markov chain, a probability value for transferring from one state information to another state information is calculated, for example, a probability value for transferring from a highly damaged state to a moderately damaged state, and if the probability value for transferring the treatment state is not greater than a preset treatment state transfer probability value, the transition from one state information to another state information is described, so that the treatment parameter data information of the current physiotherapy instrument is adjusted according to the actual treatment condition of the user. The physiotherapy instrument can be an infrared physiotherapy instrument, a microwave physiotherapy instrument and the like. The method can adjust the treatment parameter data information of the current physiotherapy instrument according to the time condition of the user, thereby improving the rationality in the physiotherapy process.
Further, in a preferred embodiment of the present invention, a bio-compensation physiotherapy method further comprises the steps of:
acquiring treatment state information in the physiotherapy process within a preset time, and constructing treatment state change curve information according to the treatment state information in the physiotherapy process within the preset time;
inputting the treatment state change curve information into a database for data matching, obtaining sample data information with highest similarity, and obtaining physiotherapy instrument treatment parameter information in the physiotherapy treatment process of the sample data information with highest similarity;
and recommending treatment parameters of the treatment state information in the physiotherapy process within the preset time according to the physiotherapy instrument treatment parameter information in the physiotherapy process of the sample data information with the highest similarity, and generating recommendation information.
It should be noted that, by the method, the treatment parameters of the physiotherapy instrument can be recommended according to the actual physiotherapy and rehabilitation conditions of the user, so that the rationality in the physiotherapy process is improved. The treatment parameter information includes treatment time, the size of the treatment parameter, and the like.
In addition, the method can further comprise the following steps:
acquiring behavior characteristic data information in a user physiotherapy process, constructing a user emotion recognition model based on a convolutional neural network, and constructing an emotion characteristic matrix according to the behavior characteristic data information in the user physiotherapy process;
Inputting the emotion feature matrix into the user emotion recognition model for coding learning, obtaining a trained user emotion recognition model, and obtaining behavior feature data information of the current user in the physiotherapy process;
inputting the behavior characteristic data information of the current user in the physiotherapy process into the user emotion recognition model for recognition, obtaining physiotherapy emotion characteristic data of the user, and displaying or playing the preset emotion characteristic data according to a preset mode when the emotion characteristic data information of the user is the preset emotion characteristic data;
and acquiring related relaxation measures of preset emotion feature data through big data, generating prompt information according to the related relaxation measures, and displaying the prompt information according to a preset mode.
It should be noted that, through identifying the emotion of the user in the physiotherapy process, when the emotion of the user is the emotional characteristics such as tension, anxiety and the like, relevant relaxation measures are displayed or played to the user, so that the treatment experience is improved.
In addition, the method can further comprise the following steps:
the method comprises the steps of obtaining treatment state information of all sub-parts of a physiotherapy part, dividing areas according to the treatment state information of all the sub-parts of the physiotherapy part, obtaining the treatment state of each sub-area, and obtaining treatment time corresponding to the relevant treatment state according to the treatment state of each sub-area;
Acquiring a contour line area in which each sub-area is positioned, displaying the contour line area in which each sub-area is positioned according to a preset mode, and acquiring treatment position information of a current physiotherapy instrument;
updating untreated parts in the contour line area where each sub-area is located according to the treatment position information of the current physiotherapy instrument, and updating the current treatment parts and the treatment time of the treated parts in the contour line area where each sub-area is located;
and acquiring the treatment time of the current treatment position and the treated position, and displaying the positions of the current treatment position and the treated position with the treatment time lower than the treatment time corresponding to the relevant treatment state according to the preset when the treatment time of the current treatment position and the treated position is lower than the treatment time corresponding to the relevant treatment state.
It should be noted that, by the method, whether the treatment process meets the preset requirement can be monitored, so that the current treatment position and the position of the treated position, the treatment time of which is lower than the treatment time corresponding to the relevant treatment state, are displayed according to the preset, and the user is prompted to perform physiotherapy compensation.
As shown in fig. 4, the second aspect of the present invention provides a bio-compensation physiotherapy system 4, which includes a memory 41 and a processor 62, wherein the memory 41 includes a bio-compensation physiotherapy method program, and when the bio-compensation physiotherapy method program is executed by the processor 62, the following steps are implemented:
Acquiring historical image data information of the physiotherapy part in the treatment process, constructing a time stamp, and constructing sample data based on a time sequence by combining the time stamp and the historical image data information of the physiotherapy part in the treatment process;
constructing a database, introducing an attention mechanism to process sample data, obtaining processed sample data, and inputting the processed sample data into the database for storage;
acquiring sample data information in the physiotherapy process through a database, and processing the sample data information through a Markov chain to acquire a treatment state transition prediction model;
and acquiring physiotherapy image data information in the physiotherapy process within the preset time, and generating compensation parameters of the physiotherapy instrument according to the physiotherapy image data information in the physiotherapy process within the preset time and the treatment state transition prediction model.
In the system, an attention introducing mechanism processes sample data, acquires processed sample data, and inputs the processed sample data into a database for storage, and the system specifically comprises the following steps:
constructing a database, introducing an attention mechanism, and calculating the attention score of each sample data through the attention mechanism to acquire the attention score information of each sample data;
Randomly selecting one of the sample data as initial sample data, calculating Euclidean distance between the initial sample data and each other sample data, and sequencing each sample data based on the value of the Euclidean distance to generate sequencing results;
dividing a database into a plurality of storage spaces, and inputting each sample data into the storage spaces for storage according to the sequencing result;
and meanwhile, merging the sample data with equal Euclidean distance, inquiring the address of each storage space configuration information, and updating the database periodically.
In the system, sample data information in the physiotherapy process is acquired through a database, and is processed through a Markov chain to acquire a treatment state transition prediction model, and the system specifically comprises the following steps:
acquiring sample data information in the physiotherapy process through a database, carrying out state description on physiotherapy image data in the sample data information in the physiotherapy process through a Markov chain, and constructing a state transition matrix of the physiotherapy process;
introducing an FCBF algorithm, carrying out symmetry measurement on sample data in a state transition matrix of the physiotherapy process through the FCBF algorithm, obtaining relevant redundant symmetric point sample data, and removing the relevant redundant symmetric point sample data from the state transition matrix of the physiotherapy process;
Acquiring a physical therapy process state transition matrix from which relevant redundant symmetric point sample data are removed, introducing a singular value decomposition algorithm, and performing dimension reduction treatment on the physical therapy process state transition matrix through the singular value decomposition algorithm to generate a covariance matrix;
and constructing a treatment state transition prediction model based on the convolutional neural network, inputting the covariance matrix into the treatment state transition prediction model for training, and outputting the treatment state transition prediction model after the treatment state transition prediction model meets the preset requirement.
In the system, physiotherapy image data information in the physiotherapy process within the preset time is obtained, and compensation parameters of the physiotherapy instrument are generated according to the physiotherapy image data information in the physiotherapy process within the preset time and a treatment state transition prediction model, and the system specifically comprises the following steps:
acquiring physiotherapy image data information in a physiotherapy process within a preset time, inputting the physiotherapy image data information in the physiotherapy process within the preset time into a treatment state transition prediction model for prediction, and acquiring a treatment state transition prediction result;
presetting relevant treatment state transition probability value information, and acquiring a treatment state transition probability value in the treatment process of the physiotherapy instrument within the next preset time according to a treatment state transition prediction result;
Judging whether the probability value of the treatment state transition is larger than the preset treatment state transition probability value, and if the probability value of the treatment state transition is not larger than the preset treatment state transition probability value, acquiring treatment parameter data information of the current physiotherapy instrument;
and acquiring a treatment state value in the current physiotherapy process through a treatment state transfer prediction result, acquiring treatment parameter data information of the physiotherapy instrument corresponding to the treatment state value in the current physiotherapy process through big data, and generating compensation parameters of the physiotherapy instrument according to the treatment parameter data information of the physiotherapy instrument corresponding to the treatment state value in the current physiotherapy process.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. A bio-compensation physiotherapy method, comprising the steps of:
acquiring historical image data information of a physiotherapy part in a treatment process, constructing a time stamp, and constructing sample data based on a time sequence by combining the time stamp and the historical image data information of the physiotherapy part in the treatment process;
constructing a database, introducing an attention mechanism to process the sample data, obtaining processed sample data, and inputting the processed sample data into the database for storage;
acquiring sample data information in the physiotherapy process through the database, and processing the sample data information through a Markov chain to acquire a treatment state transition prediction model;
acquiring physiotherapy image data information in a physiotherapy process within a preset time, and generating compensation parameters of the physiotherapy instrument according to the physiotherapy image data information in the physiotherapy process within the preset time and a treatment state transition prediction model;
the method comprises the steps of acquiring physiotherapy image data information in the physiotherapy process in preset time, and generating compensation parameters of the physiotherapy instrument according to the physiotherapy image data information in the physiotherapy process in the preset time and a treatment state transition prediction model, wherein the method specifically comprises the following steps:
Acquiring physiotherapy image data information in a physiotherapy process within a preset time, inputting the physiotherapy image data information in the physiotherapy process within the preset time into the treatment state transition prediction model for prediction, and acquiring a treatment state transition prediction result;
presetting relevant treatment state transition probability value information, and acquiring a treatment state transition probability value in the treatment process of the physiotherapy instrument within the next preset time according to the treatment state transition prediction result;
judging whether the probability value of the treatment state transition is larger than a preset treatment state transition probability value, and if the probability value of the treatment state transition is not larger than the preset treatment state transition probability value, acquiring treatment parameter data information of the current physiotherapy instrument;
and acquiring a treatment state value in the current physiotherapy process through the treatment state transfer prediction result, acquiring treatment parameter data information of the physiotherapy instrument corresponding to the treatment state value in the current physiotherapy process through big data, and generating compensation parameters of the physiotherapy instrument according to the treatment parameter data information of the physiotherapy instrument corresponding to the treatment state value in the current physiotherapy process.
2. The bio-compensation physiotherapy method according to claim 1, wherein the steps of obtaining historical image data information of the physiotherapy site during the treatment process, constructing a time stamp, and constructing sample data by combining the time stamp and the historical image data information of the physiotherapy site during the treatment process comprise:
Acquiring historical image data information of a physiotherapy part in a treatment process, cutting a non-treatment area of the historical image data information, and acquiring an interested area of the historical image data information after cutting;
smoothing the region of interest by means of an average filtering method, extracting image features of the physiotherapy parts from the image smoothing result by means of a canny operator, and obtaining image data information of the physiotherapy parts after treatment;
and constructing an image feature matrix of the physiotherapy part according to the processed image data information of the physiotherapy part, constructing a time stamp, and constructing sample data based on a time sequence by combining the time stamp and the image feature matrix of the physiotherapy part.
3. The bio-compensation physiotherapy method according to claim 1, characterized in that a database is constructed, the sample data is processed by an attention introducing mechanism, the processed sample data is obtained, and the processed sample data is input into the database for storage, specifically comprising the steps of:
constructing a database, introducing an attention mechanism, and calculating the attention score of each sample data through the attention mechanism to acquire the attention score information of each sample data;
Randomly selecting one sample data as initial sample data, calculating Euclidean distance between the initial sample data and each other sample data, and sequencing each sample data based on the Euclidean distance value to generate sequencing results;
dividing the database into a plurality of storage spaces, and inputting each sample data into the storage spaces for storage according to the sorting result;
and meanwhile, merging the sample data with equal Euclidean distance, inquiring the address of each storage space configuration information, and updating the database periodically.
4. The bio-compensation physiotherapy method according to claim 1, characterized in that the sample data information in the physiotherapy process is obtained through the database, and the sample data information is processed through a markov chain to obtain a treatment state transition prediction model, and specifically comprising the following steps:
acquiring sample data information in a physiotherapy process through the database, carrying out state description on physiotherapy image data in the sample data information in the physiotherapy process through a Markov chain, and constructing a state transition matrix in the physiotherapy process;
Introducing an FCBF algorithm, carrying out symmetry measurement on sample data in the state transition matrix of the physiotherapy process through the FCBF algorithm, obtaining relevant redundant symmetry point sample data, and removing the relevant redundant symmetry point sample data from the state transition matrix of the physiotherapy process;
acquiring a physical therapy process state transition matrix from which relevant redundant symmetric point sample data are removed, introducing a singular value decomposition algorithm, and performing dimension reduction on the physical therapy process state transition matrix through the singular value decomposition algorithm to generate a covariance matrix;
and constructing a treatment state transition prediction model based on a convolutional neural network, inputting the covariance matrix into the treatment state transition prediction model for training, and outputting the treatment state transition prediction model after the treatment state transition prediction model meets the preset requirements.
5. The bio-compensation physiotherapy method of claim 1, further comprising the steps of:
acquiring treatment state information in a physiotherapy process within a preset time, and constructing treatment state change curve information according to the treatment state information in the physiotherapy process within the preset time;
Inputting the treatment state change curve information into the database for data matching, obtaining sample data information with highest similarity, and obtaining physiotherapy instrument treatment parameter information in the physiotherapy treatment process of the sample data information with highest similarity;
and recommending treatment parameters of the treatment state information in the physiotherapy process within preset time according to the physiotherapy instrument treatment parameter information in the physiotherapy process of the sample data information with the highest similarity, and generating recommendation information.
6. A bio-compensation physiotherapy system, the system comprising a memory and a processor, the memory comprising a bio-compensation physiotherapy method program, the bio-compensation physiotherapy method program when executed by the processor implementing the steps of:
acquiring historical image data information of a physiotherapy part in a treatment process, constructing a time stamp, and constructing sample data based on a time sequence by combining the time stamp and the historical image data information of the physiotherapy part in the treatment process;
constructing a database, introducing an attention mechanism to process the sample data, obtaining processed sample data, and inputting the processed sample data into the database for storage;
Acquiring sample data information in the physiotherapy process through the database, and processing the sample data information through a Markov chain to acquire a treatment state transition prediction model;
acquiring physiotherapy image data information in a physiotherapy process within a preset time, and generating compensation parameters of the physiotherapy instrument according to the physiotherapy image data information in the physiotherapy process within the preset time and a treatment state transition prediction model;
the method comprises the steps of acquiring physiotherapy image data information in the physiotherapy process in preset time, and generating compensation parameters of the physiotherapy instrument according to the physiotherapy image data information in the physiotherapy process in the preset time and a treatment state transition prediction model, wherein the method specifically comprises the following steps:
acquiring physiotherapy image data information in a physiotherapy process within a preset time, inputting the physiotherapy image data information in the physiotherapy process within the preset time into the treatment state transition prediction model for prediction, and acquiring a treatment state transition prediction result;
presetting relevant treatment state transition probability value information, and acquiring a treatment state transition probability value in the treatment process of the physiotherapy instrument within the next preset time according to the treatment state transition prediction result;
Judging whether the probability value of the treatment state transition is larger than a preset treatment state transition probability value, and if the probability value of the treatment state transition is not larger than the preset treatment state transition probability value, acquiring treatment parameter data information of the current physiotherapy instrument;
and acquiring a treatment state value in the current physiotherapy process through the treatment state transfer prediction result, acquiring treatment parameter data information of the physiotherapy instrument corresponding to the treatment state value in the current physiotherapy process through big data, and generating compensation parameters of the physiotherapy instrument according to the treatment parameter data information of the physiotherapy instrument corresponding to the treatment state value in the current physiotherapy process.
7. The bio-compensation physiotherapy system of claim 6, wherein the attention drawing mechanism processes the sample data, obtains processed sample data, and inputs the processed sample data into the database for storage, comprising the steps of:
constructing a database, introducing an attention mechanism, and calculating the attention score of each sample data through the attention mechanism to acquire the attention score information of each sample data;
randomly selecting one sample data as initial sample data, calculating Euclidean distance between the initial sample data and each other sample data, and sequencing each sample data based on the Euclidean distance value to generate sequencing results;
Dividing the database into a plurality of storage spaces, and inputting each sample data into the storage spaces for storage according to the sorting result;
and meanwhile, merging the sample data with equal Euclidean distance, inquiring the address of each storage space configuration information, and updating the database periodically.
8. The bio-compensation physiotherapy system according to claim 6, wherein the database is used for obtaining sample data information in physiotherapy process, and the markov chain is used for processing the sample data information to obtain a treatment state transition prediction model, and the method specifically comprises the following steps:
acquiring sample data information in a physiotherapy process through the database, carrying out state description on physiotherapy image data in the sample data information in the physiotherapy process through a Markov chain, and constructing a state transition matrix in the physiotherapy process;
introducing an FCBF algorithm, carrying out symmetry measurement on sample data in the state transition matrix of the physiotherapy process through the FCBF algorithm, obtaining relevant redundant symmetry point sample data, and removing the relevant redundant symmetry point sample data from the state transition matrix of the physiotherapy process;
Acquiring a physical therapy process state transition matrix from which relevant redundant symmetric point sample data are removed, introducing a singular value decomposition algorithm, and performing dimension reduction on the physical therapy process state transition matrix through the singular value decomposition algorithm to generate a covariance matrix;
and constructing a treatment state transition prediction model based on a convolutional neural network, inputting the covariance matrix into the treatment state transition prediction model for training, and outputting the treatment state transition prediction model after the treatment state transition prediction model meets the preset requirements.
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