Dental treatment instrument for detecting oral bacteria

Abstract

The present invention relates to a dental treatment device for detecting oral bacteria, which solves the technical problem that dental treatment devices cannot detect and display oral bacteria. The dental treatment device capable of detecting oral bacteria comprises a biosensor for detecting oral bacteria, the biosensor is connected to an MCU unit, and the MCU unit is connected to a display unit; the technical solution of the display unit displaying oral bacteria effectively solves the problem and can be used in dental treatment.

Description

Technical Field

[0001] The present invention relates to the field of dental treatment instruments, and in particular to a dental treatment instrument for detecting oral bacteria. Background Art

[0002] Dental medical devices refer to a variety of small, portable tools designed specifically for use in dentistry, known internationally as dental instruments. These include surgical instruments such as dental handpieces, oral handpiece sterilizers, extraction forceps, dental elevators, dental picks, manual dental instruments, dental rotary instruments, dental syringes, and endodontic instruments. The scope of dental medical devices is vast, with numerous manufacturers, and the corresponding international group, ISO / TC106 SC4, is specifically responsible for standardization in this area.

[0003] Currently, all dental treatment devices are unable to detect oral bacteria in the oral cavity, making it impossible for people to self-check their oral bacteria status, and doctors and patients cannot communicate intuitively. Oral bacteria is one of the symptoms of dental disease. Therefore, the present invention provides a dental treatment device that can detect and intuitively display oral bacteria in the human body. Summary of the Invention

[0004] The technical problem to be solved by the present invention is the lack of oral bacteria detection in the prior art. A new dental treatment device for detecting oral bacteria is provided, which has the characteristic of being able to detect oral bacteria.

[0005] In order to solve the above technical problems, the technical solutions adopted are as follows:

[0006] A dental treatment device for detecting oral bacteria comprises a biosensor for detecting oral bacteria, the biosensor is connected to an MCU unit, and the MCU unit is connected to a display unit; the display unit displays oral bacteria information.

[0007] In the above scheme, for optimization, the biosensor further includes a power control module, a locator, a microcontroller chip, a signal transmission module, and a bacteria detection sensor; the power control module includes a normally open magnetic switch, which is placed in a permanent magnetic field device with a specific magnetic field direction when not in use. The magnetic switch is in an off state and the circuit is disconnected; when in use, it is removed from the permanent magnetic field device, the magnetic switch is closed, and the biosensor circuit is connected and starts working.

[0008] Furthermore, a signal amplification unit is connected between the biosensor and the MCU unit.

[0009] Furthermore, the signal amplification unit includes an integrated circuit LM324, the first pin of the integrated circuit LM324 is connected to a resistor R53 and a resistor R51, the other pin of the resistor R53 is connected to the W_AN signal pin, the other pin of the resistor R51 is connected to the second pin of the integrated circuit LM324 and the resistor R52, and the other pin of the resistor R52 is grounded; the third pin of the integrated circuit LM324 is connected to the capacitor C51 to the ground and connected to the resistor R50, and the other pin of the resistor R50 is connected to the HV signal pin; the fourth pin of the integrated circuit LM324 is connected to the +12V power supply pin, and the +12V power supply pin is connected to the GND pin through the capacitor C50.

[0010] Furthermore, the signal amplification circuit includes an integrated circuit LM321, the first pin of the integrated circuit LM321 is connected in series with a resistor R43 to the VS_FB signal terminal; the second pin of the integrated circuit LM321 is grounded; the third pin of the integrated circuit LM321 is connected in parallel with resistors R44 and R45, the resistor R44 is grounded, and the resistor R45 is connected to the fourth pin of the integrated circuit LM321.

[0011] Furthermore, the biosensor includes Thin-Ex-TFG structured optical fiber and oral bacteria-sensitive hydrogel, wherein Thin-Ex-TFG; the oral bacteria-sensitive hydrogel is composed of acrylic acid, hydroxyethyl methacrylate, ethylene glycol dimethacrylate and benzoin dimethyl ether.

[0012] Furthermore, the MCU unit is provided with an oral bacteria value processing unit for pre-processing the biosensor data, and the oral bacteria value processing unit performs the following steps:

[0013] Step 1: Collect the oral bacteria values ​​corresponding to the biosensor within time t and generate the time series Z t ;

[0014] Step 2: Using the correlation between abnormal symptoms and oral bacteria values ​​in historical data, a correlation function based on oral bacteria values ​​is established as a symptom prediction algorithm;

[0015] Step 3: The preprocessed time series Z t' Input the symptom prediction algorithm to obtain the symptom prediction result.

[0016] Furthermore, the disease prediction algorithm includes:

[0017] Step 1: Randomly select m × n normal oral bacteria values ​​from the oral bacteria values ​​corresponding to abnormal conditions as samples;

[0018] Step 2: Calculate the difference between the actual oral bacteria value and the sample oral bacteria value, and define the difference as a short circuit sign if it is less than a preset threshold;

[0019] Step 4: Randomly construct the calculated differences into an Euler model in the form of m rows and n columns. The Euler model is an undirected connected graph, where the corresponding differences are vertices, and the vertices are connected by Euler edges.

[0020] Step 5: Add virtual edges to the periphery of the array of the Euler model, convert the vertices with odd degrees into vertices with even degrees, and construct an Euler circuit;

[0021] Step 6: Define the priority strategy as "the larger the difference, the higher the priority", determine any vertex in the Euler circuit as the search starting point, determine whether all adjacent edges of the starting point have been traversed according to the priority strategy, and determine the next adjacent vertex to be passed;

[0022] Step 7: Repeat step 6 until all edges have been traversed, and record the vertices passed in sequence as a feasible solution, that is, the test path for calculating the oral bacteria value;

[0023] Step 8: Determine the size of the vertex with the largest difference in the test path and the threshold. If the difference is greater than or equal to the threshold, it is defined as a symptom and a symptom warning result is output; if the difference is less than the threshold, it is defined as no symptom.

[0024] Furthermore, the threshold is preset by defining the dynamic error of the system as ±E1, the pre-estimated residual as ±E2, and the fault judgment threshold V=2(|E1|+|E2|).

[0025] Furthermore, the dynamic error of the system is defined as ±E1 and the estimated residual is defined as ±E2, both of which are the responsibility of the fault decision unit;

[0026] The fault decision unit includes a user interface, a static database, a dynamic database, a knowledge base, an inference engine, a knowledge acquisition unit, a time series model recognition unit, a time series predictor, and a fault estimator; the user interface is connected to the inference engine, the static database, and the knowledge acquisition unit in parallel, the inference engine is connected to the static database, the dynamic database, and the knowledge base, and the knowledge acquisition unit is connected to the knowledge base; the static database is connected to the fault estimator and the time series model recognition unit, and the time series predictor is connected between the fault estimator and the time series model recognition unit.

[0027] The present invention utilizes biosensors, integrated with existing dental care and treatment products, to simultaneously monitor oral bacteria levels during treatment or care, alerting users to necessary treatment. Furthermore, the use of an oral bacteria estimation unit and a fault decision-making unit effectively improves oral bacteria detection and treatment capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The present invention will be further described below with reference to the accompanying drawings and examples.

[0029] Figure 1 , schematic diagram of the dental treatment instrument for detecting oral bacteria in Example 1.

[0030] Figure 2 , schematic diagram of the first signal amplification circuit in Example 1.

[0031] Figure 3 , schematic diagram of the second signal amplification circuit in Example 1.

[0032] Figure 4 , schematic diagram of the biosensor in Example 1.

[0033] Figure 5 , schematic diagram of the dental treatment system for detecting oral bacteria in Example 1. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0035] Example 1

[0036] This embodiment provides a dental treatment device for detecting oral bacteria, such as Figure 1 and Figure 5 The dental treatment device capable of detecting oral bacteria includes a biosensor for detecting oral bacteria, the biosensor is connected to an MCU unit, and the MCU unit is connected to a display unit; the display unit displays an oral bacteria index.

[0037] Preferably, if Figure 4 The biosensor includes a power control module, a locator, a microcontroller chip, a signal transmission module, and a bacteria detection sensor. The power control module includes a normally open magnetic switch. When not in use, the module is placed in a permanent magnetic field device with a specific magnetic field direction. The magnetic switch is in an off state and the circuit is disconnected. When in use, the module is removed from the permanent magnetic field device, the magnetic switch is closed, and the biosensor circuit is connected and begins to work.

[0038] Preferably, in order to better transmit the amplitude between the output of the biosensor and the signal received by the MCU unit, a signal amplification unit is connected between the biosensor and the MCU unit.

[0039] Specifically, if Figure 2The first signal amplification unit includes an integrated circuit LM324, wherein the first pin of the integrated circuit LM324 is connected to a resistor R53 and a resistor R51, the other pin of the resistor R53 is connected to the W_AN signal pin, the other pin of the resistor R51 is connected to the second pin of the integrated circuit LM324 and the resistor R52, and the other pin of the resistor R52 is grounded; the third pin of the integrated circuit LM324 is connected to the capacitor C51 to the ground and connected to the resistor R50, and the other pin of the resistor R50 is connected to the HV signal pin; the fourth pin of the integrated circuit LM324 is connected to the +12V power pin, and the +12V power pin is connected to the GND pin through the capacitor C50.

[0040] like Figure 3 The second signal amplification circuit includes an integrated circuit LM321, wherein the first pin of the integrated circuit LM321 is connected in series with a resistor R43 to the VS_FB signal terminal; the second pin of the integrated circuit LM321 is grounded; the third pin of the integrated circuit LM321 is connected in parallel with resistors R44 and R45, the resistor R44 is grounded, and the resistor R45 is connected to the fourth pin of the integrated circuit LM321.

[0041] Specifically, the MCU unit is provided with an oral bacteria value processing unit for preprocessing biosensor data, and the oral bacteria value processing unit performs the following steps:

[0042] Step 1: Collect the oral bacteria values ​​corresponding to the biosensor within time t and generate the time series Z t ;

[0043] Step 2: Using the correlation between abnormal symptoms and oral bacteria values ​​in historical data, a correlation function based on oral bacteria values ​​is established as a symptom prediction algorithm;

[0044] Step 3: The preprocessed time series Z t' Input the symptom prediction algorithm to obtain the symptom prediction result.

[0045] Specifically, the disease prediction algorithm includes:

[0046] Step 1: Randomly select m × n normal oral bacteria values ​​from the oral bacteria values ​​corresponding to abnormal conditions as samples;

[0047] Step 2: Calculate the difference between the actual oral bacteria value and the sample oral bacteria value, and define the difference as a short circuit sign if it is less than a preset threshold;

[0048] Step 4: Randomly construct the calculated differences into an Euler model in the form of m rows and n columns. The Euler model is an undirected connected graph, where the corresponding differences are vertices, and the vertices are connected by Euler edges.

[0049] Step 5: Add virtual edges to the periphery of the array of the Euler model, convert the vertices with odd degrees into vertices with even degrees, and construct an Euler circuit;

[0050] Step 6: Define the priority strategy as "the larger the difference, the higher the priority", determine any vertex in the Euler circuit as the search starting point, determine whether all adjacent edges of the starting point have been traversed according to the priority strategy, and determine the next adjacent vertex to be passed;

[0051] Step 7: Repeat step 6 until all edges have been traversed, and record the vertices passed in sequence as a feasible solution, that is, the test path for calculating the oral bacteria value;

[0052] Step 8: Determine the size of the vertex with the largest difference in the test path and the threshold. If the difference is greater than or equal to the threshold, it is defined as a symptom and a symptom warning result is output; if the difference is less than the threshold, it is defined as no symptom.

[0053] Specifically, the threshold is preset by defining the dynamic error of the system as ±E1, the pre-estimated residual as ±E2, and the fault judgment threshold V=2(|E1|+|E2|).

[0054] Specifically, the system dynamic error is defined as ±E1 and the estimated residual is defined as ±E2, both of which are the responsibility of the fault decision unit;

[0055] The fault decision unit includes a user interface, a static database, a dynamic database, a knowledge base, an inference engine, a knowledge acquisition unit, a time series model recognition unit, a time series predictor, and a fault estimator; the user interface is connected to the inference engine, the static database, and the knowledge acquisition unit in parallel, the inference engine is connected to the static database, the dynamic database, and the knowledge base, and the knowledge acquisition unit is connected to the knowledge base; the static database is connected to the fault estimator and the time series model recognition unit, and the time series predictor is connected between the fault estimator and the time series model recognition unit.

[0056] This embodiment uses a biosensor, connected to existing dental care and treatment products, to simultaneously monitor oral bacteria levels during treatment or care, alerting users to necessary treatment. It also incorporates an oral bacteria count estimation unit and a fault decision-making unit to effectively improve oral bacteria detection and treatment.

[0057] Since the fault decision unit has human-computer dialogue and explanation functions, some difficult faults can be satisfactorily resolved with simple intervention of the operator, which also increases the credibility of the fault diagnosis system.

[0058] Although the above describes the illustrative specific embodiments of the present invention so that those skilled in the art can understand the present invention, the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, all inventions and creations based on the concepts of the present invention are protected.

Claims

1. A dental treatment device for detecting oral bacteria, characterized in that: The dental treatment device for detecting oral bacteria includes a biosensor for detecting oral bacteria, the biosensor is connected to an MCU unit, and the MCU unit is connected to a display unit; the display unit displays an oral bacteria index; The biosensor includes a power control module, a locator, a microcontroller chip, a signal transmission module, and a bacteria detection sensor; The power control module includes a normally open magnetic control switch. When not in use, it is placed in a permanent magnetic field device with a specific magnetic field direction. The magnetic control switch is in an open state and the circuit is disconnected. When in use, it is removed from the permanent magnetic field device, the magnetic control switch is closed, and the biosensor circuit is connected and starts working. The biosensor comprises a Thin-Ex-TFG structured optical fiber and an oral bacteria-sensitive hydrogel, wherein the Thin-Ex-TFG is composed of acrylic acid, hydroxyethyl methacrylate, ethylene glycol dimethacrylate and benzoin dimethyl ether. The MCU unit is provided with an oral bacteria value processing unit for preprocessing biosensor data, and the oral bacteria value processing unit performs the following steps: Step 1: Collect the oral bacteria values ​​corresponding to the biosensor within time t and generate the time series Z t ; Step 2: Using the correlation between abnormal symptoms and oral bacteria values ​​in historical data, a correlation function based on oral bacteria values ​​is established as a symptom prediction algorithm; Step 3: The preprocessed time series Z t' Input the symptom prediction algorithm to obtain the symptom prediction result; Symptom prediction algorithms include: Step 1: Randomly select m × n normal oral bacteria values ​​from the oral bacteria values ​​corresponding to abnormal conditions as samples; Step 2: Calculate the difference between the actual oral bacteria value and the sample oral bacteria value, and define the difference as a short circuit sign if it is less than a preset threshold; Step 3: Randomly construct the calculated differences into an Euler model in the form of m rows and n columns. The Euler model is an undirected connected graph, where the corresponding differences are vertices, and the vertices are connected by Euler edges. Step 4: Add virtual edges to the periphery of the array of the Euler model, convert the vertices with odd degrees into vertices with even degrees, and construct an Euler circuit; Step 5: Define the priority strategy as "the larger the difference, the higher the priority", determine any vertex in the Euler circuit as the search starting point, determine whether all adjacent edges of the starting point have been traversed according to the priority strategy, and determine the next adjacent vertex to be traversed; Step 6: Repeat step 6 until all edges have been traversed, and record the vertices passed in sequence as a feasible solution, that is, the test path for calculating the oral bacteria value; Step 7: Determine the size of the vertex with the largest difference in the test path and the threshold. If the difference is greater than or equal to the threshold, it is defined as a disease and a disease warning result is output; if the difference is less than the threshold, it is defined as no disease; The threshold is preset by defining the dynamic error of the system as ±E1, the estimated residual as ±E2, and the preset fault judgment threshold V=2(|E1|+|E2|).

2. The dental treatment device for detecting oral bacteria according to claim 1, characterized in that: A signal amplification unit is connected between the biosensor and the MCU unit.

3. The dental treatment device for detecting oral bacteria according to claim 2, characterized in that: The signal amplification unit includes an integrated circuit LM324, wherein the first pin of the integrated circuit LM324 is connected to a resistor R53 and a resistor R51, the other pin of the resistor R53 is connected to the W_AN signal pin, the other pin of the resistor R51 is connected to the second pin of the integrated circuit LM324 and the resistor R52, and the other pin of the resistor R52 is grounded; the third pin of the integrated circuit LM324 is connected to the capacitor C51 to the ground and to the resistor R50, and the other pin of the resistor R50 is connected to the HV signal pin; the fourth pin of the integrated circuit LM324 is connected to the +12V power supply pin, and the +12V power supply pin is connected to the GND pin through the capacitor C50.

4. The dental treatment device for detecting oral bacteria according to claim 2, characterized in that: The signal amplification unit includes an integrated circuit LM321, wherein the first pin of the integrated circuit LM321 is connected in series with a resistor R43 to the VS_FB signal terminal; the second pin of the integrated circuit LM321 is grounded; the third pin of the integrated circuit LM321 is connected in parallel with resistors R44 and R45, the resistor R44 is grounded, and the resistor R45 is connected to the fourth pin of the integrated circuit LM321.

5. The dental treatment device for detecting oral bacteria according to claim 4, characterized in that: Define the system's dynamic error as ±E1 and the estimated residual as ±E2, both of which are the responsibility of the fault decision unit; The fault decision unit includes a user interface, a static database, a dynamic database, a knowledge base, an inference engine, a knowledge acquisition unit, a time series model recognition unit, a time series predictor, and a fault estimator; the user interface is connected to the inference engine, the static database, and the knowledge acquisition unit in parallel, the inference engine is connected to the static database, the dynamic database, and the knowledge base, and the knowledge acquisition unit is connected to the knowledge base; the static database is connected to the fault estimator and the time series model recognition unit, and the time series predictor is connected between the fault estimator and the time series model recognition unit.

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