CN113940634B - Alzheimer's disease classification diagnosis system based on high potential treatment - Google Patents
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Abstract
The invention discloses a high-potential treatment-based Alzheimer's disease classification diagnosis system, which is used for solving the problems that the existing Alzheimer's disease classification diagnosis system cannot analyze and evaluate voice data of the old to determine whether to detect Alzheimer's disease, so that symptoms cannot be found in time and the optimal treatment time is influenced; the system comprises a data acquisition end, a server, a classification diagnosis module and a high-potential treatment module; the invention processes the voice data of the user to be diagnosed to obtain a detection value, thereby reasonably determining that the user to be diagnosed detects the Alzheimer's disease, selecting the corresponding detection end by combining the number to be detected and the position spacing of the Alzheimer's disease detection equipment to detect the user to be diagnosed to obtain a detection result, processing the detection result and carrying out auxiliary treatment on the user to be diagnosed by the high-potential treatment equipment.
Description
Technical Field
The invention relates to the technical field of Alzheimer's disease classification diagnosis, in particular to a high-potential treatment-based Alzheimer's disease classification diagnosis system.
Background
Alzheimer's disease is a central nervous system variable disease, and has the symptoms of hidden attack and chronic disease course, mainly manifested by progressive memory disorder, cognitive dysfunction, personality change, language disorder and other neuropsychiatric symptoms, and seriously affecting social, professional and life functions;
the high potential therapy is to utilize high voltage alternating electric field with high voltage and low current to electrically regulate human body, regulate acid-base balance of blood, inhibit acidification of blood, and restore normal weak alkalization of the acidified blood, so as to promote metabolism and recover tissue and organ with disorder disease, and achieve the effects of treating diseases and protecting health;
the existing Alzheimer's disease classification diagnosis system cannot analyze and evaluate the voice data of the old people to determine whether to detect the Alzheimer's disease or not, so that the Alzheimer's disease can be extracted and evaluated and can be treated by high-potential treatment in time, and the curative effect of the Alzheimer's disease is improved.
Disclosure of Invention
The invention aims to solve the problems that the existing Alzheimer's disease classification diagnosis system cannot analyze and evaluate voice data of the old to determine whether to detect Alzheimer's disease, so that symptoms cannot be found in time and the optimal treatment time is affected.
The aim of the invention can be achieved by the following technical scheme:
a Alzheimer disease classification diagnosis system based on high-potential treatment comprises a data acquisition end, a server, a classification diagnosis module and a high-potential treatment module;
the data acquisition end is used for acquiring first voice data of a user to be diagnosed, playing a test voice problem and acquiring second voice data corresponding to the test voice problem replied by the user to be diagnosed, performing voice recognition on the first voice data and the second voice data and converting the voice recognition on the first voice data and the second voice data into characters to obtain a first text and a second text, and performing recognition processing on the characters in the first text to obtain a third text; transmitting the second text and the third text to a server; the third text comprises repeated sentence characters and corresponding time and repetition times;
the server receives the second text and the third text and then sends the second text and the third text to the classification diagnosis module;
the classification diagnosis module analyzes the second text and the third text after receiving the second text and the third text to obtain a check value of the user to be diagnosed, and when the check value is larger than a set threshold value, a detection signaling of the user to be diagnosed is generated and sent to the detection module; receiving and processing the detection result fed back by the detection module to obtain a treatment signaling of the user to be diagnosed, and sending the treatment signaling to the high-potential treatment module;
the detection module analyzes and processes the detection signaling to obtain a corresponding detection end, and the detection end detects a user to be diagnosed to obtain a detection result and feeds the detection result back to the detection module; the detection module sends the detection result to the classification diagnosis module;
the high-potential treatment module is used for analyzing the treatment signaling to obtain a corresponding high-potential treatment end and sending the treatment signaling to the high-potential treatment end; the high-potential treatment end is used for generating a high-voltage alternating electric field with high voltage and low current to treat the user to be diagnosed;
as a preferred embodiment of the present invention, the process of performing recognition processing on the text in the first text is: comparing the characters in the first text with the characters of the preset sentences, and copying the repeated preset sentence characters, corresponding moments and repeated times into a third text when the repeated preset sentence characters appear and the repeated times are more than two;
as a preferred embodiment of the present invention, the specific process of the classification diagnosis module for analyzing the second text and the third text is:
processing the third text, specifically: setting a plurality of preset sentence characters, wherein each preset sentence character corresponds to a semantic value, and matching the repeated sentence characters with all the preset sentence characters to obtain corresponding semantic values and marking the corresponding semantic values as YC; sequencing the repeated sentence characters according to the corresponding time, and calculating the time difference between the corresponding time of two adjacent repeated sentence characters to obtain the language weight time length; summing all the language weight time lengths, taking the average value to obtain a weight average value, and marking the weight average value as YJ; setting all preset sentences to correspond to a preset weight factor group YD; the preset weight factor group comprises preset weights k1 corresponding to the language weight time length and preset weights k2 corresponding to the weight average value; multiplying the values of the weight value and the weight average value by corresponding preset weights respectively and summing to obtain a first weight value JZ1;
processing the second text, specifically: comparing the reply characters in the second text with corresponding preset question answers to obtain the character repetition rates of the reply characters and the preset question answers, and marking the corresponding time for collecting the reply test voice questions of the reply characters as forgetting time when the character repetition rates are smaller than a set threshold value; sequencing all the forgetting moments according to the time sequence, and calculating the moment difference between two adjacent forgetting moments to obtain forgetting interval duration; then taking the time between two adjacent forgetting time points and marking the time as a middle time point, taking the middle time point as an abscissa, taking the numerical value of the corresponding forgetting interval time length as an ordinate, establishing a rectangular coordinate system, drawing all the numerical values of the forgetting interval time length into the rectangular coordinate system according to the corresponding middle time point, connecting numerical value points corresponding to the two forgetting interval time lengths in the rectangular coordinate system to obtain a forgetting line, calculating the slope of the forgetting line, and marking the slope of the forgetting line as a first slope when the included angle between the forgetting line and the abscissa is smaller than ninety degrees; when the included angle between the forgetting line and the abscissa is larger than ninety degrees, marking the slope of the forgetting line as a second slope; setting a weight coefficient of the first slope as da1; the weight coefficient of the second slope is da2; summing all values of the first slope and marking the sum of the first slopes as DX1; summing all values of the second slope and marking the second slope total value as DX2; obtaining a second language weight JZ2 by using a formula JZ=DX1×da1+DX2×da2;
multiplying the first language weight value and the second language weight value by corresponding preset weight coefficients respectively, and summing to obtain a check value of the user to be diagnosed;
as a preferred embodiment of the present invention, the specific process of the detection module for analyzing and processing the detection signaling is:
after receiving detection signaling of a user to be diagnosed, sending a position acquisition instruction to an intelligent terminal corresponding to the user to be diagnosed and acquiring the position of the user to be diagnosed, establishing a search area according to the position of the user to be diagnosed, and acquiring all Alzheimer's disease detection equipment in the search area and marking the Alzheimer's disease detection equipment as a primary selection end;
performing position interval calculation on the position of the primary selection end and the position of the user to be diagnosed to obtain a detection interval and marking the detection interval as WZ1; obtaining the number to be detected of the primary selection end and marking the number as WZ2;
normalizing the number to be detected and the position distance of the primary selection end, taking the numerical value obtained after normalization, and obtaining a bias selection value XW by using a formula XW=WZ1 xaf1+WZ2 xaf2; wherein, af1 and af2 are preset weight factors; marking a primary selection end with the smallest selection offset value as a detection end;
sending detection signaling to a detection end, wherein the number to be detected of the detection end is increased by one; meanwhile, the position of the detection end is sent to an intelligent terminal of the user to be diagnosed; after the user to be diagnosed receives the position of the detection end, the user to be diagnosed is detected through the detection end, corresponding indexes are obtained, all index data of the user to be diagnosed are marked as detection results and fed back to the detection module, and the number of the users to be detected of the detection end is reduced by one;
as a preferred embodiment of the present invention, the specific process of the classification diagnosis module receiving and processing the detection result is: acquiring values corresponding to all indexes in the detection result, multiplying all the values by weights of the corresponding indexes, and summing to obtain an evaluation value; setting a plurality of treatment signaling, wherein each treatment signaling corresponds to a numerical range, matching the evaluation value with all the numerical ranges to obtain corresponding treatment signaling, and transmitting the corresponding treatment signaling to a high-potential treatment module; wherein each treatment signaling includes a corresponding treatment duration and treatment frequency;
as a preferred embodiment of the present invention, the specific process of the high-potential treatment module receiving the treatment signaling of the user to be diagnosed for analysis is: acquiring position data of all high-potential therapeutic apparatuses, calculating the position distance between the positions of the high-potential therapeutic apparatuses and the positions of users to be diagnosed to obtain a treatment distance, and marking the high-potential therapeutic apparatus with the minimum treatment distance as a high-potential therapeutic end; after the high potential treatment end receives the treatment signaling, a high voltage alternating electric field with high voltage and low current is generated to treat the user to be diagnosed, and the treatment duration is consistent with the treatment duration in the treatment signaling;
as a preferred embodiment of the invention, the server comprises a registration module and a device acquisition module; the registration module is used for submitting registration information to register by a user, wherein the registration information comprises the age, name, identity card number and communication number and residence address of the user to be diagnosed; the equipment acquisition module is used for acquiring position data of the high-potential therapeutic instrument and the position of the Alzheimer disease detection equipment.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the voice data of the user to be diagnosed is processed to obtain a detection value, so that the user to be diagnosed is reasonably determined to detect the Alzheimer's disease, the corresponding detection end is selected by combining the number to be detected and the position spacing of the Alzheimer's disease detection equipment to detect the user to be diagnosed to obtain a detection result, and then the detection result is processed and the auxiliary treatment is carried out on the user to be diagnosed through the high-potential treatment equipment;
after the high-potential treatment end receives the treatment signaling, the high-voltage alternating electric field with high voltage and low current is generated to treat the user to be diagnosed, and the treatment duration is consistent with the treatment duration in the treatment signaling, so that reasonable auxiliary treatment is conveniently carried out on the Alzheimer disease patient.
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The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
Fig. 1 is a functional block diagram of the present invention.
Description of the embodiments
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, an alzheimer's disease classification diagnosis system based on high-potential treatment includes a data acquisition end, a server, a classification diagnosis module and a high-potential treatment module;
the data acquisition end acquires first voice data of a user to be diagnosed, plays a test voice problem and acquires second voice data corresponding to the test voice problem replied by the user to be diagnosed, carries out voice recognition on the first voice data and the second voice data and converts the voice recognition on the first voice data and the second voice data into characters to obtain a first text and a second text, and carries out recognition processing on the characters in the first text to obtain a third text, wherein the method specifically comprises the following steps of: comparing the characters in the first text with the characters of the preset sentences, and when the characters of the preset sentences repeatedly appear and the repetition number is greater than two, copying the repeatedly appearing characters of the preset sentences, corresponding moments and repetition numbers into a third text, and transmitting the second text and the third text into a server;
the server receives the second text and the third text and then sends the second text and the third text to the classified diagnosis module, and the classified diagnosis module analyzes the second text and the third text, wherein the specific analysis process is as follows:
processing the third text, setting a plurality of preset sentence characters, wherein each preset sentence character corresponds to a semantic value, and matching the repeated sentence characters with all the preset sentence characters to obtain corresponding semantic values and marking the corresponding semantic values as YC; sequencing the repeated sentence characters according to the corresponding time, and calculating the time difference between the corresponding time of two adjacent repeated sentence characters to obtain the language weight time length; summing all the language weight time lengths, taking the average value to obtain a weight average value, and marking the weight average value as YJ; setting all preset sentences to correspond to a preset weight factor group YD; the preset weight factor group comprises preset weights k1 corresponding to the language weight time length and preset weights k2 corresponding to the weight average value; multiplying the values of the weight value and the weight average value by corresponding preset weights respectively and summing to obtain a first weight value JZ1;
processing the second text, comparing the reply characters in the second text with corresponding preset question answers to obtain the character repetition rates of the reply characters and the preset question answers, and marking the corresponding time for collecting the reply test voice questions of the reply characters as forgetting time when the character repetition rates are smaller than a set threshold value; sequencing all the forgetting moments according to the time sequence, and calculating the moment difference between two adjacent forgetting moments to obtain forgetting interval duration; then taking the time between two adjacent forgetting time points and marking the time as a middle time point, taking the middle time point as an abscissa, taking the numerical value of the corresponding forgetting interval time length as an ordinate, establishing a rectangular coordinate system, drawing all the numerical values of the forgetting interval time length into the rectangular coordinate system according to the corresponding middle time point, connecting numerical value points corresponding to the two forgetting interval time lengths in the rectangular coordinate system to obtain a forgetting line, calculating the slope of the forgetting line, and marking the slope of the forgetting line as a first slope when the included angle between the forgetting line and the abscissa is smaller than ninety degrees; when the included angle between the forgetting line and the abscissa is larger than ninety degrees, marking the slope of the forgetting line as a second slope; setting a weight coefficient of the first slope as da1; the weight coefficient of the second slope is da2; summing all values of the first slope and marking the sum of the first slopes as DX1; summing all values of the second slope and marking the second slope total value as DX2; obtaining a second language weight JZ2 by using a formula JZ=DX1×da1+DX2×da2; values of da1 and da2 may be 0.79, 0.81;
multiplying the first weight and the second weight by corresponding preset weight coefficients respectively, summing to obtain a check value of the user to be diagnosed, wherein the check value is in direct proportion to the first weight and the second weight, and when the check value is greater than a set threshold, a detection signaling of the user to be diagnosed is generated and sent to a detection module; the check value is a numerical value for evaluating the probability of generating the detection signaling by the user to be diagnosed, and the larger the check value is, the larger the probability of correspondingly generating the detection signaling is; namely, the larger the value corresponding to the first language weight and the second language weight is, the larger the obtained check value is;
after receiving the detection signaling, the detection module analyzes and processes the detection signaling, and the specific process is as follows:
after receiving detection signaling of a user to be diagnosed, sending a position acquisition instruction to an intelligent terminal corresponding to the user to be diagnosed and acquiring the position of the user to be diagnosed, establishing a search area according to the position of the user to be diagnosed, and acquiring all Alzheimer's disease detection equipment in the search area and marking the Alzheimer's disease detection equipment as a primary selection end;
performing position interval calculation on the position of the primary selection end and the position of the user to be diagnosed to obtain a detection interval and marking the detection interval as WZ1; obtaining the number to be detected of the primary selection end and marking the number as WZ2;
normalizing the number to be detected and the position distance of the primary selection end, taking the numerical value obtained after normalization, and obtaining a bias selection value XW by using a formula XW=WZ1 xaf1+WZ2 xaf2; wherein, af1 and af2 are preset weight factors; the values of af1 and af2 may be 0.72;0.43; the primary selection end with the smallest deflection value is marked as a detection end;
sending detection signaling to a detection end, wherein the number to be detected of the detection end is increased by one; meanwhile, the position of the detection end is sent to an intelligent terminal of the user to be diagnosed; after the user to be diagnosed receives the position of the detection end, the user to be diagnosed is detected through the detection end, corresponding indexes are obtained, all index data of the user to be diagnosed are marked as detection results and fed back to the detection module, and the number of the users to be detected of the detection end is reduced by one;
the classification diagnosis module receives and processes the detection result, and the specific process is as follows: acquiring values corresponding to all indexes in the detection result, multiplying all the values by weights of the corresponding indexes, and summing to obtain an evaluation value; the indexes comprise indexes such as singular value decomposition matrix parameters, complexity approximate entropy, wavelet entropy and the like of alpha waves, beta waves and theta waves;
setting a plurality of treatment signaling, wherein each treatment signaling corresponds to a numerical range, matching the evaluation value with all the numerical ranges to obtain corresponding treatment signaling, and transmitting the corresponding treatment signaling to a high-potential treatment module; wherein each treatment signaling includes a corresponding treatment duration and treatment frequency;
the high potential treatment module analyzes the treatment signaling, and the specific process is as follows: acquiring position data of all high-potential therapeutic apparatuses, calculating the position distance between the positions of the high-potential therapeutic apparatuses and the positions of users to be diagnosed to obtain a treatment distance, and marking the high-potential therapeutic apparatus with the minimum treatment distance as a high-potential therapeutic end; after the high potential treatment end receives the treatment signaling, a high voltage alternating electric field with high voltage and low current is generated to treat the user to be diagnosed, and the treatment duration is consistent with the treatment duration in the treatment signaling;
the server comprises a registration module and an equipment acquisition module;
the registration module is used for submitting registration information to register by a user, wherein the registration information comprises the age, name, ID card number and communication number and residence address of the user to be diagnosed; the equipment acquisition module acquires position data of the high-potential therapeutic instrument and the position of the Alzheimer disease detection equipment; the Alzheimer's disease detection equipment is an Alzheimer's disease auxiliary therapeutic equipment utilizing high potential and ultrashort waves disclosed in patent CN 109701158A;
when the method and the device are used, a data acquisition end is used for acquiring first voice data of a user to be diagnosed, playing a test voice problem and acquiring second voice data corresponding to the test voice problem replied by the user to be diagnosed, voice recognition is carried out on the first voice data and the second voice data and the voice data are converted into characters to obtain a first text and a second text, and then the characters in the first text are subjected to recognition processing to obtain a third text; transmitting the second text and the third text to a server; the server receives the second text and the third text, then sends the second text and the third text to a classification diagnosis module, analyzes the second text through the classification diagnosis module, processes the third text to obtain a first language weight value, processes the second text to obtain a second language weight value, and generates a detection signaling of the user to be diagnosed and sends the detection signaling to a detection module when the detection signaling is larger than a set threshold value through the first language weight value and the second language weight value; receiving and processing the detection result fed back by the detection module to obtain a treatment signaling of the user to be diagnosed, sending the treatment signaling to the high-potential treatment module, and then generating a high-voltage alternating electric field with high voltage and low current through the high-potential treatment end to treat the user to be diagnosed; the detection method comprises the steps of processing voice data of a user to be diagnosed to obtain a detection value, reasonably determining that the user to be diagnosed detects Alzheimer's disease, selecting a corresponding detection end by combining the number to be detected and the position spacing of Alzheimer's disease detection equipment to detect the user to be diagnosed to obtain a detection result, processing the detection result and performing auxiliary treatment on the user to be diagnosed through high-potential treatment equipment.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (7)
1. The Alzheimer's disease classification diagnosis system based on high-potential treatment is characterized by comprising a data acquisition end, a server, a classification diagnosis module and a high-potential treatment module;
the data acquisition end is used for acquiring first voice data of a user to be diagnosed, playing a test voice problem and acquiring second voice data corresponding to the test voice problem replied by the user to be diagnosed, performing voice recognition on the first voice data and the second voice data and converting the voice recognition on the first voice data and the second voice data into characters to obtain a first text and a second text, and performing recognition processing on the characters in the first text to obtain a third text; transmitting the second text and the third text to a server; the third text comprises repeated sentence characters and corresponding time and repetition times;
the server receives the second text and the third text and then sends the second text and the third text to the classification diagnosis module;
the classification diagnosis module analyzes the second text and the third text after receiving the second text and the third text to obtain a check value of the user to be diagnosed, and when the check value is larger than a set threshold value, a detection signaling of the user to be diagnosed is generated and sent to the detection module; receiving and processing the detection result fed back by the detection module to obtain a treatment signaling of the user to be diagnosed, and sending the treatment signaling to the high-potential treatment module;
the detection module analyzes and processes the detection signaling to obtain a corresponding detection end, and the detection end detects a user to be diagnosed to obtain a detection result and feeds the detection result back to the detection module; the detection module sends the detection result to the classification diagnosis module;
the high-potential treatment module is used for analyzing the treatment signaling to obtain a corresponding high-potential treatment end and sending the treatment signaling to the high-potential treatment end; the high potential treatment end is used for generating a high voltage alternating electric field with high voltage and low current to treat the user to be diagnosed.
2. The alzheimer's disease classification and diagnosis system based on high potential therapy according to claim 1, wherein the process of recognizing the first text is: comparing the characters in the first text with the characters of the preset sentences, and copying the repeated preset sentence characters, corresponding moments and repeated times into a third text when the repeated preset sentence characters appear and the repeated times are larger than two.
3. The alzheimer's disease classification and diagnosis system based on high potential therapy according to claim 2, wherein the specific process of analyzing the second text and the third text by the classification and diagnosis module is as follows:
processing the third text, specifically: setting a plurality of preset sentence characters, wherein each preset sentence character corresponds to a semantic value, and matching repeated sentence characters with all preset sentence characters to obtain corresponding semantic values; sequencing the repeated sentence characters according to the corresponding time, and calculating the time difference between the corresponding time of two adjacent repeated sentence characters to obtain the language weight time length; summing all the language weight time lengths and taking the average value to obtain a weight average value; multiplying the values of the weight value and the weight average value by corresponding preset weights respectively and summing to obtain a first weight value;
processing the second text, specifically: comparing the reply characters in the second text with corresponding preset question answers to obtain the character repetition rates of the reply characters and the preset question answers, and marking the corresponding time for collecting the reply test voice questions of the reply characters as forgetting time when the character repetition rates are smaller than a set threshold value; sequencing all the forgetting moments according to the time sequence, and calculating the moment difference between two adjacent forgetting moments to obtain forgetting interval duration; then taking the time between two adjacent forgetting time points and marking the time as a middle time point, taking the middle time point as an abscissa, taking the numerical value of the corresponding forgetting interval time length as an ordinate, establishing a rectangular coordinate system, drawing all the numerical values of the forgetting interval time length into the rectangular coordinate system according to the corresponding middle time point, connecting numerical value points corresponding to the two forgetting interval time lengths in the rectangular coordinate system to obtain a forgetting line, calculating the slope of the forgetting line, and marking the slope of the forgetting line as a first slope when the included angle between the forgetting line and the abscissa is smaller than ninety degrees; when the included angle between the forgetting line and the abscissa is larger than ninety degrees, marking the slope of the forgetting line as a second slope; processing the first slope and the second slope to obtain a second language weight;
and multiplying the first language weight value and the second language weight value by corresponding preset weight coefficients respectively, and summing to obtain the check value of the user to be diagnosed.
4. The alzheimer's disease classification and diagnosis system based on high potential therapy according to claim 1, wherein the specific process of analyzing and processing the detection signaling by the detection module is as follows:
after receiving detection signaling of a user to be diagnosed, sending a position acquisition instruction to an intelligent terminal corresponding to the user to be diagnosed and acquiring the position of the user to be diagnosed, establishing a search area according to the position of the user to be diagnosed, and acquiring all Alzheimer's disease detection equipment in the search area and marking the Alzheimer's disease detection equipment as a primary selection end;
calculating the position distance between the position of the primary selection end and the position of the user to be diagnosed to obtain a detection distance; obtaining the number to be detected of the primary selection end;
normalizing the number to be detected and the position spacing of the primary selection end to obtain a selection offset value of the primary selection end, and marking the primary selection end with the minimum selection offset value as a detection end;
sending detection signaling to a detection end, wherein the number to be detected of the detection end is increased by one; meanwhile, the position of the detection end is sent to an intelligent terminal of the user to be diagnosed; after the user to be diagnosed receives the position of the detection end, the user to be diagnosed is detected through the detection end, corresponding indexes are obtained, all index data of the user to be diagnosed are marked as detection results and fed back to the detection module, and the number of the users to be detected of the detection end is reduced by one.
5. The alzheimer's disease classification and diagnosis system based on high potential therapy according to claim 4, wherein the specific process of the classification and diagnosis module receiving and processing the detection result is as follows: acquiring values corresponding to all indexes in the detection result, multiplying all the values by weights of the corresponding indexes, and summing to obtain an evaluation value; setting a plurality of treatment signaling, wherein each treatment signaling corresponds to a numerical range, matching the evaluation value with all the numerical ranges to obtain corresponding treatment signaling, and transmitting the corresponding treatment signaling to a high-potential treatment module; wherein each treatment signaling includes a corresponding treatment duration and treatment frequency.
6. The alzheimer's disease classification and diagnosis system based on high-potential therapy according to claim 4, wherein the specific process of the high-potential therapy module receiving the therapy signaling of the user to be diagnosed for analysis is: acquiring position data of all high-potential therapeutic apparatuses, calculating the position distance between the positions of the high-potential therapeutic apparatuses and the positions of users to be diagnosed to obtain a treatment distance, and marking the high-potential therapeutic apparatus with the minimum treatment distance as a high-potential therapeutic end; after the high potential treatment end receives the treatment signaling, a high voltage alternating electric field with high voltage and low current is generated to treat the user to be diagnosed, and the treatment duration is consistent with the treatment duration in the treatment signaling.
7. The Alzheimer's disease classification diagnosis system based on high-potential treatment according to claim 1, wherein the server comprises a registration module and a device acquisition module; the registration module is used for submitting registration information to register by a user, wherein the registration information comprises the age, name, identity card number and communication number and residence address of the user to be diagnosed; the equipment acquisition module is used for acquiring position data of the high-potential therapeutic instrument and the position of the Alzheimer disease detection equipment.
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