An intelligent heat dissipation method and system based on natural cold sources

Abstract

The present application relates to an intelligent heat dissipation method and system based on natural cold sources, including the steps of: obtaining in real time the heat load data of a data center and the water temperature data at different depths of a reservoir through sensors disposed in the reservoir and the data center, and generating a heat capacity attenuation curve of water bodies at different depths; based on the real-time heat load data of the data center and the heat capacity attenuation curves of water bodies at different depths, outputting optimal extraction parameters by using an adaptive cold source hierarchical selection algorithm; sending instructions to a water intake pump and a heat exchanger according to the optimal extraction parameters; the water intake pump receiving the instructions and adjusting the water intake temperature and extraction speed of deep water, and the heat exchanger receiving the instructions and adjusting its working state; continuously receiving and analyzing the feedback information of the sensors, dynamically adjusting the optimal extraction parameters and sending instructions to the water intake pump and the heat exchanger. The present application can determine the optimal natural cold source extraction parameters, and improve the energy utilization rate and cooling effect of the cooling system of the data center.

Description

Technical Field

[0001] This application relates to the technical field of natural cold source utilization, and particularly to an intelligent heat dissipation method and system based on natural cold source. Background Art

[0002] Data center facilities operate around the clock, consuming a large amount of energy and generating a large amount of heat. The water temperature of deep reservoirs is usually relatively stable and lower than that of the surface water. Utilizing this characteristic, the heat of the data center can be transferred to the deep water through a heat exchanger, thereby achieving a cooling effect.

[0003] In the prior art, it is difficult for the cooling system to adjust the extraction parameters of natural cold source according to actual needs, which may lead to the use of a large amount of cold source even under low load or unnecessary conditions, resulting in energy waste. Or when the temperature rises, the heat load of the data center increases, but the water temperature of the natural water source also rises, and the ability to provide cold source decreases, resulting in poor cooling effect. Summary of the Invention

[0004] In order to determine the optimal extraction parameters of natural cold source and improve the energy utilization rate and cooling effect of the data center cooling system, this application provides an intelligent heat dissipation method and system based on natural cold source.

[0005] The first invention object of this application is achieved through the following technical solutions:

[0006] An intelligent heat dissipation method based on natural cold source, comprising the steps of:

[0007] Real-time obtain the heat load data of the data center and the water temperature data at different depths of the reservoir through sensors arranged in the reservoir and the data center, and generate the heat capacity attenuation curves of water bodies at different depths;

[0008] Based on the real-time heat load data of the data center and the heat capacity attenuation curves of water bodies at different depths, use the adaptive cold source hierarchical selection algorithm to output the optimal extraction parameters;

[0009] Send instructions to the water intake pump and the heat exchanger according to the optimal extraction parameters;

[0010] The water intake pump receives the instructions and adjusts the water intake temperature and extraction speed of the deep water, and the heat exchanger receives the instructions and adjusts its working state;

[0011] Continuously receive and analyze the feedback information of the sensors, dynamically adjust the optimal extraction parameters and send instructions to the water intake pump and the heat exchanger.

[0012] By adopting the above technical solution, the sensor can obtain the thermal load of the data center and the water temperature data of the reservoir depth in real time. Combining with the heat capacity attenuation curve, it outputs the optimal cold source retrieval parameters to achieve automatic decision-making of the optimal natural cold source retrieval parameters. By continuously receiving the sensor feedback and dynamically adjusting the parameters, the system can adapt to environmental changes, maintain efficient heat dissipation performance, reduce energy waste, and improve the heat dissipation effect.

[0013] In a preferred example of the present application: The step of obtaining the thermal load data of the data center and the water temperature data at different depths of the reservoir in real time by the sensors disposed in the reservoir and the data center, and generating the heat capacity attenuation curves of the water bodies at different depths specifically includes the steps:

[0014] Arrange an optical fiber temperature sensor array at intervals in the vertical direction of the reservoir to form a vertical high-density temperature measurement network. The sensors collect water temperature data at preset time intervals to generate a vertical temperature gradient map;

[0015] Establish a three-dimensional fluid model of the reservoir by combining the data of the water flow velocity sensor, and input the real-time water temperature, flow velocity, and environmental temperature and humidity data;

[0016] Simulate the heat exchange process between the water bodies at different depths and the surrounding environment, and output the heat capacity attenuation rate data and the effective cooling duration data;

[0017] Output the heat capacity attenuation curve for each depth layer.

[0018] By adopting the above technical solution, the process of obtaining sensor data is refined, an optical fiber temperature sensor array and a water flow velocity sensor are added to generate a three-dimensional fluid model to simulate the process of water body heat exchange, and the heat capacity attenuation curve of the water body is optimized. This refined step can more accurately obtain the temperature gradient and cooling capacity at different depths of the reservoir, and improve the accuracy of cold source selection.

[0019] In a preferred example of the present application: The step of outputting the optimal retrieval parameters by using the adaptive cold source hierarchical selection algorithm based on the real-time thermal load data of the data center and the heat capacity attenuation curves of the water bodies at different depths specifically includes the steps:

[0020] Collect the real-time heat load, return water temperature, and current operating parameter data of the pump group on the data center side. Based on the pump characteristic curve, calculate the pumping energy consumption corresponding to the water intake at each depth;

[0021] Based on the heat capacity attenuation curve and the pumping energy consumption corresponding to the water intake at each depth, calculate the theoretical cooling capacity of the water bodies at each depth layer, and introduce the temperature rise attenuation correction;

[0022] Prioritize the water body at the depth layer with the highest theoretical cooling capacity and output the optimal retrieval parameters.

[0023] By adopting the above technical solution, based on data such as real-time heat load and return water temperature, combined with pumping energy consumption, the theoretical cooling capacity of water bodies at various depths is calculated, and the most effective water body layer is preferentially selected, which helps to improve the cooling efficiency, reduce energy consumption, and ensure the optimal performance of the cooling system under different load conditions.

[0024] In a preferred example of the present application: after the step of preferentially selecting the water body layer with the highest theoretical cooling capacity and outputting the optimal extraction parameters, the following steps are further included:

[0025] When it is detected that the air temperature continuously exceeds the preset threshold and the difference between the surface water temperature and the deep water temperature is greater than the preset threshold, enter the summer operation mode;

[0026] Increase the extraction volume of deep water during the night period and store it in the cold storage pool. When the air temperature rises suddenly during the day, causing the surface water temperature to rise rapidly, preferentially call the water reserved in the cold storage pool;

[0027] When it is detected that the air temperature continuously is lower than the preset threshold and the temperature difference at each depth is less than the preset threshold, enter the winter operation mode;

[0028] Preferentially extract shallow water and introduce the return water cooled by the data center into the building heating system;

[0029] When the shallow water temperature is close to the freezing point, temporarily switch to middle layer water intake.

[0030] By adopting the above technical solution, the cooling mode is adjusted according to the change of air temperature. In summer, deep water is stored in the cold storage pool, and the cold source is adjusted by using the temperature difference during the day, delaying the direct dependence on natural cold sources. In winter, shallow water is preferentially extracted, reducing the pumping energy consumption of deep water intake. The water is combined with the heating system, and through seasonal adjustment, the adaptability and energy saving of the cooling system are improved.

[0031] In a preferred example of the present application: the step of sending instructions to the water intake pump and the heat exchanger according to the optimal extraction parameters specifically includes the following steps:

[0032] Extract the reservoir depth data in the optimal extraction parameters and associate the water intake pump corresponding to the depth;

[0033] Send a start instruction to the associated water intake pump and a stop instruction to other water intake pumps;

[0034] Extract the water flow velocity data in the optimal extraction parameters, generate an instruction to adjust the water flow velocity, and send it to the associated water intake pump;

[0035] Extract the return water temperature data in the optimal extraction parameters, generate an instruction to adjust heat exchange, and send it to the heat exchanger. The instruction to adjust heat exchange is used to adjust the mixing ratio of the cooling water and the circulating water in the data center.

[0036] By adopting the above technical solution, according to the real-time heat load data, the parameter matching model outputs the optimal extraction parameters, providing an accurate control basis for the cooling system. According to the optimal extraction parameters, specific instructions are sent to the water intake pump and the heat exchanger to adjust the mixing ratio of the cooling water and the circulating water in the data center, achieving precise control of the temperature in the data center.

[0037] In a preferred example of the present application: after the steps of continuously receiving and analyzing the feedback information of the sensor, dynamically adjusting the optimal extraction parameters and sending instructions to the water intake pump and the heat exchanger, the following steps are further included:

[0038] Real-time monitor the pressure data of the water intake pump and the pipeline, and send a potential danger signal when the fluctuation range of the pressure data exceeds the preset threshold;

[0039] After receiving the potential danger signal, automatically shut down the water intake pump and start the standby cooling device.

[0040] By adopting the above technical solution, real-time monitoring of the pressure data of the water intake pump and the pipeline helps to detect potential dangers in a timely manner. When the fluctuation range of the pressure data exceeds the preset threshold, the water intake pump is automatically shut down and the standby cooling device is started, improving the safety of the system operation.

[0041] In a preferred example of the present application: after the steps of automatically shutting down the water intake pump and starting the standby cooling device after receiving the potential danger signal, the following steps are further included:

[0042] When it is detected that the air temperature suddenly rises and it is predicted that the surface water temperature will rise rapidly, extract deep low-temperature water to the cold storage pool in advance;

[0043] Mix deep water and middle-layer water for water supply according to a preset ratio to balance the pumping energy consumption and cold quantity supply;

[0044] When the return water temperature exceeds the preset threshold, automatically increase the heat exchanger area.

[0045] By adopting the above technical solution, when it is detected that the air temperature suddenly rises, extract low-temperature deep water in advance and mix it for water supply to optimize the pumping energy consumption and cold quantity supply. At the same time, when the return water temperature is too high, automatically increase the heat exchanger area, thereby optimizing the energy efficiency, responding to environmental changes in a timely manner, and reducing the heat dissipation pressure brought by high temperature.

[0046] The above second invention object of the present application is achieved through the following technical solutions:

[0047] An intelligent cooling system based on natural cold sources, comprising:

[0048] A data collection module, configured to obtain the heat load data of the data center and the water temperature data at different depths of the reservoir in real time through sensors arranged in the reservoir and the data center, and generate a heat capacity attenuation curve of water bodies at different depths;

[0049] A parameter retrieval module, which is used to output optimal retrieval parameters based on the real-time heat load data of the data center and the heat capacity attenuation curves of water bodies at different depths by using an adaptive cold source layer selection algorithm, and send instructions to the water intake pump and the heat exchanger according to the optimal retrieval parameters;

[0050] An execution module, which is used for the water intake pump to receive instructions and adjust the water intake temperature and extraction speed of deep water, and the heat exchanger to receive instructions and adjust its working state;

[0051] A feedback module, which is used to continuously receive and analyze the feedback information of the sensor, dynamically adjust the optimal retrieval parameters and send instructions to the water intake pump and the heat exchanger.

[0052] By adopting the above technical solutions, the collaborative cooperation of the data collection module, the parameter retrieval module, the execution module and the feedback module can obtain data in real time and dynamically adjust the cooling strategy, realizing intelligent and automatic heat dissipation management, and improving the heat dissipation efficiency and system response ability.

[0053] The above object three of the present application is achieved through the following technical solutions:

[0054] A computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned intelligent heat dissipation method based on natural cold source are realized.

[0055] The above object four of the present application is achieved through the following technical solutions:

[0056] A computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned intelligent heat dissipation method based on natural cold source are realized.

[0057] In summary, the present application includes at least one of the following beneficial technical effects:

[0058] 1. The sensor obtains the heat load of the data center and the water temperature data of the reservoir depth in real time, combines the heat capacity attenuation curve, and outputs the optimal cold source retrieval parameters, so as to realize the automatic decision of the optimal natural cold source retrieval parameters. By continuously receiving the sensor feedback and dynamically adjusting the parameters, the system can adapt to environmental changes, maintain high heat dissipation performance, reduce energy waste, and improve the heat dissipation effect;

[0059] 2. Based on data such as real-time heat load and return water temperature, combined with pumping energy consumption, calculate the theoretical cooling capacity of water bodies at each depth, and preferentially select the most effective water body layer, which helps to improve the cooling efficiency, reduce energy consumption, and ensure the best performance of the cooling system under different load conditions;

[0060] 3. Adjust the cooling mode according to the temperature change. Store deep water in the chilled water storage tank in summer, utilize the temperature difference during the day to regulate the cold source, delay the direct dependence on natural cold sources, and in winter, preferentially extract shallow water to reduce the pumping energy consumption of deep water intake. Combine with the heating system, and through seasonal adjustment, improve the adaptability and energy efficiency of the cooling system;

[0061] 4. Real-time monitor the pressure data of the water intake pump and pipeline, which helps to detect potential hazards in a timely manner. When the fluctuation range of the pressure data exceeds the preset threshold, automatically shut down the water intake pump and start the standby cooling device, improving the safety of system operation. Real-time monitor the pressure data of the water intake pump and pipeline, which helps to detect potential hazards in a timely manner. When the fluctuation range of the pressure data exceeds the preset threshold, automatically shut down the water intake pump and start the standby cooling device, improving the safety of system operation. Description of the Drawings

[0062] Figure 1 is a flowchart of an embodiment of an intelligent heat dissipation method and system based on natural cold sources in this application;

[0063] Figure 2 is an implementation flowchart of step S10 in an embodiment of an intelligent heat dissipation method and system based on natural cold sources in this application;

[0064] Figure 3 is an implementation flowchart of step S20 in an embodiment of an intelligent heat dissipation method and system based on natural cold sources in this application;

[0065] Figure 4 is a schematic diagram of an intelligent heat dissipation system based on natural cold sources in this application;

[0066] Figure 5 is a principle block diagram of a computer device in this application. Detailed Description of the Embodiment

[0067] The following further describes this application in detail with reference to the drawings.

[0068] In the embodiment, as Figures 1-3 shown, this application discloses an intelligent heat dissipation method based on natural cold sources, specifically including the following steps:

[0069] S10: Real-time obtain the heat load data of the data center and the water temperature data at different depths of the reservoir through the sensors set in the reservoir and the data center, and generate the heat capacity attenuation curves of water bodies at different depths;

[0070] S20: Based on the real-time heat load data of the data center and the heat capacity attenuation curves of water bodies at different depths, use the adaptive cold source stratified selection algorithm to output the optimal extraction parameters;

[0071] S30: Send instructions to the water intake pump and the heat exchanger according to the optimal extraction parameters;

[0072] S40: The water intake pump receives the instructions and adjusts the water intake temperature and extraction speed of the deep water, and the heat exchanger receives the instructions and adjusts its working state;

[0073] S50: Continuously receive and analyze the feedback information of the sensors, dynamically adjust the optimal extraction parameters, and send instructions to the water intake pump and the heat exchanger.

[0074] In this embodiment, sensors are installed in the reservoir and the data center to monitor the heat load data of the data center in real time, measure the water temperature data at different depths of the reservoir, and generate the heat capacity attenuation curves of water bodies at different depths to understand the temperature distribution and heat capacity changes of the water bodies. Based on the real-time heat load data and the heat capacity attenuation curves, the adaptive cold source stratification selection algorithm is used to calculate the optimal extraction parameters, which may include the water intake depth, water intake speed, and the working mode of the heat exchanger, etc. According to the calculated optimal extraction parameters, control instructions are sent to the water intake pump and the heat exchanger. The water intake pump adjusts the water intake temperature and extraction speed of the deep water according to the received instructions to ensure that the supplied cooling water can meet the heat dissipation requirements of the data center. The heat exchanger adjusts its working state according to the instructions to optimize the heat exchange process. Continuously receive and analyze the feedback information of the sensors, including the temperature and flow rate of the cooling water and the heat load changes of the data center, etc., dynamically adjust the optimal extraction parameters according to the feedback information, and send new instructions to the water intake pump and the heat exchanger to maintain the efficient operation of the system.

[0075] In one embodiment, step S10 specifically includes the steps:

[0076] S11: Fiber optic temperature sensor arrays are arranged at intervals in the vertical direction of the reservoir to form a vertical high-density temperature measurement network. The sensors collect water temperature data at preset time intervals and generate a vertical temperature gradient map;

[0077] S12: Establish a three-dimensional fluid model of the reservoir by combining the data of the water flow velocity sensors, and input the real-time water temperature, flow velocity, and ambient temperature and humidity data;

[0078] S13: Simulate the heat exchange process between water bodies at different depths and the surrounding environment, and output the heat capacity attenuation rate data and the effective cooling duration data;

[0079] S14: Output the heat capacity attenuation curve for each depth layer.

[0080] In this embodiment, an optical fiber temperature sensor array is arranged at intervals of 0.5 meters in the vertical direction of the reservoir to form a vertical high-density temperature measurement network. The sensor collects water temperature data every 30 seconds to generate a vertical temperature gradient map (for example, the water temperature at a depth of 10 meters is 8°C, at 20 meters is 5°C, and at 30 meters is 4°C). Combining the data of the water flow velocity sensor, a three-dimensional temperature field model of the reservoir is constructed through the Kriging interpolation algorithm to quantify the differences in the distribution of cold sources in different regions. The heat capacity attenuation rate is the rate of temperature rise (°C / h) of the water body caused by heat absorption per unit time, and the effective cooling duration is the time that the water body at the current depth can continuously supply cooling before the temperature rise does not exceed the threshold (such as 2°C).

[0081] In one embodiment, step S20 specifically includes the steps of:

[0082] S21: The data center side collects real-time heat load, return water temperature, and current operating parameter data of the pump group, and calculates the pumping energy consumption corresponding to water intake at each depth based on the pump characteristic curve;

[0083] S22: Based on the heat capacity attenuation curve and the pumping energy consumption corresponding to water intake at each depth, calculate the theoretical cooling capacity of the water body at each depth layer, and introduce temperature rise attenuation correction;

[0084] S23: Prioritize the water body at the depth layer with the highest theoretical cooling capacity and output the optimal extraction parameters.

[0085] In this embodiment, the sensors on the data center side collect real-time heat load, return water temperature, and current operating parameter data of the pump group. According to the pump characteristic curve, calculate the pumping energy consumption required for water intake from different depths, which involves factors such as the efficiency, flow rate, and head of the pump; based on the heat capacity attenuation curve, calculate the cooling capacity of the water body at different depths. The heat capacity attenuation curve reflects the trend that the temperature of the water body gradually decreases with the increase in depth. Combining the pumping energy consumption corresponding to water intake at each depth, calculate the theoretical cooling capacity of the water body at each depth layer. At the same time, factors such as the temperature, flow rate, and heat exchange efficiency of the water body should be considered, and temperature rise attenuation correction should be introduced to consider the temperature change of the water body during transmission to ensure the accuracy of the calculated cooling capacity; prioritize the water body at the depth layer with the highest theoretical cooling capacity and output the optimal extraction parameters, including the water intake depth, water intake speed, and working mode of the heat exchanger, etc. These parameters will be used to control the operation of the water intake pump and the heat exchanger to achieve an efficient heat dissipation process.

[0086] In one embodiment, after step S23, it further includes the steps of:

[0087] S240: When it is detected that the air temperature continuously exceeds the preset threshold and the difference between the surface water temperature and the deep water temperature is greater than the preset threshold, enter the summer operation mode;

[0088] S241: Increase the extraction volume of deep water during the night period and store it in the chilled water storage tank. When the surface water temperature rises rapidly due to a sudden increase in daytime temperature, give priority to using the stored water in the chilled water storage tank.

[0089] S242: When it is detected that the temperature remains lower than the preset threshold and the temperature difference at each depth is less than the preset threshold, enter the winter operation mode.

[0090] S243: Give priority to extracting shallow water and introduce the return water cooled by the data center into the building heating system.

[0091] S244: When the shallow water temperature approaches the freezing point, temporarily switch to taking water from the middle layer.

[0092] In this embodiment, when it is detected that the temperature remains higher than 25°C and the difference between the surface water temperature (0 - 10 m) and the deep water temperature (>30 m) is significant (such as the temperature difference ≥8°C), the system determines to enter the summer operation mode. During the night period with lower temperature (from 20:00 to 6:00 the next day), increase the extraction volume of deep low-temperature water (>30 m) and store it in the chilled water storage tank. The reserve volume is about 20% - 30% of the estimated daytime demand. When the surface water temperature rises rapidly due to a sudden increase in daytime temperature, give priority to using the stored water in the chilled water storage tank to delay the direct dependence on natural cold sources. Adopt a mixing ratio of "70% deep water + 30% middle water" to balance the cooling efficiency and energy consumption. For example, the deep water provides the low-temperature basis, and the middle water supplements the flow demand.

[0093] When the temperature is continuously lower than 10°C and the water temperature stratification is not obvious (the temperature difference at each depth ≤3°C), the system switches to the winter mode. Give priority to extracting the relatively cold water at a depth of 5 - 15 m to reduce the pumping energy consumption of deep water extraction. Because the temperature difference between the deep layer and the shallow layer is small in winter, introduce the return water (with a temperature higher than the ambient water) cooled by the data center into the building heating system to replace the traditional boiler heating and improve the comprehensive energy utilization rate. If the shallow water temperature approaches the freezing point (≤2°C), temporarily switch to taking water from the middle layer (15 - 25 m) to avoid ice crystal blockage of equipment.

[0094] In one embodiment, step S30 specifically includes the following steps:

[0095] S31: Extract the reservoir depth data in the optimal extraction parameters and associate the water intake pump corresponding to the corresponding depth.

[0096] S32: Send a start command to the associated water intake pump and send a stop command to other water intake pumps.

[0097] S33: Extract the water flow velocity data in the optimal extraction parameters, generate a command to adjust the water flow velocity, and send it to the associated water intake pump.

[0098] S34: Extract the return water temperature data from the optimal retrieval parameters, generate a heat exchange adjustment instruction, and send it to the heat exchanger. The heat exchange adjustment instruction is used to adjust the mixing ratio of the cooling water and the circulating water in the data center.

[0099] In this embodiment, according to the optimal retrieval parameters, specific instructions are sent to the water intake pump and the heat exchanger to adjust the mixing ratio of the cooling water and the circulating water in the data center, achieving precise control of the data center temperature.

[0100] Specifically, associate the reservoir depth data with the corresponding water intake pump to ensure that the water intake pump can draw water from the correct depth. Send a start instruction to the associated water intake pump to start drawing water from the corresponding depth. At the same time, send a shutdown instruction to other irrelevant water intake pumps to avoid unnecessary energy waste and operation errors; extract the water flow velocity data from the optimal retrieval parameters, generate an instruction to adjust the water flow velocity based on these data, and send it to the started water intake pump to control the water intake speed; extract the return water temperature data from the optimal retrieval parameters, generate an instruction to adjust the heat exchange based on the return water temperature data. The heat exchange adjustment instruction is used to adjust the mixing ratio of the cooling water and the circulating water in the data center to ensure effective heat exchange and heat dissipation efficiency.

[0101] In one embodiment, after step S50, the following steps are further included:

[0102] S51: Real-time monitor the pressure data of the water intake pump and the pipeline, and send a potential danger signal when the fluctuation range of the pressure data exceeds the preset threshold;

[0103] S52: Automatically shut down the water intake pump after receiving the potential danger signal, and start the standby cooling device.

[0104] In this embodiment, the standby cooling device is a key component of the data center or other critical facilities, used to maintain the cooling of the equipment when the main cooling system fails or is under maintenance, such as a standby cooling tower or a standby coolant circulation pump. An automatic transfer switch is set between the main cooling system and the standby cooling device to detect the main system failure and automatically switch to the standby system.

[0105] Specifically, continuously monitor the pressure data of the water intake pump and the pipeline. When the fluctuation range of the monitored pressure data exceeds the preset safety threshold and potential operation risks such as excessive pressure and abnormal temperature are identified, automatically shut down the relevant equipment to prevent the situation from deteriorating, and notify the operation and maintenance personnel of the potential danger signal through audible and visual alarms, text messages or emails. After receiving the potential danger signal, start the standby cooling device to ensure that the data center can still maintain the normal operating temperature when the main heat dissipation system is unavailable.

[0106] In one embodiment, after step S52, the following steps are further included:

[0107] S53: When it is detected that the air temperature rises suddenly and it is predicted that the surface water temperature will rise rapidly, deep low-temperature water is extracted in advance to the cold storage tank;

[0108] S54: Mix deep water and middle-layer water for water supply according to a preset ratio to balance the pumping energy consumption and cold quantity supply;

[0109] S55: When the return water temperature exceeds the preset threshold, the heat exchanger area is automatically increased.

[0110] In this embodiment, deep low-temperature water is extracted in advance to the cold storage tank, and the reserve is 20% of the daily water consumption. Water is supplied by mixing 70% deep water + 30% middle-layer water to balance the pumping energy consumption and cold quantity supply. When the return water temperature exceeds the set value, the heat exchanger area is automatically increased by 10%-15%.

[0111] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0112] In one embodiment, an intelligent heat dissipation system based on natural cold sources is provided, and the device corresponds one-to-one with an intelligent heat dissipation method based on natural cold sources in the above embodiment. As Figure 4 shown, the system includes:

[0113] A data collection module, configured to obtain the heat load data of the data center and the water temperature data at different depths of the reservoir in real time through sensors arranged in the reservoir and the data center, and generate a heat capacity attenuation curve of water bodies at different depths;

[0114] A parameter retrieval module, configured to output optimal retrieval parameters by using an adaptive cold source stratification selection algorithm based on the real-time heat load data of the data center and the heat capacity attenuation curves of water bodies at different depths, and send instructions to the water intake pump and the heat exchanger according to the optimal retrieval parameters;

[0115] An execution module, configured to receive instructions by the water intake pump and adjust the water intake temperature and extraction speed of deep water, and the heat exchanger receives instructions and adjusts its working state;

[0116] A feedback module, configured to continuously receive and analyze the feedback information of the sensors, dynamically adjust the optimal retrieval parameters and send instructions to the water intake pump and the heat exchanger.

[0117] For the specific limitations of an intelligent cooling system based on natural cold sources, reference can be made to the limitations of an intelligent cooling method based on natural cold sources in the foregoing text, which will not be elaborated herein. Each module in the above-mentioned intelligent cooling system based on natural cold sources can be implemented in whole or in part by software, hardware, and their combinations. Each of the above modules can be embedded in the processor of a computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to each of the above modules.

[0118] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 5 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements an intelligent cooling method based on natural cold sources.

[0119] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements an intelligent cooling method based on natural cold sources;

[0120] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it implements an intelligent cooling method based on natural cold sources.

[0121] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0122] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to be completed by several functional units and modules as needed, that is, the internal structure of the device can be divided into several functional units or modules to complete all or part of the functions described above.

[0123] 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 foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An intelligent heat dissipation method based on natural cooling source, characterized in that: Includes steps: Sensors installed in the reservoir and data center are used to obtain real-time heat load data of the data center and water temperature data at different depths of the reservoir, and to generate heat capacity attenuation curves of water bodies at different depths; Based on the real-time heat load data of the data center and the heat capacity attenuation curves of water bodies at different depths, the adaptive cold source stratification selection algorithm is used to output the optimal call parameters; Send instructions to the water intake pump and heat exchanger according to the optimal extraction parameters; The water pump receives the instruction and adjusts the deep water intake temperature and extraction speed, and the heat exchanger receives the instruction and adjusts the working state; Continuously receive and analyze sensor feedback information, dynamically adjust optimal extraction parameters and send instructions to water pumps and heat exchangers; The step of obtaining the heat load data of the data center and the water temperature data of different depths of the reservoir in real time through sensors arranged in the reservoir and the data center, and generating the heat capacity attenuation curves of water bodies at different depths specifically includes the following steps: An array of optical fiber temperature sensors is arranged at intervals in the vertical direction of the reservoir to form a vertical high-density temperature measurement network. The sensors collect water temperature data at preset time intervals to generate a vertical temperature gradient map. Combined with the water flow velocity sensor data, a three-dimensional fluid model of the reservoir is established, and real-time water temperature, flow velocity, and ambient temperature and humidity data are input; Simulate the heat exchange process between water bodies at different depths and the surrounding environment, and output heat capacity decay rate data and effective cooling time data; Output the heat capacity decay curve for each depth layer.

2. The intelligent heat dissipation method based on natural cooling source according to claim 1, characterized in that: The step of outputting the optimal call parameters by using the adaptive cold source stratification selection algorithm based on the real-time heat load data of the data center and the heat capacity attenuation curves of water bodies at different depths specifically includes the following steps: The data center collects real-time heat load, return water temperature and current operating parameter data of the pump group, and calculates the pumping energy consumption corresponding to water intake at each depth based on the pump characteristic curve; Based on the heat capacity decay curve and the corresponding pumping energy consumption of water extraction at each depth, the theoretical cooling capacity of water bodies at each depth layer is calculated, and the temperature rise decay correction is introduced; Prioritize the water body at the depth with the highest theoretical cooling capacity and output the optimal adjustment parameters.

3. The intelligent heat dissipation method based on natural cooling source according to claim 2 is characterized in that: After the step of preferentially selecting the water body in the depth layer with the highest theoretical cooling capacity and outputting the optimal adjustment parameters, the step further includes: When it is detected that the air temperature is continuously higher than the preset threshold, and the difference between the surface water temperature and the deep water temperature is greater than the preset threshold, the summer operation mode is entered; Increase the amount of deep water pumped during the night and store it in the cold storage pool. When the temperature rises suddenly during the day, causing the surface water temperature to rise rapidly, the cold storage pool reserve water will be used first; When it is detected that the temperature is continuously lower than the preset threshold and the temperature difference at each depth is less than the preset threshold, the winter operation mode is entered; Prioritize shallow water extraction and direct return water from data center cooling into the building heating system; When the shallow water temperature approaches freezing point, temporarily switch to the middle layer to draw water.

4. The intelligent heat dissipation method based on natural cooling source according to claim 1, characterized in that: The step of sending instructions to the water intake pump and the heat exchanger according to the optimal adjustment parameters specifically includes the steps of: Extract the reservoir depth data from the optimal extraction parameters and associate the water intake pumps at the corresponding depths; Send a start command to the associated water pump and send a shut-down command to other water pumps; Extracting water flow velocity data from the optimal call parameters, generating a water flow velocity adjustment instruction, and sending it to the associated water intake pump; The return water temperature data in the optimal call parameters is extracted, and a heat exchange adjustment instruction is generated and sent to the heat exchanger. The heat exchange adjustment instruction is used to adjust the mixing ratio of cooling water and circulating water in the data center.

5. The intelligent heat dissipation method based on natural cooling source according to claim 1, characterized in that: After the step of continuously receiving and analyzing the feedback information from the sensor, dynamically adjusting the optimal parameters and sending instructions to the water pump and the heat exchanger, the following steps are also included: Monitor the pressure data of water pumps and pipelines in real time, and send out potential danger signals when the fluctuation range of pressure data exceeds the preset threshold; Upon receiving a potential danger signal, the water intake pump is automatically shut down and the backup cooling device is started.

6. The intelligent heat dissipation method based on natural cooling source according to claim 5, characterized in that: After the step of automatically shutting down the water pump and starting the standby cooling device after receiving the potential danger signal, the step further includes: When a sudden rise in air temperature is detected and the surface water temperature is predicted to rise rapidly, deep low-temperature water is pumped into the cold storage tank in advance; Mix deep water and middle layer water in a preset ratio to supply water, balancing pumping energy consumption and cooling supply; When the return water temperature exceeds the preset threshold, the heat exchanger area is automatically increased.

7. An intelligent heat dissipation system based on natural cooling source, characterized in that: include: The data collection module is used to obtain the heat load data of the data center and the water temperature data of different depths of the reservoir in real time through sensors installed in the reservoir and the data center, and generate heat capacity decay curves of water bodies at different depths; specifically, an array of optical fiber temperature sensors is arranged at intervals in the vertical direction of the reservoir to form a vertical high-density temperature measurement network, and the sensors collect water temperature data at preset time intervals to generate a vertical temperature gradient map; a three-dimensional fluid model of the reservoir is established in combination with the water flow velocity sensor data, and real-time water temperature, flow velocity and ambient temperature and humidity data are input; the heat exchange process between water bodies at different depths and the surrounding environment is simulated, and heat capacity decay rate data and effective cooling time data are output; the heat capacity decay curve of each depth layer is output; The parameter retrieval module is used to output the optimal retrieval parameters based on the real-time heat load data of the data center and the heat capacity attenuation curves of water bodies at different depths using an adaptive cold source stratification selection algorithm, and send instructions to the water intake pump and heat exchanger according to the optimal retrieval parameters; The execution module is used for the water pump to receive instructions and adjust the water temperature and extraction speed of deep water, and the heat exchanger to receive instructions and adjust the working state; The feedback module is used to continuously receive and analyze the feedback information from the sensor, dynamically adjust the optimal parameters and send instructions to the water pump and heat exchanger.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the intelligent heat dissipation method based on a natural cold source as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of an intelligent heat dissipation method based on a natural cooling source as claimed in any one of claims 1 to 6 are implemented.

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