An intelligent gynecological operating table control system

Abstract

The present invention relates to the field of operating table control technology, specifically to an intelligent gynecological operating bed control system, the system includes a vibration control strategy design module, a damping adjustment execution module, an operating bed stability dynamic test module, and an operating environment adaptability adjustment module. In the present invention, the operating stability and accuracy of the operating bed are improved by accurately controlling the vibration frequency and adjusting the damping mechanism in real time, identifying key frequencies through dynamic data analysis, customizing the damping control strategy, finely managing the vibration of the operating bed, optimizing the damping structure to cope with specific frequencies, reducing surgical errors caused by bed vibration, and increasing surgical safety and success rate. The intelligent feedback mechanism continuously monitors and adjusts the damping effect during the operation to ensure that the surgical environment is always optimal. Real-time environmental adaptability adjustment improves the system response speed and adaptability, making the operating bed more in line with various surgical conditions and doctor needs, improving operational accuracy, reducing human errors, and improving surgical quality.

Description

Technical Field

[0001] The present invention relates to the technical field of operating table control, and in particular to an intelligent gynecological operating table control system. Background Art

[0002] The field of operating table control technology involves the development and improvement of equipment used in surgery to improve surgical efficiency, safety and comfort. The core of this field is to accurately adjust the position, angle and height of the operating table through technical means such as automation, mechanization and intelligent control systems. The control configuration includes sensors, actuators and control software to ensure that the movement of the operating table is accurate and can quickly respond to the needs of the surgical team. The development of this technology continues to incorporate more advanced communication technologies, such as wireless remote control and programmed presets, so that the operating table can better cooperate with different surgical processes and doctors' operating habits.

[0003] Among them, the intelligent gynecological operating table control system is an intelligent operating table control system specially designed for gynecological surgery. The main purpose of the system is to provide a customized and precise operating platform for gynecological surgery through a highly automated control interface and customized program settings. The system includes the function of automatically adjusting the position of the operating table to meet the needs of different surgical stages, optimize the automated control of surgical access points, and improve surgical accuracy and safety. Intelligent control can also reduce human errors during surgery through real-time feedback and adjustment mechanisms, thereby improving the success rate of surgery and ensuring patient safety.

[0004] Although the existing intelligent gynecological operating table control system has achieved automation and programmed control in terms of operating table control, it still faces the challenge of being unable to accurately respond to dynamic changes in actual operation, including the lack of sufficient sensitivity and immediate response capabilities when dealing with sudden tiny vibrations during surgery, which leads to reduced surgical accuracy and even affects patient safety. The existing technology uses a preset damping configuration and fails to make real-time adjustments based on the actual needs of the surgery, which is particularly problematic in complex or prolonged surgeries. The existing operating tables lack real-time feedback and adaptive adjustment mechanisms, and cannot make full use of advanced data analysis technology to optimize the surgical environment, resulting in poor surgical efficiency and results. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent gynecological operating table control system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: an intelligent gynecological operating table control system comprises:

[0007] The vibration control strategy design module obtains the dynamic data of the operating table from the acceleration sensor, performs dynamic adjustment analysis on the vibration frequency, identifies the key frequency components that affect the operation, generates a core frequency data set, designs the damping mechanism based on the core frequency data set, and creates an intelligent damping control scheme;

[0008] The damping adjustment execution module adjusts the response of the internal damping mechanism to the key frequency based on the intelligent damping control scheme, implements the damping adjustment, generates the damping adjustment result, performs a stability feedback test on the damping adjustment result, and generates the damping effect determination result;

[0009] The operating table stability dynamic test module uses the damping effect to determine the result, performs a multi-dimensional operating table stability test, determines the stability of the adjusted bed surface, generates a stability test result, performs an environmental matching adjustment analysis on the stability test result, evaluates whether it meets the surgical requirements, and generates an operating table stability test result;

[0010] The operating environment adaptability adjustment module adjusts the operating table settings and matches the differentiated operating environment according to the operating table stability test results, performs an environmental matching adjustment test, optimizes the operating table's response mechanism and settings, and generates an intelligent operating table adjustment plan.

[0011] As a further solution of the present invention, the step of acquiring the core frequency data set is specifically:

[0012] The dynamic data of the operating table obtained from the acceleration sensor is converted into frequency domain data through Fourier transform, and the mathematical model conversion formula is used:

[0013]

[0014] Generate frequency response data where Represents time, is the total measurement time, It's in time The vibration intensity, is the frequency The frequency response data below, represents the differential element, represents an imaginary unit;

[0015] Analyze the frequency response data by calculating the adjusted energy spectral density at each frequency point, using the energy density formula:

[0016]

[0017] Referring to the nonlinear effect of frequency, the adjusted energy spectrum data is obtained, where: Indicates frequency The adjusted energy spectrum of is the energy adjustment factor, is the frequency Frequency response data below;

[0018] Based on the adjusted energy spectrum data, the key frequencies are screened and the nonlinear threshold adjustment formula is used:

[0019]

[0020] Determine the core frequency set and generate a core frequency data set, where: is the energy threshold, is a parameter used to adjust the energy threshold sensitivity, is the core frequency dataset.

[0021] As a further solution of the present invention, the steps for obtaining the intelligent damping control solution are specifically as follows:

[0022] Extract key frequency characteristics from the core frequency data set and calculate the statistical characteristics of the frequency data, including the average frequency and standard deviation, using the formula:

[0023]

[0024]

[0025] Get frequency statistics, where is the total number of frequency values, is a single frequency value, and represent the mean frequency and standard deviation, respectively;

[0026] Based on the frequency statistics, the dynamic response characteristics of the damper are designed, and the damping coefficient is adjusted by the formula:

[0027]

[0028] Referring to the nonlinear effect, the initial damping parameters are calculated, where: is the adjustment factor, represents the initial damping parameter, and represent the mean frequency and standard deviation, respectively;

[0029] With the initial damping parameters, the nonlinear control formula is used:

[0030]

[0031] Introduce the dynamic adjustment logic of time response to generate intelligent damping control scheme, where: is the time variable, and is the time response adjustment parameter, is the damping force adjusted over time, represents the initial damping parameter.

[0032] As a further solution of the present invention, the step of obtaining the damping adjustment result is specifically:

[0033] Starting from the data received from the intelligent damping control scheme, the damping requirements corresponding to the critical frequencies are evaluated using the formula:

[0034]

[0035] The response results of the key frequencies are calculated, where: is a single critical frequency, For time, is the phase angle, Indicates frequency In time Response results;

[0036] Based on the response results of the key frequencies, the initial damping parameters are optimized using the formula:

[0037]

[0038] Generate optimized damping coefficients, where is the adjustment weight for each frequency, is the response threshold, is the optimized damping coefficient, is the number of critical frequencies, represents the initial damping parameter, is a single critical frequency;

[0039] Apply the optimized damping coefficient, adjust the internal damping mechanism, match the optimal response of the key frequency, and perform damping adjustment, through the formula:

[0040]

[0041] Generates a damping adjustment result where is the time variable, and is the time response adjustment parameter, is the damping adjustment result, is the optimized damping coefficient.

[0042] As a further solution of the present invention, the step of obtaining the damping effect determination result is specifically:

[0043] Set the initial conditions of the experiment, and calculate the initial damping response at the benchmark test frequency by adjusting the damping control scheme, using the exponential decay formula:

[0044]

[0045] in, represents the attenuation coefficient, It's in time The initial damping response of Indicates the benchmark test frequency;

[0046] The initial damping response is optimized, referring to the damping requirements under differentiated working conditions, using the formula:

[0047]

[0048] A multi-parameter response adjustment model is introduced to generate an optimized and adjusted damping response, where: is the adjustment factor, is the optimally adjusted damping response, It's in time The initial damping response of Indicates the benchmark test frequency;

[0049] The stability test is performed using the optimized damping response, and the integral evaluation method is applied to determine the stability of the overall damping effect, using the formula:

[0050]

[0051] Calculate the stability index of the damping effect and generate the damping effect determination result, where is the length of time for the evaluation, represents the damping effect determination result, is the optimally adjusted damping response, Represents time variable Infinitely small increments.

[0052] As a further solution of the present invention, the step of obtaining the stability test result is specifically as follows:

[0053] Based on the damping effect determination results, the initial test parameters of the operating table are set, referring to the reference frequency and stability baseline, using the formula:

[0054]

[0055] Using the damping effect as a baseline, an initial stability test response is calculated where represents the baseline stability level, Indicates the test frequency, represents the frequency adjustment coefficient, represents the linear adjustment coefficient of the frequency response, represents a constant term, represents the preliminary initial stability test response;

[0056] The initial stability test response is dynamically adjusted using the formula:

[0057]

[0058] The response adjustment model is introduced to simulate the impact of frequency change on stability and obtain the adjusted stability response, where: represents the basic adjustment coefficient, represents the exponential growth adjustment coefficient, represents the oscillation influence coefficient, represents the oscillation frequency adjustment coefficient, represents the adjusted stability response;

[0059] To evaluate the stability performance of the adjusted stability response, an integral method is applied to determine the overall stability of the operating table, using the formula:

[0060]

[0061] Generate stability test results where, Indicates the total duration of the test cycle. Indicates the stability test results, represents the adjusted stability response, Indicates a time interval.

[0062] As a further solution of the present invention, the steps for obtaining the operating table stability test result are specifically as follows:

[0063] The stability test results and current environmental parameters were preliminarily screened using the formula:

[0064]

[0065] Calculate the environmental adaptability index, where: represents the environmental adaptability index, Indicates the current environment parameters. is the stability test result, is the environmental sensitivity parameter;

[0066] The matching degree between the environmental adaptability index and the surgical requirements is analyzed by the formula:

[0067]

[0068] Determine whether the environmental requirements for surgery are met and obtain the environmental matching degree, where: Represents the environmental matching degree, It is the surgical requirement parameter. represents the environmental adaptability index;

[0069] According to the matching degree of the environment, whether the surgical requirements are met is evaluated by the formula:

[0070]

[0071] Generate the results of the operating table stability test, where: Indicates the results of the stability test of the operating table. Represents the environmental matching degree.

[0072] As a further solution of the present invention, the steps for obtaining the adjustment plan of the intelligently controlled operating table are specifically as follows:

[0073] Based on the stability test results of the operating table, collect stability test data, analyze and evaluate the data, and apply the formula:

[0074]

[0075] Get the environmental sensitivity score, where Indicates the results of the stability test of the operating table. Represents the stability index under ideal environment, represents the environmental sensitivity score;

[0076] Use the environmental sensitivity score to adjust the parameters of the operating table, analyze the environmental sensitivity score, calculate the adjustment coefficient, and use the formula:

[0077]

[0078] Calculate and generate adjustment parameter weights, where: and is the adjustment coefficient, represents the environmental sensitivity score, Indicates adjustment parameter weight;

[0079] Based on the adjustment parameter weights, the operating table settings are optimized and the optimization target is determined using the formula:

[0080]

[0081] Generate an intelligent operating table adjustment plan, including: Indicates the adjustment parameter weight, represents the adjustment response threshold, It indicates the adjustment plan of the intelligent operating table.

[0082] Compared with the prior art, the advantages and positive effects of the present invention are:

[0083] In the present invention, the operating stability and accuracy of the operating bed are significantly improved by precisely controlling the vibration frequency and adjusting the damping mechanism in real time. The key frequencies that affect the operation are identified through dynamic data analysis, and then the damping control strategy is customized to achieve fine management of the vibration of the operating bed. By optimizing the damping structure to cope with specific frequencies, surgical errors caused by bed vibration are reduced, thereby increasing the safety and success rate of the operation. The intelligent feedback mechanism can continuously monitor and adjust the damping effect during the operation to ensure that the operating environment is in the optimal state at all times. This real-time environmental adaptability adjustment significantly improves the response speed and adaptability of the system, so that the operating bed can better meet various surgical conditions and doctor needs. Compared with the existing technology, this system not only improves the accuracy of the operation, but also improves the overall surgical quality by reducing human errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 is a system flow chart of the present invention;

[0085] Figure 2 A flowchart of the core frequency data set in the present invention;

[0086] Figure 3 It is a flow chart of the intelligent damping control scheme in the present invention;

[0087] Figure 4 It is a flow chart of the damping adjustment result in the present invention;

[0088] Figure 5 A flow chart of the damping effect determination result in the present invention;

[0089] Figure 6 It is a flow chart of the stability test results in the present invention;

[0090] Figure 7 It is a flow chart of the stability test results of the operating table in the present invention;

[0091] Figure 8 This is a flow chart of the intelligent control operating table adjustment solution of the present invention. DETAILED DESCRIPTION

[0092] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and 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.

[0093] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined. Embodiment 1

[0094] See also Figure 1 , an intelligent gynecological operating table control system includes:

[0095] The vibration control strategy design module obtains the dynamic data of the operating table from the acceleration sensor, dynamically adjusts and analyzes the vibration frequency, identifies the key frequency components that affect the operation, generates a core frequency data set, designs the damping mechanism based on the core frequency data set, and creates an intelligent damping control solution;

[0096] The damping adjustment execution module adjusts the response of the internal damping mechanism to the key frequency based on the intelligent damping control scheme, implements the damping adjustment, generates the damping adjustment result, performs stability feedback test on the damping adjustment result, and generates the damping effect determination result;

[0097] The dynamic test module for operating table stability uses the damping effect to determine the result, conducts a multi-dimensional operating table stability test, measures the stability of the adjusted bed surface, generates stability test results, performs environmental matching adjustment analysis on the stability test results, evaluates whether the surgical requirements are met, and generates operating table stability test results;

[0098] The operating environment adaptability adjustment module adjusts the operating table settings and matches the differentiated operating environment according to the operating table stability test results, conducts environmental matching adjustment tests, optimizes the operating table's response mechanism and settings, and generates an intelligent operating table adjustment plan.

[0099] The core frequency data set includes frequency value and amplitude. The intelligent damping control scheme includes control algorithm type, feedback mechanism design, and actuator configuration. The damping adjustment results include adjusted vibration level, damping ratio, and adjustment response time. The damping effect determination results include vibration suppression efficiency and steady-state error. The stability test results include vibration attenuation effect, stability level, and performance recovery time. The operating bed stability test results include environmental adaptability, stability verification, and safety evaluation. The intelligent control operating bed adjustment scheme includes adjustment strategy details, environmental matching, and operation response optimization.

[0100] See also Figure 2, the steps to obtain the core frequency dataset are as follows:

[0101] The dynamic data of the operating table obtained from the acceleration sensor is converted into frequency domain data through Fourier transform, and the mathematical model conversion formula is used:

[0102]

[0103] Generate frequency response data where Represents time, is the total measurement time, It's in time The vibration intensity, is the frequency The frequency response data below, represents the differential element, represents an imaginary unit;

[0104] Analyze the frequency response data by calculating the adjusted energy spectral density at each frequency point, using the energy density formula:

[0105]

[0106] Referring to the nonlinear effect of frequency, the adjusted energy spectrum data is obtained, where: Indicates frequency The adjusted energy spectrum of is the energy adjustment factor, is the frequency Frequency response data below;

[0107] Based on the adjusted energy spectrum data, the key frequencies are screened and the nonlinear threshold adjustment formula is used:

[0108]

[0109] Determine the core frequency set and generate a core frequency data set, where: is the energy threshold, is a parameter used to adjust the energy threshold sensitivity, is the core frequency dataset.

[0110] frequency The frequency response data formula is:

[0111]

[0112] :time The vibration intensity under the condition is measured by the acceleration sensor;

[0113] : Frequency, set or obtained by sampling;

[0114] ; The total measurement time is determined as the measurement period;

[0115] Assume that the vibration is measured within 1 second, that is Seconds, for frequency Hz response is of interest, assuming the vibration intensity is a sine wave with an amplitude of 1, that is ,but:

[0116]

[0117] The purpose of this integration is to calculate the frequency response at 5 Hz. To perform this integration, Euler's formula for complex exponential and trigonometric functions is used. , therefore, we have:

[0118]

[0119]

[0120] Substituting this expression into the integral, we obtain:

[0121]

[0122] This integral can be calculated in two parts:

[0123]

[0124] Using trigonometric identities to reduce the integral to a solvable form, we obtain

[0125]

[0126]

[0127] The integral over the interval [0, 1] yields 0 for integer multiples of sine and cosine, so:

[0128]

[0129] This calculation result It shows that at a frequency of 5 Hz, there is no significant response due to the mismatch between the signal frequency (1 Hz) and the analysis frequency (5 Hz). This is a typical result in frequency analysis, showing the response characteristics of the vibration signal at non-intrinsic frequencies.

[0130] frequency The adjusted energy spectrum formula is:

[0131]

[0132] :Adjust parameters and set according to vibration characteristics;

[0133] : The frequency response calculated in the previous step;

[0134] Assumptions , , ;

[0135]

[0136]

[0137]

[0138] This result represents the adjusted energy spectral density at 100 Hz, which is used to determine whether it is a critical frequency.

[0139] Core frequency data set formula:

[0140]

[0141] : Threshold adjustment parameter;

[0142] Derivation process and examples:

[0143] Assumptions , ;

[0144]

[0145]

[0146]

[0147]

[0148] Because 34.0102 is not greater than 44.82, 100Hz is not the core frequency.

[0149] It shows how to determine the core frequency using detailed frequency response data and tuning parameters, the choice of which should be based on the characteristics of the vibration data in the actual application.

[0150] See also Figure 3 , the specific steps for obtaining the intelligent damping control scheme are:

[0151] Extract key frequency characteristics from the core frequency data set and calculate the statistical characteristics of the frequency data, including the mean frequency and standard deviation, using the formula:

[0152]

[0153]

[0154] Get frequency statistics, where is the total number of frequency values, is a single frequency value, and represent the mean frequency and standard deviation, respectively;

[0155] Based on the frequency statistics, the dynamic response characteristics of the damper are designed, and the damping coefficient is adjusted by the formula:

[0156]

[0157] Referring to the nonlinear effect, the initial damping parameters are calculated, where: is the adjustment factor, represents the initial damping parameter, and Represent the average frequency and standard deviation respectively; using the initial damping parameters, the nonlinear control formula is used:

[0158]

[0159] Introduce the dynamic adjustment logic of time response to generate intelligent damping control scheme, where: is the time variable, and is the time response adjustment parameter, is the damping force adjusted over time, represents the initial damping parameter.

[0160] Mean frequency and standard deviation formula:

[0161]

[0162]

[0163] : the total number of frequency values;

[0164] : single frequency value;

[0165] : average frequency;

[0166] : standard deviation of frequency;

[0167] Assume there are 5 frequency values: ;

[0168] Calculate the average frequency :

[0169]

[0170] Calculate standard deviation :

[0171]

[0172]

[0173] Average frequency Represents the average level of the measured frequency values;

[0174] Standard Deviation Indicates the fluctuation of the frequency value. The larger the frequency value, the greater the data fluctuation.

[0175] Formula and derivation of step 2

[0176] Initial damping parameter formula:

[0177]

[0178] : Adjustment coefficient to precisely control the damping response;

[0179] Assume that the setting parameters are ;

[0180] calculate :

[0181]

[0182]

[0183] Represents the damping coefficient, which affects the strength of the damping force.

[0184] The formula for the damping force that adjusts over time is:

[0185]

[0186] : Time response adjustment parameters;

[0187] Assumptions Second;

[0188] calculate :

[0189]

[0190]

[0191] Represents at time The damping force here is 24.7, which indicates the damping force at 5 seconds.

[0192] See also Figure 4 , the specific steps for obtaining the damping adjustment results are:

[0193] Starting from the data received from the intelligent damping control solution, the damping requirements corresponding to the critical frequencies are evaluated using the formula:

[0194]

[0195] The response results of the key frequencies are calculated, where: is a single critical frequency, For time, is the phase angle, Indicates frequency In time Response results;

[0196] Based on the response results of the key frequencies, the initial damping parameters are optimized using the formula:

[0197]

[0198] Generate optimized damping coefficients, where is the adjustment weight for each frequency, is the response threshold, is the optimized damping coefficient, is the number of critical frequencies, represents the initial damping parameter, is a single critical frequency;

[0199] Apply the optimized damping coefficient, adjust the internal damping mechanism, match the optimal response of the key frequency, and adjust the damping through the formula:

[0200]

[0201] Generates a damping adjustment result where is the time variable, and is the time response adjustment parameter, is the damping adjustment result, is the optimized damping coefficient.

[0202] frequency In time The response result formula is:

[0203]

[0204] time The damping force under

[0205] : key frequency;

[0206] : time point;

[0207] : initial phase angle;

[0208] Assuming critical frequency Hz, time s, initial phase angle (This is a typical initial phase, used to simplify the calculation), damping force Set to 10N (assuming the damping force is 10 Newtons at 1 second);

[0209] calculate The part inside the function: rad;

[0210] Calculate the cosine: ;

[0211] calculate :

[0212]

[0213] The results show that at 1 second, the damping response corresponding to a frequency of 5 Hz is -7.07 Newtons;

[0214] Optimized damping coefficient formula:

[0215]

[0216] : original damping coefficient;

[0217] : adjustment factor;

[0218] The frequency response calculated in the previous step;

[0219] : response threshold;

[0220] Assuming the original damping coefficient Ns / m, response threshold N, adjustment factor (assuming there is only one frequency);

[0221] calculate Parts:

[0222]

[0223] Calculate the denominator:

[0224]

[0225] Calculate the overall formula:

[0226]

[0227] The results show that the damping coefficient increased slightly after adjustment, a very minor adjustment because the response With threshold There is a big difference.

[0228] Damping adjustment result formula:

[0229]

[0230] The new damping coefficient calculated in the previous step;

[0231] : time response coefficient;

[0232] : Time response shape parameter;

[0233] : time variable;

[0234] Assumptions ,time Second;

[0235] Calculate the exponential part:

[0236]

[0237] Calculate the final damping adjustment result:

[0238]

[0239] The results showed that at 1 second, the final damping force was approximately 0.95 Newtons.

[0240] See also Figure 5 , the specific steps for obtaining the damping effect determination result are:

[0241] Set the initial conditions of the experiment, and calculate the initial damping response at the benchmark test frequency by adjusting the damping control scheme, using the exponential decay formula:

[0242]

[0243] in, represents the attenuation coefficient, It's in time The initial damping response of Indicates the benchmark test frequency;

[0244] The initial damping response is optimized, referring to the damping requirements under differentiated working conditions, using the formula:

[0245]

[0246] A multi-parameter response adjustment model is introduced to generate an optimized and adjusted damping response, where: is the adjustment factor, is the optimally adjusted damping response, It's in time The initial damping response of Indicates the benchmark test frequency;

[0247] The stability test is performed using the optimally adjusted damping response, and the integral evaluation method is applied to determine the stability of the overall damping effect, using the formula:

[0248]

[0249] Calculate the stability index of the damping effect and generate the damping effect determination result, where is the length of time for the evaluation, represents the damping effect determination result, is the optimally adjusted damping response, Represents time variable Infinitely small increments.

[0250] time The initial damped response formula is:

[0251]

[0252] This formula is used to calculate the and time The initial damped response under is the damping value obtained from the intelligent damping control scheme, is the time Function of

[0253] : This is the damping value, which can be a time function determined based on a physical model or experimental data. Assume in and is a given coefficient;

[0254] : represents the attenuation coefficient, which can be obtained through experimental adjustment and is used to adjust the intensity of the damping effect;

[0255] : Benchmark test frequency, set by the experiment;

[0256] : time variable;

[0257] Assumptions (damping unit), (1 / second), (1 / second), Hz, Second;

[0258] calculate :

[0259]

[0260] This result Indicated in Initial damped response at 10 seconds.

[0261] The damping response formula after optimization is:

[0262]

[0263] This formula is used to optimize the damping response by adjusting the factor With frequency relationship;

[0264] Use the previous ,set up , ;

[0265] calculate :

[0266]

[0267] This result represents the damped response after optimization.

[0268] The damping effect is determined by the formula:

[0269]

[0270] This formula is used to calculate the damped response In total test time The average value within is used to evaluate the stability of the damping effect;

[0271] Assumptions Seconds, assuming During this time it remained unchanged at 9.63;

[0272] calculate :

[0273]

[0274] This result Represents the stable average value of the damping effect over the entire evaluation period.

[0275] See also Figure 6 , the specific steps for obtaining the stability test results are:

[0276] Based on the damping effect determination results, the initial test parameters of the operating table are set, referring to the reference frequency and stability baseline, using the formula:

[0277]

[0278] Using the damping effect as a baseline, an initial stability test response is calculated where represents the baseline stability level, Indicates the test frequency, represents the frequency adjustment coefficient, represents the linear adjustment coefficient of the frequency response, represents a constant term, represents the preliminary initial stability test response;

[0279] The initial stability test response is dynamically adjusted using the formula:

[0280]

[0281] The response adjustment model is introduced to simulate the impact of frequency change on stability and obtain the adjusted stability response, where: represents the basic adjustment coefficient, represents the exponential growth adjustment coefficient, represents the oscillation influence coefficient, represents the oscillation frequency adjustment coefficient, represents the adjusted stability response;

[0282] To evaluate the stability performance of the adjusted stability response, an integral method was applied to determine the overall stability of the operating table using the formula:

[0283]

[0284] Generate stability test results where, Indicates the total duration of the test cycle. Indicates the stability test results, represents the adjusted stability response, Indicates a time interval.

[0285] Preliminary Initial Stability Test Response Formula:

[0286]

[0287] : Baseline stability level, assuming the maximum stable response is 100;

[0288] : test frequency;

[0289] : Frequency adjustment coefficient, assuming half attenuation;

[0290] : Linear adjustment coefficient of frequency response;

[0291] : Constant term, used to adjust baseline stability;

[0292] calculate Values:

[0293]

[0294] Calculate the logarithmic term :

[0295]

[0296] Calculate the denominator :

[0297]

[0298] Calculate the final value:

[0299]

[0300] This calculation example shows the initial stability response at a given frequency of 2 Hz. It is 57.75, which shows that the operating table has a good stability response at this frequency.

[0301] Adjusted stability response formula:

[0302]

[0303] : derived from the previous step;

[0304] : basic adjustment coefficient;

[0305] : Exponential growth adjustment coefficient;

[0306] : Oscillation influence coefficient;

[0307] : Oscillation frequency adjustment coefficient;

[0308] Calculating exponential terms :

[0309]

[0310] Calculating molecules :

[0311]

[0312] Calculate the denominator :

[0313]

[0314] Calculate the final value:

[0315]

[0316] Calculation example display It is 116.47, indicating that frequency adjustment and dynamic response increase the stability performance of the operating table under given settings.

[0317] Stability test result formula:

[0318]

[0319] : derived from the previous step;

[0320] : Total duration of the test cycle;

[0321] because is a constant, the integration becomes a simple multiplication:

[0322]

[0323] Stability test results It is 116.47, indicating that the operating table maintained a high level of stability throughout the test period.

[0324] See also Figure 7 , the specific steps for obtaining the results of the operating table stability test are:

[0325] The stability test results are initially screened with the current environmental parameters using the formula:

[0326]

[0327] Calculate the environmental adaptability index, where: represents the environmental adaptability index, Indicates the current environment parameters. is the stability test result, is the environmental sensitivity parameter;

[0328] Analyze the matching degree between environmental adaptability index and surgical requirements through the formula:

[0329]

[0330] Determine whether the environmental requirements for surgery are met and obtain the environmental matching degree, where: Represents the environmental matching degree, It is the surgical requirement parameter. represents the environmental adaptability index;

[0331] According to the matching degree of the environment, whether the surgical needs are met is evaluated through the formula:

[0332]

[0333] Generate the results of the operating table stability test, where: Indicates the results of the stability test of the operating table. Represents the environmental matching degree.

[0334] Environmental adaptability index formula:

[0335]

[0336] : Environmental parameters, such as the temperature or humidity of the operating room, are assumed to be (temperature, in °C);

[0337] : Stability test results, indicating the performance score of the operating table, assuming ;

[0338] : Environmental sensitivity parameter, controlling the influence of environmental parameters on the results, assuming ;

[0339] calculate ;

[0340] Apply the exponential function and the environmental sensitivity parameter to calculate 0.606;

[0341] Evaluate the entire fraction:

[0342]

[0343] Environmental adaptability index It means that under the current environmental parameters, the adaptability of the operating table is 62.2%.

[0344] Environmental matching formula:

[0345]

[0346] : Environmental adaptability index calculated from the previous formula, ;

[0347] : The baseline value required for the surgical environment, assumed to be 100 (standard performance index);

[0348] calculate ;

[0349] Calculate logarithm ;

[0350] Calculate the matching degree:

[0351]

[0352] Environmental Matching It indicates that the performance of the operating table is poorly matched with the operating room environment and needs further adjustment to improve compatibility.

[0353] Operating table stability test result formula:

[0354]

[0355] : The environmental matching degree calculated from the previous formula, ;

[0356] calculate ;

[0357] Calculate the ratio ;

[0358] Calculate the stability test results: (There is obviously a logical error here, and the formula or parameters need to be adjusted)

[0359] Since the calculation results in a negative value, this indicates that the formula or parameter settings need to be adjusted, because in actual circumstances the test result should be between 0% and 100%.

[0360] See also Figure 8 The specific steps for obtaining the intelligent operating table adjustment plan are as follows:

[0361] Based on the results of the operating table stability test, collect stability test data, analyze and evaluate the data, and apply the formula:

[0362]

[0363] Get the environmental sensitivity score, where Indicates the results of the stability test of the operating table. Represents the stability index under ideal environment, represents the environmental sensitivity score;

[0364] Use the environmental sensitivity score to adjust the parameters of the operating table, analyze the environmental sensitivity score, calculate the adjustment coefficient, and use the formula:

[0365]

[0366] Calculate and generate adjustment parameter weights, where: and is the adjustment coefficient, represents the environmental sensitivity score, Indicates adjustment parameter weight;

[0367] Optimize the operating table settings based on adjusting parameter weights and determine the optimization target using the formula:

[0368] Generate an intelligent operating table adjustment plan, including: Indicates the adjustment parameter weight, represents the adjustment response threshold, It indicates the adjustment plan of the intelligent operating table.

[0369] Environmental sensitivity scoring formula:

[0370]

[0371] :The results of the stability test of the operating table are obtained from the real-time feedback of the system. 8.0;

[0372] : Stability index under ideal conditions, determined by previous tests, set to ;

[0373] calculate :

[0374]

[0375] This value indicates the sensitivity score of the operating table to environmental changes under a given stability test result. The higher the value, the more sensitive the operating table is to environmental changes.

[0376] Adjust parameter weight formula:

[0377]

[0378] :Adjustment coefficient, set to ;

[0379] : Bias in the adjustment coefficient, assuming it is determined by tuning ;

[0380] use Calculate from the previous step;

[0381] calculate :

[0382]

[0383] The value represents the adjustment coefficient, which is used for the subsequent environmental adaptability adjustment of the operating table. A high value means that the operating table needs to be adjusted more to adapt to the environment.

[0384] The formula for the intelligent operating table adjustment plan:

[0385]

[0386] : Adjust the response threshold and set it to ;

[0387] use ,calculate :

[0388]

[0389] This value Represents the adaptability index of the intelligent operating table adjustment plan. The higher the value, the better the adaptability of the operating table and the better it can meet the surgical needs.

[0390] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent gynecological operating table control system, characterized in that: The system comprises: The vibration control strategy design module obtains the dynamic data of the operating table from the acceleration sensor, performs dynamic adjustment analysis on the vibration frequency, identifies the key frequency components that affect the operation, generates a core frequency data set, designs the damping mechanism based on the core frequency data set, and creates an intelligent damping control scheme; The steps for obtaining the intelligent damping control scheme are specifically as follows: Extract key frequency characteristics from the core frequency data set and calculate the statistical characteristics of the frequency data, including the average frequency and standard deviation, using the formula: ； ； Get frequency statistics, where is the total number of frequency values, is a single frequency value, and represent the mean frequency and standard deviation, respectively; Based on the frequency statistics, the dynamic response characteristics of the damper are designed, and the damping coefficient is adjusted by the formula: ； Referring to the nonlinear effect, the initial damping parameters are calculated, where: is the adjustment factor, represents the initial damping parameter, and represent the mean frequency and standard deviation, respectively; With the initial damping parameters, the nonlinear control formula is used: ； Introduce the dynamic adjustment logic of time response to generate intelligent damping control scheme, where: is the time variable, and is the time response adjustment parameter, is the damping force adjusted over time, represents the initial damping parameter; The damping adjustment execution module adjusts the response of the internal damping mechanism to the key frequency based on the intelligent damping control scheme, implements the damping adjustment, generates the damping adjustment result, performs a stability feedback test on the damping adjustment result, and generates the damping effect determination result; The steps for obtaining the damping adjustment result are specifically as follows: Starting from the data received from the intelligent damping control scheme, the damping requirements corresponding to the critical frequencies are evaluated using the formula: ； The response results of the key frequencies are calculated, where: is a single critical frequency, For time, is the phase angle, Indicates frequency In time Response results; Based on the response results of the key frequencies, the initial damping parameters are optimized using the formula: ； Generate optimized damping coefficients, where is the adjustment weight for each frequency, is the response threshold, is the optimized damping coefficient, is the number of critical frequencies, represents the initial damping parameter, is a single critical frequency; Apply the optimized damping coefficient, adjust the internal damping mechanism, match the optimal response of the key frequency, and perform damping adjustment, through the formula: ； Generates a damping adjustment result where is the time variable, and is the time response adjustment parameter, is the damping adjustment result, is the optimized damping coefficient; The steps for obtaining the damping effect determination result are specifically as follows: Set the initial conditions of the experiment, and calculate the initial damping response at the benchmark test frequency by adjusting the damping control scheme, using the exponential decay formula: ； in, represents the attenuation coefficient, It's in time The initial damping response of Indicates the benchmark test frequency; The initial damping response is optimized, referring to the damping requirements under differentiated working conditions, using the formula: ； A multi-parameter response adjustment model is introduced to generate an optimized and adjusted damping response, where: is the adjustment factor, is the optimally adjusted damping response, It's in time The initial damping response of Indicates the benchmark test frequency; The stability test is performed using the optimized damping response, and the integral evaluation method is applied to determine the stability of the overall damping effect, using the formula: ； Calculate the stability index of the damping effect and generate the damping effect determination result, where is the length of time for the evaluation, represents the damping effect determination result, is the optimally adjusted damping response, Represents time variable Infinitely small increments of The operating table stability dynamic test module uses the damping effect to determine the result, performs a multi-dimensional operating table stability test, determines the stability of the adjusted bed surface, generates a stability test result, performs an environmental matching adjustment analysis on the stability test result, evaluates whether it meets the surgical requirements, and generates an operating table stability test result; The steps for obtaining the stability test results are specifically as follows: Based on the damping effect determination results, the initial test parameters of the operating table are set, referring to the reference frequency and stability baseline, using the formula: ； Using the damping effect as a baseline, an initial stability test response is calculated where represents the baseline stability level, Indicates the test frequency, represents the frequency adjustment coefficient, represents the linear adjustment coefficient of the frequency response, represents a constant term, represents the preliminary initial stability test response; The initial stability test response is dynamically adjusted using the formula: ； The response adjustment model is introduced to simulate the impact of frequency change on stability and obtain the adjusted stability response, where: represents the basic adjustment coefficient, represents the exponential growth adjustment coefficient, represents the oscillation influence coefficient, represents the oscillation frequency adjustment coefficient, represents the adjusted stability response; To evaluate the stability performance of the adjusted stability response, an integral method is applied to determine the overall stability of the operating table, using the formula: ； Generate stability test results where, Indicates the total duration of the test cycle. Indicates the stability test results, represents the adjusted stability response, Indicates a time interval; The steps for obtaining the operating table stability test result are specifically as follows: The stability test results and current environmental parameters were preliminarily screened using the formula: ； Calculate the environmental adaptability index, where: represents the environmental adaptability index, Indicates the current environment parameters. is the stability test result, is the environmental sensitivity parameter; The matching degree between the environmental adaptability index and the surgical requirements is analyzed by the formula: ； Determine whether the environmental requirements for surgery are met and obtain the environmental matching degree, where: Represents the environmental matching degree, are the surgical requirement parameters, represents the environmental adaptability index; According to the matching degree of the environment, whether the surgical requirements are met is evaluated by the formula: ； Generate the results of the operating table stability test, where: Indicates the results of the stability test of the operating table. Represents environmental matching; The operating environment adaptability adjustment module adjusts the operating table settings and matches the differentiated operating environment according to the operating table stability test results, performs an environmental matching adjustment test, optimizes the operating table's response mechanism and settings, and generates an intelligent operating table adjustment plan.

2. The intelligent gynecological operating table control system according to claim 1 is characterized in that: The steps for obtaining the core frequency data set are specifically as follows: The dynamic data of the operating table obtained from the acceleration sensor is converted into frequency domain data through Fourier transform, and the mathematical model conversion formula is used: ； Generate frequency response data where Represents time, is the total measurement time, It's in time The vibration intensity, is the frequency The frequency response data below, represents the differential element, represents an imaginary unit; Analyze the frequency response data by calculating the adjusted energy spectral density at each frequency point, using the energy density formula: ； Referring to the nonlinear effect of frequency, the adjusted energy spectrum data is obtained, where: Indicates frequency The adjusted energy spectrum of is the energy adjustment factor, is the frequency Frequency response data below; Based on the adjusted energy spectrum data, the key frequencies are screened and the nonlinear threshold adjustment formula is used: ； Determine the core frequency set and generate a core frequency data set, where: is the energy threshold, is a parameter used to adjust the energy threshold sensitivity, is the core frequency dataset.

3. The intelligent gynecological operating table control system according to claim 1 is characterized in that: The steps for obtaining the intelligent control operating table adjustment plan are specifically as follows: Based on the stability test results of the operating table, collect stability test data, analyze and evaluate the data, and apply the formula: ； Get the environmental sensitivity score, where Indicates the results of the stability test of the operating table. Represents the stability index under ideal environment, represents the environmental sensitivity score; Use the environmental sensitivity score to adjust the parameters of the operating table, analyze the environmental sensitivity score, calculate the adjustment coefficient, and use the formula: ； Calculate and generate adjustment parameter weights, where: and is the adjustment coefficient, represents the environmental sensitivity score, Indicates adjustment parameter weight; Based on the adjustment parameter weights, the operating table settings are optimized and the optimization target is determined using the formula: ； Generate an intelligent operating table adjustment plan, including: Indicates the adjustment parameter weight, represents the adjustment response threshold, It indicates the adjustment plan of the intelligent operating table.

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