FFU cluster energy-saving control method and device based on cleanliness real-time feedback

Abstract

The invention relates to the technical field of energy-saving control, in particular to an FFU cluster energy-saving control method and device based on cleanliness real-time feedback, and the method comprises the steps: carrying out the regional division of a clean room, and obtaining the actual cleanliness data of each purification management and control unit through a dust particle sensor; determining and marking the purification demand level of each purification management and control unit; on the premise that the purification requirement levels of all purification management and control units are met, the minimum total energy consumption of an FFU cluster composed of all the FFUs serves as an optimization target, a PSO algorithm is adopted to conduct multiple rounds of iterative operation, and an FFU wind speed configuration scheme enabling the optimization target to be established is obtained through solving; and determining a target wind speed parameter corresponding to each FFU, and transmitting the target wind speed parameter to the corresponding FFU controller so as to drive a motor to adjust the rotating speed of the fan. According to the method, the total energy consumption of the FFU cluster can be reduced to the maximum extent on the premise of ensuring that the cleanliness of the whole region reaches the standard.

Description

Technical Field

[0001] This application relates to the field of energy-saving control technology, and in particular to an energy-saving control method and device for FFU clusters based on real-time cleanliness feedback. Background Technology

[0002] In high-end manufacturing sectors such as semiconductors, biopharmaceuticals, and precision electronics, cleanrooms are one of the core production environments, and their cleanliness directly affects product quality and production yield. FFUs (Fan Filter Units), as key equipment for cleanroom air purification, use fans to drive air through high-efficiency air filters to remove dust particles, microorganisms, and other pollutants from the air, maintaining a clean environment within the cleanroom.

[0003] Traditional FFU control methods generally adopt a constant speed operation mode, that is, all FFUs always operate at a fixed maximum wind speed or a preset single wind speed, regardless of whether there are differences in the actual cleanliness requirements of different areas in the cleanroom. This method has drawbacks such as excessive energy consumption and lag in cleanliness control. Therefore, there is an urgent need for a technical solution that can achieve collaborative optimization control of the FFU cluster based on real-time cleanliness feedback from different areas of the cleanroom, and minimize the total energy consumption of the cluster while ensuring that the cleanliness of the entire area meets the standards. Summary of the Invention

[0004] Therefore, it is necessary to provide an energy-saving control method and device for FFU clusters based on real-time cleanliness feedback, which can minimize the total energy consumption of the FFU cluster while ensuring that the cleanliness of the entire area meets the standards.

[0005] In a first aspect, this application provides an energy-saving control method for an FFU cluster based on real-time cleanliness feedback, the method comprising: The cleanroom is divided into areas to form multiple purification control units, and the actual cleanliness data of each purification control unit is obtained through dust particle sensors. Each purification control unit is equipped with an FFU for air purification. Obtain the preset cleanliness threshold for each purification control unit, compare the actual cleanliness data of each purification control unit with the corresponding preset cleanliness threshold, and determine and mark the purification requirement level of each purification control unit based on the comparison results. To minimize the total energy consumption of the FFU cluster composed of all FFUs while meeting the purification requirements of all purification control units, the PSO algorithm is used to perform multiple rounds of iterative calculations to obtain the FFU wind speed configuration scheme that makes the optimization objective true. According to the FFU wind speed configuration scheme, the target wind speed parameters corresponding to each FFU are determined, and the target wind speed parameters of each FFU are transmitted to the corresponding FFU controller via Ethernet to drive the motor to adjust the fan speed.

[0006] In one embodiment, the optimization objective is to minimize the total energy consumption of the FFU cluster composed of all FFUs while meeting the purification requirements of all purification control units. The PSO algorithm is used for multiple iterative calculations to obtain an FFU wind speed configuration scheme that satisfies the optimization objective. This scheme includes: Configure the particle swarm size according to the number of FFUs in the cleanroom, initialize the particle swarm, where each particle is mapped to a set of FFU wind speed combination schemes, the position parameters of the particle correspond to the wind speed values ​​of each FFU, and each particle is configured with an initial movement speed. To minimize the total energy consumption of the FFU cluster composed of all FFUs while meeting the purification requirements of all purification control units, an fitness evaluation function is constructed, which integrates energy consumption indicators and cleanliness indicators. Based on the fitness evaluation function, the particle swarm is subjected to multiple rounds of iterative calculations using the PSO algorithm combined with local search. The iteration stops when a preset termination condition is met, and the FFU wind speed combination scheme corresponding to the globally optimal position is output.

[0007] In one embodiment, the step of performing multiple rounds of iterative computation on the particle swarm using the PSO algorithm combined with local search based on the fitness evaluation function, and stopping the iteration when a preset termination condition is met, and outputting the FFU wind speed combination scheme corresponding to the globally optimal position includes: Based on the fitness evaluation function, the particle swarm is subjected to multiple rounds of iterative calculations using the PSO algorithm. After each iteration, the position parameters and velocities of the particles are updated, and the global optimal solution is determined. After determining the global optimal solution, a local search space is defined with the global optimal solution as the center, and a local search algorithm is used to optimize the solution in the local search space; The iteration stops when the preset termination condition is met, and the FFU wind speed combination scheme corresponding to the globally optimal position is output.

[0008] In one embodiment, the step of obtaining the preset cleanliness threshold corresponding to each purification control unit, comparing the actual cleanliness data of each purification control unit with the corresponding preset cleanliness threshold, and determining and marking the purification requirement level of each purification control unit based on the comparison result includes: Obtain the preset cleanliness threshold for each purification control unit, compare the actual cleanliness data of each purification control unit with the corresponding preset cleanliness threshold, and obtain the cleanliness difference. Based on the cleanliness difference, the cleanliness requirement level of each cleanliness control unit is divided, and different preset labels are used to mark the cleanliness requirement level.

[0009] In one embodiment, the step of dividing the cleanroom into multiple purification control units and acquiring the actual cleanliness data of each purification control unit using a dust particle sensor includes: The cleanroom is divided into multiple independent purification and control units using a grid-based partitioning method. Establish a sensor calibration mechanism to perform standard calibration on the dust particle sensor in order to correct the detection deviation of the dust particle sensor; The actual cleanliness data of each purification control unit is collected using calibrated dust particle sensors.

[0010] In one embodiment, the FFU cluster energy-saving control method based on real-time cleanliness feedback further includes: Multiple FFU controllers are connected via a ring network using ring network redundancy technology. When a link in the ring network fails, the ring network is automatically reconfigured so that data can be transmitted through other links, thus achieving fault switching.

[0011] In one embodiment, after determining the target wind speed parameters for each FFU according to the FFU wind speed configuration scheme, and transmitting the target wind speed parameters of each FFU to the corresponding FFU controller via Ethernet to drive the motor to adjust the fan speed, the method further includes: Obtain the energy consumption data of the FFU cluster before the fan speed adjustment, and calculate the energy consumption reduction rate based on the energy consumption data of the FFU cluster before the fan speed adjustment. The cleanliness compliance rate is obtained by statistically analyzing the proportion of time within which the cleanroom's cleanliness meets the preset standards within a preset time. The energy-saving effect is evaluated based on the energy consumption reduction rate and the cleanliness compliance rate, and the control parameters of the FFU are adjusted according to the evaluation results.

[0012] Secondly, this application also provides an FFU cluster energy-saving control device based on real-time cleanliness feedback. The device includes: The area division module is used to divide the cleanroom into areas, forming multiple purification control units, and to obtain the actual cleanliness data of each purification control unit through a dust particle sensor. Each purification control unit is equipped with an FFU for air purification. The level determination module is used to obtain the preset cleanliness threshold corresponding to each purification control unit, compare the actual cleanliness data of each purification control unit with the corresponding preset cleanliness threshold, and determine and mark the purification requirement level of each purification control unit based on the comparison results. The solution module is used to minimize the total energy consumption of the FFU cluster composed of all FFUs, under the premise of meeting the purification requirements of all purification control units. It uses the PSO algorithm to perform multiple rounds of iterative calculations to obtain the FFU wind speed configuration scheme that makes the optimization objective true. The wind speed control module is used to determine the target wind speed parameters corresponding to each FFU according to the FFU wind speed configuration scheme, and transmit the target wind speed parameters of each FFU to the corresponding FFU controller via Ethernet to drive the motor to adjust the fan speed.

[0013] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps: The cleanroom is divided into areas to form multiple purification control units, and the actual cleanliness data of each purification control unit is obtained through dust particle sensors. Each purification control unit is equipped with an FFU for air purification. Obtain the preset cleanliness threshold for each purification control unit, compare the actual cleanliness data of each purification control unit with the corresponding preset cleanliness threshold, and determine and mark the purification requirement level of each purification control unit based on the comparison results. To minimize the total energy consumption of the FFU cluster composed of all FFUs while meeting the purification requirements of all purification control units, the PSO algorithm is used to perform multiple rounds of iterative calculations to obtain the FFU wind speed configuration scheme that makes the optimization objective true. According to the FFU wind speed configuration scheme, the target wind speed parameters corresponding to each FFU are determined, and the target wind speed parameters of each FFU are transmitted to the corresponding FFU controller via Ethernet to drive the motor to adjust the fan speed.

[0014] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps: The cleanroom is divided into areas to form multiple purification control units, and the actual cleanliness data of each purification control unit is obtained through dust particle sensors. Each purification control unit is equipped with an FFU for air purification. Obtain the preset cleanliness threshold for each purification control unit, compare the actual cleanliness data of each purification control unit with the corresponding preset cleanliness threshold, and determine and mark the purification requirement level of each purification control unit based on the comparison results. To minimize the total energy consumption of the FFU cluster composed of all FFUs while meeting the purification requirements of all purification control units, the PSO algorithm is used to perform multiple rounds of iterative calculations to obtain the FFU wind speed configuration scheme that makes the optimization objective true. According to the FFU wind speed configuration scheme, the target wind speed parameters corresponding to each FFU are determined, and the target wind speed parameters of each FFU are transmitted to the corresponding FFU controller via Ethernet to drive the motor to adjust the fan speed.

[0015] In summary, this application includes the following beneficial technical effects: The cleanroom is divided into zones, forming multiple purification control units, enabling zoned monitoring and control of cleanliness. This allows for precise capture of cleanliness differences between different areas. By comparing actual cleanliness data with preset cleanliness thresholds, purification requirement levels are categorized, allowing for real-time identification of cleanliness requirement differences and targeted adjustment of FFU (Fan Filter Unit) speeds in different zones. With the optimization goal of meeting the purification needs of the entire area and minimizing the total energy consumption of the FFU cluster, the PSO (Programmable Optimal State) algorithm is used for multi-round iterative solutions. Under complex multivariate constraints, the globally optimal FFU speed configuration scheme can be found. Through differentiated speed control, the total energy consumption of the FFU cluster is minimized while ensuring that the cleanliness of the entire area meets the standards. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an energy-saving control method for an FFU cluster based on real-time cleanliness feedback in one embodiment. Figure 2 This is a flowchart illustrating an energy-saving control method for an FFU cluster based on real-time cleanliness feedback in another embodiment. Figure 3 This is a structural block diagram of an FFU cluster energy-saving control device based on real-time cleanliness feedback in one embodiment. Detailed Implementation

[0017] This invention provides an energy-saving control method and device for FFU clusters based on real-time cleanliness feedback.

[0018] The embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0019] In the description of the embodiments disclosed in this invention, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0020] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the FFU cluster energy-saving control method based on real-time cleanliness feedback in this invention includes: S100 divides the cleanroom into zones, forming multiple purification control units, and obtains the actual cleanliness data of each purification control unit through dust particle sensors.

[0021] Specifically, considering the cleanroom's production layout, varying cleanliness requirements, and FFU installation locations, the cleanroom is divided into multiple rectangular or square units of equal area or adapted to different production processes. This ensures that the cleanliness of each unit can be independently monitored and controlled. For example, a semiconductor cleanroom can be divided into cleanroom control units based on the processes of "photolithography area - etching area - thin film deposition area - material storage area." Each cleanroom control unit is equipped with at least one dust particle sensor to ensure no blind spots in cleanliness data acquisition. Simultaneously, a sensor calibration mechanism is established to standardize the dust particle sensors. These sensors detect airborne particle concentration using the laser scattering principle (the mainstream technology). After calibration, the dust particle sensors collect actual cleanliness data (particle concentration data) within each unit with a sampling period of 10-30 seconds, while simultaneously recording the collection time, sensor number, unit number, and other related information. This information is then transmitted to a host computer for processing.

[0022] In this embodiment, the cleanroom is divided into several independent purification control units, each equipped with a dust particle sensor, which enables uniform and systematic monitoring of the entire space.

[0023] S200: Obtain the preset cleanliness threshold corresponding to each purification control unit, compare the actual cleanliness data of each purification control unit with the corresponding preset cleanliness threshold, and determine and mark the purification requirement level of each purification control unit based on the comparison results.

[0024] Specifically, the preset cleanliness threshold is formulated based on the production process requirements of each purification control unit and must comply with industry standards. It supports manual adjustment or automatic system optimization. Manual adjustment is suitable for process change scenarios, while automatic optimization is based on historical data. For example, when a unit achieves a cleanliness compliance rate of ≥99.8% for 30 consecutive days and an energy consumption reduction rate of <20%, the system automatically relaxes the threshold by 5%-10% to further reduce energy consumption. The actual cleanliness data of each purification control unit is compared with the corresponding preset cleanliness threshold to calculate the cleanliness difference (cleanliness difference = actual cleanliness data - preset cleanliness threshold). Based on the cleanliness difference, purification requirements are divided into multiple levels to adapt to different cleanliness fluctuation scenarios. Different colors and icons are used to mark each unit's level in real time and display it on the host computer interface. When the highest purification requirement level is reached, i.e., an emergency requirement, a pop-up window appears on the host computer interface to prompt and trigger an alarm.

[0025] In this embodiment, the actual cleanliness data of each purification control unit is compared with the corresponding preset cleanliness threshold to determine the purification requirement level of each unit. This provides accurate guidance for subsequent FFU wind speed configuration and avoids indiscriminate adjustments.

[0026] S300 aims to minimize the total energy consumption of the FFU cluster composed of all FFUs while meeting the purification requirements of all purification control units. The PSO algorithm is used for multiple rounds of iterative calculations to obtain the FFU wind speed configuration scheme that satisfies the optimization objective.

[0027] Specifically, the prerequisite for FFU wind speed configuration is that the evolutionary requirement level corresponding to all FFUs must be met, meaning the air cleanliness requirements of each area must be satisfied. Under this premise, the optimization objective is to minimize the total energy consumption of the FFU cluster composed of multiple FFUs. Based on this optimization objective, a fitness function is constructed. This fitness function is used to evaluate the merits of candidate solutions or individuals to guide the algorithm's search process. Based on the fitness function, the PSO algorithm is used for multiple rounds of iterative calculations. After the iterations, a set of optimal FFU wind speed configurations is obtained, where the wind speed setting of each FFU ensures both the cleanliness requirements of all purification and control units and minimizes the energy consumption of the entire FFU cluster.

[0028] In this embodiment, the multi-round iteration method of PSO meets the cleanliness requirements of all purification control units while minimizing the total energy consumption of the FFU cluster, ensuring that the cleanliness of all areas meets the requirements and reducing operating costs.

[0029] The S400 determines the target wind speed parameters for each FFU according to the FFU wind speed configuration scheme, and transmits the target wind speed parameters of each FFU to the corresponding FFU controller via Ethernet to drive the motor to adjust the fan speed.

[0030] Specifically, Ethernet uses the TCP / IP protocol, which features high transmission rate and strong stability, enabling real-time parameter transmission from multiple FFUs. The target wind speed parameters of each FFU are transmitted to the corresponding FFU controller via Ethernet. Upon receiving the target wind speed parameters, the FFU controller controls the fan motor speed using a PWM (Pulse Width Modulation) signal. The motor speed is linearly related to the target wind speed (e.g., a target wind speed of 0.5 m / s corresponds to a motor speed of 1500 rpm, and 0.8 m / s corresponds to 2400 rpm). The controller performs closed-loop adjustment of the speed by real-time acquisition of the motor speed signal, ensuring that the deviation between the actual wind speed and the target wind speed is less than a preset threshold, thus guaranteeing control accuracy.

[0031] In one embodiment, such as Figure 2 As shown, S300 includes: S310: Configure the particle swarm size according to the number of FFUs in the cleanroom and initialize the particle swarm. S320, with the optimization objective of minimizing the total energy consumption of the FFU cluster composed of all FFUs while meeting the purification requirements of all purification control units, constructs a fitness evaluation function. S330, based on the fitness evaluation function, performs multiple rounds of iterative calculations on the particle swarm using the PSO algorithm combined with local search, and stops iterating when a preset termination condition is met, outputting the FFU wind speed combination scheme corresponding to the globally optimal position.

[0032] Specifically, the particle swarm is first initialized, and its size is configured based on the number of FFUs (Fan Filter Units) in the cleanroom. Each particle corresponds to a set of FFU airflow combinations, and a corresponding purification requirement level label is assigned to each particle to ensure that the airflow configuration matches the requirements. Each particle also has an initial velocity set, generated through a random function, while ensuring that the initial position and velocity meet FFU hardware constraints (such as the lower limit of airflow corresponding to the minimum motor speed and the upper limit of airflow corresponding to the maximum motor speed). The fitness function must simultaneously consider both the minimum energy consumption and the achievement of cleanliness standards, integrating energy consumption and cleanliness indicators, as shown in the following formula: Among them, energy consumption indicators , The energy consumption factor (determined by FFU signal and operating conditions) is the energy consumption coefficient. The actual air velocity of the i-th FFU in the j-th purification control unit; cleanliness index , Let j be the predicted cleanliness level of the j-th purification control unit under the current wind speed. A preset cleanliness threshold is set for this purification control unit. This is a weighting coefficient that is dynamically adjusted based on the level of purification requirements. , These are the energy consumption weight and the cleanliness weight, respectively.

[0033] Based on the fitness evaluation function, the PSO algorithm is combined with local search to perform multiple rounds of iterative calculations on the particle swarm. The iteration stops when a preset termination condition is met. The preset termination condition is that the number of iterations reaches a preset value or the time of a single iteration exceeds 5 seconds. The iteration stops when either condition is met.

[0034] In one embodiment, based on the fitness evaluation function, the particle swarm optimization (PSO) algorithm is used in combination with local search to perform multiple rounds of iterative calculations. The iteration stops when a preset termination condition is met, and the FFU wind speed combination scheme corresponding to the globally optimal position is output, including: Based on the fitness evaluation function, the particle swarm is subjected to multiple rounds of iterative calculations using the PSO algorithm. After each iteration, the position parameters and velocities of the particles are updated, and the global optimum is determined. After determining the global optimum, a local search space is defined with the global optimum as the center, and the solution is optimized using a local search algorithm in the local search space. When the preset termination condition is met, the iteration stops, and the FFU wind speed combination scheme corresponding to the global optimum position is output.

[0035] Specifically, based on the fitness evaluation function, the PSO algorithm is used to perform multiple rounds of iterative calculations on the particle swarm. In each iteration, the fitness value of each particle is calculated, the individual optimal solution (the position corresponding to the particle's historical optimal fitness) and the global optimal solution (the position corresponding to the historical optimal fitness of the entire particle swarm) are updated, and the position parameters and velocity of the particles are updated to continuously adjust the wind speed. After determining the global optimal solution, a local search space is defined with the global optimal solution as the center, and the solution is optimized in the local search space using a local search algorithm. Specifically, one FFU is randomly selected, and its wind speed is adjusted in the search space with a step size of 0.01 m / s. The fitness value of the new solution is calculated. If the fitness value of the new solution is less than that of the original solution, the wind speed of the FFU is updated; otherwise, the original wind speed is maintained, and the next FFU is adjusted. The above process is repeated until all FFUs are traversed or 10 consecutive adjustments fail to optimize the solution. The globally optimal solution after local optimization is then output.

[0036] In this embodiment, the PSO algorithm can quickly locate potential global optimal solutions during the global search phase. Then, a local search space is constructed with this optimal solution as the center. The local search algorithm is used to further refine the solution's accuracy, overcoming the premature convergence problem that may occur with simple global search. This allows the algorithm to maintain both global exploration capabilities and the advantage of local refinement during the search process, thus obtaining a more reliable optimal solution.

[0037] In one embodiment, obtaining the preset cleanliness threshold corresponding to each purification control unit, comparing the actual cleanliness data of each purification control unit with the corresponding preset cleanliness threshold, and determining and marking the purification requirement level of each purification control unit based on the comparison results includes: Obtain the preset cleanliness threshold for each purification control unit, compare the actual cleanliness data of each purification control unit with the corresponding preset cleanliness threshold, and obtain the cleanliness difference; based on the cleanliness difference, classify the purification requirement level of each purification control unit, and use different preset labels to mark the purification requirement level.

[0038] Specifically, the cleanliness preset threshold corresponding to each purification control unit is obtained. The actual cleanliness data of each purification control unit is subtracted from the corresponding cleanliness preset threshold to obtain the cleanliness difference value. Based on the cleanliness difference value, the purification requirements are divided into 4 levels. Specifically, if the cleanliness difference value is ≤ -50% of the preset threshold, that is, the actual cleanliness is much higher than the requirement, the purification requirement level is low requirement, and no enhanced purification is required. If -50% × preset threshold < cleanliness difference value ≤ 0, that is, the actual cleanliness meets the requirement, the purification requirement level is medium requirement, and basic purification is maintained. If 0 < cleanliness difference value ≤ 50% × preset threshold, that is, the actual cleanliness is close to exceeding the standard, the purification requirement level is high requirement, and the FFU air velocity needs to be increased. If the cleanliness difference value is > 50% × preset threshold, that is, the actual cleanliness exceeds the standard, the purification requirement level is emergency requirement, and the maximum air velocity purification is required.

[0039] In one embodiment, the cleanroom is divided into zones to form multiple purification control units, and the actual cleanliness data of each purification control unit is obtained through a dust particle sensor, including: The cleanroom is divided into multiple independent purification control units using a grid partitioning method; a sensor calibration mechanism is established to perform standard calibration on the dust particle sensors in order to correct the detection deviation of the dust particle sensors; and the actual cleanliness data of each purification control unit is collected through the calibrated dust particle sensors.

[0040] Specifically, based on the cleanroom's dimensions (length and width), production layout, airflow distribution characteristics, and FFU installation density, the grid size is determined, and the cleanroom is divided into multiple independent purification control units according to the grid size. A sensor calibration mechanism is established to correct detection deviations. Specifically, firstly, a calibration cycle is set according to the cleanroom's contamination risk level. Then, the outlet of the standard particle generator is aligned with the sampling port of the dust particle sensor to be calibrated, ensuring that particles enter the sensor's detection area uniformly. The sensor's detection value and the standard particle concentration value are recorded, and the detection deviation is calculated (deviation = |detected value - standard value| / standard value × 100%). If the deviation is ≤10%, the sensor is considered qualified; if the deviation is >10%, linearity error and zero-point drift are corrected using the sensor's built-in calibration software or hardware knob until the deviation is ≤10%. The calibrated dust particle sensor collects particle concentration data from each purification control unit.

[0041] In one embodiment, the FFU cluster energy-saving control method based on real-time cleanliness feedback further includes: By using ring network redundancy technology, multiple FFU controllers are connected through a ring network; when a link in the ring network fails, the ring network is automatically reconfigured so that data can be transmitted through other links, thus achieving fault switching.

[0042] Specifically, industrial-grade redundant Ethernet switches are used to connect all FFU controllers into a closed ring network via network cables or fiber optic cables. Each FFU controller is configured with dual Ethernet ports, connected to the upstream and downstream switch ports of the ring network respectively, forming a bidirectional data transmission link. MRP (Media Redundancy Protocol) or RSTP (Rapid Spanning Tree Protocol) is used for ring network management. The ring network switches monitor the link status by periodically sending test frames. The test frames are transmitted unidirectionally along the ring network. If a switch does not receive a test frame returned by the downstream switch within a preset time, it is determined that the link segment is faulty. After fault detection, the MRP / RSTP protocol automatically calculates a new transmission path, reconstructing the ring network from a closed loop into a linear link, and data is transmitted through the reverse link.

[0043] In one embodiment, after determining the target wind speed parameters for each FFU according to the FFU wind speed configuration scheme, and transmitting the target wind speed parameters of each FFU to the corresponding FFU controller via Ethernet to drive the motor to adjust the fan speed, the method further includes: The energy consumption data of the FFU cluster before the fan speed adjustment is obtained, and the energy consumption reduction rate is calculated based on the energy consumption data of the FFU cluster before the fan speed adjustment; the proportion of time within a preset time when the cleanroom cleanliness meets the preset standard is statistically analyzed to obtain the cleanliness compliance rate; the energy-saving effect is evaluated based on the energy consumption reduction rate and the cleanliness compliance rate, and the control parameters of the FFU are adjusted based on the evaluation results.

[0044] Specifically, the total energy consumption of the FFU cluster before and after the fan speed adjustment is collected. Based on the total energy consumption of the FFU cluster before and after the adjustment, the energy consumption reduction rate is calculated. A statistical period is set, and the cleanliness data of each purification control unit within the statistical period is extracted from the database. The time when the cleanliness meets the preset standard and the total time of the statistical period are counted to calculate the cleanliness compliance rate. Based on the combined effect of the energy consumption reduction rate and the cleanliness compliance rate, a graded adjustment strategy is adopted to optimize the FFU control parameters.

[0045] In one embodiment, such as Figure 3 As shown, an FFU cluster energy-saving control device based on real-time cleanliness feedback is provided, including: a zone division module 10, a level determination module 20, a scheme solution module 30, and a wind speed control module 40, wherein: The area division module 10 is used to divide the cleanroom into areas, forming multiple purification control units, and to obtain the actual cleanliness data of each purification control unit through a dust particle sensor. Each purification control unit is equipped with an FFU for air purification. The level determination module 20 is used to obtain the preset cleanliness threshold corresponding to each purification control unit, compare the actual cleanliness data of each purification control unit with the corresponding preset cleanliness threshold, and determine and mark the purification requirement level of each purification control unit based on the comparison results. The solution module 30 is used to minimize the total energy consumption of the FFU cluster composed of all FFUs, under the premise of meeting the purification requirements of all purification control units. It uses the PSO algorithm to perform multiple rounds of iterative calculations to obtain the FFU wind speed configuration scheme that makes the optimization objective valid. The wind speed control module 40 is used to determine the target wind speed parameters corresponding to each FFU according to the FFU wind speed configuration scheme, and transmit the target wind speed parameters of each FFU to the corresponding FFU controller via Ethernet to drive the motor to adjust the fan speed.

[0046] In one embodiment, the solution-solving module 30 is further configured to configure the particle swarm size according to the number of FFUs in the cleanroom, and initialize the particle swarm, wherein each particle is mapped to a set of FFU wind speed combination schemes, the position parameters of the particle correspond to the wind speed values ​​of each FFU, and each particle is configured with an initial movement speed; under the premise of meeting the purification requirements of all purification control units, the optimization objective is to minimize the total energy consumption of the FFU cluster composed of all FFUs, and a fitness evaluation function is constructed, which integrates energy consumption indicators and cleanliness indicators; based on the fitness evaluation function, the particle swarm is iteratively calculated multiple times by combining the PSO algorithm with local search, and the iteration stops when the preset termination condition is met, and the FFU wind speed combination scheme corresponding to the globally optimal position is output.

[0047] In one embodiment, the solution-solving module 30 is further configured to perform multiple rounds of iterative calculations on the particle swarm using the PSO algorithm based on the fitness evaluation function. After each iteration, the position parameters and velocities of the particles are updated, and the global optimal solution is determined. After determining the global optimal solution, a local search space is defined with the global optimal solution as the center, and the solution is optimized using a local search algorithm in the local search space. When the preset termination condition is met, the iteration stops, and the FFU wind speed combination scheme corresponding to the global optimal position is output.

[0048] In one embodiment, the solution solving module 30 is further configured to obtain the cleanliness preset threshold corresponding to each purification control unit, compare the actual cleanliness data of each purification control unit with the corresponding cleanliness preset threshold, obtain the cleanliness difference, classify the purification requirement level of each purification control unit according to the cleanliness difference, and mark the purification requirement level with different preset labels.

[0049] In one embodiment, the area division module 10 is also used to divide the cleanroom into multiple independent purification control units using a grid division method; establish a sensor calibration mechanism to perform standard calibration on the dust particle sensor to correct the detection deviation of the dust particle sensor; and collect the actual cleanliness data of each purification control unit through the calibrated dust particle sensor.

[0050] In one embodiment, the FFU cluster energy-saving control device based on real-time cleanliness feedback further includes a ring network connection module, which is used to connect multiple FFU controllers through a ring network using ring network redundancy technology; when a link in the ring network fails, the ring network is automatically reconfigured so that data is transmitted through other links to achieve fault switching.

[0051] In one embodiment, the FFU cluster energy-saving control device based on real-time cleanliness feedback further includes a control parameter adjustment module, which is used to acquire the energy consumption data of the FFU cluster before the fan speed is adjusted, and calculate the energy consumption reduction rate based on the energy consumption data of the FFU cluster before the fan speed is adjusted; statistically analyze the proportion of time within a preset time when the cleanroom cleanliness meets the preset standard to obtain the cleanliness compliance rate; evaluate the energy-saving effect based on the energy consumption reduction rate and the cleanliness compliance rate, and adjust the control parameters of the FFU based on the evaluation results.

[0052] In one embodiment, this application discloses a computer device including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor loads the computer program, it executes an FFU cluster energy-saving control method based on real-time cleanliness feedback as described in the above embodiment.

[0053] In one embodiment, this application discloses a computer-readable storage medium storing a computer program, wherein when the computer program is loaded by a processor, it executes an FFU cluster energy-saving control method based on real-time cleanliness feedback as described in the above embodiment.

[0054] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for energy-saving control of FFU clusters based on real-time cleanliness feedback, characterized in that, include: The cleanroom is divided into areas to form multiple purification control units, and the actual cleanliness data of each purification control unit is obtained through dust particle sensors. Each purification control unit is equipped with an FFU for air purification. Obtain the preset cleanliness threshold for each purification control unit, compare the actual cleanliness data of each purification control unit with the corresponding preset cleanliness threshold, and determine and mark the purification requirement level of each purification control unit based on the comparison results. To minimize the total energy consumption of the FFU cluster composed of all FFUs while meeting the purification requirements of all purification control units, the PSO algorithm is used to perform multiple rounds of iterative calculations to obtain the FFU wind speed configuration scheme that makes the optimization objective true. According to the FFU wind speed configuration scheme, the target wind speed parameters corresponding to each FFU are determined, and the target wind speed parameters of each FFU are transmitted to the corresponding FFU controller via Ethernet to drive the motor to adjust the fan speed.

2. The FFU cluster energy-saving control method based on real-time cleanliness feedback according to claim 1, characterized in that, The optimization objective is to minimize the total energy consumption of the FFU cluster composed of all FFUs while meeting the purification requirements of all purification control units. The PSO algorithm is used for multiple iterative calculations to obtain the FFU wind speed configuration schemes that satisfy the optimization objective. These schemes include: Configure the particle swarm size according to the number of FFUs in the cleanroom, initialize the particle swarm, where each particle is mapped to a set of FFU wind speed combination schemes, the position parameters of the particle correspond to the wind speed values ​​of each FFU, and each particle is configured with an initial movement speed. To minimize the total energy consumption of the FFU cluster composed of all FFUs while meeting the purification requirements of all purification control units, an fitness evaluation function is constructed, which integrates energy consumption indicators and cleanliness indicators. Based on the fitness evaluation function, the particle swarm is subjected to multiple rounds of iterative calculations using the PSO algorithm combined with local search. The iteration stops when a preset termination condition is met, and the FFU wind speed combination scheme corresponding to the globally optimal position is output.

3. The FFU cluster energy-saving control method based on real-time cleanliness feedback according to claim 2, characterized in that, The process of performing multiple rounds of iterative computation on the particle swarm using the PSO algorithm combined with local search based on the fitness evaluation function, and stopping the iteration when a preset termination condition is met, outputting the FFU wind speed combination scheme corresponding to the globally optimal position, includes: Based on the fitness evaluation function, the particle swarm is subjected to multiple rounds of iterative calculations using the PSO algorithm. After each iteration, the position parameters and velocities of the particles are updated, and the global optimal solution is determined. After determining the global optimal solution, a local search space is defined with the global optimal solution as the center, and a local search algorithm is used to optimize the solution in the local search space; The iteration stops when the preset termination condition is met, and the FFU wind speed combination scheme corresponding to the globally optimal position is output.

4. The FFU cluster energy-saving control method based on real-time cleanliness feedback according to claim 1, characterized in that, The process of obtaining the preset cleanliness threshold for each purification control unit, comparing the actual cleanliness data of each purification control unit with the corresponding preset cleanliness threshold, and determining and marking the purification requirement level of each purification control unit based on the comparison results includes: Obtain the preset cleanliness threshold for each purification control unit, compare the actual cleanliness data of each purification control unit with the corresponding preset cleanliness threshold, and obtain the cleanliness difference. Based on the cleanliness difference, the cleanliness requirement level of each cleanliness control unit is divided, and different preset labels are used to mark the cleanliness requirement level.

5. The FFU cluster energy-saving control method based on real-time cleanliness feedback according to claim 1, characterized in that, The process of dividing the cleanroom into multiple purification control units and acquiring the actual cleanliness data of each unit using a dust particle sensor includes: The cleanroom is divided into multiple independent purification and control units using a grid-based partitioning method. Establish a sensor calibration mechanism to perform standard calibration on the dust particle sensor in order to correct the detection deviation of the dust particle sensor; The actual cleanliness data of each purification control unit is collected using calibrated dust particle sensors.

6. The FFU cluster energy-saving control method based on real-time cleanliness feedback according to claim 1, characterized in that, Also includes: Multiple FFU controllers are connected via a ring network using ring network redundancy technology. When a link in the ring network fails, the ring network is automatically reconfigured so that data can be transmitted through other links, thus achieving fault switching.

7. The FFU cluster energy-saving control method based on real-time cleanliness feedback according to claim 1, characterized in that, After determining the target wind speed parameters for each FFU according to the FFU wind speed configuration scheme, and transmitting the target wind speed parameters of each FFU to the corresponding FFU controller via Ethernet to drive the motor to adjust the fan speed, the method further includes: Obtain the energy consumption data of the FFU cluster before the fan speed adjustment, and calculate the energy consumption reduction rate based on the energy consumption data of the FFU cluster before the fan speed adjustment. The cleanliness compliance rate is obtained by statistically analyzing the proportion of time within which the cleanroom's cleanliness meets the preset standards within a preset time. The energy-saving effect is evaluated based on the energy consumption reduction rate and the cleanliness compliance rate, and the control parameters of the FFU are adjusted according to the evaluation results.

8. An FFU cluster energy-saving control device based on real-time cleanliness feedback, characterized in that, include: The area division module is used to divide the cleanroom into areas, forming multiple purification control units, and to obtain the actual cleanliness data of each purification control unit through a dust particle sensor. Each purification control unit is equipped with an FFU for air purification. The level determination module is used to obtain the preset cleanliness threshold corresponding to each purification control unit, compare the actual cleanliness data of each purification control unit with the corresponding preset cleanliness threshold, and determine and mark the purification requirement level of each purification control unit based on the comparison results. The solution module is used to minimize the total energy consumption of the FFU cluster composed of all FFUs, under the premise of meeting the purification requirements of all purification control units. It uses the PSO algorithm to perform multiple rounds of iterative calculations to obtain the FFU wind speed configuration scheme that makes the optimization objective true. The wind speed control module is used to determine the target wind speed parameters corresponding to each FFU according to the FFU wind speed configuration scheme, and transmit the target wind speed parameters of each FFU to the corresponding FFU controller via Ethernet to drive the motor to adjust the fan speed.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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