Control method and device based on PLC system fuzzy algorithm

Abstract

The invention discloses a control method and device based on a PLC system fuzzy algorithm, and belongs to the technical field of water treatment. The device comprises a PLC control module which integrates a fuzzy control algorithm and is used for processing an input signal in real time and generating a control instruction; the target value setting module is used for receiving a liquid level or flow target value set by a user and displaying the liquid level or flow target value through a human-computer interaction interface (HMI); the sensor feedback module is used for acquiring an output actual value of the equipment in real time and transmitting data to the PLC control module; the fuzzy rule base is used for storing fuzzy control rules based on deviation and deviation change rate, and the fuzzy control rules define control logic in an IF-THEN form; the execution control module is used for controlling the running state of the equipment according to the defuzzified control parameters, a preset adjustment interval and a single adjustment amplitude; and the opening degree limiting module is used for setting the maximum opening degree and the minimum opening degree of the equipment, and when the control parameter exceeds the limit, the control parameter is automatically cut off to a boundary value.

Description

Technical Field

[0001] This application relates to the field of water treatment technologies, and particularly to a control method and device based on a fuzzy algorithm of a PLC system. Background Art

[0002] In the water treatment industry, the control of equipment such as variable-frequency water pumps, valves, and heaters usually relies on PID algorithms or interlock control logics. For PID control, it is necessary to accurately tune the proportional, integral, and derivative parameters. Its program logic and control parameter settings are very troublesome, with high technical requirements for operators, and it is difficult to adapt to non-linear or time-varying systems, with high complexity. The program writing of interlock control is relatively complex, with high technical requirements for programmers, which is not conducive to the later project engineering maintenance and the popularization of logic programs. Moreover, it requires the operation and maintenance personnel to have a certain understanding of the process of the system and have relatively high process operations, with high maintenance costs. Also, external disturbances (such as sudden changes in influent flow rate and sensor noise) easily cause system fluctuations and require frequent manual adjustments, with weak anti-interference ability. Summary of the Invention

[0003] Therefore, in order to solve the problems of high complexity, weak anti-interference ability, and high maintenance costs existing in the above-mentioned prior art for PID control or interlock control, the embodiments of this application provide a control method and device based on a fuzzy algorithm of a PLC system. The technical solutions are as follows: In a first aspect, the embodiments of this application provide a control device based on a fuzzy algorithm of a PLC system, including: A PLC control module, integrated with a fuzzy control algorithm, for processing input signals in real time and generating control instructions; A target value setting module, for receiving the liquid level or flow rate target value set by the user and displaying it through a human-machine interface (HMI); A sensor feedback module, for real-time collecting the actual output values of the equipment through a liquid level sensor, a flow sensor, or a temperature sensor, and transmitting the data to the PLC control module; A fuzzy rule base, storing fuzzy control rules based on the deviation and the rate of change of the deviation, and the fuzzy control rules define the control logic in the form of "IF-THEN"; An execution control module, for controlling the operating state of the equipment according to the defuzzified control parameters, at a preset adjustment interval and a single adjustment amplitude; An opening degree limiting module, for setting the maximum and minimum opening degrees of the equipment, and automatically truncating to the boundary values when the control parameters exceed the limits.

[0004] In the above-mentioned control device based on a fuzzy algorithm of a PLC system, the PLC control module includes: A deviation calculation unit for calculating the deviation (Δ) between the actual value and the target value and the deviation change rate (dΔ / dt); A fuzzification unit for converting the deviation and the deviation change rate into fuzzy quantities through a preset membership function, where the membership function includes a triangular function or a trapezoidal function, and the fuzzy sets are defined as "negative large", "negative small", "zero", "positive small", and "positive large"; A fuzzy inference engine for performing fuzzy logic operations according to the rules in the fuzzy rule base to generate a fuzzy output quantity; A defuzzification unit for converting the fuzzy output quantity into an exact control parameter by using the centroid method.

[0005] In the above control device based on the fuzzy algorithm of the PLC system, the device further includes: An anti-interference module that triggers anti-interference rules when detecting an external disturbance signal and reduces the adjustment amplitude to a preset safety value; A dynamic optimization module for automatically adjusting the rule weights of the fuzzy rule base according to historical operation data and optimizing the coverage range of the membership function to improve control stability; A data storage and analysis module for recording historical control parameters, device status, and external disturbance events, and also for generating operation reports and providing suggestions for optimizing the rule base.

[0006] In the above control device based on the fuzzy algorithm of the PLC system, the fuzzy control algorithm pauses adjustment under the following conditions: the absolute value of the deviation between the actual value and the target value is less than or equal to m% of the target value; the device is in the manual mode or the fault state.

[0007] In the above control device based on the fuzzy algorithm of the PLC system, the adjustment interval and the single adjustment amplitude are configured in the following way: the default values are fixed parameters; it supports dynamic modification through the human-machine interface (HMI), and the parameters take effect in real time after modification.

[0008] In the above control device based on the fuzzy algorithm of the PLC system, the dynamic optimization logic of the membership function includes: when the system still fails to reach the target interval after 3 consecutive adjustments, automatically narrow the coverage range of the "zero" interval to improve sensitivity; when the external disturbance frequency exceeds the threshold, expand the coverage ranges of the "positive large" and "negative large" intervals to enhance anti-interference ability.

[0009] In the above control device based on the fuzzy algorithm of the PLC system, the triggering conditions of the anti-interference module include: sudden change of sensor data; abnormal communication of external devices; the deviation between the feedback value of the actuator and the control instruction exceeds the preset threshold.

[0010] In the above control device based on the fuzzy algorithm of the PLC system, the fuzzy processing of the fuzzification unit further includes: setting a dynamic threshold for the deviation change rate (dΔ / dt), and when dΔ / dt exceeds the threshold, automatically increasing the weight of the "positive large" or "negative large" interval.

[0011] In a second aspect, an embodiment of the present application provides a control method based on the fuzzy algorithm of the PLC system, which uses the above control device based on the fuzzy algorithm of the PLC system, and includes the following steps: Set the target value of the liquid level or flow rate; Real-time collect the actual value output by the device; Calculate the deviation (Δ) between the actual value and the target value and the deviation change rate (dΔ / dt); Generate a control instruction through the fuzzy rule base, and control the operating state of the device according to the preset adjustment interval and the opening degree each time; During the adjustment process, limit the operating state of the device not to exceed the maximum opening degree and the minimum opening degree; When an external disturbance is detected, reduce the adjustment amplitude to a preset safety value, and optimize the rule weight and membership function parameters according to historical data.

[0012] In the above control method based on the fuzzy algorithm of the PLC system, the generation of the control instruction through the fuzzy rule base includes: Fuzzify the deviation (Δ) and the deviation change rate (dΔ / dt) through the membership function; Perform fuzzy inference according to the fuzzy rule base to generate a fuzzy output quantity; Use the centroid method to defuzzify to obtain the precise control parameter.

[0013] Compared with the prior art, the beneficial effects of the present invention are at least as follows: The control method and device based on the fuzzy algorithm of the PLC system of the present application only need to set the target value, adjustment interval and opening degree limit, and the number of parameters is reduced by more than 50% compared with PID control, simplifying the parameter configuration and facilitating the operator to adjust and optimize the sensitivity of the control unit proficiently; by dynamically optimizing the rule base and membership function, the liquid level or flow rate fluctuation is significantly suppressed, and the steady-state error ≤ 5%, enhancing the stability of the system; under external disturbance, the system recovery time is shortened by more than 40%, and the anti-interference ability is strong; the rule base can be intuitively modified through the HMI without reprogramming or the intervention of professional technicians, reducing the maintenance cost. Description of the Drawings

[0014] The above and other objects, features, and advantages of the present application will become more apparent by describing the embodiments of the present application in more detail with reference to the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. They are used together with the embodiments of the present application to explain the present application and do not constitute a limitation to the present application.

[0015] Figure 1 is the structural framework diagram of a control device based on the fuzzy algorithm of the PLC system provided by the embodiment of the present application; Figure 2 is the method flowchart of a control method based on the fuzzy algorithm of the PLC system provided by the embodiment of the present application; Figure 3 is the step flowchart of generating control instructions through the fuzzy rule base provided by the embodiment of the present application. Detailed Embodiments

[0016] Next, exemplary embodiments of the present application will be described in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only a part of the embodiments of the present application, and the present application is not limited by the exemplary embodiments described herein.

[0017] Embodiment 1 As Figure 1 shown, the embodiment of the present application provides a control device based on the fuzzy algorithm of the PLC system, including: The PLC control module 100 integrates the fuzzy control algorithm and is used to process the input signal in real time and generate control instructions; Fuzzy control is a non - linear control method based on fuzzy logic, which is suitable for complex systems where it is difficult to establish an accurate mathematical model. Its core idea is to convert fuzzy and inaccurate language rules into specific control actions through fuzzification of input, fuzzy inference, and defuzzification of output; the PLC control module 100 processes the input signal in real time based on the fuzzy control algorithm to generate control instructions; The target value setting module 200 is communicatively connected to the PLC control module 100 and the sensor feedback module 300, and is used to receive the liquid level or flow target value set by the user, and also includes the adjustment interval and opening limit set by the user, and displays them through the human - machine interface (HMI). The content displayed on the HMI also includes the actual output value of the device, the working range of fuzzy control, and the running time of the device; The sensor feedback module 300 collects the actual liquid level value, actual flow value, or actual temperature value after the output of the variable - frequency water pump, valve, or heater in real time through a liquid level sensor, flow sensor, or temperature sensor, and transmits the collected actual value to the PLC control module 100 for processing, and also transmits the collected actual value to the target value setting module 200 for display; Further, the PLC control module 100 includes: A deviation calculation unit 110, configured to calculate the deviation (Δ) and the deviation change rate (dΔ / dt) between the actual value and the target value. The specific process is as follows: The deviation is the difference between the target value and the actual measured value, that is: ; In a digital control system, time is discretized into a sampling period T (e.g., T = 1 second). The deviation change rate is calculated by the difference in deviations between two adjacent samplings, specifically: ; The sampling period is selected according to the dynamic characteristics of the system, generally taken as 1 / 10 to 1 / 5 of the system response time to balance the response speed and the noise resistance ability; the deviation change rate is sensitive to noise, and can be filtered by moving average filtering or first-order low-pass filtering to reduce noise interference and improve the calculation stability; A fuzzification unit 120, configured to convert the deviation and the deviation change rate into fuzzy quantities through a preset membership function. The membership function includes a triangular function or a trapezoidal function, and the fuzzy sets are defined as "negative large", "negative small", "zero", "positive small", and "positive large"; Specifically, for the process of defining the fuzzy sets of the input variables deviation (Δ) and deviation change rate (dΔ / dt): The deviation (Δ) and the deviation change rate (dΔ / dt) are divided into several linguistic variables. Taking the water treatment liquid level control as an example, they are divided into 5 levels: negative large (NB), negative small (NS), zero (ZO), positive small (PS), and positive large (PB); then, a membership function including a triangular function or a trapezoidal function is used to convert the deviation and the deviation change rate into fuzzy quantities. The example is as follows: Assume that the target liquid level value is 2.5 meters, and the allowable deviation is ±0.5 meters. The membership function of Δ is: NB (negative large): Δ ≤ -0.5 meters, and the membership degree gradually increases as Δ decreases; NS (negative small): -0.5 meters < Δ ≤ -0.2 meters, the membership degree is 0 at -0.5 meters and 1 at -0.2 meters; ZO (zero): -0.2 meters < Δ < +0.2 meters, the membership degree is 1 at ±0.2 meters and linearly decreases on both sides; PS (positive small): +0.2 meters ≤ Δ < +0.5 meters, the membership degree is 0 at +0.2 meters and 1 at +0.5 meters; PB (positive large): Δ ≥ +0.5 meters, and the membership degree gradually increases as Δ increases; The membership function of dΔ / dt is similar to the division of Δ, covering states such as rapid decline (NB), slow decline (NS), stable (ZO), slow rise (PS), rapid rise (PB), etc.; The fuzzy inference engine 130 performs fuzzy logic operations according to the rules in the fuzzy rule base 400, usually using the Mamdani or Sugeno method, generates a fuzzy output quantity, and the fuzzy set of the output variable (such as the valve opening adjustment amount) is divided into: large decrease (NB), small decrease (NS), maintain (ZO), small increase (PS), large increase (PB), and its membership function is similar to that of the input variable, covering the actual control range; The defuzzification unit 140 uses the centroid method to convert the fuzzy output quantity into an accurate control parameter. For example, if the centroid of the fuzzy set output after multiple rules are activated corresponds to a valve opening adjustment amount of -3%, then this adjustment is executed; The fuzzy rule base 400 stores fuzzy control rules based on the deviation and the rate of change of the deviation. The fuzzy control rules define the control logic in the form of "IF-THEN". Based on expert experience or system characteristics, rules in the form of "IF-THEN" are formulated to cover all input combinations. The rule representation form is: IF Δ is [A] AND dΔ / dt is [B] THEN the control quantity is [C], where [A] and [B] are input fuzzy sets, and [C] is the output fuzzy set. Taking liquid level control as an example, some rules are shown in Table 1 below; Table 1 The execution control module 500 controls the operating state of the device according to the accurate control parameters after defuzzification, according to the preset adjustment interval and the single adjustment amplitude; The opening limit module 600 is used to set the maximum and minimum openings of the device. When the control parameter exceeds the limit, it is automatically truncated to the boundary value to prevent overshoot.

[0018] Furthermore, the device of the present application further includes: The anti-interference module 700, when detecting an external disturbance signal, triggers anti-interference rules, reduces the adjustment amplitude to a preset safety value. For example, in the liquid level control scenario, when detecting a sudden change in the inlet flow rate, the adjustment amplitude is reduced to half of the original pre-adjustment amplitude to avoid overshoot; The dynamic optimization module 800 automatically adjusts the rule weights of the fuzzy rule base 400 according to historical operation data and optimizes the coverage range of the membership function to improve control stability; The data storage and analysis module 900 is used to record historical control parameters, device status and external disturbance events, and is also used to generate operation reports and provide rule base optimization suggestions.

[0019] Further, the fuzzy control algorithm pauses adjustment under the following conditions: The absolute value of the deviation between the actual value and the target value is less than or equal to m% of the target value. The value of m can be 2 - 7, and is not limited here. Taking m = 5 as an example, the range within 5% of the target value is the allowable deviation range. When the deviation is within this range, the adjustment is paused; The device is in manual mode or a fault state. This application deals with the liquid level or flow control of the device in the automatic state. When the device is in a fault state or manual mode, the adjustment is paused.

[0020] Further, the adjustment interval and the single - adjustment amplitude are configured in the following ways: The default value is a fixed parameter; It supports dynamic modification through the human - machine interface (HMI) of the target - value setting module 200, and the parameters take effect in real time after modification.

[0021] Further, the dynamic optimization logic of the membership function includes: When the system still does not reach the target interval after 3 consecutive adjustments, automatically narrow the coverage range of the "zero" interval to improve sensitivity; When the external disturbance frequency exceeds the threshold, expand the coverage ranges of the "positive large" and "negative large" intervals to enhance the anti - interference ability.

[0022] Further, the triggering conditions of the anti - interference module 700 include: The sensor data mutates. The sensor data undergoes a drastic change beyond the normal range in a short period, which may be caused by a real disturbance (such as a sudden increase in the influent flow rate) or sensor noise / failure. By calculating the difference or change rate between the current value and the previous sampled value, if the difference exceeds the preset threshold (such as the flow rate change rate > 10% / second), it is determined that the data has mutated. It can quickly identify sudden disturbances (such as pipeline bursts, pump failures), trigger the anti - interference rules, reduce the control amplitude, and avoid system oscillation; The communication with external devices is abnormal. The communication between the PLC control module 100 and external devices (such as sensors, actuators) is interrupted or the data is incorrect, resulting in the loss of control instructions or feedback information. By monitoring the communication link status and counting the device response time, if it times out, it is marked as a communication anomaly. When the data verification fails, discard the error data packet and record the anomaly event to discover the anomaly, preventing the control instructions from failing due to communication failures (such as out - of - control valve opening); The deviation between the feedback value of the actuator and the control instruction exceeds the preset threshold. There is a significant difference between the actual state of the actuator (such as valves, pumps) and the control instruction sent by the PLC, which may be caused by mechanical failures, sudden load changes, or actuator response lags. By setting the maximum allowable deviation percentage (such as the deviation percentage threshold is 20%), if the deviation exceeds the standard for multiple consecutive samples (such as 3 times), it is determined as an abnormal execution, which can timely detect mechanical jams and actuator failures (such as motor burnout), avoid equipment damage, and trigger fault tolerance control (such as switching to a standby actuator or alarm for maintenance).

[0023] Further, the fuzzification process of the fuzzification unit 120 further includes: Set a dynamic threshold for the deviation change rate (dΔ / dt). When dΔ / dt exceeds the threshold, automatically increase the weight of the "positive large" or "negative large" interval.

[0024] Specifically, set the initial threshold based on the dynamic characteristics of the system (such as the maximum allowable change rate). For example, in liquid level control, if the maximum allowable liquid level change rate of the system is ±0.1 m / s, the initial threshold can be set to ±0.08 m / s. According to the statistical distribution of dΔ / dt in historical data, if it frequently exceeds the threshold during a certain period, automatically increase the threshold (such as adjusting from ±0.08 m / s to ±0.1 m / s). When detecting external disturbances (such as sudden changes in the inlet flow rate), temporarily reduce the threshold to improve sensitivity; when dΔ / dt exceeds the dynamic threshold, the system determines that the current state is a rapid change state, and it is necessary to enhance the response ability to large deviations. If dΔ / dt > threshold (positive rapid change), at this time, it is necessary to increase the weight of the rules related to the "positive large" (PB) interval. If dΔ / dt < -threshold (negative rapid change), at this time, it is necessary to increase the weight of the rules related to the "negative large" (NB) interval, and then recalculate the fuzzy inference result according to the new weight to generate a control instruction.

[0025] Embodiment 2 The embodiment of the present application provides a control method based on the fuzzy algorithm of the PLC system, using a control device based on the fuzzy algorithm of the PLC system in the above Embodiment 1, such as Figure 2 shown, including the following steps: S100. Set the target value of the liquid level or flow rate; S200. Real-time collect the actual value output by the device; S300. Calculate the deviation (Δ) between the actual value and the target value and the deviation change rate (dΔ / dt); S400. Generate a control instruction through the fuzzy rule base 400, and control the operating state of the device according to the preset adjustment interval and the opening degree adjusted each time; S500. During the adjustment process, limit the operating state of the device not to exceed the maximum opening degree and the minimum opening degree; S600. When an external disturbance is detected, reduce the adjustment amplitude to a preset safety value, and optimize the rule weights and membership function parameters according to historical data.

[0026] Further, as Figure 3 shown, the control instruction is generated through the fuzzy rule base in step S400, including: S410. Fuzzify the deviation (Δ) and the deviation change rate (dΔ / dt) through the membership function; S420. Perform fuzzy inference according to the fuzzy rule base to generate a fuzzy output quantity; S430. Use the centroid method to defuzzify to obtain the precise control parameter.

[0027] Specifically, taking the liquid level control of a sewage treatment tank as an example, a certain sewage treatment tank needs to maintain the liquid level at a set value (such as 2.5 meters) to prevent overflow or pump dry running. Referring to Figures 1-3 shown, first, set the target liquid level to 2.5 meters through the HMI, the adjustment interval is 10 seconds, the single - time adjustment amplitude is ±5%, the maximum valve opening is set to 80%, and the minimum opening is 20%; the actual liquid level value (such as 2.6 meters) is collected in real - time through the liquid level sensor and transmitted to the PLC control module 100; the deviation calculation unit 110 calculates the deviation Δ = +0.1 meters, and the deviation change rate dΔ / dt = +0.02 meters / second. The fuzzification unit 120 maps Δ and dΔ / dt to the fuzzy set through the triangular membership function. Δ = +0.1 meters corresponds to "positive small", and dΔ / dt = +0.02 meters / second corresponds to "positive small"; the fuzzy rule base 400 matches the rule: IF Δ is "positive small" AND dΔ / dt is "positive small" THEN the valve opening is reduced by 3%. The fuzzy inference engine 130 performs Mamdani inference to generate the fuzzy output quantity "small reduction". The defuzzification unit 140 uses the centroid method to calculate the precise adjustment amount as the valve opening is reduced by 3%; the execution control module 500 drives the valve opening to be adjusted from 70% to 67%, and the opening limit module 600 ensures that the valve opening is not lower than 20%; if a sudden increase in the influent flow rate is detected (the disturbance signal exceeds the threshold), the anti - interference module 700 is triggered, and the single - time adjustment amplitude is reduced from ±5% to ±2.5%. The data storage and analysis module 900 records the adjustment parameters and disturbance events of this time and generates a report recommendation: "Increase the rule weight in the 'positive small' interval"; the dynamic optimization module 800 detects that the target interval has not been reached after 3 consecutive adjustments and automatically reduces the range of the "zero" interval (from ±0.05 meters to ±0.03 meters) to improve the sensitivity.

[0028] In summary, for the control method and device based on the fuzzy algorithm of the PLC system in this embodiment, the target value setting module receives the liquid level or flow target value set by the user and displays it through the human-machine interface (HMI), simplifying the parameter configuration process and facilitating the operator to proficiently adjust and optimize the sensitivity of the control unit; by dynamically optimizing the rule base and membership function, the liquid level or flow fluctuation is significantly suppressed, and the steady-state error is ≤5%, enhancing the stability of the system; under external disturbances, the system recovery time is significantly shortened and the anti-interference ability is strong; the rule base can be intuitively modified through the HMI without reprogramming or the intervention of professional technicians, reducing the maintenance cost.

[0029] The basic principles of the present application have been described above in combination with specific embodiments. It should be understood that the above disclosed specific details are only for the purpose of illustration and easy understanding, rather than limitation, and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A control device based on a fuzzy algorithm of a PLC system, characterized in that: The device comprises: PLC control module, integrated with fuzzy control algorithm, used to process input signals and generate control instructions in real time; The target value setting module is used to receive the liquid level or flow target value set by the user and display it through the human-machine interface (HMI); The sensor feedback module collects the actual output value of the equipment in real time through the liquid level sensor, flow sensor or temperature sensor, and transmits the data to the PLC control module; A fuzzy rule base storing fuzzy control rules based on deviations and deviation change rates, wherein the fuzzy control rules define control logic in an "IF-THEN" form; The execution control module controls the operation state of the equipment according to the defuzzified control parameters, the preset adjustment interval and the single adjustment range; The opening limit module is used to set the maximum and minimum opening of the equipment. When the control parameters exceed the limit, they are automatically truncated to the boundary value.

2. The control device based on the fuzzy algorithm of the PLC system according to claim 1, characterized in that: The PLC control module comprises: Deviation calculation unit, used to calculate the deviation (Δ) between the actual value and the target value and the deviation change rate (dΔ / dt); A fuzzification unit, used for converting the deviation and the deviation change rate into a fuzzy quantity through a preset membership function, wherein the membership function includes a triangular function or a trapezoidal function, and defines a fuzzy set as "negative large", "negative small", "zero", "positive small", and "positive large"; The fuzzy reasoning engine performs fuzzy logic operations according to the rules in the fuzzy rule base and generates fuzzy output quantities; The defuzzification unit converts the fuzzy output into precise control parameters using the centroid method.

3. The control device based on the fuzzy algorithm of the PLC system according to claim 1, characterized in that: The device further comprises: The anti-interference module, when detecting an external disturbance signal, triggers the anti-interference rule and reduces the adjustment amplitude to a preset safety value; Dynamic optimization module, which automatically adjusts the rule weights of the fuzzy rule base according to historical operation data and optimizes the coverage of the membership function to improve control stability; The data storage and analysis module is used to record historical control parameters, equipment status and external disturbance events, and is also used to generate operation reports and provide rule base optimization suggestions.

4. The control device based on the fuzzy algorithm of the PLC system according to claim 1, characterized in that: The fuzzy control algorithm suspends adjustment under the following conditions: The absolute value of the deviation between the actual value and the target value is less than or equal to m% of the target value; The device is in manual mode or in a fault condition.

5. The control device based on the fuzzy algorithm of the PLC system according to claim 1, characterized in that: The adjustment interval and single adjustment amplitude are configured in the following way: The default values ​​are fixed parameters; Supports dynamic modification through the human-machine interface (HMI), and the modified parameters take effect in real time.

6. The control device based on the fuzzy algorithm of the PLC system as claimed in claim 3, characterized in that: The dynamic optimization logic of the membership function includes: When the system fails to reach the target interval after three consecutive adjustments, the coverage of the "zero" interval is automatically reduced to improve sensitivity; When the external disturbance frequency exceeds the threshold, the coverage of the "positive large" and "negative large" intervals is expanded to enhance the anti-interference capability.

7. The control device based on the fuzzy algorithm of the PLC system as claimed in claim 3, characterized in that: The triggering conditions of the anti-interference module include: Sensor data mutation; External device communication is abnormal; The deviation between the actuator feedback value and the control command exceeds the preset threshold.

8. The control device based on the PLC system fuzzy algorithm as claimed in claim 2, characterized in that: The fuzzification processing of the fuzzification unit further includes: Set a dynamic threshold for the deviation change rate (dΔ / dt). When dΔ / dt exceeds the threshold, the weight of the "positive large" or "negative large" interval is automatically increased.

9. A control method based on a PLC system fuzzy algorithm, using a control device based on a PLC system fuzzy algorithm according to any one of claims 1 to 8, characterized in that: The following steps are involved: Set target value of liquid level or flow rate; Collect the actual value of the device output in real time; Calculate the deviation (Δ) between the actual value and the target value and the rate of change of the deviation (dΔ / dt); Generate control instructions through the fuzzy rule base to control the operating status of the equipment according to the preset adjustment interval and each adjustment opening; During the adjustment process, limit the operating state of the equipment to not exceed the maximum opening and minimum opening; When external disturbances are detected, the adjustment range is reduced to the preset safety value, and the rule weights and membership function parameters are optimized based on historical data.

10. The control method based on the fuzzy algorithm of the PLC system according to claim 9, characterized in that: The generating of control instructions by using a fuzzy rule base comprises: The deviation (Δ) and the deviation change rate (dΔ / dt) are fuzzified through the membership function; Perform fuzzy reasoning according to the fuzzy rule base to generate fuzzy output quantities; The centroid method is used to defuzzify and obtain precise control parameters.

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