Intelligent Temperature Control Method and System for Solid State Drives Based on Edge Computing

Abstract

The present invention relates to the field of intelligent temperature control technology, and particularly to an intelligent temperature control method and system for a solid-state drive based on edge computing. The method includes the following steps: obtaining solid-state drive array data; performing array topology analysis on the solid-state drive array data to generate solid-state drive array topology data; constructing an initial self-organizing thermal dissipation network based on the solid-state drive array topology data to obtain an initial self-organizing thermal dissipation network; deploying edge computing nodes for the initial self-organizing thermal dissipation network and collecting temperature data of the solid-state drive according to the edge computing nodes; and optimizing the network thermal conduction path of the initial self-organizing thermal dissipation network through the temperature data to generate a self-organizing thermal dissipation optimized network. The present invention realizes SSD array-level intelligent temperature control through self-organizing thermal dissipation network, edge computing optimization, thermal-electric mode conversion and long-term prediction, and improves the heat dissipation efficiency, response speed and long-term stability.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent temperature control, and particularly to an intelligent temperature control method and system for a solid-state drive based on edge computing. Background Art

[0002] Initially, SSD temperature control mainly relied on passive heat dissipation, such as heat sinks and thermal grease. With the development of the PCIe and NVMe protocols, the data transfer rate has increased significantly, and the heat generation problem of SSDs has become increasingly severe. Some high-end products have started to adopt active heat dissipation, such as fans and metal heat dissipation casings. At the same time, temperature sensors have been integrated into the SSD controller to achieve basic overheat protection. With the rise of edge computing, computing tasks tend to be distributed, and SSDs need to operate stably for a long time in high-temperature environments. Intelligent temperature control technology gradually uses AI algorithms combined with edge computing to achieve real-time temperature monitoring, load balancing, and dynamic frequency adjustment. Some SSDs also use phase change material heat dissipation and liquid cooling technology to reduce the temperature peak. In addition, combined with the data prediction ability of edge computing nodes, the SSD temperature control strategy can be adaptively adjusted according to the workload and environmental temperature to achieve efficient heat dissipation and performance balance. However, currently, traditional SSD temperature control strategies are usually based on single-disk monitoring and regulation, such as simply triggering frequency reduction based on temperature thresholds, lacking a global perspective, and it is difficult to achieve balanced heat dissipation in a multi-disk array. At the same time, traditional temperature control means mostly use frequency reduction or air cooling, which affects the performance of the SSD, and the heat dissipation method is single, and factors such as current and voltage are not fully utilized for collaborative optimization, resulting in low heat dissipation efficiency and heat dissipation accuracy of the solid-state drive temperature control. Summary of the Invention

[0003] Based on this, it is necessary to provide an intelligent temperature control method and system for a solid-state drive based on edge computing to solve at least one of the above technical problems.

[0004] To achieve the above object, an intelligent temperature control method for a solid-state drive based on edge computing, the method includes the following steps:

[0005] Step S1: Obtain solid-state drive array data; perform array topology analysis on the solid-state drive array data to generate solid-state drive array topology data; based on the solid-state drive array topology data, construct an initial self-organizing heat dissipation network for the solid-state drive array data to obtain an initial self-organizing heat dissipation network;

[0006] Step S2: Deploy edge computing nodes for the initial self-organizing heat dissipation network, and collect temperature data of the solid-state drive according to the edge computing nodes; optimize the network heat conduction path of the initial self-organizing heat dissipation network through the temperature data to generate a self-organizing heat dissipation optimized network;

[0007] Step S3: Calculate the current thermodynamic state of the solid-state drive array, perform a thermal-electric mode conversion on the temperature data according to the current thermodynamic state, and generate thermal flow dynamic equal-temperature regulation data; perform a thermal path pre-adjustment on the self-organizing thermal dissipation optimization network through the thermal flow dynamic equal-temperature regulation data to generate hard disk global thermal equilibrium optimization data;

[0008] Step S4: Predict the long-term change trend of the temperature data, and perform a hard disk advance optimization feedback on the hard disk global thermal equilibrium optimization data according to the long-term change trend to generate a solid-state drive intelligent temperature control strategy.

[0009] Through the construction and optimization of the self-organizing thermal dissipation network, the present invention establishes an efficient heat conduction path, enabling heat to be quickly and evenly distributed and dissipated, avoiding local overheating phenomena, and improving the overall heat dissipation efficiency. The edge computing node is used to monitor the hard disk temperature in real time, and through the thermal flow dynamic equal-temperature regulation technology, global thermal equilibrium is achieved, ensuring that the temperature of all hard disks is distributed within the optimal range, and enhancing the stability of the system. Excessive temperature will cause the performance of the solid-state drive to decline or even data damage. Through the intelligent temperature control strategy, the impact of high temperature can be effectively reduced, ensuring the stability of the hard disk read and write speed and reducing the I / O performance loss caused by temperature fluctuations. Prolonged exposure to high temperature environments will accelerate hard disk wear, and this method makes the temperature control more accurate through an advance optimization feedback mechanism, thereby reducing hard disk aging caused by overheating and increasing the service life of the hard disk. By using a long-term temperature change trend prediction model, the heat dissipation strategy is adjusted in advance to avoid the impact of sudden temperature changes, achieve active temperature control, and reduce emergency frequency reduction or shutdown protection triggered by sudden overheating of the system. Due to thermal management optimization, the hard disk operates in a reasonable temperature range for a long time, which can significantly reduce physical damage caused by thermal expansion and contraction, as well as failures of the controller and NAND chips due to overheating, and improve the reliability of data storage. Overheating will cause the bit error rate (BER) of the stored data to rise, affecting data integrity. This method ensures that the temperature of the data storage environment is appropriate through thermal equilibrium optimization and intelligent temperature control strategies, reducing the risk of data damage caused by temperature fluctuations. Therefore, the present invention realizes SSD array-level intelligent temperature control through a self-organizing thermal dissipation network, edge computing optimization, thermal-electric mode conversion, and long-term prediction, improving heat dissipation efficiency, response speed, and long-term stability.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain solid-state drive array data;

[0012] Step S12: Perform an array topology analysis on the solid-state drive array data to generate solid-state drive array topology data;

[0013] Step S13: Integrate intelligent materials into the solid - state drive array using the solid - state drive array topology data to generate solid - state drive array integrated material data, where the intelligent material integration includes integrating phase - change materials, thermoelectric materials, and intelligent thermal conductive films;

[0014] Step S14: Identify the micro - fluidic heat conduction path for the solid - state drive array topology data based on the solid - state drive array integrated material data to generate hard - disk self - assembly microstructure data;

[0015] Step S15: Implant local sensing units into the solid - state drive array topology data through the hard - disk self - assembly microstructure data to generate an initial self - organizing heat dissipation network, where the local sensing units include temperature sensors and heat - flow sensing devices.

[0016] Through the integration of intelligent materials (phase - change materials, thermoelectric materials, intelligent thermal conductive films) and the optimization of micro - fluidic heat conduction paths, the present invention realizes efficient heat - dissipation management, improves the overall heat - balance ability, and reduces the risk of local overheating. The intelligent thermal conductive film and thermoelectric materials are used to optimize the heat distribution. Combined with the micro - fluidic heat conduction path, the heat - transfer efficiency is improved, enabling the rapid diffusion of heat energy and reducing the problem of excessive local temperature. By implanting local sensing units (temperature sensors, heat - flow sensing devices), real - time temperature monitoring of the hard - disk array is achieved, enabling the system to accurately sense temperature changes and optimize the heat - management strategy. Combining the identification of micro - fluidic heat conduction paths and the integration of intelligent materials, an automatically adjustable self - organizing heat dissipation network is realized, improving the intelligent level of heat management and reducing manual intervention. Overheating can cause the expansion and contraction of materials, affecting the long - term stability of storage chips. Through the heat - buffering effect of phase - change materials, the physical stress of the hard disk caused by severe temperature fluctuations can be effectively reduced, improving the system reliability. Using intelligent materials for heat - management optimization enables the hard disk to operate in a stable state at low temperatures, reducing the damage to storage units caused by high temperatures, extending the hard - disk life, and at the same time reducing the replacement and maintenance costs caused by overheating damage. Through the high - precision temperature monitoring of local sensing units and the rapid thermoelectric conversion ability of thermoelectric materials, the hard - disk system can quickly adjust the heat - dissipation strategy to avoid damage caused by sudden temperature changes. Traditional heat - dissipation solutions usually rely on air - cooling or liquid - cooling systems, with high energy consumption. This method uses intelligent materials (thermoelectric materials, phase - change materials) and micro - fluidic heat - conduction mechanisms to make heat - dissipation more efficient, thereby reducing additional energy consumption and improving the energy - efficiency ratio of the system.

[0017] Preferably, step S2 includes the following steps:

[0018] Step S21: Deploy edge - computing nodes for the initial self - organizing heat dissipation network to obtain edge - computing nodes of the heat - dissipation network;

[0019] Step S22: Based on a preset time period, the temperature sensor is used to sense the hard disk temperature of the edge computing node of the heat dissipation network to generate hard disk temperature sensing data; the hard disk temperature sensing data is preprocessed to generate standard hard disk temperature sensing data, wherein the data preprocessing includes data cleaning, data denoising and data standardization;

[0020] Step S23: performing temperature distribution analysis on the standard hard disk temperature sensing data to generate a hard disk thermal distribution matrix; performing thermal conduction path optimization on the hard disk self-assembled microstructure data based on the hard disk thermal distribution matrix to generate thermal conduction path optimization data;

[0021] Step S24: regional temperature discrimination is performed on the hard disk temperature sensing data according to the thermal conduction path optimization data. When the hard disk temperature sensing data is greater than or equal to the preset temperature threshold, the initial self-organizing heat dissipation network is reorganized locally through the edge computing node of the heat dissipation network to generate a self-organizing heat dissipation optimization network.

[0022] The present invention transfers data processing from the cloud to the local through the deployment of edge computing nodes of the heat dissipation network, improves the real-time performance of temperature data collection and calculation, reduces data transmission delay, and realizes efficient distributed thermal management. Data preprocessing (data cleaning, data denoising, and data standardization) is adopted to reduce sensor noise interference, ensure that the acquired hard disk temperature data is accurate and reliable, and provide high-quality data support for subsequent temperature control optimization. The hard disk temperature distribution is analyzed by the hard disk heat distribution matrix, and the heat conduction path optimization is combined to make the heat flow more uniform, improve the overall heat dissipation effect, and reduce local overheating. The regional temperature discrimination mechanism is adopted. When the hard disk temperature exceeds the preset threshold, the heat path reorganization is automatically triggered, so that the heat energy can be quickly diffused to avoid the impact of temperature accumulation on the hard disk performance. Through the local heat path reorganization, the heat channel can be intelligently adjusted according to the real-time temperature change to form a self-organized heat dissipation optimization network, ensure that the hard disk operates within a reasonable temperature range, and improve the long-term stability of the system. Traditional air cooling or liquid cooling heat dissipation methods often rely on high-power consumption equipment. This solution is based on thermal conduction path optimization and edge computing collaborative management to reduce unnecessary heat dissipation resource consumption and improve the overall energy efficiency ratio.

[0023] Preferably, optimizing the heat conduction path of the hard disk self-assembly microstructure data based on the hard disk heat distribution matrix includes:

[0024] Confirming the state of the phase change material in the hard disk self-assembly microstructure data based on the hard disk heat distribution matrix, and obtaining the phase change material state data, wherein the phase change material state data includes solid state data and liquid state data;

[0025] The heat flow direction and heat exchange rate of the SSD array data are calculated through the hard disk heat distribution matrix, and a local heat flow model is constructed based on the heat flow direction and heat exchange rate;

[0026] Leverage the local heat flux model to locate the hot spots in the hard disk for the initial self-organizing heat dissipation network, generating the hard disk hot spot areas; conduct regional thermal buffer analysis on the hard disk hot spot areas to generate hard disk area thermal buffer data; based on the dynamic phase change regulation formula, perform local phase change control on the hard disk hot spot areas according to the phase change material state data, generating the dynamic adjustment data of the solid-liquid regions of the phase change material. The formula for local phase change control is as follows:

[0027] ;

[0028] In the formula, is the solid-liquid conversion ratio of the phase change material at position , is the current temperature of the solid-state drive array at position , is the optimal operating temperature set by the system, is the influence weight of the adjusted temperature deviation on the solid-liquid conversion ratio of the PCM, is the temperature of the solid-state drive array changing with time , is the influence weight of the adjusted temperature change rate on the solid-liquid conversion ratio of the PCM, is the abscissa of the position, is the ordinate of the position;

[0029] Based on the thermoelectric material in the hard disk self-assembled microstructure data, confirm the electric field application mode of the hard disk hot spot areas, and calculate the potential difference of the hard disk hot spot areas according to the electric field application mode to obtain the potential difference of the hot spot areas; use the heat flux guidance optimization formula to control the electric field distribution of the thermoelectric material for the potential difference of the hot spot areas, generating the dynamic electric field regulation data. The heat flux guidance optimization formula is as follows:

[0030] ;

[0031] In the formula, is the local potential difference of the phase change material at position , is the current temperature of the solid-state drive array at position , is the optimal operating temperature set by the system, is the influence weight of the controlled temperature deviation on the potential difference of the TEG, is the temperature of the solid-state drive array changing with time , is the influence weight of the controlled temperature change rate on the potential difference of the TEG, is the abscissa of the position, is the ordinate of the position;

[0032] Optimize the thermal diffusion path of the self-assembled microstructure data of the hard disk according to the dynamic adjustment data of the solid-liquid regions of the phase change material and the dynamic electric field control data, and generate optimized thermal conductivity path data.

[0033] Through the dynamic phase change regulation formula, the present invention realizes the intelligent solid-liquid conversion of the phase change material under different temperature conditions, enabling heat to be quickly absorbed and exported in the high-temperature region, reducing local overheating, and improving the thermal diffusion efficiency. Combining the analysis of the heat flow direction and the heat exchange rate, a local heat flow model is constructed, which can accurately identify and guide the heat flow path, enabling heat to diffuse more efficiently to the low-temperature region and enhancing the overall heat dissipation capacity. By accurately positioning the hot spots of the hard disk through the local heat flow model and combining with the regional heat buffer analysis, it helps to pre-sense the high-temperature region and take corresponding temperature control measures to avoid system failure caused by excessive temperature. Using the state data (solid / liquid) of the phase change material, it can be dynamically regulated according to the real-time change of the hard disk temperature, making the temperature fluctuation smooth, reducing the material stress caused by too high or too low temperature, and improving the long-term stability and reliability of the hard disk. By calculating the potential difference in the hot spot region, reasonably adjusting the electric field application mode, and utilizing the electric field effect of the thermoelectric material to further optimize the heat diffusion path, making the thermal conductivity path more efficient and reducing heat accumulation. Using the heat flow guidance optimization formula to dynamically regulate the potential difference of the TEG (thermoelectric material) based on the temperature deviation and the temperature change rate can improve the thermal energy conversion efficiency, convert part of the excess heat into electrical energy, and improve the energy utilization rate of the system. By combining the phase change material regulation and the dynamic electric field control, the heat diffusion path can be optimized, enabling heat to diffuse outward along the shortest path, reducing the ineffective heat dissipation power consumption, and improving the overall energy efficiency.

[0034] Preferably, the calculation of the current thermodynamic state of the solid-state drive array in step S3 includes:

[0035] Extract the operation data of the solid-state drive array and calculate the total power consumption of the solid-state drive array according to the operation data;

[0036] Analyze the thermal power distribution of the solid-state drive array through the total power consumption of the solid-state drive array;

[0037] Perform a temperature field simulation on the thermal power distribution of the solid-state drive array based on the finite element method to generate an array temperature field model;

[0038] Evaluate the heat exchange amount of the solid-state drive array according to the array temperature field model and perform curve conversion to obtain a heat exchange curve;

[0039] Screen the time points with a slope of 0 in the heat exchange curve and mark them as thermal steady-state time data;

[0040] Perform a thermal runaway risk analysis on the operating data of the solid-state drive array using the heat exchange amount and thermal steady-state time data of the solid-state drive array, thereby generating the current thermodynamic state of the solid-state drive array.

[0041] By extracting the operating data of the solid-state drive array and calculating its total power consumption, the present invention can accurately grasp the energy consumption of the hard disk, provide data support for subsequent thermal management optimization, help reduce unnecessary power consumption, and improve the overall energy efficiency ratio. By using the thermal power distribution analysis method, the heat distribution in different regions can be identified, the high-temperature area and low-temperature area can be found, and more refined temperature management can be achieved to avoid performance degradation or equipment damage caused by overheating in the hot spot area. By using the finite element method (FEM) to simulate the temperature field, the temperature distribution of the solid-state drive array under different power consumption conditions can be accurately calculated, an array temperature field model can be established, the accuracy of temperature prediction can be improved, and the hard disk can be ensured to operate within the optimal temperature range. By calculating the heat exchange amount and performing curve conversion, the heat flow of the hard disk array can be quantified, the heat dissipation effect can be accurately evaluated, data support can be provided for the heat dissipation optimization strategy, and the refinement degree of thermal management can be improved. By using the heat exchange curve analysis, the time point with a heat exchange slope of 0 is screened to automatically identify the time when the hard disk reaches the thermal steady state, which helps optimize the heat dissipation strategy, avoid temperature overshoot, and improve the long-term operation stability of the hard disk. By combining the heat exchange amount and thermal steady-state time data, a thermal runaway risk analysis is performed on the hard disk operating state to discover potential abnormal temperature trends in advance, avoid hardware damage, data loss, or system crashes caused by overheating, and improve the security and reliability of the storage system.

[0042] Preferably, the thermal-electric mode conversion of the temperature data according to the current thermodynamic state in step S3 includes:

[0043] Perform a thermal aggregation analysis on the temperature data according to the current thermodynamic state to generate thermal aggregation data;

[0044] Use the thermal aggregation data to perform a clustering analysis on the solid-state drive array data to generate a hard disk hot spot area, a hard disk cooling area, and a hard disk neutral area, and eliminate the hard disk neutral area;

[0045] Perform thermal-electric mode analysis on the hard disk hot spot area, the hard disk cooling area, and the hard disk neutral area respectively through the thermoelectric materials in the solid-state drive array integrated material data to generate the Peltier effect mode and the Seebeck effect mode;

[0046] Calculate the optimal heat flow guiding paths for the hard disk hot spot area and the hard disk cooling area, and adjust the electric field directions of the Peltier effect mode and the Seebeck effect mode through the optimal heat flow guiding paths to generate thermal flow dynamic isothermal regulation data.

[0047] Through thermal aggregation analysis, the present invention can accurately identify the heat distribution, generate thermal aggregation data, provide a scientific basis for subsequent thermal management, optimize the heat dissipation strategy, and improve the overall system energy efficiency. By using clustering analysis, the solid-state drive array is divided into a hard disk hot spot area, a hard disk cooling area, and a hard disk neutral area, and the neutral area is excluded to ensure that the heat dissipation strategy only targets key areas, reduce unnecessary heat dissipation power consumption, and improve the heat dissipation efficiency. By integrating thermoelectric materials in the solid-state drive array with material data, the Peltier effect mode and the Seebeck effect mode are analyzed for different regions respectively to achieve the conversion of thermal energy into electrical energy, improve the intelligence of thermal management, and recover some energy to reduce the system power consumption. Calculate the optimal heat flow guiding path to ensure that heat can be efficiently transferred from the hot spot area to the cooling area, reduce local overheating, optimize the overall temperature distribution, and improve the long-term operation stability of the hard disk. Through the thermal flow dynamic temperature equalization control data, adjust the electric field directions of the Peltier effect and the Seebeck effect to achieve dynamic temperature equilibrium, prevent local overheating or overcooling, ensure that the hard disk operates within the optimal working temperature range, and extend the service life. By adopting an advanced thermal-electric mode conversion strategy, the temperature fluctuation of the hard disk can be effectively reduced, the mechanical stress caused by thermal expansion and contraction can be reduced, thereby reducing hard disk failures and improving the data security and reliability of the storage system.

[0048] Preferably, the thermal path pre-adjustment of the self-organizing thermal dissipation optimization network by the thermal flow dynamic temperature equalization control data in step S3 includes:

[0049] Perform a thermal transmission topology structure mapping on the self-organizing thermal dissipation optimization network by the thermal flow dynamic temperature equalization control data to generate a thermal transmission path diagram; extract the thermal transmission path impedance characteristic data from the thermal transmission path diagram;

[0050] Set an optimization target based on the thermal transmission path impedance characteristic data, and perform dynamic path allocation on the thermal transmission path diagram according to the optimization target to generate thermal path control data;

[0051] Use the thermal path control data to perform global network thermal equilibrium optimization on the self-organizing thermal dissipation optimization network to generate hard disk global thermal equilibrium optimization data.

[0052] The present invention maps the thermal transmission topological structure of the self-organizing thermal dissipation optimization network through thermal flow dynamic temperature equalization control data, can accurately draw the thermal flow transmission path in the network, and identify key thermal transmission channels. This step optimizes the distribution of thermal flow in the hard disk array, enables heat to be more efficiently guided to the heat dissipation area, avoids the concentration of hot spots, and improves the overall heat dissipation efficiency. Extracting the impedance characteristics of the thermal transmission channels from the thermal transmission channel map can understand the impedance characteristics of each thermal channel, so as to identify which areas have overheating problems and further strengthen thermal management. By extracting thermal impedance data, more refined thermal flow distribution can be achieved, reducing unnecessary energy loss. Based on the impedance characteristic data of the thermal transmission channels, an optimization goal is set, and through dynamic path allocation, precise control of the thermal flow path can be achieved, flexibly adjusting the heat dissipation capacity of each area, ensuring that the hot spot area is effectively cooled, while avoiding overcooling of other areas, thereby improving the energy efficiency and stability of the system. Using the thermal channel control data to perform global thermal equilibrium optimization on the self-organizing thermal dissipation optimization network can balance the thermal flow states of all hard disks in the network, enabling the entire hard disk array system to maintain the best operating temperature during operation, effectively reducing the impact of temperature fluctuations on the hard disk performance, and prolonging the hard disk life. Through global thermal equilibrium optimization, the burden on the heat dissipation device is reduced, and at the same time, the utilization efficiency of thermal energy is improved. A more energy-saving heat dissipation solution is achieved, which not only reduces the energy consumption of the cooling system, but also reduces the demand for additional energy, thereby reducing the energy consumption of the entire hard disk array system.

[0053] Preferably, step S4 includes the following steps:

[0054] Step S41: Analyze the long-term change trend of the temperature data to generate long-term temperature trend data;

[0055] Step S42: Predict the potential hot spot areas of the solid-state drive array data according to the long-term temperature trend data to generate potential hot spot area prediction data;

[0056] Step S43: Optimize and feedback the hard disk heat dissipation path in advance for the hard disk global thermal equilibrium optimization data through the potential hot spot area prediction data to generate an intelligent temperature control strategy for the solid-state drive.

[0057] By analyzing the long-term change trend of temperature data, the present invention can predict the long-term change of the temperature of the hard disk array, identify the rules of temperature fluctuations and potential problems in advance. The long-term trend data of temperature provides a reliable basis for subsequent thermal management strategies, helps to optimize the long-term operation and maintenance of the hard disk array, and prevents hard disk failures caused by abnormal temperatures. Based on the long-term trend data of temperature, predicting potential hot spots in the hard disk array can identify the overheated areas in advance, facilitating the implementation of targeted thermal control measures. This predictive ability provides foresight for thermal management, enabling effective intervention before hot spots occur in the hard disk array, reducing losses and failure risks caused by overheating. Through the predicted data of potential hot spots, optimizing the heat dissipation path in advance for the global thermal balance optimization data of the hard disk can adjust the heat flow path before actual overheating occurs, ensuring that heat can be effectively dissipated to the designated heat dissipation area. This step can not only improve the heat flow distribution efficiency, but also optimize the heat dissipation system of the hard disk array, reduce the system burden. The generated intelligent temperature control strategy for solid-state drives can dynamically adjust the heat dissipation configuration of the hard disk array according to the long-term temperature change trend and the prediction of potential hot spots, enabling it to maintain the optimal temperature level under different working conditions. The intelligent temperature control strategy enhances the adaptive ability of the hard disk array, enabling it to optimize and adjust according to real-time and historical temperature data, effectively avoiding abnormal temperature conditions, and improving the overall system stability and operation efficiency.

[0058] Preferably, step S42 includes the following steps:

[0059] Step S421: Calculate the temperature change rate of the long-term temperature trend data to obtain the long-term temperature change rate;

[0060] Step S422: Perform temperature fluctuation analysis on the long-term temperature change rate through Fourier transform to generate temperature fluctuation data;

[0061] Step S423: Calculate the hot spot confidence of the solid-state drive array data based on the temperature fluctuation data, and analyze the hot spot diffusion trend of the solid-state drive array data based on the hot spot confidence to generate hot spot clusters; divide the hot spot clusters into data sets to generate a model training set and a model test set;

[0062] Step S424: Train the model training set through a convolutional neural network algorithm to generate a pre-model for predicting hot spot areas; use the model test set to optimize and iterate the pre-model for predicting hot spot areas to generate a model for predicting hot spot areas;

[0063] Step S425: Import the solid-state drive array data into the hot spot area prediction model to predict potential hot spot areas, thereby generating hot spot area prediction data.

[0064] The calculation of the long-term temperature change rate in the present invention (step S421) helps to accurately capture the temperature change trend and reflect the temperature fluctuation speed of the hard disk array in different time periods. This data provides a key input for subsequent thermal management strategies, enabling the system to perform real-time temperature control on the hard disk array based on the dynamic temperature change rate and avoid sudden temperature anomalies. Analyzing the temperature fluctuation through Fourier transform (step S422) can analyze the temperature fluctuation characteristics of the hard disk array from the perspective of the frequency domain. This method can help the system identify the periodic and non-periodic changes of the temperature fluctuation, providing a more accurate fluctuation characteristic analysis for predicting potential problems and adjusting the heat dissipation scheme of the hard disk array. Calculating the hotspot confidence based on the temperature fluctuation data (step S423) can predict the hotspot areas in the hard disk array and determine the expansion path of the hotspot areas through the hotspot diffusion trend analysis. This step can detect and mark in advance the areas with large temperature fluctuations and overheating in the hard disk array, greatly improving the early warning ability of the system and reducing hard disk failures caused by heat concentration. In the hotspot cluster dataset division (step S423), the hard disk array data is divided into a training set and a test set, ensuring the scientificity and effectiveness of the model training and optimization process. Through this division, the accurate matching of the training set and the test set can ensure the high-precision prediction ability of the final model in actual applications and improve the accurate identification ability of the system for hotspot areas.

[0065] In this specification, a solid-state drive intelligent temperature control system based on edge computing is provided for implementing the above-mentioned solid-state drive intelligent temperature control method based on edge computing. The solid-state drive intelligent temperature control system based on edge computing includes:

[0066] A network construction module, configured to obtain solid-state drive array data; perform array topology analysis on the solid-state drive array data to generate solid-state drive array topology data; and construct an initial self-organizing heat dissipation network for the solid-state drive array data based on the solid-state drive array topology data to obtain an initial self-organizing heat dissipation network;

[0067] A heat conduction path optimization module, configured to deploy edge computing nodes for the initial self-organizing heat dissipation network and collect temperature data of the solid-state drive according to the edge computing nodes; optimize the network heat conduction path of the initial self-organizing heat dissipation network through the temperature data to generate a self-organizing heat dissipation optimized network;

[0068] A heat flow path optimization module, configured to calculate the current thermodynamic state of the solid-state drive array and perform a heat-electric mode conversion on the temperature data according to the current thermodynamic state to generate heat flow dynamic equal-temperature regulation data; pre-adjust the heat path of the self-organizing heat dissipation optimized network through the heat flow dynamic equal-temperature regulation data to generate hard disk global heat balance optimization data;

[0069] A pre-feedback module is used to predict the long-term change trend of temperature data, and based on the long-term change trend, it provides an early optimization feedback for the hard disk global thermal equilibrium optimization data to generate an intelligent temperature control strategy for the solid-state drive.

[0070] The beneficial effects of the present invention are as follows: Through the acquisition of solid-state drive array data and array topology analysis (network construction module), the state information of the hard disk array can be comprehensively collected, and detailed hard disk array structure data can be obtained through array topology analysis. This provides a solid data foundation and an accurate network structure view for subsequent thermal management, optimization, and intelligent adjustment, enabling the system to deeply understand the working environment of the hard disk array. The deployment of edge computing nodes and temperature data collection (thermal conduction path optimization module) enables the system to monitor the temperature data of the hard disk array in real time, quickly respond, and adjust the thermal management strategy. The deployment of edge computing nodes not only improves the accuracy of temperature data collection but also makes the temperature control system have a higher response speed and intelligent level, adjusting the heat dissipation path of the hard disk array in real time to avoid overheating. The generation of hard disk global thermal equilibrium optimization data (heat flow path optimization module) improves the heat dissipation efficiency of the hard disk array by optimizing the heat flow path and balancing the overall heat distribution of the hard disk array, thus avoiding local overheating. This optimization can effectively improve the working performance and lifespan of the hard disk array, while reducing energy consumption and extending the stable operation time of the system. Through the prediction of long-term temperature change trends (pre-feedback module), the system can optimize the hard disk array in advance according to the long-term trend of temperature data. This prediction ability enables the system to anticipate possible thermal problems in advance and adjust the temperature control strategy of the hard disk array according to the predicted data, thus achieving more intelligent and precise thermal management, improving the system's ability to respond to abnormal situations, and reducing potential overheating risks. Therefore, the present invention realizes intelligent temperature control at the SSD array level through self-organizing heat dissipation network, edge computing optimization, thermoelectric mode conversion, and long-term prediction, improving heat dissipation efficiency, response speed, and long-term stability. Description of the Drawings

[0071] Figure 1 It is a schematic diagram of the step flow of a method for intelligent temperature control of a solid-state drive based on edge computing;

[0072] Figure 2 is Figure 1 a schematic diagram of the detailed implementation step flow of step S2 in

[0073] Figure 3 is Figure 1 a schematic diagram of the detailed implementation step flow of step S4 in

[0074] The realization, functional features, and advantages of the object of the present invention will be further described in conjunction with embodiments and with reference to the drawings. Detailed Embodiments

[0075] The technical method of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work belong to the scope of protection of the present invention.

[0076] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0077] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0078] To achieve the above object, please refer to Figures 1 to 3 , an intelligent temperature control method for a solid-state drive based on edge computing, the method comprising the following steps:

[0079] Step S1: Obtain solid-state drive array data; perform array topology analysis on the solid-state drive array data to generate solid-state drive array topology data; based on the solid-state drive array topology data, construct an initial self-organizing heat dissipation network for the solid-state drive array data to obtain an initial self-organizing heat dissipation network;

[0080] Step S2: Deploy edge computing nodes for the initial self-organizing heat dissipation network and collect temperature data of the solid-state drive according to the edge computing nodes; optimize the network heat conduction path of the initial self-organizing heat dissipation network through the temperature data to generate a self-organizing heat dissipation optimized network;

[0081] Step S3: Calculate the current thermodynamic state of the solid-state drive array, and perform a thermal-electric mode conversion on the temperature data according to the current thermodynamic state to generate thermal flow dynamic temperature equalization control data; pre-adjust the heat path of the self-organizing heat dissipation optimized network through the thermal flow dynamic temperature equalization control data to generate hard disk global heat balance optimization data;

[0082] Step S4: Predict the long-term change trend of temperature data, and perform early optimization feedback on the hard disk for the global thermal equilibrium optimization data of the hard disk according to the long-term change trend, so as to generate an intelligent temperature control strategy for the solid-state drive.

[0083] Through the construction and optimization of the self-organizing heat dissipation network, the present invention establishes an efficient heat conduction path, enables heat to be distributed and dissipated quickly and evenly, avoids local overheating phenomena, and improves the overall heat dissipation efficiency. The edge computing node is used to monitor the hard disk temperature in real time, and through the thermal flow dynamic temperature equalization control technology, global thermal equilibrium is achieved, ensuring that the temperature distribution of all hard disks is within the optimal range and improving the stability of the system. Excessive temperature will cause the performance of the solid-state drive to decline or even data damage. Through the intelligent temperature control strategy, the influence of high temperature can be effectively reduced, the read and write speed of the hard disk can be ensured to be stable, and the I / O performance loss caused by temperature fluctuations can be reduced. Prolonged exposure to high temperatures will accelerate the wear of the hard disk, and this method makes the temperature control more accurate through the early optimization feedback mechanism, thereby reducing the hard disk aging caused by overheating and increasing the service life of the hard disk. By using the long-term temperature change trend prediction model, the heat dissipation strategy is adjusted in advance to avoid the impact caused by sudden temperature changes, achieve active temperature control, and reduce the emergency frequency reduction or shutdown protection triggered by sudden overheating of the system. Due to the thermal management optimization, the hard disk runs in a reasonable temperature range for a long time, which can greatly reduce the physical damage caused by thermal expansion and contraction, as well as the failures of the controller and NAND chips due to overheating, and improve the reliability of data storage. Overheating will cause the bit error rate (BER) of the stored data to rise, affecting data integrity. This method ensures that the temperature of the data storage environment is appropriate through thermal equilibrium optimization and intelligent temperature control strategy, reducing the risk of data damage caused by temperature fluctuations. Therefore, the present invention realizes the intelligent temperature control at the SSD array level through the self-organizing heat dissipation network, edge computing optimization, thermal-electric mode conversion and long-term prediction, improving the heat dissipation efficiency, response speed and long-term stability.

[0084] In the embodiment of the present invention, referring to Figure 1 As shown, it is a schematic diagram of the step flow of an intelligent temperature control method for a solid-state drive based on edge computing according to the present invention. In this example, the intelligent temperature control method for a solid-state drive based on edge computing includes the following steps:

[0085] Step S1: Obtain the solid-state drive array data; perform array topology analysis on the solid-state drive array data to generate solid-state drive array topology data; based on the solid-state drive array topology data, construct an initial self-organizing heat dissipation network for the solid-state drive array data to obtain an initial self-organizing heat dissipation network;

[0086] In the embodiments of the present invention, by connecting a solid-state drive array (SSD Array), the storage management interface (such as NVMe, SAS, or SATA protocol) is called to obtain information such as the hard disk operating status and data storage structure. Key data metrics are collected, such as disk I / O rate, storage capacity distribution, temperature sensor data, data block access frequency, etc. An original solid-state drive array data set is formed, and a connection structure diagram of the solid-state drive array is constructed, including RAID levels, controller distribution, and data channel paths. The data block distribution, index structure, hot data distribution, etc. are analyzed. Based on the sensor data, the temperature distribution of each storage unit is calculated, hot spots are identified, and solid-state drive array topology data is generated for subsequent construction of a heat dissipation network. Based on the solid-state drive array topology data, the SSD units are temperature partitioned to form a mapping of hot data regions, and the heat diffusion relationship between storage units is calculated using a heat conduction model (such as the Fourier heat conduction equation) to establish a preliminary heat dissipation network structure. A dynamic adjustment strategy based on the data access pattern (such as LSTM or reinforcement learning) is used to optimize the data block migration path and reduce hot spot accumulation. Combining a cold and hot data distribution strategy based on load balancing, the data write and read priorities of the storage units are automatically adjusted to improve the overall heat dissipation efficiency. The initial balance degree of the heat dissipation network is calculated to ensure no overload points, and an initial self-organizing heat dissipation network is generated, including: a heat map (showing the temperature distribution), a heat migration path (data block migration strategy), and a balance degree index (measuring the heat dissipation balance).

[0087] Step S2: Deploy edge computing nodes for the initial self-organizing heat dissipation network, and collect the temperature data of the solid-state drive according to the edge computing nodes; optimize the network heat conduction path of the initial self-organizing heat dissipation network through the temperature data to generate a self-organizing heat dissipation optimized network;

[0088] In the embodiments of the present invention, a reasonable deployment location of edge computing nodes is selected according to the physical distribution of a solid-state drive (SSD). Edge computing devices with low latency and high computing capabilities are selected, such as embedded processors or FPGAs. An initial self-organizing heat dissipation network is deployed inside the SSD cluster to establish an initial communication link. A data transmission protocol between nodes is set to ensure that edge nodes can collect data in real time and optimize the network. Temperature sensors (such as NTC thermistors, MEMS temperature sensors) are deployed on the surface of the SSD and at key heat dissipation components. A data sampling period is set, such as temperature collection every 10 ms, to ensure data real-time performance. The temperature time series change curve of the solid-state drive is calculated to form a temperature data stream. Through time series analysis, abnormal temperature fluctuations are identified and the future temperature trend is predicted. Based on the SSD temperature data, a heat conduction mathematical model is constructed, and the thermal conductivity parameters between nodes are set. The finite element analysis (FEA) or the finite difference method (FDM) is used to calculate the heat transfer path. According to the heat flow distribution, the heat conduction path is dynamically adjusted to reduce the heat accumulation in the hot spot area. An adaptive load balancing algorithm is adopted to evenly distribute the heat and reduce the local temperature peak. The machine learning optimization algorithm (such as reinforcement learning or neural network) is combined to adjust the heat dissipation path between nodes. According to the temperature feedback, the heat dissipation air duct and the heat flow channel of the heat dissipation material are optimized in real time. The optimized heat conduction path is recorded to form a new self-organizing heat dissipation optimization network topology. This network has better heat conduction performance and improves the overall heat management efficiency of the solid-state drive.

[0089] Step S3: Calculate the current thermodynamic state of the solid-state drive array, perform a thermal-electric mode conversion on the temperature data according to the current thermodynamic state to generate thermally dynamic isothermal regulation data; perform a pre-adjustment of the heat path on the self-organizing heat dissipation optimization network through the thermally dynamic isothermal regulation data to generate global hard disk thermal equilibrium optimization data;

[0090] In the embodiments of the present invention, by collecting temperature data of various parts of the solid-state drive, a temperature distribution model of the hard disk array is constructed. The finite element analysis (FEA) or the finite difference method (FDM) is used to calculate the steady-state and transient temperature fields of the hard disk array. Parameters such as the specific heat capacity, thermal conductivity, and heat convection coefficient of each SSD unit are calculated. The heat flux between each SSD unit is calculated to evaluate the heat exchange efficiency. Using the thermal-electric simulation method, the SSD temperature distribution is converted into a thermal-electric equivalent circuit (similar to a thermal resistance network). The equivalent thermoelectric parameters are set: Thermal resistance (Rth): corresponding to resistance, representing the resistance of heat flow. Heat capacity (Cth): corresponding to capacitance, representing the heat storage capacity. Heat flow (Ith): corresponding to current, representing the heat transfer rate. Using the neural network mapping algorithm or the least squares regression, the temperature data is converted into equivalent thermal-electric signal data. The thermal voltage (Vth) and thermal current (Ith) distributions in the SSD array are calculated to generate thermal flow dynamic temperature equalization control data. According to the heat flow gradient, the temperature equalization target is set. Using the optimal control algorithm (such as model predictive control MPC) to calculate the optimal heat regulation strategy to form the thermal flow dynamic temperature equalization control data. According to the thermal flow dynamic temperature equalization control data, the heat conduction path of the self-organizing heat dissipation optimization network is dynamically adjusted. Using the adaptive heat dissipation optimization algorithm to ensure that the heat is evenly distributed among each SSD unit. Combining reinforcement learning, the heat path is optimized through the feedback mechanism. Calculate the optimal heat dissipation air duct structure to ensure an efficient heat conduction path. Calculate the global heat flow balance factor of the SSD array to optimize the heat exchange path. Using the multi-objective optimization method (such as the genetic algorithm) to optimize the heat balance regulation strategy to generate the global heat balance optimization data of the hard disk, which is used as the input of the heat dissipation system to ensure temperature balance during long-term operation.

[0091] Step S4: Predict the long-term change trend of the temperature data, and perform hard disk advance optimization feedback on the global hard disk heat balance optimization data according to the long-term change trend to generate an intelligent temperature control strategy for the solid-state drive.

[0092] In the embodiments of the present invention, by collecting temperature data, heat flow data, and heat dissipation efficiency data from multiple edge computing nodes. Denoising and interpolation are performed to ensure data integrity. The ARIMA (Autoregressive Integrated Moving Average) model or the LSTM (Long Short-Term Memory Network) is used to predict the temperature change trend. Identify temperature anomaly points to avoid hot spot accumulation. Combining the thermal-electric conversion model, the temperature data is mapped into thermal voltage (Vth) and thermal current (Ith) data streams. Construct an equivalent heat flow network topology diagram to form a global heat flow model. Using the finite element method (FEM) to calculate the thermal conductivity matrix between each SSD unit. Combining the thermal resistance network (Thermal Resistance Network) to analyze the heat flow channel, find the high-blocking area and optimize the heat exchange path. Set the optimization goal to make the temperatures of all SSD units tend to be balanced: , where is the temperature of the th SSD unit, is the global average SSD temperature, is the total number of SSD units. Particle Swarm Optimization (PSO) or Simulated Annealing Algorithm (SA) is used for thermal equilibrium optimization calculation. The optimal heat flow distribution matrix is calculated to ensure the global temperature tends to be stable. Based on the optimization calculation results, an intelligent temperature control strategy is generated to dynamically adjust the heat dissipation mode of the SSD. Combining with the PID control algorithm, the fan speed, heat pipe power, and heat sink thermal conductivity are adjusted in real time. Combining with the workload scheduling, the task allocation of the SSD is intelligently adjusted to reduce the load of high-temperature SSD units and moderately increase the load of low-temperature SSD units. Calculate the dynamic load migration strategy to avoid local overheating. Deep Reinforcement Learning (DQN) is used to optimize the heat dissipation path based on historical temperature data and the current working state. By intelligently adjusting the heat dissipation components (such as heat pipes, heat sinks, liquid cooling modules), the heat transfer direction is optimized. For high-temperature areas, phase change materials or thermoelectric cooling devices are enabled for active heat dissipation. The air duct distribution is adjusted for low-temperature areas to improve the global average temperature efficiency.

[0093] Preferably, step S1 includes the following steps:

[0094] Step S11: Obtain the solid-state drive array data;

[0095] Step S12: Perform array topology analysis on the solid-state drive array data to generate solid-state drive array topology data;

[0096] Step S13: Use the solid-state drive array topology data to perform intelligent material integration on the solid-state drive array to generate solid-state drive array integrated material data, where the intelligent material integration includes integrating phase change materials, thermoelectric materials, and intelligent thermal conductive films;

[0097] Step S14: Identify the microfluidic heat conduction path based on the solid-state drive array integrated material data for the solid-state drive array topology data to generate hard disk self-assembled microstructure data;

[0098] Step S15: Implant local sensing units into the solid-state drive array topology data through the hard disk self-assembled microstructure data to generate an initial self-organizing heat dissipation network, where the local sensing units include temperature sensors and heat flow sensing devices.

[0099] In the embodiments of the present invention, an original data set is extracted from a solid-state drive storage system, including but not limited to hard disk models, storage capacities, physical connection methods (SATA, NVMe, PCIe), array configurations (RAID 0, RAID 1, RAID5, etc.), and hard disk operating state parameters (temperature, power consumption, read / write speed, etc.). Data acquisition can be automatically performed using an intelligent monitoring system, and the acquired data is formatted to form a solid-state drive array data set to ensure the accuracy of subsequent analysis. The topology modeling method is used to analyze the array structure. Specifically, based on the hard disk interface type and physical connection method, a topology node graph of the solid-state drive array is constructed; the data transmission path, power supply path, and heat dissipation channel are analyzed to establish a complete topology structure; based on thermodynamic analysis and data flow calculation, the solid-state drive array topology is optimized and adjusted to improve data throughput efficiency and thermal management capabilities; the above analysis results are integrated to generate solid-state drive array topology data, providing input data for subsequent steps. Phase change materials (PCMs) are introduced into key areas of the hard disk array (such as high-power consumption chip areas, storage controller modules) to improve heat absorption capacity and reduce local temperature peaks; thermoelectric materials (such as Bi2Te3-based thermoelectric modules) are used to generate thermoelectric power, and the energy consumption efficiency is optimized through an energy recovery system; intelligent thermal conductive films are introduced between storage units to enhance heat conduction capabilities, and combined with adaptive heat dissipation technology to improve the overall heat dissipation performance. Based on the solid-state drive array topology data, high heat flux regions are identified, and a microfluidic channel structure is designed at the corresponding positions; combined with the distribution of intelligent materials, the microfluidic flow path is optimized to achieve uniform heat dissipation; micro-nano structures are introduced on the surface of the hard disk housing and PCB board to enhance heat exchange efficiency, and hard disk self-assembled micro-structure data is generated to provide a basis for the construction of subsequent heat dissipation networks. Temperature sensors are arranged around the hard disk control chip, storage unit, and radiator to monitor temperature changes in real time; MEMS heat flow sensors are arranged in microfluidic channels and key heat dissipation areas to sense the characteristics of heat flow distribution; based on the data collected by the sensing unit, an adaptive thermal management algorithm is used to optimize the heat dissipation path, generating an initial self-organizing heat dissipation network to improve the heat dissipation efficiency and stability of the solid-state drive array.

[0100] As an example of the present invention, refer to Figure 2 shown, in this example, step S2 includes:

[0101] Step S21: Deploy edge computing nodes for the initial self-organizing heat dissipation network to obtain edge computing nodes of the heat dissipation network;

[0102] Step S22: Based on a preset time period, use a temperature sensor to sense the hard disk temperature of the edge computing node of the heat dissipation network, and generate hard disk temperature sensing data; perform data preprocessing on the hard disk temperature sensing data to generate standard hard disk temperature sensing data, where data preprocessing includes data cleaning, data denoising, and data standardization;

[0103] Step S23: Perform temperature distribution analysis on the standard hard disk temperature sensing data to generate a hard disk heat distribution matrix; optimize the heat conduction path for the hard disk self-assembled microstructure data based on the hard disk heat distribution matrix to generate heat conduction path optimization data;

[0104] Step S24: Based on the heat conduction path optimization data, perform regional temperature discrimination on the hard disk temperature sensing data. When the hard disk temperature sensing data is greater than or equal to a preset temperature threshold, the edge computing node of the heat dissipation network performs local heat path recombination on the initial self-organizing heat dissipation network to generate a self-organizing heat dissipation optimization network.

[0105] In the embodiment of the present invention, according to the topological structure of the initial self-organizing heat dissipation network, nodes with strong computing power, large data flow and drastic temperature changes are selected as edge computing nodes; microprocessing units (MCUs) or low-power edge computing modules are deployed on the selected edge computing nodes to realize local thermal management calculations; the data interaction strategy between the edge computing nodes and the central processing unit (CPU) is optimized to ensure that the calculation results can be fed back to the heat dissipation network in real time to realize dynamic thermal management; the configuration and optimization of the edge computing nodes are completed to form a heat dissipation network edge computing node cluster with intelligent computing capabilities. In each preset time period, the temperature sensor of the edge computing node collects the temperature data of the hard disk; the collected hard disk temperature perception data is cleaned and optimized, including: removing abnormal values, such as data deviations caused by sampling errors or hardware failures; using sliding average filtering or wavelet transform methods to remove high-frequency noise and improve data accuracy; normalizing the data or Z-score standardization to ensure the consistency of different batches of data; after data preprocessing, high-quality, analyzable standard hard disk temperature perception data is obtained to provide input for subsequent analysis. Use interpolation algorithms or thermal field simulation algorithms (such as finite element analysis FEM) to analyze the temperature perception data of standard hard disks; generate temperature gradient distribution on the surface and inside of the hard disk, and construct a hard disk thermal distribution matrix, which describes the temperature differences at different locations; based on the hard disk thermal distribution matrix, determine the relative positions of high-temperature areas and low-temperature areas, and identify the main heat conduction pathways; combine the hard disk self-assembly microstructure data to optimize the microfluidic channel, intelligent thermal film layout and thermoelectric material distribution to enhance local heat dissipation capabilities; generate thermal path optimization data as input for subsequent thermal management decisions. Set a temperature threshold (such as 70°C) and monitor the temperature data of each hard disk area in real time; if the temperature of a certain area reaches or exceeds the threshold, it is determined that the area needs to optimize the heat dissipation strategy; trigger the edge computing node of the heat dissipation network to execute a dynamic thermal management algorithm and recalculate the optimal thermal conduction path; optimize the local heat path by controlling the microfluidic channel, adjusting the thermal conductivity of the intelligent thermal film, adjusting the power of the thermoelectric material, etc.; form a self-organized heat dissipation optimization network, which can be adaptively adjusted according to the operating status of the hard disk to improve heat dissipation efficiency and extend the life of the equipment.

[0106] Preferably, optimizing the heat conduction path of the hard disk self-assembly microstructure data based on the hard disk heat distribution matrix includes:

[0107] Confirming the state of the phase change material in the hard disk self-assembly microstructure data based on the hard disk heat distribution matrix, and obtaining the phase change material state data, wherein the phase change material state data includes solid state data and liquid state data;

[0108] The heat flow direction and heat exchange rate of the SSD array data are calculated through the hard disk heat distribution matrix, and a local heat flow model is constructed based on the heat flow direction and heat exchange rate;

[0109] Use the local heat flux model to locate the hot spots in the hard disk for the initial self-organizing heat dissipation network, and generate the hard disk hot spot regions; perform regional heat buffer analysis on the hard disk hot spot regions to generate hard disk regional heat buffer data; based on the dynamic phase change regulation formula, perform local phase change control on the hard disk hot spot regions according to the phase change material state data, and generate the dynamic adjustment data of the solid-liquid regions of the phase change material. The formula for local phase change control is as follows:

[0110] ;

[0111] In the formula, is the solid-liquid conversion ratio of the phase change material at position , is the current temperature of the solid-state drive array at position , is the optimal operating temperature set by the system, is the influence weight of the adjusted temperature deviation on the solid-liquid conversion ratio of the PCM, is the temperature of the solid-state drive array changing with time , is the influence weight of the adjusted temperature change rate on the solid-liquid conversion ratio of the PCM, is the abscissa of the position, is the ordinate of the position;

[0112] Based on the thermoelectric material in the hard disk self-assembled microstructure data, confirm the electric field application mode of the hard disk hot spot regions, and calculate the potential difference of the hard disk hot spot regions according to the electric field application mode to obtain the potential difference of the hot spot regions; use the heat flux guidance optimization formula to control the electric field distribution of the thermoelectric material for the potential difference of the hot spot regions, and generate the dynamic electric field regulation data. The heat flux guidance optimization formula is as follows:

[0113] ;

[0114] In the formula, is the local potential difference of the phase change material at position , is the current temperature of the solid-state drive array at position , is the optimal operating temperature set by the system, is the influence weight of the controlled temperature deviation on the potential difference of the TEG, is the temperature of the solid-state drive array changing with time , is the influence weight of the controlled temperature change rate on the potential difference of the TEG, is the abscissa of the position, is the ordinate of the position;

[0115] Optimize the heat diffusion path of the hard disk self-assembled microstructure data according to the dynamic adjustment data of the solid-liquid regions of the phase change material and the dynamic electric field regulation data, and generate heat conduction path optimization data.

[0116] In the embodiment of the present invention, in the initial self-organizing heat dissipation network, key heat dissipation positions are selected for the deployment of edge computing nodes to form a set of edge computing nodes of the heat dissipation network. A distributed control strategy is adopted to enable the edge computing nodes to independently process local temperature perception, heat flow optimization, and heat path recombination tasks. High-precision temperature sensors are used to regularly collect the hard disk temperature data of the edge computing nodes of the heat dissipation network to generate initial hard disk temperature perception data. Abnormal data is removed to ensure the accuracy of the temperature data, and the Kalman filter or wavelet transform is used to remove the noise in the temperature signal to improve the data quality. The data is normalized to unify the analysis standard and generate standard hard disk temperature perception data. Based on the standard hard disk temperature perception data, a hard disk heat distribution matrix is constructed to describe the temperature changes in different regions. The state of the phase change material in the hard disk self-assembled microstructure is confirmed through the heat distribution matrix to generate phase change material state data (including solid state data and liquid state data). Calculate the heat flow direction and heat exchange rate of the solid-state drive array data, and establish a local heat flow model. Using the local heat flow model and combining with the heat distribution matrix, identify the regions with higher temperatures to generate hard disk hot spot region data. Calculate the heat transfer delay and heat capacity of the hot spot regions to generate hard disk region heat buffer data. Adopt the dynamic phase change regulation formula: ; In the formula, is the solid-liquid conversion ratio of the phase change material at position , is the current temperature of the solid-state drive array at position , is the optimal operating temperature set by the system, is the influence weight of the adjusted temperature deviation on the solid-liquid conversion ratio of the PCM, is the temperature of the solid-state drive array changing with time , is the influence weight of the adjusted temperature change rate on the solid-liquid conversion ratio of the PCM, is the abscissa of the position, is the ordinate of the position; Calculate the electric field application mode of the hard disk hot spot region and calculate the potential difference of the hot spot region. Adopt the heat flow guiding optimization formula for electric field regulation: ; In the formula, is the local potential difference of the phase change material at position , is the current temperature of the solid-state drive array at position , is the optimal operating temperature set by the system, To control the influence weight of temperature deviation on the TEG potential difference, is the temperature of the solid-state drive array over time rate of change, To control the influence weight of the rate of temperature change on the TEG potential difference, is the abscissa of the position, is the ordinate of the position; Dynamically adjust data according to the solid-liquid regions of the phase change material and optimize data of the dynamic electric field to regulate the heat conduction path of the self-assembled microstructure of the hard disk, generating the final optimized data of the heat conduction path. Based on the optimized data of the heat conduction path, analyze the hard disk temperature perception data. If the temperature of a certain area is greater than or equal to the set threshold, trigger local heat path recombination. Dynamically adjust the heat diffusion path through the edge computing nodes of the heat dissipation network, enabling the heat in the overheated area to be more effectively dissipated. Combine the phase state adjustment of the phase change material and the electric field optimization of the thermoelectric material to achieve more efficient heat dissipation management, and finally generate a self-organizing heat dissipation optimization network.

[0117] Preferably, the calculation of the current thermodynamic state of the solid-state drive array in step S3 includes:

[0118] Extract the operation data of the solid-state drive array, and calculate the total power consumption of the solid-state drive array according to the operation data;

[0119] Analyze the thermal power distribution of the solid-state drive array through the total power consumption of the solid-state drive array;

[0120] Perform a temperature field simulation on the thermal power distribution of the solid-state drive array based on the finite element method to generate an array temperature field model;

[0121] Evaluate the heat exchange amount of the solid-state drive array according to the array temperature field model and perform curve conversion to obtain a heat exchange curve;

[0122] Screen the time points with a slope of 0 in the heat exchange curve and mark them as thermal steady-state time data;

[0123] Use the heat exchange amount and thermal steady-state time data of the solid-state drive array to perform a thermal runaway risk analysis on the operation data of the solid-state drive array, thereby generating the current thermodynamic state of the solid-state drive array.

[0124] In the embodiment of the present invention, by collecting the key operation parameters of the solid-state drive array, including: workload (read / write IOPS, throughput), power consumption (instantaneous power, current, voltage), temperature data (sensor temperature, ambient temperature), fan status (rotation speed, heat dissipation efficiency), generate an operation data set of the solid-state drive array. Calculate the power based on the voltage V and current I of the hard disk: P = V×I; sum up all hard disk nodes to obtain the total power consumption of the array: ; where is the number of hard disks, is the power consumption of the th hard disk. Record the total power consumption data as the basis for subsequent thermal analysis. According to the physical structure and power consumption distribution of the solid-state drive array, calculate the thermal power of different regions: where

[0125] Preferably, the thermal-electric mode conversion of the temperature data according to the current thermodynamic state in step S3 includes:

[0126] Conduct thermal aggregation analysis on the temperature data according to the current thermodynamic state to generate thermal aggregation data;

[0127] Use the thermal aggregation data to conduct clustering analysis on the solid-state drive array data to generate a hot spot area, a cooling area, and a neutral area of the hard disk, and eliminate the neutral area of the hard disk;

[0128] Conduct thermal-electric mode analysis on the hot spot area, the cooling area, and the neutral area of the hard disk respectively through the thermoelectric materials in the solid-state drive array integration material data to generate the Peltier effect mode and the Seebeck effect mode;

[0129] Calculate the optimal heat flow guiding paths for the hot spot area and the cooling area of the hard disk, and adjust the electric field directions of the Peltier effect mode and the Seebeck effect mode through the optimal heat flow guiding paths to generate thermal flow dynamic isothermal regulation data.

[0130] In the embodiment of the present invention, through the current thermodynamic state data calculated by the previous steps.

[0131] The temperature sensor data of the solid-state drive array (temperature distribution of each hard disk).

[0132] The operating load information of the device (such as IOPS, throughput, etc.). Using spatial interpolation methods (such as Kriging interpolation, inverse distance weighted method), continuous modeling is performed on the temperature data of different hard disks to obtain a heat aggregation function, calculate the heat aggregation degree of different regions, form a heat aggregation data matrix, set K = 3, and perform temperature clustering on all hard disks to obtain: the hot spot area of the hard disk (high temperature area), the cooling area of the hard disk (low temperature area), and the neutral area of the hard disk (moderate temperature, no significant heat dissipation requirement). Since the neutral area of the hard disk has no obvious influence on heat flow control, it does not participate in the subsequent heat - electricity mode conversion, and only the hot spot area and the cooling area are retained for heat - electricity optimization. In the hot spot area of the hard disk, active heat dissipation is carried out using the Peltier effect: Qc = πI; where: Qc is the heat dissipation, π is the Peltier coefficient, and I is the current. By controlling the direction of the current, the heat in the hot spot area is actively extracted to reduce the temperature. In the cooling area of the hard disk, the Seebeck effect is used to recover thermal energy: V = S·ΔT; where: V is the thermal voltage, S is the Seebeck coefficient, and ΔT is the temperature difference. This mode is used to convert the temperature difference into electrical energy to optimize the energy utilization rate. A heat conduction model is established using the Fourier heat conduction equation: q = -k▽T; where: q is the heat flux density, k is the thermal conductivity of the material, and ▽T is the temperature gradient. The shortest path algorithm (such as the Dijkstra algorithm) is used to calculate the optimal heat flow path from the hot spot area to the cooling area: ; where: is the thermal resistance of each point on the path. The electric field directions of the Peltier and Seebeck modes are optimized according to the heat flow path to enable efficient heat transfer. By optimizing the current direction, the electric field direction is matched with the heat flow path: E = -▽V; where: E is the electric field and V is the electric potential, generating heat flow dynamic isothermal regulation data for real - time adjustment of the working modes of the Peltier effect and the Seebeck effect to improve the heat dissipation efficiency.

[0133] Preferably, the heat path pre - adjustment of the self - organizing heat dissipation optimization network by the heat flow dynamic isothermal regulation data in step S3 includes:

[0134] Performing a heat transfer topology structure mapping on the self - organizing heat dissipation optimization network by the heat flow dynamic isothermal regulation data to generate a heat transfer path diagram; extracting the impedance characteristics of the heat transfer path of the heat transfer path diagram to obtain heat transfer path impedance characteristic data;

[0135] Setting an optimization target based on the heat transfer path impedance characteristic data, and performing dynamic path allocation on the heat transfer path diagram according to the optimization target to generate heat path regulation data;

[0136] Using the heat path regulation data to perform global network heat balance optimization on the self - organizing heat dissipation optimization network to generate hard disk global heat balance optimization data.

[0137] In the embodiments of the present invention, by inputting thermal dynamic uniform temperature regulation data, including the temperature distribution, heat flow path, and heat balance control strategy of the hard disk array. Spatial distribution data of the hard disk array (such as hard disk positions, heat source positions, etc.). Modeling the heat transfer network of the hard disk array according to the thermal dynamic uniform temperature regulation data: graphically representing the heat connection relationships between each hard disk and the heat dissipation devices (such as heat sinks, fans, etc.) in the array. Each node (hard disk or heat dissipation device) represents a heat source or a heat dissipation point, each edge represents a heat flow path, and the weight represents the thermal resistance (the impedance characteristic of the heat path), generating a heat transfer path graph, in the form of a directed graph. For each path in the heat transfer path graph, calculate its thermal impedance characteristic. The thermal impedance is calculated according to the heat conduction equation: ; where is the temperature difference, is the heat flow rate, is the length of the transmission path, is the thermal conductivity of the material, is the cross-sectional area of the heat transfer path. Extract the thermal impedance characteristic data of each path to form an impedance matrix. Based on the thermal impedance characteristic data of the heat transfer path, set optimization objectives, mainly including: ensuring that heat can be efficiently transferred from the hot spot area to the cooling area by reducing the thermal resistance on the heat transfer path. Optimizing the path allocation to achieve thermal balance of the hard disk array and avoid local overheating or insufficient cooling. According to the set optimization objectives, optimize the heat flow path allocation through a dynamic path allocation algorithm (such as the greedy algorithm or the simulated annealing algorithm): dynamically adjust the allocation of the heat flow path to make the temperature distribution as balanced as possible and reduce the temperature difference between the hot spot area and the cooling area. Determine the heat flow rate allocation of each heat flow path so that the heat flow passes through the path with the minimum thermal resistance, generate heat path regulation data, and record the heat flow rate and adjustment strategy of each heat flow path. In the self-organizing heat dissipation optimization network, perform global network thermal balance optimization. This process adjusts the heat flow path and heat source distribution through a global optimization algorithm (such as the genetic algorithm or the particle swarm optimization) to optimize the heat transfer network. Considering the thermal behavior and heat transfer interaction relationships of each hard disk in the hard disk array, ensure that the overall heat distribution is uniform. Optimize the synergistic effect of the heat source and the heat dissipation device so that the heat flow can be maximally dispersed in the hard disk array. Output the global thermal balance optimization data of the hard disk, including the optimized heat flow path, the thermal regulation strategy of the heat dissipation device, and the temperature distribution data under the thermal balance state. Ensure that the temperature difference between all hard disks in the array is minimized and effectively avoid hot spot problems.

[0138] As an example of the present invention, referring to Figure 3 shown, in this example, step S4 includes:

[0139] Step S41: Analyze the long-term change trend of the temperature data to generate long-term temperature trend data;

[0140] Step S42: Predict potential hot spot areas for the solid-state drive array data based on the long-term temperature trend data to generate potential hot spot area prediction data;

[0141] Step S43: Advance optimize and feedback the hard disk heat dissipation path for the hard disk global thermal balance optimization data through the potential hot spot area prediction data to generate an intelligent temperature control strategy for the solid-state drive.

[0142] In an embodiment of the present invention, historical temperature data is input, including temperature change data of the solid state drive array in different time periods. Timestamp data: time stamp of temperature data, used to analyze the temperature change trend. Based on historical temperature data, the long-term change trend of temperature data is analyzed by time series analysis method (such as sliding average method, exponential smoothing method, or trend analysis algorithm). The average change trend of temperature in different time dimensions such as year, month, day, hour, etc. is calculated to identify seasonal fluctuations, periodic changes and trend changes of temperature. Regression analysis (such as linear regression, polynomial regression) or machine learning method (such as support vector regression) is used to predict the temperature change trend in the future. According to the above analysis, long-term temperature trend data is generated, which includes the long-term change trend of temperature, periodic fluctuation law, and future predicted temperature curve. The long-term temperature trend data is in the following form: T(t)=T0+ΔT(t), where T(t) represents the predicted temperature at time t, T0 is the initial temperature, and ΔT(t) is the temperature change value obtained based on time series analysis. Based on the long-term temperature trend data and the working data of the hard disk array, the potential hotspot areas are predicted using thermodynamic modeling and machine learning methods (such as clustering algorithms, decision trees, random forests, etc.). Considering the usage load, power consumption data and temperature change trend of the hard disk, the areas where the temperature will rise due to increased load and power consumption in the future period are identified. Different areas of the hard disk array are partitioned to analyze the thermal sensitivity of each area under different load and temperature conditions. Using the heat conduction model, the temperature changes of each hard disk and its surrounding areas are predicted, and then the potential hotspot areas are identified. The temperature prediction results of each area are combined with the actual load conditions to identify the areas that will become hotspots in the future, and the potential hotspot area prediction data is generated. The data includes the predicted temperature of each hard disk and hard disk array area and the potential hotspot areas. Example of hotspot area prediction data: predicted temperature and hotspot warning value of each hard disk area. According to the potential hotspot area prediction data, the current heat dissipation path is optimized in advance. The optimization goal is to avoid overheating near the hotspot area as much as possible, and to adjust the heat flow path so that the heat flow can be smoothly transferred and dissipated to the cooling area. Redesign the heat flow path using heat conduction simulation and optimization algorithms, such as particle swarm optimization (PSO) and simulated annealing. Enhance the heat flow transmission path in the hot spot area by adjusting the heat sink layout and fan speed regulation of the hard disk array to avoid heat accumulation. Generate an intelligent temperature control strategy for the solid-state drive based on the optimized heat dissipation path and predicted data. The strategy includes: adjusting the fan speed and the working status of the cooling device according to the actual temperature data, and performing temperature control in a timely manner. Before the potential hot spot area appears, the system automatically warns and makes necessary heat flow adjustments. Based on the temperature change trend and the predicted data of the hot spot area, dynamically adjust the heat flow path to achieve global thermal balance optimization.

[0143] Preferably, step S42 includes the following steps:

[0144] Step S421: Calculate the temperature change rate for the long-term temperature trend data to obtain the long-term temperature change rate;

[0145] Step S422: Conduct temperature fluctuation analysis on the long-term temperature change rate through Fourier transform to generate temperature fluctuation data;

[0146] Step S423: Calculate the hot spot confidence for the solid-state drive array data based on the temperature fluctuation data, and conduct hot spot diffusion trend analysis on the solid-state drive array data based on the hot spot confidence to generate hot spot clusters; Divide the hot spot clusters into data sets to generate a model training set and a model test set;

[0147] Step S424: Train the model training set through the convolutional neural network algorithm to generate a preliminary hot spot area prediction model; Use the model test set to optimize and iterate the preliminary hot spot area prediction model to generate a hot spot area prediction model;

[0148] Step S425: Import the solid-state drive array data into the hot spot area prediction model to predict the potential hot spot area, thereby generating hot spot area prediction data.

[0149] In the embodiments of the present invention, the time difference is performed on the long-term trend data of temperature to calculate the change rate of temperature. The calculation of the temperature change rate can be carried out on various time scales (such as hours, days, months) to generate the corresponding long-term temperature change rate data. The frequency domain analysis is performed on the long-term temperature change rate data through Fourier transform. Fourier transform can help extract the periodic fluctuation part in the temperature change. The data after Fourier transform can show the temperature fluctuation intensity at different frequencies, revealing the periodicity and fluctuation pattern of the temperature change. Based on the temperature fluctuation data, by analyzing the temperature change rate of each hard disk area, the hot spot confidence of each hard disk area is calculated. The hot spot confidence can represent the possibility of a certain hard disk area becoming a hot spot. According to the hot spot confidence data, the impact of temperature fluctuation on the hard disk array is analyzed, and the diffusion trend of hot spots is predicted. A spatial diffusion model can be used to consider the spatial propagation characteristics of temperature change to generate the diffusion trend of hot spot areas. Based on the hot spot confidence and diffusion trend, the areas in the hard disk array are subjected to clustering analysis to form multiple hot spot clusters. Each hot spot cluster contains hard disk areas with similar temperature change characteristics. Clustering algorithms such as K-means or DBSCAN can be used for clustering to divide the hot spot clusters into a data set for generating a model training set and a model test set; a convolutional neural network (CNN) is used to train the model training set. CNN can effectively process data with spatial structure characteristics, so it is suitable for hot spot area prediction in this task. The spatial characteristics of the data are extracted through multiple convolutional layers to generate a pre-model for hot spot area prediction. Through cross-validation and hyperparameter adjustment, the pre-model obtained by training is optimized and iterated using the model test set, and parameters such as the network structure and learning rate of CNN are adjusted to improve the prediction accuracy of the model. The backpropagation algorithm is used to optimize the model so that it can better capture the laws of temperature change and hot spot diffusion. The hot spot area prediction model, that is, the optimized and trained convolutional neural network model, can be used to predict the hot spot areas for future temperature data. The real-time data of the solid-state drive array is input into the trained hot spot area prediction model, and the model predicts the possible future hot spot areas according to the input data. The prediction results include the temperature prediction of each hard disk area, the hot spot confidence, and whether the area is likely to become a hot spot.

[0150] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be included in the present invention.

[0151] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A solid state hard disk intelligent temperature control method based on edge computing, characterized in that: The following steps are involved: Step S1: Acquire solid state drive array data; Perform array topology analysis on solid-state drive array data to generate solid-state drive array topology data; use solid-state drive array topology data to perform intelligent material integration on solid-state drive array to generate solid-state drive array integrated material data, wherein the intelligent material integration includes integrated phase change materials, thermoelectric materials, and intelligent thermal conductive films; perform microfluidic thermal conduction pathway identification on solid-state drive array topology data based on solid-state drive array integrated material data to generate hard disk self-assembly microstructure data; implant local sensing units into solid-state drive array topology data through hard disk self-assembly microstructure data to generate an initial self-organized heat dissipation network, wherein the local sensing units include temperature sensors and heat flow sensing devices; Step S2: deploy edge computing nodes on the initial self-organizing heat dissipation network, and collect temperature data of the solid-state hard disk according to the edge computing nodes; optimize the network thermal conductivity path of the initial self-organizing heat dissipation network through the temperature data to generate a self-organizing heat dissipation optimization network; Step S3: Calculate the current thermodynamic state of the solid-state hard disk array, and perform thermal-electric mode conversion on the temperature data according to the current thermodynamic state to generate heat flow dynamic temperature control data; pre-adjust the heat path of the self-organizing heat dissipation optimization network through the heat flow dynamic temperature control data to generate hard disk global thermal balance optimization data; Step S4: predicting the long-term change trend of the temperature data, and performing hard disk optimization feedback in advance on the hard disk global thermal balance optimization data according to the long-term change trend, and generating a solid state hard disk intelligent temperature control strategy.

2. The solid state hard disk intelligent temperature control method based on edge computing according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: deploy edge computing nodes on the initial self-organizing heat dissipation network to obtain edge computing nodes of the heat dissipation network; Step S22: Based on a preset time period, the temperature sensor is used to sense the hard disk temperature of the edge computing node of the heat dissipation network to generate hard disk temperature sensing data; the hard disk temperature sensing data is preprocessed to generate standard hard disk temperature sensing data, wherein the data preprocessing includes data cleaning, data denoising and data standardization; Step S23: performing temperature distribution analysis on the standard hard disk temperature sensing data to generate a hard disk thermal distribution matrix; performing thermal conduction path optimization on the hard disk self-assembled microstructure data based on the hard disk thermal distribution matrix to generate thermal conduction path optimization data; Step S24: regional temperature discrimination is performed on the hard disk temperature sensing data according to the thermal conduction path optimization data. When the hard disk temperature sensing data is greater than or equal to the preset temperature threshold, the initial self-organizing heat dissipation network is reorganized locally through the edge computing node of the heat dissipation network to generate a self-organizing heat dissipation optimization network.

3. The solid state hard disk intelligent temperature control method based on edge computing according to claim 2 is characterized in that: The optimization of the thermal path of the hard disk self-assembly microstructure data based on the hard disk thermal distribution matrix includes: Confirming the state of the phase change material in the hard disk self-assembly microstructure data based on the hard disk heat distribution matrix, and obtaining the phase change material state data, wherein the phase change material state data includes solid state data and liquid state data; The heat flow direction and heat exchange rate of the SSD array data are calculated through the hard disk heat distribution matrix, and a local heat flow model is constructed based on the heat flow direction and heat exchange rate; The local heat flow model is used to locate the hard disk hotspot area of ​​the initial self-organized heat dissipation network and generate the hard disk hotspot area; the regional thermal buffer analysis of the hard disk hotspot area is performed to generate the hard disk regional thermal buffer data; based on the dynamic phase change control formula, the local phase change control of the hard disk hotspot area is performed according to the phase change material state data to generate the phase change material solid-liquid area dynamic adjustment data, where the local phase change control formula is as follows: ； In the formula, For phase change material in position The solid-liquid conversion ratio at For SSD arrays in location The current temperature at Set the optimal operating temperature for the system. In order to adjust the influence weight of temperature deviation on the PCM solid-liquid conversion ratio, is the rate of change of SSD array temperature T with time t, To adjust the weight of the influence of the temperature change rate on the PCM solid-liquid conversion ratio, i is the horizontal coordinate of the position, and j is the vertical coordinate of the position; Based on the thermoelectric material in the hard disk self-assembly microstructure data, the electric field application mode of the hard disk hot spot area is confirmed, and the potential difference of the hard disk hot spot area is calculated according to the power plant application mode to obtain the hot spot area potential difference; the hot spot area potential difference is controlled by the thermoelectric material electric field distribution using the heat flow guidance optimization formula to generate dynamic electric field control data, where the heat flow guidance optimization formula is as follows: ； In the formula, For phase change material in position The local potential difference at For SSD arrays in location The current temperature at Set the optimal operating temperature for the system. In order to control the influence weight of temperature deviation on TEG potential difference, is the rate of change of SSD array temperature T with time t, To control the weight of the influence of the temperature change rate on the TEG potential difference, i is the horizontal coordinate of the position and j is the vertical coordinate of the position; According to the dynamic adjustment data of the solid-liquid region of the phase change material and the dynamic electric field control data, the heat diffusion path of the hard disk self-assembly microstructure data is optimized to generate the thermal conduction path optimization data.

4. The solid state hard disk intelligent temperature control method based on edge computing according to claim 1, characterized in that: The step S3 of calculating the current thermodynamic state of the solid state drive array includes: Extracting operation data of the solid state drive array, and calculating the total power consumption of the solid state drive array according to the operation data; Analyze the thermal power distribution of the SSD array through the total power consumption of the SSD array; Based on the finite element method, the temperature field of the thermal power distribution of the solid-state drive array is simulated to generate the array temperature field model; According to the array temperature field model, the heat exchange amount of the solid state drive array is evaluated and the curve is converted to obtain a heat exchange curve; The time point with a slope of 0 in the heat exchange curve was selected and marked as thermal steady-state time data; The heat exchange amount and thermal steady-state time data of the solid-state drive array are used to perform thermal runaway risk analysis on the operation data of the solid-state drive array, thereby generating the current thermodynamic state of the solid-state drive array.

5. The solid state hard disk intelligent temperature control method based on edge computing according to claim 1 is characterized in that: The step S3 of converting the temperature data into a thermal-electrical mode according to the current thermodynamic state includes: Perform thermal aggregation analysis on the temperature data according to the current thermodynamic state to generate thermal aggregation data; Use thermal clustering data to perform cluster analysis on SSD array data to generate hard disk hotspots, hard disk cooling areas, and hard disk neutral areas, and eliminate hard disk neutral areas; Through the thermoelectric materials in the SSD array integrated material data, the hot spot area, cooling area and neutral area of ​​the hard disk are analyzed for thermo-electric mode, and the Peltier effect mode and Seebeck effect mode are generated. The optimal heat flow guiding path for the hard disk hot spot area and the hard disk cooling area is calculated, and the electric field direction of the Peltier effect mode and the Seebeck effect mode is adjusted through the optimal heat flow guiding path to generate heat flow dynamic temperature control data.

6. The solid state hard disk intelligent temperature control method based on edge computing according to claim 1 is characterized in that: The step S3 of pre-adjusting the heat path of the self-organized heat dissipation optimization network by using the heat flow dynamic temperature control data includes: The heat transfer topology structure of the self-organizing heat dissipation optimization network is mapped through the heat flow dynamic temperature control data to generate a heat transfer path diagram; the heat transfer path impedance characteristics are extracted from the heat transfer path diagram to obtain the heat transfer path impedance characteristic data; The optimization target is set based on the impedance characteristic data of the heat transfer path, and the heat transfer path diagram is dynamically allocated according to the optimization target to generate the heat path control data; The thermal path control data is used to optimize the global network thermal balance of the self-organizing heat dissipation optimization network and generate the global thermal balance optimization data of the hard disk.

7. The solid state hard disk intelligent temperature control method based on edge computing according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Analyze the long-term trend of temperature data to generate long-term trend data of temperature; Step S42: predicting potential hotspot areas of the solid state disk array data according to the long-term temperature trend data to generate potential hotspot area prediction data; Step S43: Pre-optimize the hard disk heat dissipation path and provide feedback to the hard disk global thermal balance optimization data through the potential hot spot area prediction data, and generate an intelligent temperature control strategy for the solid state hard disk.

8. The solid state hard disk intelligent temperature control method based on edge computing according to claim 7 is characterized in that: Step S42 includes the following steps: Step S421: Calculate the temperature change rate of the temperature long-term trend data to obtain the temperature long-term change rate; Step S422: Performing temperature fluctuation analysis on the long-term temperature change rate by Fourier transform to generate temperature fluctuation data; Step S423: performing hotspot confidence calculation on the solid state drive array data based on the temperature fluctuation data, and performing hotspot diffusion trend analysis on the solid state drive array data based on the hotspot confidence to generate hotspot clusters; dividing the hotspot clusters into data sets to generate a model training set and a model test set; Step S424: Perform model training on the model training set by using a convolutional neural network algorithm to generate a hotspot area prediction pre-model; perform model optimization iteration on the hotspot area prediction pre-model by using a model test set to generate a hotspot area prediction model; Step S425: importing the solid state disk array data into the hot spot area prediction model to perform potential hot spot area prediction, thereby generating hot spot area prediction data.

9. A solid state hard disk intelligent temperature control system based on edge computing, characterized in that: Used to execute the solid-state hard disk intelligent temperature control method based on edge computing as claimed in claim 1, the solid-state hard disk intelligent temperature control system based on edge computing includes: A network construction module is used to obtain solid-state drive array data; perform array topology analysis on solid-state drive array data to generate solid-state drive array topology data; use solid-state drive array topology data to perform intelligent material integration on solid-state drive array to generate solid-state drive array integrated material data, wherein the intelligent material integration includes integrated phase change materials, thermoelectric materials, and intelligent thermal conductive films; perform microfluidic thermal conduction pathway identification on solid-state drive array topology data based on solid-state drive array integrated material data to generate hard disk self-assembly microstructure data; perform local sensing unit implantation on solid-state drive array topology data through hard disk self-assembly microstructure data to generate an initial self-organized heat dissipation network, wherein the local sensing unit includes a temperature sensor and a heat flow sensing device; The heat conduction path optimization module is used to deploy edge computing nodes for the initial self-organizing heat dissipation network and collect temperature data of the solid-state hard disk according to the edge computing nodes; the network heat conduction path of the initial self-organizing heat dissipation network is optimized through the temperature data to generate a self-organizing heat dissipation optimization network; The heat flow path optimization module is used to calculate the current thermodynamic state of the solid-state hard disk array, and convert the temperature data into a thermal-electrical mode according to the current thermodynamic state to generate heat flow dynamic temperature control data; the heat flow dynamic temperature control data is used to pre-adjust the heat path of the self-organizing heat dissipation optimization network to generate hard disk global thermal balance optimization data; The forward feedback module is used to predict the long-term change trend of temperature data, and perform hard disk optimization feedback in advance on the hard disk global thermal balance optimization data based on the long-term change trend to generate an intelligent temperature control strategy for the solid-state hard disk.

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