Control method of pneumoperitoneum machine and pneumoperitoneum machine

Abstract

The present application relates to a control method of an insufflator and an insufflator, wherein the control method of the insufflator provided by the present application performs fuzzy reasoning and defuzzification on three PID control parameters, namely, proportional control parameter, integral control parameter and differential control parameter, by fuzzifying the pressure deviation value and the rate of change of the pressure deviation value in a fuzzy controller, outputs the proportional control parameter, integral control parameter and differential control parameter of the PID controller, and further inputs the three PID control parameters into the PID controller so that the PID controller adjusts the PWM parameter change and generates a PWM voltage pulse signal to adjust the opening of the proportional valve. The PWM current control method based on fuzzy PID overcomes the disadvantage that the traditional PID control cannot adjust the PID parameters in real time, improves the anti-interference performance of the entire system, and can perform continuous proportional and uninterrupted control and adjustment of the output pressure and flow in real time, thereby achieving the purpose of constant pressure variable current stable gas supply, and the control effect is good.

Description

Technical Field

[0001] The present application relates to the technical field of insufflator, and in particular to a control method of an insufflator and an insufflator. Background Art

[0002] The insufflator is a medical device that outputs a certain pressure of CO2 gas to a detection cavity. When the predetermined target pressure is reached, the insufflator automatically stops the intake of gas and maintains a certain amount of gas to keep the detection cavity in a predetermined pressure inflation state, so that the pressure in the detection cavity remains at a constant value.

[0003] When the pressure in the detection cavity is higher than the set pressure due to overfilling or other factors, the insufflator can also release the pressure in time to keep the pressure at a constant level. When the gas flow rate output by the insufflator reaches the predetermined flow rate, the insufflator can maintain inflation at the predetermined flow rate. When the gas flow rate output by the insufflator is not equal to the predetermined flow rate, the insufflator can adjust the output gas flow rate to make the output gas flow rate reach the predetermined flow rate. It can be seen that the insufflator needs to have stable pressure control and flow control methods.

[0004] The traditional control method of the insufflator is relatively simple, and the pressure and flow output by the insufflator are controlled only through feedback control. The control effect is general, and the anti-interference ability and anti-interference recovery ability are poor. The pressure and flow output by the insufflator are easily affected by external environmental factors. Summary of the invention

[0005] Based on this, it is necessary to provide a control method for an insufflator to address the problems that the control method of a traditional insufflator has poor anti-interference ability and anti-interference recovery ability, and the pressure and flow rate output by the insufflator are easily affected by external environmental factors.

[0006] On the one hand, the present application provides a control method of a pneumoperitoneum machine.

[0007] The method comprises:

[0008] Obtaining the air pressure value in the gas pipeline and the preset target air pressure value, calculating the difference between the preset target air pressure value and the air pressure value in the gas pipeline, and using the difference as the current air pressure deviation value;

[0009] Obtain historical air pressure deviation values ​​from the database, and use the difference between the current air pressure deviation value and the historical air pressure deviation value as the air pressure deviation value change rate;

[0010] Perform fuzzy calculation based on the current air pressure deviation value and the rate of change of the air pressure deviation value to generate proportional control parameters, integral control parameters and differential control parameters;

[0011] Generate a PWM voltage pulse signal according to a proportional control parameter, an integral control parameter and a differential control parameter;

[0012] The PWM voltage pulse signal is sent to the proportional valve, and the opening of the proportional valve is adjusted according to the PWM voltage pulse signal.

[0013] On the other hand, the present application also provides a pneumoperitoneum machine, the pneumoperitoneum machine comprising:

[0014] Insufflator body;

[0015] Gas pipelines;

[0016] The gas pipeline is arranged between the pneumoperitoneum machine body and the pneumoperitoneum needle;

[0017] A proportional valve is installed on the gas pipeline;

[0018] A pressure sensor is arranged on the inner wall of the gas pipeline;

[0019] The pneumoperitoneum machine body comprises a main controller, a fuzzy controller and a PID controller, and the main controller is used to execute the control method of the pneumoperitoneum machine as mentioned above.

[0020] The present application relates to a control method of an insufflator and an insufflator, wherein the control method of the insufflator provided by the present application obtains the deviation value between the air pressure value in the gas transmission pipeline and the preset target air pressure value as the air pressure deviation value, and obtains the difference between the air pressure deviation value and the historical air pressure deviation value as the air pressure deviation value change rate, and in the fuzzy controller, after the air pressure deviation value and the air pressure deviation value change rate are fuzzified, the three PID control parameters of the proportional control parameter, the integral control parameter and the differential control parameter are fuzzy reasoning, defuzzification and other steps are performed, and the three PID control parameters of the proportional control parameter, the integral control parameter and the differential control parameter of the PID controller are output, and the three PID control parameters of the PID controller are further input into the PID controller so that the PID controller adjusts the PWM parameter change, and then completes the subsequent control, and generates a PWM voltage pulse signal to adjust the opening of the proportional valve. The PWM current control method based on fuzzy PID overcomes the disadvantage that the traditional PID control cannot adjust the PID parameters in real time, improves the anti-disturbance performance of the entire system, and can perform continuous proportional and uninterrupted control and adjustment of the output pressure and flow in real time, thereby achieving the purpose of constant pressure variable current stable gas supply, and the control effect is good. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of a control method for a pneumoperitoneum machine provided in one embodiment of the present application.

[0022] Figure 2 A schematic diagram of the structure of a pneumoperitoneum machine provided in one embodiment of the present application.

[0023] Figure 3A schematic structural diagram of a pneumoperitoneum machine provided in another embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of this application more clear, the following is a further detailed description of this application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain this application and are not used to limit this application.

[0025] On the one hand, the present application provides a control method for a pneumoperitoneum machine. It should be noted that the control method for a pneumoperitoneum machine provided in the present application is applied to a pressure experiment of a pneumoperitoneum machine, and the detection cavity into which the pneumoperitoneum needle of the pneumoperitoneum machine is inserted is a plastic model. The detection cavity is specifically a cavity with a cavity made of a plastic material with good sealing performance.

[0026] In addition, the control method of the insufflator provided in the present application does not limit its execution subject. Optionally, the execution subject of the control method of the insufflator provided in the present application can be a insufflator. Specifically, the execution subject of the control method of the insufflator provided in the present application can be a main controller in the insufflator body in the insufflator.

[0027] The pneumoperitoneum machine body comprises a main controller, a fuzzy controller and a PID controller. The main controller is connected to the fuzzy controller, the main controller is connected to the PID controller, and the PID controller is connected to the proportional valve.

[0028] like Figure 1 As shown, in one embodiment of the present application, the control method of the pneumoperitoneum machine includes:

[0029] S100, obtaining the air pressure value in the gas pipeline and the preset target air pressure value, calculating the difference between the preset target air pressure value and the air pressure value in the gas pipeline, and using the difference as the current air pressure deviation value.

[0030] Specifically, a pressure sensor is provided in the gas pipeline, and the pressure sensor obtains the air pressure value in the gas pipeline in real time and returns it to the main controller in the main body of the pneumoperitoneum machine. The main controller then obtains the preset target air pressure value stored in the local database, and subtracts the preset target air pressure value from the air pressure value in the gas pipeline, and uses the difference as the current air pressure deviation value, recorded as E.

[0031] S200, obtaining historical air pressure deviation values ​​from a database, and taking the difference between the current air pressure deviation value and the historical air pressure deviation value as the air pressure deviation value change rate.

[0032] Specifically, the historical air pressure deviation value is the “current air pressure deviation value” obtained when S100 was last executed. The last time is the time node of S100 that is closest to the current system time.

[0033] The difference between the current air pressure deviation value and the historical air pressure deviation value is taken as the air pressure deviation value change rate, which is recorded as EC.

[0034] S300, performing fuzzy calculation according to the current air pressure deviation value and the rate of change of the air pressure deviation value to generate a proportional control parameter, an integral control parameter and a differential control parameter.

[0035] Specifically, the main controller inputs the calculated current pressure deviation value and pressure deviation value change rate into the fuzzy controller, and controls the fuzzy controller to calculate the proportional control parameter, integral control parameter and differential control parameter. The proportional control parameter is recorded as Kp, the integral control parameter is recorded as Ki, and the differential control parameter is recorded as Kd.

[0036] S400, generating a PWM voltage pulse signal according to a proportional control parameter, an integral control parameter, and a differential control parameter.

[0037] Specifically, the proportional control parameter, the integral control parameter and the differential control parameter are three PID control parameters. These three PID control parameters are input into the PID controller. The PID controller controls the duty cycle, period and other parameters of PWM (Pulse width modulation), thereby controlling the current. The controlled current adjusts the output flow of the proportional valve, and the purpose of changing the output air pressure of the insufflator is achieved according to the proportional relationship between the output flow of the proportional valve and the pressure.

[0038] S500, sending a PWM voltage pulse signal to a proportional valve, and adjusting the opening of the proportional valve according to the PWM voltage pulse signal.

[0039] Specifically, the proportional valve receives a PWM voltage pulse signal with a variable duty cycle. The average voltage divided by the coil resistance of the proportional valve is the coil current value, which ultimately generates electromagnetic force through the coil current value to move the valve core of the proportional valve, thereby changing the opening of the proportional valve.

[0040] In this embodiment, by obtaining the deviation value between the air pressure value in the gas pipeline and the preset target air pressure value as the air pressure deviation value, and obtaining the difference between the air pressure deviation value and the historical air pressure deviation value as the air pressure deviation value change rate, the air pressure deviation value and the air pressure deviation value change rate are fuzzified in the fuzzy controller, and the three PID control parameters of the proportional control parameter, the integral control parameter and the differential control parameter are fuzzy reasoning, defuzzification and other steps are performed, and the three PID control parameters of the proportional control parameter, the integral control parameter and the differential control parameter of the PID controller are output, and the three PID control parameters are further input into the PID controller so that the PID controller adjusts the PWM parameter change, and then completes the subsequent control, and generates a PWM voltage pulse signal to adjust the opening of the proportional valve. The PWM current control method based on fuzzy PID overcomes the disadvantage that the traditional PID control cannot adjust the PID parameters in real time, improves the anti-disturbance performance of the entire system, and can perform continuous proportional and uninterrupted control and adjustment of the output pressure and flow in real time, thereby achieving the purpose of constant pressure variable flow stable gas supply, and the control effect is good.

[0041] In one embodiment of the present application, the S300 includes:

[0042] S310, setting a fuzzy range of a pressure deviation value according to a preset target air pressure value.

[0043] Specifically, optionally, as an optional implementation, the preset target air pressure value is used as the maximum boundary value of the pressure deviation value fuzzy range, and the negative value of the preset target air pressure value is used as the minimum boundary value of the pressure deviation value fuzzy range.

[0044] For example, the preset target air pressure value is 12 mmHg (millimeters of mercury, one of the commonly used units of air pressure), then the pressure deviation value fuzzy range can be set to be greater than or equal to -12 mmHg and less than or equal to 12 mmHg, which is recorded as [-12,12].

[0045] S320, equidistantly intercepting n boundary value points in the pressure deviation value fuzzy range, and creating n+1 pressure deviation value fuzzy sub-ranges according to the n boundary value points, where n is a positive integer greater than or equal to 1.

[0046] Specifically, following the above example, the fuzzy range of the pressure deviation value is [-12, 12], then n boundary value points are intercepted equidistantly within the fuzzy range of the pressure deviation value.

[0047] In one embodiment of the present application, n is 7.

[0048] Specifically, the fuzzy range of the pressure deviation value is [-12, 12]. When n is 7, the 7 boundary value points are -12, -8, -4, 0, 4, 8 and 12. Then 8 fuzzy sub-ranges of pressure deviation values ​​are created, namely [-∞, -12], [--12, -8], [-8, -4], [-4, 0], [0, 4], [4, 8], [8, 12] and [12, +∞].

[0049] Furthermore, each boundary value point is taken as a fuzzy subset of pressure deviation value, and each fuzzy subset of pressure deviation value is numbered, so 7 fuzzy subsets of pressure deviation value are obtained: NB is -12, NM is -8, NS is -4, ZE is 0, PS is 4, PM is 8, and PB is 12.

[0050] S330, based on the current air pressure deviation value and the rate of change of the air pressure deviation value, a fuzzy algorithm is used to calculate the membership of the proportional control parameter at each boundary value point in the fuzzy range of the pressure deviation value, and the membership that is not 0 is selected as the fuzzy membership of the proportional control parameter to obtain at least one fuzzy membership of the proportional control parameter.

[0051] Specifically, following the above embodiment, this step is to calculate the membership of the proportional control parameter at each boundary value point in [-12,12] according to the current air pressure deviation value and the rate of change of the air pressure deviation value by using a fuzzy algorithm. In fact, it is to calculate the membership of the proportional control parameter at the seven boundary value points of NB, NM, NS, ZE, PS, PM and PB.

[0052] According to this step, we can obtain the distribution of proportional control parameters on NB, NM, NS, ZE, PS, PM and PB.

[0053] S340, obtaining a test parameter range of the proportional control parameter by testing and adjusting the parameters, as the proportional test parameter range.

[0054] Specifically, test parameter adjustment is a test method in which the resulting proportional test parameter range is equal to a "theoretical" parameter range.

[0055] S350, based on at least one proportional control parameter fuzzy membership and a proportional test parameter range, obtain a proportional control parameter using a defuzzification algorithm.

[0056] Specifically,

[0057] This step is equivalent to coupling the measured situation (at least one proportional control parameter fuzzy membership) and the theoretical situation (proportional test parameter range) to each other, and finally obtaining the proportional control parameter. The calculation method of the integral control parameter is the same as S310-S350. The calculation method of the differential control parameter is the same as S310-S350.

[0058] In one embodiment of the present application, after S320 and before S330, S300 further includes:

[0059] S321, taking each boundary value point as a fuzzy subset of pressure deviation value.

[0060] Specifically, the fuzzy range of the pressure deviation value is [-12, 12]. When n is 7, the 7 boundary value points are -12, -8, -4, 0, 4, 8 and 12. Then -12, -8, -4, 0, 4, 8 and 12 are 7 fuzzy subsets of the pressure deviation value. Each boundary value point is a fuzzy subset of the pressure deviation value.

[0061] S322, obtaining a sequence number set {V1, V2, V3...Vm}, wherein the sequence number set includes a plurality of sequence numbers, and the plurality of sequence numbers are arranged in a preset order in the sequence number set, and m is the total number of sequence numbers.

[0062] Specifically, a sequence number set {V1, V2, V3...Vm} is created. For example, V1 uses NB, V2 uses NM, V3 uses NS, and so on. In one implementation, a sequence number set {NB, NM, NS, ZE, PS, PM, PB...} is obtained. It can be seen that the preset order is the arrangement order of NB-NM-NS-ZE-PS-PM-PB...

[0063] S323, sorting the n boundary value points within the fuzzy range of the pressure deviation value in ascending order of boundary value, and assigning a serial number to each boundary value point in ascending order, and assigning the serial numbers in accordance with the preset order.

[0064] Specifically, when this step is executed, for example, the 7 boundary values ​​are sorted in ascending order to obtain -12, -8, -4, 0, 4, 8 and 12, and then assigned serial numbers according to the preset order NB-NM-NS-ZE-PS-PM-PB..., so -12 (NB), -8 (NM), -4 (NS), 0 (ZE), 4 (PS), 8 (PM) and 12 (PB) are obtained.

[0065] S324, assigning a serial number to each boundary value point within the fuzzy range of the pressure deviation value as a fuzzy subset serial number.

[0066] Specifically, for example, NB is the fuzzy subset number of the boundary value point -12.

[0067] In one embodiment of the present application, the S330 includes:

[0068] S331, obtain multiple pressure deviation value fuzzy sub-ranges, match the current air pressure deviation value with the multiple pressure deviation value fuzzy sub-ranges one by one, obtain the pressure deviation value fuzzy sub-range to which the current air pressure deviation value belongs, and use the pressure deviation value fuzzy sub-range to which the current air pressure deviation value belongs as the first hit fuzzy sub-range.

[0069] Specifically, this embodiment introduces a specific implementation method of using a fuzzy algorithm to calculate the membership degree of the proportional control parameter at each boundary value point in the fuzzy range of the pressure deviation value.

[0070] The multiple fuzzy sub-ranges of pressure deviation values ​​obtained by the above-mentioned content are obtained through this step S331.

[0071] Following the above embodiment, [-∞, -12], [--12, -8], [-8, -4], [-4, 0], [0, 4], [4, 8], [8, 12] and [12, +∞] are obtained.

[0072] The current air pressure deviation value is further matched with these pressure deviation value fuzzy sub-ranges one by one to obtain the pressure deviation value fuzzy sub-range to which the current air pressure deviation value belongs, and the pressure deviation value fuzzy sub-range to which the current air pressure deviation value belongs is used as the first hit fuzzy sub-range.

[0073] For example, if the current air pressure deviation value is 6HHmg, then the pressure deviation value fuzzy sub-range to which the current air pressure deviation value belongs is [4, 8], that is, the first hit fuzzy sub-range is [4, 8].

[0074] S332, obtaining two fuzzy subsets of pressure deviation values ​​included in the first hit fuzzy sub-range.

[0075] Specifically, the aforementioned content has divided the pressure deviation value fuzzy subsets into NB (-12), NM (-8, NS (-4), ZE (0), PS (4), PM (8) and PB (12). It can be understood that the two pressure deviation value fuzzy subsets contained in the first hit fuzzy sub-range are obtained, and the two boundary value points contained in the first hit fuzzy sub-range are 4 and 8, then the two pressure deviation value fuzzy subsets contained in the first hit fuzzy sub-range are PS (4) and PM (8) respectively.

[0076] S333, based on the two pressure deviation value fuzzy subsets included in the first hit fuzzy sub-range and the current air pressure deviation value, calculate the membership of the current air pressure deviation value on each pressure deviation value fuzzy subset, and incorporate the obtained membership that is not 0 into the current air pressure deviation value membership fuzzy set.

[0077] Specifically, this step is to calculate the membership of the current air pressure deviation value 6HHmg in PS(4), and the membership of the current air pressure deviation value 6HHmg in PS(4) in PM(8). If the obtained membership is 0, it will be discarded, and the obtained membership that is not 0 will be included in the fuzzy set of the current air pressure deviation value membership.

[0078] S334, matching the pressure deviation value change rate with multiple pressure deviation value fuzzy sub-ranges one by one to obtain the pressure deviation value fuzzy sub-range to which the pressure deviation value change rate belongs, and taking the pressure deviation value fuzzy sub-range to which the pressure deviation value change rate belongs as the second hit fuzzy subset.

[0079] Specifically, this step is consistent with the principle of S331, the difference is that this step matches the change rate of the air pressure deviation value with multiple pressure deviation value fuzzy sub-ranges [-∞, -12], [-12, -8], [-8, -4], [-4, 0], [0, 4], [4, 8], [8, 12] and [12, +∞] one by one.

[0080] S335, obtaining two fuzzy subsets of pressure deviation values ​​included in the second hit fuzzy sub-range.

[0081] Specifically, this step is consistent with the principle of S332 and will not be repeated here.

[0082] S336, based on the two pressure deviation value fuzzy subsets contained in the second hit fuzzy sub-range and the pressure deviation value change rate, calculate the membership of the pressure deviation value change rate on each pressure deviation value fuzzy subset, and include the obtained membership that is not 0 into the pressure deviation value change rate membership fuzzy set.

[0083] Specifically, the principle of this step is consistent with that of S336 and will not be repeated here.

[0084] S337, selecting one membership from each of the current air pressure deviation value membership fuzzy set and the air pressure deviation value change rate membership fuzzy set, and forming an array in pairs.

[0085] Specifically, for example, the current air pressure deviation value membership fuzzy set is {0.4, 0.6}, and the air pressure deviation value change rate membership fuzzy set is {0.3, 0.7}. 0.4 and 0.3 can form an array (0.4, 0.3).

[0086] S338, repeatedly execute S337 to obtain at least one array.

[0087] Specifically, the group array needs to be exhaustive, that is, it is guaranteed that the arrays contain at least one different element after comparison. For example, the current air pressure deviation membership fuzzy set is {0.4, 0.6}, and the air pressure deviation change rate membership fuzzy set is {0.3, 0.7}, then the resulting arrays are (0.4, 0.3), (0.4, 0.7), (0.6, 0.3), (0.6, 0.7), a total of 4 arrays.

[0088] S339, calling the fuzzy rule table, obtaining the fuzzy membership of the proportional control parameter corresponding to each array in the fuzzy rule table, and obtaining at least one fuzzy membership of the proportional control parameter.

[0089] Specifically, the fuzzy rule table is a table of preset fuzzy membership of proportional control parameters, and a corresponding fuzzy membership of proportional control parameters can be found in the table according to each (pressure deviation value membership, pressure deviation value change rate membership) array. Therefore, if there are multiple arrays, multiple fuzzy memberships of proportional control parameters will be obtained.

[0090] In one embodiment of the present application, the S333 includes:

[0091] S333a, determining whether the current air pressure deviation value is equal to the minimum boundary value of the first hit fuzzy sub-range or the maximum boundary value of the first hit fuzzy sub-range.

[0092] S333b: If the current air pressure deviation value is equal to the minimum boundary value of the first hit fuzzy sub-range or the maximum boundary value of the first hit fuzzy sub-range, then obtain the pressure deviation value fuzzy subset corresponding to the minimum boundary value or the maximum boundary value.

[0093] S333c, record the membership of the current air pressure deviation value on the pressure deviation value fuzzy subset corresponding to the minimum boundary value or the maximum boundary value as 1, and include the membership into the current air pressure deviation value membership fuzzy set.

[0094] Specifically, for example, the current air pressure deviation value is 8Hmmg, which is exactly equal to the maximum boundary value of [4, 8], and the pressure deviation value fuzzy subset corresponding to the boundary value 8 is PM, then the membership of the current air pressure deviation value in PM is recorded as 1, and 1 is included in the current air pressure deviation value membership fuzzy set. At this time, the current air pressure deviation value has a membership of 0 on NB (-12), NM (-8), NS (-4), ZE (0), PS (4) and PB (12), and a membership of 0 will not be included in the current air pressure deviation value membership fuzzy set.

[0095] In one embodiment of the present application, in one embodiment of the present application, the S333 further includes:

[0096] S333e, if the current air pressure deviation value is not equal to the minimum boundary value of the first hit fuzzy sub-range, and is also not equal to the maximum boundary value of the first hit fuzzy sub-range, then obtain the fuzzy subset of the pressure deviation value corresponding to the minimum boundary value, and obtain the fuzzy subset of the pressure deviation value corresponding to the maximum boundary value.

[0097] S333f, respectively calculating the membership degree K1 of the current air pressure deviation value on the pressure deviation value fuzzy subset corresponding to the minimum boundary value, and the membership degree K2 of the current air pressure deviation value on the pressure deviation value fuzzy subset corresponding to the maximum boundary value.

[0098] S333g, including both K1 and K2 into the current air pressure deviation value membership fuzzy set.

[0099] Specifically, for example, the current air pressure deviation value is 6Hmmg, and the first hit fuzzy sub-range is [4,8], but 6 is neither equal to the minimum boundary value 4 nor the maximum boundary value 8, because 6 falls in [4,8]. Therefore, the pressure deviation value fuzzy subset PS corresponding to the minimum boundary value 4 and the pressure deviation value fuzzy subset PM corresponding to the minimum boundary value 8 are obtained. The membership K1 of the current air pressure deviation value 6Hmmg on PS is calculated, and the membership K2 of the current air pressure deviation value 6Hmmg on PM is calculated, and the membership K1 of the current air pressure deviation value 6Hmmg on PS is calculated.

[0100] In one embodiment of the present application, the S333f includes:

[0101] S333f1, calculate the membership degree K1 on the fuzzy subset of the pressure deviation value corresponding to the current air pressure deviation value and the minimum boundary value according to formula 1.

[0102]

[0103] Among them, K1 is the membership degree of the pressure deviation value fuzzy subset corresponding to the current air pressure deviation value and the minimum boundary value, E is the current air pressure deviation value, x is the fuzzy subset number of the pressure deviation value fuzzy subset corresponding to the minimum boundary value, y is the fuzzy subset number of the pressure deviation value fuzzy subset corresponding to the maximum boundary value, is the minimum boundary value, is the maximum boundary value.

[0104] Specifically, following the above example, the current pressure deviation value E is 6Hmmg, x is PS, y is PM, then So

[0105] S333f2, calculate the membership degree K2 on the fuzzy subset of the pressure deviation value corresponding to the current air pressure deviation value and the maximum boundary value according to formula 2.

[0106]

[0107] Among them, K2 is the membership degree of the pressure deviation value fuzzy subset corresponding to the current air pressure deviation value and the maximum boundary value, E is the current air pressure deviation value, x is the fuzzy subset number of the pressure deviation value fuzzy subset corresponding to the minimum boundary value, y is the fuzzy subset number of the pressure deviation value fuzzy subset corresponding to the maximum boundary value, is the minimum boundary value, is the maximum boundary value.

[0108] Specifically, following the above example, the current pressure deviation value E is 6Hmmg, x is PS, y is PM, then So

[0109] Furthermore, in the above example, the subsequent S333g is executed to include both K1 and K2 into the current air pressure deviation value membership fuzzy set, and then the air pressure deviation value membership fuzzy set is {0.5, 0.5}.

[0110] The following describes how S339 retrieves the fuzzy rule table, obtains the fuzzy membership of the proportional control parameter corresponding to each array in the fuzzy rule table, and obtains the specific execution of at least one fuzzy membership of the proportional control parameter.

[0111] For example, the current air pressure deviation value membership fuzzy set obtained by executing S331 to S333 is {E's membership a in PS, E's membership 1-a in PM}, and the air pressure deviation value change rate membership fuzzy set obtained by executing subsequent S334 to S336 is {EC's membership b in NM, EC's membership 1-b in NS}.

[0112] Further execute S337 and S338 to obtain four arrays, namely A1 = (E's membership in PS a, EC's membership in NM b), A2 = (E's membership in PS a, EC's membership in NS 1-b), A3 = (E's membership in PM 1-a, EC's membership in NM b), A4 = (E's membership in PM 1-a, EC's membership in NS 1-b). Further execute S339. The fuzzy rule table is retrieved as shown in Table 1.

[0113] Table 1 - Fuzzy rules table

[0114]

[0115] Each cell in Table 1 has 3 serial numbers. The first serial number is related to the proportional control parameter, the first serial number is related to the integral control parameter, and the first serial number is related to the differential control parameter. For example, the cell PB / NB / PS corresponding to the first row in the first column means that PB is related to the proportional control parameter, NB is related to the integral control parameter, and PS is related to the differential control parameter.

[0116] Since this embodiment is to calculate the proportional control parameters, we query the first sequence number.

[0117] S339 From the above Table 1, we can know that A1 = (E's membership a in PS, EC's membership b in NM), and its corresponding column PS, row NM corresponds to the cell PS / NS / ZE, whose first serial number is PS, then we get the first proportional control parameter fuzzy membership: the fuzzy membership of the proportional control parameter on PS is a×b.

[0118] A2 = (E's membership in PS a, EC's membership in NS 1-b), the corresponding column PS, the row NS corresponds to the cell ZE / ZE / ZE, and its first serial number is ZE, then the second proportional control parameter fuzzy membership is obtained: the fuzzy membership of the proportional control parameter on ZE is a×(1-b).

[0119] A3=(1-a, membership of E in PM, b, membership of EC in NM), the corresponding column PM, the corresponding cell of row NM is ZE / ZE / NS, and its first serial number is ZE. Then the fuzzy membership of the third proportional control parameter is obtained: the fuzzy membership of the proportional control parameter on ZE is b×(1-a).

[0120] A4=(E's membership in PM is 1-a, EC's membership in NS is 1-b, the corresponding column is PM, the corresponding cell in row NS is NS / PS / PS, and its first serial number is NS, then the fourth proportional control parameter fuzzy membership is obtained: the fuzzy membership of the proportional control parameter on NS is (1-a)×(1-b).

[0121] Combining the fuzzy membership of the above four proportional control parameters, we get the following results:

[0122] The fuzzy membership of the proportional control parameter on PS is a×b, the fuzzy membership of the proportional control parameter on ZE is a×(1-b)+b×(1-a), and the fuzzy membership of the proportional control parameter on NS is (1-a)×(1-b).

[0123] In the above content, we mentioned that the calculation method of the integral control parameter is the same as S310-S360. The calculation method of the differential control parameter is the same as S310-S360. Then when the integral control parameter is calculated and executed in S339, the second serial number in each unit in Table 1 is queried. When the differential control parameter is calculated and executed in S339, the third serial number in each unit in Table 1 is queried. The specific table lookup steps are not repeated here.

[0124] In one embodiment of the present application, S360 includes:

[0125] S361, intercept n boundary value points equidistantly within the range of the proportional test parameter.

[0126] Specifically, note that n in S361 is equal to n in S320.

[0127] For example, after obtaining the ratio test parameter range of [-6,6], that is, the range greater than or equal to -6 and less than or equal to 6, then when n is 7 in S320, n is also 7 in S361, and 7 boundary value points are obtained, namely -6, -4, -2, 0, 2, 4 and 6.

[0128] S362, taking each boundary value point as a pressure deviation value test subset.

[0129] Specifically, the principle is the same as S321, -6, -4, -2, 0, 2, 4 and 6 are 7 pressure deviation value test subsets. Each boundary value point is a pressure deviation value test subset.

[0130] S363, obtain the sequence number set {V1, V2, V3...Vm}.

[0131] Specifically, it should be noted that the sequence number set used in this step is completely consistent with the sequence number set used in the aforementioned S322, so the exact same set of sequence numbers is also used when assigning sequence numbers subsequently.

[0132] S364, sorting the n boundary value points within the ratio test parameter range in ascending order of boundary value, and assigning a serial number to each boundary value point in ascending order, and assigning the serial numbers according to the preset order.

[0133] Specifically, the aforementioned S323 uses the sequence number set {NB, NM, NS, ZE, PS, PM, PB...}, and the preset order is NB-NM-NS-ZE-PS-PM-PB..., then this step must also use the same sequence number set {NB, NM, NS, ZE, PS, PM, PB...}, and use the same preset order NB-NM-NS-ZE-PS-PM-PB... to assign sequences to ensure alignment.

[0134] When this step is executed specifically, the principle is the same as S323. The 7 boundary values ​​are sorted in ascending order to obtain -6, -4, -2, 0, 2, 4 and 6, and then assigned serial numbers according to the preset order NB-NM-NS-ZE-PS-PM-PB..., so -6 (NB), -4 (NM), -2 (NS), 0 (ZE), 2 (PS), 4 (PM) and 6 (PB) are obtained.

[0135] S365, assigning a serial number to each boundary value point within the ratio test parameter range as a test subset serial number.

[0136] Specifically, for example, NB is the test subset number of the boundary value point -6. The principle of this step is consistent with that of S324.

[0137] In one embodiment of the present application, S360 further includes:

[0138] S366, obtaining the fuzzy subset sequence number of the pressure deviation value fuzzy subset to which the fuzzy membership degree of each proportional control parameter belongs, and obtaining a plurality of fuzzy subset sequence numbers.

[0139] Specifically, following the results obtained from the table lookup in the previous example, the fuzzy membership of the proportional control parameter on PS is a×b, the fuzzy membership of the proportional control parameter on ZE is a×(1-b)+b×(1-a), and the fuzzy membership of the proportional control parameter on NS is (1-a)×(1-b), so we get three fuzzy subset numbers PS, ZE and NS.

[0140] S367, searching for a test subset serial number that is the same as each fuzzy subset serial number, and obtaining a plurality of test subset serial numbers.

[0141] Specifically, this step means that we also need to find three test subset numbers PS, ZE and NS, which are equal to the three fuzzy subset numbers respectively, indicating alignment, so as to facilitate the subsequent acquisition of the boundary values ​​within the range of the proportional test parameters corresponding to the test subset numbers.

[0142] S368, obtaining the boundary value within the range of the proportional test parameter corresponding to each test subset serial number.

[0143] Specifically, since the boundary value points within the proportional test parameter range are: -6 (NB), -4 (NM), -2 (NS), 0 (ZE), 2 (PS), 4 (PM) and 6 (PB), this step obtains the boundary value 2 corresponding to PS, the boundary value 0 corresponding to ZE, and the boundary value -2 corresponding to NS.

[0144] S369, calculate the proportional control parameter according to Formula 3.

[0145] K p =O V1 ×U V1 +O V2 ×U V2 ...+O Vm ×U Vm Formula 3.

[0146] Among them, Kp is the proportional control parameter, Vm is the fuzzy subset number, O Vm is the fuzzy membership of the proportional control parameter on the pressure deviation value fuzzy subset number Vm, U Vm It is the boundary value within the range of the proportion test parameter corresponding to the test subset number that is the same as the fuzzy subset number.

[0147] Specifically, S366 to S367 are steps of aligning the fuzzy subset number and the test subset number.

[0148] Following the above example, combining the fuzzy membership of the above four proportional control parameters, we get the following result:

[0149] The fuzzy membership of the proportional control parameter on PS is a×b, the fuzzy membership of the proportional control parameter on ZE is a×(1-b)+b×(1-a), and the fuzzy membership of the proportional control parameter on NS is (1-a)×(1-b).

[0150] The proportional control parameter corresponds to a boundary value of 2 at PS, a boundary value of 0 at ZE, and a boundary value of -2 at NS.

[0151] Then finally calculated according to formula 3:

[0152] K p =O PS ×U PS +O ZE ×U ZE +O NS ×U NS ,

[0153] That is, Kp=ab×2+[a(1-b)+b(1-a)]×0+[(1-a)×(1-b)]×(-2).

[0154] The present application also provides a pneumoperitoneum machine.

[0155] like Figure 2 As shown, in one embodiment of the present application, the pneumoperitoneum machine includes a pneumoperitoneum machine body 100 , a gas supply pipeline 200 , a pneumoperitoneum needle 300 , a proportional valve 400 and a pressure sensor 500 .

[0156] The pneumoperitoneum machine body 100 includes a main controller 110, a fuzzy controller 120 and a PID controller 130. The main controller 110 is used to execute the control method of the pneumoperitoneum machine mentioned in any of the above embodiments.

[0157] Specifically, the main controller 110 is connected to the fuzzy controller 120 , the main controller 110 is connected to the PID controller 130 , and the PID controller 110 is connected to the proportional valve 400 .

[0158] In another embodiment of the present application, the pneumoperitoneum machine further includes a gas source 600, such as Figure 3 As shown, the gas source 600 is connected to the insufflator body 100. The gas source 600 may be a gas cylinder containing CO2 gas.

[0159] After the CO2 gas in the gas source 600 is transported to the insufflator body 100, it is transported into the gas supply pipeline 200. The PID controller 130 uses the control method of the insufflator mentioned in any of the aforementioned embodiments to control the opening of the proportional valve 400, thereby controlling the output flow and output gas pressure of the insufflator.

[0160] The technical features of the above-described embodiments may be arbitrarily combined, and the execution order of the method steps is not limited. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0161] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A control method for an insufflator, characterized in that: The method comprises: Obtaining the air pressure value in the gas pipeline and the preset target air pressure value, calculating the difference between the preset target air pressure value and the air pressure value in the gas pipeline, and using the difference as the current air pressure deviation value; Obtain historical air pressure deviation values ​​from the database, and use the difference between the current air pressure deviation value and the historical air pressure deviation value as the air pressure deviation value change rate; Perform fuzzy calculation based on the current air pressure deviation value and the rate of change of the air pressure deviation value to generate proportional control parameters, integral control parameters and differential control parameters; Generate a PWM voltage pulse signal according to a proportional control parameter, an integral control parameter and a differential control parameter; Sending a PWM voltage pulse signal to a proportional valve, and adjusting the opening of the proportional valve according to the PWM voltage pulse signal; The fuzzy calculation is performed based on the current air pressure deviation value and the air pressure deviation value change rate to generate proportional control parameters, integral control parameters and differential control parameters, including: Set the fuzzy range of pressure deviation value according to the preset target air pressure value; In the fuzzy range of the pressure deviation value, n boundary value points are intercepted at equal intervals, and n+1 fuzzy sub-ranges of the pressure deviation value are created according to the n boundary value points; n is a positive integer greater than or equal to 1; Based on the current air pressure deviation value and the air pressure deviation value change rate, the membership of the proportional control parameter at each boundary value point in the fuzzy range of the pressure deviation value is calculated using a fuzzy algorithm, and the membership that is not 0 is selected as the fuzzy membership of the proportional control parameter to obtain at least one fuzzy membership of the proportional control parameter; The test parameter range of the proportional control parameter is obtained by testing and adjusting the parameters, and is used as the proportional test parameter range; Based on at least one proportional control parameter fuzzy membership and a proportional test parameter range, a proportional control parameter is obtained by using a defuzzification algorithm.

2. The control method of the pneumoperitoneum machine according to claim 1, characterized in that: Before the method of calculating the membership of the proportional control parameter at each boundary value point in the fuzzy range of the pressure deviation value based on the current pressure deviation value and the pressure deviation value change rate by using a fuzzification algorithm, screening the membership that is not 0 as the proportional control parameter fuzzy membership, and obtaining at least one proportional control parameter fuzzy membership, the method of performing fuzzy calculation according to the current pressure deviation value and the pressure deviation value change rate to generate the proportional control parameter, the integral control parameter and the differential control parameter also includes: Take each boundary value point as a fuzzy subset of pressure deviation value; Get a sequence number set {V1, V2, V3...Vm}, where the sequence number set contains multiple sequence numbers, and the multiple sequence numbers are arranged in a preset order in the sequence number set; m is the total number of sequence numbers; Sorting the n boundary value points within the fuzzy range of the pressure deviation value in ascending order of the boundary value, and assigning a serial number to each boundary value point in ascending order, and assigning the serial numbers in accordance with the preset order; The serial number assigned to each boundary value point within the fuzzy range of the pressure deviation value is used as the fuzzy subset serial number.

3. The control method of the pneumoperitoneum machine according to claim 2, characterized in that: The method comprises: calculating the membership of the proportional control parameter at each boundary value point in the fuzzy range of the pressure deviation value based on the current pressure deviation value and the pressure deviation value change rate by using a fuzzy algorithm, selecting the membership that is not 0 as the fuzzy membership of the proportional control parameter, and obtaining at least one fuzzy membership of the proportional control parameter, including: Acquire multiple pressure deviation value fuzzy sub-ranges, match the current air pressure deviation value with the multiple pressure deviation value fuzzy sub-ranges one by one, obtain the pressure deviation value fuzzy sub-range to which the current air pressure deviation value belongs, and use the pressure deviation value fuzzy sub-range to which the current air pressure deviation value belongs as the first hit fuzzy sub-range; Obtain two fuzzy subsets of pressure deviation values ​​included in the first hit fuzzy sub-range; According to the two pressure deviation value fuzzy subsets contained in the first hit fuzzy sub-range and the current air pressure deviation value, the membership of the current air pressure deviation value on each pressure deviation value fuzzy subset is calculated, and the obtained membership that is not 0 is included in the current air pressure deviation value membership fuzzy set; Matching the pressure deviation value change rate with multiple pressure deviation value fuzzy sub-ranges one by one to obtain the pressure deviation value fuzzy sub-range to which the pressure deviation value change rate belongs, and taking the pressure deviation value fuzzy sub-range to which the pressure deviation value change rate belongs as the second hit fuzzy subset; Obtain two pressure deviation value fuzzy subsets included in the second hit fuzzy sub-range; According to the two pressure deviation value fuzzy subsets contained in the second hit fuzzy sub-range and the pressure deviation value change rate, the membership of the pressure deviation value change rate on each pressure deviation value fuzzy subset is calculated, and the obtained membership that is not 0 is included in the pressure deviation value change rate membership fuzzy set; Select one membership from each of the current air pressure deviation membership fuzzy set and the air pressure deviation value change rate membership fuzzy set to form an array in pairs; Repeatedly executing the step of selecting a membership from the current air pressure deviation value membership fuzzy set and the air pressure deviation value change rate membership fuzzy set, and forming an array in pairs, to obtain at least one array; The fuzzy rule table is retrieved to obtain the fuzzy membership of the proportional control parameter corresponding to each array in the fuzzy rule table, and at least one fuzzy membership of the proportional control parameter is obtained.

4. The control method of the pneumoperitoneum machine according to claim 3, characterized in that: The method of calculating the membership of the current air pressure deviation value on each pressure deviation value fuzzy subset based on the two pressure deviation value fuzzy subsets included in the first hit fuzzy sub-range and the current air pressure deviation value, and incorporating the obtained membership that is not 0 into the current air pressure deviation value membership fuzzy set includes: Determine whether the current air pressure deviation value is equal to the minimum boundary value of the first hit fuzzy sub-range or the maximum boundary value of the first hit fuzzy sub-range; If the current air pressure deviation value is equal to the minimum boundary value of the first hit fuzzy sub-range or the maximum boundary value of the first hit fuzzy sub-range, then obtaining the pressure deviation value fuzzy subset corresponding to the minimum boundary value or the maximum boundary value; The membership of the current air pressure deviation value on the pressure deviation value fuzzy subset corresponding to the minimum boundary value or the maximum boundary value is recorded as 1, and the membership is included in the current air pressure deviation value membership fuzzy set.

5. The control method of the pneumoperitoneum machine according to claim 4, characterized in that: The method further comprises: calculating the membership of the current air pressure deviation value on each pressure deviation value fuzzy subset according to the two pressure deviation value fuzzy subsets included in the first hit fuzzy sub-range and the current air pressure deviation value, and incorporating the obtained membership that is not 0 into the current air pressure deviation value membership fuzzy set; If the current air pressure deviation value is not equal to the minimum boundary value of the first hit fuzzy sub-range, and is also not equal to the maximum boundary value of the first hit fuzzy sub-range, then obtain the pressure deviation value fuzzy subset corresponding to the minimum boundary value, and obtain the pressure deviation value fuzzy subset corresponding to the maximum boundary value; Calculate the membership degree K1 of the current air pressure deviation value on the fuzzy subset of the pressure deviation value corresponding to the minimum boundary value, and the membership degree K2 of the current air pressure deviation value on the fuzzy subset of the pressure deviation value corresponding to the maximum boundary value respectively; Both K1 and K2 are included in the current air pressure deviation value membership fuzzy set.

6. The control method of the pneumoperitoneum machine according to claim 5, characterized in that: The respectively calculating the membership degree K1 of the current air pressure deviation value on the pressure deviation value fuzzy subset corresponding to the minimum boundary value, and the membership degree K2 of the current air pressure deviation value on the pressure deviation value fuzzy subset corresponding to the maximum boundary value, comprises: The membership degree K1 of the current air pressure deviation value on the fuzzy subset of the pressure deviation value corresponding to the minimum boundary value is calculated according to formula 1; Among them, K1 is the membership degree of the current air pressure deviation value on the pressure deviation value fuzzy subset corresponding to the minimum boundary value, E is the current air pressure deviation value, x is the fuzzy subset number of the pressure deviation value fuzzy subset corresponding to the minimum boundary value, y is the fuzzy subset number of the pressure deviation value fuzzy subset corresponding to the maximum boundary value, is the minimum boundary value, is the maximum boundary value; According to formula 2, the fuzzy membership degree K2 of the current air pressure deviation value on the fuzzy subset of the pressure deviation value corresponding to the maximum boundary value is calculated; Among them, K2 is the membership degree of the current air pressure deviation value on the pressure deviation value fuzzy subset corresponding to the maximum boundary value, E is the current air pressure deviation value, x is the fuzzy subset number of the pressure deviation value fuzzy subset corresponding to the minimum boundary value, y is the fuzzy subset number of the pressure deviation value fuzzy subset corresponding to the maximum boundary value, is the minimum boundary value, is the maximum boundary value.

7. The control method of the pneumoperitoneum machine according to claim 4 or 6, characterized in that: The method of obtaining the proportional control parameter by using a defuzzification algorithm based on at least one proportional control parameter fuzzy membership and a proportional test parameter range includes: Intercept n boundary value points equidistantly within the range of the proportional test parameters; Take each boundary value point as a pressure deviation value test subset; Get the sequence number set {V1, V2, V3...Vm}; Sorting the n boundary value points within the ratio test parameter range in order of the boundary value from small to large, and assigning a serial number to each boundary value point in order from small to large, and assigning the serial numbers in accordance with the preset order; The serial number assigned to each boundary value point within the ratio test parameter range is used as the test subset serial number.

8. The control method of the pneumoperitoneum machine according to claim 7, characterized in that: The method of obtaining the proportional control parameter by using a defuzzification algorithm based on at least one proportional control parameter fuzzy membership and a proportional test parameter range also includes: Obtaining the fuzzy subset sequence number of the pressure deviation value fuzzy subset to which the fuzzy membership degree of each proportional control parameter belongs, and obtaining a plurality of fuzzy subset sequence numbers; Searching for the test subset serial number that is the same as each fuzzy subset serial number, and obtaining multiple test subset serial numbers; Obtain the boundary value within the range of the proportion test parameter corresponding to each test subset serial number; Calculate the proportional control parameters according to formula 3; Kp=OV1×UV1+OV2×UV2...+OVm×UVm Formula 3; Among them, Kp is the proportional control parameter, Vm is the fuzzy subset number, OVm is the fuzzy membership of the proportional control parameter on the pressure deviation value fuzzy subset with fuzzy subset number Vm, and UVm is the boundary value within the proportional test parameter range corresponding to the test subset number with the same fuzzy subset number.

9. A pneumoperitoneum machine, characterized in that: include: Insufflator body; Gas pipelines; The gas pipeline is arranged between the pneumoperitoneum machine body and the pneumoperitoneum needle; A proportional valve is installed on the gas pipeline; A pressure sensor is arranged on the inner wall of the gas pipeline; The pneumoperitoneum machine body comprises a main controller, a fuzzy controller and a PID controller, and the main controller is used to execute the control method of the pneumoperitoneum machine as described in any one of claims 1-8.

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