Radio frequency internal diathermy health care equipment management system based on the Internet of Things

Abstract

The present invention discloses a radio frequency internal diathermy health care equipment management system based on the Internet of Things. The present invention relates to the field of intelligent temperature control technology and solves the problem that the radio frequency parameters are adjusted at a slow rate, resulting in poor effect of the entire physical therapy process. The present invention enables the health care equipment to quickly reach the highest power, so that the preparation time is fully reduced, and then slowly reduces it during the physical therapy process. The radio frequency output power can be gradually reduced after the high power is initially turned on, so that the human body is kept in a hot state, and step-by-step analysis and processing are performed in sequence, which can not only make the generated radio frequency power most suitable for the corresponding personnel, but also make the working parameters and working state of the health care equipment reach the best, and determine the power change logic of this working process according to the past change curve, so as to achieve better health care equipment management effect.

Description

Technical Field

[0001] The present invention relates to the field of intelligent temperature control technology, and in particular to a radio frequency internal diathermy health care equipment management system based on the Internet of Things. Background Art

[0002] Through intelligent temperature control, remote control, temperature warning, data transmission and other methods, the RF output power is adjusted for different individuals to solve the irreversible damage to the skin, cells and nerve tissues caused by improper operation of RF equipment on the human body.

[0003] The application with patent application number CN106362296A provides a radio frequency gynecological physiotherapy instrument, including a 360-degree surround field radiator, a 40.68MHz solid-state power source, a control circuit and a power supply connected in sequence; the 360-degree surround field radiator includes a sealed shell, a sealed cover, a power line, a temperature measuring line, a cylindrical spiral line, and a temperature sensor; the sealed cover is fixed at the end opening of the sealed shell, and the sealed shell and the sealed cover 2 form a closed space, and the sealed shell is filled with a water-soluble liquid; the cylindrical spiral line and the temperature sensor are both arranged in the sealed shell; the power line passes through the sealed cover from the outside of the sealed shell and is connected to the cylindrical spiral line; the temperature measuring line passes through the sealed cover from the outside of the sealed shell and is connected to the temperature sensor. The physiotherapy instrument uses a 360-degree surround field radiator to heat evenly in the body, and pioneeringly adopts the method of in vivo radio frequency non-invasive treatment. The radio frequency has good penetration, stable frequency, uniform heating, and very obvious treatment effect.

[0004] The original RF treatment method uses fixed RF parameters to perform heating therapy on different people, which will cause discomfort to some people. In addition, during the execution of the corresponding equipment, the RF parameters are adjusted at a slow rate, resulting in poor results in the entire therapy process. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a radio frequency internal heat health care equipment management system based on the Internet of Things, which solves the problem that the radio frequency parameters are adjusted at a slow rate, resulting in poor results in the entire physical therapy process.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A radio frequency internal diathermy health care equipment management system based on the Internet of Things, comprising:

[0007] The personnel data acquisition end collects basic parameters such as height, weight, gender, age, etc. of the participants, and transmits the collected basic parameters to the personnel data processing end;

[0008] The personnel data processing end processes the collected basic parameters in sequence, prioritizes the basal metabolic rate generated by different genders, determines the body surface area, and finally calculates the intelligently adjusted RF output power P. The specific method is as follows:

[0009] Calculate your basal metabolic rate (BMR) based on age, sex and height:

[0010] Men: BMR = (10 × weight) + (6.25 × height) - (5 × age) + 5;

[0011] Women: BMR = (10 × weight) + (6.25 × height) - (5 × age) - 161;

[0012] Then calculate the body surface area (BSA) based on weight and height:

[0013] BSA=0.007184×height 0.725 ×weight 0.425 ;

[0014] The basic linear value PL0 of RF output power is calculated based on BMR and BSA:

[0015] PL0 = BMR × BSA × CL0, CL0 is the initial linear coefficient, and a suitable value can be set according to the requirement of short-term high power.

[0016] The coefficient (C0) of RF output power is adjusted according to gender and age, limiting the maximum value to no more than 800 watts and gradually decreasing within a set time range:

[0017] For adults:

[0018] Male: C0 = min(1 + age / 100, 800 / PL0);

[0019] Female: C0 = min(1 + age / 120,800 / PL0);

[0020] For children:

[0021] C0=min(1+age / 150,800 / PL0);

[0022] A linear coefficient curve (CLt) within a limited time range t (in seconds) can be implemented using linear interpolation;

[0023] The initial time t0 corresponds to the initial linear coefficient CL0;

[0024] The final time tf corresponds to the final linear coefficient CLf;

[0025] At the time t between t0 and tf, the corresponding linear coefficient CLt is calculated by linear interpolation. The calculation formula of linear interpolation is:

[0026] CLt=CL0+(CLf-CL0)*((t-t0) / (tf-t0)), where CL0 represents the initial linear coefficient, CLf represents the final linear coefficient, t is the current time, t0 is the start time, and tf is the final time;

[0027] Finally, the intelligently adjusted RF output power P is calculated:

[0028] P = PL0 × C0 × CLt, linear interpolation can be used to calculate the linear coefficient CLt between the time range t0 to tf;

[0029] The equipment data acquisition end determines the past power regulation data generated under different working parameters of different health care equipment, where the different working parameters include some parameters selected under the equipment operation state, excluding voltage values, and transmits the confirmed past power regulation data to the preliminary processing end;

[0030] The preliminary processing end receives the past power regulation data generated by the specified health care equipment, and then constructs the corresponding power regulation curve. By analyzing the trend changes between the curves, the optimal working parameters of this health care equipment are locked. The specific method is as follows:

[0031] Receiving several groups of power regulation data generated under the same working parameters, using the voltage value as the horizontal coordinate axis and the power regulation data as the vertical coordinate axis, constructing a set of two-dimensional coordinate systems, and then confirming a set of change curves according to the changes between the power regulation data, and then sequentially processing the power regulation data generated under other working parameters to confirm several groups of change curves;

[0032] Determine the slope of the line segments between different adjacent points in the change curve, and mark the determined slope as X i-k , where k represents the line segments between different adjacent points, and the resulting groups of slopes X i-k Perform summation to determine the standard rate B i , where i represents different change curves;

[0033] Then from several standard rates B i In the above example, we select a set of maximum values ​​B imax , B imax The corresponding working parameters are calibrated as optimal working parameters and calibrated as standard parameters;

[0034] The comprehensive analysis end, according to the determined RF output power P and the determined standard parameters, locks the fastest climbing voltage change value and the slowly decreasing voltage change value from the change curve corresponding to the standard parameters, and generates a set of power change logic, specifically:

[0035] Determine the trend change point from the change curve corresponding to the standard parameter. The slopes of the line segments before and after the trend change point are different. Mark the trend change point in the change curve.

[0036] The climbing line segments between adjacent trend change points are determined, the power parameter at the rear end of the climbing line segment is greater than the power parameter at the front end, and then the slopes of different climbing line segments are determined;

[0037] From several groups of slopes, select the group of slope values ​​with the largest value, and record the initial value and final value of the voltage in this line segment, and use: change value = final value - initial value to determine the fastest climbing voltage change value;

[0038] Determine the descending line segment between adjacent trend change points, the power parameter at the rear end of the descending line segment is smaller than the power parameter at the front end, and then determine the slopes of different descending line segments, select a group of slope values ​​with the smallest value from several groups of slopes, and record the initial value and the final value of the voltage in this line segment, and use: change value = initial value - final value to determine the slowly decreasing voltage change value;

[0039] Generate power change logic: prioritize using the fastest climbing voltage change value as the standard for voltage increase, and stop when the power parameter reaches the RF output power P, then slowly reduce the voltage change value as the standard for voltage decrease, and stop when the power parameter reaches 0, and transmit the generated power change logic to the control terminal, which then executes it according to the power change logic to control the health care equipment.

[0040] The present invention provides a radio frequency internal diathermy health care equipment management system based on the Internet of Things. Compared with the prior art, it has the following beneficial effects:

[0041] The present invention analyzes the height, weight and other basic parameters of the participants in turn, and determines different radio frequency output frequencies according to different service objects, thereby improving the comprehensiveness of the health care equipment service process and the equipment management effect;

[0042] The health care equipment can quickly reach the highest power, so that its preparation time can be greatly reduced, and then slowly reduced during the subsequent physical therapy process. The RF output power can be turned on at high power at the beginning and then gradually reduced, or vice versa, to keep the human body in a continuous state of heat, and perform step-by-step analysis and processing in sequence. It can not only make the generated RF power most suitable for the corresponding personnel, but also make the working parameters and working state of the health care equipment reach the best, and determine the power change logic of this working process based on the past change curve, so as to achieve better health care equipment management effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the principle framework of the present invention;

[0044] Figure 2 It is a schematic diagram of the radio frequency output power conversion process of the present invention. DETAILED DESCRIPTION

[0045] 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 only 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.

[0046] Embodiment 1

[0047] See also Figure 1 , the present application provides a radio frequency internal diathermy health care equipment management system based on the Internet of Things, including a personnel data acquisition terminal, a personnel data processing terminal, an equipment data acquisition terminal, a preliminary processing terminal, a comprehensive analysis terminal and a control terminal;

[0048] The personnel data acquisition end is electrically connected to the personnel data processing end input node, the device data acquisition end is electrically connected to the preliminary processing end input node, the personnel data processing end and the preliminary processing end are both electrically connected to the comprehensive analysis end input node, and the comprehensive analysis end is electrically connected to the control terminal input node;

[0049] The personnel data acquisition end collects basic parameters such as height, weight, gender, age, etc. of the participants, and transmits the collected basic parameters to the personnel data processing end, wherein the collected basic parameters can be collected by a specific collection instrument or manually input;

[0050] Combination Figure 2The personnel data processing end processes the collected basic parameters in sequence, prioritizes the basal metabolic rate generated by different genders, determines the body surface area, and finally calculates the intelligently adjusted RF output power P, and transmits the processed RF output power P to the comprehensive analysis end, wherein the specific method of sequential processing is:

[0051] Calculate your basal metabolic rate (BMR) based on age, sex and height:

[0052] Men: BMR = (10 × weight) + (6.25 × height) - (5 × age) + 5;

[0053] Women: BMR = (10 × weight) + (6.25 × height) - (5 × age) - 161;

[0054] Then calculate the body surface area (BSA) based on weight and height:

[0055] BSA=0.007184×height 0.725 ×weight 0.425 ;

[0056] The basic linear value PL0 of RF output power is calculated based on BMR and BSA:

[0057] PL0 = BMR × BSA × CL0, CL0 is the initial linear coefficient, and a suitable value can be set according to the requirement of short-term high power.

[0058] The coefficient (C0) of RF output power is adjusted according to gender and age, limiting the maximum value to no more than 800 watts and gradually decreasing within a set time range:

[0059] For adults:

[0060] Male: C0 = min(1 + age / 100, 800 / PL0);

[0061] Female: C0 = min(1 + age / 120,800 / PL0);

[0062] For children:

[0063] C0=min(1+age / 150,800 / PL0);

[0064] A linear coefficient curve (CLt) within a limited time range t (in seconds) can be implemented using linear interpolation;

[0065] The initial time t0 corresponds to the initial linear coefficient CL0;

[0066] The final time tf corresponds to the final linear coefficient CLf;

[0067] At the time t between t0 and tf, the corresponding linear coefficient CLt is calculated by linear interpolation. The calculation formula of linear interpolation is:

[0068] CLt=CL0+(CLf-CL0)*((t-t0) / (tf-t0)), where CL0 represents the initial linear coefficient, CLf represents the final linear coefficient, t is the current time, t0 is the start time, and tf is the final time;

[0069] Finally, the intelligently adjusted RF output power P is calculated:

[0070] P=PL0×C0×CLt, linear interpolation can be used to calculate the linear coefficient CLt between the time range t0 to tf.

[0071] The device data acquisition end determines the past power adjustment data generated under different working parameters of different health care equipment, wherein the different working parameters include some parameters selected under the operation state of the equipment, excluding voltage values, and transmits the confirmed past power adjustment data to the preliminary processing end;

[0072] The preliminary processing end receives the past power adjustment data generated by the designated health care equipment, and then constructs a corresponding power adjustment curve. By analyzing the trend changes between the curves, the optimal working parameters belonging to the health care equipment are locked, and the optimal working parameters are calibrated as the standard parameters of the health care equipment. The specific method of locking the optimal working parameters is:

[0073] Receiving several groups of power regulation data generated under the same working parameters, using the voltage value as the horizontal coordinate axis and the power regulation data as the vertical coordinate axis, constructing a set of two-dimensional coordinate systems, and then confirming a set of change curves according to the changes between the power regulation data, and then sequentially processing the power regulation data generated under other working parameters to confirm several groups of change curves;

[0074] Determine the slope of the line segments between different adjacent points in the change curve, and mark the determined slope as X i-k , where k represents the line segments between different adjacent points, and the resulting groups of slopes X i-k Perform summation to determine the standard rate B i , where i represents different change curves;

[0075] Then from several standard rates B i In the above example, we select a set of maximum values ​​B imax , B imax The corresponding working parameters are calibrated as optimal working parameters and calibrated as standard parameters;

[0076] Specifically, when the health care equipment is performing heat therapy, the power adjustment rate will be different due to different working parameters. Therefore, in the actual treatment process, the best set of working parameters for the health care equipment is selected from several sets of different working parameters. These working parameters are some parameters set during the operation of the equipment so that the corresponding equipment can operate normally.

[0077] Embodiment 2

[0078] The comprehensive analysis end, according to the determined RF output power P and the determined standard parameters, locks the fastest climbing voltage change value and the slowly decreasing voltage change value from the change curve corresponding to the standard parameters, and generates a set of power change logic, and transmits the generated power change logic to the control terminal, wherein the specific method of generating the power change logic is:

[0079] Determine the trend change point from the change curve corresponding to the standard parameter. The slopes of the line segments before and after the trend change point are different. Mark the trend change point in the change curve.

[0080] The climbing line segments between adjacent trend change points are determined, and the power parameter of the rear end of the climbing line segment is greater than the power parameter of the front end, and then the slopes of different climbing line segments are determined. Since the method for determining the slope is a prior art, it will not be described in detail here. The slope k = (y1-y2) / (x1-x2);

[0081] From several groups of slopes, select a group of slope values ​​with the largest value, and record the initial value and the final value of the voltage in this line segment, and use: change value = final value - initial value to determine the fastest climbing voltage change value, where this line segment is a climbing line segment, so the voltage parameter between them is also rising, so the final value generated is greater than the initial value, so the corresponding change value can be determined;

[0082] Determine the descending line segment between adjacent trend change points. The power parameter at the rear end of the descending line segment is smaller than the power parameter at the front end. Then determine the slopes of different descending line segments. Select a group of slope values ​​with the smallest value from several groups of slopes. Record the initial value and the final value of the voltage in this line segment. Use: change value = initial value - final value to determine the slowly decreasing voltage change value. Therefore, the line segment is a descending line segment. Therefore, the generated power parameter is also in a descending state. When the power parameter decreases, the voltage value related to it will also decrease accordingly. In the process of decrease, its initial value is greater than the final value, so the corresponding change value can be determined.

[0083] Generate power change logic: prioritize using the fastest climbing voltage change value as the standard to increase the voltage, stop when the power parameter reaches the RF output power P, then slowly reduce the voltage change value as the standard to reduce the voltage, stop when the power parameter reaches 0, and transmit the generated power change logic to the control terminal;

[0084] Specifically, in order to enable the corresponding equipment to quickly reach the corresponding RF output power, the fastest climbing standard is adopted to determine the fastest climbing voltage change value. Then, after the climb is completed, in order to ensure that the duration is long enough, a slow descent method is adopted to descend in sequence, so that the entire physical therapy process is optimized, the entire thermal therapy effect is enhanced, and better equipment management effects are achieved.

[0085] The control terminal controls the health care equipment according to the power change logic and performs heat therapy on the therapist.

[0086] In view of the above-mentioned rapid heating and slow cooling method, another group of thermal therapy methods is also included as a further embodiment of the present application;

[0087] Through the comprehensive analysis end, according to the determined RF output power P and the determined standard parameters, the slowest rising voltage change value and the fastest decreasing voltage change value are locked from the change curve corresponding to the standard parameters, and a set of power change logic is generated, and the generated power change logic is transmitted to the control terminal;

[0088] The slowest climbing voltage change value is determined by the determined climbing line segment, and a set of slope values ​​with the smallest slope value is determined from the climbing line segment, thereby locking the corresponding slowest climbing voltage change value, which is opposite to the fastest climbing voltage change value mentioned above;

[0089] Similarly, from the descending line segment, determine a group of slope values ​​with the largest slope value, so as to lock the corresponding fastest decreasing change value;

[0090] Generate the corresponding power change logic, preferentially use the slowest climbing voltage change value as the standard to increase the voltage, stop when the power parameter reaches the RF output power P, then use the fastest decreasing voltage change value as the standard to decrease the voltage, stop when the power parameter reaches 0, and transmit the generated power change logic to the control terminal.

[0091] Specifically, in order to enable the health care equipment to quickly reach the highest power during normal heat therapy, so that the preparation time can be fully reduced, and then slowly reduced during the subsequent physical therapy process, the RF output power can be gradually reduced after initially turning on the high power, so that the human body can be kept in a hot state;

[0092] Similarly, the method of slowly heating up and then cooling down quickly can also keep the human body in a hot state and fully guarantee the thermal therapy effect it needs;

[0093] Carrying out step-by-step analysis and processing in sequence can not only make the generated RF power most suitable for the corresponding personnel, but also optimize the working parameters and working status of the health care equipment. Based on the past change curve, the power change logic of this working process can be determined, thereby achieving better health care equipment management effects.

[0094] Embodiment 3

[0095] The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0096] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0097] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The radio frequency internal diathermy health care equipment management system based on the Internet of Things is characterized by: include: The personnel data acquisition end collects the basic parameters of the height, weight, gender, and age of the participants, and transmits the collected basic parameters to the personnel data processing end; The personnel data processing end processes the collected basic parameters in sequence, prioritizes the basal metabolic rate generated by different genders, determines the body surface area, and finally calculates the intelligently adjusted RF output power P, and transmits the processed RF output power P to the comprehensive analysis end. The specific method is as follows: Calculate your basal metabolic rate (BMR) based on age, sex and height: Men: BMR = (10 × weight) + (6.25 × height) - (5 × age) + 5; Women: BMR = (10 × weight) + (6.25 × height) - (5 × age) - 161; Then calculate the body surface area (BSA) based on weight and height: BSA=0.007184×height 0.725 ×weight 0.425 ; The basic linear value PL0 of RF output power is calculated based on BMR and BSA: PL0 = BMR × BSA × CL0, CL0 is the initial linear coefficient, and a suitable value can be set according to the requirement of short-term high power. The coefficient (C0) of RF output power is adjusted according to gender and age, limiting the maximum value to no more than 800 watts and gradually decreasing within a set time range: For adults: Male: C0 = min(1 + age / 100, 800 / PL0); Female: C0 = min(1 + age / 120,800 / PL0); for child: C0=min(1+age / 150,800 / PL0); A linear coefficient curve (CLt) within a limited time range t (in seconds) can be implemented using linear interpolation; The initial time t0 corresponds to the initial linear coefficient CL0; The final time tf corresponds to the final linear coefficient CLf; At the time t between t0 and tf, the corresponding linear coefficient CLt is calculated by linear interpolation. The calculation formula of linear interpolation is: CLt=CL0+(CLf-CL0)*((t-t0) / (tf-t0)), where CL0 represents the initial linear coefficient, CLf represents the final linear coefficient, t is the current time, t0 is the start time, and tf is the final time; Finally, the intelligently adjusted RF output power P is calculated: P = PL0 × C0 × CLt, linear interpolation can be used to calculate the linear coefficient CLt between the time range t0 to tf; The equipment data acquisition end determines the past power regulation data generated under different working parameters of different health care equipment, where the different working parameters include some parameters selected under the equipment operation state, excluding voltage values, and transmits the confirmed past power regulation data to the preliminary processing end; The preliminary processing end receives the past power regulation data generated by the specified health care equipment, and then constructs the corresponding power regulation curve. By analyzing the trend changes between the curves, the optimal working parameters of this health care equipment are locked, and the optimal working parameters are calibrated as the standard parameters of this health care equipment. The specific method is as follows: Receiving several groups of power regulation data generated under the same working parameters, using the voltage value as the horizontal coordinate axis and the power regulation data as the vertical coordinate axis, constructing a set of two-dimensional coordinate systems, and then confirming a set of change curves according to the changes between the power regulation data, and then sequentially processing the power regulation data generated under other working parameters to confirm several groups of change curves; Determine the slope of the line segments between different adjacent points in the change curve, and mark the determined slope as X i-k , where k represents the line segments between different adjacent points, and the resulting groups of slopes X i-k Perform summation to determine the standard rate B i , where i represents different change curves; Then from several standard rates B i In the above example, we select a set of maximum values ​​B imax , B imax The corresponding working parameters are calibrated as the optimal working parameters and calibrated as the standard parameters; The comprehensive analysis end, according to the determined RF output power P and the determined standard parameters, locks the fastest climbing voltage change value and the slowly decreasing voltage change value from the change curve corresponding to the standard parameters, and generates a set of power change logic, and transmits the generated power change logic to the control terminal, specifically in the following manner: Determine the trend change point from the change curve corresponding to the standard parameter. The slopes of the line segments before and after the trend change point are different. Mark the trend change point in the change curve. The climbing line segments between adjacent trend change points are determined, the power parameter at the rear end of the climbing line segment is greater than the power parameter at the front end, and then the slopes of different climbing line segments are determined; From several groups of slopes, select the group of slope values ​​with the largest value, and record the initial value and final value of the voltage in this line segment, and use: change value = final value - initial value to determine the fastest climbing voltage change value; Determine the descending line segment between adjacent trend change points, the power parameter at the rear end of the descending line segment is smaller than the power parameter at the front end, and then determine the slopes of different descending line segments. From several groups of slopes, select a group of slope values ​​with the largest value, and record the initial value and the final value of the voltage in this line segment, and use: change value = initial value - final value to determine the slowly decreasing voltage change value; Generate power change logic: prioritize using the fastest climbing voltage change value as the standard for voltage increase, stop when the power parameter reaches the RF output power P, then slowly reduce the voltage change value as the standard for voltage decrease, stop when the power parameter reaches 0, and transmit the generated power change logic to the control terminal.

2. The radio frequency internal diathermy health care equipment management system based on the Internet of Things according to claim 1 is characterized in that: The control terminal controls the health care equipment according to the power change logic and execution.

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