Intelligent heat dissipation method and system based on natural cold source

Abstract

The invention relates to an intelligent heat dissipation method and system based on a natural cold source, and the method comprises the steps: obtaining heat load data of a data center and water temperature data of different depths of the reservoir in real time through sensors disposed in the reservoir and the data center, and generating heat capacity attenuation curves of water bodies of different depths; based on the real-time heat load data of the data center and the heat capacity attenuation curves of the water bodies of different depths, outputting optimal calling parameters by utilizing a self-adaptive cold source hierarchical selection algorithm; instructions are sent to a water taking pump and a heat exchanger according to the optimal calling parameters; the water taking pump receives the instruction and adjusts the water taking temperature and the pumping speed of the deep water, and the heat exchanger receives the instruction and adjusts the working state; and feedback information of the sensor is continuously received and analyzed, optimal calling parameters are dynamically adjusted, and instructions are sent to the water taking pump and the heat exchanger. The method has the advantages that the optimal natural cold source calling parameters are determined, and the energy utilization rate and the cooling effect of the data center cooling system are improved.

Description

Technical Field

[0001] The present application relates to the technical field of natural cooling source utilization, and in particular to an intelligent heat dissipation method and system based on natural cooling source. Background Art

[0002] Data center facilities operate around the clock, consuming a lot of energy and generating a lot of heat. The water temperature in deep reservoirs is usually relatively stable and lower than the surface water temperature. By utilizing this characteristic, the heat from 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 parameters of the natural cooling source according to actual needs, which may result in the use of a large amount of cooling source when the load is low or unnecessary, resulting in energy waste. When the temperature rises, the heat load of the data center increases, but the water temperature of the natural water source will also increase, and the ability to provide cooling source will decrease, resulting in poor cooling effect. Summary of the invention

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

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: An intelligent heat dissipation method based on a natural cold source comprises the following 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 feedback information from sensors, dynamically adjust optimal parameters and send instructions to water pumps and heat exchangers.

[0006] By adopting the above technical solution, the sensor obtains the heat load of the data center and the water temperature data of the reservoir depth in real time, and outputs the optimal cold source call parameters in combination with the heat capacity attenuation curve to achieve automatic decision-making on the optimal natural cold source call parameters. By continuously receiving sensor feedback and dynamically adjusting parameters, the system can adapt to environmental changes, maintain efficient heat dissipation performance, reduce energy waste, and improve heat dissipation effects.

[0007] In a preferred example of the present application, 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.

[0008] By adopting the above technical solution, the sensor data acquisition process was refined, and an optical fiber temperature sensor array and a water flow velocity sensor were added to generate a three-dimensional fluid model, simulate the process of heat exchange in the water body, and optimize the thermal capacity attenuation curve of the water body. This refinement step can more accurately obtain the temperature gradient and cooling capacity at different depths of the reservoir, thereby improving the accuracy of the cold source selection.

[0009] In a preferred example of the present application, the step of outputting the optimal call parameters 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 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.

[0010] By adopting the above technical solution, based on real-time heat load, return water temperature and other data, combined with pumping energy consumption, the theoretical cooling capacity of water bodies at various depths is calculated, and the most effective water layer is selected first, which helps to improve cooling efficiency, reduce energy consumption, and ensure the optimal performance of the cooling system under different load conditions.

[0011] In a preferred example of the present application: after the step of preferentially selecting the water body at the depth 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.

[0012] By adopting the above technical solution, the cooling mode is adjusted according to the temperature changes. In summer, deep water is stored in the cold storage pool, and the temperature difference during the day is used to adjust the cold source, delaying the direct dependence on natural cold sources. In winter, shallow water is preferentially extracted to reduce the pumping energy consumption of deep water. The water is combined with the heating system to improve the adaptability and energy saving of the cooling system through seasonal adjustment.

[0013] 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 call 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.

[0014] By adopting the above technical solution, the parameter matching model outputs the optimal call parameters according to the real-time heat load data, providing an accurate control basis for the cooling system. According to the optimal call parameters, specific instructions are sent to the water intake pump and heat exchanger to adjust the mixing ratio of cooling water and circulating water in the data center, thereby achieving precise control of the temperature of the data center.

[0015] In a preferred example of the present application: after the step of continuously receiving and analyzing the feedback information of the sensor, dynamically adjusting the optimal call parameters and sending instructions to the water pump and the heat exchanger, the step further includes: 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.

[0016] By adopting the above technical solution, real-time monitoring of the pressure data of the water intake pump and pipeline can help to promptly detect potential dangers. When the pressure data fluctuation exceeds the preset threshold, the water intake pump is automatically shut down and the backup cooling device is started, thereby improving the safety of system operation.

[0017] In a preferred example of the present application: 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.

[0018] By adopting the above technical solution, when a sudden rise in temperature is detected, low-temperature deep water is extracted in advance and mixed with the water supply to optimize pumping energy consumption and cold supply. At the same time, the heat exchanger area is automatically increased when the return water temperature is too high, thereby optimizing energy efficiency, responding to environmental changes in a timely manner, and reducing the heat dissipation pressure caused by high temperature.

[0019] The second object of the invention is achieved by the following technical solutions: An intelligent heat dissipation system based on a natural cold source, comprising: The data collection module is used 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 installed in the reservoir and the data center, and to generate heat capacity attenuation curves of water bodies at different depths; 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.

[0020] By adopting the above technical solution, the coordinated cooperation of the data collection module, parameter retrieval module, execution module and feedback module can obtain data in real time and dynamically adjust the cooling strategy, realize intelligent and automated heat dissipation management, and improve heat dissipation efficiency and system responsiveness.

[0021] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned intelligent heat dissipation method based on a natural cold source when executing the computer program.

[0022] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium 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 a natural cold source are implemented.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: 1. The sensor obtains the data center's heat load and the water temperature data at the reservoir depth in real time, and outputs the optimal cold source call parameters in combination with the heat capacity attenuation curve to automatically decide the optimal natural cold source call parameters. By continuously receiving sensor feedback and dynamically adjusting parameters, the system can adapt to environmental changes, maintain efficient heat dissipation performance, reduce energy waste, and improve heat dissipation effects; 2. Based on real-time heat load, return water temperature and other data, combined with pumping energy consumption, the theoretical cooling capacity of each depth of water is calculated, and the most effective water layer is selected first, which helps to improve cooling efficiency, reduce energy consumption, and ensure the best performance of the cooling system under different load conditions; 3. Adjust the cooling mode according to the temperature changes. In summer, deep water is stored in the cold storage pool, and the temperature difference during the day is used to adjust the cold source, delaying the direct dependence on natural cold sources. In winter, shallow water is drawn first to reduce the pumping energy consumption of deep water. Water is combined with the heating system to improve the adaptability and energy saving of the cooling system through seasonal adjustment; 4. Real-time monitoring of the pressure data of the water intake pump and pipeline helps to detect potential dangers in time. When the pressure data fluctuation exceeds the preset threshold, the water intake pump is automatically shut down and the backup cooling device is started, improving the safety of system operation. Real-time monitoring of the pressure data of the water intake pump and pipeline helps to detect potential dangers in time. When the pressure data fluctuation exceeds the preset threshold, the water intake pump is automatically shut down and the backup cooling device is started, improving the safety of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of an embodiment of an intelligent heat dissipation method and system based on a natural cold source of the present application; Figure 2 This is a flowchart of an implementation of step S10 in an intelligent heat dissipation method and system embodiment based on a natural cold source of the present application; Figure 3This is a flowchart of an implementation of step S20 in an intelligent heat dissipation method and system embodiment based on a natural cold source of the present application; Figure 4 This is a schematic diagram of an intelligent heat dissipation system based on a natural cold source in the present application; Figure 5 This is a principle block diagram of a computer device of the present application. DETAILED DESCRIPTION

[0025] The present application is further described in detail below in conjunction with the accompanying drawings.

[0026] In the following embodiments, Figure 1-3 As shown, the present application discloses an intelligent heat dissipation method based on a natural cold source, which specifically includes the following steps: S10: The heat load data of the data center and the water temperature data at different depths of the reservoir are obtained in real time through sensors installed in the reservoir and the data center, and heat capacity attenuation curves of water bodies at different depths are generated; 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, the adaptive cold source stratification selection algorithm is used to output the optimal call parameters; S30: Sending instructions to the water intake pump and the heat exchanger according to the optimal adjustment parameters; S40: the water pump receives the instruction and adjusts the water temperature and extraction speed of the deep water, and the heat exchanger receives the instruction and adjusts the working state; S50: Continuously receive and analyze feedback information from sensors, dynamically adjust optimal parameters, and send instructions to water pumps and heat exchangers.

[0027] 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 heat capacity decay curves of water bodies at different depths to understand the temperature distribution and heat capacity changes of the water body. Based on the real-time heat load data and the heat capacity decay curve, an adaptive cold source stratification selection algorithm is used to calculate the optimal call parameters. These parameters may include water intake depth, water intake speed, and working mode of the heat exchanger. According to the calculated optimal call 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. The feedback information of the sensor, including the temperature and flow of the cooling water and the heat load changes of the data center, is continuously received and analyzed. The optimal call parameters are dynamically adjusted according to the feedback information, and new instructions are sent to the water intake pump and the heat exchanger to maintain the efficient operation of the system.

[0028] In one embodiment, step S10 specifically includes the steps of: S11: An optical fiber temperature sensor array is arranged at intervals in the vertical direction of the reservoir to form a vertical high-density temperature measurement network. The sensor collects water temperature data at preset time intervals to generate a vertical temperature gradient map; S12: Establish a three-dimensional fluid model of the reservoir in combination with the water flow velocity sensor data, and input real-time water temperature, flow velocity, and ambient temperature and humidity data; S13: 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; S14: Output the heat capacity attenuation curve for each depth layer.

[0029] 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 (such as: the water temperature is 8°C at a depth of 10 meters, 5°C at 20 meters, and 4°C at 30 meters). Combined with the water flow velocity sensor data, a three-dimensional temperature field model of the reservoir is constructed through the Kriging interpolation algorithm (Kriging) to quantify the differences in the distribution of cold sources in different regions. The heat capacity decay rate is the rate of temperature rise of the water body due to heat absorption per unit time (°C / h), and the effective cooling time is the time that the water body at the current depth can continue to provide cooling before the temperature rise does not exceed the threshold (such as 2°C).

[0030] In one embodiment, step S20 specifically includes the steps of: S21: 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; S22: Based on the heat capacity decay curve and the pumping energy consumption corresponding to the water intake at each depth, the theoretical cooling capacity of the water body at each depth layer is calculated, and the temperature rise decay correction is introduced; S23: Prioritize the water body in the depth layer with the highest theoretical cooling capacity and output the optimal adjustment parameters.

[0031] In this embodiment, the sensor on 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 required for taking water from different depths according to the characteristic curve of the water pump, which involves factors such as the efficiency, flow rate and head of the water pump; based on the heat capacity decay curve, the cooling capacity of water bodies at different depths is calculated. The heat capacity decay curve reflects the trend that the temperature of the water body gradually decreases with the increase of depth. Combined with the pumping energy consumption corresponding to the water extraction at each depth, the theoretical cooling capacity of the water body at each depth layer is calculated. At the same time, factors such as the temperature, flow rate and heat exchange efficiency of the water body should be considered, and a temperature rise decay correction is introduced to consider the temperature change of the water body during the transmission process to ensure that the calculated cooling capacity is more accurate; the water body at the depth layer with the highest theoretical cooling capacity is preferentially selected, and the optimal extraction parameters are output, including water extraction depth, water extraction speed and working mode of the heat exchanger, etc. These parameters will be used to control the operation of the water extraction pump and the heat exchanger to achieve an efficient heat dissipation process.

[0032] In one embodiment, after step S23, the method further includes the following steps: S240: 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; S241: Increase the amount of deep water pumped during the night and store it in the cold storage tank. When the temperature rises suddenly during the day, causing the surface water temperature to rise rapidly, the cold storage tank reserve water is used first; S242: 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, entering the winter operation mode; S243: Prioritize shallow water extraction and direct the return water from data center cooling into the building heating system; S244: When the shallow water temperature approaches freezing point, temporarily switch to the middle layer to draw water.

[0033] In this embodiment, when it is detected that the temperature is continuously higher than 25°C, and the surface water temperature (0-10 meters) and the deep water temperature (>30 meters) differ significantly (such as a temperature difference of ≥8°C), the system determines to enter the summer operation mode. During the night period (20:00 to 6:00 the next day) when the temperature is lower, the extraction of deep low-temperature water (>30 meters) is increased and stored in the cold storage pool. The reserve amount is about 20%-30% of the estimated daytime demand. When the daytime temperature rises suddenly and causes the surface water temperature to rise rapidly, the cold storage pool reserve water is used first to delay direct dependence on natural cold sources. The mixing ratio of "70% deep water + 30% middle water" is used to balance cooling efficiency and energy consumption. For example, deep water provides a low-temperature basis, and middle water supplements flow demand.

[0034] When the air temperature is below 10°C for a long time and the water temperature stratification is not obvious (the temperature difference at each depth is ≤3°C), the system switches to winter mode, giving priority to extracting colder water at a depth of 5-15 meters to reduce the pumping energy consumption of deep water extraction. Because the temperature difference between deep and shallow layers is small in winter, the return water after cooling the data center (with a higher temperature than ambient water) is introduced into the building heating system to replace traditional boiler heating and improve the comprehensive energy utilization rate. If the shallow water temperature is close to the freezing point (≤2°C), it will temporarily switch to the middle layer (15-25 meters) to extract water to avoid ice crystals clogging the equipment.

[0035] In one embodiment, step S30 specifically includes the following steps: S31: extracting reservoir depth data in the optimal call parameters and associating a water intake pump at a corresponding depth; S32: Sending a start command to the associated water pump, and sending a shut-down command to other water pumps; S33: extracting water flow velocity data from the optimal call parameters, generating a water flow velocity adjustment instruction, and sending the instruction to the associated water intake pump; S34: extracting the return water temperature data in the optimal call parameters, generating a heat exchange adjustment instruction, and sending it 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.

[0036] In this embodiment, specific instructions are sent to the water intake pump and the heat exchanger according to the optimal adjustment parameters to adjust the mixing ratio of cooling water and data center circulating water, thereby achieving precise control of the temperature of the data center.

[0037] Specifically, the reservoir depth data is associated with the corresponding water intake pump to ensure that the water intake pump can draw water from the correct depth, and a start instruction is sent to the associated water intake pump to start drawing water from the corresponding depth. At the same time, a shutdown instruction is sent to other unrelated water intake pumps to avoid unnecessary energy waste and operational errors; water flow velocity data is extracted from the optimal extraction parameters, and instructions for adjusting the water flow velocity are generated based on these data and sent to the already started water intake pump to control the water intake speed; return water temperature data is extracted from the optimal extraction parameters, and instructions for adjusting heat exchange are generated based on the return water temperature data. The heat exchange adjustment instructions are used to adjust the mixing ratio of cooling water and circulating water in the data center to ensure effective heat exchange and heat dissipation efficiency.

[0038] In one embodiment, step S50 further includes the following steps: S51: monitor the pressure data of the water pump and pipeline in real time, and send out a potential danger signal when the fluctuation range of the pressure data exceeds a preset threshold; S52: After receiving a potential danger signal, the water pump is automatically shut down and the backup cooling device is started.

[0039] In this embodiment, the backup cooling device is a key component of a data center or other critical facilities, and is used to keep the equipment cooled when the main cooling system fails or is under maintenance, such as a backup cooling tower or a backup coolant circulation pump, and an automatic transfer switch is provided between the main cooling system and the backup cooling device to detect failures in the main system and automatically switch to the backup system.

[0040] Specifically, the pressure data of the water intake pump and pipeline are continuously monitored. When the fluctuation range of the monitored pressure data exceeds the preset safety threshold and potential operating risks such as excessive pressure and abnormal temperature are identified, the relevant equipment is automatically shut down to prevent the situation from deteriorating. The operation and maintenance personnel are notified of potential danger signals through sound and light alarms, text messages or emails. After receiving the potential danger signals, the backup cooling device is started to ensure that the data center can maintain normal operating temperature when the main cooling system is unavailable.

[0041] In one embodiment, step S52 further includes the following steps: S53: 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 to the cold storage tank in advance; S54: Mix deep water and middle layer water in a preset ratio to supply water, balancing pumping energy consumption and cooling supply; S55: When the return water temperature exceeds the preset threshold, the heat exchanger area is automatically increased.

[0042] In this embodiment, deep low-temperature water is pumped into the cold storage tank in advance, and the reserve amount is 20% of the average daily water consumption. The water is mixed in the ratio of 70% deep water + 30% middle layer water to balance the pumping energy consumption and the cold supply. When the return water temperature exceeds the set value, the heat exchanger area is automatically increased by 10%-15%.

[0043] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0044] In one embodiment, a smart heat dissipation system based on a natural cold source is provided, and the device corresponds one-to-one with a smart heat dissipation method based on a natural cold source in the above embodiment. Figure 4 As shown, the system includes: The data collection module is used 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 installed in the reservoir and the data center, and to generate heat capacity attenuation curves of water bodies at different depths; 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.

[0045] For the specific definition of an intelligent heat dissipation system based on a natural cold source, please refer to the definition of an intelligent heat dissipation method based on a natural cold source in the above text, which will not be repeated here. Each module in the above-mentioned intelligent heat dissipation system based on a natural cold source can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0046] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As 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, an intelligent heat dissipation method based on a natural cold source is implemented.

[0047] In one embodiment, a computer device is provided, 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, an intelligent heat dissipation method based on a natural cooling source is implemented; In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, an intelligent heat dissipation method based on a natural cooling source is implemented.

[0048] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0049] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and 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.

[0050] The embodiments described above 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, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions 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 feedback information from sensors, dynamically adjust optimal parameters and send instructions to water pumps and heat exchangers.

2. The intelligent heat dissipation method based on natural cooling source according to claim 1, characterized in that: 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.

3. 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.

4. The intelligent heat dissipation method based on natural cooling source according to claim 3 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.

5. 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.

6. 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.

7. The intelligent heat dissipation method based on natural cooling source according to claim 6, 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.

8. 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 at different depths of the reservoir in real time through sensors installed in the reservoir and the data center, and to generate heat capacity attenuation curves of water bodies at different depths; 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.

9. 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 7 are implemented.

10. 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 7 are implemented.

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