Cabinet spray temperature control method and system based on environmental parameter sensing

Abstract

The application discloses a cabinet spray temperature control method and system based on environmental parameter sensing, relates to the technical field of data acquisition and control, and comprises the following steps: acquiring an environmental parameter sensing flow and a plurality of cabinet data acquisition flows in real time, performing diffusion migration characteristic modeling, establishing a spray diffusion correlation model, performing heat propagation trend prediction and humidity accumulation coupling analysis on a plurality of spray cabinets, and acquiring multi-cabinet heat and humidity coupling distribution results; performing multi-spray parameter collaborative dynamic adjustment on the plurality of spray cabinets according to the multi-cabinet heat and humidity coupling distribution results, acquiring a spray collaboration initial scheme, performing compensation analysis, and acquiring a lag compensation scheme and a price linkage compensation scheme; and performing collaborative optimization on the spray collaboration initial scheme to acquire a spray collaboration optimization scheme. The application solves the technical problem that the cabinet spray temperature control in the prior art is difficult to adapt to multi-cabinet heat and humidity coupling changes, thereby leading to low temperature control precision, and achieves the technical effect of improving the multi-cabinet spray temperature control precision.

Description

Technical Field

[0001] This invention relates to the field of data acquisition and control technology, specifically to a cabinet spray temperature control method and system based on environmental parameter sensing. Background Technology

[0002] Communication equipment rooms typically house multiple high-density racks. These racks differ in equipment load, heat intensity, airflow organization, and spatial location, easily leading to localized hotspots and heat migration. Spray temperature control, as a method of rack cooling, can reduce the internal temperature by adjusting parameters such as spray pressure, flow rate, and duration. However, in scenarios with multiple racks operating simultaneously, the cooling energy from the spray diffuses and migrates between racks with the airflow, and humidity accumulates continuously in overlapping spray areas, causing temperature and humidity changes to interact. If spray control relies solely on the local temperature of a single rack or a fixed control strategy, it struggles to reflect the thermal and humidity coupling changes between multiple racks in a timely manner. This can easily result in insufficient localized cooling, humidity buildup, or unreasonable allocation of spray resources, affecting the overall temperature control accuracy of the communication equipment room. Summary of the Invention

[0003] This application provides a cabinet spray temperature control method and system based on environmental parameter sensing, which is used to address the technical problem in the prior art that cabinet spray temperature control is difficult to adapt to the thermal and humidity coupling changes of multiple cabinets, resulting in low temperature control accuracy.

[0004] In view of the above problems, this application provides a cabinet spray temperature control method and system based on environmental parameter sensing.

[0005] The first aspect of this application provides a cabinet spray temperature control method based on environmental parameter sensing, the method comprising: The system acquires real-time environmental parameter sensing streams from the communication equipment room and multiple cabinet data acquisition streams from various spray cabinets within the room. Based on these data streams, it models the diffusion and migration characteristics of the multiple spray cabinets, establishing a spray diffusion correlation model. Using this model, it predicts heat propagation trends and performs humidity accumulation coupling analysis on the spray cabinets, obtaining multi-cabinet thermal-humidity coupling distribution results. Based on these results, it dynamically adjusts multiple spray parameters collaboratively, obtaining an initial spray coordination scheme. It then performs thermal-humidity coupling hysteresis compensation analysis on the initial scheme, obtaining a hysteresis compensation scheme, and performs peak-valley electricity price linkage compensation analysis, obtaining an electricity price linkage compensation scheme. Finally, it performs collaborative optimization on the initial scheme, obtaining a spray coordination optimization scheme.

[0006] A second aspect of this application provides a cabinet spray temperature control system based on environmental parameter sensing, the system comprising: The system comprises the following modules: a data acquisition module for real-time acquisition of environmental parameter sensing streams from the communication equipment room and data acquisition streams from multiple spray cabinets within the room; a modeling module for modeling the diffusion and migration characteristics of the multiple spray cabinets based on the environmental parameter sensing streams and the data acquisition streams, establishing a spray diffusion correlation model; a prediction and analysis module for predicting heat propagation trends and performing humidity accumulation coupling analysis on the multiple spray cabinets based on the spray diffusion correlation model, obtaining the multi-cabinet thermal-humidity coupling distribution results; a dynamic adjustment module for dynamically adjusting multiple spray parameters in a coordinated manner based on the multi-cabinet thermal-humidity coupling distribution results, obtaining an initial spray coordination scheme; a compensation analysis module for performing thermal-humidity coupling hysteresis compensation analysis on the initial spray coordination scheme, obtaining a hysteresis compensation scheme, and performing peak-valley electricity price linkage compensation analysis on the initial spray coordination scheme, obtaining an electricity price linkage compensation scheme; and a collaborative optimization module for performing collaborative optimization on the initial spray coordination scheme based on the hysteresis compensation scheme and the electricity price linkage compensation scheme, obtaining a spray collaborative optimization scheme.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application acquires real-time environmental parameter sensing streams from a communication equipment room, as well as data acquisition streams from multiple spray cabinets within the communication equipment room. Based on the environmental parameter sensing streams and the multiple cabinet data acquisition streams, it models the diffusion and migration characteristics of the multiple spray cabinets, establishing a spray diffusion correlation model. Based on the spray diffusion correlation model, it performs heat propagation trend prediction and humidity accumulation coupling analysis on the multiple spray cabinets, obtaining multi-cabinet thermal-humidity coupling distribution results. Based on the multi-cabinet thermal-humidity coupling distribution results, it performs coordinated dynamic adjustment of multiple spray parameters on the multiple spray cabinets, obtaining an initial spray coordination scheme. It performs thermal-humidity coupling hysteresis compensation analysis on the initial spray coordination scheme, obtaining a hysteresis compensation scheme, and performs peak-valley electricity price linkage compensation analysis on the initial spray coordination scheme, obtaining an electricity price linkage compensation scheme. Based on the hysteresis compensation scheme and the electricity price linkage compensation scheme, it performs coordinated optimization of the initial spray coordination scheme, obtaining a spray coordination optimization scheme. This invention addresses the technical problem in existing technologies where cabinet spray temperature control is difficult to adapt to the thermal and humidity coupling changes of multiple cabinets, resulting in low temperature control accuracy. By establishing a spray diffusion correlation model based on environmental parameter sensing flow and cabinet data acquisition flow, and performing thermal and humidity coupling analysis and spray parameter collaborative optimization accordingly, the technical effect of improving the spray temperature control accuracy of multiple cabinets is achieved. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A schematic diagram of the cabinet spray temperature control method based on environmental parameter sensing provided in this application embodiment; Figure 2 A schematic diagram of the cabinet spray temperature control system based on environmental parameter sensing provided in this application embodiment.

[0010] Figure labeling: Data acquisition module 11, modeling module 12, predictive analysis module 13, dynamic adjustment module 14, compensation analysis module 15, collaborative optimization module 16. Detailed Implementation

[0011] This application provides a cabinet spray temperature control method and system based on environmental parameter sensing. It addresses the technical problem in the prior art where cabinet spray temperature control is difficult to adapt to the thermal and humidity coupling changes of multiple cabinets, resulting in low temperature control accuracy. The method establishes a spray diffusion correlation model based on environmental parameter sensing flow and cabinet data acquisition flow, and performs thermal and humidity coupling analysis and spray parameter co-optimization accordingly, thereby improving the technical effect of multi-cabinet spray temperature control accuracy.

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0013] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0014] Example 1, as Figure 1 As shown, this application provides a cabinet spray temperature control method based on environmental parameter sensing, the method comprising: Step S100: Real-time acquisition of environmental parameter sensing streams from the communication equipment room, and multiple cabinet data acquisition streams from multiple spray cabinets within the communication equipment room.

[0015] Furthermore, the method provided in the application embodiments also includes: Each cabinet's data acquisition stream includes equipment load data, cabinet temperature data, spray pressure data, spray flow rate data, cabinet humidity data, and airflow direction data for each spray cabinet.

[0016] In this embodiment, temperature, humidity, airflow, and environmental disturbance sampling points are set up within the communication equipment room to continuously monitor the overall environmental status of the room. Data on changes in ambient temperature, humidity, airflow speed, airflow direction, and environmental disturbances are collected according to a preset sampling period. The collected data is then aggregated chronologically to obtain an environmental parameter sensing stream for the communication equipment room. Simultaneously, corresponding data sampling points are set up at multiple spray cabinets within the communication equipment room, enabling each spray cabinet to independently collect its own operational status data. The data collected by each spray cabinet is synchronized with the environmental parameter sensing stream of the communication equipment room in time.

[0017] For each spray cabinet, equipment load data is collected by reading the operating power, service occupancy rate, or equipment operating status of the devices within the cabinet; temperature data is collected using temperature sensors located on the air inlet, air outlet, or heat-generating areas within the cabinet; spray pressure data is collected using pressure sensors located in the spray pipes or at the nozzle tips; spray flow data is collected using flow sensors located on the spray supply pipes; cabinet humidity data is collected using humidity sensors located inside the cabinet or in the spray-affected area; and airflow direction data is collected using airflow detection devices located in the cabinet ducts, air inlets / outlets, or adjacent cabinet passageways. The equipment load data, cabinet temperature data, spray pressure data, spray flow data, cabinet humidity data, and airflow direction data collected for each spray cabinet are correlated according to the collection time to form corresponding cabinet data acquisition streams, thus obtaining multiple cabinet data acquisition streams for multiple spray cabinets within the communication equipment room.

[0018] Step S200: Based on the environmental parameter sensing stream and the multiple cabinet data acquisition streams, model the diffusion migration characteristics of the multiple spray cabinets and establish a spray diffusion correlation model.

[0019] In this embodiment, based on the environmental parameter sensing stream and multiple cabinet data acquisition streams, the spray characteristics and direction of multiple spray cabinets are first identified and calibrated to determine the diffusion coverage of the spray in each spray cabinet and adjacent areas, thus obtaining the spray diffusion coverage relationship. Then, combined with the temperature gradient analysis inside the cabinet and the temperature difference coupling analysis between cabinets, the migration direction and influence of the spray cold energy inside and between cabinets are determined, thus obtaining the spray cold energy migration relationship. At the same time, the environmental airflow characteristics in the communication equipment room are identified based on the environmental parameter sensing stream, and the influence of environmental disturbances on spray diffusion and cold energy migration is analyzed to obtain the environmental disturbance influence relationship. Finally, a graph neural network is used to fuse and model the spray diffusion coverage relationship, the spray cold energy migration relationship, and the environmental disturbance influence relationship to generate a spray diffusion association model for characterizing the diffusion migration characteristics between multiple spray cabinets.

[0020] Furthermore, in the method provided in the application embodiment, modeling the diffusion migration characteristics of the multiple spray cabinets based on the environmental parameter sensing stream and the multiple cabinet data acquisition streams, and establishing a spray diffusion correlation model, further includes: Spray characteristics are identified and directional calibration is performed based on the data acquisition streams from multiple cabinets to obtain the spray diffusion coverage relationship; cabinet temperature gradient analysis and inter-cabinet temperature difference coupling analysis are performed based on the data acquisition streams from multiple cabinets to obtain the spray cold energy migration relationship; environmental airflow characteristics are identified and disturbance impact analysis is performed based on the environmental parameter sensing stream to obtain the environmental disturbance impact relationship; the spray diffusion coverage relationship, the spray cold energy migration relationship, and the environmental disturbance impact relationship are fused and modeled using a graph neural network to generate the spray diffusion association model.

[0021] In this embodiment, when identifying spray characteristics and calibrating directions based on data acquisition streams from multiple cabinets, the spray pressure data, spray flow rate data, cabinet humidity data, and airflow direction data of each spray cabinet are correlated according to the acquisition time. The spray start time, spray duration, and spray stop time are determined based on changes in spray pressure and spray flow rate data. Within the spray duration, changes in spray pressure, spray flow rate, and cumulative spray output are determined, thereby identifying the spray output characteristics of each spray cabinet. Subsequently, the cabinet humidity data before and after spray start are compared to identify the areas within the cabinet with increased humidity and adjacent spray cabinet areas. The diffusion direction of the spray medium within and between cabinets is determined by combining this with airflow direction data. When adjacent spray cabinets show a corresponding increase in humidity within the same spray duration, and the area of ​​increased humidity aligns with the airflow direction, a spray diffusion coverage association is established between the corresponding spray cabinets. By associating and organizing the spray output characteristics, humidity increase areas, diffusion directions, and overlapping coverage areas of multiple spray cabinets, the spray diffusion coverage relationship is obtained.

[0022] When performing internal temperature gradient analysis and inter-rack temperature difference coupling analysis based on multiple rack data acquisition streams, the equipment load data and internal temperature data of each spray rack are correlated according to the acquisition time, and the internal temperature distribution of the spray rack is determined based on the acquisition location of the internal temperature data. By comparing the internal temperature data at different locations within the same spray rack, the high-temperature region, low-temperature region, and the direction of the temperature gradient from the high-temperature region to the low-temperature region are identified, thus obtaining the internal temperature gradient. Subsequently, the internal temperature data of adjacent spray racks or spray racks with airflow connections are compared to determine the temperature difference changes between racks, and the equipment load data is used to determine whether the temperature difference changes mainly correspond to the spraying effect. When the temperature of a spray rack drops after spraying, and adjacent spray racks show synchronous or delayed temperature drops in the corresponding airflow direction, it is determined that the sprayed cooling capacity migrates from that spray rack to the adjacent spray rack. By correlating the internal temperature gradient direction, inter-rack temperature difference changes, temperature drops before and after spraying, and airflow direction data, the sprayed cooling capacity migration relationship is obtained.

[0023] When identifying environmental airflow characteristics and analyzing disturbance effects based on environmental parameter sensing streams, the environmental temperature, humidity, airflow velocity, airflow direction, and environmental disturbance data in the sensing streams are organized according to the acquisition time and time-corresponding to data acquisition streams from multiple cabinets. By continuously comparing airflow velocity and airflow direction data, the dominant airflow direction, airflow velocity changes, and airflow direction changes within the communication equipment room are determined, thus acquiring environmental airflow characteristics. Subsequently, the environmental airflow characteristics are correlated with the spray diffusion coverage and spray cold energy migration relationships to determine whether changes in environmental airflow and environmental disturbances cause shifts in spray diffusion direction, changes in spray coverage, changes in cold energy migration direction, or changes in cold energy migration intensity. When these changes correspond in time and are consistent in direction with changes in environmental airflow characteristics, the impact of environmental airflow and disturbances on spray diffusion and spray cold energy migration is determined. By correlating environmental airflow characteristics, changes in spray diffusion, and changes in spray cold energy migration, the environmental disturbance impact relationship is obtained.

[0024] When using a graph neural network to fuse and model the relationships between spray diffusion coverage, spray cold energy migration, and environmental disturbance, multiple spray cabinets are treated as nodes in the graph neural network. The equipment load data, internal temperature data, spray pressure data, spray flow rate data, cabinet humidity data, and airflow direction data for each spray cabinet are used as node features. Connections between nodes are established based on these relationships. For spray cabinets with correlations in these areas, connections are established between corresponding nodes, with diffusion direction, coverage area, cold energy migration direction, cold energy migration degree, and disturbance degree used as connection features. The graph neural network aggregates information between adjacent spray cabinets based on node and connection features, enabling each spray cabinet node to obtain its associated spray diffusion impact, spray cold energy migration impact, and environmental disturbance impact. After aggregation and modeling, a spray diffusion correlation model is generated to characterize the spray diffusion coverage, spray cold energy migration, and environmental disturbance impact among multiple spray cabinets.

[0025] Step S300: Based on the spray diffusion correlation model, perform heat propagation trend prediction and humidity accumulation coupling analysis on the multiple spray cabinets to obtain the heat and humidity coupling distribution results of the multiple cabinets.

[0026] In this embodiment, based on the spray diffusion association model, the heat diffusion path between multiple spray cabinets is first analyzed to determine the direction, range, and trend of heat transfer between different spray cabinets, thus obtaining the heat propagation trend between cabinets. Then, humidity migration analysis is performed on the overlapping area of ​​spray diffusion between multiple spray cabinets to determine the direction, range, and trend of humidity accumulation within and between cabinets under the spray effect, thus obtaining the humidity accumulation trend. Subsequently, based on the heat propagation trend and humidity accumulation trend between cabinets, the degree of mutual influence between temperature propagation and humidity accumulation is analyzed to obtain the heat-humidity coupling intensity distribution results. Finally, based on the heat propagation trend, humidity accumulation trend, and heat-humidity coupling intensity distribution results between cabinets, multi-level distribution calibration is performed on the hot spot diffusion area, humidity accumulation area, and heat-humidity coupling risk area in multiple spray cabinets to generate multi-cabinet heat-humidity coupling distribution results.

[0027] Furthermore, in the method provided in the application embodiment, the heat propagation trend prediction and humidity accumulation coupling analysis of the multiple spray cabinets are performed based on the spray diffusion correlation model to obtain the heat and humidity coupling distribution results of the multiple cabinets, and the method further includes: The heat diffusion path between the multiple spray cabinets is analyzed based on the spray diffusion association model to obtain the heat propagation trend between the cabinets; humidity migration analysis is performed on the overlapping area of ​​spray diffusion between the multiple spray cabinets based on the spray diffusion association model to obtain the cumulative humidity change trend; thermal-humidity coupling intensity analysis is performed based on the heat propagation trend between the cabinets and the cumulative humidity change trend to obtain the thermal-humidity coupling intensity distribution result; multi-level distribution calibration of hot spot diffusion area, humidity accumulation area and thermal-humidity coupling risk area of ​​the multiple spray cabinets is performed based on the heat propagation trend between the cabinets, the cumulative humidity change trend and the thermal-humidity coupling intensity distribution result to generate the multi-cabinet thermal-humidity coupling distribution result.

[0028] In this embodiment, when analyzing the heat diffusion path between multiple spray cabinets based on the spray diffusion association model, the connection relationship, cold energy migration direction, spray diffusion direction, and coverage area between the multiple spray cabinets are read from the spray diffusion association model. The internal temperature data of each spray cabinet is arranged according to the acquisition time to form a temperature change sequence for each spray cabinet. The internal temperature data of spray cabinets with connection relationships are compared. When the internal temperature of one spray cabinet is higher than that of the adjacent spray cabinet, and the temperature difference between the two reaches a preset inter-cabinet temperature difference threshold, the direction from the spray cabinet with higher temperature to the adjacent spray cabinet with lower temperature is determined as the heat diffusion direction. When the temperature difference reaches the preset inter-cabinet temperature difference threshold for multiple consecutive acquisition times, and the internal temperature of the adjacent spray cabinet increases in subsequent acquisition times compared to the previous acquisition time, this direction is determined as the heat diffusion path. The temperature drop inside the cabinet after spraying is compared with a preset temperature drop threshold. When the temperature drop does not reach the preset threshold, the heat diffusion path is determined to be a continuous propagation trend; when the temperature drop reaches the preset threshold, the heat diffusion path is determined to be a weakening trend. By analyzing the heat diffusion direction, heat diffusion path, temperature difference duration, and temperature drop after spraying among multiple spray cabinets in a time-by-time manner, the heat propagation trend between cabinets is obtained.

[0029] Next, when performing humidity migration analysis on the overlapping areas of spray diffusion among multiple spray cabinets based on the spray diffusion correlation model, the spray diffusion coverage relationship, diffusion direction, and overlapping coverage area among multiple spray cabinets are read from the spray diffusion correlation model. The cabinet humidity data of each spray cabinet is arranged according to the collection time to form a humidity change sequence for each spray cabinet. The cabinet humidity data before and after spraying are compared. When the cabinet humidity data of a certain spray cabinet increases after spraying starts relative to the cabinet humidity data before spraying starts, and the increase reaches a preset humidity increment threshold, the corresponding area of ​​that spray cabinet is identified as the humidity generation area. When the cabinet humidity data of an adjacent spray cabinet or the spray diffusion overlapping area increases at subsequent collection times relative to the cabinet humidity data before spraying starts, and the increase reaches a preset humidity increment threshold, the direction from the humidity generation area to the adjacent spray cabinet or the spray diffusion overlapping area is identified as the humidity migration direction. The humidity data of the cabinets within the overlapping area of ​​the spray diffusion is continuously analyzed. When the humidity data of the cabinets in this area is higher than the humidity data of the cabinets before the spray starts for multiple consecutive collection times, and the difference reaches the preset humidity increment threshold, it is determined that there is humidity accumulation in the overlapping area of ​​the spray diffusion. By sorting out the humidity migration direction, the overlapping area of ​​the spray diffusion, the humidity increase rate, and the duration of humidity in multiple spray cabinets time-by-time, the trend of humidity accumulation is obtained.

[0030] Subsequently, when analyzing the thermal-humidity coupling intensity based on the heat transfer trend and humidity accumulation trend between server racks, multiple spray racks and overlapping spray diffusion areas were matched according to the same acquisition time, and the temperature and humidity changes within the same area were read separately. Temperature changes were determined by the increase in temperature inside the rack, the duration of the temperature difference between racks, and the number of heat diffusion paths; humidity changes were determined by the increase in humidity within the rack, the duration of humidity increase, and the number of overlapping spray diffusion paths. When the same area simultaneously meets the following conditions within the same acquisition time period: the increase in temperature inside the rack reaches a preset temperature increment threshold, the increase in humidity within the rack reaches a preset humidity increment threshold, the duration of temperature increase reaches a preset temperature duration, and the duration of humidity increase reaches a preset humidity duration, the area is identified as having thermal-humidity coupling. The thermal-humidity coupling intensity level is determined based on the increase in temperature inside the cabinet, the increase in humidity inside the cabinet, the duration of the temperature increase, and the duration of the humidity increase. When all the above parameters reach the first-level preset range, it is calibrated as low-level thermal-humidity coupling intensity; when all the above parameters reach the second-level preset range, it is calibrated as medium-level thermal-humidity coupling intensity; and when all the above parameters reach the third-level preset range, it is calibrated as high-level thermal-humidity coupling intensity. The thermal-humidity coupling intensity distribution results are obtained in this way.

[0031] Finally, based on the heat propagation trend, humidity accumulation trend, and heat-humidity coupling intensity distribution results between cabinets, multi-level distribution calibration of hot spot diffusion areas, humidity accumulation areas, and heat-humidity coupling risk areas for multiple spray cabinets is performed. The hot spot diffusion area is calibrated according to the heat propagation trend between cabinets. When the temperature inside a certain spray cabinet is higher than that inside the adjacent spray cabinet, and the temperature difference between the two reaches the preset temperature difference threshold between cabinets, and the heat diffusion path extends outward from the spray cabinet, the spray cabinet and the adjacent area through which the heat diffusion path passes are calibrated as the hot spot diffusion area. When the temperature drop inside the spray cabinet after spraying does not reach the preset temperature drop threshold, the area where the spray cabinet is located is simultaneously calibrated as the hot spot diffusion area. Humidity clustering areas are identified based on the cumulative humidity trend. When the humidity data of a spray cabinet or a cabinet in an overlapping spray diffusion area increases compared to the humidity data before spraying starts, and the increase reaches a preset humidity increment threshold, and the duration reaches a preset humidity duration, this area is identified as a humidity clustering area. When the spray diffusion coverage of multiple spray cabinets overlaps in the same area, and the humidity data of the cabinets in that area meets the above conditions, the overlapping area is identified as a humidity clustering area. Heat and humidity coupling risk areas are identified based on the heat and humidity coupling intensity distribution results. When the same area simultaneously belongs to a hotspot diffusion area and a humidity clustering area, and the corresponding heat and humidity coupling intensity level of this area is low, medium, or high, this area is identified as a low-level, medium-level, or high-level heat and humidity coupling risk area, respectively. By performing multi-level distribution identification of hotspot diffusion areas, humidity clustering areas, and heat and humidity coupling risk areas, multi-cabinet heat and humidity coupling distribution results are generated.

[0032] Step S400: Based on the multi-cabinet thermal-humidity coupling distribution results, perform multi-spray parameter coordinated dynamic adjustment on the multiple spray cabinets to obtain the initial spray coordination scheme.

[0033] In this embodiment, based on the multi-cabinet thermal-humidity coupling distribution results, a historical spray control search analysis is first performed on multiple spray cabinets to determine the historical spray control values ​​corresponding to hotspot diffusion areas, humidity accumulation areas, and thermal-humidity coupling risk areas, thus obtaining a first spray search range. Then, weights are allocated according to spray regulation evaluation indicators to establish a spray coordination scoring model for evaluating the regulation effects of different spray candidate schemes. Subsequently, within the first spray search range, collaborative decision-making on spray parameters is performed on multiple spray cabinets to generate multiple spray candidate schemes. These multiple spray candidate schemes are then scored using the spray coordination scoring model, and the spray candidate scheme with the highest collaborative score is selected as the current guiding spray scheme. Next, the first spray search range is narrowed and adjusted based on the current guiding spray scheme to obtain a second spray search range. Finally, based on the spray coordination scoring model, the current guiding spray scheme is iteratively updated within the second spray search range until a preset number of iterations is met, thus obtaining an initial spray coordination scheme.

[0034] Furthermore, in the method provided in the application embodiment, the method further includes: dynamically adjusting the multiple spray parameters of the multiple spray cabinets in a coordinated manner based on the multi-cabinet thermal-humidity coupling distribution results to obtain an initial spray coordination scheme; Based on the multi-cabinet thermal-humidity coupling distribution results, a historical spray control search analysis is performed on the multiple spray cabinets to obtain a first spray search range. A spray coordination scoring model is established by weighting the spray adjustment evaluation indicators. Based on the first spray search range, collaborative spray parameter decisions are made for the multiple spray cabinets to obtain multiple spray candidate schemes. The multiple spray candidate schemes are scored according to the spray coordination scoring model, and the spray candidate scheme with the highest spray coordination score is selected as the current guiding spray scheme. The first spray search range is narrowed and adjusted based on the current guiding spray scheme to obtain a second spray search range. Based on the spray coordination scoring model and the second spray search range, the current guiding spray scheme is iteratively updated to obtain the initial spray coordination scheme that satisfies a preset number of iterations.

[0035] Furthermore, the method provided in the application embodiments also includes: The evaluation indicators for spray regulation include spray coverage adequacy, spray diffusion effectiveness, and spray resource utilization rate.

[0036] In this embodiment, based on the multi-cabinet thermal-humidity coupling distribution results, historical spray control records of multiple spray cabinets are searched to match historical control data corresponding to the current hotspot diffusion area, humidity accumulation area, and thermal-humidity coupling risk area, thereby obtaining multiple cabinet-matched spray control sets. Then, effective control samples are screened for each cabinet-matched spray control set, eliminating historical samples with abnormal heat dissipation response or incomplete data. The historical control parameters such as spray pressure, spray flow rate, spray duration, and spray interval in the retained samples are statistically analyzed to obtain multiple spray control value ranges. Subsequently, the historical heat dissipation effect is evaluated based on the cooling effect, humidity change, and spray resource consumption corresponding to each historical control parameter, obtaining the historical performance degree of each parameter. Finally, the upper and lower limits of the multiple spray control value ranges are corrected based on the historical performance degree of each parameter, so that parameter value ranges with good historical heat dissipation effects are retained and enhanced, while parameter value ranges with unsatisfactory historical heat dissipation effects are compressed or eliminated, generating the first spray search range.

[0037] Next, a spray coordination scoring model is established by assigning weights based on the spray regulation evaluation indicators. In this process, spray coverage adequacy, spray diffusion effectiveness, and spray resource utilization rate are used as spray regulation evaluation indicators, and a pre-determined first weight, second weight, and third weight are assigned to spray coverage adequacy, spray diffusion effectiveness, and spray resource utilization rate, respectively. The sum of the first weight, second weight, and third weight is one. For any candidate spray scheme, the adequacy of spray coverage is obtained by dividing the overlapping area of ​​the spray action area and the target adjustment area by the total area of ​​the target adjustment area, where the target adjustment area includes the hot spot diffusion area and the heat and humidity coupling risk area in the multi-cabinet heat and humidity coupling distribution results; the effectiveness of spray diffusion is obtained by dividing the area of ​​the effective diffusion area by the total area of ​​the spray action area, where the effective diffusion area is the spray action area where the spray diffusion direction is towards the hot spot diffusion area or the heat and humidity coupling risk area and does not fall into the humidity accumulation area; the spray resource utilization rate is obtained by subtracting the absolute value of the difference between the actual spray resource consumption and the reference spray resource consumption from 1 to the reference spray resource consumption, where the actual spray resource consumption is obtained by multiplying the spray flow rate of each participating spray cabinet by the spray duration and then summing the results, and the reference spray resource consumption is obtained by dividing the total temperature exceedance by the average cooling amount corresponding to the unit spray resource consumption in the historical spray control records. Using spray coverage adequacy, spray diffusion effectiveness, and spray resource utilization rate as input functions, and the first, second, and third weights as coefficients, a spray synergy scoring model is established. The spray synergy score is equal to the sum of the products of the first weight and spray coverage adequacy, the second weight and spray diffusion effectiveness, and the third weight and spray resource utilization rate. The expression for the spray synergy scoring model is: Spray Synergy Score = First Weight × Spray Coverage Adequacy + Second Weight × Spray Diffusion Effectiveness + Third Weight × Spray Resource Utilization Rate.

[0038] Subsequently, based on the first spray search range, collaborative decision-making on spray parameters is performed for multiple spray cabinets. First, the first spray search range corresponding to each spray cabinet is read, which includes the range of spray pressure, spray flow rate, spray duration, and spray interval. Then, the lower limit, median, and upper limit values ​​are selected as candidate values ​​within the spray pressure, spray flow rate, spray duration, and spray interval ranges for each spray cabinet, respectively. Next, the candidate values ​​of spray pressure, spray flow rate, spray duration, and spray interval for the same spray cabinet are combined to form a single-cabinet spray parameter combination for that spray cabinet. Finally, the single-cabinet spray parameter combinations of multiple spray cabinets are combined according to the same adjustment cycle, so that each combination includes the spray pressure, spray flow rate, spray duration, and spray interval corresponding to multiple spray cabinets, thereby obtaining multiple spray candidate schemes.

[0039] Next, multiple spray candidate schemes are scored according to the spray coordination scoring model. When selecting the spray candidate scheme with the highest spray coordination score as the current guiding spray scheme, the spray action area, target adjustment area, effective diffusion area, total spray action area, actual spray resource consumption, and reference spray resource consumption are determined for each spray candidate scheme. Based on this, the spray coverage sufficiency, spray diffusion effectiveness, and spray resource utilization rate of the spray candidate scheme are calculated. Then, the calculated spray coverage sufficiency, spray diffusion effectiveness, and spray resource utilization rate are substituted into the spray coordination scoring model to obtain the corresponding spray coordination score for the spray candidate scheme. After scoring all spray candidate schemes, the multiple spray coordination scores are compared numerically, and the spray candidate scheme with the highest spray coordination score is selected as the current guiding spray scheme. When two or more spray candidate schemes have the same maximum spray coordination score, the spray candidate scheme with the lowest actual spray resource consumption is selected as the current guiding spray scheme.

[0040] Next, when adjusting the first spray search range according to the current guided spray scheme, the spray pressure, spray flow rate, spray duration, and spray interval of each spray cabinet in the current guided spray scheme are used as the center value of the corresponding spray parameters to narrow the first spray search range. For any spray parameter of any spray cabinet, the interval length of the spray parameter in the first spray search range is first calculated, then half of the interval length is taken as the new interval length, and new upper and lower limits are formed by extending upwards and downwards from the value of the spray parameter in the current guided spray scheme as the center. When the new lower limit is lower than the original lower limit of the first spray search range, the original lower limit of the first spray search range is used as the new lower limit; when the new upper limit is higher than the original upper limit of the first spray search range, the original upper limit of the first spray search range is used as the new upper limit. The above-mentioned narrowing adjustment is performed on the spray pressure, spray flow rate, spray duration, and spray interval of multiple spray cabinets to obtain the second spray search range.

[0041] Finally, based on the spray coordination scoring model, the current guided spray scheme is iteratively updated according to the second spray search range. In this process, the second spray search range is used as the current spray search range. Within this range, spray parameter coordination decisions are re-performed to generate multiple new spray candidate schemes. Then, the spray coordination scoring model scores each of these new candidate schemes, and the candidate scheme with the highest new coordinated spray score is selected to update the current guided spray scheme. Next, the current spray search range is further narrowed and adjusted based on the updated guided spray scheme to obtain the next round of spray search range. This process of spray parameter coordination decision-making, scoring, selection, and narrowing adjustment is repeated until the preset number of iterations is reached. When the preset number of iterations is reached, the last selected guided spray scheme is determined as the initial spray coordination scheme.

[0042] Furthermore, in the method provided in the application embodiment, the process of performing a spray control history search analysis on the multiple spray cabinets based on the multi-cabinet thermal-humidity coupling distribution results to obtain a first spray search range also includes: Based on the multi-rack heat and humidity coupling distribution results, historical spray control records are searched for the multiple spray racks to obtain multiple rack-matched spray control sets; effective control samples are screened and parameter values ​​are statistically analyzed for each rack-matched spray control set to obtain multiple spray control value ranges; historical heat dissipation effect is evaluated for each historical control parameter within the multiple rack-matched spray control sets to obtain the historical performance degree of each parameter; the upper and lower limits of the multiple spray control value ranges are corrected based on the historical performance degree of each parameter to generate the first range of the spray search.

[0043] In this embodiment, when searching historical spray control records for multiple spray cabinets based on the multi-cabinet thermal-humidity coupling distribution results, the system first reads the area calibration results, cabinet temperature data, cabinet humidity data, and equipment load data corresponding to each spray cabinet within the current adjustment cycle. The area calibration results include hotspot diffusion areas, humidity accumulation areas, and thermal-humidity coupling risk areas. Then, historical records for the same spray cabinet are extracted from the historical spray control records according to the spray cabinet number. The system calculates the difference between the current cabinet temperature data and the cabinet temperature before spraying in the historical records, the difference between the current cabinet humidity data and the cabinet humidity before spraying in the historical records, and the difference between the current equipment load data and the equipment load before spraying in the historical records. When the absolute value of the temperature difference does not exceed a preset temperature matching threshold, the absolute value of the humidity difference does not exceed a preset humidity matching threshold, the absolute value of the equipment load difference does not exceed a preset load matching threshold, and the current area calibration result is the same as or belongs to the same risk level as the area calibration result in the historical records, the historical record is included in the cabinet-matched spray control set for the corresponding spray cabinet. The historical spray control records of multiple spray cabinets are searched in the manner described above to obtain multiple matching spray control sets for each cabinet. Each matching spray control set includes the spray pressure, spray flow rate, spray duration, spray interval, cabinet temperature before spraying, cabinet temperature after spraying, cabinet humidity before spraying, cabinet humidity after spraying, and equipment load before spraying for the corresponding spray cabinet under similar heat and humidity conditions and similar load conditions.

[0044] Next, when selecting effective control samples and statistically analyzing parameter values ​​based on the matching spray control set for each cabinet, the completeness of each historical record is first checked. Historical records that simultaneously include spray pressure, spray flow rate, spray duration, spray interval, cabinet temperature before spray, cabinet temperature after spray, cabinet humidity before spray, cabinet humidity after spray, and equipment load before spray are retained, while historical records with missing fields are removed. Then, the cabinet temperature drop and cabinet humidity status after spray are calculated for each retained historical record. The cabinet temperature drop is equal to the cabinet temperature before spray minus the cabinet temperature after spray. When the cabinet temperature drop reaches the preset cooling requirement and the cabinet humidity after spray does not exceed the preset humidity limit, the historical record is determined as a valid control sample. When the cabinet temperature drop does not reach the preset cooling requirement, or the cabinet humidity after spray exceeds the preset humidity limit, the historical record is not considered a valid control sample. Subsequently, the parameter values ​​of the effective control samples matched to the spray control set for each cabinet are statistically analyzed. The spray pressure, spray flow rate, spray duration and spray interval in the effective control samples are read respectively. The minimum value of each spray parameter is taken as the lower limit of the corresponding spray control value range, and the maximum value of each spray parameter is taken as the upper limit of the corresponding spray control value range, thereby obtaining multiple spray control value ranges.

[0045] Subsequently, when evaluating the historical heat dissipation effect based on each historical control parameter within the multi-rack matching spray control set, the following calculations are performed for each valid control sample: cabinet temperature drop, cabinet humidity increase, and historical spray consumption. The cabinet temperature drop equals the cabinet temperature before spraying minus the cabinet temperature after spraying; the cabinet humidity increase equals the cabinet humidity after spraying minus the cabinet humidity before spraying; and the historical spray consumption equals the spray flow rate multiplied by the spray duration. Then, the cabinet temperature drop, cabinet humidity increase, and historical spray consumption are converted into cooling evaluation values, humidity evaluation values, and consumption evaluation values, respectively, ranging from zero to one. The cooling evaluation value is obtained by dividing the cabinet temperature drop by the preset maximum cooling amount; if the result is greater than one, it is rounded to one. The humidity evaluation value is obtained by subtracting the ratio of the cabinet humidity increase to the preset allowable humidity increase from one; if the result is less than zero, it is rounded to zero. The consumption evaluation value is obtained by subtracting the ratio of the historical spray consumption to the preset upper limit of spray consumption from one; if the result is less than zero, it is rounded to zero. For a given historical value of the same spray parameter, all valid control samples containing that historical value are statistically analyzed. The average cooling evaluation value, average humidity evaluation value, and average consumption evaluation value of these valid control samples are calculated separately. The average cooling evaluation value, average humidity evaluation value, and average consumption evaluation value are then weighted and summed according to predetermined cooling weight, humidity weight, and consumption weight to obtain the historical performance degree of the parameter corresponding to that historical value. The historical performance degree of the parameters is calculated separately for each historical value of spray pressure, spray flow rate, spray duration, and spray interval in the same manner to obtain the historical performance degree of each parameter.

[0046] Finally, when adjusting the upper and lower limits of multiple spray control value ranges based on the historical performance of each parameter, the spray pressure value range, spray flow value range, spray duration value range, and spray interval value range corresponding to each spray cabinet are first mapped to the historical performance of each parameter. For any spray parameter, its historical values ​​are arranged in ascending order, and historical values ​​that reach the preset performance threshold are selected. The lowest historical value that reaches the preset performance threshold is used as the lower limit of the adjusted range, and the highest historical value that reaches the preset performance threshold is used as the upper limit of the adjusted range. If the lower limit of the original spray control value range is lower than the lower limit of the adjusted range, the lower limit of the range is raised to the lower limit of the adjusted range; if the upper limit of the original spray control value range is higher than the upper limit of the adjusted range, the upper limit of the range is lowered to the upper limit of the adjusted range; if both the upper and lower limits of the original spray control value range are between the lower and upper limits of the adjusted range, the original upper and lower limits remain unchanged. The upper and lower limits of multiple spray control value ranges for spray pressure, spray flow rate, spray duration and spray interval of multiple spray cabinets are corrected according to the above method to generate the first range of spray search.

[0047] Step S500: Perform thermal-humid coupling hysteresis compensation analysis on the initial spray coordination scheme to obtain the hysteresis compensation scheme, and perform peak-valley electricity price linkage compensation analysis on the initial spray coordination scheme to obtain the electricity price linkage compensation scheme.

[0048] In this embodiment, when performing thermal-humidity coupling hysteresis compensation analysis on the initial spray coordination scheme, the temperature response delay of multiple spray cabinets after spray execution is first identified according to the initial spray coordination scheme, and multiple cabinet thermal hysteresis change sequences are obtained; then the humidity accumulation delay after spray execution is identified, and multiple cabinet humidity hysteresis change sequences are obtained; subsequently, the multiple cabinet thermal hysteresis change sequences and multiple cabinet humidity hysteresis change sequences are time-aligned and coupled and superimposed to obtain the thermal-humidity coupling hysteresis sequence; finally, based on the thermal-humidity coupling hysteresis sequence, the spray start time, spray duration and spray interval of multiple spray cabinets are compensated and adjusted to generate a hysteresis compensation scheme including spray start time advance compensation, spray duration limit compensation and spray interval dynamic compensation.

[0049] When performing peak-valley electricity price linkage compensation analysis on the initial spray coordination scheme, the following steps are taken: First, based on the peak-valley electricity price information of the communication equipment room, low-price pre-cooling compensation is performed on the initial spray coordination scheme during low-price periods to obtain the first electricity price linkage compensation decision; then, high-price limiting compensation is performed on the initial spray coordination scheme during high-price periods to obtain the second electricity price linkage compensation decision; subsequently, electricity price switching smooth transition compensation is performed on the initial spray coordination scheme during the peak-valley electricity price switching period to obtain the third electricity price linkage compensation decision; finally, the first, second, and third electricity price linkage compensation decisions are time-series aligned and analyzed to generate an electricity price linkage compensation scheme for optimizing the spray execution period and spray parameters.

[0050] Furthermore, in the method provided in the application embodiments, performing thermo-humid coupling hysteresis compensation analysis on the initial spray coordination scheme to obtain the hysteresis compensation scheme further includes: Based on the initial spray coordination scheme, temperature response delay is identified in the multiple spray cabinets to obtain multiple cabinet thermal hysteresis change sequences; based on the initial spray coordination scheme, humidity accumulation delay is identified in the multiple spray cabinets to obtain multiple cabinet humidity hysteresis change sequences; time alignment and coupling superposition are performed on the multiple cabinet thermal hysteresis change sequences and the multiple cabinet humidity hysteresis change sequences to obtain a thermal-humidity coupled hysteresis sequence; based on the thermal-humidity coupled hysteresis sequence, spray start-up time advance compensation, spray duration limit compensation, and spray interval dynamic compensation are performed on the multiple spray cabinets to generate the hysteresis compensation scheme.

[0051] In this embodiment, when identifying the temperature response delay of multiple spray cabinets according to the initial spray coordination scheme, the spray start time, spray duration, spray pressure, spray flow rate, and cabinet temperature data of each spray cabinet are read. Using the spray start time as the time reference and the cabinet temperature at the spray start time as the reference temperature, calculations are performed for each data collection time after spray start. The spray start time is subtracted from the data collection time to obtain the corresponding relative time; the cabinet temperature at that data collection time is subtracted from the reference temperature to obtain the cabinet temperature drop. When the cabinet temperature drop first reaches a preset temperature response threshold, the corresponding relative time is determined as the temperature response delay. The spray cabinet number, relative time, cabinet temperature drop, and temperature response delay are arranged in chronological order to obtain a sequence of thermal hysteresis changes for multiple cabinets.

[0052] Next, when identifying the humidity accumulation delay for multiple spray cabinets according to the initial spray coordination scheme, the spray start time, spray duration, spray pressure, spray flow rate, and cabinet humidity data for each spray cabinet are read. Using the spray start time as the time reference and the cabinet humidity at the spray start time as the baseline humidity, calculations are performed for each data collection time after spray start. The relative time is obtained by subtracting the spray start time from the data collection time; the cabinet humidity at that data collection time is subtracted from the baseline humidity to obtain the cabinet humidity increase. When the cabinet humidity increase first reaches the preset humidity response threshold, the corresponding relative time is determined as the humidity accumulation delay. After spraying stops, if the cabinet humidity increase continues to reach the preset humidity response threshold, the humidity accumulation lag period is obtained by subtracting the spray stop time from the moment the cabinet humidity increase first falls below the preset humidity response threshold. The spray cabinet number, relative time, cabinet humidity increase, humidity accumulation delay, and humidity accumulation lag period are arranged in chronological order to obtain multiple cabinet humidity lag change sequences.

[0053] Subsequently, when performing time-series alignment and coupling overlay based on multiple cabinet thermal hysteresis change sequences and multiple cabinet humidity hysteresis change sequences, the spray start-up time of the same spray cabinet is used as a unified time reference. The relative thermal hysteresis time is obtained by subtracting the spray start-up time from the temperature acquisition time, and the relative humidity hysteresis time is obtained by subtracting the spray start-up time from the humidity acquisition time. A relative time series is established according to a preset unified sampling period, and the cabinet temperature drop and cabinet humidity increase are assigned to the corresponding relative time. For each relative time, if the cabinet temperature drop does not reach the preset temperature response threshold, the temperature hysteresis is marked as one; otherwise, it is zero. If the cabinet humidity increase reaches the preset humidity response threshold, the humidity hysteresis is marked as one; otherwise, it is zero. The temperature hysteresis mark and the humidity hysteresis mark are added together to obtain the thermal-humidity coupling hysteresis value, and a thermal-humidity coupling hysteresis sequence is generated in chronological order.

[0054] Finally, when performing spray start-up compensation, spray duration limitation compensation, and spray interval dynamic compensation on multiple spray cabinets based on the thermal-humidity coupling hysteresis sequence, the temperature response delay, humidity accumulation hysteresis period, and thermal-humidity coupling hysteresis value of each spray cabinet are read. Spray start-up compensation is obtained by subtracting the temperature response delay from the original spray start-up time; spray duration limitation compensation is obtained by subtracting the product of the humidity accumulation hysteresis period and a preset limitation coefficient from the original spray duration, and it is not less than the preset minimum spray duration; spray interval dynamic compensation is adjusted according to the thermal-humidity coupling hysteresis value. When the temperature hysteresis is marked as one and the humidity hysteresis is marked as zero, the original spray interval is subtracted from the preset interval step size; when the humidity hysteresis is marked as one, the original spray interval is added to the preset interval step size, ensuring that the adjusted spray interval is between the preset minimum spray interval and the preset maximum spray interval. The compensated spray start-up time, spray duration, and spray interval are written into the spray execution parameters of the corresponding spray cabinet to generate a hysteresis compensation scheme.

[0055] Furthermore, the method provided in the application embodiments, which performs peak-valley electricity price linkage compensation analysis on the initial spray coordination scheme to obtain an electricity price linkage compensation scheme, further includes: Based on the peak and valley electricity price information of the communication equipment room, the initial spray coordination scheme is compensated for low electricity price pre-cooling to obtain a first decision on electricity price linkage compensation; based on the peak and valley electricity price information, the initial spray coordination scheme is compensated for high electricity price limiting to obtain a second decision on electricity price linkage compensation; based on the peak and valley electricity price information, the initial spray coordination scheme is compensated for smooth transition of electricity price switching to obtain a third decision on electricity price linkage compensation; the first, second, and third decisions on electricity price linkage compensation are time-series aligned and sorted to generate the electricity price linkage compensation scheme.

[0056] In this embodiment, when performing low-price pre-cooling compensation for the initial spray coordination scheme based on the peak-valley electricity price information of the communication equipment room, the start and end times of the low-price period in the peak-valley electricity price information are read, and the spray start time, spray duration, spray pressure, spray flow rate, and spray interval of each spray cabinet in the initial spray coordination scheme are read. The spray start time of each spray cabinet is compared with the low-price period. When the spray start time of a certain spray cabinet is after the low-price period, and the spray cabinet belongs to at least one of the hotspot diffusion area and the heat and humidity coupling risk area, part of the spray execution volume of the spray cabinet is moved forward to the low-price period. The forward spray duration is the smaller value between the remaining duration of the low-price period and the preset pre-cooling duration upper limit. The forward spray execution volume is equal to the original spray flow rate multiplied by the forward spray duration. The forward spray pressure does not exceed the original spray pressure, and the forward spray flow rate does not exceed the original flow rate. Thus, the pre-cooling spray parameters within the low-price period are formed, and the first decision of electricity price linkage compensation is obtained.

[0057] Next, when applying high-price limiting compensation to the initial spray coordination scheme based on peak-valley electricity price information, the start and end times of the high-price period are read from the peak-valley electricity price information, and it is determined whether the spray execution period of each spray cabinet in the initial spray coordination scheme falls within the high-price period. When the spray execution period of a certain spray cabinet falls within the high-price period, and the spray cabinet does not belong to the advanced heat and humidity coupling risk area, and the internal temperature of the cabinet has not reached the preset high-temperature protection threshold, its spray pressure, spray flow rate, and spray duration are limited. The spray pressure limit is equal to the original spray pressure multiplied by the preset pressure limit coefficient, the spray flow rate limit is equal to the original spray flow rate multiplied by the preset flow rate limit coefficient, and the spray duration limit is equal to the original spray duration multiplied by the preset duration limit coefficient. When the spray cabinet belongs to the advanced heat and humidity coupling risk area, or the internal temperature of the cabinet reaches the preset high-temperature protection threshold, the spray pressure, spray flow rate, and spray duration in the original initial spray coordination scheme are maintained without reduction. By limiting the spray parameters during the high-price period, the second decision for electricity price linkage compensation is obtained.

[0058] Subsequently, when performing price switching smoothing compensation on the initial spray coordination scheme based on peak-valley electricity price information, the price switching times for low-price periods, high-price periods, and the periods between low and high-price periods are read. A smoothing transition window is determined using a preset transition duration before and after the price switching time. For spray execution parameters falling within the smoothing transition window, the time difference between the spray execution time and the price switching time is calculated, and this time difference is divided by the preset transition duration to obtain the transition ratio. When switching from a low-price period to a high-price period, the spray flow adjustment value is equal to the original spray flow minus the difference between the original spray flow and the spray flow after the high-price limit, multiplied by the transition ratio. The spray duration adjustment value is equal to the original spray duration minus the difference between the original spray duration and the spray duration after the high-price limit, multiplied by the transition ratio. When switching from a high-price period to a low-price period, the spray flow rate adjustment value is equal to the spray flow rate after the high-price limit plus the difference between the original spray flow rate and the spray flow rate after the high-price limit, multiplied by the transition ratio. The spray duration adjustment value is equal to the spray duration after the high-price limit plus the difference between the original spray duration and the spray duration after the high-price limit, multiplied by the transition ratio. By continuously adjusting the spray flow rate and spray duration before and after the price switch, the third decision for price linkage compensation is obtained.

[0059] Finally, when aligning the first, second, and third decisions regarding electricity price linkage compensation in a timely manner, the unified adjustment cycle was used as the time axis. The low-price pre-cooling compensation, high-price limiting compensation, and electricity price switching smooth transition compensation were each mapped to the spray execution period of each spray cabinet. If multiple compensation decisions exist for the same spray cabinet simultaneously within the same time period, it is first determined whether the cabinet temperature reaches the preset high-temperature protection threshold and whether the spray cabinet belongs to an advanced heat and humidity coupling risk area. When the cabinet temperature reaches the preset high-temperature protection threshold, or the spray cabinet belongs to an advanced heat and humidity coupling risk area, the spray pressure, spray flow rate, and spray duration from the original initial spray coordination scheme are used as the final compensation parameters for that period. If the cabinet temperature does not reach the preset high-temperature protection threshold, and the spray cabinet does not belong to an advanced heat and humidity coupling risk area, it is further determined whether the spray execution period falls within the smooth transition window. When the spray execution period falls within the smooth transition window, the spray parameters after electricity price switching smooth transition compensation are used as the final compensation parameters. If the spraying execution period does not fall within the smooth transition window, the final compensation parameters are determined based on the electricity price period to which that period belongs. For low-price periods, the spraying parameters after low-price pre-cooling compensation are used; for high-price periods, the spraying parameters after high-price limiting compensation are used. The final determined spraying start time, spraying duration, spraying pressure, spraying flow rate, and spraying interval are written into the corresponding spraying cabinet's spraying execution parameters in chronological order to generate an electricity price-linked compensation scheme.

[0060] Step S600: Perform collaborative optimization on the initial spray coordination scheme according to the lag compensation scheme and the electricity price linkage compensation scheme to obtain the spray coordination optimization scheme.

[0061] In this embodiment, when optimizing the initial spray coordination scheme based on the lag compensation scheme and the electricity price linkage compensation scheme, the spray start time, spray duration, spray pressure, spray flow rate, and spray interval of each spray cabinet in the initial spray coordination scheme are first used as basic parameters, and the compensation results of the corresponding spray cabinet in the lag compensation scheme are read. When correcting the spray start time, the original spray start time is subtracted from the advance compensation amount to obtain the first spray start time; when correcting the spray duration, the duration after the spray duration limit compensation is replaced with the original spray duration to obtain the first spray duration; when correcting the spray interval, the interval after the spray interval dynamic compensation is replaced with the original spray interval to obtain the first spray interval. Thus, the spray start time, spray duration, and spray interval of each spray cabinet after lag compensation, together with the original spray pressure and the original spray flow rate, constitute the first spray execution parameters.

[0062] Subsequently, the execution parameters of the first spray are correlated with the electricity price linkage compensation scheme to determine the peak-valley electricity price period to which the first spray start time and its corresponding spray execution period belong. If the spray execution period is in a low electricity price period, the spray start time, spray duration, spray pressure, and spray flow rate are adjusted according to the low electricity price pre-cooling compensation; if the spray execution period is in a high electricity price period, the spray duration, spray pressure, and spray flow rate are adjusted according to the high electricity price limiting compensation; if the spray execution period is in a smooth transition period of electricity price switching, the spray duration, spray pressure, and spray flow rate are adjusted according to the smooth transition compensation of electricity price switching. After completing the electricity price linkage correction, the adjacent spray execution periods of the same spray cabinet are checked sequentially to ensure that the spray interval meets the first spray interval. The corrected spray start time, spray duration, spray pressure, spray flow rate, and spray interval are then integrated according to the spray cabinet number and execution time to obtain a spray collaborative optimization scheme.

[0063] In summary, the embodiments of this application have at least the following technical effects: This application acquires real-time environmental parameter sensing streams from a communication equipment room, as well as data acquisition streams from multiple spray cabinets within the communication equipment room. Based on the environmental parameter sensing streams and the multiple cabinet data acquisition streams, it models the diffusion and migration characteristics of the multiple spray cabinets, establishing a spray diffusion correlation model. Based on the spray diffusion correlation model, it performs heat propagation trend prediction and humidity accumulation coupling analysis on the multiple spray cabinets, obtaining multi-cabinet thermal-humidity coupling distribution results. Based on the multi-cabinet thermal-humidity coupling distribution results, it performs coordinated dynamic adjustment of multiple spray parameters on the multiple spray cabinets, obtaining an initial spray coordination scheme. It performs thermal-humidity coupling hysteresis compensation analysis on the initial spray coordination scheme, obtaining a hysteresis compensation scheme, and performs peak-valley electricity price linkage compensation analysis on the initial spray coordination scheme, obtaining an electricity price linkage compensation scheme. Based on the hysteresis compensation scheme and the electricity price linkage compensation scheme, it performs coordinated optimization of the initial spray coordination scheme, obtaining a spray coordination optimization scheme. This invention addresses the technical problem in existing technologies where cabinet spray temperature control is difficult to adapt to the thermal and humidity coupling changes of multiple cabinets, resulting in low temperature control accuracy. By establishing a spray diffusion correlation model based on environmental parameter sensing flow and cabinet data acquisition flow, and performing thermal and humidity coupling analysis and spray parameter collaborative optimization accordingly, the technical effect of improving the spray temperature control accuracy of multiple cabinets is achieved.

[0064] Example 2, based on the same inventive concept as the cabinet spray temperature control method based on environmental parameter sensing in the previous examples, such as... Figure 2 As shown, this application provides a cabinet spray temperature control system based on environmental parameter sensing. The system and method embodiments in this application are based on the same inventive concept. The system includes: The data acquisition module 11 is used to acquire environmental parameter sensing streams from the communication equipment room in real time, as well as data acquisition streams from multiple spray cabinets within the communication equipment room; the modeling module 12 is used to model the diffusion migration characteristics of the multiple spray cabinets based on the environmental parameter sensing streams and the multiple cabinet data acquisition streams, and establish a spray diffusion correlation model; the prediction and analysis module 13 is used to predict the heat propagation trend and perform humidity accumulation coupling analysis on the multiple spray cabinets based on the spray diffusion correlation model, and obtain the multi-cabinet heat and humidity coupling distribution results; the dynamic adjustment module 14 is used to perform multi-spray parameter coordinated dynamic adjustment on the multiple spray cabinets based on the multi-cabinet heat and humidity coupling distribution results, and obtain an initial spray coordination scheme; the compensation analysis module 15 is used to perform heat and humidity coupling lag compensation analysis on the initial spray coordination scheme, obtain a lag compensation scheme, and perform peak-valley electricity price linkage compensation analysis on the initial spray coordination scheme, and obtain an electricity price linkage compensation scheme; the collaborative optimization module 16 is used to perform collaborative optimization on the initial spray coordination scheme based on the lag compensation scheme and the electricity price linkage compensation scheme, and obtain a spray collaborative optimization scheme.

[0065] Furthermore, the system is also used to implement the following functions: Based on the data acquisition streams from the multiple cabinets, spray characteristics are identified and direction is determined to obtain the spray diffusion coverage relationship; Based on the data acquisition streams from multiple cabinets, internal temperature gradient analysis and inter-cabinet temperature difference coupling analysis are performed to obtain the spray cold energy migration relationship; based on the environmental parameter sensing stream, environmental airflow characteristics are identified and disturbance impact analysis is performed to obtain the environmental disturbance impact relationship; the spray diffusion coverage relationship, the spray cold energy migration relationship, and the environmental disturbance impact relationship are fused and modeled using a graph neural network to generate the spray diffusion association model.

[0066] Furthermore, the system is also used to implement the following functions: The heat diffusion path between the multiple spray cabinets is analyzed based on the spray diffusion association model to obtain the heat propagation trend between the cabinets; humidity migration analysis is performed on the overlapping area of ​​spray diffusion between the multiple spray cabinets based on the spray diffusion association model to obtain the cumulative humidity change trend; thermal-humidity coupling intensity analysis is performed based on the heat propagation trend between the cabinets and the cumulative humidity change trend to obtain the thermal-humidity coupling intensity distribution result; multi-level distribution calibration of hot spot diffusion area, humidity accumulation area and thermal-humidity coupling risk area of ​​the multiple spray cabinets is performed based on the heat propagation trend between the cabinets, the cumulative humidity change trend and the thermal-humidity coupling intensity distribution result to generate the multi-cabinet thermal-humidity coupling distribution result.

[0067] Furthermore, the system is also used to implement the following functions: Based on the multi-cabinet thermal-humidity coupling distribution results, a historical spray control search analysis is performed on the multiple spray cabinets to obtain a first spray search range. A spray coordination scoring model is established by weighting the spray adjustment evaluation indicators. Based on the first spray search range, collaborative spray parameter decisions are made for the multiple spray cabinets to obtain multiple spray candidate schemes. The multiple spray candidate schemes are scored according to the spray coordination scoring model, and the spray candidate scheme with the highest spray coordination score is selected as the current guiding spray scheme. The first spray search range is narrowed and adjusted based on the current guiding spray scheme to obtain a second spray search range. Based on the spray coordination scoring model and the second spray search range, the current guiding spray scheme is iteratively updated to obtain the initial spray coordination scheme that satisfies a preset number of iterations.

[0068] Furthermore, the system is also used to implement the following functions: Based on the multi-rack heat and humidity coupling distribution results, historical spray control records are searched for the multiple spray racks to obtain multiple rack-matched spray control sets; effective control samples are screened and parameter values ​​are statistically analyzed for each rack-matched spray control set to obtain multiple spray control value ranges; historical heat dissipation effect is evaluated for each historical control parameter within the multiple rack-matched spray control sets to obtain the historical performance degree of each parameter; the upper and lower limits of the multiple spray control value ranges are corrected based on the historical performance degree of each parameter to generate the first range of the spray search.

[0069] Furthermore, the system is also used to implement the following functions: Based on the initial spray coordination scheme, temperature response delay is identified in the multiple spray cabinets to obtain multiple cabinet thermal hysteresis change sequences; based on the initial spray coordination scheme, humidity accumulation delay is identified in the multiple spray cabinets to obtain multiple cabinet humidity hysteresis change sequences; time alignment and coupling superposition are performed on the multiple cabinet thermal hysteresis change sequences and the multiple cabinet humidity hysteresis change sequences to obtain a thermal-humidity coupled hysteresis sequence; based on the thermal-humidity coupled hysteresis sequence, spray start-up time advance compensation, spray duration limit compensation, and spray interval dynamic compensation are performed on the multiple spray cabinets to generate the hysteresis compensation scheme.

[0070] Furthermore, the system is also used to implement the following functions: Based on the peak and valley electricity price information of the communication equipment room, the initial spray coordination scheme is compensated for low electricity price pre-cooling to obtain a first decision on electricity price linkage compensation; based on the peak and valley electricity price information, the initial spray coordination scheme is compensated for high electricity price limiting to obtain a second decision on electricity price linkage compensation; based on the peak and valley electricity price information, the initial spray coordination scheme is compensated for smooth transition of electricity price switching to obtain a third decision on electricity price linkage compensation; the first, second, and third decisions on electricity price linkage compensation are time-series aligned and sorted to generate the electricity price linkage compensation scheme.

[0071] Furthermore, the system is also used to implement the following functions: Each cabinet's data acquisition stream includes equipment load data, cabinet temperature data, spray pressure data, spray flow rate data, cabinet humidity data, and airflow direction data for each spray cabinet.

[0072] Furthermore, the system is also used to implement the following functions: The evaluation indicators for spray regulation include spray coverage adequacy, spray diffusion effectiveness, and spray resource utilization rate.

[0073] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0074] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A cabinet spray temperature control method based on environmental parameter sensing, characterized in that, The method includes: Real-time acquisition of environmental parameter sensing streams from the communication equipment room, as well as multiple cabinet data acquisition streams from multiple spray cabinets within the communication equipment room; Based on the environmental parameter sensing stream and the multiple cabinet data acquisition streams, the diffusion migration characteristics of the multiple spray cabinets are modeled, and a spray diffusion correlation model is established. Based on the spray diffusion correlation model, heat propagation trend prediction and humidity accumulation coupling analysis are performed on the multiple spray cabinets to obtain the heat and humidity coupling distribution results of the multiple cabinets. Based on the multi-cabinet thermal-humidity coupling distribution results, the multiple spray cabinets are dynamically adjusted in a coordinated manner to obtain an initial spray coordination scheme. A thermal-humid coupling hysteresis compensation analysis is performed on the initial spray coordination scheme to obtain a hysteresis compensation scheme, and a peak-valley electricity price linkage compensation analysis is performed on the initial spray coordination scheme to obtain an electricity price linkage compensation scheme. The initial spray coordination scheme is optimized based on the lag compensation scheme and the electricity price linkage compensation scheme to obtain the optimized spray coordination scheme.

2. The cabinet spray temperature control method based on environmental parameter sensing as described in claim 1, characterized in that, Based on the environmental parameter sensing stream and the multiple cabinet data acquisition streams, the diffusion migration characteristics of the multiple spray cabinets are modeled, and a spray diffusion correlation model is established, including: Based on the data acquisition streams from the multiple cabinets, spray characteristics are identified and direction is determined to obtain the spray diffusion coverage relationship; Based on the data acquisition streams from the multiple cabinets, internal temperature gradient analysis and inter-cabinet temperature difference coupling analysis are performed to obtain the migration relationship of spray cooling capacity. Based on the environmental parameters, the environmental airflow characteristics are identified and the disturbance impact is analyzed to obtain the environmental disturbance impact relationship; The spray diffusion correlation model is generated by fusing and modeling the spray diffusion coverage relationship, the spray cold energy migration relationship, and the environmental disturbance influence relationship through a graph neural network.

3. The cabinet spray temperature control method based on environmental parameter sensing as described in claim 1, characterized in that, Based on the spray diffusion correlation model, heat propagation trend prediction and humidity accumulation coupling analysis are performed on the multiple spray cabinets to obtain the multi-cabinet heat and humidity coupling distribution results, including: Based on the spray diffusion association model, the heat diffusion path between the multiple spray cabinets is analyzed to obtain the heat propagation trend between the cabinets; Based on the spray diffusion correlation model, humidity migration analysis is performed on the overlapping area of ​​spray diffusion among the multiple spray cabinets to obtain the cumulative humidity change trend. The thermal-humidity coupling intensity analysis is performed based on the heat propagation trend between the cabinets and the humidity accumulation trend to obtain the thermal-humidity coupling intensity distribution results. Based on the heat propagation trend between the cabinets, the cumulative humidity change trend, and the heat-humidity coupling intensity distribution results, multi-level distribution calibration of the hot spot diffusion area, humidity accumulation area, and heat-humidity coupling risk area of ​​the multiple spray cabinets is performed to generate the heat-humidity coupling distribution results of the multiple cabinets.

4. The cabinet spray temperature control method based on environmental parameter sensing as described in claim 1, characterized in that, Based on the multi-cabinet thermal-humidity coupling distribution results, the multiple spray cabinets are dynamically adjusted in a coordinated manner using multiple spray parameters to obtain an initial spray coordination scheme, including: Based on the multi-cabinet thermal-humidity coupling distribution results, a spray control history search and analysis is performed on the multiple spray cabinets to obtain the first spray search range; A spray synergy scoring model is established by assigning weights to spray regulation evaluation indicators. Based on the first range of spray search, the spray parameters of the multiple spray cabinets are collaboratively decided to obtain multiple spray candidate schemes; The multiple spray candidate schemes are scored according to the spray coordination scoring model, and the spray candidate scheme with the highest spray coordination score is selected as the current guiding spray scheme. Based on the current guided spray scheme, the first spray search range is narrowed and adjusted to obtain a second spray search range; Based on the spray coordination scoring model, the current guided spray scheme is iteratively updated according to the second range of spray search to obtain the initial spray coordination scheme that meets the preset number of iterations.

5. The cabinet spray temperature control method based on environmental parameter sensing as described in claim 4, characterized in that, Based on the multi-cabinet thermal-humidity coupling distribution results, a historical spray control search analysis is performed on the multiple spray cabinets to obtain a first spray search range, including: Based on the multi-cabinet thermal-humidity coupling distribution results, historical spray control records of the multiple spray cabinets are searched to obtain multiple cabinet matching spray control sets; Based on the matching spray control set for each cabinet, effective control samples are screened and parameter values ​​are statistically analyzed to obtain multiple spray control value ranges. Based on each historical control parameter in the multiple cabinet matching spray control set, the historical heat dissipation effect is evaluated, and the historical performance of each parameter is obtained. Based on the historical performance of each parameter, the upper and lower limits of the multiple spray control value ranges are adjusted to generate the first range for spray search.

6. The cabinet spray temperature control method based on environmental parameter sensing as described in claim 1, characterized in that, A thermo-humid coupling hysteresis compensation analysis is performed on the initial spray coordination scheme to obtain the hysteresis compensation scheme, including: Based on the initial spray coordination scheme, temperature response delay is identified in the multiple spray cabinets to obtain multiple cabinet thermal hysteresis change sequences. Based on the initial spray coordination scheme, the humidity accumulation delay of the multiple spray cabinets is identified to obtain the humidity lag change sequence of the multiple cabinets. Based on the multiple cabinet thermal hysteresis change sequences and the multiple cabinet humidity hysteresis change sequences, time sequence alignment and coupling superposition are performed to obtain the thermal-humidity coupled hysteresis sequence; Based on the thermal-humidity coupling hysteresis sequence, the spray start-up time of the multiple spray cabinets is compensated in advance, the spray duration is limited, and the spray interval is dynamically compensated to generate the hysteresis compensation scheme.

7. The cabinet spray temperature control method based on environmental parameter sensing as described in claim 1, characterized in that, A peak-valley electricity price linkage compensation analysis is performed on the initial spray coordination scheme to obtain an electricity price linkage compensation scheme, including: Based on the peak and off-peak electricity price information of the communication equipment room, the initial spray coordination scheme is compensated for low electricity price pre-cooling, and the first decision of electricity price linkage compensation is obtained. Based on the peak and valley electricity price time information, the initial spray coordination scheme is subjected to high electricity price limit compensation to obtain the second decision of electricity price linkage compensation. Based on the peak and valley electricity price time period information, the initial spray coordination scheme is compensated for smooth transition of electricity price switching, and a third decision on electricity price linkage compensation is obtained. The electricity price linkage compensation first decision, the electricity price linkage compensation second decision, and the electricity price linkage compensation third decision are time-series aligned and sorted to generate the electricity price linkage compensation scheme.

8. The cabinet spray temperature control method based on environmental parameter sensing as described in claim 1, characterized in that, Each cabinet's data acquisition stream includes equipment load data, cabinet temperature data, spray pressure data, spray flow rate data, cabinet humidity data, and airflow direction data for each spray cabinet.

9. The cabinet spray temperature control method based on environmental parameter sensing as described in claim 4, characterized in that, The evaluation indicators for spray regulation include spray coverage adequacy, spray diffusion effectiveness, and spray resource utilization rate.

10. A cabinet spray temperature control system based on environmental parameter sensing, characterized in that, The system is used to execute the cabinet spray temperature control method based on environmental parameter sensing as described in any one of claims 1-9, and the system includes: The data acquisition module is used to acquire environmental parameter sensing streams of the communication equipment room in real time, as well as multiple cabinet data acquisition streams of multiple spray cabinets in the communication equipment room; The modeling module is used to model the diffusion migration characteristics of the multiple spray cabinets based on the environmental parameter sensing stream and the multiple cabinet data acquisition stream, and to establish a spray diffusion correlation model. The predictive analysis module is used to predict the heat propagation trend and perform humidity accumulation coupling analysis on the multiple spray cabinets based on the spray diffusion correlation model, and obtain the heat and humidity coupling distribution results of the multiple cabinets; The dynamic adjustment module is used to dynamically adjust multiple spray parameters of the multiple spray cabinets in a coordinated manner based on the heat and humidity coupling distribution results of the multiple cabinets, and to obtain an initial spray coordination scheme. The compensation analysis module is used to perform thermal-humid coupling hysteresis compensation analysis on the initial spray coordination scheme to obtain the hysteresis compensation scheme, and to perform peak-valley electricity price linkage compensation analysis on the initial spray coordination scheme to obtain the electricity price linkage compensation scheme. The collaborative optimization module is used to perform collaborative optimization on the initial spray coordination scheme based on the lag compensation scheme and the electricity price linkage compensation scheme, and obtain the spray collaborative optimization scheme.

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