Refrigeration equipment control method and system for communication machine room

Abstract

The invention discloses a refrigeration equipment control method and system for a communication machine room, and relates to the field of heat dissipation control, and the method comprises the steps: obtaining machine room indoor temperature and equipment heating data of the communication machine room, and determining a preset refrigeration index according to the machine room indoor temperature and the equipment heating data; the operation parameters of the air conditioning equipment are adjusted to refrigeration operation parameters; when the temperature difference value between the indoor temperature of the machine room and the outdoor environment temperature is higher than a preset starting threshold value, starting parameters of the natural cold energy exchange unit are determined; the natural cold energy exchange unit is controlled to operate according to the starting parameters, and operating parameters of the air conditioning equipment are adjusted to be optimized operating parameters; determining a temperature change trend curve of the outdoor environment temperature based on the weather forecast information, and determining an optimization adjustment node according to the temperature change trend curve; and when the adjusting nodes are optimized, the starting parameters of the natural cold energy exchange unit and the operation parameters of the air conditioning equipment are adjusted. By implementing the application, the energy use of the refrigeration equipment in the communication machine room can be saved.

Description

Technical Field

[0001] The present application relates to the field of heat dissipation control, and in particular to a method and system for controlling refrigeration equipment for a communication room. Background Art

[0002] With the rapid development of communication technology, the scale of communication rooms continues to expand, and the density of equipment continues to increase, resulting in an increase in heat generation in the room. The normal operation of communication equipment has strict requirements on the ambient temperature, so room temperature control has become a key factor in ensuring the stable operation of communication equipment.

[0003] At present, the temperature control of communication rooms mainly adopts air conditioning cooling. By installing multiple air conditioners in the room, the room temperature is maintained by controlling the start and stop of the air conditioners according to the indoor temperature setting value. Some rooms also use a simple air duct system to deliver cold air to the equipment area through fans to achieve a cooling effect. This method can basically meet the temperature control requirements of communication equipment and ensure the normal operation of the equipment.

[0004] However, in actual applications, relying solely on air conditioning for cooling will result in high energy consumption in the computer room. Especially in transitional periods such as spring and autumn, although the outdoor temperature is low, the computer room still needs to continue to use air conditioning for cooling, which will cause a lot of energy waste. Summary of the invention

[0005] The present application provides a method and system for controlling refrigeration equipment for a communication room, which are used to save energy usage of refrigeration equipment in the communication room.

[0006] In a first aspect, the present application provides a method for controlling refrigeration equipment for a communication room, which is applied to a refrigeration control system, the method comprising: obtaining the indoor temperature of the communication room and the equipment heating data, and determining a preset refrigeration index based on the indoor temperature of the communication room and the equipment heating data; determining the refrigeration operation parameters corresponding to the preset refrigeration index, and adjusting the operating parameters of the air-conditioning equipment to the refrigeration operation parameters; obtaining the outdoor ambient temperature of the communication room, and when the temperature difference between the indoor temperature of the room and the outdoor ambient temperature is higher than a preset activation threshold, determining the activation parameters of the natural cooling energy exchange group according to the temperature difference; controlling the operation of the natural cooling energy exchange group according to the activation parameters, and adjusting the operating parameters of the air-conditioning equipment to the optimized operating parameters according to the operating status data of the natural cooling energy exchange group; determining the temperature change trend curve of the outdoor ambient temperature based on weather forecast information, and determining the optimization adjustment node according to the temperature change trend curve; at the optimization adjustment node, adjusting the activation parameters of the natural cooling energy exchange group and the operating parameters of the air-conditioning equipment.

[0007] In the above embodiment, the refrigeration control system monitors the room temperature and equipment heating conditions in real time, dynamically adjusts the refrigeration strategy based on the outdoor temperature, enables natural cold energy exchange at the right time, and optimizes the adjustment parameters in advance based on the weather forecast to predict temperature changes, thereby realizing intelligent collaborative control of the refrigeration system and improving refrigeration efficiency and energy utilization.

[0008] In combination with some embodiments of the first aspect, in some embodiments, when the temperature difference between the indoor temperature of the computer room and the outdoor ambient temperature is higher than a preset activation threshold, before the step of determining the activation parameters of the natural cooling energy exchange group based on the temperature difference, the method also includes: obtaining the initial energy consumption data of the air-conditioning equipment in the communication room when it is operating alone within the preset temperature difference range, and the optimized energy consumption data after enabling the natural cooling energy exchange group; based on the initial energy consumption data and the optimized energy consumption data, calculating the energy-saving benefits of enabling the natural cooling energy exchange group at different temperature difference nodes; calculating the unit usage cost of the natural cooling energy exchange group at the outdoor ambient temperature; determining the minimum temperature difference node where the energy-saving benefit is greater than the unit usage cost as the preset activation threshold.

[0009] In the above embodiment, the refrigeration control system determines the optimal activation threshold by analyzing the energy consumption data and energy-saving benefits under different temperature difference conditions and combining the use cost of the natural cooling energy exchange unit, ensuring that natural cooling is enabled only when it is economically beneficial, thereby avoiding additional costs caused by blind use.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the step of calculating the unit usage cost of the natural cooling energy exchange unit at the outdoor ambient temperature specifically includes: real-time collection of ambient humidity data of the area where the computer room is located; determining the dehumidification cost of the natural cooling energy exchange unit based on the ambient humidity data; calculating the operating cost of the natural cooling energy exchange unit at the outdoor ambient temperature; determining the unit usage cost of the natural cooling energy exchange unit based on the dehumidification cost and the operating cost.

[0011] In the above embodiment, the refrigeration control system monitors the ambient humidity in real time, comprehensively considers the dehumidification cost and the operating cost, and accurately calculates the overall use cost of the natural cold energy exchange unit, thereby providing a reliable economic basis for the selection of the refrigeration method.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the steps of obtaining the indoor temperature and equipment heating data of the communication room, and determining the preset cooling index based on the indoor temperature and equipment heating data of the communication room, specifically include: real-time collection of power consumption data, equipment operation status data and equipment distribution location data of communication equipment in the communication room; inputting the power consumption data, equipment operation status data and equipment distribution location data into the equipment heat generation model to obtain the equipment heating trend within a preset time period in the future; collecting the indoor temperature distribution data of the communication room, and calculating the minimum cooling capacity required to maintain the safe operation of equipment in each area based on the temperature distribution data, equipment heating trend and temperature sensitivity of various types of equipment, to obtain the preset cooling index.

[0013] In the above embodiment, the refrigeration control system collects equipment power consumption, operating status and distribution location data, combines the equipment heat generation model to predict the heating trend, and considers temperature sensitivity to accurately calculate the minimum required cooling capacity, thereby achieving accurate planning of refrigeration resources.

[0014] In combination with some embodiments of the first aspect, in some embodiments, the temperature change trend curve of the outdoor ambient temperature is determined based on weather forecast information, and the step of determining the optimized adjustment node according to the temperature change trend curve specifically includes: obtaining weather forecast information for a preset time period in the future, and determining the initial temperature change curve of the outdoor ambient temperature based on the weather forecast information; identifying the temperature change inflection point according to the initial temperature change curve, and determining the initial adjustment node in combination with the adjustment characteristics of the refrigeration equipment; within a preset time period before the initial adjustment node, correcting the initial temperature change curve according to the real-time collected outdoor ambient temperature to obtain a temperature change trend curve; based on the actual change characteristics of the temperature change trend curve and the equipment response time, correcting the initial adjustment node to an optimized adjustment node.

[0015] In the above embodiment, the refrigeration control system analyzes weather forecast information to identify the inflection point of temperature change, corrects the prediction curve in combination with real-time temperature data, and determines the optimal adjustment timing based on the equipment response characteristics, thereby achieving precise adjustment of the refrigeration system.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of adjusting the enabling parameters of the natural cold energy exchange group and the operating parameters of the air-conditioning equipment, the method also includes: collecting temperature field distribution data of the communication room in the cold channel and the hot channel; when a local hot spot is detected in the communication room according to the temperature field distribution data, calculating the temperature gradient between the hot spot area and the surrounding area; based on the temperature gradient, adjusting the air volume distribution, air supply temperature and air supply direction of the air supply outlets around the hot spot area.

[0017] In the above embodiment, the refrigeration control system detects local hot spots by real-time monitoring of the temperature field distribution, analyzes the temperature gradient and accurately adjusts the air supply parameters, thereby achieving balanced control of the room temperature and avoiding local overheating that affects the safe operation of the equipment.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of adjusting the air volume distribution, air supply temperature and air supply direction of the air supply outlets around the hot spot area based on the temperature gradient, the method also includes: real-time collection of the adjusted temperature field distribution data, and calculation of the temperature uniformity index and energy consumption change data before and after the hot spot is eliminated; determining the air supply adjustment effect based on the temperature uniformity index and the energy consumption change data; when the air supply adjustment effect is lower than the preset effect threshold, generating ventilation warning information including the hot spot location, the degree of temperature abnormality and suggested improvement measures.

[0019] In the above embodiment, the refrigeration control system establishes a complete temperature anomaly handling mechanism by evaluating the air supply adjustment effect, issuing warnings in a timely manner and providing improvement suggestions when the effect is not good, thereby ensuring the reliability of refrigeration control.

[0020] In a second aspect, an embodiment of the present application provides a refrigeration control system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the refrigeration control system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, and when the above-mentioned computer program product runs on a refrigeration control system, the above-mentioned refrigeration control system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a refrigeration control system, causes the refrigeration control system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0023] It can be understood that the refrigeration control system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The cooling index is determined by obtaining the room temperature and equipment heating data, the air conditioning operating parameters are adjusted, and the natural cold energy exchange is enabled when the indoor and outdoor temperature difference is appropriate, and the cooling strategy is dynamically optimized. At the same time, based on the weather forecast, the temperature change trend is predicted, and the operating parameters are adjusted in advance at the best time, and a complete set of intelligent collaborative control mechanisms is established. The problem of excessive energy consumption and low cooling efficiency caused by relying solely on air conditioning in related technologies is effectively solved, and efficient use of cooling resources and energy saving and consumption reduction are achieved, reducing operating costs.

[0025] 2. The real-time collection of power consumption, operating status and distribution location data of communication equipment is used to predict future heating trends in combination with the equipment heat generation model, and the minimum cooling capacity required to maintain safe operation of the equipment is accurately calculated based on temperature distribution data and equipment temperature sensitivity. This effectively solves the problems of unreasonable cooling capacity configuration and serious energy waste in related technologies, and realizes the precise placement and on-demand allocation of cooling resources.

[0026] 3. By collecting the temperature distribution data of the hot and cold channels in the computer room, real-time monitoring of local hot spots, and combining the temperature gradient analysis of the hot spot area and the surrounding area, the air supply parameters can be accurately adjusted. This effectively solves the problem of uneven temperature distribution in the computer room and local overheating affecting the safe operation of equipment in related technologies, and realizes balanced control and refined management of the computer room temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of a method for controlling refrigeration equipment for a communication room in an embodiment of the present application; Figure 2 is another flow chart of the method for controlling refrigeration equipment for a communication room in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of a refrigeration control system in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] It should be noted that the refrigeration control system in this application includes a PLC control module, a data acquisition module (including an indoor temperature sensor, an outdoor temperature sensor, an electric energy sensor, a current and voltage sensor), a natural cooling energy exchange unit, air conditioners (multiple units), air inlet ducts, air supply ducts, etc. Among them, the PLC control module obtains the temperature inside and outside the machine room through the indoor and outdoor temperature sensors of the data acquisition module, compares the temperature set by the PLC control module, and controls the start and stop of the natural cooling energy exchange unit.

[0031] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0032] With the rapid deployment of 5G networks, the equipment density and power consumption of communication rooms are constantly increasing. Taking the core communication room of a provincial capital city as an example, a large number of 5G baseband processing units, transmission equipment and servers are deployed in the room, and the equipment power consumption density reaches 8-12kW / cabinet. Such a high heat density makes the temperature control of the room face huge challenges, especially during peak usage periods such as the Spring Festival, the temperature of a single cabinet area may exceed 35°C, seriously affecting the reliability of the equipment. At the same time, the continuous use of air conditioners for cooling leads to high energy consumption, and electricity expenses account for more than 40% of operating costs. Therefore, how to reduce cooling energy consumption while ensuring the reliable operation of equipment has become an urgent problem to be solved.

[0033] In the related art, the temperature management of the equipment room can be achieved by using an air conditioning refrigeration control method with a fixed temperature threshold, which consumes a lot of energy. The following describes a scenario in which the refrigeration equipment control method for a communication equipment room in the related art is used.

[0034] A communication room uses a traditional constant temperature control solution, setting the return air temperature of the air conditioner to 24°C. When the temperature exceeds the threshold, the cooling starts, and when it falls below the threshold, it stops. Although this method is simple to operate, it has obvious defects: first, the fixed temperature threshold cannot adapt to changes in equipment load, resulting in over-cooling in some areas and too high temperatures in other areas; second, in spring and autumn when the outdoor temperature is suitable, the air conditioner still needs to be turned on for cooling, resulting in energy waste.

[0035] The use of the refrigeration equipment control method for a communication room in the embodiment of the present application can achieve refrigeration energy saving. The following describes a scenario in which the refrigeration equipment control method for a communication room in the present application is used.

[0036] By adopting the control method for refrigeration equipment for communication rooms in the embodiment of the present application, the room realizes precise refrigeration based on load perception. The system dynamically adjusts the refrigeration strategy by real-time monitoring of data such as equipment heat generation and temperature distribution. Natural cooling is automatically enabled when the outdoor temperature is suitable, and the operating parameters are optimized in advance according to the weather forecast. At the same time, a rapid response mechanism for local hotspots is established to accurately control the temperature distribution by adjusting the air supply parameters. After adopting this solution, the temperature control of the computer room is more uniform and stable, and the temperature fluctuations in each area are controlled within the range of ±1°C. More importantly, full use of natural cooling and intelligent adjustment strategies has reduced refrigeration energy consumption by 25%, saving more than 100,000 yuan in electricity expenses annually.

[0037] It can be seen that the method for controlling refrigeration equipment for a communication room in the embodiment of the present application can achieve precise temperature control while also saving energy and reducing consumption.

[0038] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of a method for controlling refrigeration equipment for a communication room in an embodiment of the present application.

[0039] S101. Acquire indoor temperature and equipment heating data of a communication room, and determine a preset cooling index according to the indoor temperature and equipment heating data of the room.

[0040] Among them, the indoor temperature of the computer room refers to the temperature data of the internal space of the communication room, including the temperature distribution in different areas and at different heights; the equipment heat data refers to the heat data generated by the communication equipment during operation, including the power consumption data of the equipment, the heat dissipation power and other information; the preset cooling index represents the cooling demand target value calculated based on the temperature and heat data, which is used to guide the operation control of the subsequent cooling equipment; the communication room refers to a special room for placing communication equipment, which is usually equipped with a temperature control system and ventilation facilities.

[0041] During the normal operation of the communication room, the refrigeration control system needs to monitor the temperature conditions in the room and the heat generated by the equipment in real time in order to adjust the refrigeration strategy in a timely manner. Specifically, the refrigeration control system first collects real-time temperature data through temperature sensors distributed throughout the room, and obtains the power consumption data and operating status of each communication device; then inputs these data into the preset heat balance model, considers the heat dissipation characteristics of the equipment, spatial position relationship and other factors, and calculates the minimum cooling capacity required to maintain the normal operation of the equipment; finally, the calculation result is used as the preset refrigeration index for subsequent refrigeration control.

[0042] In some embodiments, the collection of room temperature and equipment heating data and the determination of cooling indicators can be achieved in a variety of ways: Optionally, the cooling control system can establish a three-dimensional temperature field model of the room, use an interpolation algorithm to process discrete temperature sampling point data, generate a complete temperature distribution map, calculate the heat accumulation rate in combination with the equipment power consumption data, and finally determine the required cooling capacity based on the heat balance equation; Optionally, the cooling control system can also use machine learning methods to analyze the correlation between historical temperature data and equipment operation data, establish a prediction model, predict the cooling demand in the future based on the currently collected data, and use the prediction result as the cooling indicator. It is understandable that other mathematical models or algorithms can also be used to implement temperature monitoring and cooling demand calculation, which is not limited here.

[0043] S102: Determine refrigeration operation parameters corresponding to preset refrigeration indicators, and adjust the operation parameters of the air-conditioning equipment to the refrigeration operation parameters.

[0044] Among them, the preset cooling index refers to the target cooling demand value calculated based on the room temperature and equipment heating conditions; the cooling operation parameters represent the specific control parameters required for the operation of the air-conditioning equipment, including compressor frequency, fan speed, refrigerant flow, target temperature and other technical indicators; air-conditioning equipment refers to the refrigeration device used for room temperature regulation, usually including precision air conditioners, air-cooled units and other equipment; operating parameter adjustment refers to the process of real-time optimization and control of the working status of the air-conditioning equipment according to the cooling demand.

[0045] After obtaining the preset refrigeration index, the refrigeration control system needs to convert the refrigeration demand into specific executable equipment control instructions. Specifically, the refrigeration control system first calculates the optimal combination of operating parameters required to meet the refrigeration demand based on the preset refrigeration index, combined with the performance curve and operating characteristics of the air-conditioning equipment; then evaluates the current operating status of the air-conditioning equipment and generates a parameter adjustment strategy for transitioning from the current state to the target state; finally, through the control interface, the various operating parameters of the air-conditioning equipment are gradually adjusted, including adjusting the compressor frequency to change the cooling capacity, adjusting the fan speed to optimize the air supply effect, setting the return air temperature threshold to control the start and stop, etc., until the expected refrigeration effect is achieved.

[0046] In some embodiments, the determination and adjustment of refrigeration operating parameters can be achieved in a variety of ways: Optionally, the refrigeration control system can adopt a parameter optimization method based on a thermodynamic model, by establishing a mathematical model of the refrigeration system, considering multiple objective functions such as refrigeration capacity, energy consumption ratio, equipment life, etc., using a multi-objective optimization algorithm to calculate the optimal operating parameters, and then using a PID controller to achieve smooth adjustment of the parameters; Optionally, the refrigeration control system can also be based on fuzzy control theory, using preset refrigeration indicators and current operating status as input variables, and by setting fuzzy rules and membership functions, inferring appropriate parameter adjustment amounts to achieve intelligent control of refrigeration equipment. It is understandable that other control algorithms or optimization strategies can also be used to achieve the determination and adjustment process of operating parameters, which is not limited here.

[0047] S103, obtaining the outdoor ambient temperature of the communication room, and when the temperature difference between the indoor temperature of the room and the outdoor ambient temperature is higher than a preset activation threshold, determining the activation parameters of the natural cooling energy exchange group according to the temperature difference.

[0048] Among them, the outdoor ambient temperature refers to the atmospheric temperature outside the communication room; the temperature difference represents the absolute difference between the indoor and outdoor temperatures; the preset activation threshold refers to the minimum temperature difference requirement for triggering the start of the natural cooling energy exchange unit; the natural cooling energy exchange unit refers to an energy-saving device that uses the indoor and outdoor temperature difference for heat exchange; the activation parameters include operating control parameters such as heat exchanger opening, fan speed, and heat exchange flow rate.

[0049] The refrigeration control system needs to continuously monitor the indoor and outdoor temperature conditions to determine whether the conditions for using natural cooling are met. Specifically, the refrigeration control system collects ambient temperature data in real time through the outdoor temperature sensor and compares and calculates it with the temperature inside the computer room; when the temperature difference exceeds the pre-set activation threshold, the system will calculate the optimal natural cooling energy exchange parameters based on factors such as the current temperature difference and heat exchange requirements; these parameters involve multiple aspects such as heat exchange efficiency, energy consumption level, and system stability, ensuring that the natural cooling system can operate efficiently and reliably.

[0050] In some embodiments, temperature difference monitoring and enabling parameter determination can be achieved in a variety of ways: Optionally, the refrigeration control system can adopt a dynamic threshold adjustment method, establish a temperature difference-benefit model by analyzing historical operating data, dynamically calculate the optimal enabling threshold based on current electricity prices, equipment efficiency and other factors, and then calculate the heat exchanger opening and fan speed based on heat transfer theory, and achieve parameter optimization through model predictive control; Optionally, the refrigeration control system can also adopt an adaptive control strategy, establish a feedback adjustment mechanism through real-time monitoring of heat exchange efficiency and energy consumption data, and automatically adjust the enabling parameters according to the actual operating results to ensure that the system always operates under optimal conditions. It is understandable that other intelligent algorithms or control strategies can also be used to achieve the enabling control of the natural cooling system, which is not limited here.

[0051] S104. Control the operation of the natural cooling energy exchange unit according to the enabled parameters, and adjust the operating parameters of the air-conditioning equipment to optimized operating parameters according to the operating status data of the natural cooling energy exchange unit.

[0052] Among them, the operating status data of the natural cooling energy exchange unit includes operating indicators such as heat exchange efficiency, energy consumption, and system stability; optimized operating parameters refer to the air-conditioning equipment control parameters that are readjusted after considering the natural cooling effect; the operating parameter adjustment process refers to the coordinated control strategy of the air-conditioning and natural cooling systems.

[0053] The refrigeration control system needs to coordinate the operation of natural cooling energy exchange and traditional air conditioning. Specifically, the refrigeration control system first starts the natural cooling energy exchange unit according to the activation parameters and continuously monitors its operating status; then, according to the actual cooling effect of natural cooling, it dynamically calculates the remaining cooling demand; finally, according to the remaining cooling demand, it optimizes and adjusts the operating parameters of the air conditioning equipment to achieve the best coordination of the two cooling methods. Specifically, when the outdoor temperature reaches a certain temperature, the temperature difference between the natural cooling energy outside the computer room and the computer room is used to cool the computer room through the heat exchange core and the internal and external circulation fans in the natural cooling energy exchange unit; by adjusting the return air temperature of the air conditioner to reduce the start-up time of the air conditioner in the computer room, an energy-saving system is achieved. When the outdoor temperature cannot cool, the air conditioner starts cooling, and the internal circulation fan of the natural cooling energy unit adjusts the temperature flow in the computer room to prevent local overheating.

[0054] S105: Determine a temperature change trend curve of the outdoor ambient temperature based on the weather forecast information, and determine an optimization adjustment node according to the temperature change trend curve.

[0055] Among them, weather forecast information includes meteorological data such as temperature and humidity in the future; the temperature trend curve refers to the predicted curve of outdoor temperature changes over time; the optimization adjustment node indicates the time point when the cooling strategy needs to be adjusted.

[0056] The refrigeration control system needs to have predictive adjustment capabilities. Specifically, the refrigeration control system first obtains weather forecast data provided by the meteorological department, and combines historical data to establish a temperature change model; then identifies the key time points and change trends of temperature changes through data analysis; finally, based on the adjustment characteristics and response time of the refrigeration equipment, the best parameter adjustment time is determined in advance to achieve refrigeration control adjustment.

[0057] In some embodiments, temperature trend prediction and adjustment node determination can be achieved in a variety of ways: Optionally, the refrigeration control system can use a time series analysis method, combine historical meteorological data and weather forecast information to establish an ARIMA prediction model, predict future temperature change trends through the model, and then combine the temperature change rate and equipment adjustment characteristics to determine the key adjustment time point; Optionally, the refrigeration control system can also use a deep learning network to establish a temperature prediction model by training historical data, combine multi-source meteorological information to improve prediction accuracy, and determine the best adjustment time through sensitivity analysis. It is understandable that other prediction algorithms can also be used to achieve temperature trend analysis and adjustment optimization, which is not limited here.

[0058] It should be noted that the temperature prediction model is based on the ARIMA algorithm framework and combines deep learning methods to build a complete temperature change prediction system. The model training phase requires a large amount of historical temperature series data, weather forecast information, and equipment operation records as input, and optimizes model parameters by minimizing two key indicators: prediction error and time response delay. The core architecture of the model includes a time series feature extractor, a trend analyzer, and a periodicity recognition module. These components work together to achieve accurate modeling of temperature changes. In actual operation, the model continuously receives real-time temperature data and environmental parameters, and outputs future temperature change trends through complex mathematical operations.

[0059] It should be noted that the application of ARIMA (autoregressive integrated moving average model) in temperature trend prediction mainly includes three core steps: first, the historical temperature data is tested for stationarity and differential processing is performed to eliminate the trend and periodicity effects of the time series; then the model order is determined by autocorrelation function (ACF) and partial autocorrelation function (PACF) analysis, and a mathematical model containing autoregressive terms, differential terms and moving average terms is established; finally, the model parameters are determined using the maximum likelihood estimation method. In practical applications, the model will be corrected in combination with weather forecast data, and the prediction results will be continuously updated through a sliding window method. For example, when it is detected that the outdoor temperature may drop by 5 degrees in the next 4 hours, the system will adjust the cooling strategy 2 hours in advance to ensure full utilization of natural cooling resources. The prediction results of the model will also be compared and verified with historical data of the same period, and the model parameters will be continuously optimized by calculating the prediction error.

[0060] S106. When optimizing the adjustment node, adjust the activation parameters of the natural cooling energy exchange unit and the operating parameters of the air-conditioning equipment.

[0061] Among them, the optimized adjustment node refers to the optimal adjustment time predetermined by the system; parameter adjustment includes the coordinated optimization of natural cooling energy exchange units and air-conditioning equipment; the adjustment of activation parameters and operating parameters needs to take into account equipment characteristics and environmental changes.

[0062] The refrigeration control system needs to achieve dynamic optimization of the refrigeration strategy at the best time. Specifically, when the refrigeration control system reaches the predetermined optimization adjustment node, it first evaluates the current refrigeration effect and energy consumption; then adjusts the operating parameters of the natural cooling energy exchange unit in advance according to the predicted temperature change trend; and adjusts the working state of the air-conditioning equipment accordingly to ensure that the entire refrigeration system can smoothly transition to the new operating state and maintain the continuity and stability of the refrigeration effect.

[0063] Specifically, the PLC control module in the refrigeration control system controls the start and stop of the supply and exhaust fans of the natural cooling energy exchange unit according to the indoor and outdoor temperature values, and accumulates the start time, while detecting the current, voltage and required power, so as to determine the operating status of the natural cooling energy exchange unit. The PLC control module adjusts the start and stop temperature of the natural cooling energy exchange unit according to the indoor temperature changes and the heat exchange amount of the natural cooling energy exchange unit, and reduces the start time of the air conditioner in the computer room by adjusting the return air temperature of the air conditioner to achieve an energy-saving system. At the same time, the PLC control module gives priority to accepting the host computer's instructions to control the natural cooling energy exchange unit, and regularly uploads information such as temperature, unit operating time, required power, unit failure, etc. to the host computer to facilitate monitoring of the computer room.

[0064] In some embodiments, parameter optimization and adjustment can be achieved in a variety of ways: Optionally, the refrigeration control system can use a model predictive control method to establish a comprehensive optimization framework that includes equipment characteristics, environmental impacts, and energy consumption models, and calculate the optimal control sequence through rolling optimization to achieve a smooth transition and continuous optimization of the refrigeration equipment; Optionally, the refrigeration control system can also be based on an event-driven control strategy, through real-time monitoring of system status and environmental changes, trigger parameter optimization calculations at critical moments, dynamically adjust the refrigeration strategy, and ensure that the system always maintains optimal performance. It is understandable that other optimization control methods can also be used to achieve dynamic adjustment of the refrigeration system, which is not limited here.

[0065] It should be noted that the specific implementation process of the model predictive control method (MPC) in the optimization of the refrigeration system includes the following key links: first, a mathematical model including the dynamic characteristics of the refrigeration equipment, the law of heat transfer and energy consumption is established. The model needs to consider factors such as equipment startup delay and temperature response characteristics; then a multi-objective optimization function including refrigeration effect, energy efficiency and equipment life is defined, and corresponding constraints are set, such as temperature control range, equipment operation restrictions, etc.; on this basis, the system predicts the system state in the future for a period of time in each control cycle and solves the optimal control sequence. The optimization solution process adopts a rolling time domain strategy, that is, only the first step of the current optimal solution is executed each time, and then the entire prediction sequence is recalculated according to the latest state. This method can effectively deal with complex problems such as system delay, constraints and multivariable coupling, and realize the optimal control of the refrigeration system. For example, when a load change is predicted, the MPC controller will adjust the operating parameters of the refrigeration equipment in advance to avoid temperature fluctuations while ensuring that energy consumption is minimized.

[0066] The above embodiment mainly includes the basic refrigeration control process. In actual application, it is also possible to add humidity control, airflow organization optimization and other functional modules according to the specific scene requirements to further improve the system performance. The following is a supplement to the scene of this embodiment.

[0067] As the usage time increases, the system continues to accumulate operating data and optimize the control strategy. By analyzing historical data, the system finds the usage patterns of equipment in different areas, such as the higher load of computing-intensive servers during the daytime on weekdays and the increased load of storage devices during backup in the early morning. Based on this, the system pre-adjusts the cooling capacity of the corresponding area to achieve precise cooling. At the same time, the system incorporates weather forecast information into the decision-making model and adjusts the cooling strategy in advance when future temperature changes are expected. For example, when a cold wave is predicted to come, the natural cooling ratio is increased in advance; when it is predicted that the weather will turn from cloudy to sunny, it is adjusted to the optimal hybrid cooling mode in time. This predictive adjustment further improves the efficiency of the system, reducing the cooling energy consumption by another 15% compared with the original solution.

[0068] After combining the above scenarios, the following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the method for controlling refrigeration equipment for a communication room in an embodiment of the present application.

[0069] S201. Acquire the indoor temperature of the communication room and the heating data of the equipment, and determine the preset cooling index according to the indoor temperature of the room and the heating data of the equipment.

[0070] Referring to step S101 , the refrigeration control system determines a preset refrigeration index.

[0071] In some embodiments, the refrigeration control system will perform refrigeration planning based on the equipment heat generation model; that is, the refrigeration control system will collect power consumption data, equipment operating status data and equipment distribution location data of the communication equipment in the communication room in real time; the power consumption data, equipment operating status data and equipment distribution location data are then input into the equipment heat generation model to obtain the equipment heating trend within a preset time period in the future; by collecting the indoor temperature distribution data of the communication room, and finally according to the temperature distribution data, equipment heating trend and temperature sensitivity of various types of equipment, the minimum cooling capacity required to maintain the safe operation of equipment in each area is calculated to obtain the preset refrigeration index.

[0072] Among them, the equipment heat generation model refers to a mathematical model that describes the heat generation law of communication equipment; power consumption data indicates the energy consumption during the operation of the equipment; equipment operation status data refers to information such as the equipment's workload and operating parameters; equipment distribution location data refers to the spatial layout information of the equipment in the computer room; equipment heating trend indicates the prediction of heat generation in the future; temperature distribution data refers to the real-time temperature value of each point in the computer room; temperature sensitivity is used to indicate the equipment's ability to withstand temperature changes; and minimum cooling capacity refers to the cooling demand required to maintain normal operation of the equipment.

[0073] The refrigeration control system needs to continuously predict and plan the refrigeration demand during the operation of the equipment. Specifically, the refrigeration control system first establishes an equipment database containing equipment types, power characteristics and heat dissipation characteristics, and collects real-time operation data through a distributed sensor network; then the collected data is input into a comprehensive heat generation model that considers heat conduction, heat convection and heat radiation, and calculates the heat accumulation in the future period; finally, combined with the temperature distribution measurement results and the equipment operating temperature requirements, the required cooling capacity of each area is determined through heat balance calculation to form a complete refrigeration planning solution.

[0074] In some embodiments, refrigeration planning based on equipment heat generation can be achieved in a variety of ways: Optionally, the refrigeration control system can use a thermodynamic modeling method to first build a three-dimensional heat transfer model that takes into account the equipment layout, then calculate the heat distribution by solving the energy conservation equation and the heat conduction equation, and finally use the finite element method to perform numerical simulation to predict future heat change trends; Optionally, the refrigeration control system can also use a machine learning method to train the equipment thermal characteristic model by analyzing historical operating data, and dynamically update the prediction results in combination with real-time monitoring data to achieve accurate prediction of refrigeration demand. It is understandable that other numerical calculation or prediction methods can also be used to achieve refrigeration planning, which is not limited here.

[0075] It should be noted that the equipment heat generation model adopts a deep learning architecture and is trained and optimized by inputting a large amount of historical operation data. The model first collects the equipment's power consumption data, operating status information, ambient temperature records, and spatial distribution parameters of the equipment. After preprocessing, these data are input into the neural network for training. During the training process, the model continuously adjusts the network parameters by comparing the error between the predicted heat and the actual measured value, and finally establishes accurate heat prediction capabilities. The core structure of the model contains mathematical expressions that describe equipment characteristics, heat conduction laws, and spatial relationships, and can accurately depict the complex relationship between heat generation and various influencing factors. In actual applications, the model receives real-time collected equipment operating parameters and environmental data, and outputs predicted values ​​for heat generation in the future.

[0076] S202: Determine refrigeration operation parameters corresponding to preset refrigeration indicators, and adjust the operation parameters of the air-conditioning equipment to the refrigeration operation parameters.

[0077] Referring to step S102, the refrigeration control system adjusts the refrigeration operation parameters of the air-conditioning equipment.

[0078] S203, obtaining the initial energy consumption data of the air-conditioning equipment in the communication room when it operates alone within a preset temperature difference range, and the optimized energy consumption data after the natural cooling energy exchange group is enabled.

[0079] Among them, the preset temperature difference range refers to the indoor and outdoor temperature difference range where natural cooling may be enabled; the initial energy consumption data refers to the energy consumption when only air-conditioning equipment is used, including indicators such as electricity consumption and operating time; the optimized energy consumption data refers to the comprehensive energy consumption data after combining natural cooling; energy consumption data collection refers to recording the energy usage during equipment operation through energy consumption metering equipment.

[0080] The refrigeration control system needs to evaluate the energy consumption performance of the two refrigeration methods under different working conditions. Specifically, the refrigeration control system first records the energy consumption data of the air-conditioning equipment when it is operating independently within a given temperature difference range, including the operating power and COP value under different load and temperature difference conditions; then, the natural cooling energy exchange unit is enabled under the same conditions to collect comprehensive energy consumption data including the two refrigeration methods; finally, the collected data is standardized and an energy consumption database is established to provide data support for subsequent energy-saving analysis.

[0081] S204. Based on the initial energy consumption data and the optimized energy consumption data, calculate the energy saving benefits of enabling the natural cooling energy exchange group at different temperature difference nodes.

[0082] Among them, temperature difference nodes refer to sampling points with different indoor and outdoor temperature differences; energy saving benefits refer to the energy costs saved after using natural cooling; the calculation process includes energy consumption difference analysis and economic benefit evaluation; temperature difference node analysis refers to the process of evaluating energy saving effects under different temperature difference conditions.

[0083] The refrigeration control system needs to determine the economic benefits of natural cooling through data analysis. Specifically, the refrigeration control system first classifies and organizes the collected energy consumption data according to the temperature difference nodes; then calculates the energy consumption difference before and after using natural cooling at each temperature difference node; finally, combined with energy price factors, quantifies the economic savings under different temperature difference conditions and forms an energy saving benefit curve.

[0084] In some embodiments, the calculation and analysis of energy-saving benefits can be achieved in a variety of ways: Optionally, the refrigeration control system can use a comprehensive evaluation method to establish an economic model that includes electricity costs, maintenance costs, and equipment depreciation, and calculate the actual cost savings by combining time electricity prices and load characteristics, and evaluate the impact of different factors on energy-saving benefits through sensitivity analysis; Optionally, the refrigeration control system can also use data mining technology to analyze historical operating data to discover energy consumption patterns, establish an energy-saving effect prediction model, and achieve accurate evaluation of energy-saving potential under different working conditions. It is understandable that other economic analysis or optimization methods can also be used to achieve energy-saving benefit calculations, which are not limited here.

[0085] S205. Calculate the unit usage cost of the natural cooling energy exchange unit at the outdoor ambient temperature.

[0086] Among them, the unit use cost includes equipment operating electricity charges, maintenance costs, depreciation costs, etc.; outdoor ambient temperature is a key factor affecting equipment efficiency; cost calculation needs to take into account various expenditures throughout the life cycle of the equipment.

[0087] The refrigeration control system needs to evaluate the overall operating cost of the natural cooling equipment. Specifically, the refrigeration control system first collects the technical parameters and operating characteristics of the equipment; then calculates the actual operating power consumption of the equipment based on the outdoor temperature conditions; and finally comprehensively considers factors such as maintenance costs and life loss to obtain a complete cost evaluation result.

[0088] In some embodiments, the calculation and evaluation of the use cost can be achieved in a variety of ways: Optionally, the refrigeration control system can establish a comprehensive cost accounting model, by considering the full life cycle costs such as equipment purchase costs, installation and commissioning costs, operation and maintenance costs, and scrapping costs, combined with the equipment life and discount rate, to calculate the equivalent use cost per unit time; Optionally, the refrigeration control system can also adopt a dynamic cost evaluation method, by real-time monitoring of equipment performance parameters, combined with preventive maintenance strategies, and dynamically updating the cost prediction model to achieve accurate cost accounting. It is understandable that other cost analysis or evaluation methods can also be used to achieve use cost calculation, which is not limited here.

[0089] In some embodiments, the refrigeration control system needs to perform humidity control; that is, the refrigeration control system will collect the environmental humidity data of the area where the computer room is located in real time; then determine the dehumidification cost of the natural cooling energy exchange unit based on the environmental humidity data; at the same time, calculate the operating cost of the natural cooling energy exchange unit under the outdoor ambient temperature; finally, determine the unit use cost of the natural cooling energy exchange unit based on the dehumidification cost and the operating cost.

[0090] Among them, environmental humidity data refers to the air moisture content information in the computer room and surrounding areas; dehumidification cost refers to the energy expenditure required to reduce air humidity; operating cost refers to the electricity and maintenance costs required for the normal operation of the equipment; unit use cost refers to the sum of dehumidification cost and operating cost.

[0091] The refrigeration control system needs to consider the additional costs caused by humidity when performing natural cooling. Specifically, the refrigeration control system first obtains indoor and outdoor relative humidity and absolute humidity data through the humidity sensor network; then calculates the energy consumption required to achieve the dehumidification target based on the target humidity requirements; at the same time, considers the operating efficiency of the equipment under the current temperature and humidity conditions and calculates the basic operating cost; finally, the two parts of the cost are weighted and counted to obtain a comprehensive cost indicator.

[0092] In some embodiments, the calculation of humidity control costs can be achieved in a variety of ways: Optionally, the refrigeration control system can establish a heat and moisture treatment model based on the enthalpy-humidity diagram, calculate the energy demand of the air state change process, analyze the dehumidification efficiency in combination with the equipment performance curve, and then quantify the treatment cost by considering the energy price factor.

[0093] It should be noted that the heat and moisture treatment model is based on the basic principles of thermodynamics, and a complete air state processing framework is constructed through the theory of enthalpy-humidity diagram. A large amount of air state parameters, energy consumption data and equipment performance records need to be input during the training process of the model, which are used to verify and optimize the calculation accuracy of the model. The core structure of the model contains a detailed mathematical description of the heat and moisture exchange process, and combines the performance characteristic curve of the refrigeration equipment to form a complete state calculation system. In specific applications, the model receives the current air state parameters and target parameters as input, and outputs the optimal processing path and required energy through complex thermodynamic calculations, providing precise guidance for dehumidification control and energy consumption optimization. The model not only takes into account the basic heat and moisture exchange process, but also includes multiple practical factors such as equipment efficiency and energy cost to ensure that the calculation results have practical engineering application value.

[0094] Among them, the construction of the heat and moisture treatment model based on the enthalpy-humidity diagram first needs to determine the air state point in the enthalpy-humidity coordinate system, the horizontal axis represents the moisture content (d), the vertical axis represents the specific enthalpy value (h), and the complete state grid is constructed through isotherms, relative humidity lines and enthalpy value lines. When performing dehumidification analysis, the model determines the initial state point A (d1, h1) based on the measured dry bulb temperature and relative humidity, and then determines the target state point B (d2, h2) according to the equipment requirements. The change path between the state points reflects the heat and moisture treatment process, where the vertical downward straight line represents isenthalpic cooling and dehumidification, and the straight line tilted to the upper left represents cooling and dehumidification. By calculating the enthalpy difference Δh and moisture content change Δd between the state points, combined with the air handling capacity G, the sensible heat load Qs=G×cp×Δt and the latent heat load Ql=G×r×Δd can be obtained, where cp is the constant pressure specific heat capacity and r is the latent heat of vaporization. For example, when air at 25°C and 70% relative humidity needs to be treated to 20°C and 50% relative humidity, the total heat that needs to be removed can be determined using the psychrometric diagram and the required energy input can be calculated based on the energy efficiency ratio (COP) of the equipment.

[0095] S206. Determine the minimum temperature difference node where the energy-saving benefit is greater than the unit use cost as the preset activation threshold.

[0096] Among them, the minimum temperature difference node refers to the critical temperature difference value that meets the economic requirements; the preset activation threshold is used to determine whether to enable natural cooling; the temperature difference node selection needs to balance energy-saving benefits and operating costs.

[0097] The refrigeration control system needs to determine the economic activation conditions for natural cooling. Specifically, the refrigeration control system first compares and analyzes the energy-saving benefit curve with the use cost curve; then uses numerical calculations to find the minimum temperature difference point where the benefit curve exceeds the cost curve; finally, the temperature difference value is set as the activation threshold for natural cooling to ensure the economy of the system operation.

[0098] In some embodiments, the determination of the activation threshold can be achieved in a variety of ways: Optionally, the refrigeration control system can use an optimization algorithm to establish an objective function that includes energy-saving benefits and usage costs, consider constraints such as equipment start-stop losses and adjustment lags, solve the optimal activation temperature difference, and verify the stability of the results through sensitivity analysis; Optionally, the refrigeration control system can also use a fuzzy decision method, by establishing an evaluation system that considers multiple influencing factors, combining expert experience to set fuzzy rules, comprehensively evaluate the economic efficiency of different temperature difference nodes, and select the optimal activation threshold. It is understandable that other decision optimization or evaluation methods can also be used to determine the activation threshold, which is not limited here.

[0099] S207, obtaining the outdoor ambient temperature of the communication room, and when the temperature difference between the indoor temperature of the room and the outdoor ambient temperature is higher than a preset activation threshold, determining the activation parameters of the natural cooling energy exchange group according to the temperature difference.

[0100] Referring to step S103, the refrigeration control system determines the activation parameters of the free cooling energy exchange unit.

[0101] S208. Control the operation of the natural cooling energy exchange unit according to the enabled parameters, and adjust the operating parameters of the air-conditioning equipment to optimized operating parameters according to the operating status data of the natural cooling energy exchange unit.

[0102] Referring to step S104, the refrigeration control system adjusts the operating parameters of the air-conditioning equipment.

[0103] S209: Determine a temperature change trend curve of the outdoor ambient temperature based on the weather forecast information, and determine an optimization adjustment node according to the temperature change trend curve.

[0104] Referring to step S105 , the refrigeration control system determines the optimal adjustment node.

[0105] In some embodiments, the refrigeration control system needs to monitor temperature changes in real time; that is, the refrigeration control system will obtain weather forecast information for a preset time period in the future, and determine the initial temperature change curve of the outdoor ambient temperature based on the weather forecast information; identify the temperature change inflection point according to the initial temperature change curve, and determine the initial adjustment node in combination with the adjustment characteristics of the refrigeration equipment; within a preset time period before the initial adjustment node, correct the initial temperature change curve according to the real-time collected outdoor ambient temperature to obtain a temperature change trend curve; based on the actual change characteristics of the temperature change trend curve and the equipment response time, correct the initial adjustment node to an optimized adjustment node.

[0106] Among them, weather forecast information refers to meteorological data such as temperature and humidity in the future period; the initial temperature change curve represents the predicted temperature change trend; the temperature change inflection point refers to the moment when the temperature trend changes; the adjustment characteristics refer to the time characteristics of the equipment's response to control instructions; the initial adjustment node indicates the time point when the equipment parameters are expected to be adjusted; the temperature change trend curve refers to the corrected temperature change prediction curve.

[0107] The refrigeration control system needs to determine the optimal adjustment time during the temperature change process. Specifically, the refrigeration control system first obtains the forecast data provided by the meteorological department and constructs a temperature change model through numerical analysis methods; then uses derivative analysis to identify the key time points of temperature change, and determines the preliminary adjustment plan in combination with characteristics such as equipment startup delay; finally, the prediction model is continuously corrected through real-time temperature data to optimize the selection of adjustment time points to ensure the control effect.

[0108] In some embodiments, temperature change monitoring and adjustment optimization can be achieved in a variety of ways: Optionally, the refrigeration control system can use a time series analysis method to analyze the temperature change pattern by establishing an ARIMA prediction model, combining wavelet transform to identify the temperature inflection point, and considering the equipment response characteristics to determine the optimal adjustment time.

[0109] It should be noted that the application of the ARIMA forecasting model (an autoregressive integrated moving average model) in temperature forecasting first requires a stationarity test of the temperature time series data, and the augmented Dickey-Fuller test (ADF) is used to determine whether the sequence has a unit root. For non-stationary sequences, it is necessary to convert them into stationary sequences through differential operations, and the differential order d represents the order of the integral term. Then, the order of the autoregressive term p and the moving average term q is determined by analyzing the autocorrelation function (ACF) and partial autocorrelation function (PACF). The model can be expressed as: Xt=φ1Xt₋1+...+φpXt₋p+εt+θ1εt₋1+...+θqεt₋q, where φᵢ is the autoregressive coefficient, θᵢ is the moving average coefficient, and εt is the white noise sequence. The model parameters are determined by the maximum likelihood estimation method, and the AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) are used for model selection. For example, when predicting temperature changes within 24 hours on a certain day, an ARIMA (2, 1, 1) model can be established based on the temperature data of the previous few days, which includes a second-order autoregressive term, a first-order difference, and a first-order moving average term.

[0110] S210. When optimizing the adjustment node, adjust the activation parameters of the natural cooling energy exchange unit and the operating parameters of the air-conditioning equipment.

[0111] Referring to step S106, the refrigeration control system will adjust the natural cooling energy exchange unit and the air conditioning equipment.

[0112] S211. Collect temperature field distribution data in the cold channel and hot channel of the communication room.

[0113] Among them, the cold channel refers to the air supply area in the front of the cabinet in the computer room, which is used to supply cold air to the equipment; the hot channel refers to the return air area in the rear of the cabinet in the computer room, which is used to discharge the heat generated by the equipment; the temperature field distribution data represents the distribution state of temperature in space, including the temperature value and its spatial coordinate information; data acquisition refers to the process of obtaining real-time temperature information through a temperature sensor network.

[0114] The refrigeration control system needs to continuously monitor the temperature distribution of the hot and cold channels during the normal operation of the communication room to ensure the uniformity of the cooling effect. Specifically, the refrigeration control system first collects temperature data in three dimensions: height, depth, and width through the temperature sensor array distributed at different positions in the hot and cold channels; then performs spatial interpolation processing on the collected discrete temperature data to build a complete three-dimensional temperature field model; finally, through data visualization technology, a temperature distribution cloud map is generated to intuitively display the temperature distribution characteristics in the room.

[0115] S212: When a local hot spot is detected in the communication room according to the temperature field distribution data, the temperature gradient of the hot spot area and the surrounding area is calculated.

[0116] Among them, local hot spots refer to areas where the temperature is significantly higher than the surrounding environment; temperature gradient represents the rate of change of temperature in space, including magnitude and direction information; hot spot area refers to the range where the temperature exceeds the set threshold; surrounding area refers to the affected spatial range around the hot spot.

[0117] The refrigeration control system needs to detect and analyze temperature anomalies in the computer room in a timely manner. Specifically, the refrigeration control system first performs threshold analysis on the temperature field distribution data to identify the area with abnormal temperature; then establishes a local coordinate system with the hot spot as the center to calculate the temperature change rate in each direction; finally, the main direction of heat accumulation and diffusion trend are determined through gradient analysis to provide a basis for subsequent adjustments.

[0118] S213. Based on the temperature gradient, adjust the air volume distribution, air supply temperature and air supply direction of the air supply outlets around the hot spot area.

[0119] Among them, the air supply outlet refers to the terminal equipment that supplies cold air to the computer room; the air volume distribution refers to the air supply volume of each air supply outlet; the air supply temperature refers to the temperature of the cold air output by the air supply outlet; and the air supply direction refers to the spatial direction of the airflow organization.

[0120] The refrigeration control system needs to optimize the air supply precisely for the hot spots found. Specifically, the refrigeration control system first determines the cooling demand of the hot spot area based on the temperature gradient information; then calculates the optimal adjustment plan for the surrounding air outlets, including air volume redistribution, temperature setting value adjustment, and guide plate angle change; finally, by coordinating and controlling multiple air supply parameters, a targeted airflow organization plan is formed to achieve precise cooling of the hot spot area.

[0121] In some embodiments, the optimization and adjustment of air supply parameters can be achieved in a variety of ways: Optionally, the refrigeration control system can establish an airflow organization optimization model, calculate the optimal air supply parameter combination by solving the heat balance equation and the fluid motion equation, and achieve directional optimization of the air supply effect based on energy consumption constraints and equipment limitations; Optionally, the refrigeration control system can also adopt a dynamic response control strategy, adjust the air supply parameters in real time according to the hot spot temperature change trend, ensure the adjustment effect by establishing a temperature feedback mechanism, and continuously optimize the control strategy according to the actual cooling effect. It is understandable that other control algorithms or optimization methods can also be used to achieve precise adjustment of air supply parameters, which are not limited here.

[0122] In some embodiments, the refrigeration control system needs to issue warning information in a timely manner; that is, the refrigeration control system will collect the adjusted temperature field distribution data in real time, calculate the temperature uniformity index and energy consumption change data before and after the hot spot is eliminated; determine the air supply adjustment effect based on the temperature uniformity index and energy consumption change data; when the air supply adjustment effect is lower than the preset effect threshold, generate ventilation warning information including the hot spot location, temperature abnormality degree and suggested improvement measures.

[0123] Among them, temperature field distribution data refers to the distribution of temperature in space; temperature uniformity index indicates the degree of discreteness of temperature distribution; energy consumption change data refers to the energy consumption during the adjustment process; air supply adjustment effect refers to the actual result of improving temperature distribution; ventilation warning information contains specific descriptions of abnormal situations and handling suggestions.

[0124] The refrigeration control system needs to monitor and warn of abnormal temperature conditions in a timely manner. Specifically, the refrigeration control system first continuously collects the adjusted temperature distribution data through the temperature sensor network; then calculates statistical indicators such as the standard deviation of the temperature field to evaluate the degree of improvement in temperature uniformity; at the same time, analyzes the energy consumption during the adjustment process and comprehensively evaluates the adjustment effect; finally, when the effect is not ideal, generates a warning report containing detailed information to guide subsequent improvements.

[0125] In some embodiments, the generation and processing of warning information can be achieved in a variety of ways: Optionally, the refrigeration control system can establish a multi-level warning mechanism, by setting multiple evaluation indicators such as temperature uniformity and energy efficiency, using a fuzzy comprehensive evaluation method to calculate the adjustment effect score, and generate corresponding levels of warning information according to different severity levels; Optionally, the refrigeration control system can also use an expert system based on a knowledge base to automatically generate a detailed report containing cause analysis and solutions by analyzing historical failure cases and combining current abnormal characteristics. It is understandable that other analysis methods or evaluation systems can also be used to achieve the processing of warning information, which is not limited here.

[0126] It should be noted that the determination of the weights of evaluation indicators in the multi-level early warning mechanism adopts a combination of the analytic hierarchy process (AHP) and the entropy weight method. First, the pairwise comparison matrix A = (aᵢⱼ) n × n between indicators is constructed through expert judgment, where aᵢⱼ represents the importance of indicator i relative to indicator j. The subjective weight vector w1 is obtained by calculating the maximum eigenvalue λmax of the matrix and the corresponding eigenvector. At the same time, the information entropy of each indicator is calculated based on historical data, eᵢ = -k∑ (pᵢⱼlnpᵢⱼ), where pᵢⱼ is the normalized value of the jth sample of the i-th indicator, and k is a constant. The objective weight vector w2 is obtained by information entropy calculation. The final comprehensive weight is obtained by w = αw1 + (1-α) w2, where α is the adjustment coefficient of the subjective and objective weights. For example, the weights of the temperature uniformity index, energy efficiency index, and equipment safety margin index may be 0.4, 0.35, and 0.25, respectively. These weight values ​​can be dynamically adjusted as operating experience accumulates and system performance changes.

[0127] In the embodiment of the present application, a refrigeration demand prediction method based on the equipment heat generation model is adopted, a hybrid refrigeration system including natural cooling is established, and a multi-level linkage intelligent control strategy is designed, so that accurate perception and dynamic adjustment of the computer room environment can be achieved. The system collects equipment operation data, environmental parameters and weather information in real time, and combines mathematical models for intelligent analysis and decision-making, effectively solving the problems of delayed response, extensive control, and energy waste in traditional refrigeration systems, thereby achieving an improvement in refrigeration effects and a significant reduction in operating costs. At the same time, the system's early warning mechanism and adaptive optimization function ensure the safe and stable operation of the equipment and improve the overall reliability and operational efficiency of the data center.

[0128] The following describes the refrigeration control system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of a physical device structure of a refrigeration control system in an embodiment of the present application.

[0129] It should be noted that Figure 3 The structure of the refrigeration control system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0130] like Figure 3As shown, the refrigeration control system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302 and the RAM 303 are connected to each other through the bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0131] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0132] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0133] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0134] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0135] Specifically, the refrigeration control system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the refrigeration equipment control method for a communication room provided in the above embodiment is implemented.

[0136] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the refrigeration control system described in the above embodiment; or may exist independently without being assembled into the refrigeration control system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the refrigeration control system, the refrigeration control system implements the refrigeration equipment control method for a communication room provided in the above embodiment.

[0137] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0138] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0139] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for controlling refrigeration equipment for a communication room, characterized in that: Applied to a refrigeration control system, the method comprises: Acquire the indoor temperature of the communication room and the heating data of the equipment, and determine the preset cooling index according to the indoor temperature of the communication room and the heating data of the equipment; Determine the refrigeration operation parameters corresponding to the preset refrigeration index, and adjust the operation parameters of the air-conditioning equipment to the refrigeration operation parameters; Acquire the outdoor ambient temperature of the communication room, and when the temperature difference between the indoor temperature of the room and the outdoor ambient temperature is higher than a preset activation threshold, determine the activation parameters of the natural cooling energy exchange group according to the temperature difference; Controlling the operation of the natural cooling energy exchange unit according to the activation parameters, and adjusting the operating parameters of the air-conditioning equipment to optimized operating parameters according to the operating status data of the natural cooling energy exchange unit; Determine a temperature change trend curve of the outdoor ambient temperature based on weather forecast information, and determine an optimization adjustment node according to the temperature change trend curve; During the optimization adjustment node, the activation parameters of the natural cooling energy exchange group and the operating parameters of the air-conditioning equipment are adjusted.

2. The method according to claim 1, characterized in that When the temperature difference between the indoor temperature of the computer room and the outdoor ambient temperature is higher than a preset activation threshold, before the step of determining the activation parameters of the natural cooling energy exchange unit according to the temperature difference, the method further includes: Obtaining the initial energy consumption data of the communication room when the air conditioning equipment is operating alone within a preset temperature difference range, and the optimized energy consumption data after the natural cooling energy exchange group is enabled; Based on the initial energy consumption data and the optimized energy consumption data, calculate the energy saving benefits of enabling natural cooling energy exchange groups at different temperature difference nodes; Calculate the unit use cost of the natural cooling energy exchange unit at the outdoor ambient temperature; The minimum temperature difference node where the energy saving benefit is greater than the unit use cost is determined as the preset activation threshold.

3. The method according to claim 2, characterized in that The step of calculating the unit use cost of the natural cooling energy exchange unit at the outdoor ambient temperature specifically includes: Collect environmental humidity data of the area where the computer room is located in real time; Determining the dehumidification cost of the natural cooling energy exchange unit according to the environmental humidity data; Calculating the operating cost of the natural cooling energy exchange unit at the outdoor ambient temperature; The unit use cost of the natural cooling energy exchange unit is determined according to the dehumidification cost and the operating cost.

4. The method according to claim 1, characterized in that: The step of obtaining the indoor temperature of the communication room and the heating data of the equipment, and determining the preset cooling index according to the indoor temperature of the communication room and the heating data of the equipment, specifically includes: Real-time collection of power consumption data, equipment operation status data and equipment distribution location data of the communication equipment in the communication room; Inputting the power consumption data, the device operation status data and the device distribution location data into a device heat generation model to obtain a device heating trend within a future preset time period; The temperature distribution data of the equipment room of the communication room is collected, and the minimum cooling capacity required to maintain the safe operation of the equipment in each area is calculated according to the temperature distribution data, the heating trend of the equipment and the temperature sensitivity of various types of equipment to obtain the preset cooling index.

5. The method according to claim 1, characterized in that: The step of determining the temperature change trend curve of the outdoor ambient temperature based on weather forecast information, and determining the optimization adjustment node according to the temperature change trend curve specifically includes: Acquire weather forecast information for a preset time period in the future, and determine an initial temperature change curve of the outdoor ambient temperature based on the weather forecast information; Identifying the temperature change inflection point according to the initial temperature change curve, and determining the initial adjustment node in combination with the adjustment characteristics of the refrigeration equipment; In a preset time period before the initial adjustment node, the initial temperature change curve is corrected according to the outdoor ambient temperature collected in real time to obtain a temperature change trend curve; Based on the actual change characteristics of the temperature change trend curve and the device response time, the initial adjustment node is corrected to be an optimized adjustment node.

6. The method according to claim 1, characterized in that After the step of adjusting the activation parameters of the natural cooling energy exchange unit and the operating parameters of the air conditioning equipment, the method further includes: Collecting temperature field distribution data of the communication room in the cold channel and the hot channel; When a local hot spot is detected in the communication room according to the temperature field distribution data, the temperature gradient of the hot spot area and the surrounding area is calculated; Based on the temperature gradient, the air volume distribution, air supply temperature and air supply direction of the air supply outlets around the hot spot area are adjusted.

7. The method according to claim 6, characterized in that After the step of adjusting the air volume distribution, air supply temperature and air supply direction of the air supply outlets around the hot spot area based on the temperature gradient, the method further includes: Collect the adjusted temperature field distribution data in real time, and calculate the temperature uniformity index and energy consumption change data before and after the hot spot is eliminated; Determining the air supply adjustment effect according to the temperature uniformity index and the energy consumption change data; When the air supply adjustment effect is lower than a preset effect threshold, ventilation warning information including the hot spot location, the degree of temperature abnormality and suggested improvement measures is generated.

8. A refrigeration control system, characterized in that: The refrigeration control system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the refrigeration control system to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a refrigeration control system, the refrigeration control system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product runs on a refrigeration control system, the refrigeration control system is enabled to perform the method according to any one of claims 1 to 7.

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