Intelligent precise temperature and humidity regulation and control system and method for operating room of obstetrics and gynecology department

Abstract

The invention relates to the technical field of medical environment control, in particular to an intelligent precise temperature and humidity regulation and control system and method for an operating room of the obstetrics and gynecology department. The method comprises the steps that an environment sensing unit collects operating room temperature and humidity data in real time based on a multi-sensor fusion module, and corrects the temperature and humidity data in real time in combination with personnel density; the central control unit adjusts temperature and humidity set values based on the personnel density, optimizes control parameters through a fuzzy-PID hybrid algorithm, and generates a temperature and humidity adjusting instruction and a fresh air control signal; the dynamic regulation unit executes dynamic regulation and control by utilizing a temperature and humidity coupling control equation for personnel density compensation and combining a Lyapunov convergence function; and the safety monitoring unit performs anomaly detection on the temperature and humidity data of the operating room based on a dual-sensor redundancy check algorithm, and adopts a personnel density adaptive redundancy check threshold. According to the invention, anomaly detection is carried out based on a dual-sensor redundancy check algorithm and an adaptive threshold, and the reliability of temperature and humidity data and the safety of the system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical environment control, in particular to an intelligent gynecological operating room temperature and humidity precise regulation system and method thereof. BACKGROUND

[0002] The intelligent gynecological operating room temperature and humidity precise regulation system and method thereof is an automatic control system specially designed for gynecological operating room environment, aiming to provide the most suitable temperature and humidity conditions to ensure the safety and efficiency of the operation. The system monitors the temperature and humidity changes in the operating room in real time through high-precision sensors, and analyzes the data using intelligent algorithms to automatically adjust the working status of air conditioners, humidifiers or dehumidifiers and other equipment, ensuring that the environmental parameters are strictly controlled within the set range. In addition, the system may also include remote monitoring and operation functions, allowing medical staff to adjust the settings or view historical records at any time, thereby providing a stable and comfortable environment for gynecological operations.

[0003] In the existing intelligent gynecological operating room temperature and humidity precise regulation system and method thereof, the traditional system uses fixed temperature and humidity set values, which cannot adapt to the sudden changes in heat and humidity load caused by the flow of personnel in the operating room. The traditional PID control has lag and overshoot in adjusting non-linear and time-varying systems. Temperature and humidity regulation and fresh air system operate independently, resulting in energy waste. Fixed threshold detection is prone to false positives or false negatives when personnel are concentrated. Therefore, an intelligent gynecological operating room temperature and humidity precise regulation system and method thereof is provided. SUMMARY

[0004] The present application aims to provide an intelligent gynecological operating room temperature and humidity precise regulation system and method to solve the problems of the existing intelligent gynecological operating room temperature and humidity precise regulation system and method, such as the use of fixed temperature and humidity set values by traditional systems, which cannot adapt to the sudden changes in heat and humidity load caused by the flow of personnel in the operating room, the lag and overshoot of traditional PID control in adjusting non-linear and time-varying systems, and the energy waste caused by the independent operation of temperature and humidity regulation and fresh air system.

[0005] To achieve the above-mentioned purpose, on the one hand, the present application aims to provide an intelligent gynecological operating room temperature and humidity precise regulation system, which comprises:

[0006] An environment perception unit, which acquires real-time operating room temperature and humidity data based on a multi-sensor fusion module, and combines personnel density to correct the temperature and humidity data in real time, obtaining corrected temperature and humidity data;

[0007] A central control unit, which adjusts the temperature and humidity set values based on personnel density, optimizes control parameters through fuzzy-PID hybrid algorithm, and generates temperature and humidity regulation instructions and fresh air control signals;

[0008] The dynamic adjustment unit uses a temperature and humidity coupling control equation with personnel density compensation to perform dynamic regulation and control in combination with a Lyapunov convergence function, evaluates the deviation between the system state and the set value, and optimizes the core parameters of the central control unit.

[0009] The safety monitoring unit performs abnormality detection on the operating room temperature and humidity data based on a double-sensor redundancy check algorithm, and adopts a personnel density adaptive redundancy check threshold to provide a fault tolerance mechanism for the central control unit.

[0010] As a further improvement of the technical solution, the multi-sensor fusion module includes a temperature sensor module, a humidity sensor module, and a personnel density sensor module.

[0011] The temperature sensor module is used to monitor the temperature changes in the operating room in real time.

[0012] The humidity sensor module is used to measure the humidity level in the operating room.

[0013] The personnel density sensor module determines the personnel density by detecting the number and distribution of personnel in the operating room, and is used to adjust the environmental control strategy according to the number of people.

[0014] As a further improvement of the technical solution, the specific steps of the environmental perception unit in real-time correction of temperature and humidity data in combination with personnel density are as follows:

[0015] Obtain the original temperature and humidity and personnel density information from the collected multi-source data.

[0016] Use a personnel density adaptive temperature and humidity fusion correction method to linearly adjust the weight of the temperature and humidity correction equation according to the personnel density, and then calculate the final temperature and humidity value.

[0017] As a further improvement of the technical solution, the specific steps of the central control unit in adjusting the temperature and humidity set value based on personnel density and optimizing the control parameters through a fuzzy-PID hybrid algorithm to generate temperature and humidity adjustment instructions and fresh air control signals are as follows:

[0018] Receive real-time personnel density And the preset reference temperature and humidity 、 Adjust the temperature and humidity set value based on the set value correction equation.

[0019] Output the temperature and humidity adjustment instructions according to the temperature and humidity set value through the fuzzy-PID hybrid algorithm.

[0020] According to the personnel density adjustment, the fresh air demand is adjusted, the fresh air control signal is output through the dynamic compensation algorithm, and the coupling relationship between the fresh air control signal and the temperature and humidity control is analyzed.

[0021] As a further improvement of the technical solution, the specific steps of outputting the temperature and humidity adjustment instruction according to the temperature and humidity set value through the fuzzy-PID hybrid algorithm are:

[0022] The temperature and humidity deviation is calculated in real time, and the basic control amount is generated through PID operation;

[0023] The input personnel density change rate and the absolute value of the temperature and humidity deviation, according to the fuzzy rule base, output the correction coefficient;

[0024] According to the correction coefficient The temperature and humidity control instruction is calculated.

[0025] As a further improvement of the technical solution, the specific steps of adjusting the fresh air demand according to the personnel density, outputting the fresh air control signal through the dynamic compensation algorithm, and analyzing the coupling relationship between the fresh air control signal and the temperature and humidity control are:

[0026] According to the area of the operating room and the air exchange rate The basic fresh air volume is calculated , and the actual fresh air demand is obtained by combining the personnel density dynamic adjustment .

[0027] Based on the fresh air demand, the opening degree of the fresh air valve is determined through proportional calculation , and the fan speed is calculated using the square law formula .

[0028] According to the actual fresh air demand The coupling relationship between the fresh air control signal and the temperature and humidity control is analyzed.

[0029] As a further improvement of the technical solution, the specific process of the temperature and humidity coupling control equation compensated by the personnel density, combined with the Lyapunov convergence function to perform dynamic regulation and control, and evaluate the deviation between the system state and the set value is:

[0030] The temperature change rate and the humidity change rate are calculated by the temperature and humidity coupling control equation compensated by the personnel density.

[0031] According to the temperature deviation and the humidity deviation The Lyapunov function is constructed to evaluate the deviation between the system state and the set value.

[0032] As a further improvement of the technical solution, the temperature deviation and The specific steps of constructing the Lyapunov function to evaluate the deviation between the system state and the set value are:

[0033] According to the temperature deviation and the humidity deviation , combined with the personnel density , a Lyapunov function is constructed ;

[0034] Considering the influence of signal interference on accuracy, the system state is dynamically corrected by introducing a signal interference variable , and an improved Lyapunov function is constructed based on the Lyapunov direct method ;

[0035] The control law is designed so that the time derivative of the Lyapunov function is negative, and it is ensured to converge to zero in an exponential form.

[0036] As a further improvement of the technical solution, the safety monitoring unit detects the operating room temperature and humidity data based on a double sensor redundancy verification algorithm, and adopts a personnel density adaptive redundancy verification threshold. The specific process is as follows:

[0037] According to the difference between the readings of the two sensors, and combined with the sensitive coefficient of the personnel density, the difference degree is calculated ;

[0038] A dynamic abnormal threshold value that changes with the personnel density is set , and an alarm is triggered when the difference degree exceeds this threshold value;

[0039] When the difference degree is between 80% and 100% of the threshold value, weighted mean correction is started, and the corrected readings are generated by combining the data of the two sensors.

[0040] On the other hand, the present application provides an intelligent gynecological operating room temperature and humidity precision control method based on the above-mentioned intelligent gynecological operating room temperature and humidity precision control system, comprising the following steps:

[0041] S10.1, real-time acquisition of operating room temperature and humidity data by multi-sensor fusion module, and real-time correction of temperature and humidity data combined with personnel density;

[0042] S10.2, adjusting the temperature and humidity set value based on the personnel density, optimizing the control parameters by fuzzy-PID hybrid algorithm, generating temperature and humidity adjustment instructions and fresh air control signals;

[0043] S10.3, the temperature and humidity coupling control equation compensated by the personnel density is used to perform dynamic regulation combined with Lyapunov convergence function, and deviation between the system state and the set value is evaluated;

[0044] S10.4, the double-sensor redundancy check algorithm is used for abnormal detection of the operating room temperature and humidity data, and the redundancy check threshold value is adaptively adjusted according to the personnel density.

[0045] Compared with the prior art, the beneficial effects of the present application are:

[0046] 1. In the intelligent gynecological operating room temperature and humidity precise regulation system and method, the temperature and humidity data are collected and corrected in real time through multi-sensor fusion, ensuring high-precision measurement of the environmental parameters in the operating room, and the accuracy of temperature and humidity data collection is improved by dynamically adjusting according to the personnel density; the temperature and humidity set value is adjusted according to the real-time personnel density, and the control parameters are optimized through the fuzzy-PID hybrid algorithm to generate accurate temperature and humidity regulation instructions and fresh air control signals, realizing efficient and flexible environmental control strategy.

[0047] 2. In the intelligent gynecological operating room temperature and humidity precise regulation system and method, the temperature and humidity coupling control equation compensated by the personnel density is used to perform dynamic regulation combined with Lyapunov convergence function, ensuring that the deviation between the set value and the actual value converges to zero quickly, enhancing the stability and response speed of the system. The double-sensor redundancy check algorithm and adaptive threshold are used for abnormal detection, improving the reliability of temperature and humidity data and the safety of the system, and enhancing the fault tolerance of the system, ensuring the safety and stability of the operating environment. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The overall flowchart of the present application is shown in the figure;

[0049] The meanings of the various labels in the figure are as follows:

[0050] 1. Environment perception unit; 11, multi-sensor fusion module; 111, temperature sensor module; 112, humidity sensor module; 113, personnel density sensor module; 2, central control unit; 3, dynamic regulation unit; 4, safety monitoring unit. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0052] Embodiment 1: Please refer to Figure 1 As shown in the drawings, an intelligent gynecological operating room temperature and humidity precise control system is provided, which comprises an environment perception unit 1. The environment perception unit 1 collects operating room temperature and humidity data in real time based on a multi-sensor fusion module 11, and combines personnel density to correct the temperature and humidity data in real time, to obtain corrected temperature and humidity data.

[0053] In this embodiment, the multi-sensor fusion module 11 comprises a temperature sensor module 111, a humidity sensor module 112, and a personnel density sensor module 113.

[0054] Further, in order to detect the carbon dioxide concentration level in the operating room in real time, as an important basis for evaluating air quality and adjusting fresh air volume, a carbon dioxide concentration sensor module is added to the multi-sensor fusion module 11. This module can use an infrared gas sensor or a laser scattering type carbon dioxide detector to output the carbon dioxide concentration value (unit: ppm) in real time and upload it to the central control unit for decision reference.

[0055] The temperature sensor module 111 is used to monitor the temperature change in the operating room in real time.

[0056] The humidity sensor module 112 is used to measure the humidity level in the operating room.

[0057] The personnel density sensor module 113 determines the personnel density by detecting the number and distribution of personnel in the operating room, and is used for the system to adjust the environmental control strategy according to the number of people.

[0058] Specifically, in the intelligent gynecological operating room temperature and humidity precise control system, the multi-sensor fusion module 11 integrates the temperature sensor module 111, the humidity sensor module 112, and the personnel density sensor module 113, achieving comprehensive monitoring and dynamic adjustment of environmental parameters. The temperature sensor module 111 monitors and adjusts the indoor temperature in real time to ensure that it is maintained within a safe and comfortable range. The humidity sensor module 112 accurately measures the air humidity to ensure that it meets the standard requirements of medical operations. The personnel density sensor module 113 detects the number and distribution of personnel in the operating room, and optimizes the temperature and humidity control strategy and fresh air supply according to the actual number of people, thereby improving the comfort and air quality of the operating room environment, and providing the best working and treatment conditions for patients and medical staff. This integrated system effectively improves the intelligent level and energy efficiency ratio of the operating room environment management.

[0059] In this embodiment, the specific steps for the environment perception unit 1 to correct the temperature and humidity data in real time in combination with the personnel density are as follows:

[0060] Obtain original temperature and humidity and personnel density information from the collected multi-source data;

[0061] Specifically, the original temperature, humidity, and population density information is as follows:

[0062] raw temperature reading :

[0063] ;

[0064] In the formula, Indicates the first Readings from each temperature sensor module; Gaussian noise representing temperature; Indicates the index variable for the temperature sensor module reading; Indicates the total number of temperature sensor modules;

[0065] raw humidity readings :

[0066] ;

[0067] In the formula, Indicates the first Readings from each humidity sensor module; Gaussian noise representing humidity; Indicates the index variable for the humidity sensor module reading; Indicates the total number of humidity sensor modules;

[0068] Personnel density reading :

[0069] ;

[0070] In the formula, Indicates the first Individual in time Does the status indicator variable exist in the operating room? Represents a personnel index variable; Indicates the total number of people;

[0071] By using a temperature and humidity fusion correction method that adapts to personnel density, the weights of the temperature and humidity correction equations are linearly adjusted according to personnel density, and then the final temperature and humidity values ​​are calculated.

[0072] Specifically, the temperature and humidity fusion correction method adapted to personnel density adjusts the weights of the temperature and humidity correction equations linearly according to personnel density, and then calculates the final temperature and humidity values. The specific calculation process is as follows:

[0073] The temperature and humidity correction equation is:

[0074] ;

[0075] In the formula, represents the corrected temperature reading; represents the corrected humidity reading; represents the weight of ; the weight of ; represents the weight of ; the weight of ; represents the maximum number of people;

[0076] ; ; ;

[0077] Further, in the intelligent obstetric operating room temperature and humidity precise control system, the original temperature and humidity data and the number of people are collected in real time by the multi-sensor fusion module, and the weight of the temperature and humidity correction equation is linearly adjusted according to the number of people by using the temperature and humidity fusion correction method adaptive to the number of people. The final temperature and humidity value is calculated. This method not only improves the accuracy of temperature and humidity data collection, but also dynamically optimizes the environmental control strategy according to the actual number of people, ensuring that the operating room always maintains the best temperature and humidity conditions. This helps to improve the safety and success rate of surgical operations, while enhancing the comfort of patients and medical staff, and reflects the highly intelligent and refined management capabilities. Finally, this precise control mechanism provides a solid guarantee for achieving an efficient, energy-saving and comfortable operating environment.

[0078] In this embodiment, in the standard obstetric clean operating room environment, if the temperature sensor is set to 3, the humidity sensor is set to 2, the maximum number of people (i.e. the number of people) is 10, and the current number of people in the room is 6 (including 2 doctors, 3 nurses and 1 patient), then

[0079] The temperature sensor real-time collection readings are monitored to be 23.8℃, 24.0℃ and 23.9℃, respectively, and the Gaussian noise expectation value is approximately 0, so the original temperature reading ;

[0080] The humidity sensor real-time collection readings are monitored to be 52%RH and 54%RH, respectively, so the original humidity reading ;

[0081] Further, a set of engineering feasible weight parameters are taken: ; ; ; ;

[0082] And make temperature correction:

[0083] ;

[0084] Wherein, the personnel density term is used to compensate the sensor average value for heat load offset, and is linked with the set value in subsequent control, rather than directly equivalent to the absolute temperature of the environment.

[0085] Humidity correction:

[0086] .

[0087] The intelligent gynecological operating room temperature and humidity precise regulation system further comprises a central control unit 2, which adjusts the temperature and humidity set value based on the personnel density, optimizes the control parameters through a fuzzy-PID hybrid algorithm, generates temperature and humidity adjustment instructions and fresh air control signals;

[0088] In the embodiment, the specific steps of the central control unit 2 adjusting the temperature and humidity set value based on the personnel density, optimizing the control parameters through the fuzzy-PID hybrid algorithm, and generating the temperature and humidity adjustment instructions and the fresh air control signals are as follows:

[0089] Receiving real-time personnel density And the preset reference temperature and humidity , Adjust the temperature and humidity set value based on the set value correction equation;

[0090] Specifically, the temperature and humidity set value is adjusted based on the set value correction equation;

[0091] Temperature set value Is:

[0092] ;

[0093] In the formula, Indicates the temperature change amount caused by the presence of personnel;

[0094] Humidity set value Is:

[0095] ;

[0096] In the formula, Indicates the humidity change amount caused by the presence of personnel;

[0097] Output the temperature and humidity adjustment instructions through the fuzzy-PID hybrid algorithm according to the temperature and humidity set value;

[0098] In the embodiment, the specific steps of outputting the temperature and humidity adjustment instructions through the fuzzy-PID hybrid algorithm according to the temperature and humidity set value are as follows:

[0099] Real-time calculation of temperature and humidity deviation, and generation of basic control quantity by PID operation;

[0100] Specifically, the temperature and humidity deviation is calculated as:

[0101]

[0102]

[0103] In the formula, Indicates the temperature deviation; Indicates the humidity deviation;

[0104] An independent PID control law is established for temperature and humidity respectively, and the corresponding basic control quantity is generated as follows:

[0105]

[0106] In the formula, Indicates the current time; Indicates the integral variable, which is used to represent the past time; Indicates the basic temperature control signal calculated by the PID controller, which is the output quantity of the temperature PID controller; Indicates the proportional gain coefficient of the temperature controller, which is used to adjust the direct response strength of the temperature error; Indicates the integral gain coefficient of the temperature controller, which is used to compensate for long-term temperature deviation and enhance steady-state performance; Indicates the differential gain coefficient of the temperature controller, which is used to respond to the rate of change of temperature error and improve dynamic response speed; Indicates the integral term of temperature deviation, which represents the accumulation of temperature error from the starting time to the present; Indicates the rate of change of temperature error, which is the first-order derivative of error with respect to time; Indicates the basic humidity control signal calculated by the PID controller, which is the output quantity of the humidity PID controller; Indicates the proportional gain coefficient of the humidity controller; Indicates the integral gain coefficient of the humidity controller; Indicates the differential gain coefficient of the humidity controller; Indicates the rate of change of humidity error; Indicates the integral term of humidity deviation;

[0107] Input personnel density rate of change And the absolute value of temperature and humidity deviation, according to the fuzzy rule base to output correction coefficient;

[0108]

[0109] In the formula,​​​​ represents a time interval;

[0110] fuzzy rule base:

[0111] temperature control rule:

[0112] enhance control when people increase and temperature difference is large:

[0113] ;

[0114] weaken control when people decrease and temperature difference is small:

[0115] ;

[0116] humidity control rule:

[0117] enhance control when people increase and humidity difference is large:

[0118] ;

[0119] weaken control when people decrease and humidity difference is small:

[0120] ;

[0121] fresh air volume control rule:

[0122] increase fresh air when people increase and air quality is poor:

[0123] ;

[0124] decrease fresh air when people decrease and air quality is good:

[0125] ;

[0126] wherein, according to the change rate of the people density and the real-time carbon dioxide concentration value, the fresh air control rule is executed, when people increase and the carbon dioxide concentration exceeds the preset upper limit (such as 800ppm), the system increases the fresh air volume to speed up the air replacement rate; when people decrease and the carbon dioxide concentration is lower than the lower limit (such as 600ppm), the fresh air volume is reduced to reduce energy consumption, this rule is based on the dynamic change of actual air quality and heat and humidity load, and realizes the high-efficiency and energy-saving fresh air control strategy;

[0127] output correction coefficient :

[0128] ;

[0129] wherein, represents an output mapping; represents the absolute value of the error;

[0130] According to the correction coefficient The temperature and humidity control instructions are calculated.

[0131] The temperature control instruction is:

[0132] ;

[0133] In the formula, represents the basic temperature control signal calculated by the PID controller; represents the temperature correction coefficient;

[0134] The humidity control instruction is:

[0135] ;

[0136] In the formula, represents the basic humidity control signal calculated by the PID controller; represents the humidity correction coefficient;

[0137] According to the personnel density adjustment of fresh air quantity demand, the fresh air control signal is output through the dynamic compensation algorithm, and the coupling relationship between the fresh air control signal and the temperature and humidity control is analyzed.

[0138] Further, in the intelligent gynecological operating room temperature and humidity precise regulation system, based on the personnel density adjustment of temperature and humidity set value and through the fuzzy-PID hybrid algorithm optimization control parameter method, the temperature and humidity adjustment instruction and fresh air control signal can be dynamically generated. This method first calculates the adjusted temperature and humidity set value according to the real-time personnel density and the preset reference temperature and humidity, and then uses the fuzzy-PID hybrid algorithm to calculate the temperature and humidity deviation and generate the basic control quantity in real time, and combines the personnel density change rate and the error absolute value to output the correction coefficient through fuzzy logic, and finally generates accurate temperature and humidity control instructions. In addition, the system also dynamically adjusts the fresh air quantity demand according to the personnel density to ensure air quality. This method not only improves the precision and response speed of the operating room environment control, but also effectively adapts to the influence of personnel density changes, thereby providing a safer, more comfortable and efficient operating environment for patients and medical staff. This reflects the intelligence and high efficiency of the system management ability, which helps to improve the success rate of surgery and the rehabilitation effect of patients.

[0139] In this embodiment, the specific steps of adjusting the fresh air quantity demand according to the personnel density, outputting the fresh air control signal through the dynamic compensation algorithm, and analyzing the coupling relationship between the fresh air control signal and the temperature and humidity control are as follows:

[0140] According to the area of the operating room and the air exchange rate , the basic fresh air quantity is calculated The actual fresh air demand is obtained by combining the personnel density dynamic adjustment ;

[0141] The basic fresh air demand is:

[0142] ;

[0143] The actual fresh air demand is obtained by combining the personnel density dynamic adjustment :

[0144] ;

[0145] In the formula, is a personnel density compensation coefficient;

[0146] The opening of the fresh air valve is determined by proportional calculation based on the fresh air demand , and the fan speed is calculated by a square law formula ;

[0147] The opening of the fresh air valve is determined by proportional calculation .

[0148] .

[0149] In the formula, is the maximum fresh air demand;

[0150] The fan speed is calculated by a square law formula :

[0151] .

[0152] In the formula, is the minimum fan speed; is the maximum fan speed;

[0153] The actual fresh air demand is used to analyze the coupling relationship between the fresh air control signal and the temperature and humidity control.

[0154] Specifically, the coupling relationship between the fresh air control signal and the temperature and humidity control is:

[0155] .

[0156] .

[0157] In the formula, is the final temperature control signal; is the final humidity control signal; is the temperature control weight coefficient; is the humidity control weight coefficient; Integral gain of temperature PID control; Integral gain of humidity PID control; Temperature-fresh air coupling coefficient; Humidity-fresh air coupling coefficient;

[0158] Specifically, if the area of the obstetrics and gynecology operating room is 60 square meters, the standard air change rate is 12 times per hour, the maximum number of personnel is 10, the current number of personnel is 6, and the personnel density compensation coefficient is 0.5, the system maximum fresh air volume is 900 cubic meters per hour;

[0159] The basic fresh air volume is ;

[0160] After personnel density compensation, the fresh air demand is:

[0161] ;

[0162] Since the system maximum fresh air capacity is , the limiting control is executed;

[0163] If is , is , the fan speed is :

[0164] ;

[0165] Further, in the intelligent obstetrics and gynecology operating room temperature and humidity precise control system, the method of adjusting fresh air demand based on personnel density and outputting fresh air control signal through dynamic compensation algorithm can ensure that the air quality in the operating room always maintains the best state. This method first calculates the basic fresh air volume according to the area of the operating room and the air change rate, and then dynamically adjusts the actual fresh air demand in combination with the real-time personnel density, so as to accurately respond to the changes in air quality and heat and humidity load caused by the change in the number of people. Then, the opening of the fresh air valve is determined through proportional calculation, and the fan speed is optimized using the square law formula to reduce energy consumption at low load. In addition, the dynamic adjustment unit uses the temperature and humidity coupling control equation and Lyapunov convergence function of personnel density compensation to realize rapid and stable deviation convergence, ensuring that the error between the set value and the actual value rapidly decreases to zero. This method not only improves the precision and efficiency of environmental control, but also enhances the energy saving and stability of the system, providing a safer, more comfortable and efficient operating environment for patients and medical staff. This reflects the highly intelligent and refined management ability, which helps to improve the success rate of surgery and the rehabilitation effect of patients.

[0166] The intelligent gynecological operating room temperature and humidity precise regulation system further comprises a dynamic adjustment unit 3, the dynamic adjustment unit 3 utilizes a temperature and humidity coupling control equation compensated by personnel density, performs dynamic regulation in combination with a Lyapunov convergence function, evaluates the deviation between the system state and the set value, and optimizes the core parameters of the central control unit 2;

[0167] In the embodiment, the specific process of evaluating the deviation between the system state and the set value by utilizing the temperature and humidity coupling control equation compensated by personnel density and in combination with the Lyapunov convergence function is as follows:

[0168] The temperature change rate and the humidity change rate are calculated by the temperature and humidity coupling control equation compensated by personnel density ;

[0169] Specifically, the temperature change rate and the humidity change rate are as follows:

[0170] ;

[0171] In the formula, represents the attenuation coefficient of the temperature deviation; represents the attenuation coefficient of the humidity deviation; represents the control gain coefficient of the temperature; represents the control gain coefficient of the humidity; represents the personnel heat load coefficient; represents the personnel humidity load coefficient; The Lyapunov function is constructed according to the temperature deviation and the humidity deviation. In the embodiment, the Lyapunov function is constructed based on the Lyapunov direct method according to the temperature deviation and the humidity deviation and in combination with the personnel density ;

[0172] In the formula, the temperature deviation is as follows:

[0173] In the embodiment, the Lyapunov function is constructed based on the Lyapunov direct method according to the temperature deviation and the humidity deviation and in combination with the personnel density ; ;

[0174] In the formula, the temperature deviation is as follows:

[0175] ; The humidity deviation is as follows:

[0176]

[0177] ​​​​​​​​​​​ ;

[0178] in, for:

[0179] ;

[0180] In the formula, Weighting coefficients representing personnel density;

[0181] Considering that signal interference affects accuracy, a signal interference variable is introduced. To dynamically correct the system state, an improved Lyapunov function is constructed based on the direct Lyapunov method. ;

[0182] Among them, the improved Lyapunov function for:

[0183] ;

[0184] In the formula, The weighting coefficient representing signal interference;

[0185] Lyapunov functions are analyzed using the chain rule. By derivation, the Lyapunov function is obtained. time derivative ;

[0186] The chain rule is a differentiation rule in calculus used to calculate the derivative of a composite function. It involves decomposing a complex function layer by layer, differentiating each layer, and multiplying the results sequentially. This is a fundamental tool for dealing with differentiation problems of nested functions and has wide applications in mathematical analysis, physical modeling, and engineering calculations.

[0187] Specifically, time derivative for:

[0188] ;

[0189] In the formula, express The positive constant gain of the squared term; express The positive constant gain of the squared term; Represents the exponential convergence coefficient;

[0190] The control law is designed to make the time derivative of the Lyapunov function... It is negative and ensures that it converges to zero exponentially.

[0191] The control law is:

[0192] ;

[0193] When , ensure the exponential convergence.

[0194] Further, in the intelligent gynecological operating room temperature and humidity precise control system, the temperature and humidity coupling control equation with personnel density compensation is used in combination with Lyapunov convergence function for dynamic regulation and control, which can realize the rapid convergence of the deviation between the set value and the actual value. By calculating the change rate of temperature and humidity, and adjusting the control parameters according to the real-time personnel density, the system can accurately respond to environmental changes, and ensure that the temperature and humidity always remain in the best range. Lyapunov function is constructed to evaluate the deviation between the system state and the set value, and a control law is designed to make the time derivative of the function negative, thereby ensuring the stability and rapid convergence of the system. This method not only improves the accuracy and response speed of temperature and humidity control, but also effectively adapts to the impact of personnel density changes, providing a safer, more comfortable and efficient operating environment for patients and medical staff, enhancing the intelligence and energy saving of the system, and helping to improve the success rate of surgery and the rehabilitation effect of patients. This mechanism reflects the ability of highly intelligent and fine management, ensuring the optimal control of the operating room environment.

[0195] The intelligent gynecological operating room temperature and humidity precise control system also includes a safety monitoring unit 4, which detects abnormality of the operating room temperature and humidity data based on a double-sensor redundancy verification algorithm, and adopts a personnel density adaptive redundancy verification threshold to provide a fault tolerance mechanism for the central control unit 2.

[0196] In this embodiment, the specific process of the safety monitoring unit 4 detecting abnormality of the operating room temperature and humidity data based on a double-sensor redundancy verification algorithm and adopting a personnel density adaptive redundancy verification threshold is as follows:

[0197] According to the difference between the readings of the two sensors, and combining the personnel density sensitivity coefficient, the difference degree is calculated ;

[0198] Specifically, :

[0199] ;

[0200] In the formula, and represent the measurement values of the two sensors at time ; represents the absolute difference between the readings of the two sensors; represents the personnel density sensitivity coefficient (preferably 0.5);

[0201] A dynamic abnormal threshold is set which adjusts with the change of personnel density trigger an alarm when the difference exceeds this threshold;

[0202] dynamic abnormal threshold is:

[0203]

[0204] wherein, represents the base abnormal threshold (preferably 0.8℃);

[0205]

[0206] When the difference is between 80% and 100% of the threshold, a weighted mean correction is initiated, combining the data from both sensors to generate a corrected reading.

[0207] When a weighted mean correction is initiated:

[0208] wherein, represents the corrected sensor reading.

[0209] In particular, in the intelligent obstetric operating room temperature and humidity precise control system, based on the double sensor redundancy verification algorithm for abnormal detection and the method of personnel density adaptive redundancy verification threshold, the reliability of environmental data and the safety of the system can be significantly improved. By calculating the difference between the readings of the two sensors, and dynamically adjusting the threshold of this difference according to the personnel density, the system can effectively identify abnormal situations and trigger an alarm when necessary. When the difference exceeds the set dynamic abnormal threshold, the system will trigger an alarm to alert potential problems; when the difference is between 80% and 100% of the threshold, the system initiates a weighted mean correction, combining the data from both sensors to generate a more accurate reading, ensuring accurate control of environmental parameters. This method not only enhances the fault tolerance and stability of the system, but also adapts to environmental changes under different personnel densities, providing a safer and more reliable operating environment for patients and medical staff, thereby improving the success rate of surgery and the rehabilitation effect of patients. This mechanism reflects the ability of highly intelligent and fine management, ensuring the efficiency and accuracy of the operating room environment monitoring.

[0210] Embodiment 2:

[0211] The difference between Embodiment 2 and Embodiment 1 of the present application is that this embodiment introduces an intelligent obstetric operating room temperature and humidity precise control method used in an intelligent obstetric operating room temperature and humidity precise control system.

[0212] An intelligent obstetric operating room temperature and humidity precise control method based on the above intelligent obstetric operating room temperature and humidity precise control system, comprising the following steps:​​​

[0213] S10.1, real-time acquisition of operating room temperature and humidity data by a multi-sensor fusion module, and real-time correction of temperature and humidity data combined with personnel density;

[0214] S10.2, adjusting the temperature and humidity set value based on the personnel density, optimizing the control parameters through a fuzzy-PID hybrid algorithm, generating temperature and humidity adjustment instructions and fresh air control signals;

[0215] S10.3, using the temperature and humidity coupling control equation compensated by the personnel density, combining the Lyapunov convergence function to perform dynamic regulation and control, and evaluating the deviation between the system state and the set value;

[0216] S10.4, abnormal detection of operating room temperature and humidity data based on a dual-sensor redundancy verification algorithm, and using a personnel density adaptive redundancy verification threshold.

[0217] Specifically, the intelligent gynecological operating room temperature and humidity precise regulation method collects and corrects temperature and humidity data in real time through multi-sensor fusion, dynamically adjusts the set value based on personnel density, optimizes the control parameters through a fuzzy-PID hybrid algorithm, uses a temperature and humidity coupling control equation combined with Lyapunov convergence function to realize rapid and stable deviation convergence, and simultaneously uses a dual-sensor redundancy verification algorithm and an adaptive threshold for abnormal detection. This method ensures accurate control and rapid response of the operating room environmental parameters, enhances the stability and reliability of the system, and provides a safer and more comfortable operating environment, thereby improving the success rate of surgery and the rehabilitation effect of patients, and reflecting the ability of highly intelligent and fine management.

[0218] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application.

Claims

1. A smart obstetric and gynecological operating room temperature and humidity precise control system, characterized in that, include: The environmental sensing unit (1) collects operating room temperature and humidity data in real time based on the multi-sensor fusion module (11), and corrects the temperature and humidity data in real time by combining the personnel density to obtain the corrected temperature and humidity data. The central control unit (2) adjusts the temperature and humidity setpoints based on personnel density, optimizes control parameters through a fuzzy-PID hybrid algorithm, and generates temperature and humidity adjustment commands and fresh air control signals. The dynamic adjustment unit (3) uses the temperature and humidity coupling control equation of personnel density compensation and combines the Lyapunov convergence function to perform dynamic regulation, evaluate the deviation between the system state and the set value, and optimize the core parameters of the central control unit (2). The safety monitoring unit (4) performs abnormal detection of operating room temperature and humidity data based on the dual-sensor redundancy check algorithm, and adopts a redundancy check threshold that is adaptive to personnel density to provide a fault tolerance mechanism for the central control unit (2).

2. The intelligent obstetric and gynecological operating room temperature and humidity precise control system according to claim 1, characterized in that: The multi-sensor fusion module (11) includes a temperature sensor module (111), a humidity sensor module (112), and a personnel density sensor module (113). The temperature sensor module (111) is used to monitor temperature changes in the operating room in real time. The humidity sensor module (112) is used to measure the humidity level in the operating room; The personnel density sensor module (113) determines the personnel density by detecting the number and distribution of personnel in the operating room, and is used by the system to adjust the environmental control strategy according to the number of people.

3. The intelligent obstetric and gynecological operating room temperature and humidity precise control system according to claim 2, characterized in that, The specific steps of the environmental sensing unit (1) in real-time correction of temperature and humidity data based on personnel density are as follows: Raw temperature, humidity, and population density information were obtained by collecting multi-source data. By using a temperature and humidity fusion correction method that is adaptive to personnel density, the weights of the temperature and humidity correction equation are linearly adjusted according to personnel density, and then the final temperature and humidity values ​​are calculated.

4. The intelligent obstetric and gynecological operating room temperature and humidity precise control system according to claim 1, characterized in that: The specific steps by which the central control unit (2) adjusts the temperature and humidity setpoints based on personnel density, optimizes control parameters through a fuzzy-PID hybrid algorithm, and generates temperature and humidity adjustment commands and fresh air control signals are as follows: Receive real-time population density With preset reference temperature and humidity , The temperature and humidity setpoints are adjusted based on the setpoint correction equation. Based on the setpoints for temperature and humidity, a fuzzy-PID hybrid algorithm is used to output temperature and humidity adjustment commands. The system adjusts the fresh air volume requirement based on the personnel density, outputs a fresh air control signal through a dynamic compensation algorithm, and analyzes the coupling relationship between the fresh air control signal and the temperature and humidity control.

5. The intelligent obstetric and gynecological operating room temperature and humidity precise control system according to claim 4, characterized in that, The specific steps for outputting temperature and humidity adjustment commands based on the temperature and humidity setpoints using a fuzzy-PID hybrid algorithm are as follows: Real-time calculation of temperature and humidity deviation, and generation of basic control variables through PID calculation; Input population density change rate The absolute value of the temperature and humidity deviation is used to output a correction coefficient based on the fuzzy rule base. According to the correction factor The temperature and humidity control commands are calculated.

6. The intelligent obstetric and gynecological operating room temperature and humidity precise control system according to claim 4, characterized in that: The specific steps for adjusting the fresh air volume demand based on personnel density, outputting a fresh air control signal through a dynamic compensation algorithm, and analyzing the coupling relationship between the fresh air control signal and temperature and humidity control are as follows: Based on the size of the operating room and ventilation rate Calculate the basic fresh air volume The actual fresh air demand is then obtained by dynamically adjusting the personnel density. ; Based on the fresh air demand, the opening degree of the fresh air valve is determined through proportional calculation. The fan speed was calculated using the square law formula. ; Based on actual fresh air demand Analyze the coupling relationship between fresh air control signals and temperature and humidity control.

7. The intelligent obstetric and gynecological operating room temperature and humidity precise control system according to claim 1, characterized in that: In the dynamic adjustment unit (3), the specific process of performing dynamic regulation using the temperature and humidity coupling control equation with personnel density compensation and the Lyapunov convergence function is as follows: Calculate the rate of temperature change using the temperature and humidity coupled control equation with personnel density compensation. and the rate of change of humidity ; Based on temperature deviation and humidity deviation Construct Lyapunov functions to evaluate the deviation between the system state and the setpoint.

8. The intelligent obstetric and gynecological operating room temperature and humidity precise control system according to claim 7, characterized in that: According to temperature deviation and humidity deviation The specific steps for constructing a Lyapunov function to evaluate the deviation between the system state and the setpoint are as follows: Based on temperature deviation and humidity deviation and combined with population density Lyapunov functions are constructed based on the direct Lyapunov method. ; By introducing signal interference variables To dynamically correct the system state, an improved Lyapunov function is constructed based on the direct Lyapunov method. ; Using the chain rule to analyze Lyapunov functions By derivation, the Lyapunov function is obtained. time derivative .

9. The intelligent obstetric and gynecological operating room temperature and humidity precise control system according to claim 1, characterized in that: The specific process by which the safety monitoring unit (4) performs anomaly detection on the operating room temperature and humidity data based on a dual-sensor redundancy check algorithm and adopts a redundancy check threshold that is adaptive to personnel density is as follows: The degree of difference is calculated based on the difference in readings from the two sensors, combined with the sensitivity coefficient of personnel density. ; Set a dynamic anomaly threshold that adjusts according to changes in population density. An alarm is triggered when the difference exceeds this threshold. When the difference is between 80% and 100% of the threshold, weighted mean correction is initiated, and the corrected reading is generated by combining the data from the two sensors.

10. A method for precise control of room temperature and humidity in intelligent obstetric and gynecological operating rooms, based on the intelligent obstetric and gynecological operating room room temperature and humidity precise control system as described in any one of claims 1-9, characterized in that, Includes the following steps: S10.

1. Real-time acquisition of operating room temperature and humidity data through a multi-sensor fusion module, and real-time correction of temperature and humidity data based on personnel density; S10.2 Adjust the temperature and humidity setpoints based on personnel density, optimize control parameters through a fuzzy-PID hybrid algorithm, and generate temperature and humidity adjustment commands and fresh air control signals; S10.3 Utilize the temperature and humidity coupled control equation with personnel density compensation, and combine it with the Lyapunov convergence function to perform dynamic regulation and evaluate the deviation between the system state and the set value. S10.

4. Anomaly detection is performed on the operating room temperature and humidity data based on a dual-sensor redundancy check algorithm, and a redundancy check threshold adaptive to personnel density is adopted.

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