Heat dissipation method for deep geothermal-data center waste heat combined supply and related device

Abstract

The invention relates to the technical field of data center heat dissipation, in particular to a deep geothermal-data center waste heat combined supply heat dissipation method and related device.A deep geothermal basic data set and a data center basic data set are obtained, and a geothermal coupling waste heat recovery path is established; afterheat information of the data center is obtained, a geothermal coupling heat dissipation scheme is obtained according to the afterheat information of the data center and the geothermal coupling afterheat recovery path, heat dissipation control is conducted on the data center, and heat dissipation is completed. According to the method, through effective matching of a deep geothermal basic data set and a data center basic data set, a geothermal coupling waste heat recovery path is accurately established according to the characteristics of a deep geothermal system and the heat dissipation requirement of a data center; according to data center waste heat information, a geothermal coupling waste heat recovery path is dynamically adjusted, a scientific heat dissipation mechanism is customized, efficient coupling of a deep geothermal system and a data center heat dissipation system is achieved, and the technical problems of energy matching, heat transfer and the like of the deep geothermal system and the data center heat dissipation system are solved.

Description

Technical Field

[0001] The present invention relates to the field of data center heat dissipation technology, and in particular to a deep geothermal-data center waste heat combined heat dissipation method and related devices. Background Art

[0002] Deep geothermal energy originates from the Earth's interior, has high temperatures (reaching over 150°C), and is stable, clean, and renewable. Deep geothermal technology involves extracting heat from high-temperature reservoirs 1,000 to 5,000 meters underground through geothermal wells. This energy can be used to drive heat pumps or directly for heating, power generation, and other applications. Data center waste heat refers to the underutilized heat generated by servers, storage devices, and other devices during data center operation. This generally refers to large data centers that require continuous operation, generating large amounts of waste heat at relatively stable temperatures. Data center waste heat is considered low-grade thermal energy. Deep geothermal-data center waste heat cogeneration combines deep geothermal energy with data center waste heat to provide stable thermal energy for urban heating, industrial heating, and other applications, achieving efficient energy supply and cascaded utilization.

[0003] The current deep geothermal-data center waste heat cogeneration sector faces numerous technical challenges that need to be addressed. First, the poor compatibility of deep geothermal systems with data centers in the deployment of cooling hardware makes it difficult to accurately establish an efficient geothermal-coupled waste heat recovery pathway. This results in low heat transfer efficiency and prevents the full utilization of deep geothermal energy for heat dissipation. Second, data center waste heat monitoring methods are limited, and the resulting waste heat information is incomplete and inaccurate, making it difficult to accurately reflect the actual heating conditions in each area of ​​the data center. Cooling decisions based on inaccurate waste heat information often result in geothermal-coupled cooling solutions that lack scientific and effective scientific principles and fail to achieve precise cooling of the data center. Furthermore, even when cooling solutions are in place, the lack of comprehensive control mechanisms and technologies makes it difficult to ensure effective implementation in actual operation. This significantly reduces the cooling effectiveness of deep geothermal-data center waste heat cogeneration, severely impacting the stable operation of data centers and the overall energy utilization efficiency. Therefore, a method for achieving precise data center heat dissipation is urgently needed to improve overall energy utilization efficiency. Summary of the Invention

[0004] In response to the problem of poor heat dissipation accuracy in data centers in the prior art, the present invention provides a heat dissipation method and related devices using deep geothermal energy and data center waste heat combined.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: The present invention provides a heat dissipation method for combined deep geothermal and data center waste heat, comprising: Obtain deep geothermal basic data sets and data center basic data sets; Based on the deep geothermal basic data set and the data center basic data set, a geothermal coupled waste heat recovery path is established; Obtain data center waste heat information; According to the waste heat information of the data center and the geothermal coupling waste heat recovery path, the geothermal coupling heat dissipation solution is obtained, and the heat dissipation of the data center is controlled to complete the heat dissipation.

[0006] Optionally, the deep geothermal basic data set includes the temperature, flow rate and geological structure of the geothermal system.

[0007] Optionally, the data center basic data set includes the scale, equipment layout and power of the data center.

[0008] Optionally, the method for establishing a geothermal coupled waste heat recovery path based on the deep geothermal basic data set and the data center basic data set is: Match the deep geothermal basic dataset with the data center basic dataset to obtain the relationship between the deep geothermal system and the data center; Based on the relationship between the deep geothermal system and the data center, the airflow and temperature field distribution inside the data center under different cooling scenarios are simulated to obtain the evaluation results of the cooling effect of different cooling scenarios; According to the evaluation results of the heat dissipation effects of different heat dissipation scenarios, the heat dissipation scenario with the best comprehensive performance is obtained as the geothermal coupling waste heat recovery path.

[0009] Optionally, the method for obtaining data center waste heat information is: Conduct waste heat monitoring on data centers and obtain waste heat monitoring data sets; Perform data cleaning on the residual heat monitoring data set to remove abnormal and redundant data and obtain the residual heat information of the data center.

[0010] Optionally, the method of obtaining a geothermal coupling heat dissipation solution based on the data center waste heat information and the geothermal coupling waste heat recovery path, and performing heat dissipation control on the data center to complete the heat dissipation is as follows: Based on the data center waste heat information, the heat dissipation capacity of the geothermal coupled waste heat recovery path is quantitatively evaluated to obtain the heat dissipation capacity matching coefficient; According to the heat dissipation capacity matching coefficient, the geothermal coupling heat dissipation solution is obtained and the heat dissipation of the data center is controlled.

[0011] Optionally, it also includes a monitoring and early warning process for the geothermal coupled waste heat recovery path, specifically: Performing real-time monitoring on the geothermal coupled waste heat recovery path to obtain recovery path monitoring data; performing anomaly detection on the geothermal coupled waste heat recovery path according to the recovery path monitoring data, and determining an anomaly coefficient of the recovery path; If the recovery path abnormality coefficient is greater than or equal to a preset recovery path abnormality threshold, a recovery path abnormality warning signal is generated and a warning is issued.

[0012] A deep geothermal-data center waste heat combined heat dissipation system, comprising: Basic data set acquisition module: used to obtain deep geothermal basic data sets and data center basic data sets; Path establishment module: used to establish geothermal coupled waste heat recovery paths based on deep geothermal basic data sets and data center basic data sets; Waste heat information acquisition module: used to obtain waste heat information of the data center; Heat dissipation control module: used to obtain the geothermal coupling heat dissipation solution based on the data center waste heat information and the geothermal coupling waste heat recovery path, and to control the heat dissipation of the data center to complete the heat dissipation.

[0013] A terminal 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 method when executing the computer program.

[0014] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a heat dissipation method for deep geothermal-data center waste heat co-supply. The method obtains a deep geothermal basic data set and a data center basic data set, and establishes a geothermal-coupled waste heat recovery path; then, the data center waste heat information is obtained, and based on the data center waste heat information and the geothermal-coupled waste heat recovery path, a geothermal-coupled heat dissipation solution is obtained, and the heat dissipation of the data center is controlled to complete the heat dissipation. This method effectively matches the deep geothermal basic dataset with the data center basic dataset. Based on the characteristics of the deep geothermal system (such as temperature, flow, water quality, etc.) and the cooling requirements of the data center (such as heat load distribution, cooling period, etc.), it achieves seamless integration of deep geothermal energy and data center waste heat, improves the adaptability of the deep geothermal system and the data center, and accurately establishes a geothermal coupling waste heat recovery path. This can fully utilize deep geothermal energy for heat dissipation, improve heat transfer efficiency, and achieve cascade utilization of energy. Then, based on the data center waste heat information, the geothermal coupling waste heat recovery path is dynamically adjusted to customize a scientific cooling mechanism based on the actual heating conditions of each area of ​​the data center. The dynamic adjustment mechanism enables the cooling to quickly respond to changes in the heating conditions of the data center and adjust the cooling strategy in time, avoiding equipment failure and energy waste caused by insufficient or excessive cooling. This greatly improves the stability and reliability of data neutrality, ensures the normal operation of the data center, and improves the comprehensive energy utilization efficiency. It achieves efficient coupling of the deep geothermal system and the data center cooling system, and solves the technical difficulties between the two in energy matching and heat transfer.

[0016] The present invention provides a deep geothermal-data center waste heat co-supply heat dissipation system. The system, through the high integration of a basic data set acquisition module, a path establishment module, a waste heat information acquisition module, and a heat dissipation control module, realizes the acquisition of a deep geothermal basic data set and a data center basic data set, and establishes a geothermal-coupled waste heat recovery path. Then, the data center waste heat information is acquired, and based on the data center waste heat information and the geothermal-coupled waste heat recovery path, a geothermal-coupled heat dissipation solution is obtained, and a heat dissipation control process is performed on the data center. The real-time data exchange and collaborative work between the modules greatly improves the system's operational efficiency and stability. The basic data set acquisition module is responsible for acquiring the deep geothermal basic data set and the data center basic data set, laying the foundation for the subsequent matching of the deep geothermal system and the data center, and providing the prerequisite for establishing a geothermal-coupled waste heat recovery path. The path establishment module is responsible for establishing a geothermal-coupled waste heat recovery path based on the deep geothermal basic data set and the data center basic data set, providing conditions for subsequent heat dissipation control. The waste heat information acquisition module is responsible for acquiring data center waste heat information. The heat dissipation control module dynamically adjusts the geothermal-coupled heat dissipation solution based on this information and the established geothermal-coupled waste heat recovery path. When the heat output of a certain area in the data center changes, the system can quickly adjust the distribution of geothermal energy and waste heat to ensure heat dissipation while further improving energy utilization efficiency. The system has a simple structure and a highly integrated modular design, achieving efficient energy utilization, significantly improved heat dissipation, considerable economic benefits, outstanding environmental benefits, and technological innovation and industry leadership. It provides a sustainable, green, and efficient solution for data center heat dissipation, has broad application prospects and market potential, and is of great significance in promoting green development and energy transformation in the data center industry.

[0017] The present invention also provides a terminal device, comprising 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 method when executing the computer program; the processor is capable of rapidly executing the above-mentioned acquisition of deep geothermal basic data sets and data center basic data sets, establishment of a thermally coupled waste heat recovery path, acquisition of waste heat information from the data center, and heat dissipation processes of the data center, thereby ensuring accurate matching and high efficiency of heat dissipation of the data center; the computer program in the memory can be modified and optimized according to actual needs to adapt to the heat dissipation requirements of different scenarios.

[0018] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above method; the computer-readable storage medium (such as a solid-state drive (SSD) and a Flash memory) has high-speed reading capabilities and can quickly load the computer program into the processor for execution, ensuring the timeliness of dynamic matching of heat dissipation in the data center. It has the characteristics of flexibility and portability, high reliability and stability, support for large-scale data storage, easy integration and expansion, reduced development and maintenance costs, high security, energy saving and environmental protection, support for multiple application scenarios, and promotion of standardization and normalization. It provides strong support for promoting green development and energy transformation in the data center industry and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The figure is a flow chart of a heat dissipation method for combining deep geothermal energy with waste heat from a data center according to the present invention.

[0020] Figure 2 This is a structural diagram of a deep geothermal-data center waste heat combined heat dissipation system of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] The present invention will be further described in detail below with reference to specific embodiments, which are intended to explain the present invention rather than to limit it.

[0024] See also Figure 1 The present invention discloses a heat dissipation method for deep geothermal-data center waste heat co-generation, comprising: S1: Acquire deep geothermal basic dataset and data center basic dataset, specifically: First, the basic information of the deep geothermal system is comprehensively collected, including data such as temperature, flow, geological structure, etc., to construct a deep geothermal basic data set; when collecting the basic information of the deep geothermal system, a variety of technical means are used to conduct an all-round detection of the deep geothermal system: through geological exploration, the distribution, thickness and rock characteristics of the underground heat reservoir are understood; with the help of temperature measuring instruments, geothermal data at different depths are accurately obtained; with flow monitoring equipment, the flow and flow rate of geothermal fluids are measured; in addition, the chemical composition of geothermal fluids and other information are analyzed, and these rich collected data are sorted and summarized to form a deep geothermal basic data set.

[0025] Basic data such as the data center's size, equipment layout, and power consumption are collected to form a data center basic data set. For each data center, detailed information is compiled. First, the data center's size, including computer room area and number of server racks, is recorded. Second, the server type, power consumption, and layout are recorded to clarify the heat dissipation power density in different areas. Furthermore, the data center's existing cooling facilities, such as the cooling capacity of the air conditioning system and the layout of the ventilation ducts, are also monitored. This data is integrated to form a complete data center basic data set, providing accurate information for cooling hardware deployment decisions.

[0026] S2: Based on the deep geothermal basic dataset and the data center basic dataset, establish a geothermal coupled waste heat recovery path, specifically: S21: Match the deep geothermal basic dataset with the data center basic dataset to obtain the relationship between the deep geothermal system and the data center: This involves deeply analyzing the data center's basic datasets to extract key information such as the data center's equipment layout, power consumption, heat generation characteristics, and the spatial structure of the computer room. Furthermore, data on geothermal resource temperature distribution, geological structure, thermal conductivity, and mining techniques are accurately extracted from the deep geothermal basic dataset. Using Geographic Information System (GIS) technology, the spatial information of the data center is spatially matched with the geographic distribution of the deep geothermal system, visually demonstrating the relationship between the deep geothermal system and the data center.

[0027] S22: Based on the relationship between the deep geothermal system and the data center, simulate the airflow and temperature field distribution inside the data center under different cooling scenarios to obtain the evaluation results of the cooling effect of different cooling scenarios: Computational Fluid Dynamics (CFD) software was used to simulate the airflow and temperature distribution within the data center under different cooling hardware layout scenarios to evaluate cooling effectiveness. Based on this, multi-objective optimization algorithms, such as genetic algorithms, were used to iteratively optimize various layout options, including geothermal heat exchanger type, quantity, and location, as well as the routing and diameter of the circulation pipes, with the goals of maximizing cooling efficiency, minimizing costs, and maximizing system stability. Through multiple rounds of calculation and screening, a cooling hardware layout with the best overall performance was determined, ultimately establishing a geothermal-coupled waste heat recovery path.

[0028] S23: Based on the evaluation results of the heat dissipation effects of different heat dissipation scenarios, the heat dissipation scenario with the best overall performance is obtained as the geothermal coupling waste heat recovery path: Working strictly in accordance with the cooling hardware layout plan, various cooling hardware devices, such as heat exchangers, coolant pipes, and cooling fans, were precisely installed within the data center. Furthermore, the data center's cooling hardware was properly connected to the deep geothermal system's related facilities to ensure coolant circulation between them. During the installation process, each connection point was rigorously inspected to prevent leaks and other problems. After installation and commissioning, a geothermal-coupled waste heat recovery path was established.

[0029] The method for establishing a geothermal-coupled waste heat recovery pathway based on the deep geothermal and data center basic datasets essentially involves analyzing these two datasets to make decisions about cooling hardware placement, determining the hardware type, quantity, and installation location, and ultimately developing a cooling hardware deployment plan. The cooling hardware is then installed according to the plan, and all pipes and wiring between the data center and the deep geothermal system are connected, generating a geothermal-coupled waste heat recovery pathway. This pathway not only serves as a channel for dissipating data center waste heat but also provides a crucial foundation for the combined supply of deep geothermal and data center waste heat. Subsequent data center waste heat monitoring, cooling decision-making, and heat dissipation control will all be centered around this pathway.

[0030] S3: Obtain data center waste heat information, specifically: S31: Perform residual heat monitoring on the data center and obtain a residual heat monitoring data set: To comprehensively and accurately monitor the data center's waste heat, various monitoring devices, such as high-precision temperature sensors and heat flow sensors, are deployed at key locations within the data center, such as near core heat-generating components like the server's central processing unit (CPU) and graphics processing unit (GPU), as well as at the air inlets and outlets of the computer room. These sensors collect real-time data such as temperature and heat flux density at various locations within the data center and record it at regular intervals. Over time, this vast amount of monitoring data accumulates, forming a rich, albeit potentially chaotic and redundant, waste heat monitoring dataset. This dataset covers waste heat data from various areas of the data center at different times.

[0031] S32: Clean the residual heat monitoring data set to remove abnormal and redundant data and obtain the residual heat information of the data center: Because residual heat monitoring datasets may contain erroneous data due to factors such as sensor failure and electromagnetic interference, as well as duplicate and irrelevant data, these data can affect the true assessment of the data center's residual heat situation. By setting reasonable thresholds, abnormal data that clearly exceeds the normal range is screened out and removed. Next, methods such as data interpolation are used to supplement small amounts of missing data. At the same time, duplicate data records are removed to avoid data redundancy. After this series of data cleaning operations, the processed datasets are organized and summarized to generate data center residual heat information that accurately and clearly reflects the actual situation of residual heat generation and distribution in the data center, providing a reliable basis for subsequent cooling decisions and control.

[0032] S4: Based on the data center waste heat information and the geothermal coupling waste heat recovery path, obtain the geothermal coupling heat dissipation solution, and control the heat dissipation of the data center to complete the heat dissipation, specifically:

[0033] S41: Based on the data center waste heat information, quantitatively evaluate the heat dissipation capacity of the geothermal coupled waste heat recovery path and obtain the heat dissipation capacity matching coefficient: The Analytic Hierarchy Process (AHP) combined with a fuzzy comprehensive evaluation method was used to determine the heat dissipation capacity matching coefficient. First, a hierarchical model was constructed, with the target layer being the evaluation of the heat dissipation capacity matching between the geothermal coupled waste heat recovery pathway and the data center's waste heat. The criterion layer included data center waste heat factors (such as total waste heat volume and waste heat distribution uniformity) and geothermal coupled waste heat recovery pathway factors (such as heat exchanger efficiency, coolant flow rate and velocity, and pipeline thermal conductivity). The indicator layer contained specific quantitative indicators for each criterion. Expert scoring was used to determine the relative importance of indicators at each level. A judgment matrix was constructed, and the maximum eigenvalue and corresponding eigenvector of the judgment matrix were calculated using the square root method or the eigenroot method to obtain the weight vector for each indicator. Furthermore, quantitative scores were assigned to each indicator of the data center waste heat information and the geothermal coupled waste heat recovery pathway. Heat exchanger efficiency was categorized into different levels based on actual values ​​and assigned corresponding scores. Next, a comprehensive evaluation was conducted using the fuzzy transformation principle, establishing a fuzzy relationship matrix whose elements represent the degree of membership of each indicator to different evaluation levels. The weight vector was then combined with the fuzzy relationship matrix to produce a comprehensive evaluation vector, reflecting the degree of membership of the geothermal-coupled waste heat recovery path at different evaluation levels. Finally, a weighted average was taken of the comprehensive evaluation vector, multiplying the scores corresponding to different evaluation levels by the degree of membership and then summing the results to obtain a value between 0 and 1, which is the heat dissipation matching coefficient. The closer the heat dissipation matching coefficient is to 1, the higher the degree of heat dissipation matching.

[0034] S42: Based on the heat dissipation capacity matching coefficient, obtain a geothermal coupling heat dissipation solution and perform heat dissipation control on the data center: The resulting heat dissipation capacity matching coefficient is compared with a pre-set heat dissipation capacity matching threshold. This heat dissipation capacity matching threshold, determined based on extensive experimental data and engineering experience, serves as a metric for determining whether the geothermal-coupled waste heat recovery pathway can meet the data center's cooling needs. When the heat dissipation capacity matching coefficient is greater than or equal to the threshold, the geothermal-coupled waste heat recovery pathway has sufficient capacity to meet the data center's current cooling needs. At this point, the internal instruction generation module is triggered, and according to pre-set program logic, it rapidly generates a special electrical signal or digital code—this is the geothermal-coupled heat dissipation command. This command, using a specific communication protocol, is quickly and accurately transmitted via data transmission lines to various execution units related to the cooling system, such as controllers controlling the opening and closing of heat exchanger valves and drivers regulating the speed of coolant pumps. This provides a trigger signal for subsequent cooling decisions and implementation based on the command, ensuring timely and efficient cooling of the data center. The pre-built geothermal-coupled heat dissipation decision model is then invoked, and detailed data center waste heat information, including real-time waste heat generation and temperature distribution in each area of ​​the data center, is input into the model to determine the degree of cooling requirements in each area. The model also incorporates relevant parameters of the geothermal-coupled waste heat recovery pathway, such as the heat exchange capacity of the heat exchanger, the coolant circulation flow rate, and the thermal conductivity of the pipelines. The model then integrates this data to simulate the cooling effects of the data center under different cooling strategies. For example, the model adjusts the coolant flow distribution in different pipe areas based on the waste heat distribution and optimizes the operating frequency of the heat exchanger to achieve optimal cooling efficiency. After multiple rounds of simulation and analysis, the optimal combination is selected from a wide range of possible cooling strategies, ultimately generating a complete geothermal-coupled cooling solution. This ensures that the waste heat generated by the data center can be efficiently transferred through the geothermal-coupled waste heat recovery pathway, achieving the goal of stable and energy-efficient cooling for the data center.

[0035] Specifically, when a geothermal coupling heat dissipation instruction is received, the geothermal coupling heat dissipation decision model is activated through a specific software interface or signal transmission mechanism.

[0036] Data center waste heat information (such as temperature and power consumption in different areas) and geothermal-coupled waste heat recovery path information (such as heat exchanger efficiency and pipeline length) were organized into a structured dataset and divided into training and test sets. A random forest algorithm was used. Random forests consist of multiple decision trees. During the training phase, samples were randomly drawn with replacement from the training set to construct each decision tree. When splitting a node, each decision tree randomly selected a subset of features from all available features to increase diversity between trees. During the tree growth process, metrics such as the Gini impurity measure were used to measure node impurity. The optimal features and split points were selected until a preset stopping condition was met (such as the number of node samples falling below a threshold or the tree depth reaching a maximum limit). After training, each decision tree in the random forest was able to predict the input information and recommend a cooling solution. Ultimately, the results of all decision trees were combined through majority voting to arrive at a unified cooling solution prediction. The trained random forest model is fed with test data to evaluate its performance. Based on the results, the model's parameters (such as the number of decision trees and the maximum depth of each tree) are adjusted to further optimize the model. Once the model achieves satisfactory performance, the actual data center waste heat information and geothermal coupling waste heat recovery path information are fed into the model. The model then outputs a suitable geothermal coupling heat dissipation solution that takes into account the data center's cooling requirements and the characteristics of the geothermal coupling system, achieving efficient heat dissipation and effective energy utilization.

[0037] If the heat dissipation capacity matching coefficient is less than the heat dissipation capacity matching threshold, the auxiliary heat dissipation path is activated; then, according to the waste heat information of the data center, waste heat is distributed between the geothermal coupling waste heat recovery path and the auxiliary heat dissipation path to obtain waste heat allocated by a first path and a second path; based on the waste heat allocated by the first path and the waste heat allocated by the second path, a heat dissipation decision is made for the geothermal coupling waste heat recovery path and the auxiliary heat dissipation path to obtain the geothermal coupling heat dissipation solution. Specifically, when the heat dissipation capacity matching coefficient falls below the heat dissipation capacity matching threshold, it means that the geothermal-coupled waste heat recovery path alone cannot meet the data center's cooling needs, and auxiliary cooling paths are activated. Supplementary cooling paths include air cooling systems, additional liquid cooling equipment, and other options. A genetic algorithm is used to allocate waste heat. First, a detailed analysis of the data center's waste heat information is performed to understand the waste heat generation in each area. Performance parameters of the geothermal-coupled waste heat recovery path and auxiliary cooling paths, such as heat dissipation capacity and energy consumption, are also determined. Next, the waste heat allocation plan is encoded into chromosomes, with each chromosome representing a possible combination of waste heat allocation using the primary and secondary paths. An initial population is randomly generated, with each individual in the population representing a chromosome. To evaluate the performance of each individual, a fitness function is defined, with objectives such as minimizing energy consumption, maximizing heat dissipation efficiency, and meeting the data center's cooling needs being the goal. A selection operation is then performed on the initial population, selecting superior individuals based on their fitness values. Individuals with higher fitness have a greater chance of being selected for the next generation. A crossover operation then occurs, randomly selecting two selected individuals and exchanging some of their genetic fragments to generate new individuals, increasing population diversity. At the same time, individual genes are mutated with a certain probability to prevent the algorithm from falling into a local optimum. Selection, crossover, and mutation operations are repeated to evolve the population generation by generation. The fitness of each individual is calculated in each generation. The algorithm terminates when the preset number of iterations is reached, or when the fitness of the population converges to a satisfactory level. Finally, the individual with the highest fitness is selected from the final population, and its corresponding residual heat allocation combination becomes the desired residual heat allocation for the first and second paths.

[0038] Based on the obtained waste heat distribution for the first path and the second path, detailed heat dissipation decisions are made for the geothermal coupling waste heat recovery path and the auxiliary heat dissipation path. For the geothermal coupling waste heat recovery path, based on the allocated waste heat amount, combined with the specifications of the heat exchanger, the parameters of the coolant, etc., key parameters such as the time required for the heat exchange process, the flow rate and flow velocity of the coolant are calculated, and the power of the coolant pump and the opening of the valve are adjusted in real time through the automatic control system to ensure that the waste heat can be efficiently recovered. For the auxiliary heat dissipation path, based on its allocated waste heat amount and combined with the performance curve of the heat dissipation equipment, the number of devices to be opened, the operating power and the working time are determined, and the operating status of the equipment is adjusted in real time through devices such as inverters, ultimately forming a geothermal coupling heat dissipation solution.

[0039] One possible implementation method also includes a monitoring and early warning process for the geothermal coupled waste heat recovery path, specifically: Performing real-time monitoring on the geothermal coupled waste heat recovery path to obtain recovery path monitoring data; performing anomaly detection on the geothermal coupled waste heat recovery path according to the recovery path monitoring data, and determining an anomaly coefficient of the recovery path; If the recovery path abnormality coefficient is greater than or equal to a preset recovery path abnormality threshold, a recovery path abnormality warning signal is generated and a warning is issued.

[0040] Specifically, real-time monitoring is achieved through a sensor network deployed at key points along the geothermal-coupled waste heat recovery pathway. High-precision temperature sensors are installed at the inlet and outlet of the heat exchanger to accurately capture temperature changes in the coolant and geothermal fluid. Flow sensors and pressure sensors are placed along the pipeline, collecting coolant flow rate and in-pipe pressure data once per second to ensure that flow fluctuations and pressure anomalies are captured promptly. Vibration sensors are installed on the surfaces of vulnerable equipment such as pumps and valves to monitor the vibration frequency and amplitude of the equipment during operation in real time. All sensors aggregate data to a central control system via industrial Ethernet or wireless transmission modules. After data cleaning and format conversion, the data is integrated into recovery path monitoring data containing multi-dimensional information such as temperature series, flow parameters, pressure curves, and vibration spectra, providing basic support for subsequent anomaly analysis and system optimization.

[0041] Based on historical recovery path monitoring data, threshold ranges for normal operation are determined for various monitoring indicators, such as temperature, pressure, and flow rate. These threshold ranges are determined by analyzing a large amount of data from periods of normal operation and calculating the mean and standard deviation of each indicator. For example, the normal temperature range is set as the mean plus or minus two standard deviations. Next, a sliding window approach is used to process the real-time recovery path monitoring data. A sliding window of appropriate size is set, for example, every 10 consecutive data points as a window. Within each window, the deviation of each monitoring indicator's actual value from the corresponding threshold range is calculated. For data points that exceed the threshold range, a deviation score is assigned based on the magnitude of the deviation. Next, a weight is assigned to each monitoring indicator based on its importance to the normal operation of the geothermal-coupled waste heat recovery path. For example, the heat exchanger inlet and outlet temperatures have a significant impact on waste heat recovery efficiency and can be assigned a higher weight. The deviation scores of each monitoring indicator within each window are multiplied by the corresponding weights and summed to obtain a comprehensive deviation value for that window. Finally, the comprehensive deviation values ​​of all windows are averaged and normalized, and the result is mapped to the range of 0 to 1. The resulting value is the recovery path anomaly coefficient. The closer the coefficient is to 1, the greater the possibility of an abnormality in the recovery path; the closer it is to 0, the more normal the operation status is.

[0042] The calculated recovery path anomaly coefficient is continuously compared in real time with a pre-set recovery path anomaly threshold. Once the recovery path anomaly coefficient is detected to be greater than or equal to the threshold, it indicates that the operation status of the geothermal coupling waste heat recovery path has deviated from the normal range and there is a potential failure risk. A recovery path anomaly warning signal is immediately generated. This warning signal is conveyed to relevant management personnel through various means, such as audible and visual alarms, SMS notifications, and system pop-up reminders, so that they can take timely measures to ensure the stable operation of the geothermal coupling waste heat recovery path and avoid adverse effects on the entire data center cooling system due to equipment failure or operational anomalies.

[0043] See also Figure 2 A deep geothermal-data center waste heat combined heat dissipation system, characterized by comprising: Basic data set acquisition module: used to obtain deep geothermal basic data sets and data center basic data sets; Path establishment module: used to establish geothermal coupled waste heat recovery paths based on deep geothermal basic data sets and data center basic data sets; Waste heat information acquisition module: used to obtain waste heat information of the data center; Heat dissipation control module: used to obtain the geothermal coupling heat dissipation solution based on the data center waste heat information and the geothermal coupling waste heat recovery path, and to control the heat dissipation of the data center to complete the heat dissipation. Furthermore, the system is also used to achieve the following functions: Basic information of the deep geothermal system is collected to obtain a deep geothermal basic data set; a data center basic data set is obtained; a heat dissipation hardware layout decision is made based on the data center basic data set and the deep geothermal basic data set to obtain a heat dissipation hardware layout plan; heat dissipation hardware is laid out according to the heat dissipation hardware layout plan to generate the geothermal coupled waste heat recovery path.

[0044] Furthermore, the system is also used to implement the following functions: Performing residual heat monitoring on the data center to obtain a residual heat monitoring data set; and performing data cleaning on the residual heat monitoring data set to generate residual heat information of the data center.

[0045] Furthermore, the system is also used to implement the following functions: A heat dissipation capability matching evaluation is performed on the geothermal coupling waste heat recovery path according to the waste heat information of the data center to obtain a heat dissipation capability matching coefficient; a determination is made as to whether the heat dissipation capability matching coefficient is greater than or equal to a heat dissipation capability matching threshold; if the heat dissipation capability matching coefficient is greater than or equal to the heat dissipation capability matching threshold, a geothermal coupling heat dissipation instruction is generated; based on the geothermal coupling heat dissipation instruction, a heat dissipation decision is made according to the waste heat information of the data center and the geothermal coupling waste heat recovery path to generate the geothermal coupling heat dissipation solution.

[0046] Furthermore, the system is also used to implement the following functions: Based on the geothermal coupling heat dissipation instruction, a geothermal coupling heat dissipation decision model is activated; the data center waste heat information and the geothermal coupling waste heat recovery path are input into the geothermal coupling heat dissipation decision model to obtain the geothermal coupling heat dissipation solution.

[0047] Furthermore, the system is also used to implement the following functions: If the heat dissipation capacity matching coefficient is less than the heat dissipation capacity matching threshold, the auxiliary heat dissipation path is activated; according to the waste heat information of the data center, the waste heat is distributed between the geothermal coupling waste heat recovery path and the auxiliary heat dissipation path to obtain a first path allocation of waste heat and a second path allocation of waste heat; according to the first path allocation of waste heat and the second path allocation of waste heat, a heat dissipation decision is made for the geothermal coupling waste heat recovery path and the auxiliary heat dissipation path to obtain the geothermal coupling heat dissipation solution.

[0048] Furthermore, the system is also used to implement the following functions: The geothermal coupling waste heat recovery path is monitored in real time to obtain recovery path monitoring data; an anomaly detection is performed on the geothermal coupling waste heat recovery path based on the recovery path monitoring data to determine a recovery path anomaly coefficient; if the recovery path anomaly coefficient is greater than or equal to a recovery path anomaly threshold, a recovery path anomaly warning signal is generated.

[0049] The system has a simple structure and, through a highly integrated modular design, achieves efficient energy utilization, significant improvement in heat dissipation, considerable improvement in economic benefits, outstanding environmental benefits, as well as technological innovation and industry leadership. It provides a sustainable, green and efficient solution for data center heat dissipation, has broad application prospects and market potential, and is of great significance for promoting the green development and energy transformation of the data center industry.

[0050] The present invention provides a terminal device comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of each of the aforementioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of each module / unit in each of the aforementioned apparatus embodiments are implemented.

[0051] The computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to accomplish the present invention.

[0052] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0053] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0054] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0055] If the module / unit integrated in the terminal device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0056] The above description is merely a preferred embodiment of the present invention and is not intended to impose any limitation on the technical solution of the present invention. Those skilled in the art should understand that, without departing from the spirit and principles of the present invention, the technical solution can also be subjected to several simple modifications and replacements, and these modifications and replacements are also within the scope of protection covered by the claims.

Claims

1. A heat dissipation method for deep geothermal and data center waste heat co-generation, characterized in that: include: Obtain deep geothermal basic data sets and data center basic data sets; Based on the deep geothermal basic data set and the data center basic data set, a geothermal coupled waste heat recovery path is established; Obtain data center waste heat information; According to the waste heat information of the data center and the geothermal coupling waste heat recovery path, the geothermal coupling heat dissipation solution is obtained, and the heat dissipation of the data center is controlled to complete the heat dissipation.

2. The heat dissipation method of deep geothermal-data center waste heat co-generation according to claim 1 is characterized in that: The deep geothermal basic data set includes the temperature, flow rate and geological structure of the geothermal system.

3. The heat dissipation method of deep geothermal-data center waste heat co-generation according to claim 1 is characterized in that: The data center basic data set includes the scale, equipment layout and power of the data center.

4. The heat dissipation method of deep geothermal-data center waste heat co-generation according to claim 1 is characterized in that: The method for establishing a geothermal coupled waste heat recovery path based on the deep geothermal basic data set and the data center basic data set is: Match the deep geothermal basic dataset with the data center basic dataset to obtain the relationship between the deep geothermal system and the data center; Based on the relationship between the deep geothermal system and the data center, the airflow and temperature field distribution inside the data center under different cooling scenarios are simulated to obtain the evaluation results of the cooling effect of different cooling scenarios; According to the evaluation results of the heat dissipation effects of different heat dissipation scenarios, the heat dissipation scenario with the best comprehensive performance is obtained as the geothermal coupling waste heat recovery path.

5. The heat dissipation method of deep geothermal-data center waste heat co-generation according to claim 1 is characterized in that: The method for obtaining data center waste heat information is: Conduct waste heat monitoring on data centers and obtain waste heat monitoring data sets; Perform data cleaning on the residual heat monitoring data set to remove abnormal and redundant data and obtain the residual heat information of the data center.

6. The heat dissipation method of deep geothermal-data center waste heat co-generation according to claim 1 is characterized in that: The method for obtaining a geothermal coupling heat dissipation solution based on the data center waste heat information and the geothermal coupling waste heat recovery path, and performing heat dissipation control on the data center to complete the heat dissipation is as follows: Based on the data center waste heat information, the heat dissipation capacity of the geothermal coupled waste heat recovery path is quantitatively evaluated to obtain the heat dissipation capacity matching coefficient; According to the heat dissipation capacity matching coefficient, the geothermal coupling heat dissipation solution is obtained and the heat dissipation of the data center is controlled.

7. The heat dissipation method of deep geothermal-data center waste heat co-generation according to claim 1 is characterized in that: It also includes the monitoring and early warning process of the geothermal coupled waste heat recovery path, specifically: Performing real-time monitoring on the geothermal coupled waste heat recovery path to obtain recovery path monitoring data; performing anomaly detection on the geothermal coupled waste heat recovery path according to the recovery path monitoring data, and determining an anomaly coefficient of the recovery path; If the recovery path abnormality coefficient is greater than or equal to a preset recovery path abnormality threshold, a recovery path abnormality warning signal is generated and a warning is issued.

8. A deep geothermal-data center waste heat combined heat dissipation system, characterized in that: include: Basic data set acquisition module: used to obtain deep geothermal basic data sets and data center basic data sets; Path establishment module: used to establish geothermal coupled waste heat recovery paths based on deep geothermal basic data sets and data center basic data sets; Waste heat information acquisition module: used to obtain waste heat information of the data center; Heat dissipation control module: used to obtain the geothermal coupling heat dissipation solution based on the data center waste heat information and the geothermal coupling waste heat recovery path, and to control the heat dissipation of the data center to complete the heat dissipation.

9. A terminal device comprising 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 method according to 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 the method according to any one of claims 1 to 7 are implemented.

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