Optimization control method for water-cooling refrigeration station system of public building

Abstract

The invention discloses a water-cooling refrigeration station system optimization control method for a public building, and relates to the technical field of exhibition hall refrigeration control, and the method comprises the steps: carrying out the three-dimensional dynamic grid modeling of the whole exhibition hall space, monitoring the basic sensible heat and solar radiation conditions in the exhibition hall in real time, obtaining the heat load weight, and dividing the exhibition hall into different regions; according to the heat source distribution value of each exhibition hall partition, a physical field coupling thermal load prediction model is set, and thermal load prediction values of the exhibition hall partitions are predicted in combination with airflow, heat conduction and solar radiation heat absorption of the glass curtain walls; according to the thermal load threshold value, whether the next step is carried out or not is judged; and a rotational flow air port with an angle adjusting mechanism is installed beside the main machine room, and the air supply angle and the air speed are adjusted according to the thermal load predicted value of the exhibition hall partition. The problems that when a large-area French window or a glass curtain wall is directly irradiated by sunlight in summer, instantaneous large load increase is caused, and air supply dead corners and uneven layering are caused by a high-density showcase and a temporary partition are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of exhibition hall refrigeration control, and particularly to an optimized control method for a water-cooled refrigeration station system for public buildings. Background Art

[0002] Public buildings - especially large convention centers and exhibition halls - have very high requirements for the energy efficiency and environmental comfort of the refrigeration system. Water-cooled systems often have large capacities and drastic load changes. If the control strategy is not refined enough, it will lead to high energy consumption, uneven local temperature distribution, and frequent start and stop of equipment.

[0003] Most existing projects still adopt empirical static reset strategies, simply adjusting the chilled water supply temperature up and down according to the outdoor air or return water temperature; at the same time, the system generally relies on classical PID or rule control, and has a slow response to drastic fluctuations in heating and cooling loads, making it difficult to achieve optimal operation of the whole plant, and often presenting operation inefficiencies such as "thermal stratification" and "large water volume and small temperature difference".

[0004] With the drastic changes in the exhibition scale and the number of visitors, the indoor cooling load may fluctuate several times within a short period. The response of the traditional control system is difficult to meet the requirements; moreover, large-area floor-to-ceiling windows or glass curtain walls will cause an instantaneous large load increase under direct sunlight in summer. Without quick linkage between sunshading and refrigeration, it is easy to cause local high-temperature areas. Coupled with complex air flow organization, high-density display cabinets and temporary partitions often change the indoor air flow channels, resulting in dead air zones and uneven stratification of the air supply.

[0005] Therefore, the present invention provides an optimized control method for a water-cooled refrigeration station system for public buildings. Summary of the Invention

[0006] The purpose of the present invention is to provide an optimized control method for a water-cooled refrigeration station system for public buildings to solve the existing problems mentioned in the above background art.

[0007] To achieve the above purpose, the present invention provides the following technical solution: An optimized control method for a water-cooled refrigeration station system for public buildings, comprising the following steps: S1. Perform three-dimensional dynamic grid modeling on the entire exhibition hall space, monitor the basic sensible heat and solar radiation in the exhibition hall in real time, obtain the heat load weight, and divide the exhibition hall into zones; S2. Set a physical field coupled heat load prediction model according to the heat source distribution value of each exhibition hall zone, and combine air flow, heat conduction, and solar radiation heat absorption of the glass curtain wall to predict the heat load prediction value of the exhibition hall zone; S3. When the heat load prediction value is greater than the set heat load threshold, run the dynamic sunshading interlock strategy and then return to S2; when the heat load prediction value is less than or equal to the set heat load threshold, directly run S4; S4. Install a swirl air outlet with an angle adjustment mechanism beside the main computer room, and adjust the air supply angle and air speed according to the predicted value of the heat load of the exhibition hall partition.

[0008] A further improvement of the present invention lies in that the specific steps of S1 include: S11. Discretize the exhibition hall space into voxels according to N×M×H to obtain an exhibition hall voxel set ; Each voxel has a plane center coordinate ; S12. Collect the basic sensible heat Qther, transient temperature rise , solar radiation load Isol and space occlusion rate Cpar of each voxel unit; S13. Normalize and sum the data in step S12 to obtain the heat load weight of each voxel unit ; S14. Automatically segment into exhibition hall partitions according to the heat load weight through load homogeneous density peak clustering.

[0009] A further improvement of the present invention lies in that the basic sensible heat is obtained by deploying a pyroelectric infrared or UWB positioning personnel counter, combining with the input of equipment power, and calculating the sensible heat power in each voxel unit in real time to obtain the basic sensible heat Qther; Install a spectral sensor above the voxel near the window to obtain the total radiation of 400–1100nm in real time as the real-time solar irradiance G, then the solar radiation load , where represents the lighting roof area corresponding to the voxel unit, represents the glass heat absorption coefficient.

[0010] A further improvement of the present invention lies in that the specific implementation steps of the load homogeneous density peak clustering include: S141. Introduce a heat load difference weight and Euclidean distance to calculate the mixed distance between each voxel , where λ represents a control coefficient used to control the relative weight of space and load; S142. Calculate the local density of voxel i , where represents the density truncation distance, taking the 2% quantile of all ; S143. Traverse the local density of all voxels around voxel i in ascending order of distance. When a new voxel with a local density greater than that of voxel i is traversed, stop the traversal, extract this voxel as the candidate voxel cluster center, and extract its mixed distance with voxel i ; S144. Calculate the standard value of the local density of all voxels and the standard value of the mixing distance , extract the candidate voxel cluster centers that meet and as the voxel of the voxel cluster center; S145. For non - central voxels, add them to the voxel cluster of the voxel that is the nearest and has a higher density to obtain a preliminary exhibition hall partition.

[0011] A further improvement of the present invention lies in that the specific implementation steps of the load homogeneous density peak clustering further include calculating the area of each voxel cluster , if is less than the set minimum cluster area, then extract and merge the clusters that are spatially adjacent to and have the smallest difference in load mean values until all cluster areas meet the requirement of being greater than the set minimum cluster area to obtain the exhibition hall partition.

[0012] A further improvement of the present invention lies in that the specific steps of S2 include: S21. Establish a multi - physical - field coupling model, and introduce the coupling of air flow, heat conduction, and solar radiation heat absorption of the glass curtain wall to calculate the heat source distribution value of each exhibition hall partition; S22. Create a physical - field coupling heat load prediction model. Through edge computing nodes, real - time summarize the temperature, wind speed, direct and diffuse solar radiation intensities measured by spectral sensors provided by the sensor network, as well as the geometric and point cloud data after BIM zoning, and perform deep fusion and processing through a spatio - temporal graph convolutional network. The model input tensor includes five dimensions: basic sensible heat, solar radiation load, transient temperature rise, air flow velocity, and spatial occlusion rate, and outputs the heat load prediction values of each partition within the next 15 minutes .

[0013] A further improvement of the present invention lies in that the dynamic sunshade interlock strategy includes using continuously adjustable electrochromic glass, calculating the transmittance reduction S according to the degree of regional refrigeration overload , represents the heat load threshold, where , represents the scale from the heat load threshold to the maximum heat load, and calculate the target optical transmittance of any area where the heat load prediction value is greater than the set heat load threshold , represents the optical transmittance at full transparency, represents the optical transmittance at the darkest state, and send the target optical transmittance to the electrochromic glass controller.

[0014] A further improvement of the present invention lies in that after the electrochromic glass transmittance changes, the solar radiation load in the solar radiation heat absorption term of the glass curtain wall in step S21 is updated to , write the new back to S2 and re-predict ; if the after re-prediction, then the sunshade interlock is released and it jumps to S4.

[0015] A further improvement of the present invention lies in that S4 includes the following specific steps: S41. Define the control objectives, including the water temperature wte, the angle of each air outlet and the wind speed wv; S42. Randomly generate a set of PID control parameters and the corresponding control objective output combinations; S43. Calculate the exhibition hall temperature uniformity index Ute and the regional temperature error index E; S44. Take the PID control parameters and the temperature uniformity index Ute and the regional temperature error index E as constraints, and define the hard constraint function Mte; S45. Extract the control objective output combination sequence corresponding to the PID control parameters that satisfy the hard constraint function Mte ; S46. Set the soft constraint penalty function Jte, and extract the sequence .

[0016] A further improvement of the present invention lies in that the exhibition hall temperature uniformity index Ute and the regional temperature error index E are obtained by extracting the temperature of the i-th exhibition hall partition after sunshading and the set target temperature , the maximum temperature difference between intervals ; first calculate the normalized standard deviation of all exhibition hall partitions , and obtain the exhibition hall temperature uniformity index ; the regional temperature error index E is obtained by measuring the deviation of the temperature from the target temperature , then .

[0017] A further improvement of the present invention lies in that the constraint formula of the hard constraint function Mte is , where s.t. means to satisfy simultaneously; the constraint formula of the soft constraint penalty function Jte is: ; where, represents the constraint weight of the temperature error term, represents the constraint weight of the temperature uniformity term.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention firstly uses the load homogeneity density peak clustering algorithm, combined with "mixed distance" and "minimum area constraint", to automatically divide the exhibition hall into load homogeneous zones, thus solving the problem that the air conditioning zones of traditional exhibition halls are rough and static, and cannot cope with the uneven heat load caused by exhibit replacement, changes in crowd density and changes in light. Secondly, a multi-physics field heat load prediction model that couples airflow, heat conduction, and solar radiation absorption is constructed, and combined with a spatiotemporal graph convolutional network to achieve heat load prediction for the next 15 minutes, with high prediction accuracy and timely response. Electrochromic glass is used to dynamically change the transmittance according to the heat load, and graded shading is performed by region to avoid cold load overload and improve energy efficiency. After the shading adjustment, the heat load prediction model is fed back in real time to form a closed-loop control link of prediction-linkage-feedback-correction. This solves the problem of the shading strategy being out of touch with the actual load, which is prone to excessive or insufficient shading. Finally, based on the iterative search and optimization of PID parameters, dual constraints of temperature uniformity and regional error index were defined to balance overall comfort and local accuracy; the problem that fixed air supply angle and wind speed could not adapt to the dynamic heat load differences in partitions, resulting in uneven local heating and cooling was solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of a water-cooling refrigeration station system optimization control method for public buildings of the present invention; Figure 2 This is a flow chart of a physical field coupled heat load prediction model of the present invention; Figure 3 The present invention is a flow chart of a method for adjusting air supply angle and wind speed in a water-cooled refrigeration station system optimization control method for public buildings. DETAILED DESCRIPTION

[0020] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0021] The term "and / or" is merely a description of the association relationship between associated objects, indicating that three possible relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist at the same time, and B exists alone.

[0022] Example 1 Figure 1 The flowchart of the water-cooling refrigeration station system optimization control method for public buildings disclosed in this embodiment is shown, and the steps are as follows: S1. Perform three-dimensional dynamic grid modeling on the entire exhibition hall space, monitor the basic sensible heat and solar radiation in the exhibition hall in real time, obtain the heat load weight, and divide the exhibition hall into zones. The specific steps are as follows: S11. Install Velodyne VLP-16 radars at the main roads and high altitudes in the exhibition hall, with a horizontal accuracy of ±3 cm and a scanning frequency of 20 Hz. Register the real-time point cloud with the BIM (Revit / IFC) model through the ICP algorithm, and remove static obstacles such as the ground and stairs, only retaining display cabinets and temporary partitions. Discretize the exhibition hall space into voxels according to N×M×H to obtain the exhibition hall voxel set ; Each voxel has a planar center coordinate ; S12. Collect the basic sensible heat Qther, transient temperature rise , solar radiation load Isol, and space occlusion rate Cpar of each voxel unit; The basic sensible heat is obtained by deploying pyroelectric infrared or UWB positioning personnel counters, combined with equipment power entry and reading the socket circuit of the exhibition area through Modbus, and calculating the sensible heat power in each voxel unit in real time to obtain the basic sensible heat Qther; and place a temperature sensor at the center of each voxel, sampling once every 5 s, and calculating the instantaneous temperature rise rate, that is, the transient temperature rise ; Install a spectral sensor (Hamamatsu C12880MA) above the voxel near the window to obtain the total radiation of 400–1100 nm in real time as the real-time solar irradiance G; then the solar radiation load , where represents the daylighting roof area corresponding to the voxel unit, represents the glass heat absorption coefficient; the laser point cloud data calculates the proportion of the point cloud in the voxel cone through PCL to obtain the space occlusion rate Cpar; S13. Normalize the above four types of data and then sum them up with weights to obtain the heat load weight of each voxel unit ; S14. Then call the DPC algorithm based on k-d tree accelerated nearest neighbor search to form a load homogeneous density peak clustering, automatically divide it into exhibition hall zones according to the heat load weight, ensure the load homogeneity of each region and the area is not less than 5 m²; finally, write the zone number into the BMS zone table to realize the association with the subsequent controller; The specific implementation steps of the load homogeneous density peak clustering are as follows: S141. To balance spatial adjacency and load similarity, introduce a "hybrid distance", and calculate the hybrid distance between each voxel through the heat load difference weight and the Euclidean distance , where λ represents the control coefficient, which is used to control the relative weight of space and load; , so that the two are dimensioned; S142. Calculate the local density of voxel i , where represents the density truncation distance, and take the 2% quantile of all ; This Gaussian kernel density can smoothly take into account the influence of several surrounding adjacent points; S143. Traverse the local densities of all voxels around voxel i in ascending order of distance. When the local density of a newly traversed voxel is greater than the local density of voxel i, stop traversing, extract this voxel as the candidate voxel cluster center, and extract its mixing distance from voxel i ; S144. Calculate the standard value of the local density of all voxels and the standard value of the mixing distance , extract the candidate voxel cluster center that satisfies and as the voxel cluster center voxel; S145. For non-center voxels, add them to the voxel cluster of the voxel that is the closest and has a higher density to obtain a preliminary exhibition hall partition; S146. Calculate the area of each voxel cluster . If is less than the set minimum cluster area, then extract the cluster that is spatially adjacent to and has the smallest load mean difference for merging until all cluster areas satisfy being greater than the set minimum cluster area to obtain the exhibition hall partition.

[0023] S2. According to the heat source distribution value of each exhibition hall partition, set a physical field coupled heat load prediction model, and combine air flow, heat conduction, and solar radiation heat absorption of the glass curtain wall to predict the heat load prediction value of the exhibition hall partition; S3. When the heat load prediction value is greater than the set heat load threshold, run the dynamic sunshade interlock strategy and then return to S2; when the heat load prediction value is less than or equal to the set heat load threshold, directly run S4; S4. Install a swirl air outlet with an angle adjustment mechanism beside the main computer room, and adjust the air supply angle and wind speed according to the heat load prediction value of the exhibition hall partition.

[0024] Embodiment 2 Based on the same inventive concept as Embodiment 1, this embodiment provides a specific implementation manner of a physical field coupled heat load prediction model and a dynamic sunshade interlock strategy for implementing the implementation steps of steps S2 and S3 in Embodiment 1, Figure 2 shows a flowchart of a physical field coupled heat load prediction model in this embodiment. The specific steps include: S21. Establish a multi-physical-field coupling model, and introduce the coupling of air flow, heat conduction, and solar radiation heat absorption of the glass curtain wall to calculate the heat source distribution value of each exhibition hall area; the specific formula is expressed as: , where T represents the temperature of the exhibition hall area. In the formula, describes the influence of heat conduction, represents the target temperature, h represents the convective heat transfer coefficient, and the empirical value is taken ; S22. Create a physical-field coupling heat load prediction model. Through the edge computing node, the temperature, wind speed, direct and scattered solar radiation intensities measured by the spectral sub-sensors provided by the sensor network, as well as the geometric and point cloud data after BIM zoning are summarized in real time, and deep fusion and processing are carried out through the spatio-temporal graph convolutional network. The model input tensor includes five dimensions: basic sensible heat, solar radiation load, transient temperature rise, air flow velocity, and spatial occlusion rate, and outputs the heat load prediction value of each area within the next 15 minutes .

[0025] The dynamic sunshade interlock strategy includes using continuously adjustable electrochromic glass, and its optical transmittance ranges from fully transparent to the darkest and can vary linearly. According to the degree of overloading of regional refrigeration calculate the reduction in transmittance S, represents the heat load threshold, where , represents the scale from the heat load threshold to the maximum heat load, and calculate the target optical transmittance of any area where the heat load prediction value is greater than the set heat load threshold , represents the optical transmittance at full transparency, represents the optical transmittance at the darkest, and send the target optical transmittance to the electrochromic glass controller. After the electrochromic glass transmittance changes, the solar radiation load in the solar radiation heat absorption term of the glass curtain wall in step S21 is updated to , and the new is written back to S2, and re-prediction is carried out ; if the re-predicted , then the sunshade interlock is released, and it jumps to S4

[0026] Embodiment 3 Figure 3 shows a flow chart of the air supply angle and wind speed adjustment method for an optimized control method of a water-cooled refrigeration station system for public buildings in this embodiment. Based on the inventive concepts of Embodiment 1 and Embodiment 2, this embodiment provides an air supply angle and wind speed adjustment method for realizing the implementation steps of step S4 in Embodiment 1. The specific steps include: S41. Define control objectives, including water temperature wte, the angle of each air outlet and wind speed wv; S42. Randomly generate a set of PID control parameters and the corresponding control objective output combinations; S43. Calculate the exhibition hall temperature uniformity index Ute and the regional temperature error index E; The exhibition hall temperature uniformity index Ute and the regional temperature error index E are obtained by extracting the temperature of the i-th exhibition hall partition after shading and the set target temperature , the maximum temperature difference between partitions Calculated; First, calculate the normalized standard deviation of all exhibition hall partitions , to obtain the exhibition hall temperature uniformity index , such that , where , represents the average temperature of all exhibition hall partitions, and it is required that , represents the minimum required temperature uniformity; The regional temperature error index E is obtained by measuring the deviation of the temperature from the target temperature , then .

[0027] S44. Take the PID control parameters and the temperature uniformity index Ute, the regional temperature error index E as constraints, and define the hard constraint function Mte; Ensure that the exhibition hall temperature uniformity is greater than the lowest uniformity threshold and the temperature is close to the target temperature; The constraint formula of the hard constraint function Mte is , where s.t. means satisfying simultaneously; S45. Extract the control objective output combination sequence corresponding to the PID control parameters that satisfy the hard constraint function Mte ; S46. Set the soft constraint penalty function Jte: ; Among them, represents the constraint weight of the temperature error term, represents the constraint weight of the temperature uniformity term.

[0028] Extract the sequence The control objective output combination sequence corresponding to the minimum value of the soft constraint penalty function Jte in is used as the final output .

[0029] By simultaneously monitoring the "overall temperature difference distribution" (through ), avoid simply pursuing the average value and covering up local hot spots / cold spots.

[0030] The set values such as the threshold and weight can be set according to the default settings of the present invention or can be set by the operator himself / herself.

[0031] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0032] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0033] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0034] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0035] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative rather than restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit of the present invention and the scope protected by the claims. All of these are within the protection scope of the present invention.

Claims

1. A water-cooling refrigeration station system optimization control method for public buildings, characterized in that: The following steps are involved: S1. Conduct three-dimensional dynamic grid modeling of the entire exhibition hall space, monitor the basic sensible heat and solar radiation in the exhibition hall in real time, obtain the heat load weight, and divide the exhibition hall into zones; S2. According to the heat source distribution value of each exhibition hall partition, a physical field coupled heat load prediction model is set up to predict the heat load prediction value of the exhibition hall partition by combining airflow, heat conduction and solar radiation absorption of glass curtain wall; S3: When the predicted heat load value is greater than the set heat load threshold, the dynamic sunshade interlocking strategy is run and then returns to S2; when the predicted heat load value is less than or equal to the set heat load threshold, S4 is directly run; S4. Install a swirl air outlet with an angle adjustment mechanism next to the main machine room, and adjust the air supply angle and wind speed according to the predicted heat load value of the exhibition hall partition.

2. The water-cooling refrigeration station system optimization control method for public buildings according to claim 1, characterized in that: The specific steps of S1 include: S11. Discretize the exhibition hall space according to N×M×H voxels to obtain the exhibition hall voxel set ; Each voxel Has plane center coordinates ; S12, collect the basic sensible heat Qther and transient temperature rise of each voxel unit , solar radiation load Isol and space shading rate Cpar; S13, normalize the data in step S12 and perform weighted summation to obtain the heat load weight of each voxel unit ; S14. By clustering the load homogeneity density peaks, the exhibition hall is automatically divided into zones according to the heat load weight.

3. The water-cooling refrigeration station system optimization control method for public buildings according to claim 2, characterized in that: The basic sensible heat is calculated by deploying a pyroelectric infrared or UWB positioning personnel counter, combining the equipment power input, and calculating the sensible heat power in each voxel unit in real time to obtain the basic sensible heat Qther; A spectral sensor is installed above the window voxel to obtain the total radiation of 400-1100nm in real time as the real-time solar irradiance G, then the solar radiation load ,in, represents the lighting top area corresponding to the voxel unit, Represents the heat absorption coefficient of glass.

4. The water-cooling refrigeration station system optimization control method for public buildings according to claim 2, characterized in that: The specific implementation steps of load homogeneity density peak clustering include: S141, introduce heat load difference weight and Euclidean distance to calculate the mixed distance between each voxel , where λ represents the control coefficient, which is used to control the relative weight of space and load; S142, calculate the local density of voxel i ,in, represents the density cutoff distance, taking all 2% quantile; S143, traverse the local densities of all voxels around voxel i in order from small to large distances. When the local density of a new voxel is greater than the local density of voxel i, stop traversing, extract the voxel as the candidate voxel cluster center, and extract its mixed distance with voxel i ; S144. Calculate the local density standard value of all voxels and hybrid distance standard value , extract the content and The candidate voxel cluster center is taken as the voxel cluster center voxel; S145. For non-central voxels, add the voxel clusters of the nearest voxels with higher density to obtain preliminary pavilion partitions.

5. The water-cooling refrigeration station system optimization control method for public buildings according to claim 4, characterized in that: The load homogeneous density peak clustering step further includes calculating the area of ​​each voxel cluster. ,like If the cluster area is smaller than the set minimum, then extract The clusters that are spatially adjacent and have the smallest load mean difference are merged until the area of ​​all clusters is larger than the set minimum cluster area, and the exhibition hall partition is obtained.

6. The water-cooling refrigeration station system optimization control method for public buildings according to claim 2, characterized in that: The specific steps of S2 include: S21. Establish a multi-physics field coupling model, introduce the coupling of airflow, heat conduction and solar radiation absorption of glass curtain wall to calculate the heat source distribution value of each exhibition hall partition; S22. Create a physical field coupled heat load prediction model, and use edge computing nodes to aggregate the temperature, wind speed, direct and scattered solar radiation intensity measured by spectral sensors provided by the sensor network in real time, as well as the geometry and point cloud data after BIM partitioning, and perform deep fusion and processing through the spatiotemporal graph convolution network. The model input tensor includes five dimensions: basic sensible heat, solar radiation load, transient temperature rise, airflow velocity, and space shading rate, and outputs the heat load forecast value of each partition in the next 15 minutes. .

7. The water-cooling refrigeration station system optimization control method for public buildings according to claim 1, characterized in that: The dynamic shading interlocking strategy includes the use of continuously adjustable electrochromic glass, which changes color according to the degree of regional cooling overload. Calculate the transmittance drop S, represents the heat load threshold, where , Represents a scale from the heat load threshold to the maximum heat load, and calculates the target optical transmittance for any area where the predicted heat load value is greater than the set heat load threshold , Indicates the optical transmittance when fully transparent, Indicates the optical transmittance at the darkest time. Sent to the electrochromic glass controller.

8. The water-cooling refrigeration station system optimization control method for public buildings according to claim 7, characterized in that: The dynamic sunshade interlocking strategy also includes that after the transmittance of the electrochromic glass changes, the solar radiation load in the solar radiation heat absorption item of the glass curtain wall in step S21 is updated to , the new Write back to S2 and re-predict ; If the re-forecasted , the sunshade interlock is released and jumps to S4.

9. The water-cooling refrigeration station system optimization control method for public buildings according to claim 1, characterized in that: The S4 comprises the following specific steps: S41. Define control targets, including water temperature wte, angle of each air outlet and wind speed wv; S42, randomly generate a set of PID control parameters and the corresponding control target output combination; S43, calculating the exhibition hall temperature uniformity index Ute and the regional temperature error index E; S44, PID control parameters With temperature uniformity index Ute and regional temperature error index E as constraints, hard constraint function Mte is defined; S45, extracting the control target output combination sequence corresponding to the PID control parameters that satisfy the hard constraint function Mte ; S46. Set soft constraint penalty function Jte and extract sequence The control target output combination sequence corresponding to the minimum value of the soft constraint penalty function Jte is used as the final output .

10. The water-cooling refrigeration station system optimization control method for public buildings according to claim 9, characterized in that: The pavilion temperature uniformity index Ute and the regional temperature error index E are obtained by extracting the temperature of the ith pavilion partition after shading. and the set target temperature , Maximum temperature difference between zones Calculated; first calculate the normalized standard deviation of all pavilion partitions , get the exhibition hall temperature uniformity index ; The regional temperature error index E is calculated by measuring the temperature and the target temperature The deviation of .

11. The water-cooling refrigeration station system optimization control method for public buildings according to claim 9, characterized in that: The constraint formula of the hard constraint function Mte is: , where st means simultaneous satisfaction; the constraint formula of the soft constraint penalty function Jte is: ； in, represents the temperature error term constraint weight, Represents the temperature uniformity constraint weight.

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