Clean room environment thermal parameter intelligent management system based on cloud-side cooperation

Abstract

The invention discloses an intelligent management system for thermal parameters of a clean room environment based on cloud-side collaboration, and relates to the technical field of parameter management. Comprising a unit partition module which is used for performing region division on a target region and dividing the target region into at least two partitions to obtain region division items; and the parameter calculation module is used for performing independent real-time calculation control on the target parameter based on the region division item, obtaining a real-time target parameter calculation result of the region division item, and obtaining a region real-time data set. According to the invention, through dynamic partitioning and real-time calculation, the response speed and adjustment precision of the thermal parameters of the clean room are improved, the unit partitioning module performs region division based on the coverage rate or type of parameter adjustment equipment, independent monitoring of each partition is ensured, hysteresis of traditional centralized control or a single edge strategy is avoided, and the efficiency of the system is improved. Local disturbance can be rapidly responded, and rapid pre-adjustment is achieved.

Description

Technical Field

[0001] This invention relates to the field of parameter management technology, specifically to an intelligent management system for cleanroom environmental thermal parameters based on cloud-edge collaboration. Background Technology

[0002] A cleanroom is a sealed space that uses engineering design, special building materials, and strict operating procedures to control environmental parameters such as the concentration of airborne particles, microorganisms, and aerosols, as well as temperature, humidity, and air pressure. Its core objective is to limit contaminants to acceptable levels to meet the stringent cleanliness requirements of specific processes or activities. The key thermal parameters are the concentration of airborne particles, dust, microorganisms, and aerosols, as well as temperature, humidity, and air pressure. When adjusting these parameters, the core technologies of existing management methods include multi-source sensing and Internet of Things integration, high-precision sensor networks, equipment status sensing, and edge computing nodes to ensure the stability of parameters within the cleanroom.

[0003] A smart industrial park zero-carbon management system and its management method, disclosed in patent publication number CN118365263A, maximizes energy utilization and minimizes carbon emissions through technological and management innovation. Secondly, the new zero-carbon management industrial park adopts advanced energy-saving technologies and equipment, improving energy efficiency and reducing energy waste. It can monitor environmental parameters such as temperature, humidity, and light intensity within the park in real time and transmit the data to the cloud for analysis and processing. It connects to energy management equipment, including electricity meters, water meters, and gas meters, to monitor and analyze the park's energy consumption. Through intelligent analysis and scheduling functions, it achieves energy efficiency management and optimization of key power consumption, power generation, and energy storage systems. AI peak-shaving and valley-filling algorithms improve energy utilization and optimize carbon emission indicators. Simultaneously, key equipment monitoring indicators are displayed clearly on an IOC (Integrated Circuit) screen, enabling remote management of electricity consumption and carbon emission indicators.

[0004] The aforementioned and similar technical solutions, when managing and adjusting the thermal parameters within a cleanroom, are affected by factors such as process switching, batch characteristics, external energy prices, and weather. This makes it difficult for centralized control or single edge control to respond quickly to disturbances in local areas, further leading to significant fluctuations in parameters such as temperature, humidity, pressure difference, and wind speed in certain areas. This affects cleanliness and process stability, and makes it impossible to make corresponding dynamic parameter adjustments based on the specific thermal parameters of the cleanroom. Consequently, the management response speed is slow, affecting the cleanliness adjustment rate and process stability. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent management system for cleanroom environmental thermal parameters based on cloud-edge collaboration, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent management system for cleanroom environmental thermal parameters based on cloud-edge collaboration, comprising:

[0007] Unit partitioning module: Divides the target area into at least two partitions to obtain the area partitioning items;

[0008] Parameter calculation module: Based on the region division item, the target parameter is independently calculated and controlled in real time, and the real-time target parameter calculation result of the region division item is obtained to obtain the region real-time dataset. The region real-time dataset includes at least two region real-time data items composed of the real-time target parameter data calculation results of the region division item.

[0009] Strategy pre-adjustment module: Obtain the target parameters to be adjusted, obtain the target parameter items, and based on the target parameter items, perform regional independent target parameter pre-adjustment on the regional division items through the target parameter calculation method to obtain regional pre-adjustment items, and perform resource optimization allocation on the target parameters within the regional division items based on the regional pre-adjustment items;

[0010] Trend Feedback Module: The module updates the regional pre-adjustment items in real time using an update algorithm to obtain updated data items, and then performs further resource optimization and allocation based on the updated data items.

[0011] Node scheduling module: It sends updated data items to the cloud, builds a multi-objective model to generate control scheduling algorithms, outputs scheduling data, performs system scheduling on edge devices based on the scheduling data, obtains collaborative scheduling items, forms multi-region collaborative control, and further optimizes resource allocation results.

[0012] Furthermore, the method for obtaining the region division items includes:

[0013] The target area includes the target cleanroom. The overall area information of the target cleanroom is obtained to obtain the target area item. At the same time, based on the target area item, the parameter adjustment equipment of the target cleanroom is obtained to obtain the parameter adjustment equipment item.

[0014] Based on the parameter adjustment equipment item, the categories are divided to obtain at least one parameter category item. Based on the parameter category item, the target area item is divided by average to obtain at least one area category item. The area category item corresponds to the parameter category item.

[0015] Based on the correspondence between area division items and parameter type items, the target cleanroom is classified and divided, thereby obtaining the area division items.

[0016] Furthermore, the method for obtaining the region division items also includes:

[0017] Based on the parameter type item, the coverage rate of the parameter adjustment device is obtained, and the rate information item is obtained. Based on the rate information item, the target area item is divided into coverage areas to obtain at least one area division item. The area division items correspond to the parameter type items respectively.

[0018] Based on the correspondence between area division items and parameter type items, the target cleanroom is classified and divided, thereby obtaining the area division items.

[0019] Furthermore, the method for obtaining the real-time regional dataset includes:

[0020] Based on the area division, the temperature, humidity, air pressure and particulate concentration parameters of each area division in the target clean room are obtained by the parameter acquisition device to obtain the real-time data set of the area.

[0021] Furthermore, the method for obtaining the real-time regional dataset also includes:

[0022] Based on the partitioning results of the region partitioning items, the parameter acquisition device is partitioned to obtain at least two parameter device partitioning items;

[0023] Based on the parameter device partitioning items, the temperature parameters, humidity parameters, air pressure parameters, and particle concentration parameters of each region partitioning item are obtained to obtain the partitioned dataset. The average parameter information of the partitioned dataset is then obtained to obtain the real-time regional dataset.

[0024] Furthermore, the method for obtaining the regional pre-adjustment item includes:

[0025] Based on the target parameter item, the parameter difference between the real-time regional dataset and the target parameter item is obtained to obtain the parameter supplement item;

[0026] Based on the parameter supplement, a supplement threshold is set, which is a fixed time value. Based on the supplement threshold, the parameters are adjusted through a parameter adjustment device to obtain the regional pre-adjustment item.

[0027] Furthermore, the method for obtaining the updated data item includes:

[0028] Set an acquisition interval, continuously acquire target parameter change information of region division items based on the acquisition interval, and obtain parameter trend items by acquiring target parameter change information of region division items in real time.

[0029] Based on the parameter trend item, with the target parameter item as the trend extreme value, it is determined whether the parameter trend item has reached the trend extreme value. When the parameter trend item reaches the trend extreme value, the region division item corresponding to the trend extreme value and the corresponding parameter adjustment device are used as the termination adjustment, and then the updated data item is obtained.

[0030] Furthermore, the method for obtaining the cooperative scheduling item includes:

[0031] Based on the region division item, obtain the working energy consumption information of the parameter adjustment equipment corresponding to the region division item to obtain the energy consumption data item;

[0032] Using the location of the parameter adjustment device as the judgment point, the nearby parameter adjustment devices of the judgment point are obtained as auxiliary scheduling points to obtain auxiliary scheduling items;

[0033] Based on the updated data items and energy consumption data items, the working energy consumption information of the auxiliary scheduling items is comprehensively calculated and compared with the data. The auxiliary scheduling items are used as the scheduling items, and the system scheduling is carried out in conjunction with the updated data items to obtain the collaborative scheduling items.

[0034] Compared with the prior art, the beneficial effects of the present invention are:

[0035] This cloud-edge collaborative intelligent management system for cleanroom environmental thermal parameters improves the response speed and adjustment accuracy of cleanroom thermal parameters through dynamic zoning and real-time calculation. The unit zoning module divides areas based on the coverage rate or type of parameter adjustment equipment, ensuring independent monitoring of each zone and avoiding the lag of traditional centralized control or single edge strategies. It can quickly respond to local disturbances. The parameter calculation module collects zone data in real time through parameter acquisition equipment and calculates the real-time dataset of the area. Combined with the strategy pre-adjustment module, the system sets supplementary thresholds based on the target parameter difference to achieve rapid pre-adjustment. The trend feedback module monitors the parameter trend and terminates the adjustment in real time when the target extreme value is reached to prevent over-adjustment or under-adjustment. It effectively responds to process switching or batch characteristic changes and reduces parameter fluctuations. It not only solves the problem of slow response of traditional systems, but also ensures the stability of the cleanroom environment and reduces the scrap rate caused by parameter loss through independent zoning and real-time updates. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the overall process of the present invention;

[0037] Figure 2 This is a schematic diagram of the process for obtaining the region division items in this invention;

[0038] Figure 3 This is a schematic diagram showing the distribution of regional classification items in this invention;

[0039] Figure 4 This is a schematic diagram of the second method for obtaining region division items according to the present invention;

[0040] Figure 5 This is a schematic diagram of the second type of region division item in this invention;

[0041] Figure 6 This is a schematic diagram of the integrated equipment group distribution of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Process switching is a significant cause of fluctuations in cleanroom thermal parameters. Different processes often have significantly different requirements for parameters such as temperature, humidity, pressure differential, and air velocity. If adjustments to thermal parameters fail to respond promptly or accurately to process changes, parameter fluctuations will occur, affecting product quality and yield. Secondly, batch characteristics also impact the cleanroom's thermal environment. Different batches of materials may have different thermal and humidity characteristics. Furthermore, the size and quantity of batches also affect the overall heat and humidity loads. Centralized control systems typically adjust based on preset parameters and fixed control logic, making it difficult to flexibly adjust according to actual conditions. Single-edge control strategies may only focus on parameters in a localized area, ignoring the overall environmental coordination. Both of these control strategies lack the ability to quickly respond to disturbances in localized areas, leading to significant fluctuations in parameters such as temperature, humidity, pressure differential, and air velocity in certain areas. The technical solution provided in this application improves the response speed and adjustment accuracy of cleanroom thermal parameters through dynamic zoning and real-time calculation. The unit zoning module divides areas based on the coverage rate or type of parameter adjustment equipment, ensuring independent monitoring of each zone and avoiding the lag of traditional centralized control or single-edge strategies. It can quickly respond to local disturbances. The parameter calculation module collects zone data in real time through parameter acquisition equipment and calculates the real-time dataset of the area. Combined with the strategy pre-adjustment module, the system sets a supplementary threshold based on the target parameter difference to achieve rapid pre-adjustment. The trend feedback module monitors the parameter trend and terminates the adjustment immediately when the target extreme value is reached to prevent over-adjustment or under-adjustment. This mechanism effectively copes with process switching or batch characteristic changes, reduces parameter fluctuations, not only solves the problem of slow response in traditional systems, but also ensures the stability of the cleanroom environment through independent zoning and real-time updates, reducing the scrap rate caused by parameter malfunction. Figure 1 As shown, it includes a unit partitioning module, a parameter calculation module, a strategy pre-tuning module, a trend feedback module, and a node scheduling module.

[0044] Unit partitioning module: Divides the target area into at least two partitions to obtain the area partitioning items.

[0045] It is important to note that, such as Figure 2As shown, the method for obtaining the area division item includes: the target area includes the target cleanroom; the overall area information of the target cleanroom is obtained to obtain the target area item; simultaneously, based on the target area item, the parameter adjustment equipment of the target cleanroom is obtained to obtain the parameter adjustment equipment item; the parameter adjustment equipment item is classified into categories to obtain at least one parameter category item; the target area item is averaged and divided according to the parameter category item to obtain at least one area division item, and the area division item corresponds to the parameter category item; based on the correspondence between the area division item and the parameter category item, the target cleanroom is classified and divided to obtain the area division item.

[0046] Specifically, the first method of dividing the target area is based on equal division of area. First, the overall area information of the target cleanroom is obtained. Then, the parameter control equipment of the target cleanroom is obtained. Thermal parameters include temperature, humidity, air pressure, and particulate concentration. Therefore, the parameter control equipment includes temperature control components, humidity control components, air pressure control components, and particulate concentration control components. Moreover, the number of parameter control devices is not limited to one. For example, a cleanroom may have two temperature control components, three humidity control components, one air pressure control component, and three particulate concentration control components. Then, the parameter control equipment is classified into four categories. Since the parameter control equipment includes temperature control components, humidity control components, air pressure control components, and particulate concentration control components, it is divided into four categories. The four parameter category items are obtained by dividing the area equally based on the parameter category items, thus obtaining the area division items.

[0047] In the specific implementation process, such as Figure 3 As shown, a cleanroom A requires thermal parameter adjustment. The area of ​​the cleanroom is 100㎡, and the parameter adjustment equipment information is as follows: two temperature control components, two humidity control components, four air pressure control components, and four particulate concentration control components. Therefore, there are four types of parameters. Based on these parameter types, the cleanroom is divided into four equal areas: first, temperature control components 1 and 2, with an average area of ​​50㎡ each; second, humidity control components 1 and 2, with an average area of ​​50㎡ each; third, air pressure control components 1, 2, 3, and 4, with an average area of ​​25㎡ each; and finally, particulate concentration control components 1, 2, 3, and 4, with an average area of ​​25㎡ each. Thus, there are four types of area divisions.

[0048] It is important to note that, such as Figure 4As shown, the method for obtaining the area division item also includes: obtaining the coverage rate of the parameter adjustment device based on the parameter type item to obtain the rate information item; dividing the target area item by coverage rate based on the rate information item to obtain at least one area division item, and the area division item corresponds to the parameter type item respectively; classifying the target cleanroom based on the correspondence between the area division item and the parameter type item to obtain the area division item.

[0049] Specifically, the second method for dividing the target area is based on the coverage rate of different parameter adjustment devices. First, the coverage rate of each parameter adjustment device is obtained. Since the parameter adjustment devices include temperature adjustment components, humidity adjustment components, air pressure adjustment components, and particulate concentration adjustment components, the coverage rate of each device is obtained separately. That is, under the premise of unified control parameters, the effective coverage area of ​​each device is obtained. For example, when the temperature is adjusted by the temperature adjustment component, under the premise of the same temperature setting, the same wind speed, and the same air outlet direction, the effective coverage area of ​​each temperature adjustment component within a specified time is obtained. Then, the area is divided according to the coverage area, and thus the area division item is obtained.

[0050] In the specific implementation process, such as Figure 5 As shown, a cleanroom B requires thermal parameter adjustment. The area of ​​the cleanroom is 100㎡. The parameter adjustment equipment information is as follows: two temperature control components, two humidity control components, two air pressure control components, and two particulate concentration control components. Therefore, there are four types of parameters. The coverage rates of these devices are obtained as follows: the coverage rates of temperature control components 11 and 22 are 60:40, so the area division based on temperature is 6:4; the coverage rates of humidity control components 11 and 22 are 55:45, so the area division based on humidity is 5.5:4.5; the coverage rates of air pressure control components 11 and 22 are 50:50, so the area division based on air pressure is 5:5; and the coverage rates of particulate concentration control components 11 and 22 are 70:30, so the area division based on particulate concentration is 7:3. Thus, there are four types of area division items.

[0051] Parameter calculation module: Based on the region division item, it independently calculates and controls the target parameters in real time, obtains the real-time target parameter calculation results of the region division item, and obtains the real-time dataset of the region.

[0052] It should be noted that the regional real-time dataset includes at least two regional real-time data items consisting of the calculation results of real-time target parameter data from the regional division items. The method for obtaining the regional real-time dataset includes: based on the regional division items, the temperature parameters, humidity parameters, air pressure parameters, and particle concentration parameters of each regional division item in the target clean room are obtained through parameter acquisition devices to obtain the regional real-time dataset.

[0053] Specifically, the parameter acquisition equipment includes a comprehensive equipment group consisting of temperature parameter acquisition equipment, humidity parameter acquisition equipment, air pressure parameter acquisition equipment, and particulate concentration parameter acquisition equipment. At this time, temperature parameters are acquired through the temperature parameter acquisition equipment, humidity parameters are acquired through the humidity parameter acquisition equipment, air pressure parameters are acquired through the air pressure parameter acquisition equipment, and particulate concentration parameters are acquired through the particulate concentration parameter acquisition equipment, thereby obtaining the real-time regional dataset for each regional division item.

[0054] It should be noted that the method for obtaining the regional real-time dataset also includes: dividing the parameter acquisition device into partitions based on the partitioning results of the regional partitioning items to obtain at least two parameter device partitioning items; obtaining the temperature parameters, humidity parameters, air pressure parameters, and particle concentration parameters of each regional partitioning item based on the parameter device partitioning items to obtain the partitioned dataset; obtaining the average parameter information of the partitioned dataset to obtain the regional real-time dataset.

[0055] Specifically, since the area division item divides the cleanroom into multiple areas, and the size of each area may vary, when installing the parameter acquisition equipment, the parameter acquisition equipment can also be divided into zones based on the area division results, resulting in at least two parameter equipment zone items. Each parameter equipment zone item corresponds to an area division item, and there may be more than one parameter acquisition device within each parameter equipment zone item. Then, based on the divided parameter equipment zone items, the temperature parameters, humidity parameters, air pressure parameters, and particulate concentration parameters of the corresponding area division items are obtained to obtain the zone dataset. The average parameter information of the zone dataset is then obtained to obtain the real-time regional dataset.

[0056] In the specific implementation process, such as Figure 6As shown, a cleanroom C requires thermal parameter adjustment. The area of ​​the cleanroom is 100㎡. The parameter adjustment equipment includes two temperature control components, two humidity control components, two air pressure control components, and two particulate concentration control components. Therefore, there are four types of parameters. The coverage rates of these devices are then determined. The coverage rates of temperature control components 11 and 22 are 60:40, resulting in a temperature-based area ratio of 6:4. The coverage rates of humidity control components 11 and 22 are 55:45, resulting in a humidity-based area ratio of 5.5:4.5. The coverage rates of air pressure control components 11 and 22 are 50:50, resulting in a pressure-based area ratio of 5:5. The coverage rates of particulate concentration control components 11 and 22 are also determined. The coverage rates of the concentration adjustment component 22 are 70:30, and the area division based on particle concentration is 7:3. The parameter acquisition equipment installed inside the clean room is a comprehensive equipment group consisting of temperature parameter acquisition equipment, humidity parameter acquisition equipment, air pressure parameter acquisition equipment, and particle concentration parameter acquisition equipment. A total of ten such devices are installed, spaced 1m apart, and arranged in a straight line. Since the area division for temperature is 6:4, for humidity it is 5.5:4.5, for air pressure it is 5:5, and for particle concentration it is 7:3, the distribution of temperature parameter acquisition equipment, humidity parameter acquisition equipment, air pressure parameter acquisition equipment, and particle concentration parameter acquisition equipment in the comprehensive equipment group is 6:4, 6:4, 5:5, and 7:3, respectively. At this time, the average parameter information of the partition dataset is obtained, and then the real-time regional dataset is obtained.

[0057] Strategy pre-adjustment module: Obtain the target parameters to be adjusted, obtain the target parameter items, and based on the target parameter items, perform regional independent target parameter pre-adjustment on the regional division items through the target parameter calculation method to obtain the regional pre-adjustment items.

[0058] It should be noted that resource optimization allocation is performed on the target parameters within the regional division item based on the regional pre-adjustment item. The method for obtaining the regional pre-adjustment item includes: obtaining the parameter difference between the real-time dataset of the region and the target parameter item based on the target parameter item to obtain the parameter supplement item; setting a supplement threshold based on the parameter supplement item, the supplement threshold being a fixed time value; and adjusting the parameters through a parameter adjustment device based on the supplement threshold to obtain the regional pre-adjustment item.

[0059] Specifically, the set replenishment threshold is 5 seconds, meaning that the parameters are adjusted by the parameter adjustment device at the specified replenishment threshold.

[0060] Trend Feedback Module: The module updates the regional pre-adjustment items in real time using an update algorithm to obtain updated data items, and then performs further resource optimization and allocation based on the updated data items.

[0061] It should be noted that the method for obtaining updated data items includes: setting an acquisition interval, continuously acquiring target parameter change information of the region division item based on the acquisition interval, acquiring target parameter change information of the region division item in real time, and obtaining parameter trend items; based on the parameter trend items, taking the target parameter item as the trend extreme value, determining whether the parameter trend item has reached the trend extreme value, when the parameter trend item reaches the trend extreme value, taking the region division item corresponding to the trend extreme value and the corresponding parameter adjustment device as the termination adjustment, and thus obtaining the updated data items.

[0062] Specifically, the acquisition interval is set to 0.1s. Based on the acquisition interval, the target parameter change information of the region division item is continuously acquired. Since the target parameters include temperature, humidity, air pressure and particle concentration, there are four parameter trend items. At this time, the target parameter item is used as the trend extreme value. It is determined whether the parameter trend item has reached the trend extreme value. When the parameter trend item reaches the trend extreme value, the region division item corresponding to the trend extreme value and the corresponding parameter adjustment device are used as the termination adjustment, and then the updated data item is obtained.

[0063] In the specific implementation process, the area ratio of a certain cleanroom is divided as follows: temperature 6:4 (area 1 and area 2); humidity 5.5:4.5 (area 11 and area 22); air pressure 5:5 (area 111 and area 222); and particulate concentration 7:3 (area 1111 and area 2222). The target parameters are set to achieve the following: temperature (set 1), humidity (set 2), air pressure (set 3), and particulate concentration (set 4). Parameters are adjusted using parameter adjustment devices. During adjustment, area 1 first reaches set 1, at which point the adjustment is terminated using the device corresponding to area 1. The device corresponding to area 2 continues adjustment, then area 22 first reaches set 2, at which point the adjustment is terminated using the device corresponding to area 22. The device corresponding to area 1 continues adjustment, and so on, thus obtaining updated data.

[0064] Node scheduling module: It sends updated data items to the cloud, builds a multi-objective model to generate a control scheduling algorithm, outputs scheduling data, and performs system scheduling on edge devices based on the scheduling data to obtain collaborative scheduling items.

[0065] It is important to note that, based on the collaborative scheduling items, multi-region collaborative control is formed to further optimize resource allocation results. The methods for obtaining collaborative scheduling items include: based on the region division item, obtaining the working energy consumption information of the parameter adjustment equipment corresponding to the region division item to obtain energy consumption data items; using the location of the parameter adjustment equipment as the judgment point, obtaining the parameter adjustment equipment near the judgment point as auxiliary scheduling points to obtain auxiliary scheduling items; based on the updated data items and energy consumption data items, comprehensively calculating the working energy consumption information comparison data of the auxiliary scheduling items, using the auxiliary scheduling items as scheduling, and cooperating with the updated data items to perform system scheduling, thereby obtaining collaborative scheduling items.

[0066] Specifically, although the cleanroom is divided into zones, it remains a unified whole. When some parameter regulating devices are operating, even if they prioritize their own zone's requirements, they can still affect other zones. To address this, by acquiring the energy consumption information of each parameter regulating device, using its location as a reference point, and identifying neighboring devices as auxiliary scheduling points, an auxiliary scheduling item is obtained. Based on updated data and energy consumption data, the energy consumption information of the auxiliary scheduling item is comprehensively calculated. When the energy consumption of the auxiliary scheduling item is low, it is used for scheduling, in conjunction with the updated data item, to perform system scheduling, thus obtaining a coordinated scheduling item. This achieves a multi-zone coordinated thermal parameter regulation effect.

[0067] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended embodiments and their equivalents.

Claims

1. A cloud-edge collaborative intelligent management system for cleanroom environmental thermal parameters, comprising: Unit partitioning module: Divides the target area into at least two partitions to obtain the area partitioning items; Parameter calculation module: Based on the region division item, the target parameter is independently calculated and controlled in real time, and the real-time target parameter calculation result of the region division item is obtained to obtain the region real-time dataset. The region real-time dataset includes at least two region real-time data items composed of the real-time target parameter data calculation results of the region division item. Its characteristic is that it further includes: Strategy pre-adjustment module: Obtain the target parameters to be adjusted, obtain the target parameter items, and based on the target parameter items, perform regional independent target parameter pre-adjustment on the regional division items through the target parameter calculation method to obtain regional pre-adjustment items, and perform resource optimization allocation on the target parameters within the regional division items based on the regional pre-adjustment items; Trend Feedback Module: The module updates the regional pre-adjustment items in real time using an update algorithm to obtain updated data items, and then performs further resource optimization and allocation based on the updated data items. Node scheduling module: It sends updated data items to the cloud, builds a multi-objective model to generate control scheduling algorithms, outputs scheduling data, performs system scheduling on edge devices based on the scheduling data, obtains collaborative scheduling items, forms multi-region collaborative control, and further optimizes resource allocation results. 2.The cloud-edge collaborative cleanroom environment thermal parameter intelligent management system according to claim 1, characterized in that: The method for obtaining the region division items includes: The target area includes the target cleanroom. The overall area information of the target cleanroom is obtained to obtain the target area item. At the same time, based on the target area item, the parameter adjustment equipment of the target cleanroom is obtained to obtain the parameter adjustment equipment item. Based on the parameter adjustment equipment item, the categories are divided to obtain at least one parameter category item. Based on the parameter category item, the target area item is divided by average to obtain at least one area category item. The area category item corresponds to the parameter category item. Based on the correspondence between area division items and parameter type items, the target cleanroom is classified and divided, thereby obtaining the area division items. 3.The cloud-edge collaborative cleanroom environment thermal parameter intelligent management system according to claim 2, characterized in that: The method for obtaining the region division items also includes: Based on the parameter type item, the coverage rate of the parameter adjustment device is obtained, and the rate information item is obtained. Based on the rate information item, the target area item is divided into coverage areas to obtain at least one area division item. The area division items correspond to the parameter type items respectively. Based on the correspondence between area division items and parameter type items, the target cleanroom is classified and divided, thereby obtaining the area division items.

4. The cloud-edge collaboration based cleanroom environment thermal parameter intelligent management system according to claim 1, characterized in that: The methods for obtaining the real-time dataset of the region include: Based on the area division, the temperature, humidity, air pressure and particulate concentration parameters of each area division in the target clean room are obtained by the parameter acquisition device to obtain the real-time data set of the area.

5. The cloud-edge collaboration based cleanroom environment thermal parameter intelligent management system according to claim 4, characterized in that: The method for obtaining the real-time dataset of the region also includes: Based on the partitioning results of the region partitioning items, the parameter acquisition device is partitioned to obtain at least two parameter device partitioning items; Based on the parameter device partitioning items, the temperature parameters, humidity parameters, air pressure parameters, and particle concentration parameters of each region partitioning item are obtained to obtain the partitioned dataset. The average parameter information of the partitioned dataset is then obtained to obtain the real-time regional dataset. 6.The cloud-edge collaborative cleanroom environment thermal parameter intelligent management system according to claim 1, characterized in that: The method for obtaining the regional preset items includes: Based on the target parameter item, the parameter difference between the real-time regional dataset and the target parameter item is obtained to obtain the parameter supplement item; Based on the parameter supplement, a supplement threshold is set, which is a fixed time value. Based on the supplement threshold, the parameters are adjusted through a parameter adjustment device to obtain the regional pre-adjustment item. 7.The cloud-edge collaborative cleanroom environment thermal parameter intelligent management system according to claim 1, characterized in that: The method for obtaining the updated data item includes: Set an acquisition interval, continuously acquire target parameter change information of region division items based on the acquisition interval, and obtain parameter trend items by acquiring target parameter change information of region division items in real time. Based on the parameter trend item, with the target parameter item as the trend extreme value, it is determined whether the parameter trend item has reached the trend extreme value. When the parameter trend item reaches the trend extreme value, the region division item corresponding to the trend extreme value and the corresponding parameter adjustment device are used as the termination adjustment, and then the updated data item is obtained.

8. The intelligent management system for cleanroom environmental thermal parameters based on cloud-edge collaboration according to claim 1, characterized in that: The method for obtaining the coordinated scheduling item includes: Based on the region division item, obtain the working energy consumption information of the parameter adjustment equipment corresponding to the region division item to obtain the energy consumption data item; Using the location of the parameter adjustment device as the judgment point, the nearby parameter adjustment devices of the judgment point are obtained as auxiliary scheduling points to obtain auxiliary scheduling items; Based on the updated data items and energy consumption data items, the working energy consumption information of the auxiliary scheduling items is comprehensively calculated and compared with the data. The auxiliary scheduling items are used as the scheduling items, and the system scheduling is carried out in conjunction with the updated data items to obtain the collaborative scheduling items.

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