Control method of negative pressure isolation device

Abstract

The invention discloses a control method of a negative pressure isolation device, and belongs to the technical field of medical isolation facilities. Firstly, flow characteristics of an air inlet and an air outlet of the device are analyzed, a mathematical model of a control system is established, and air pressure difference values between the interior of the device and the external environment at all moments are calculated according to the mathematical model; then, establishing a control model of the system; and finally, a PID controller is constructed according to the control model of the system, and PID parameters are adjusted by using a fuzzy RBF neural network. The input quantity of the fuzzy RBF neural network comprises an air pressure difference value deviation and an air pressure difference value deviation change rate, and the output quantity of the fuzzy RBF neural network comprises a proportionality coefficient, an integral coefficient and a differential coefficient; parameters of a PID controller are adjusted in real time according to the output quantity of the fuzzy RBF neural network, and the rotating speed of a draught fan located at an air outlet of the negative pressure isolation device is controlled through the PID controller. According to the method, the rotating speed of the draught fan can be rapidly and accurately adjusted, the air pressure difference value can be rapidly and accurately recovered to be within the set threshold value range, the response speed and anti-interference performance of a control system are improved, and the device can adapt to the complex and changeable working environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical isolation facilities, and specifically is a control method for a negative pressure isolation device. Background Art

[0002] A negative pressure isolation device is a device that isolates infectious disease patients or suspected patients from the outside world. It can effectively avoid cross-infection and disease transmission, and is used for patient isolation and transfer. To achieve stable operation of the negative pressure isolation device, it is necessary to maintain a stable air pressure difference between the external environment and the inside of the device. During the use of negative pressure isolation devices, they are often affected by complex environments, such as collisions, bumps, etc. The air pressure difference is not easy to maintain stability, and the air pressure difference changes greatly after being disturbed, and cannot be quickly restored to the set threshold range, resulting in slow response speed of the control system and poor anti-interference, which makes the device unable to adapt to complex and changeable working environments.

[0003] In response to the above problems, the present invention proposes a control method for a negative pressure isolation device, which can accurately and quickly adjust the air pressure difference, improve the responsiveness and robustness of the control system, and enable the device to adapt to complex and changeable working environments. Summary of the invention

[0004] In view of the deficiencies in the prior art, the technical problem to be solved by the present invention is to provide a control method for a negative pressure isolation device.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A control method for a negative pressure isolation device, characterized in that the method comprises the following steps:

[0007] Step 1: Establish the mathematical model of the control system, expressed as:

[0008]

[0009] In the formula, Δp is the pressure difference, p1 is the atmospheric pressure, and p2 is the internal pressure of the device. is the instantaneous rate of change of the pressure difference, Q2 is the outlet flow rate, V is the device volume, s2 is the inlet area, γ is the correction coefficient, ρ is the gas density, R is the gas constant, and T is the absolute temperature;

[0010] Step 2: Establish the control model of the system, expressed as:

[0011]

[0012] Where G(s) is the transfer function of the control system, a1, a2, b1 and b2 are all constants, and s is the Laplace operator;

[0013] Step 3: Build a PID controller based on the system control model and use fuzzy RBF neural network to adjust PID parameters;

[0014] The input of the fuzzy RBF neural network includes the pressure difference deviation and the rate of change of the pressure difference deviation, and the output includes the proportional coefficient K p , integral coefficient K i and the differential coefficient K d ; The parameters of the PID controller are adjusted in real time according to the output of the fuzzy RBF neural network, and the speed of the fan located at the air outlet of the negative pressure isolation device is controlled by the PID controller;

[0015] The pressure difference deviation is the difference between the current pressure difference and the target pressure difference. The current pressure difference is calculated based on the mathematical model of the control system.

[0016] Compared with the prior art, the present invention has the following advantages:

[0017] 1. The flow characteristics of the air inlet and outlet of the negative pressure isolation device were analyzed, the mathematical model of the control system was established, and the theoretical calculation formula for the air pressure difference was proposed.

[0018] 2. The PID parameters are adjusted in real time by combining fuzzy control with RBF neural network, so that the PID controller can quickly and accurately adjust the fan speed, so that the air pressure difference can be quickly and accurately restored to the set threshold range, improving the response speed and anti-interference ability of the control system, so that the negative pressure isolation device can adapt to complex and changeable working environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is the overall control schematic diagram;

[0020] Figure 2 It is a flow chart for updating PID parameters. DETAILED DESCRIPTION

[0021] Specific embodiments are given below in conjunction with the accompanying drawings. The specific embodiments are only used to introduce the technical solutions of the present invention in detail and are not intended to limit the protection scope of the present application.

[0022] The present invention provides a control method for a negative pressure isolation device (hereinafter referred to as method, see Figures 1-2 ), including the following steps:

[0023] Step 1: Analyze the flow characteristics of the air inlet and outlet of the negative pressure isolation device, establish a mathematical model of the control system, and calculate the air pressure difference at each moment according to the mathematical model;

[0024] The stable operation of the negative pressure isolation device depends on maintaining a stable pressure difference between the internal and external environments. The pressure difference is mainly determined by the inlet and outlet flow rates. It is generally believed that the inlet flow rate is quantitative, and the outlet air flow rate is controlled by the fan located at the outlet. In the early stage of the device operation, the internal air pressure is approximately equal to the external atmospheric pressure, and the pressure difference is about 0. As the device runs, the fan speed increases, the internal air pressure of the device gradually decreases, and the pressure difference gradually increases. When the pressure difference reaches the target value, the fan speed tends to stabilize. Therefore, the outlet flow rate is adjusted by controlling the fan speed, and then the pressure difference is adjusted.

[0025] First, a mathematical model of the control system is established. To simplify the modeling, it is assumed that the gas is an ideal gas, there is no leakage in the device, and the internal gas is evenly distributed with no heat exchange with the outside world.

[0026] (1) Analysis of air inlet flow characteristics

[0027] The fluid continuity equation indicates that the mass of fluid flowing through any cross section per unit time must be equal. Take a section of fluid flowing in the flow field, and the cross-sectional areas at both ends are s1 and s2 respectively; since the mass of fluid flowing through any cross section per unit time must be equal, we can get:

[0028] ρ1v1ds1=ρ2v2ds2 (1)

[0029] Where ρ1 and ρ2 represent the density of the fluid flowing through the micro-sections ds1 and ds2, respectively; v1 and v2 represent the instantaneous velocity of the fluid flowing through the micro-sections ds1 and ds2, respectively.

[0030] When the fluid is incompressible, ρ1=ρ2, then equation (1) is simplified to:

[0031] v1ds1=v2ds2 (2)

[0032] Integrating equation (2), we get the flow rate of the fluid flowing through the two sections:

[0033]

[0034] make Then we have:

[0035] q1=q2 (4)

[0036] In the formula, q1 and q2 are the flow rates of the fluid flowing through the two cross sections respectively;

[0037] According to the relationship between flow rate, flow velocity and cross-sectional area, equation (4) can be written as:

[0038] u1s1=u2s2 (5)

[0039] In the formula, u1 and u2 are the average velocities of the fluid flowing through the two cross sections respectively. It can be seen from formula (5) that the velocity of the fluid flowing through the cross section is inversely proportional to the cross-sectional area.

[0040] Newton's second law shows that the acceleration of an object is proportional to the force and inversely proportional to the mass of the object. The same applies to streamline motion. Take one point in the α direction and follow Newton's second law to get:

[0041] ∑F α =ma α (6)

[0042] In the formula, F α represents the resultant force on the point in the α direction, m represents the mass of the point, and a α Represents the acceleration of the point in the α direction.

[0043] When the internal friction between the gases is ignored and only the pressure on both sides and the gravity component of the point in the α direction need to be considered, equation (6) can be written as:

[0044]

[0045] Where g represents the acceleration due to gravity, θ represents the angle between the tangent direction of the streamline at that point and the vertical direction y, p represents the pressure at that point, s represents the area at that point, and u represents the average velocity at that point.

[0046] And from m = ρdsdα, Substituting it into formula (7) we can get:

[0047]

[0048] Where h is the height of the point and ρ is the gas density;

[0049] Then by Substituting it into equation (8) and dividing each term by ρ, we can obtain:

[0050]

[0051] Assuming that the gas flows steadily and is incompressible, integrating equation (9) yields the Bernoulli equation:

[0052]

[0053] Where C is a constant, For kinetic energy, is the flow energy and gy is the potential energy.

[0054] From formula (10), we can see that the fluid enters from the front section of the inlet, flows through the inlet and flows through the rear section. The pressures of the front section and the rear section are p1 and p2 respectively, and the average velocities are u1 and u2 respectively. When the gas flows steadily and the compressibility and friction are ignored, the sum of the kinetic energy, potential energy and flow energy of the particles along the streamline is equal to a constant. Assuming that the cross-section heights are equal, that is, h1 = h2, then:

[0055]

[0056] In the formula, h w It is the local pressure loss.

[0057] Since the front cross-sectional area of ​​the air inlet is much larger than the rear cross-sectional area, it can be seen from equation (5) that u1 is much smaller than u2, so u1 is ignored and Substituting it into equation (11), we can get the average velocity u2 of the gas flowing through the air inlet:

[0058]

[0059] In the formula, p1 is the atmospheric pressure, p2 is the internal pressure of the device, and γ is the correction coefficient;

[0060] In summary, the air inlet flow rate Q1 is:

[0061]

[0062] In formula (13), s2 is the air inlet area;

[0063] (2) Analysis of outlet flow characteristics

[0064] The outlet flow is different from the inlet flow. The outlet flow is determined by the wind speed of the fan. There is a nonlinear relationship between the wind speed and the fan speed and is expressed by h(u). Therefore, the outlet flow Q2 can be expressed as:

[0065] Q2=h(u)×s3 (14)

[0066] Where s3 is the outlet area.

[0067] From the gas complete state equation, we know that p = ρRT, where R is the gas constant and T is the absolute temperature. From the above formula, we can also get:

[0068]

[0069] In the formula, m V is the mass of gas in the device, V is the volume of the device;

[0070] According to the law of conservation of mass, after multiplying equation (14) and equation (13) by ρ at the same time, the difference can be obtained to obtain the change of gas mass in the device at a certain moment with time, as shown below:

[0071]

[0072] Since the device volume V, atmospheric pressure p1, and gas density ρ are constants, equation (16) can be simplified as:

[0073]

[0074] In formula (17) Therefore, we can get:

[0075]

[0076] Finally, the mathematical model of the control system is shown in formula (19), which is used to calculate the pressure difference at each moment;

[0077]

[0078] Where Δp is the pressure difference, is the instantaneous rate of change of the pressure difference.

[0079] Step 2: Establish the control model of the system;

[0080] The control system of the negative pressure isolation device is a single-input single-output system. The input is the fan speed and the output is the air pressure difference. The control system model is as follows:

[0081] y(k)=-a1y(k-1)-a2y(k-2)+b1x(k-1)+b2x(k-2)+e(k) (20)

[0082] Where y(k), y(k-1) and y(k-2) are the outputs at time k, k-1 and k-2 respectively, x(k), x(k-1) and x(k-2) are the inputs at time k, k-1 and k-2 respectively, e(k) is the pressure difference deviation at time k; a1, a2, b1 and b2 are all constants, and their values ​​can be obtained by least squares fitting;

[0083] According to the control system model, the transfer function is:

[0084]

[0085] The z plane is mapped to the s plane through bilinear transformation. The bilinear transformation formula is:

[0086]

[0087] Take t = 0.1, and the control model of the system is:

[0088]

[0089] Where G(s) is the transfer function of the control system and s is the Laplace operator.

[0090] Step 3: Build a PID controller based on the system control model and use fuzzy RBF neural network to adjust PID parameters;

[0091] Fuzzy control can adjust PID parameters according to fuzzy rules to adapt to dynamic changes of the system and environmental changes; RBF neural network is a local approximation neural network that can learn the complex nonlinear characteristics of the system, provide more accurate parameter optimization, and solve local minima problems; therefore, adopting a PID control strategy based on fuzzy RBF neural network and combining PID control with fuzzy RBF neural network can enhance the robustness of the control system.

[0092] The input of the fuzzy RBF neural network includes the pressure difference deviation e and the pressure difference deviation change rate e c , the output includes the proportionality factor K p , integral coefficient K i and the differential coefficient K d Three parameters. The pressure difference deviation is the difference between the current pressure difference and the target pressure difference. The current pressure difference is calculated according to formula (19).

[0093] The fuzzy RBF neural network includes an input layer, a fuzzification layer, a fuzzy reasoning layer and an output layer; the input layer includes two nodes, representing the pressure difference deviation e and the pressure difference deviation change rate e respectively. c , the input layer directly passes the input to the next layer, and the output of the input layer is expressed as:

[0094] f1(i)=x i ,i=1,2 (24)

[0095] In the formula, x i , f1(i) are the input and output of node i respectively;

[0096] The fuzzy layer is used to calculate the pressure difference deviation e and the pressure difference deviation change rate e c Fuzzy processing is performed, and the fuzzy set includes 7 fuzzy variables: "Negative big (NB)", "Negative middle (NM)", "Negative small (NS)", "Zero (ZO)", "Positive small (PS)", "Positive middle (PM)" and "Positive big (PB)". The Gaussian function is used as the membership function, and the expression is:

[0097]

[0098] In the formula, c ij and b ijrepresent the mean and standard deviation of the ith input variable and the jth membership function respectively, and n is the number of fuzzy variables.

[0099] The fuzzy reasoning layer matches the fuzzy rules by connecting with the simulation layer, and fuzzy operations are implemented between nodes. A node in this layer is a fuzzy rule. Each input of all nodes in this layer is multiplied by the fuzzy rules contained in all nodes in this layer, and then the sum is obtained to obtain the fuzzy output. Pressure difference deviation e and pressure difference deviation change rate e c After fuzzification, the outputs are all 7, which are combined in pairs to form 49 fuzzy rules. The output of the fuzzy reasoning layer is expressed as:

[0100]

[0101] Where N represents the number of nodes in the fuzzy reasoning layer;

[0102] The output layer includes three nodes. The output of each node is the weighted sum of all outputs of the fuzzy inference layer. The output of each node is expressed as:

[0103]

[0104] Where w(l,j) is the connection weight between the lth node of the output layer and the fuzzy reasoning layer;

[0105] From formula (27), we can get the output K of the fuzzy RBF neural network: p , K i and K d It is expressed as:

[0106]

[0107] Where w(1,j), w(2,j) and w(3,j) are the connection weights between the three nodes of the output layer and the fuzzy inference layer;

[0108] K p , K i and K d It acts on the PID controller to adjust the parameters of the PID controller in real time, and controls the fan speed through the PID controller to maintain a stable air pressure difference.

[0109] Any matters not described in the present invention are applicable to the prior art.

Claims

1. A control method for a negative pressure isolation device, characterized in that: The method comprises the following steps: Step 1: Establish the mathematical model of the control system, expressed as: In the formula, Δp is the pressure difference, p1 is the atmospheric pressure, and p2 is the internal pressure of the device. is the instantaneous rate of change of the pressure difference, Q2 is the outlet flow rate, V is the device volume, s2 is the inlet area, γ is the correction coefficient, ρ is the gas density, R is the gas constant, and T is the absolute temperature; Step 2: Establish the control model of the system, expressed as: Where G(s) is the transfer function of the control system, a1, a2, b1 and b2 are all constants, and s is the Laplace operator; Step 3: Build a PID controller based on the system control model and use fuzzy RBF neural network to adjust PID parameters; The input of the fuzzy RBF neural network includes the pressure difference deviation and the rate of change of the pressure difference deviation, and the output includes the proportional coefficient K p , integral coefficient K i and the differential coefficient K d ; The parameters of the PID controller are adjusted in real time according to the output of the fuzzy RBF neural network, and the speed of the fan located at the air outlet of the negative pressure isolation device is controlled by the PID controller; The pressure difference deviation is the difference between the current pressure difference and the target pressure difference. The current pressure difference is calculated based on the mathematical model of the control system.

2. The control method of the negative pressure isolation device according to claim 1, characterized in that: The outlet flow rate satisfies the following formula: Q2=h(u)×s3 (14) Where h(u) is the relationship function between wind speed and fan speed, and s3 is the outlet area.

3. The control method of the negative pressure isolation device according to claim 1 or 2, characterized in that: The fuzzy RBF neural network includes an input layer, a fuzzification layer, a fuzzy reasoning layer and an output layer. The input layer contains two nodes, and the output of the input layer is expressed as: f1(i)=x i ,i=1,2 (24) In the formula, x i , f1(i) are the input and output of node i respectively; The fuzzification layer performs fuzzy processing on the pressure difference deviation and the rate of change of the pressure difference deviation, and the membership function is: In the formula, c ij and b ij Represent the mean and standard deviation of the ith input variable and the jth membership function, respectively, and n is the number of fuzzy variables; The output of the fuzzy inference layer is expressed as: Where N represents the number of nodes in the fuzzy reasoning layer; The output layer consists of three nodes, and the output of each node is expressed as: Where w(l,j) is the connection weight between the lth node of the output layer and the fuzzy reasoning layer; The output K of the fuzzy RBF neural network p , K i and K d It is expressed as: Where w(1,j), w(2,j) and w(3,j) are the connection weights between the three nodes of the output layer and the fuzzy inference layer.

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