CN117612724B - Privacy assessment method and system for female vaginal relaxation state - Google Patents

Privacy assessment method and system for female vaginal relaxation state Download PDF

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CN117612724B
CN117612724B CN202410090076.1A CN202410090076A CN117612724B CN 117612724 B CN117612724 B CN 117612724B CN 202410090076 A CN202410090076 A CN 202410090076A CN 117612724 B CN117612724 B CN 117612724B
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CN117612724A (en
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顾琪琪
郑伟峰
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Nanjing Maidou Health Technology Co ltd
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    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
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    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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Abstract

The invention relates to the technical field of privacy maintenance, and discloses a privacy assessment method and a system for female vaginal relaxation state, wherein the privacy assessment method comprises the following steps: acquiring pregnancy information filled by a detected person; placing the vaginal electrode of the myoelectric sensor correctly in the vagina; acquiring a pelvic floor electromyographic signal, and comprehensively evaluating the functions and performances of pelvic floor muscles according to the judgment rules of six detection stages; converting the pelvic floor electromyographic signals into digital signals, obtaining basic data required to be obtained in each stage through various algorithms, calculating five sub-item scores according to the judging rules of the six detection stages, calculating total scores through the five sub-item scores, and outputting the detection total scores in a graphical mode. The invention detects the function of female privacy and simultaneously detects the pelvic floor dysfunction, gives visual and intuitive result data and discovers the problem of pelvic floor muscle injury earlier.

Description

Privacy assessment method and system for female vaginal relaxation state
Technical Field
The invention relates to the technical field of privacy maintenance, in particular to a privacy assessment method and system for female vaginal relaxation state.
Background
In recent years, many women have higher requirements on their own appearance and health, and are paying more attention to maintenance and health care, but the phenomena of aging appearance, low immunity, deformed stature, incoordination of the life of the couple and the like are still increasing year by year. In order to better solve the most interesting various beauty, slimming and health problems of women, the basic carrier of the body is needed to be started, and the female privacy maintenance concept provides a brand new space for women. The detection of female privacy function at present is most prone to go to medical institutions or evaluate privacy function by way of online consultation. The former two ways occupy a great deal of time from registering, seeking medical attention and detecting a series of processes, and meanwhile, the traditional vaginal palpation is a method widely used by physiotherapists to evaluate the contraction capacity of pelvic floor muscles at present, a tester directly senses the muscle strength of the vaginal contraction in a finger diagnosis mode and grades and scores muscles by combining an Oxford scoring system, and the vaginal palpation method is inductive and simple and is commonly used, but is subjectively judged by a diagnostician, and the diagnostician is required to have a skilled experience, so the method has a certain measurement error; the latter, through simple consultation, does not give the user a complete and intuitive test result.
Disclosure of Invention
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the invention provides a privacy assessment method and a privacy assessment system for a female vaginal relaxation state, which solve the problems that the existing vaginal relaxation state detection method is not accurate enough and not comprehensive enough.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, the present invention provides a method of privacy assessment of a female vaginal relaxation state comprising:
acquiring pregnancy information filled by a detected person;
placing the vaginal electrode correctly in the vagina;
acquiring a pelvic floor myoelectric signal, and comprehensively evaluating functions and performances of pelvic floor muscles through a judging rule of six detection stages and an improved ascending stage slope algorithm and a contraction algorithm;
the pelvic floor electromyographic signals are converted into digital signals, vaginal basic data which are required to be obtained in each stage are obtained through various algorithms, five sub-item scores are calculated according to the judging rules of six detection stages, the total score is calculated through the five sub-item scores, and the detection total score is output in a graphical mode.
As a preferred embodiment of the method for evaluating privacy of a female vaginal relaxation state according to the present invention, wherein: the six detection phases include: a pre-resting stage, a fast-acting stage, a slow-acting stage, a post-resting stage, a progressive control stage and a continuous A3 reflex stage;
The judging rule of the pre-resting stage comprises the following steps:
acquiring a front resting stage average value first_m and a template matching degree first_t of pelvic floor muscle data and vagina template data in a front resting stage through an average value algorithm and a template matching degree algorithm;
if first_m <2, the calculation formula of the average value score first_ms in the previous resting stage is: first_ms=90+ (2-first_m) ×5;
if 2 is less than or equal to first_m <10, the calculation formula of the average value score first_ms in the previous resting stage is as follows: first_ms= -1.125 x first_m× first_m+2.25xfirst_m+90
If first_m is more than or equal to 10, the average value score of the previous resting stage is first_ms=0;
if first_t >0, the calculation formula of the template matching degree score first_ts in the previous resting stage is as follows: first_ts=100×first_t;
if first_t <0, the template matching degree score first_ts=0 in the previous resting stage;
the previous rest score first_s=first_ms×0.8+first_ts×0.2;
the judging rule of the fast muscle stage comprises the following steps:
obtaining a maximum value second_b of the fast muscle stage and a slope second_r of the fast muscle stage rising stage through a maximum value algorithm and a rising stage slope algorithm;
if second_b >100, the fast muscle stage maximum score second_bs=100;
if 100 is greater than or equal to second_b >5, the calculation formula of the fast muscle stage maximum value score second_bs is: second_bs= (second_b×second_b)/(95- (second_b) +19);
If second_b is less than or equal to 5, the fast muscle stage maximum score second_bs=0;
if second_r >96, the fast muscle stage ascending stage slope score second_rs=100;
if 96 is greater than or equal to second_b >1, the calculation formula of the fast muscle stage ascending stage slope score second_rs is: second_rs= - (4× (second_r-96) × (second_r-96)) ∈1+100;
if second_b is less than or equal to 1, second_rs=0;
fast muscle score second_s=second_bs×0.7+second_rs×0.3;
the judging rule of the slow muscle stage comprises the following steps:
obtaining a slow muscle stage contraction average value third_m and a slow muscle stage contraction variability third_v through an average value algorithm and a variability algorithm;
if third_m <5, then the slow muscle stage contraction average score third_ms=0;
if 5 is less than or equal to third_m <100, the calculation formula of the slow muscle stage contraction average value score third_ms is as follows:
Third_ms＝-(4×(Third_m-100)×(Third_m-100))÷361+100；
if third_m is more than or equal to 100, the slow muscle stage contraction average score third_ms=100;
if third_v <0.1, the slow muscle stage contraction variability score third_vs is calculated by the following formula: third_vs=95+ (0.1-third_v) ×5++0.1;
if 0.1 is less than or equal to third_v <0.5, the calculation formula of the slow muscle stage contraction variability score third_vs is as follows: third_vs= -237.5×third_v+118.75;
If third_v is greater than or equal to 0.5, then the slow muscle stage contraction variability score third_vs=0;
slow muscle score third_s=third_ms×0.5+third_vs×0.5;
the judging rule of the post-resting stage comprises the following steps:
acquiring a post-resting stage average value Fourier_m, and template matching degree Fourier_t of pelvic floor muscle data and vagina template data through an average value algorithm and a template matching degree algorithm;
if Fourth_m <2, the calculation formula of the post-rest stage average value score Fourth_ms is: fourthms=90+ (2-fourthm) ×5;
if 2 is less than or equal to Fourth_m <10, the calculation formula of the average value score Fourth_ms in the post-rest stage is as follows: fourth_ms= -1.125×Fourth_mX Fourth_m+2.25XFourth_m+90;
if the Fourier_m is more than or equal to 10, the average value score of the post-resting stage is Fourier_ms=0;
if Fourth_t >0, the calculation formula of the template matching degree score Fourth_ts in the later rest stage is as follows: fourier_ts=100×fourier_t;
if the Fourier_t is less than 0, the template matching degree score of the post-rest stage is Fourier_ts=0;
post-rest score fourier_s=fourier_ms×0.8+fourier_ts×0.2;
the judging rule of the progressive control stage comprises the following steps:
acquiring a matching deviation value Fifth_g of a progressive control stage through a matching deviation value algorithm and a template matching degree algorithm, acquiring a template matching degree Fifth_t of pelvic floor muscle data and vaginal template data of the progressive control stage, and acquiring a shrinkage variability Fifth_v of the progressive control stage through a variability algorithm;
If Fifth_g is less than 10, the calculation formula of the coincidence deviation value score in the progressive control stage is as follows: fifth_gs=100-4×fifth_g;
if the Fifth_g is less than or equal to 10 and is less than 20, the calculation formula of the coincidence deviation value score in the progressive control stage is as follows: fifth_gs=120-6×fifth_g;
if Fifth_g is more than or equal to 20, the gradual control stage anastomosis deviation value is divided into Fifth_gs=0;
if Fifth_t >0, the template matching degree of pelvic floor muscle data and vaginal template data in the progressive control stage is Fifth_ts=100×Fifth_t;
if Fifth_t is less than 0, the template matching degree Fifth_ts=0 of pelvic floor muscle data and vaginal template data in the gradual control stage;
if Fifth_v <0.1, the calculation formula of the gradual control stage shrinkage variability score is: fifth_vs=95+ (0.1-Fifth_v). Times.5.0.1;
if 0.1 is less than or equal to Fifth_v <0.5, the calculation formula of the shrinkage variability score in the progressive control stage is as follows: fifth_vs= -237.5×Fifth_v+118.75;
if Fifth_v is more than or equal to 20, the gradual control stage shrinkage variability score Fifth_vs=0;
the judging rule of the continuous A3 reflection stage comprises the following steps:
acquiring a continuous A3 reflection stage anastomosis deviation value Sixth_g and template matching degree Sixth_t of pelvic floor muscle data and vagina template data through an anastomosis deviation value algorithm and a template matching degree algorithm;
If Sixth_g is less than 10, the calculation formula of the continuous A3 reflection stage coincidence deviation value score is as follows: sixth_gs=100-4×sixth_g;
if 10 is less than or equal to Sixth_g <20, the calculation formula of the continuous A3 reflection stage coincidence deviation value score is as follows: sixth_gs=120-6×sixth_g;
if Sixth_g is more than or equal to 20, the consistent deviation value score Sixth_gs=0 in the continuous A3 reflection stage;
if Sixth_t >0, the calculation formula of the template matching degree score of the continuous A3 reflection stage is as follows: sixth_ts=100×sixth_t;
if sixth_t <0, the template matching degree score of the continuous A3 reflection stage is sixth_ts=0;
as a preferred embodiment of the method for evaluating privacy of a female vaginal relaxation state according to the present invention, wherein: the rising stage slope algorithm comprises the following steps:
assigning an X array, wherein the value is 0 to N-1, and N time data are counted;
assigning the myoelectricity data of the pelvic floor muscles corresponding to the n time data into a Y array, and totaling n pieces of myoelectricity data of the pelvic floor muscles;
calculating the average value mu X of the X array;
calculating the average value mu Y of the Y array;
cycling for N times, and calculating the difference multiplication and cross sum of the X array and the Y array;
cycling for N times, and calculating the square sum of the average differences squared of the X arrays;
the slope Tup is expressed as: tup=crossSum/squareSum.
As a preferred embodiment of the method for evaluating privacy of a female vaginal relaxation state according to the present invention, wherein: the formula for calculating the sum of the difference and the average difference between the X array and the Y array is expressed as follows:
where Xi is the ith number of the X array, yi is the ith number of the Y array, μX is the average value of the X array, and μY is the average value of the Y array;
the formula for calculating the sum of squares of the mean differences of the X array is expressed as follows:
where Xi is the ith number of the X array and μX is the average of the X arrays.
As a preferred embodiment of the method for evaluating privacy of a female vaginal relaxation state according to the present invention, wherein: the method comprises the steps of obtaining a slow muscle stage contraction average value third_m and a slow muscle stage contraction variability third_v through an average algorithm and a variability algorithm, obtaining a gradual control stage contraction variability Fifth_v through a variability algorithm, obtaining vaginal data of a pelvic floor muscle contraction stage through a contraction algorithm, and obtaining the contraction average value and the contraction variability of the data of the pelvic floor muscle contraction stage through the average algorithm and the variability algorithm, wherein the contraction algorithm comprises the following steps:
circulating vagina template data, and setting the data of a first point of vagina detection as base;
Processing subsequent data of the vaginal template data, and setting an array for storing myoelectricity data of pelvic floor muscles in a contraction stage;
judging the next data of each piece of vaginal template data, if the next is not equal to the base, and the next is identical to the next after 10 pieces of continuous vaginal data, considering the next as the vaginal data in the contraction stage, and acquiring the time t corresponding to the next at the moment;
aiming at time t, myoelectricity data u corresponding to t is obtained from myoelectricity data of pelvic floor muscles and put into an array;
and (3) until the circulation is finished, acquiring data of which the array is the pelvic floor muscle contraction stage.
As a preferred embodiment of the method for evaluating privacy of a female vaginal relaxation state according to the present invention, wherein: calculating five sub-item scores according to the judging rules of the six detection stages, wherein the five sub-item scores are respectively an explosive force score, a diastole force score, a persistence force score, a control force score and an excitement score;
the explosive force score is equal to the fast muscle score;
the calculation formula of the diastolic force score is as follows: first_s×0.5+fourier_s×0.5;
the endurance score is equal to the slow muscle score;
the calculation formula of the control force score is as follows:
First_ts×0.05+Fourth_ts×0.05+Third_vs×0.2+Fifth_ts×0.2+Fifth_vs×0.15
+Sixth_ts×0.35；
the calculation formula of the excitement score is as follows:
Fifth_gs×0.15+Fifth_ts×0.15+Sixth_gs×0.25+Sixth_ts×0.25+Second_s×0.2。
As a preferred embodiment of the method for evaluating privacy of a female vaginal relaxation state according to the present invention, wherein: the total score is calculated through five sub-item scores, wherein the calculation formula of the total score is expressed as follows:
total score = persistence score x 0.25+ explosive force score x 0.25+ diastolic force score x 0.2+ controlling force score x 0.1+ excitability score x 0.2.
In a second aspect, the present invention provides a privacy assessment system for female vaginal relaxation states, comprising:
the acquisition module acquires pregnancy information filled by a detected person;
a placement module for correctly placing the vaginal electrode in the vagina;
the evaluation module acquires the myoelectric signals of the pelvic floor muscles and comprehensively evaluates the functions and performances of the pelvic floor muscles through the combination of the judging rules of six detection stages and an improved ascending stage slope algorithm and a shrinkage algorithm;
the calculation module converts the pelvic floor electromyographic signals into digital signals, obtains vaginal basic data required to be obtained in each stage through various algorithms, calculates five sub-item scores according to the judgment rules of six detection stages, calculates total scores through the five sub-item scores, and outputs the detection total scores in a graphical mode.
In a third aspect, the present invention provides a computing device comprising:
A memory for storing a program;
a processor for executing the computer executable instructions which when executed by the processor perform the steps of the privacy assessment method for female vaginal relaxation states.
In a fourth aspect, the present invention provides a computer-readable storage medium comprising: the program, when executed by a processor, performs the steps of the privacy assessment method for female vaginal relaxation states.
The invention has the beneficial effects that: the invention comprehensively evaluates the privacy function, and the detection method covers different aspects of the privacy function, including resting state, muscle strength, muscle coordination, reflecting capacity and the like, can comprehensively evaluate the function and performance of pelvic floor muscles and identify the existing problems. Through a set of programs which are independently developed, by adding a detection stage and a new result calculation method, the functions of female privacy are detected, simultaneously, pelvic floor dysfunction is detected, visual and visual result data are provided, myoelectricity data, namely voltage, used by the invention is the earliest abnormal myoelectricity signal in the muscle injury process, so that the problem of pelvic floor muscle injury can be found more accurately and earlier than pressure by myoelectricity.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a basic flow diagram of a method for evaluating privacy of a female vaginal relaxation state according to one embodiment of the present invention;
FIG. 2 is a system model diagram of a method for privacy assessment of female vaginal relaxation states in accordance with one embodiment of the present invention;
FIG. 3 is a system-wide flowchart of a second embodiment of a method for privacy assessment of vaginal relaxation in women according to one embodiment of the present invention;
FIG. 4 is a graph showing the detection effect of the previous resting stage in a second embodiment of a method for evaluating the privacy of a female vaginal relaxation state according to an embodiment of the present invention;
FIG. 5 is a graph showing the effect of rapid muscle phase detection in a second embodiment of a method for evaluating privacy of a female vaginal relaxation state according to an embodiment of the present invention;
FIG. 6 is a diagram showing the effect of slow muscle stage detection in a second embodiment of a method for evaluating privacy of a female vaginal relaxation state according to an embodiment of the present invention;
FIG. 7 is a graph showing the detection effect of the post-resting stage in a second embodiment of a method for evaluating the privacy of a female vaginal relaxation state according to one embodiment of the present invention;
FIG. 8 is a graph showing the effect of detecting the progressive control stage in a second embodiment of a method for evaluating the privacy of a female vaginal relaxation state according to an embodiment of the present invention;
FIG. 9 is a graph showing the effect of detecting successive A3 reflex phases in a second embodiment of a method for evaluating privacy of a female vaginal relaxation state according to an embodiment of the present invention;
fig. 10 is a diagram showing the effect of the total score in the form of graphics in the second embodiment of the method for evaluating the privacy of female vaginal relaxation according to an embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Example 1
Referring to fig. 1-3, for one embodiment of the present invention, a method for privacy assessment of female vaginal relaxation status is provided, as shown in fig. 1, comprising the steps of:
s1: acquiring pregnancy information filled by a detected person;
furthermore, the person to be detected manually triggers the privacy detection function, and the data transmission module is used for acquiring the history personal information UserInfo of the person to be detected, wherein the information comprises Birthday, delivery times, abortion times Gp and privacy Demand.
Judging whether the data storage module of the cloud contains the historical personal information of the detected person, if so, executing decryption, otherwise, filling again.
User personal information is anonymized and encrypted by a data security encryption module, and vaginal data is reversely processed in the step.
Anonymization and encryption processing reverse processing operation:
1. processing the personal information through a base64_decode to obtain strA;
2. performing AES decryption by using a specific key and a specific key offset iv for strA to obtain a character string strB;
3. analyzing strB into a JSON object, and taking out a sign value, a nonce value and a timestamp value;
4. the JSON object is taken out of sign data, and a character string strC is spliced by using a format of a Url key value pair;
5. splicing secret on the strC to obtain strD, wherein the secret value is a 16-bit character string agreed by the server and the client;
6. carrying out md5 operation on the strD to obtain strE;
7. converting strE into capitalized characters and generating a signature B;
8. and comparing sign B with sign, returning JSON data if the sign B is the same as the sign, and otherwise, returning null data.
If no history data exists, the step is directly executed after the history data is judged, and the interface data are all filled, otherwise, a detected person can carry out secondary adjustment on the Delivery times delay, the abortion times Gp and the private Demand in the personal information according to the personal Demand. (wherein, the Demand includes daily maintenance, privacy anti-aging, privacy nursing, privacy comprehensive improvement, privacy tightening, libido improvement, orgasm enhancement, privacy relaxation, sexual sensitivity improvement, water wettability improvement, privacy reaction enhancement, private pain relief, pelvic pain relief, lower abdominal pain relief, privacy sealing restoration)
S2: placing the vaginal electrode correctly in the vagina;
furthermore, the system relies on the vaginal electrode, and the person to be tested needs to correctly place the vaginal electrode into the vagina during this step.
Template information is a file which guides a detected person to detect and is used as a data calculation reference, and the base64_decode used herein can directly analyze JSON data of the template data.
The vaginal template data is a pair of key values of a plurality of times and template values, only a timer is needed to be used for customizing and obtaining the vaginal data in the pair, the time is used as an X axis on a screen, and the template value is used as a Y axis, so that the template line can be drawn.
The person to be detected performs movements such as contraction and relaxation on pelvic floor muscles according to the template line prompt on the equipment screen to generate a pelvic floor myoelectric signal.
The vaginal electrode collects electric signals under the movement of pelvic floor muscles, and the electric signals are processed into digital models through vaginal clicking.
Processing myoelectric voltage value, processing the exceeding range, judging the acquired vagina data testVal, setting the vagina data lower than 0 mu V to be 0 mu V;
Processing myoelectric voltage values, processing the exceeding range, and writing files.
1. Writing the template point value template Val and the acquired vaginal data testVal into JSON data according to the corresponding time of the template point; 2. serializing the JSON data into a character string strA;3. strA is encrypted by AES, and strB is obtained by processing the strA through a specific encryption Key; 4. strB is written to file.
The personal information in the history information is stored and correlated with the file in the myoelectricity data and the vagina template data.
S3: acquiring a pelvic floor myoelectric signal, and comprehensively evaluating functions and performances of pelvic floor muscles through a judging rule of six detection stages and an improved ascending stage slope algorithm and a contraction algorithm;
further, the six detection phases include: a pre-resting stage, a fast-acting stage, a slow-acting stage, a post-resting stage, a progressive control stage and a continuous A3 reflex stage;
the judging rule of the pre-resting stage comprises the following steps:
acquiring a front resting stage average value first_m and a template matching degree first_t of pelvic floor muscle data and vagina template data in a front resting stage through an average value algorithm and a template matching degree algorithm;
If first_m <2, the calculation formula of the average value score first_ms in the previous resting stage is: first_ms=90+ (2-first_m) ×5;
if 2 is less than or equal to first_m <10, the calculation formula of the average value score first_ms in the previous resting stage is as follows: first_ms= -1.125 x first_m× first_m+2.25xfirst_m+90
If first_m is more than or equal to 10, the average value score of the previous resting stage is first_ms=0;
if first_t >0, the calculation formula of the template matching degree score first_ts in the previous resting stage is as follows: first_ts=100×first_t;
if first_t <0, the template matching degree score first_ts=0 in the previous resting stage;
the previous rest score first_s=first_ms×0.8+first_ts×0.2;
the judging rule of the fast muscle stage comprises the following steps:
obtaining a maximum value second_b of the fast muscle stage and a slope second_r of the fast muscle stage rising stage through a maximum value algorithm and a rising stage slope algorithm;
if second_b >100, the fast muscle stage maximum score second_bs=100;
if 100 is greater than or equal to second_b >5, the calculation formula of the fast muscle stage maximum value score second_bs is: second_bs= (second_b×second_b)/(95- (second_b) +19);
if second_b is less than or equal to 5, the fast muscle stage maximum score second_bs=0;
if second_r >96, the fast muscle stage ascending stage slope score second_rs=100;
If 96 is greater than or equal to second_b >1, the calculation formula of the fast muscle stage ascending stage slope score second_rs is: second_rs= - (4× (second_r-96) × (second_r-96)) ∈1+100;
if second_b is less than or equal to 1, second_rs=0;
fast muscle score second_s=second_bs×0.7+second_rs×0.3;
the judging rule of the slow muscle stage comprises the following steps:
obtaining a slow muscle stage contraction average value third_m and a slow muscle stage contraction variability third_v through an average value algorithm and a variability algorithm;
if third_m <5, then the slow muscle stage contraction average score third_ms=0;
if 5 is less than or equal to third_m <100, the calculation formula of the slow muscle stage contraction average value score third_ms is as follows:
Third_ms＝-(4×(Third_m-100)×(Third_m-100))÷361+100；
if third_m is more than or equal to 100, the slow muscle stage contraction average score third_ms=100;
if third_v <0.1, the slow muscle stage contraction variability score third_vs is calculated by the following formula: third_vs=95+ (0.1-third_v) ×5++0.1;
if 0.1 is less than or equal to third_v <0.5, the calculation formula of the slow muscle stage contraction variability score third_vs is as follows: third_vs= -237.5×third_v+118.75;
if third_v is greater than or equal to 0.5, then the slow muscle stage contraction variability score third_vs=0;
slow muscle score third_s=third_ms×0.5+third_vs×0.5;
the judging rule of the post-resting stage comprises the following steps:
Acquiring a post-resting stage average value Fourier_m, and template matching degree Fourier_t of pelvic floor muscle data and vagina template data through an average value algorithm and a template matching degree algorithm;
if Fourth_m <2, the calculation formula of the post-rest stage average value score Fourth_ms is: fourthms=90+ (2-fourthm) ×5;
if 2 is less than or equal to Fourth_m <10, the calculation formula of the average value score Fourth_ms in the post-rest stage is as follows: fourth_ms= -1.125×Fourth_mX Fourth_m+2.25XFourth_m+90;
if the Fourier_m is more than or equal to 10, the average value score of the post-resting stage is Fourier_ms=0;
if Fourth_t >0, the calculation formula of the template matching degree score Fourth_ts in the later rest stage is as follows: fourier_ts=100×fourier_t;
if the Fourier_t is less than 0, the template matching degree score of the post-rest stage is Fourier_ts=0;
post-rest score fourier_s=fourier_ms×0.8+fourier_ts×0.2;
the judging rule of the progressive control stage comprises the following steps:
acquiring a matching deviation value Fifth_g of a progressive control stage through a matching deviation value algorithm and a template matching degree algorithm, acquiring a template matching degree Fifth_t of pelvic floor muscle data and vaginal template data of the progressive control stage, and acquiring a shrinkage variability Fifth_v of the progressive control stage through a variability algorithm;
If Fifth_g is less than 10, the calculation formula of the coincidence deviation value score in the progressive control stage is as follows: fifth_gs=100-4×fifth_g;
if the Fifth_g is less than or equal to 10 and is less than 20, the calculation formula of the coincidence deviation value score in the progressive control stage is as follows: fifth_gs=120-6×fifth_g;
if Fifth_g is more than or equal to 20, the gradual control stage anastomosis deviation value is divided into Fifth_gs=0;
if Fifth_t >0, the template matching degree of pelvic floor muscle data and vaginal template data in the progressive control stage is Fifth_ts=100×Fifth_t;
if Fifth_t is less than 0, the template matching degree Fifth_ts=0 of pelvic floor muscle data and vaginal template data in the gradual control stage;
if Fifth_v <0.1, the calculation formula of the gradual control stage shrinkage variability score is: fifth_vs=95+ (0.1-Fifth_v). Times.5.0.1;
if 0.1 is less than or equal to Fifth_v <0.5, the calculation formula of the shrinkage variability score in the progressive control stage is as follows: fifth_vs= -237.5×Fifth_v+118.75;
if Fifth_v is more than or equal to 20, the gradual control stage shrinkage variability score Fifth_vs=0;
the judging rule of the continuous A3 reflection stage comprises the following steps:
acquiring a continuous A3 reflection stage anastomosis deviation value Sixth_g and template matching degree Sixth_t of pelvic floor muscle data and vagina template data through an anastomosis deviation value algorithm and a template matching degree algorithm;
If Sixth_g is less than 10, the calculation formula of the continuous A3 reflection stage coincidence deviation value score is as follows: sixth_gs=100-4×sixth_g;
if 10 is less than or equal to Sixth_g <20, the calculation formula of the continuous A3 reflection stage coincidence deviation value score is as follows: sixth_gs=120-6×sixth_g;
if Sixth_g is more than or equal to 20, the consistent deviation value score Sixth_gs=0 in the continuous A3 reflection stage;
if Sixth_t >0, the calculation formula of the template matching degree score of the continuous A3 reflection stage is as follows: sixth_ts=100×sixth_t;
if sixth_t <0, the template matching degree score of the continuous A3 reflection stage is sixth_ts=0;
s4: the pelvic floor electromyographic signals are converted into digital signals, vaginal basic data which are required to be obtained in each stage are obtained through various algorithms, five sub-item scores are calculated according to the judging rules of six detection stages, the total score is calculated through the five sub-item scores, and the detection total score is output in a graphical mode.
Further, the average value is formulated as:
where x1, x2, x3, …, xn is each number of the selected sequence array and n is the number of sequence array data.
Further, the sample standard deviation formula is expressed as:
where μ is the average value of the calculated sequence arrays, x1, x2, x3, …, xn is each number of the selected sequence arrays, and n is the number of sequence array data.
Further, the variability formula is expressed as:
wherein μ is the average value of the calculated sequence array, and σ is the standard deviation of the sequence array.
Still further, the maximum value algorithm is represented in the present system using Xmax.
The maximum value algorithm is to use a loop to traverse each number, initially set a maximum value xmax=x1, then compare Xmax with x2, assign x2 to Xmax if x2 is greater than Xmax, otherwise, do not change, then judge x3, and so on until xn is reached, and then calculate Xmax. Often, a maximum value algorithm is built in a common programming development language, and the maximum value algorithm can be directly used.
Further, the rising phase slope algorithm comprises the following steps:
assigning an X array, wherein the value is 0 to N-1, and N time data are counted; n=n, i.e. the values are the same;
assigning the myoelectricity data of the pelvic floor muscles corresponding to the n time data into a Y array, and totaling n pieces of myoelectricity data of the pelvic floor muscles;
calculating the average value mu X of the X array;
calculating the average value mu Y of the Y array;
cycling for N times, and calculating the difference multiplication and cross sum of the X array and the Y array;
cycling for N times, and calculating the square sum of the average differences squared of the X arrays;
the slope Tup is expressed as: tup=crossSum/squareSum.
The formula for calculating the sum of the difference and the average difference between the X array and the Y array is expressed as:
where Xi is the ith number of the X array, yi is the ith number of the Y array, μX is the average value of the X array, and μY is the average value of the Y array;
the formula for calculating the sum of squares of the mean differences of the X array is expressed as follows:
where Xi is the ith number of the X array and μX is the average of the X arrays.
Further, the calculation formula of the anastomosis deviation value algorithm is expressed as follows:
/>
wherein, testVal1, testVal2, testVal3, …, testVal is each number of the detection sequence array, templateVal1, templateVal2, templateVal3, …, templateVal is each number of the template sequence array, and n is the number of the sequence array data.
Further, the calculation formula of the template matching degree algorithm is expressed as follows:
the calculation formula of the covariance formula is expressed as:
wherein Sxy is the sample covariance of the test value sequence and the template value sequence, σx is the sample standard deviation of the test value sequence, and σy is the sample standard deviation of the template value sequence.
A contraction algorithm comprising the steps of:
circulating vagina template data, and setting the data of a first point of vagina detection as base;
processing subsequent data of the vaginal template data, and setting an array for storing myoelectricity data of pelvic floor muscles in a contraction stage;
Judging the next data of each piece of vaginal template data, if the next is not equal to the base, and the next is identical to the next after 10 pieces of continuous vaginal data, considering the next as the vaginal data in the contraction stage, and acquiring the time t corresponding to the next at the moment;
aiming at time t, myoelectricity data u corresponding to t is obtained from myoelectricity data of pelvic floor muscles and put into an array;
and (3) until the circulation is finished, acquiring data of which the array is the pelvic floor muscle contraction stage.
Further, the five score scores are respectively a explosive force score, a diastole force score, a persistence force score, a control force score and an excitement score;
the explosive force score is equal to the fast muscle score;
the calculation formula of the tension score is as follows: first_s×0.5+fourier_s×0.5;
the endurance score is equal to the slow muscle score;
the calculation formula of the control force score is as follows:
First_ts×0.05+Fourth_ts×0.05+Third_vs×0.2+Fifth_ts×0.2+Fifth_vs×0.15
+Sixth_ts×0.35；
the calculation formula of the excitement score is as follows:
Fifth_gs×0.15+Fifth_ts×0.15+Sixth_gs×0.25+Sixth_ts×0.25+Second_s×0.2。
further, the total score is calculated by five component scores, wherein the calculation formula of the total score is expressed as:
total score = persistence score x 0.25+ explosive force score x 0.25+ diastolic force score x 0.2+ controlling force score x 0.1+ excitability score x 0.2.
It should be noted that the pre-resting stage may detect pelvic floor muscle relaxation ability. When the average value score first_ms and the template matching degree score first_ts are higher, the problems that the pelvic floor muscles are strong in relaxation capacity, low in tension and in a relaxation state, can be fully relaxed, excessive tension and spasm are reduced and the like are illustrated;
The fast muscle stage can detect pelvic floor muscle contractility. When the maximum value score second_bs and the rising stage slope score second_rs are higher, the fact that the pelvic floor muscles have high fast muscle fiber proportion is indicated, and under the condition, the contraction capacity of the pelvic floor muscles is high;
the slow muscle stage can detect pelvic floor muscle endurance and endurance. When the average score of the contraction phase is third_ms and the variability score of the contraction phase is third_vs, the proportion of slow muscle fibers in pelvic floor muscles is higher, and in this case, the persistence and endurance of the pelvic floor muscles are better;
the post-exercise recovery can be detected during the post-resting phase. When the average value score Fourth_ms and the template matching degree score Fourth_ts are higher, the pelvic floor muscle is proved to be in a state of being relaxed after a series of high-intensity movements;
the progressive control phase may detect the control capacity of the pelvic floor muscles. When three scores of the match deviation value score Fifth_gs, the template matching degree score Fifth_ts and the contraction phase variability score Fifth_vs are higher, the control capability of pelvic floor muscles is generally better, and progressive control contraction training can be effectively completed. This also means that individuals have better pelvic floor muscle condition and may have lower risk in problems such as urinary incontinence, pelvic organ prolapse, etc.;
The continuous A3 reflex phase can detect the excitement of pelvic floor muscles, and the curve simulates the electric signal in the case of female sexual high tide. When the two scores of the match deviation value score Sixth_gs and the template matching degree score Sixth_ts are higher, the individual has better pelvic floor muscle sphincter reflex capability. This may also indicate that the pelvic floor muscles react faster to rapid contractions and relaxations, which is also beneficial for sexual and reproductive health.
The present embodiment also provides a privacy assessment system for female vaginal relaxation states, comprising:
the acquisition module acquires pregnancy information filled by a detected person;
a placement module for correctly placing the vaginal electrode in the vagina;
the evaluation module acquires the myoelectric signals of the pelvic floor muscles and comprehensively evaluates the functions and performances of the pelvic floor muscles through the combination of the judging rules of six detection stages and an improved ascending stage slope algorithm and a shrinkage algorithm;
the calculation module converts the pelvic floor electromyographic signals into digital signals, obtains vaginal basic data required to be obtained in each stage through various algorithms, calculates five sub-item scores according to the judgment rules of six detection stages, calculates total scores through the five sub-item scores, and outputs the detection total scores in a graphical mode.
Further, as shown in fig. 2, the method further includes:
the template display module is used for drawing template information used in detection on a screen, reading out template numerical values in a template file, drawing the template numerical values on the screen, informing a user of each detection stage more intuitively, and guiding the user to exert force.
And the data acquisition module is used for acquiring myoelectric signals of the pelvic floor surface by the vaginal electrode, converting the myoelectric signals into digital signals, and acquiring vaginal data at the moment, namely, the pelvic floor surface voltage value U.
The data storage module has the main function of storing the collected voltage value U into a file through an encryption algorithm. Meanwhile, before detection, the user stores filled personal information (including Birthday, delivery times, abortion times Gp and private Demand).
And the data transmission module is mainly used for uploading the file and the personal information to the server and correlating the data.
And the data calculation module decrypts the file through a decryption algorithm corresponding to the encryption algorithm to obtain the acquired voltage value U and personal information. By calculating the scores of the pre-resting stage, the fast-muscular stage, the slow-muscular stage, the post-resting stage, the progressive control stage and the continuous A3 reflection stage, a total score is calculated, and a endurance score, an explosive force score, a diastolic force score, a control force score and an excitement score are specifically defined.
The result display module is used for displaying the effect of the jade graph for the user on the screen according to the scores obtained by the data calculation module; reading the template value and the detection value of each stage by reading the file stored during detection, and displaying the template value and the detection value on a screen through a myoelectric curve; the test values calculated from the vaginal data detected at each stage are presented on the screen in tabular form.
And the suggestion generation module is used for providing a sentence of general condition overview and restoration suggestions of the private function by combining cloud intelligent recommendation through comprehensive item scores, ages, delivery times, abortion times and private demands.
The log recording module is used for recording and storing the circulation condition and the abnormality occurrence problem of the data in the data storage module, the data transmission module and the data calculation module one by one.
The data security encryption module is mainly used for encrypting and decrypting a file storage mode, anonymizing processing is carried out for storing personal information of a user, and rights management is carried out for a user detection result.
Still further, still include:
a memory for storing a program;
and the processor is used for loading the program to execute the privacy assessment method of the female vaginal relaxation state.
The present embodiment also provides a computer-readable storage medium storing a program which, when executed by a processor, implements the privacy assessment method of female vaginal relaxation state.
The storage medium proposed in this embodiment belongs to the same inventive concept as the privacy assessment method of female vaginal relaxation state proposed in the above embodiment, and technical details not described in detail in this embodiment can be seen in the above embodiment, and this embodiment has the same advantageous effects as the above embodiment.
From the above description of embodiments, it will be clear to a person skilled in the art that the present invention may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, etc., including several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to execute the method of the embodiments of the present invention.
Example 2
Referring to fig. 3-10, for one embodiment of the present invention, a method for evaluating privacy of a female vaginal relaxation state is provided, and in order to verify the beneficial effects thereof, a scientific demonstration is made by specific embodiments and implementation effects.
The whole flow of this embodiment is shown in fig. 3 as follows:
detecting the user A through the myoelectric sensor; the historical data information of the user A is specifically: birth date: 1 month 2 days 1990, 1 labor time and 0 abortion time;
step 1) [ beginning ] the operation is in the guide process of procedure, and the manual clicking of the start button triggers the privacy detection function.
Step 2) [ acquire historical personal information ] acquire the historical personal information of the detected person through the data transmission module;
and 3, anonymizing and encrypting the personal information of the user through a data security encryption module, wherein the step is to reversely process the data.
Step 4) [ placing vaginal electrode during detection stage ] the system relies on vaginal electrode, and the person to be detected needs to place the vaginal electrode correctly into the vagina during this step.
Step 5) [ parse template information ] the base64_decode used herein directly parses JSON data of the template data.
And 6, customizing and acquiring data in the array by using a timer aiming at template data to draw a template line on a screen of the equipment, setting the time on the screen to be about 10s, drawing the template line by using a template value as a fixed reference value.
And 7, the detected person performs movements such as contraction, relaxation and the like on the pelvic floor muscle according to the template line prompt on the equipment screen to generate a pelvic floor myoelectric signal.
Step 8) [ data acquisition ] the vaginal electrode acquires an electric signal under the movement of pelvic floor muscles, and the electric signal is processed into a digital model through vaginal clicking.
Step 9) [ processing myoelectric voltage values ] processing the myoelectric voltage values, processing the exceeding range, judging the acquired data testVal, setting the data lower than 0 mu V to be 0 mu V;
step 10) [ save myoelectricity data and template data ] process myoelectricity voltage value, process the exceeding range, and write in the file.
Step 11) [ save personal information and associate ] save personal information in the history information, and associate with the file in step 12.
Step 12) [ calculation of detection results ] the result data of the entire detection process is calculated according to the method described by my invention. Including pre-resting stage, fast-muscular stage, slow-muscular stage, post-resting stage, progressive control stage, various scores of successive A3 reflex stages, total score, and specifically defined endurance score, explosive force score, diastolic force score, controlling force score, excitement score.
Wherein the effect patterns of the pre-resting stage, the fast-muscular stage, the slow-muscular stage, the post-resting stage, the progressive control stage and the continuous A3 reflection stage are respectively shown in fig. 4, fig. 5, fig. 6, fig. 7, fig. 8 and fig. 9
Step 13) [ show detection results ] show the total score in the detection results, and the endurance score, the explosive score, the diastolic score, the control score and the excitement score which are specially defined, and the process is shown in a form of a jade pattern, as shown in fig. 10.
As can be seen from the last fig. 10, the a user has a low control score and excitement score, and there is a possibility that there is a problem such as urinary incontinence, pelvic organ prolapse, and poor reflex ability of pelvic floor sphincter muscle.
If the diastolic force score is lower than 80 points, prompting that the excessive activity of pelvic floor muscles, sexual intercourse pain, vulvar pain and other excessive activity type pelvic floor dysfunction possibly exists;
if the explosive force score is lower than 80 minutes, prompt that the muscle strength of the fast muscle is insufficient, and the loose basin bottom dysfunction such as fecal incontinence, vaginal relaxation and the like is possible;
if the endurance score is lower than 80 points, the slow muscle strength is insufficient, and the relaxation type pelvic floor dysfunction such as uterine prolapse, vaginal front and rear wall expansion and the like is possible;
If the control force score is lower than 80 points, the risk is possibly higher on the problems of urinary incontinence, pelvic organ prolapse and the like;
if the excitement score is less than 80 points, the possible poor pelvic floor sphincter reflex capability is suggested, and the sexual life and reproductive health are affected to a certain extent.
By comprehensively analyzing the test results, doctors or rehabilitative operators can formulate personalized rehabilitation training schemes according to individual conditions so as to achieve better treatment effects; cloud autonomy can be realized subsequently. And (5) intelligent recommendation.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.

Claims (6)

1. A method of privacy assessment of a female vaginal relaxation state, comprising:
acquiring pregnancy information filled by a detected person;
placing the vaginal electrode of the myoelectric sensor correctly in the vagina;
acquiring a pelvic floor myoelectric signal, and comprehensively evaluating functions and performances of pelvic floor muscles through a judging rule of six detection stages and an improved ascending stage slope algorithm and a contraction algorithm;
Converting the pelvic floor electromyographic signals into digital signals, obtaining vaginal basic data required to be obtained in each stage through various algorithms, calculating five sub-item scores according to the judgment rules of six detection stages, calculating total scores through the five sub-item scores, and outputting the detection total scores in a graphical mode;
the six detection phases include: a pre-resting stage, a fast-acting stage, a slow-acting stage, a post-resting stage, a progressive control stage and a continuous A3 reflex stage;
the judging rule of the pre-resting stage comprises the following steps:
acquiring a front resting stage average value first_m and a template matching degree first_t of pelvic floor muscle data and vagina template data in a front resting stage through an average value algorithm and a template matching degree algorithm;
if first_m <2, the calculation formula of the average value score first_ms in the previous resting stage is: first_ms=90+ (2-first_m) ×5;
if 2 is less than or equal to first_m <10, the calculation formula of the average value score first_ms in the previous resting stage is as follows: first_ms= -1.125 x first_m× first_m+2.25xfirst_m+90
If first_m is more than or equal to 10, the average value score of the previous resting stage is first_ms=0;
if first_t >0, the calculation formula of the template matching degree score first_ts in the previous resting stage is as follows: first_ts=100×first_t;
If first_t <0, the template matching degree score first_ts=0 in the previous resting stage;
the previous rest score first_s=first_ms×0.8+first_ts×0.2;
the judging rule of the fast muscle stage comprises the following steps:
obtaining a maximum value second_b of the fast muscle stage and a slope second_r of the fast muscle stage rising stage through a maximum value algorithm and a rising stage slope algorithm;
if second_b >100, the fast muscle stage maximum score second_bs=100;
if 100 is greater than or equal to second_b >5, the calculation formula of the fast muscle stage maximum value score second_bs is: second_bs= (second_b×second_b)/(95- (second_b) +19);
if second_b is less than or equal to 5, the fast muscle stage maximum score second_bs=0;
if second_r >96, the fast muscle stage ascending stage slope score second_rs=100;
if 96 is greater than or equal to second_b >1, the calculation formula of the fast muscle stage ascending stage slope score second_rs is: second_rs= - (4× (second_r-96) × (second_r-96)) ∈1+100;
if second_b is less than or equal to 1, second_rs=0;
fast muscle score second_s=second_bs×0.7+second_rs×0.3;
the judging rule of the slow muscle stage comprises the following steps:
obtaining a slow muscle stage contraction average value third_m and a slow muscle stage contraction variability third_v through an average value algorithm and a variability algorithm;
If third_m <5, then the slow muscle stage contraction average score third_ms=0;
if 5 is less than or equal to third_m <100, the calculation formula of the slow muscle stage contraction average value score third_ms is as follows: third_ms= - (4× (third_m-100) × (third_m-100)) ∈+100;
if third_m is more than or equal to 100, the slow muscle stage contraction average score third_ms=100;
if third_v <0.1, the slow muscle stage contraction variability score third_vs is calculated by the following formula: third_vs=95+ (0.1-third_v) ×5++0.1;
if 0.1 is less than or equal to third_v <0.5, the calculation formula of the slow muscle stage contraction variability score third_vs is as follows: third_vs= -237.5×third_v+118.75;
if third_v is greater than or equal to 0.5, then the slow muscle stage contraction variability score third_vs=0;
slow muscle score third_s=third_ms×0.5+third_vs×0.5;
the judging rule of the post-resting stage comprises the following steps:
acquiring a post-resting stage average value Fourier_m, and template matching degree Fourier_t of pelvic floor muscle data and vagina template data through an average value algorithm and a template matching degree algorithm;
if Fourth_m <2, the calculation formula of the post-rest stage average value score Fourth_ms is: fourthms=90+ (2-fourthm) ×5;
if 2 is less than or equal to Fourth_m <10, the calculation formula of the average value score Fourth_ms in the post-rest stage is as follows: fourth_ms= = material
1.125×Fourth_m×Fourth_m+2.25×Fourth_m+90；
If the Fourier_m is more than or equal to 10, the average value score of the post-resting stage is Fourier_ms=0;
if Fourth_t >0, the calculation formula of the template matching degree score Fourth_ts in the later rest stage is as follows: fourier_ts=100×fourier_t;
if the Fourier_t is less than 0, the template matching degree score of the post-rest stage is Fourier_ts=0;
post-rest score fourier_s=fourier_ms×0.8+fourier_ts×0.2;
the judging rule of the progressive control stage comprises the following steps:
acquiring a matching deviation value Fifth_g of a progressive control stage through a matching deviation value algorithm and a template matching degree algorithm, acquiring a template matching degree Fifth_t of pelvic floor muscle data and vaginal template data of the progressive control stage, and acquiring a shrinkage variability Fifth_v of the progressive control stage through a variability algorithm;
if Fifth_g is less than 10, the calculation formula of the coincidence deviation value score in the progressive control stage is as follows: fifth_gs=100-4×fifth_g;
if the Fifth_g is less than or equal to 10 and is less than 20, the calculation formula of the coincidence deviation value score in the progressive control stage is as follows: fifth_gs=120-6×fifth_g;
if Fifth_g is more than or equal to 20, the gradual control stage anastomosis deviation value is divided into Fifth_gs=0;
if Fifth_t >0, the template matching degree of pelvic floor muscle data and vaginal template data in the progressive control stage is Fifth_ts=100×Fifth_t;
If Fifth_t is less than 0, the template matching degree Fifth_ts=0 of pelvic floor muscle data and vaginal template data in the gradual control stage;
if Fifth_v <0.1, the calculation formula of the gradual control stage shrinkage variability score is: fifth_vs=95+ (0.1-Fifth_v). Times.5.0.1;
if 0.1 is less than or equal to Fifth_v <0.5, the calculation formula of the shrinkage variability score in the progressive control stage is as follows: fifth_vs= -237.5×Fifth_v+118.75;
if Fifth_v is more than or equal to 20, the gradual control stage shrinkage variability score Fifth_vs=0;
the judging rule of the continuous A3 reflection stage comprises the following steps:
acquiring a continuous A3 reflection stage anastomosis deviation value Sixth_g and template matching degree Sixth_t of pelvic floor muscle data and vagina template data through an anastomosis deviation value algorithm and a template matching degree algorithm;
if Sixth_g is less than 10, the calculation formula of the continuous A3 reflection stage coincidence deviation value score is as follows: sixth_gs=100-4×sixth_g;
if 10 is less than or equal to Sixth_g <20, the calculation formula of the continuous A3 reflection stage coincidence deviation value score is as follows: sixth_gs=120-6×sixth_g;
if Sixth_g is more than or equal to 20, the consistent deviation value score Sixth_gs=0 in the continuous A3 reflection stage;
if Sixth_t >0, the calculation formula of the template matching degree score of the continuous A3 reflection stage is as follows: sixth_ts=100×sixth_t;
If sixth_t <0, the template matching degree score of the continuous A3 reflection stage is sixth_ts=0;
the improved rising phase slope algorithm comprises the following steps:
assigning an X array, wherein the value is 0 to N-1, and N time data are counted;
assigning the myoelectricity data of the pelvic floor muscles corresponding to the n time data into a Y array, and totaling n pieces of myoelectricity data of the pelvic floor muscles;
calculating the average value mu X of the X array;
calculating the average value mu Y of the Y array;
cycling for N times, and calculating the difference multiplication and cross sum of the X array and the Y array;
cycling for N times, and calculating the square sum of the average differences squared of the X arrays;
the slope Tup is expressed as: tup = crossSum/squareSum;
the improved contraction algorithm further comprises the steps of obtaining data of a contraction stage of the pelvic floor muscle through the contraction algorithm, and obtaining contraction average value and contraction variability of the data of the contraction stage of the pelvic floor muscle through an average value algorithm and a variability algorithm, wherein the contraction algorithm comprises the following steps of:
circulating vagina template data, and setting the data of a first point of vagina detection as base;
processing subsequent data of the vaginal template data, and setting an array for storing myoelectricity data of pelvic floor muscles in a contraction stage;
judging the next data of each piece of vaginal template data, if the next is not equal to the base, and the next is identical to the next after 10 pieces of continuous vaginal data, considering the next as the vaginal data in the contraction stage, and acquiring the time t corresponding to the next at the moment;
Aiming at time t, acquiring vaginal myoelectricity data u corresponding to t from pelvic floor myoelectricity data, and putting the vaginal myoelectricity data u into an array;
until the circulation is finished, acquiring data of an array serving as a pelvic floor muscle contraction stage;
calculating five sub-item scores according to the judging rules of the six detection stages, wherein the five sub-item scores are respectively a explosive force score, a diastole force score, a persistence force score, a control force score and an excitement degree score;
the explosive force score is equal to the fast muscle score;
the calculation formula of the diastolic force score is as follows: first_s×0.5+fourier_s×0.5;
the endurance score is equal to the slow muscle score;
the calculation formula of the control force score is as follows:
First_ts×0.05+Fourth_ts×0.05+Third_vs×0.2+Fifth_ts×0.2+
Fifth_vs×0.15+Sixth_ts×0.35；
the calculation formula of the excitement score is as follows:
Fifth_gs×0.15+Fifth_ts×0.15+Sixth_gs×0.25+Sixth_ts×0.25+Second_s×0.2。
2. the method for privacy assessment of a female vaginal relaxation state of claim 1, wherein: the formula for calculating the sum of the difference and the average difference between the X array and the Y array is expressed as follows:
wherein X is i Is the ith number of the X array, Y i Is the ith number, mu of the Y array x Is the average value of X array, mu y Is the average value of the Y array;
the formula for calculating the sum of squares of the mean differences of the X array is expressed as follows:
wherein X is i Is the ith number of X array, mu x Is the average of the X arrays.
3. A method of privacy assessment of a female vaginal relaxation state as claimed in claim 2, wherein: the total score is calculated through five sub-item scores, wherein the calculation formula of the total score is expressed as follows:
total score = endurance score x 0.25+ explosive force score x 0.25+ diastolic force score
X 0.2+ control force score x 0.1+ excitement score x 0.2.
4. An evaluation system based on the female vaginal relaxation state privacy evaluation method as claimed in any one of claims 1 to 3, characterized in that:
the acquisition module acquires pregnancy information filled by a detected person;
the placing module is used for correctly placing the vaginal electrode of the myoelectric sensor in the vagina;
the evaluation module acquires the myoelectric signals of the pelvic floor muscles and comprehensively evaluates the functions and performances of the pelvic floor muscles through the combination of the judging rules of six detection stages and an improved ascending stage slope algorithm and a shrinkage algorithm;
the calculation module converts the pelvic floor electromyographic signals into digital signals, obtains vaginal basic data required to be obtained in each stage through various algorithms, calculates five sub-item scores according to the judgment rules of six detection stages, calculates total scores through the five sub-item scores, and outputs the detection total scores in a graphical mode.
5. An electronic device, comprising:
a memory for storing a program;
a processor for loading the program to perform the steps of the privacy assessment method of female vaginal relaxation state of any of claims 1-3.
6. A computer-readable storage medium storing a program, wherein the program, when executed by a processor, implements the steps of the privacy assessment method of female vaginal relaxation states as claimed in any one of claims 1 to 3.
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