Distributed collaborative fresh air handling unit energy-saving control system

Abstract

The application relates to the technical field of air conditioning treatment, in particular to an energy-saving regulation and control system for fresh air handling units based on distributed cooperation, which comprises the following modules: a fresh air handling unit data acquisition module, which acquires the running power of a fresh air handling unit and the environmental parameters of the region where the fresh air handling unit is located; a fresh air handling unit ventilation quality acquisition module, which compares the difference between the environmental parameters of each region and the corresponding threshold value, and obtains the proportional coefficient adjustment effect after PID parameter adjustment each time; a fresh air handling unit power analysis module, which takes each region where a fresh air handling unit is located as a target region in turn, and analyzes the influence degree of the running power change of the fresh air handling unit in other regions on the air condition of the target region; and a fresh air handling unit PID coefficient adjustment module, which screens the main influence regions and determines the proportional coefficient of the target region after current PID parameter adjustment. The application aims to adaptively correct the proportional coefficient of the fresh air handling unit in each region and reduce the energy consumption of the fresh air handling unit.

Description

Technical Field

[0001] This application relates to the field of air conditioning technology, specifically to an energy-saving control system for fresh air handling units based on distributed collaborative technology. Background Technology

[0002] In today's society, with people's increasing focus on both indoor air quality and energy conservation, the importance of fresh air systems in various buildings is becoming increasingly prominent. Traditional centralized fresh air systems, due to problems such as energy waste and difficulty in adapting to the differentiated ventilation needs of different areas, are gradually failing to meet the complex requirements of modern buildings. Distributed fresh air unit systems have emerged to address this issue. These systems consist of multiple small fresh air units distributed throughout the building, with each unit providing fresh air to its respective area relatively independently. This layout allows for greater system flexibility and better adaptability to the personalized needs of different areas.

[0003] Traditional fresh air systems often suffer from energy waste during operation, especially in large buildings or areas. A single, centralized control system struggles to meet the diverse ventilation needs of different areas, leading to low system efficiency and high energy consumption. Therefore, a method is needed to coordinate multiple fresh air units and adjust the power of each unit to achieve energy savings.

[0004] In the regulation of multiple fresh air handling units, PID control algorithms are generally used to adjust the ventilation parameters of each zone. However, existing PID algorithms often use fixed parameter settings and do not consider the impact of changes in the power of fresh air handling units between zones and the influence of natural ventilation conditions on the regulation effect. In reality, changes in the fan power of adjacent zones may interfere with the regulation results of the current zone, leading to a decrease in PID control accuracy, thereby affecting air quality control and increasing energy consumption. Summary of the Invention

[0005] In view of the above, it is necessary to provide a distributed collaborative energy-saving control system for wind turbine units to more accurately remove interference noise signals from vibration signals during wind turbine operation and improve the accuracy of vibration fault diagnosis for wind turbine units.

[0006] One embodiment of this application provides an energy-saving control system for a fresh air handling unit based on distributed collaborative operation, the system comprising:

[0007] The fresh air handling unit data acquisition module is used to obtain the operating power of each fresh air handling unit and the environmental parameters of the area where it is located.

[0008] The fresh air handling unit ventilation quality acquisition module is used to obtain the proportional coefficient adjustment effect of each area after each PID parameter adjustment based on the differences between the environmental parameters and their corresponding thresholds at all times during the time period from each PID parameter adjustment to the next PID parameter adjustment of the fresh air handling unit in each area.

[0009] The fresh air handling unit power analysis module is used to sequentially select the area where each fresh air handling unit is located as the target area, select any area other than the target area, and perform a first clustering of the operating power of all areas at all historical moments based on the historical operating power changes of all other areas except the target area; perform a second clustering of the operating power of any area in each cluster obtained from the first clustering; and based on the dispersion of the environmental parameters of the target area at the time corresponding to each operating power in the cluster obtained from the second clustering, combined with the similarity of the overall distribution of operating power and environmental parameters, obtain the degree of influence of the fresh air handling unit in any area on the air conditions of the target area.

[0010] The fresh air handling unit's PID coefficient adjustment module is used to perform threshold segmentation of the impact of all air conditions on the target area, and to screen the main affected areas. For each main affected area, a third clustering is performed on the historical power change values ​​of all PID parameter adjustments that meet the proportional coefficient condition, and an adjustment reference cluster is selected. Based on the proportional parameter change of each element in the adjustment reference cluster corresponding to the target area, the proportional coefficient adjustment factor for the target area is obtained. Based on the proportional coefficient adjustment factor for the target area, the proportional coefficient before the current PID parameter adjustment, and its adjustment effect, the proportional coefficient after the current PID parameter adjustment for the target area is determined.

[0011] The specific steps for obtaining the proportional coefficient adjustment effect of each region after each PID parameter adjustment include:

[0012] The environmental parameters include temperature data and carbon dioxide concentration data;

[0013] The difference between the temperature data and the temperature threshold at each time in each region is recorded as the first difference; the difference between the carbon dioxide concentration data and the carbon dioxide concentration threshold at each time in each region is recorded as the second difference; and the normalized value obtained by adding the first difference and the second difference at each time in each region is used as the air quality index at each time in each region.

[0014] The negative correlation mapping result of the air quality index dispersion at all times within the time period corresponding to each PID parameter adjustment for each region is used as the proportional coefficient adjustment effect for each region after each PID parameter adjustment.

[0015] Specifically, the distance metric for the first clustering is the absolute value of the difference between the operating power of each region except for any of the regions at all times of all pairwise combinations in history.

[0016] The specific process for obtaining the degree of influence of the fresh air handling unit in any of the regions on the air quality of the target region is as follows:

[0017] The temperature data and carbon dioxide concentration of the target area at each time corresponding to each element in each cluster obtained by the second clustering are obtained, and the dispersion of various environmental parameters of the target area at each time corresponding to all elements in each cluster is analyzed.

[0018] Calculate the mean of each cluster element obtained from the second clustering and the mean of various environmental parameters of the target region;

[0019] Based on the similarity between the sequence of mean values ​​of elements in all clusters and the sequence of mean values ​​of environmental parameters in the target area, and combined with the dispersion of all environmental parameters corresponding to each cluster, the influence factor of the fresh air handling unit in any area of ​​each cluster obtained in the first cluster on the air condition of the target area is obtained.

[0020] The average value of the air condition influence factor of the fresh air handling unit in any region of the first cluster obtained by the first clustering is taken as the degree of influence of the fresh air handling unit in any region on the air condition of the target region.

[0021] Specifically, the influence factor of the fresh air handling unit in any region of each cluster obtained from the first clustering on the air quality of the target region is as follows:

[0022] The sequences composed of the average operating power of any region in all clusters obtained from the second clustering, the sequences composed of the average temperature data of the target region, and the sequences composed of the average carbon dioxide concentration of the target region are respectively denoted as the first sequence, the second sequence, and the third sequence;

[0023] The similarity between the first sequence and the second sequence and the third sequence is obtained, and denoted as the first similarity and the second similarity, respectively.

[0024] Calculate the product of the dispersion of temperature data and the dispersion of carbon dioxide data in each cluster obtained from the second clustering. Sum the products corresponding to all clusters obtained from the second clustering and perform a positive fusion with the first similarity and the second similarity. This result is used as the influence factor of the fresh air unit in any region of each cluster obtained from the first clustering on the air condition of the target region.

[0025] The specific steps for obtaining the main affected areas during the screening include:

[0026] The impact of all air conditions in the target area is segmented by a threshold to obtain an impact threshold. Areas with impact values ​​greater than the impact threshold are extracted and selected as the main impact areas.

[0027] Specifically, the proportional coefficient condition is the same as the proportional coefficient when the target region is not currently adjusted by PID parameters.

[0028] Specifically, the adjustment reference cluster is the cluster whose element mean is closest to the change in operating power of the target region when the PID parameters are not currently adjusted.

[0029] The specific steps for obtaining the scaling factor adjustment factor for the target region include:

[0030] The difference between the proportional parameter obtained from the target region of the next PID parameter adjustment for each element in the reference cluster will be used as the proportional parameter change value for the target region corresponding to each element.

[0031] Obtain the average change value of the proportional parameter corresponding to all elements in the adjusted reference cluster;

[0032] The proportion of the influence of each major influence area on the air quality of the target area is calculated as the weight of the average change value of the proportional parameter of each major influence area on the air quality of the target area. The weighted sum is then used to obtain the proportional coefficient adjustment factor of the target area.

[0033] The specific formula for determining the proportional coefficient after adjusting the current PID parameters in the target area is: Hc a =Hv a ×Hi a +(1-Hv a )×Ht a In the formula, Hc a Hv represents the proportional gain after adjusting the current PID parameters for the target region a; a This indicates the effect of proportional coefficient adjustment on target region a before the current PID parameter adjustment; Hi a Ht represents the proportional coefficient before the current PID parameter adjustment; a This represents the scaling factor adjustment factor for the target region a.

[0034] This application has at least the following beneficial effects:

[0035] This application analyzes the differences between environmental parameters and their corresponding thresholds at all times during the period between each PID parameter adjustment and the next PID parameter adjustment for the fresh air handling unit. This analysis reveals the proportional gain adjustment effect for each region after each PID parameter adjustment, aiding in the analysis and evaluation of the proportional gain adjustment effect for each region. These differences provide a clearer understanding of the actual effect of PID adjustment on environmental parameter control, thus providing data support for the next adjustment. The region where each fresh air handling unit is located is sequentially designated as the target region. Cluster analysis is used to analyze the impact of power changes in other regions on the control status of the target region. The first clustering identifies the power change patterns of different regions at historical moments; the second clustering further refines the power change characteristics of different regions under different conditions. Based on the clustering results, the impact of the fresh air handling unit on the air quality of the target region can be quantified.

[0036] By thresholding the impact of air conditions on the target area, the regions most significantly affecting air quality can be identified. This allows for concentrated resource optimization of these high-impact areas, improving air quality control effectiveness. Analyzing the changes in the proportional parameters of each element in the reference cluster relative to the target area yields a proportional coefficient adjustment factor for the target area. Introducing this factor facilitates dynamic adjustment of the proportional coefficient, enhancing the flexibility and adaptability of air quality control. Adaptively correcting the proportional coefficient of each area's fresh air handling unit allows PID control parameters to more accurately adapt to the actual needs of different areas, avoiding over- or under-adjustment that might occur with fixed parameters, thus reducing unnecessary energy consumption. Furthermore, evaluating the performance of the fresh air handling unit under the current PID parameters based on the proportional coefficient adjustment effect allows for timely adjustment of the proportional coefficient, considering factors such as the impact of other areas, to optimize the operating power of the fresh air handling unit and maintain stable air quality with minimal energy consumption. This not only improves energy efficiency but also achieves energy-saving goals. Attached Figure Description

[0037] Figure 1 A block diagram of a distributed collaborative fresh air handling unit energy-saving control system provided in this application;

[0038] Figure 2 A flowchart illustrating the specific PID parameter adjustment of the target area fresh air handling unit provided in this application. Detailed Implementation

[0039] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application's specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0041] It should also be noted that the terms "first" and "second" in this application and its accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the method. Without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged, and some steps can also be deleted.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0043] The following description, in conjunction with the accompanying drawings, details the specific scheme of the energy-saving control system for a new air handling unit based on distributed collaboration provided in this application.

[0044] Please see Figure 1 The diagram illustrates a block diagram of a distributed collaborative fresh air handling unit energy-saving control system according to an embodiment of this application. The system includes: a fresh air handling unit data acquisition module, a fresh air handling unit ventilation quality acquisition module, a fresh air handling unit power analysis module, and a fresh air handling unit PID coefficient adjustment module.

[0045] This application first proposes a distributed collaborative energy-saving control system for fresh air handling units, applied in the field of air conditioning technology. The system includes:

[0046] Fresh air unit data acquisition module: acquires the operating power of each fresh air unit and the environmental parameters of the area.

[0047] The system collects real-time data on the operating power of the fresh air handling units in the factory at each sampling point. The fresh air handling units in the area to be regulated are numbered; in this application, there are a total of m fresh air handling units that collaboratively regulate the ventilation system of the entire factory. The area where each unit is located is analyzed, and environmental parameters such as temperature and carbon dioxide concentration in the corresponding area are collected by sensors, facilitating timely adjustment of the fresh air handling unit operating parameters based on the environmental conditions of the monitored area.

[0048] In this embodiment, operating power, temperature data, and carbon dioxide concentration data are collected every minute. It should be noted that all environmental parameters are collected synchronously; the time interval for adjusting the PID parameters is set to 10 minutes.

[0049] The fresh air handling unit ventilation quality acquisition module obtains the proportional coefficient adjustment effect of each area after each PID parameter adjustment by analyzing the differences between environmental parameters and their corresponding thresholds at all times during the time period from each PID parameter adjustment to the next PID parameter adjustment.

[0050] For air quality, the closer the relevant environmental parameters are to the preset threshold and the higher their stability, the more ideal the proportional control effect of the fresh air handling units in the area. The area corresponding to the *a*th fresh air handling unit is referred to as area *a*. This application uses area *a* as an example for analysis. The difference between the temperature data and the temperature threshold at each moment in area *a* is recorded as the first difference; the difference between the carbon dioxide concentration data and the carbon dioxide concentration threshold at each moment in area *a* is recorded as the second difference. The normalized value obtained by adding the first difference and the second difference at each moment in area *a* is used as the air quality index at each moment in area *a*, which is used to measure the proportional control effect of the fresh air handling units at each moment in area *a*. In this embodiment, the temperature threshold T = 20℃ and the carbon dioxide concentration threshold C = 0.01 are set; the differences between variables are calculated using the absolute value of the difference, and the normalization method uses the maximum-minimum value normalization method.

[0051] The time period from each PID parameter adjustment to the next PID parameter adjustment is defined as the time period corresponding to each PID parameter adjustment. The changes in relevant parameters at various times within each time period for all areas where fresh air units are located are analyzed. Specifically, the negative correlation mapping result of the dispersion of the air quality index at all times within the time period corresponding to each PID parameter adjustment for region a is used as the proportional coefficient adjustment effect for region a during each PID parameter adjustment. In this embodiment, the dispersion between multiple variables is measured using variance. The negative correlation mapping of variables is specifically the reciprocal of the variables. To prevent the denominator from being zero in the negative correlation mapping result, a preset parameter is added to the denominator; in this embodiment, the value is 0.01.

[0052] It should be understood that the larger the value of the proportional coefficient adjustment effect, the more significant the effect of the operating power of the fresh air unit in region a adjusted under the corresponding PID parameter proportional coefficient on improving the air quality of region a.

[0053] The fresh air handling unit power analysis module sequentially uses the area where each fresh air handling unit is located as the target area. It then selects any area other than the target area and performs a first clustering of the operating power of all areas at all historical moments based on the historical operating power changes of all other areas. A second clustering is then performed on the operating power of any area within each cluster obtained from the first clustering. Based on the dispersion of environmental parameters of the target area corresponding to each operating power in the cluster obtained from the second clustering, and combined with the overall similarity between the operating power and environmental parameters, the module obtains the degree of influence of the fresh air handling unit in any area on the air quality of the target area.

[0054] Since the operation of fresh air handling units in different areas of a factory can affect the ventilation effect in other areas, this application analyzes historical data to assess the impact of fresh air handling units in each area on the ventilation quality of other areas during each PID parameter adjustment. Based on these assessment results, the impact of fresh air handling units in other areas on the air quality of the target area can be taken into account during the control process, and corresponding adjustments can be made according to the degree of impact, thereby achieving the goals of energy saving and optimized control.

[0055] This application analyzes region a as the target region, examining the impact of fresh air handling units in region b on the ventilation effect of target region a. To analyze the impact of power changes in the fresh air handling units in region b on the air quality of region a, it is necessary to ensure that the power of fresh air handling units in other regions, except region b, remains relatively close. Only when the power of fresh air handling units in other regions is consistent can the specific impact of power fluctuations in the fresh air handling units in region b on the air quality of region a be more accurately assessed. First, the operating power of all regions at all historical moments is clustered. The absolute value of the difference between the operating power of each region (excluding region b) at all pairwise historical moments is used as the metric distance for the first clustering. In this embodiment, the DBSCAN clustering algorithm is used for clustering, and the number of clusters obtained from the first clustering is denoted as L.

[0056] Furthermore, if the environmental parameters in region a change predictably as the operating power of the fresh air handling unit in region b increases or decreases, it indicates that the fresh air handling unit b has a greater impact on the air quality in region a. A second clustering is performed on the operating power of region b in each cluster, and the absolute value of the difference in operating power between regions b is used as the distance metric for the second clustering. In this embodiment, the DBSCAN clustering algorithm is used for clustering, and the number of clusters obtained in the second clustering is denoted as R. The environmental parameters of the target region a at each time corresponding to each element in each cluster obtained in the second clustering are obtained. The average operating power of region b, the average temperature data of the target region a, and the average carbon dioxide concentration of the target region a are calculated in each cluster obtained in the second clustering. The sequence composed of the average operating power of region b in all clusters is denoted as the first sequence. The sequence composed of the average temperature data of the target region a in all clusters is denoted as the second sequence. The second sequence is defined as follows: a sequence composed of the average carbon dioxide concentrations of target region a in all clusters is designated as the third sequence; the similarity between the first sequence and the second and third sequences is obtained, and these are designated as the first similarity and the second similarity, respectively; in this embodiment, the similarity between sequences is calculated using the Pearson correlation coefficient; the product of the dispersion of temperature data and the dispersion of carbon dioxide data in target region a in each cluster obtained from the second clustering is calculated, and the products corresponding to all clusters obtained from the second clustering are accumulated and positively fused with the first and second similarities, which is used as the influence factor of the fresh air handling unit of region b in each cluster obtained from the first clustering on the air quality of region a; the mean of the influence factor of the fresh air handling unit of region b in all clusters obtained from the first clustering on the air quality of region a is used as the degree of influence of region b on the air quality of target region a. In this embodiment, the dispersion of multiple variables is calculated using variance; the positive fusion of multiple variables is calculated using multiplication.

[0057] It should be understood that the stronger the correlation between the distribution of the operating power of the fresh air handling units in region b and the distribution of carbon dioxide concentration and temperature data in target region a, and the smaller the variance of carbon dioxide concentration and temperature values ​​in each cluster, the greater the influence of the operating power of the fresh air handling units in region b on the air conditions of target region a.

[0058] The fresh air handling unit PID coefficient adjustment module performs threshold segmentation on the impact of all air conditions on the target area to screen the main affected areas; it performs a third clustering on the operating power changes of all historical PID parameter adjustments that meet the proportional coefficient conditions for each main affected area to screen the adjustment reference cluster; based on the proportional parameter changes of each element in the adjustment reference cluster corresponding to the target area, it obtains the proportional coefficient adjustment factor for the target area; based on the proportional coefficient adjustment factor of the target area, the proportional coefficient before the current PID parameter is not adjusted, and the proportional coefficient adjustment effect, it determines the proportional coefficient of the target area after the current PID parameter adjustment.

[0059] Based on the above method, the proportional coefficient adjustment effect after each PID parameter adjustment in each region is obtained. Furthermore, based on the impact of changes in the operating power of fresh air handling units in all regions on target region a, the proportional coefficient of the fresh air handling units in target region a after PID parameter adjustment is obtained. Specifically, if the operating power of fresh air handling units in other regions changes significantly, and target region a was not adjusted before the PID parameter adjustment (i.e., the result of the previous PID parameter adjustment), the smaller the proportional coefficient adjustment effect in the current time period, the more necessary the current PID parameter adjustment is. Specifically: First, the impact of the operating power of fresh air handling units in all regions on the air quality of target region a is obtained. The Otsu threshold algorithm is used to process the impact on all air quality levels corresponding to target region a to obtain an impact threshold. Regions exceeding the impact threshold are extracted, and the selected regions are taken as the main affected regions.

[0060] The power change of the fresh air handling unit in the main affected area c is analyzed. The operating power change values ​​of the main affected area c for all historical PID parameter adjustments corresponding to the time period when the proportional coefficient is the same as that of the target area a when the PID parameter is not adjusted are extracted. That is, the difference between the power at the end time and the operating power at the beginning time of each historical PID parameter adjustment time period. The operating power change values ​​of all historical PID parameter adjustments are clustered for the third time, and Euclidean distance is used as the distance metric. The mean power change value in each cluster is calculated, and the cluster closest to the operating power change value of the target area a when the PID parameter is not adjusted is selected as the adjustment reference cluster.

[0061] To obtain the proportional coefficient change value of the target area for each power change value in the adjustment reference cluster within the corresponding time period and the next time period, specifically: the difference between the proportional parameter obtained in the target area a for each element in the adjustment reference cluster corresponding to the previous PID parameter adjustment and the next PID parameter adjustment is used as the proportional parameter change value for the target area a corresponding to each element; the mean of the proportional parameter change values ​​corresponding to all elements in the adjustment reference cluster is obtained; the proportion of the influence of each major influence area on the air quality of the target area a is calculated as the weight of the mean of the proportional parameter change values ​​of the target area a corresponding to each major influence area, and the weighted sum is used to obtain the proportional coefficient adjustment factor for the target area a; based on the proportional coefficient adjustment factor for the target area a, the proportional coefficient before the current PID parameter is not adjusted, and its adjustment effect, the proportional coefficient after the current PID parameter adjustment is determined, specifically by the formula: Hc a =Hv a ×Hi a +(1-Hv a )×Ht a In the formula, Hc a Hv represents the proportional gain after adjusting the current PID parameters for the target region a; a This indicates the effect of proportional coefficient adjustment on target region a before the current PID parameter adjustment; Hi a Ht represents the proportional coefficient before the current PID parameter adjustment; a This represents the scaling factor adjustment factor for the target region a.

[0062] Based on this, each region is analyzed sequentially as the target region to obtain the proportional coefficient of the fresh air unit after the current PID parameter adjustment in each region.

[0063] The specific flowchart for adjusting the PID parameters of the fresh air handling unit in the target area is as follows: Figure 2 As shown.

[0064] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0065] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A fresh air handling unit energy-saving regulation system based on distributed cooperation, characterized in that, The system comprises: The fresh air unit data acquisition module is used for acquiring the running power of each fresh air unit and the environmental parameters of the area where the fresh air unit is located; The fresh air unit ventilation quality acquisition module is used for obtaining the proportional coefficient adjustment effect of each area after each PID parameter adjustment according to the difference between the environmental parameters of all time points in the time period from each PID parameter adjustment to the next PID parameter adjustment of the fresh air unit of each area and the corresponding threshold value of the fresh air unit; The fresh air unit power analysis module is used for taking the area where each fresh air unit is located as a target area in turn, selecting any area except the target area, and performing first clustering on the running power of all areas at all historical time points based on the running power change of all areas except the any area at the historical time points; performing second clustering on the running power of the any area in each clustering cluster obtained through the first clustering; obtaining the air condition influence degree of the fresh air unit of the any area on the target area based on the discrete degree of the environmental parameters of the target area at the corresponding time point of each running power in the clustering cluster obtained through the second clustering and the overall distribution similarity between the running power and the environmental parameters; The fresh air unit PID coefficient adjustment module is used for performing threshold segmentation on all air condition influence degrees of the target area, screening main influence areas; performing third clustering on the running power change value of all historical PID parameter adjustments that meet the proportional coefficient condition of each main influence area, screening adjustment reference clustering clusters; obtaining the proportional coefficient adjustment factor of the target area according to the proportional parameter change of each element in the adjustment reference clustering cluster corresponding to the target area; and determining the proportional coefficient of the target area after the current PID parameter adjustment based on the proportional coefficient adjustment factor of the target area, the proportional coefficient before the current PID parameter adjustment and the proportional coefficient adjustment effect of the proportional coefficient.

2. The distributed coordination based fresh air handling unit energy saving regulation system of claim 1, wherein, The specific steps for obtaining the proportional coefficient adjustment effect of each area after each PID parameter adjustment include: The environmental parameters include temperature data and carbon dioxide concentration data; The difference between the temperature data of each area at each time point and the temperature threshold value is recorded as a first difference; the difference between the carbon dioxide concentration data of each area at each time point and the carbon dioxide concentration threshold value is recorded as a second difference; and the normalized value obtained by adding the first difference and the second difference of each area at each time point is taken as the air quality index of each area at each time point; The negative correlation mapping result of the discrete degree of the air quality index of each area at all time points in the time period corresponding to each PID parameter adjustment is taken as the proportional coefficient adjustment effect of each area after each PID parameter adjustment.

3. The distributed coordination based fresh air handling unit energy saving regulation system of claim 1, wherein, The measurement distance of the first clustering is specifically the absolute value of the difference between the running power of each area except the any area in all two-by-two combinations of historical time points.

4. The distributed coordination based fresh air handling unit energy saving regulation system of claim 2, wherein, The specific process for obtaining the air condition influence degree of the fresh air unit of the any area on the target area includes: obtaining temperature data and carbon dioxide concentration of the target region corresponding to each element in each cluster obtained by the second clustering, and analyzing discrete degrees of various environmental parameters of the target region corresponding to each element in each cluster; calculating element mean value of each cluster obtained by the second clustering and mean value of various environmental parameters of the target region; obtaining an air condition influencing factor of the fresh air handling unit of any region in each cluster obtained by the first clustering on the air condition of the target region based on a similarity degree between a sequence composed of element mean values in all clusters and a sequence composed of mean values of environmental parameters of the target region, and combining discrete degrees of all environmental parameters corresponding to each cluster; taking a mean value of the air condition influencing factor of the fresh air handling unit of any region in all clusters obtained by the first clustering as an air condition influencing degree of the fresh air handling unit of any region on the target region.

5. The distributed coordination based fresh air handling unit energy saving regulation system of claim 4, wherein, The air condition influencing factor of the fresh air handling unit of any region in each cluster obtained by the first clustering is obtained by: taking a sequence composed of mean values of running power of any region in all clusters obtained by the second clustering, a sequence composed of mean values of temperature data of the target region, and a sequence composed of mean values of carbon dioxide concentration of the target region as a first sequence, a second sequence and a third sequence respectively; obtaining similarity degrees of the first sequence with the second sequence and the third sequence respectively, and taking the similarity degrees as a first similarity degree and a second similarity degree respectively; calculating a product of discrete degrees of temperature data and discrete degrees of carbon dioxide data of the target region in each cluster obtained by the second clustering, adding the products corresponding to all clusters obtained by the second clustering, and taking a result of forward fusion of the first similarity degree and the second similarity degree as the air condition influencing factor of the fresh air handling unit of any region in each cluster obtained by the first clustering on the target region.

6. The distributed coordination based fresh air handling unit energy regulation system of claim 1, wherein, The specific obtaining steps of the main influencing region include: performing threshold segmentation on all air condition influencing degrees obtained for the target region to obtain an influencing degree threshold, extracting a region greater than the influencing degree threshold, and taking the extracted region as the main influencing region.

7. The distributed coordination based fresh air handling unit energy regulation system of claim 1, wherein, The proportional coefficient condition is specifically the same as the proportional coefficient when the target region is not currently subjected to PID parameter adjustment.

8. The distributed coordination based fresh air handling unit energy regulation system of claim 1, wherein, The adjustment reference cluster is specifically a cluster in which the element mean value is closest to a running power change value of the target region when the target region is not currently subjected to PID parameter adjustment.

9. The distributed coordination based fresh air handling unit energy regulation system of claim 1, wherein, The specific steps of obtaining the proportional coefficient adjustment factor of the target region include: taking a difference between a next PID parameter adjustment proportional parameter of the target region and a corresponding PID parameter adjustment of each element in the adjustment reference cluster as a proportional parameter change value of the target region corresponding to each element; obtaining a mean value of proportional parameter change values corresponding to all elements in the adjustment reference cluster; and obtaining a proportional coefficient adjustment factor of the target region based on the mean value of the proportional parameter change values corresponding to all elements in the adjustment reference cluster. The proportion of the air condition influence degree of each main influence area on the target area in the air condition influence degree of all main influence areas on the target area is calculated as the weight of the average value of the proportion parameter change value of each main influence area corresponding to the target area, and the proportion coefficient adjustment factor of the target area is obtained by weighted summation.

10. The distributed coordination based fresh air handling unit energy regulation system of claim 1, wherein, The formula for determining the proportional coefficient after adjusting the current PID parameters in the target area is: Hc a =Hv a ×Hi a +(1-Hv a )×Ht a In the formula, Hc a Hv represents the proportional gain after adjusting the current PID parameters for the target region a; a This indicates the effect of proportional coefficient adjustment on target region a before the current PID parameter adjustment; Hi a represents the proportional coefficient before the current PID parameter adjustment; Ht a represents the proportional coefficient adjustment factor of the target region a.

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