Bioelectric signal control method, device and storage medium for hemorrhoid rehabilitation
By obtaining and analyzing the patient's hemorrhoid type, inflammation degree and pain status, and dynamically adjusting bioelectric signal parameters, the problem that existing equipment is difficult to adapt to the pathological status of different patients is solved, and the personalized effect of hemorrhoid rehabilitation treatment is improved.
Patent Information
- Application Number
- CN202411975743.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-31
- Publication Date
- 2025-04-04
AI Technical Summary
Existing bioelectric stimulation equipment is difficult to adapt to the pathological state of different patients when treating hemorrhoids, resulting in poor treatment results.
By obtaining the target user's hemorrhoid type, inflammation degree and pain condition, numerical processing is performed to obtain the target encoding information, the pathological state is analyzed using a pre-trained neural network model, and the parameters of the bioelectric signal are dynamically adjusted to achieve personalized treatment.
It improves the personalized effect of hemorrhoid rehabilitation treatment and enhances the user experience.
Smart Images
Figure CN119746271A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of signal processing technology, and in particular to a bioelectric signal control method, device, equipment and storage medium for hemorrhoid rehabilitation. Background Art
[0002] Hemorrhoids are a common anorectal disease, usually caused by increased venous pressure in the anus or rectum, and are often accompanied by symptoms such as bleeding, pain, and itching. Existing treatments for hemorrhoids include medication, surgery, and physical therapy. However, these treatments often have certain side effects or a long recovery period. In recent years, bioelectric stimulation, as a non-invasive treatment method, has been widely used in various rehabilitation treatment fields, especially in improving blood circulation, reducing inflammation, and relieving muscle spasms.
[0003] Although existing bioelectric stimulation devices have been used to treat hemorrhoids, the optimal parameters of electrical stimulation will be affected by factors such as the patient's condition, physique, age, and gender. Traditional electrical stimulation devices often use bioelectric signals with fixed frequency, intensity, and pulse width. This method is difficult to adapt to the pathological conditions of different patients, thus affecting the user experience. Summary of the invention
[0004] The embodiments of the present application provide a bioelectric signal control method, device, equipment and storage medium for hemorrhoid rehabilitation, which controls the bioelectric signal parameters according to the target user's hemorrhoid type, inflammation level and pain condition, so as to achieve personalized treatment for the target user's pathological condition and improve the user experience.
[0005] In a first aspect, an embodiment of the present application provides a bioelectric signal control method for hemorrhoid rehabilitation, comprising: obtaining the hemorrhoid type, degree of inflammation and pain condition of the target user; digitizing the hemorrhoid type, degree of inflammation and pain condition to obtain target coding information; analyzing the target coding information based on a pre-trained neural network model to obtain the pathological state of the target user; and dynamically adjusting the parameters of the bioelectric signal based on the pathological state.
[0006] In the second aspect, an embodiment of the present application provides a bioelectric signal control device for hemorrhoid rehabilitation, including: an acquisition module for acquiring the hemorrhoid type, inflammation level and pain condition of the target user; a processing module for digitizing the hemorrhoid type, inflammation level and pain condition to obtain target coding information; an analysis module for analyzing the target coding information based on a pre-trained neural network model to obtain the pathological state of the target user; an adjustment module for dynamically adjusting the parameters of the bioelectric signal based on the pathological state.
[0007] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory and a processing module; the memory is used to store a computer program; the processing module is used to execute the computer program and, when executing the computer program, implement the steps of the bioelectric signal control method for hemorrhoid rehabilitation as described in the first aspect above.
[0008] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program; when the computer program is executed by one or more processing modules, the one or more processing modules execute the steps of the bioelectric signal control method for hemorrhoid rehabilitation as described in the first aspect above.
[0009] The embodiment of the present application provides a bioelectric signal control method, device, equipment and storage medium for hemorrhoid rehabilitation, wherein the bioelectric signal control method for hemorrhoid rehabilitation includes: obtaining the hemorrhoid type, inflammation degree and pain condition of the target user; digitizing the hemorrhoid type, inflammation degree and pain condition to obtain target coding information; analyzing the target coding information based on a pre-trained neural network model to obtain the pathological state of the target user; dynamically adjusting the parameters of the bioelectric signal based on the pathological state. By controlling the bioelectric signal parameters according to the hemorrhoid type, inflammation degree and pain condition of the target user, personalized treatment can be achieved for the pathological state of the target user, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS
[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.
[0011] Figure 1 A schematic flow chart of a bioelectric signal control method for hemorrhoid rehabilitation provided in an embodiment of the present application;
[0012] Figure 2 A schematic diagram of the structure of a bioelectric signal control device for hemorrhoid rehabilitation provided in an embodiment of the present application;
[0013] Figure 3 A schematic block diagram of a bioelectric signal control device for hemorrhoid rehabilitation provided in an embodiment of the present application. DETAILED DESCRIPTION
[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0015] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.
[0016] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.
[0017] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.
[0018] The technical solution provided by this application will be described in detail below with reference to the accompanying drawings.
[0019] See also Figure 1 , Figure 1 The flow chart of the bioelectric signal control method for hemorrhoid rehabilitation provided in the embodiment of the present application is shown in FIG. The bioelectric signal control method for hemorrhoid rehabilitation provided in the embodiment of the present application is implemented by hemorrhoid therapy or rehabilitation equipment. The embodiment of the present application does not impose any limitation on hemorrhoid therapy or rehabilitation equipment. Specifically, Figure 1 As shown, the bioelectric signal control method for hemorrhoid rehabilitation includes steps S101 to S104. The details are as follows:
[0020] S101: Obtain the hemorrhoid type, inflammation degree, and pain condition of the target user.
[0021] In the present application, a user operation interface is displayed for the target user to select and input the type of hemorrhoids, the degree of inflammation and the pain condition. The target user can obtain the corresponding type of hemorrhoids, the degree of inflammation and the pain condition in advance through multi-dimensional diagnostic means, including but not limited to medical consultation, physical examination or other necessary auxiliary examinations and pain scoring systems, and the above-mentioned information is selected or input through the user operation interface provided by the hemorrhoid therapy or rehabilitation equipment of the present application for acquisition by the hemorrhoid therapy or rehabilitation equipment. Among them, the hemorrhoid type includes internal hemorrhoids, external hemorrhoids or mixed hemorrhoids, the degree of inflammation includes mild, moderate or severe, the pain condition includes mild, moderate or severe, etc.
[0022] S102: Numerically process the hemorrhoid type, inflammation degree and pain condition to obtain target coding information.
[0023] Exemplarily, the target coding information includes a combination of a hemorrhoid type coding value, an inflammation degree coding value, and a pain condition coding value. The hemorrhoid type, the inflammation degree, and the pain condition are numerically processed to obtain the target coding information, including: mapping the hemorrhoid type to a coding value to obtain a hemorrhoid type coding value, including an internal hemorrhoid coding value, an external hemorrhoid coding value, or a mixed hemorrhoid coding value; mapping the inflammation degree to a coding value to obtain an inflammation degree coding value, including a mild coding value, a moderate coding value, or a severe coding value; mapping the pain condition to a coding value to obtain a pain coding value, including a mild pain value, a moderate pain value, or a severe pain value; combining the hemorrhoid type coding value, the inflammation degree coding value, and the pain coding value to obtain the target coding information.
[0024] For example, assuming that the target user's hemorrhoid type, inflammation level, and pain condition are digitized, the hemorrhoid type coding value is 1, the inflammation level coding value is 1, and the pain coding value is 2, then the corresponding target coding information is expressed as [1 1 2].
[0025] In the present application, the hemorrhoid type code value is represented by a value of 1 for internal hemorrhoids, a value of 2 for external hemorrhoids, and a value of 3 for mixed hemorrhoids; the inflammation degree code value is represented by a value of 1 for mild code value, a value of 2 for moderate code value, and a value of 3 for severe code value; the pain code value is represented by a value of 1 for mild pain value, a value of 2 for moderate pain value, and a value of 3 for severe pain value.
[0026] By numerically coding the hemorrhoid type, inflammation degree and pain condition, target coding information is obtained to facilitate subsequent data analysis and treatment.
[0027] S103: Analyze the target coding information based on the pre-trained neural network model to obtain the pathological state of the target user.
[0028] In the present application, the pre-trained neural network model includes an input layer, a hidden layer and an output layer; the target coding information is analyzed based on the pre-trained neural network model to obtain the pathological state of the target user, including: receiving the target coding information at the input layer, standardizing the target coding information, and inputting the standardized data into the hidden layer; extracting nonlinear features from the standardized data in the hidden layer, and inputting the nonlinear features into the output layer; predicting the pathological state of the target user based on the nonlinear features at the output layer.
[0029] The hidden layer includes a series of neuron nodes. The nonlinear feature extracted from the jth neuron in the hidden layer is expressed as:
[0030]
[0031] Among them, b j is the bias term of the jth neuron in the hidden layer, ω j is the weight parameter between the input layer and the jth neuron in the hidden layer, and x is the output of the input layer.
[0032] After the output layer weights the output of the hidden layer, the pathological state of the target user is predicted. Exemplarily, after the output layer weights the output of the hidden layer, the pathological state of the target user is predicted, which is expressed as:
[0033]
[0034] Among them, h j is the output value of the jth neuron in the hidden layer, ω jk is the weight parameter between the output of the jth neuron in the hidden layer and the output layer, b k is the bias term of the output layer, and y is the final prediction result of the output layer.
[0035] S104: Dynamically adjust the parameters of the bioelectric signal based on the pathological state.
[0036] In the present application, pathological conditions include mild, moderate and severe; based on the pathological conditions, the parameters of the bioelectric signal are dynamically adjusted, including: based on the pathological conditions, traversing the pre-established mapping relationship between the pathological conditions and the parameters of the bioelectric signal, obtaining the parameters of the target bioelectric signal that matches the pathological condition, adjusting the parameters of the bioelectric signal to be output to the parameters of the target bioelectric signal, and outputting them.
[0037] Through the above analysis, it can be known that the bioelectric signal control method for hemorrhoid rehabilitation provided by the embodiment of the present application includes: obtaining the hemorrhoid type, degree of inflammation and pain condition of the target user; digitizing the hemorrhoid type, degree of inflammation and pain condition to obtain target coding information; analyzing the target coding information based on a pre-trained neural network model to obtain the pathological state of the target user; dynamically adjusting the parameters of the bioelectric signal based on the pathological state. By controlling the bioelectric signal parameters according to the hemorrhoid type, degree of inflammation and pain condition of the target user, personalized treatment can be achieved for the pathological state of the target user, thereby improving the user experience.
[0038] See also Figure 2 As shown, Figure 2 This is a schematic diagram of the structure of a bioelectric signal control device for hemorrhoid rehabilitation provided in an embodiment of the present application. Figure 2 It can be seen that the bioelectric signal control device 20 for hemorrhoid rehabilitation provided in the embodiment of the present application includes:
[0039] An acquisition module 210 is used to acquire the hemorrhoid type, inflammation degree and pain condition of the target user;
[0040] The processing module 220 is used to perform numerical processing on the hemorrhoid type, inflammation degree and pain condition to obtain target coding information;
[0041] An analysis module 230 is used to analyze the target coding information based on a pre-trained neural network model to obtain the pathological state of the target user;
[0042] The adjustment module 240 is used to dynamically adjust the parameters of the bioelectric signal based on the pathological state.
[0043] In one embodiment, the processing module 220 includes:
[0044] A first mapping unit is used to map the hemorrhoid type to a coded value to obtain a hemorrhoid type coded value;
[0045] A second mapping unit is used to map the inflammation degree into a coded value to obtain a coded value of the inflammation degree;
[0046] A third mapping unit is used to map the pain condition to a coded value to obtain a pain coding value;
[0047] The combining unit is used to combine the hemorrhoid type coding value, the inflammation degree coding value and the pain coding value to obtain target coding information.
[0048] In one embodiment, the pre-trained neural network model includes an input layer, a hidden layer, and an output layer;
[0049] The analysis module 230 includes:
[0050] A processing unit, used for receiving target coding information at the input layer, performing standardization processing on the target coding information, and inputting the standardized data into the hidden layer;
[0051] An extraction unit, used for extracting nonlinear features from the standardized data in the hidden layer, and inputting the nonlinear features into the output layer;
[0052] The prediction unit is used to predict the pathological state of the target user based on the nonlinear features at the output layer.
[0053] In one embodiment, the hidden layer includes a series of neuron nodes, and the nonlinear feature extracted from the jth neuron of the hidden layer is expressed as:
[0054]
[0055] Among them, b j is the bias term of the jth neuron in the hidden layer, ω j is the weight parameter between the input layer and the jth neuron in the hidden layer, and x is the output of the input layer.
[0056] In one embodiment, after the output layer weights the output of the hidden layer, the pathological state of the target user is predicted.
[0057] In one embodiment, after the output layer weights the output of the hidden layer, the pathological state of the target user is predicted, which is expressed as:
[0058]
[0059] Among them, h j is the output value of the jth neuron in the hidden layer, ω jk is the weight parameter between the output of the jth neuron in the hidden layer and the output layer, b k is the bias term of the output layer, and y is the final prediction result of the output layer.
[0060] In one embodiment, the pathological conditions include mild, moderate and severe; the adjustment module 240 includes:
[0061] An obtaining unit, used for traversing a pre-established mapping relationship between pathological states and parameters of bioelectric signals based on the pathological state, to obtain parameters of a target bioelectric signal matching the pathological state;
[0062] The adjustment unit is used to adjust the parameters of the bioelectric signal to be output to the parameters of the target bioelectric signal and output it.
[0063] It should be noted that the specific implementation process of the above modules or units can refer to the specific implementation process of each step in the previous method embodiment, which will not be repeated here.
[0064] See also Figure 3 As shown, Figure 3 A schematic block diagram of a bioelectric signal control device for hemorrhoid rehabilitation provided in an embodiment of the present application.
[0065] Exemplarily, the bioelectric signal control device 30 for hemorrhoid rehabilitation includes a processing module 301 and a memory 302 .
[0066] Exemplarily, the processing module 301 and the memory 302 are connected via a bus 303 , and the bus 303 is, for example, an I2C (Inter-integrated Circuit) bus.
[0067] Specifically, the processing module 301 may be a micro-controller unit (MCU), a central processing unit (CPU) or a digital signal processing module (DSP).
[0068] Specifically, the memory 302 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.
[0069] The processing module 301 is used to run the computer program stored in the memory 302, and implement the steps of the above-mentioned bioelectric signal control method for hemorrhoid rehabilitation when executing the computer program.
[0070] Exemplarily, the processing module 301 is used to run the computer program stored in the memory 302, and implement the following steps when executing the computer program:
[0071] Obtain the target user's hemorrhoid type, inflammation level, and pain condition;
[0072] The hemorrhoid type, inflammation degree and pain condition are numerically processed to obtain target coding information;
[0073] Analyze the target encoding information based on the pre-trained neural network model to obtain the pathological state of the target user;
[0074] Based on the pathological state, the parameters of the bioelectric signal are dynamically adjusted.
[0075] In one embodiment, the hemorrhoid type, inflammation degree and pain condition are numerically processed to obtain target coding information, including:
[0076] Map the hemorrhoid type to the encoding value to obtain the hemorrhoid type encoding value;
[0077] Mapping the inflammation degree into coded values to obtain the inflammation degree coded value;
[0078] Mapping the pain condition to a coded value to obtain a pain coding value;
[0079] The hemorrhoid type coding value, the inflammation degree coding value and the pain coding value are combined to obtain target coding information.
[0080] In one embodiment, the pre-trained neural network model includes an input layer, a hidden layer, and an output layer;
[0081] Analyzing the target coding information based on the pre-trained neural network model to obtain the pathological state of the target user, including: receiving the target coding information at the input layer, standardizing the target coding information, and inputting the standardized data into the hidden layer;
[0082] Extract nonlinear features from the standardized data in the hidden layer and input the nonlinear features into the output layer;
[0083] The pathological state of the target user is predicted based on the nonlinear features in the output layer.
[0084] In one embodiment, the hidden layer includes a series of neuron nodes, and the nonlinear feature extracted from the jth neuron of the hidden layer is expressed as:
[0085]
[0086] Among them, b j is the bias term of the jth neuron in the hidden layer, ω j is the weight parameter between the input layer and the jth neuron in the hidden layer, and x is the output of the input layer.
[0087] In one embodiment, after the output layer weights the output of the hidden layer, the pathological state of the target user is predicted.
[0088] In one embodiment, after the output layer weights the output of the hidden layer, the pathological state of the target user is predicted, which is expressed as:
[0089]
[0090] Among them, h j is the output value of the jth neuron in the hidden layer, ω jk is the weight parameter between the output of the jth neuron in the hidden layer and the output layer, b kis the bias term of the output layer, and y is the final prediction result of the output layer.
[0091] In one embodiment, the pathological state includes mild, moderate and severe; based on the pathological state, the parameters of the bioelectric signal are dynamically adjusted, including:
[0092] Traversing a pre-established mapping relationship between pathological states and parameters of bioelectric signals based on the pathological state to obtain parameters of a target bioelectric signal that matches the pathological state;
[0093] The parameters of the bioelectric signal to be output are adjusted to the parameters of the target bioelectric signal and output.
[0094] The specific principles and implementation methods of the bioelectric signal control device for hemorrhoid rehabilitation provided in the embodiment of the present application are similar to the implementation of the bioelectric signal control method for hemorrhoid rehabilitation in the aforementioned embodiment, and will not be repeated here.
[0095] The embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by the processing module, the processing module implements the following steps:
[0096] Obtain the target user's hemorrhoid type, inflammation level, and pain condition;
[0097] The hemorrhoid type, inflammation degree and pain condition are numerically processed to obtain target coding information;
[0098] Analyze the target encoding information based on the pre-trained neural network model to obtain the pathological state of the target user;
[0099] Based on the pathological state, the parameters of the bioelectric signal are dynamically adjusted.
[0100] In one embodiment, the hemorrhoid type, inflammation degree and pain condition are numerically processed to obtain target coding information, including:
[0101] Map the hemorrhoid type to the encoding value to obtain the hemorrhoid type encoding value;
[0102] Mapping the inflammation degree into coded values to obtain the inflammation degree coded value;
[0103] Mapping the pain condition to a coded value to obtain a pain coding value;
[0104] The hemorrhoid type coding value, the inflammation degree coding value and the pain coding value are combined to obtain target coding information.
[0105] In one embodiment, the pre-trained neural network model includes an input layer, a hidden layer, and an output layer;
[0106] Analyzing the target coding information based on the pre-trained neural network model to obtain the pathological state of the target user, including: receiving the target coding information at the input layer, standardizing the target coding information, and inputting the standardized data into the hidden layer;
[0107] Extract nonlinear features from the standardized data in the hidden layer and input the nonlinear features into the output layer;
[0108] The pathological state of the target user is predicted based on the nonlinear features in the output layer.
[0109] In one embodiment, the hidden layer includes a series of neuron nodes, and the nonlinear feature extracted from the jth neuron of the hidden layer is expressed as:
[0110]
[0111] Among them, b j is the bias term of the jth neuron in the hidden layer, ω j is the weight parameter between the input layer and the jth neuron in the hidden layer, and x is the output of the input layer.
[0112] In one embodiment, after the output layer weights the output of the hidden layer, the pathological state of the target user is predicted.
[0113] In one embodiment, after the output layer weights the output of the hidden layer, the pathological state of the target user is predicted, which is expressed as:
[0114]
[0115] Among them, h j is the output value of the jth neuron in the hidden layer, ω jk is the weight parameter between the output of the jth neuron in the hidden layer and the output layer, b k is the bias term of the output layer, and y is the final prediction result of the output layer.
[0116] In one embodiment, the pathological state includes mild, moderate and severe; based on the pathological state, the parameters of the bioelectric signal are dynamically adjusted, including:
[0117] Traversing a pre-established mapping relationship between pathological states and parameters of bioelectric signals based on the pathological state to obtain parameters of a target bioelectric signal that matches the pathological state;
[0118] The parameters of the bioelectric signal to be output are adjusted to the parameters of the target bioelectric signal, and output. The computer-readable storage medium may be an internal storage unit of the bioelectric signal control device for hemorrhoid rehabilitation in the aforementioned embodiment, such as a hard disk or memory of the bioelectric signal control device for hemorrhoid rehabilitation. The computer-readable storage medium may also be an external storage device of the bioelectric signal control device for hemorrhoid rehabilitation, such as a plug-in hard disk equipped on the bioelectric signal control device for hemorrhoid rehabilitation, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc.
[0119] It should be understood that the terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
[0120] It will also be understood that the term "and / or" used in this application and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
[0121] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.
Claims
1. A bioelectric signal control method for hemorrhoid rehabilitation, characterized in that: include: Obtain the target user's hemorrhoid type, inflammation level, and pain condition; Numerically processing the hemorrhoid type, the inflammation degree and the pain condition to obtain target coding information; Analyzing the target coding information based on a pre-trained neural network model to obtain the pathological state of the target user; Based on the pathological state, the parameters of the bioelectric signal are dynamically adjusted.
2. The bioelectric signal control method for hemorrhoid rehabilitation according to claim 1, characterized in that: The hemorrhoid type, the inflammation degree and the pain condition are numerically processed to obtain target coding information, including: Map the hemorrhoid type to the encoding value to obtain the hemorrhoid type encoding value; Mapping the inflammation degree into coded values to obtain the inflammation degree coded value; Mapping the pain condition to a coded value to obtain a pain coding value; The hemorrhoid type coding value, the inflammation degree coding value and the pain coding value are combined to obtain target coding information.
3. The bioelectric signal control method for hemorrhoid rehabilitation according to claim 2, characterized in that: The pre-trained neural network model includes an input layer, a hidden layer and an output layer; The target coding information is analyzed based on a pre-trained neural network model to obtain the pathological state of the target user, including: receiving the target coding information at the input layer, standardizing the target coding information, and inputting the standardized data into the hidden layer; Extracting nonlinear features from the standardized data in the hidden layer, and inputting the nonlinear features into the output layer; The pathological state of the target user is predicted at the output layer based on the nonlinear features.
4. The bioelectric signal control method for hemorrhoid rehabilitation according to claim 3, characterized in that: The hidden layer includes a series of neuron nodes, and the nonlinear feature extracted from the jth neuron of the hidden layer is expressed as: Among them, b j is the bias term of the jth neuron in the hidden layer, ω j is the weight parameter between the input layer and the jth neuron in the hidden layer, and x is the output of the input layer.
5. The bioelectric signal control method for hemorrhoid rehabilitation according to claim 4, characterized in that: The output layer predicts the pathological state of the target user after weighting the output of the hidden layer.
6. The bioelectric signal control method for hemorrhoid rehabilitation according to claim 5, characterized in that: After the output layer weights the output of the hidden layer, the pathological state of the target user is predicted, which is expressed as: Among them, h j is the output value of the jth neuron in the hidden layer, ω jk is the weight parameter between the output of the jth neuron in the hidden layer and the output layer, b k is the bias term of the output layer, and y is the final prediction result of the output layer.
7. The bioelectric signal control method for hemorrhoid rehabilitation according to claim 1, characterized in that: The pathological state includes mild, moderate and severe; the dynamically adjusting the parameters of the bioelectric signal based on the pathological state includes: Traversing a pre-established mapping relationship between pathological states and parameters of bioelectric signals based on the pathological state to obtain parameters of a target bioelectric signal that matches the pathological state; The parameters of the bioelectric signal to be output are adjusted to the parameters of the target bioelectric signal and then output.
8. A bioelectric signal control device for hemorrhoid rehabilitation, characterized in that: include: An acquisition module is used to obtain the hemorrhoid type, inflammation degree and pain condition of the target user; A processing module, used for performing numerical processing on the hemorrhoid type, the inflammation degree and the pain condition to obtain target coding information; An analysis module, used for analyzing the target coding information based on a pre-trained neural network model to obtain the pathological state of the target user; The adjustment module is used to dynamically adjust the parameters of the bioelectric signal based on the pathological state.
9. An electronic device, characterized in that: include: Memory and processing modules; The memory is used to store computer programs; The processing module is used to execute the computer program and implement the steps of the bioelectric signal control method for hemorrhoid rehabilitation as described in any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that: The computer readable storage medium stores a computer program; When the computer program is executed by one or more processing modules, the one or more processing modules execute the bioelectric signal control method for hemorrhoid rehabilitation as described in any one of claims 1 to 7.