Adaptive adjustment method for fresh air system of dust-free workshop

Abstract

The application discloses a dust-free workshop fresh air system adaptive adjustment method, the early warning method adjusts the fresh air system adjustment stage in the dust-free workshop fresh air system implementation stage to different levels of fresh air system adaptive adjustment artificial intelligence control valve, then when each fresh air system artificial intelligence control valve occurs, the control and supervision value is matched with the corresponding artificial intelligence control valve dynamic setting interval, and when the control and supervision value of the fresh air system artificial intelligence control valve exceeds the corresponding artificial intelligence control valve dynamic setting interval, the corresponding adjustment control signal is sent; the dust-free workshop fresh air system adaptive adjustment method in the application can adjust the control signal of each fresh air system adjustment stage before the fresh air system implementation, so that the fresh air system risk control effect of the dust-free workshop fresh air system implementation can be improved.

Description

Technical Field

[0001] This invention relates to the field of semiconductor manufacturing, and more particularly to an adaptive adjustment method for a fresh air system in a cleanroom. Background Technology

[0002] The production environment for semiconductor chips has extremely high requirements, necessitating production in cleanrooms. Cleanrooms are equipped with fresh air systems, which consist of low-temperature air supply units, air purification devices, total heat exchangers, and automatic control systems.

[0003] Traditionally, the air intake volume of a fresh air system is controlled using simple methods, which cannot achieve adaptive adjustment according to the needs of a cleanroom. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of the existing technology, the present invention provides an adaptive adjustment method for a cleanroom fresh air system.

[0005] The technical solution adopted in this invention is an adaptive adjustment method for a cleanroom fresh air system. This method adjusts the fresh air system adjustment stage in the implementation phase of the cleanroom fresh air system to different levels of adaptive adjustment artificial intelligence control valves. Then, when each fresh air system artificial intelligence control valve is activated, its control and monitoring values ​​are matched with the corresponding dynamic setting range of the artificial intelligence control valve. When the control and monitoring values ​​of the fresh air system artificial intelligence control valve exceed the corresponding dynamic setting range of the artificial intelligence control valve, a corresponding adjustment control signal is issued.

[0006] Preferably, the specific steps include:

[0007] Step Q1: Configure a PID controller in the fresh air system, and adjust the fresh air system adjustment stage in the implementation phase of the cleanroom fresh air system to different levels of adaptive adjustment artificial intelligence control valves; construct a fuzzy neural network, and then generate the dynamic setting range of the artificial intelligence control valves of each fresh air system according to the progress of the cleanroom fresh air system, as well as the dynamic setting range of the fresh air system implementation stage.

[0008] Step Q2: Set the air intake volume state variable, import it into the fuzzification module, and generate fuzzy rules. The fuzzification module is used to perform fuzzy quantization and normalization on the state variable. When the AI ​​control valve of the fresh air system is activated, the control and monitoring values ​​of the AI ​​control valve of the fresh air system are obtained. The fuzzy rules are imported into the neural network module to obtain weighting coefficients. Then, the control and monitoring values ​​of the AI ​​control valve of the fresh air system are matched with the corresponding dynamic setting range of the AI ​​control valve. When the control and monitoring values ​​are greater than the corresponding dynamic setting range of the AI ​​control valve, the corresponding adjustment control signal is issued.

[0009] Step Q3: The output layer of the neural network is output to correspond to the adjustable parameters of the PID controller. The weighting coefficients are adjusted according to the operating status of the fresh air system to obtain the PID parameters under optimal control. The corresponding control and monitoring values ​​are obtained during the implementation phase of the fresh air system. Then, the control and monitoring values ​​during the implementation phase of the fresh air system are matched with the corresponding dynamic setting range of the fresh air system implementation. When the control and monitoring values ​​are greater than the corresponding dynamic setting range of the fresh air system implementation, the corresponding adjustment control signal is issued. The PID controller performs closed-loop control on the controlled object.

[0010] Preferably, in step Q1, when the control and monitoring value of the AI ​​control valve of the previous fresh air system is less than its corresponding AI control valve dynamic setting range, the AI ​​control valve dynamic setting range of the AI ​​control valve of the subsequent fresh air system is adjusted according to the control and monitoring value and the AI ​​control valve dynamic setting range of the previous fresh air system AI control valve.

[0011] Preferably, step Q3 specifically includes the following steps:

[0012] Step Q31: After the submitted information for the implementation phase of the fresh air system is approved, obtain the preliminary risk level and progress risk level of the implementation phase of the fresh air system;

[0013] Step Q32: Match the preliminary risk level with the corresponding dynamic setting range of the fresh air system. If the preliminary risk level is greater than the dynamic setting range of the fresh air system, issue the corresponding adjustment control signal; otherwise, proceed to the next step.

[0014] Step Q33: Match the progress risk level with the corresponding dynamic setting range of the fresh air system. If the progress risk level is greater than the dynamic setting range of the fresh air system, issue the corresponding adjustment control signal; otherwise, proceed to the next step.

[0015] Step Q34: Proceed to the PID parameter adjustment and calculation stage for the cleanroom.

[0016] Preferably, the dynamic setting interval for the implementation of the fresh air system is 99% of the progress of the fresh air system in the cleanroom.

[0017] Preferably, in step Q1, the fresh air system adjustment stage in the implementation phase of the cleanroom fresh air system is adjusted to the fresh air system start-up AI control valve, fresh air system mid-term AI control valve, fresh air system modification AI control valve, and fresh air system completion AI control valve.

[0018] This invention also discloses an adaptive adjustment method for a cleanroom fresh air system, which is based on the aforementioned method for implementing control and adjustment signals for the fresh air system, specifically including:

[0019] The cleanroom fresh air system adjustment module is used to adjust the fresh air system adjustment phase during the implementation of the cleanroom fresh air system to different levels of adaptive adjustment artificial intelligence control valves for the fresh air system.

[0020] The cleanroom fresh air system artificial intelligence control valve monitoring module is used to obtain the operation and monitoring values ​​of the fresh air system artificial intelligence control valve when the fresh air system artificial intelligence control valve is activated, as well as the progress of the fresh air system in the cleanroom.

[0021] The cleanroom fresh air system operation control module is used to input the operation and monitoring values ​​of the fresh air system's artificial intelligence control valve into a pre-established fresh air system adjustment and control model and obtain the fresh air system adjustment and control results output by the model. The fresh air system adjustment and control model generates a dynamic setting range for the corresponding artificial intelligence control valve based on the progress of the fresh air system. Then, it matches the operation and monitoring values ​​of the artificial intelligence control valve with the corresponding dynamic setting range. When the operation and monitoring values ​​exceed the corresponding dynamic setting range, it outputs a corresponding fresh air system risk fault and issues an adjustment control signal as the fresh air system adjustment and control result.

[0022] Preferably, the fresh air system adjustment and control model is established based on the numerical values ​​of various dimensions of the fresh air system and historical fresh air system information; the numerical values ​​of various dimensions of the fresh air system include the artificial intelligence control valve of the fresh air system, the start and end time of the artificial intelligence control valve of the fresh air system, the risk factors of the fresh air system, the human work content of the fresh air system, and the progress of the fresh air system; the fresh air system adjustment and control model calculates the corresponding dynamic setting range of the artificial intelligence control valve based on the artificial neural network model.

[0023] Compared with existing technologies, the method and system for implementing control and adjustment of fresh air system control signals in this invention have the following advantages:

[0024] In this invention, the fresh air system adjustment phase during the implementation of a cleanroom fresh air system is adjusted to use adaptive adjustment AI control valves of different levels. The control and monitoring values ​​of each AI control valve are managed, preventing the cleanroom from exceeding set standards during fresh air system adjustments. This helps to mitigate fresh air system risks during implementation, improving the control effectiveness and contributing to cleanroom quality improvement. Secondly, based on the progress of the fresh air system and historical data or experience, this invention pre-calculates and generates dynamic setting ranges for the AI ​​control valves and the fresh air system implementation, resulting in better control over the AI ​​control valves and equipment status during implementation, further enhancing the control effectiveness of the cleanroom fresh air system. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is the first flowchart of the adaptive adjustment method for the fresh air system in a cleanroom according to Embodiment 1 of the present invention;

[0027] Figure 2 This is the second flowchart of the adaptive adjustment method for the fresh air system in a cleanroom according to Embodiment 2 of the present invention;

[0028] Figure 3 This is a block diagram illustrating the adaptive adjustment method for the fresh air system in a cleanroom according to Embodiment 3 of the present invention. Detailed Implementation

[0029] It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. The following describes the application in further detail with reference to the accompanying drawings and specific embodiments.

[0030] Example 1:

[0031] This embodiment discloses an adaptive adjustment method for a cleanroom fresh air system.

[0032] The adaptive adjustment method for cleanroom fresh air systems involves adjusting the fresh air system adjustment phase during the cleanroom fresh air system implementation stage to different levels of adaptive adjustment artificial intelligence control valves. Then, when each artificial intelligence control valve operates, its control and monitoring values ​​are compared with the corresponding dynamic setting range of the artificial intelligence control valve. When the control and monitoring values ​​of the fresh air system's artificial intelligence control valve exceed the corresponding dynamic setting range, a corresponding adjustment control signal is issued. Combined with... Figure 1 As shown, the specific steps include the following:

[0033] Step Q1: Configure a PID controller in the fresh air system, and adjust the fresh air system adjustment stage in the implementation phase of the cleanroom fresh air system to different levels of adaptive adjustment artificial intelligence control valves; construct a fuzzy neural network, and then generate the dynamic setting range of the artificial intelligence control valves of each fresh air system according to the progress of the cleanroom fresh air system, as well as the dynamic setting range of the fresh air system implementation stage.

[0034] Step Q2: Set the air intake volume state variable, import it into the fuzzification module, and generate fuzzy rules. The fuzzification module is used to perform fuzzy quantization and normalization on the state variable. When the AI ​​control valve of the fresh air system is activated, the control and monitoring values ​​of the AI ​​control valve of the fresh air system are obtained. The fuzzy rules are imported into the neural network module to obtain weighting coefficients. Then, the control and monitoring values ​​of the AI ​​control valve of the fresh air system are matched with the corresponding dynamic setting range of the AI ​​control valve. When the control and monitoring values ​​are greater than the corresponding dynamic setting range of the AI ​​control valve, the corresponding adjustment control signal is issued.

[0035] Step Q3: The output layer of the neural network is output to correspond to the adjustable parameters of the PID controller. The weighting coefficients are adjusted according to the operating status of the fresh air system to obtain the PID parameters under optimal control. The corresponding control and monitoring values ​​are obtained during the implementation phase of the fresh air system. Then, the control and monitoring values ​​during the implementation phase of the fresh air system are matched with the corresponding dynamic setting range of the fresh air system implementation. When the control and monitoring values ​​are greater than the corresponding dynamic setting range of the fresh air system implementation, the corresponding adjustment control signal is issued. The PID controller performs closed-loop control on the controlled object.

[0036] In this invention, the fresh air system adjustment phase during the implementation of a cleanroom fresh air system is adjusted to use adaptive adjustment AI control valves of different levels. The control and monitoring values ​​of each AI control valve are managed, preventing the cleanroom from exceeding set standards during fresh air system adjustments. This helps to mitigate fresh air system risks during implementation, improving the control effectiveness and contributing to cleanroom quality improvement. Secondly, based on the progress of the fresh air system and historical data or experience, this invention pre-calculates and generates dynamic setting ranges for the AI ​​control valves and the fresh air system implementation, resulting in better control over the AI ​​control valves and equipment status during implementation, further enhancing the control effectiveness of the cleanroom fresh air system.

[0037] In the specific implementation process, in step Q1, the risk level value of the artificial intelligence control valve of each artificial intelligence control valve of the cleanroom is generated according to the progress of the fresh air system; in step Q2, when the artificial intelligence control valve of the fresh air system is activated, the operation and monitoring value of the artificial intelligence control valve of the fresh air system is matched with the corresponding artificial intelligence control valve risk level value, and when the operation and monitoring value is less than the corresponding artificial intelligence control valve risk level value, the corresponding adjustment control signal is issued.

[0038] The invention also sets a risk level value for the artificial intelligence control valve, and can issue a corresponding adjustment control signal when the control and monitoring values ​​are less than the corresponding risk level value of the artificial intelligence control valve. That is, it can issue adjustment control when the control and monitoring values ​​are significantly inconsistent with the health status of the equipment, so as to remind relevant personnel to check, thereby helping to improve the quality of the cleanroom.

[0039] In the specific implementation process, combined with Figure 2 As shown, step Q3 specifically includes the following steps:

[0040] Step Q31: After the submitted information for the implementation phase of the fresh air system is approved, obtain the preliminary risk level and progress risk level of the implementation phase of the fresh air system; the dynamic setting interval for the implementation of the fresh air system is 99% of the progress of the fresh air system.

[0041] Step Q32: Match the preliminary risk level with the corresponding dynamic setting range of the fresh air system. If the preliminary risk level is greater than the dynamic setting range of the fresh air system, issue the corresponding adjustment control signal; otherwise, proceed to the next step.

[0042] Step Q33: Match the progress risk level with the corresponding dynamic setting range of the fresh air system. If the progress risk level is greater than the dynamic setting range of the fresh air system, issue the corresponding adjustment control signal; otherwise, proceed to the next step.

[0043] Step Q34: Proceed to the PID parameter adjustment and calculation stage for the cleanroom.

[0044] During the implementation phase of the fresh air system, this invention first verifies the submitted information for the fresh air system implementation. After the submitted information is approved, it manages the faults during the implementation phase and can issue corresponding adjustment control signals, thereby improving the control effect of the fresh air system implementation in the cleanroom. Secondly, this invention performs two different risk controls on the operation and monitoring values ​​during the fresh air system implementation phase, which is beneficial to improving the control effect of the fresh air system implementation in the cleanroom.

[0045] In the specific implementation process, in step Q1, the fresh air system adjustment stage in the implementation phase of the cleanroom fresh air system is adjusted to the fresh air system start-up AI control valve, fresh air system mid-term AI control valve, fresh air system modification AI control valve, and fresh air system completion AI control valve.

[0046] In this invention, the artificial intelligence control valves of each fresh air system are adjusted according to the adjustment stage of the fresh air system in the cleanroom, so that the adjusted artificial intelligence control valves of the fresh air system can be adapted to the actual fresh air system adjustment process in the cleanroom. This is conducive to improving the control effect of each artificial intelligence control valve of the fresh air system and improving the control effect of the fresh air system in the cleanroom.

[0047] In specific implementation, the dynamic setting range of the AI ​​control valve at the start of the fresh air system is 40% of the progress of the fresh air system, and the risk level value of the AI ​​control valve at the start of the fresh air system is 20% of the progress of the fresh air system; the dynamic setting range of the AI ​​control valves at the middle stage and the modification stage is 70% of the progress of the fresh air system, and the risk level value of the AI ​​control valves at the middle stage and the modification stage is 50% of the progress of the fresh air system; the dynamic setting range of the AI ​​control valve at the completion stage is 80% of the progress of the fresh air system, and the risk level value of the AI ​​control valve at the completion stage is 60% of the progress of the fresh air system.

[0048] In this invention, by setting the dynamic setting range and risk level value of the artificial intelligence control valves of each fresh air system, the control effect of the equipment status of each fresh air system artificial intelligence control valve and the fresh air system implementation stage is improved. It can also issue adjustment control when the control and monitoring values ​​are significantly inconsistent with the set standards to remind relevant personnel to check the equipment status information, thereby improving the control effect of the fresh air system implementation in the cleanroom and helping to improve the quality of the cleanroom.

[0049] Example 2:

[0050] This embodiment, based on Embodiment 1, discloses a method for updating the dynamic setting range of an artificial intelligence control valve.

[0051] In step Q1 of this embodiment, when the control and monitoring value of the AI ​​control valve of the previous fresh air system is less than its corresponding dynamic setting range of AI control valve, the dynamic setting range of AI control valve of the next fresh air system is adjusted according to the control and monitoring value of the AI ​​control valve of the previous fresh air system and the corresponding dynamic setting range of AI control valve.

[0052] In this invention, the dynamic setting range of the AI ​​control valve of the next fresh air system can be adjusted based on the difference between the control and monitoring values ​​of the AI ​​control valve of the previous fresh air system and the dynamic setting range of the AI ​​control valve. This allows the final fresh air system control in the cleanroom to better match the progress of the fresh air system, thereby enabling better equipment status management of each AI control valve in the fresh air system and improving the management effect of the fresh air system in the cleanroom.

[0053] For example, the pre-set dynamic setting range for the AI ​​control valve at the start of the fresh air system is 40% of the fresh air system's progress; the dynamic setting range for the AI ​​control valves at the mid-stage and modification stages is 70%; and the dynamic setting range for the AI ​​control valve at the completion stage is 80%. Therefore, when the control and monitoring value of the AI ​​control valve at the start of the fresh air system is 25% of the fresh air system's progress, the difference between this value and the dynamic setting range is 15%. Thus, in this embodiment, the dynamic setting range for the next AI control valve at the start of the fresh air system, the mid-stage AI control valve, and the modification AI control valve is adjusted to 75% of the fresh air system's progress. When the value of the artificial intelligence control valve of the fresh air system at the start of operation and monitoring is 38% of the progress of the fresh air system, the difference between it and the dynamic setting range of the artificial intelligence control valve is 2%. Therefore, in this embodiment, the dynamic setting range of the artificial intelligence control valve of the next fresh air system artificial intelligence control valve, the artificial intelligence control valve in the middle of the fresh air system, and the artificial intelligence control valve of the fresh air system modification is adjusted to 69% of the progress of the fresh air system.

[0054] Specifically, when the difference between the control and monitoring values ​​of the AI ​​control valve of the current fresh air system and the dynamic setting range of the AI ​​control valve is greater than 10%, the dynamic setting range of the AI ​​control valve of the next fresh air system will be adjusted upwards accordingly; when the difference is less than 5%, the dynamic setting range of the AI ​​control valve of the next fresh air system will be adjusted downwards accordingly. The specific adjustment values ​​are determined based on historical data or the corresponding cleanroom. This ensures that the final fresh air system control in the cleanroom is more closely aligned with the progress of the fresh air system development.

[0055] Example 3:

[0056] Based on Embodiment 1, this embodiment further discloses a control signal system for implementing management and adjustment of a fresh air system.

[0057] Combination Figure 3 As shown, the adaptive adjustment method for the fresh air system in a cleanroom specifically includes: a cleanroom fresh air system adjustment module, used to adjust the fresh air system adjustment stage in the implementation phase of the cleanroom fresh air system to different levels of adaptive adjustment artificial intelligence control valves for the fresh air system;

[0058] The cleanroom fresh air system artificial intelligence control valve monitoring module is used to obtain the operation and monitoring values ​​of the fresh air system artificial intelligence control valve when the fresh air system artificial intelligence control valve is activated, as well as the progress of the fresh air system in the cleanroom.

[0059] The cleanroom fresh air system operation control module is used to input the operation and monitoring values ​​of the fresh air system's artificial intelligence control valve into the pre-established fresh air system adjustment and control model and obtain the fresh air system adjustment and control results output by the fresh air system adjustment and control model;

[0060] The fresh air system adjustment and control model generates a dynamic setting range for the AI ​​control valve of the corresponding fresh air system based on the progress of the fresh air system. Then, it matches the control and monitoring values ​​of the AI ​​control valve with the corresponding dynamic setting range. When the control and monitoring values ​​exceed the corresponding dynamic setting range, it outputs a corresponding fresh air system risk fault and issues an adjustment control signal as the result of the fresh air system adjustment and control. In this embodiment, the fresh air system adjustment and control model is established based on various dimensions of the fresh air system and historical fresh air system information. These dimensions include the AI ​​control valve, its start and end times, risk factors, human intervention, and the progress of the fresh air system. The model calculates the corresponding dynamic setting range for the AI ​​control valve using an artificial neural network model.

[0061] In this invention, the cleanroom fresh air system adjustment module adjusts the fresh air system adjustment phase during the cleanroom fresh air system implementation stage to different levels of adaptive adjustment artificial intelligence control valves. Combined with the cleanroom fresh air system artificial intelligence control valve monitoring module and the cleanroom fresh air system operation control module, the control and monitoring values ​​of each fresh air system artificial intelligence control valve are managed. This prevents the cleanroom fresh air system adjustment from exceeding the set standards, thereby mitigating fresh air system risks during the implementation stage and improving the control effect of the cleanroom fresh air system implementation, thus contributing to the improvement of cleanroom quality. Secondly, the fresh air system adjustment control model in this invention pre-calculates and generates corresponding dynamic setting ranges for the artificial intelligence control valves and the dynamic setting range for the fresh air system implementation based on the progress of the fresh air system and historical data or experience values. This results in better control of the artificial intelligence control valves and equipment status during the fresh air system implementation stage, further enhancing the control effect of the cleanroom fresh air system implementation.

[0062] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0063] The above descriptions are merely embodiments of the present invention. Commonly known structures and characteristics of the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field to which the invention pertains as of the application date or priority date, are aware of all prior art in that field, and have the ability to apply conventional experimental methods as of that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. An adaptive adjustment method for a cleanroom fresh air system, characterized in that: The fresh air system adjustment phase in the implementation phase of the cleanroom fresh air system is adjusted to different levels of adaptive adjustment artificial intelligence control valves for the fresh air system. Then, when each fresh air system artificial intelligence control valve is activated, its control and monitoring values ​​are matched with the corresponding dynamic setting range of the artificial intelligence control valve. When the control and monitoring values ​​of the fresh air system artificial intelligence control valve exceed the corresponding dynamic setting range of the artificial intelligence control valve, a corresponding adjustment control signal is issued. Specifically, the steps include the following: Step Q1: Configure a PID controller in the fresh air system, and adjust the fresh air system adjustment stage in the implementation phase of the cleanroom fresh air system to different levels of adaptive adjustment artificial intelligence control valves; construct a fuzzy neural network, and then generate the dynamic setting range of the artificial intelligence control valves of each fresh air system according to the progress of the cleanroom fresh air system, as well as the dynamic setting range of the fresh air system implementation stage. Step Q2: Set the air intake volume state variable, import it into the fuzzification module, and generate fuzzy rules. The fuzzification module is used to perform fuzzy quantization and normalization on the state variable. When the AI ​​control valve of the fresh air system is activated, the control and monitoring values ​​of the AI ​​control valve of the fresh air system are obtained. The fuzzy rules are imported into the neural network module to obtain weighting coefficients. Then, the control and monitoring values ​​of the AI ​​control valve of the fresh air system are matched with the corresponding dynamic setting range of the AI ​​control valve. When the control and monitoring values ​​are greater than the corresponding dynamic setting range of the AI ​​control valve, the corresponding adjustment control signal is issued. Step Q3: The output layer of the neural network outputs the adjustable parameters corresponding to the PID controller. Based on the operating status of the fresh air system, the weighting coefficients are adjusted to obtain the PID parameters under optimal control. During the implementation phase of the fresh air system, the corresponding control and monitoring values ​​are obtained. Then, the control and monitoring values ​​during the implementation phase of the fresh air system are matched with the corresponding dynamic setting range of the fresh air system implementation. When the control and monitoring values ​​are greater than the corresponding dynamic setting range of the fresh air system implementation, the corresponding adjustment control signal is issued. The PID controller performs closed-loop control on the controlled object. In step Q1, the fresh air system adjustment stage in the implementation phase of the cleanroom fresh air system is adjusted to the fresh air system start-up AI control valve, fresh air system mid-term AI control valve, fresh air system modification AI control valve, and fresh air system completion AI control valve.

2. The adaptive adjustment method for a cleanroom fresh air system as described in claim 1, characterized in that: In step Q1, the risk level value of the AI ​​control valve for each AI control valve of the cleanroom is also generated based on the progress of the cleanroom's fresh air system. In step Q2, when the AI ​​control valve of the fresh air system is activated, the control and monitoring values ​​of the AI ​​control valve are matched with the corresponding AI control valve risk level value, and a corresponding adjustment control signal is issued when the control and monitoring values ​​are less than the corresponding AI control valve risk level value.

3. The adaptive adjustment method for a cleanroom fresh air system as described in claim 1, characterized in that: In step Q1, when the control and monitoring value of the AI ​​control valve of the previous fresh air system is less than its corresponding AI control valve dynamic setting range, the AI ​​control valve dynamic setting range of the AI ​​control valve of the subsequent fresh air system is adjusted according to the control and monitoring value and the AI ​​control valve dynamic setting range of the previous fresh air system AI control valve.

4. The adaptive adjustment method for a cleanroom fresh air system as described in claim 1, characterized in that, Step Q3 specifically includes the following steps: Step Q31: After the submitted information for the implementation phase of the fresh air system is approved, obtain the preliminary risk level and progress risk level of the implementation phase of the fresh air system; Step Q32: Match the preliminary risk level with the corresponding dynamic setting range of the fresh air system. If the preliminary risk level is greater than the dynamic setting range of the fresh air system, issue the corresponding adjustment control signal; otherwise, proceed to the next step. Step Q33: Match the progress risk level with the corresponding dynamic setting range of the fresh air system. If the progress risk level is greater than the dynamic setting range of the fresh air system, issue the corresponding adjustment control signal; otherwise, proceed to the next step. Step Q34: Proceed to the PID parameter adjustment and calculation stage for the cleanroom.

5. The adaptive adjustment method for a cleanroom fresh air system as described in claim 4, characterized in that: The dynamic setting range for the implementation of the fresh air system is 99% of the progress of the fresh air system in the cleanroom.

6. The adaptive adjustment method for a cleanroom fresh air system as described in any one of claims 1-5, characterized in that: This method is implemented through the following modules: The cleanroom fresh air system adjustment module is used to adjust the fresh air system adjustment phase during the implementation of the cleanroom fresh air system to different levels of adaptive adjustment artificial intelligence control valves for the fresh air system. The cleanroom fresh air system artificial intelligence control valve monitoring module is used to obtain the operation and monitoring values ​​of the fresh air system artificial intelligence control valve when the fresh air system artificial intelligence control valve is activated, as well as the progress of the fresh air system in the cleanroom. The cleanroom fresh air system operation control module is used to input the operation and monitoring values ​​of the fresh air system's artificial intelligence control valve into the pre-established fresh air system adjustment and control model and obtain the fresh air system adjustment and control results output by the fresh air system adjustment and control model; The fresh air system adjustment and control model generates a dynamic setting range for the AI ​​control valve of the corresponding fresh air system based on the progress of the fresh air system. Then, it matches the control and monitoring values ​​of the AI ​​control valve with the corresponding dynamic setting range. When the control and monitoring values ​​are greater than the corresponding dynamic setting range, it outputs the corresponding fresh air system risk fault and issues an adjustment control signal as the result of the fresh air system adjustment and control.

7. The adaptive adjustment method for a cleanroom fresh air system as described in claim 6, characterized in that: The fresh air system adjustment and control model is established based on the numerical values ​​of various dimensions of the fresh air system and historical fresh air system information; the numerical values ​​of various dimensions of the fresh air system include the artificial intelligence control valve of the fresh air system, the start and end time of the artificial intelligence control valve of the fresh air system, the risk factors of the fresh air system, the human work content of the fresh air system, and the progress of the fresh air system. The fresh air system adjustment and control model calculates the corresponding dynamic setting range of the artificial intelligence control valve based on the artificial neural network model.

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