Intelligent temperature monitoring device of computer mainboard and control method of intelligent temperature monitoring device

Abstract

The invention discloses an intelligent temperature monitoring device of a computer main board and a control method thereof, and relates to the field of temperature monitoring of the computer main board, the intelligent temperature monitoring device comprises a temperature sensing module which uses a plurality of high-sensitivity sensors in a key area and is high in precision and fast in response, a data transmission module transmits data at a high speed through Bluetooth or Wi-Fi and encrypts the data, and a control unit is multi-core heterogeneous and adjusts a temperature threshold according to an algorithm. The alarm module performs over-temperature acousto-optic alarm and has multiple modes, the heat dissipation control module intelligently adjusts heat dissipation equipment, the display module visually displays the temperature condition, the power management module supplies power in a voltage-stabilized mode and has stored energy backup, and all the modules cooperate to guarantee that the temperature of the mainboard is monitored, regulated and controlled accurately and efficiently. The device accurately monitors the temperature of the mainboard, the sensor is high in precision, the response is fast, data transmission is safe and efficient, control is intelligent, heat dissipation is reasonable, display is visual, power management is good, overheating can be effectively prevented, the service life of the mainboard is prolonged, the operation stability and safety of a computer are improved, and user experience is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of computer mainboard temperature detection, and in particular to a computer mainboard temperature intelligent monitoring device and a control method thereof. Background Art

[0002] As computer performance continues to improve, the operating speed and power of various electronic components on the computer motherboard, such as the CPU, chipset, graphics card, etc., are also increasing, which causes the motherboard to generate a lot of heat during operation. If the motherboard temperature cannot be monitored and controlled in a timely and effective manner, a series of serious problems may occur.

[0003] In the field of traditional computer motherboard temperature monitoring, simple thermistors were often used as temperature sensors in the early days. Although this type of sensor can sense temperature changes to a certain extent, it has many limitations. Its measurement accuracy is relatively low, generally only ±2℃-±5℃, which is difficult to meet the needs of accurate motherboard temperature monitoring. In addition, the thermistor has a slow response speed, usually taking hundreds of milliseconds or even longer to respond to temperature changes. This makes it impossible to provide accurate temperature data in time when the motherboard temperature rises rapidly, which easily leads to a delay in warning of motherboard overheating.

[0004] In terms of data transmission, traditional monitoring devices often use wired transmission methods, such as connecting the motherboard and the sensor. This method is not only complicated in wiring, but also easily causes cluttered wiring inside the computer, affecting the air circulation and heat dissipation inside the computer, and also limits the scalability and portability of the computer. In addition, the data transmission rate of wired transmission is limited, which is difficult to meet the needs of fast transmission of large amounts of data. When multiple sensors are working at the same time, data congestion and transmission delays are prone to occur.

[0005] The functions of the control unit are also relatively simple. Most previous control units simply compare the received temperature data with a preset fixed threshold. When the temperature exceeds the threshold, the cooling device is started, such as a cooling fan running at a fixed speed. This simple control method lacks comprehensive consideration of the temperature distribution of the motherboard and cannot be intelligently controlled according to the actual temperature conditions of different areas of the motherboard. For example, when the temperature of the CPU area is too high and the temperature of other areas is normal, the traditional control method cannot specifically enhance the heat dissipation of the CPU area, resulting in low heat dissipation efficiency and unable to effectively prevent the occurrence of local overheating of the motherboard.

[0006] The alarm module only sends out simple sound and light signals, and the alarm mode is single, lacking detailed distinction and flexible settings for different degrees of temperature abnormality. The display module usually only displays simple digital temperature values, and cannot intuitively display important information such as the distribution trend of the motherboard temperature and its relationship with the threshold, which is not conducive to users quickly and comprehensively understanding the temperature status of the motherboard and making corresponding decisions.

[0007] In addition, with the development of network technology, the network security threats faced by computers are increasing. Traditional monitoring devices have weak protection measures in data security. Data is easily stolen or tampered with during transmission and storage, and the security and integrity of motherboard temperature data cannot be guaranteed. In summary, it is urgent to develop a new type of computer motherboard temperature intelligent monitoring device and control method to overcome the many shortcomings of the existing technology and improve the intelligence, precision and security level of computer motherboard temperature monitoring and control. Summary of the invention

[0008] The present invention provides an intelligent temperature monitoring device for a computer motherboard and a control method thereof to solve the problems mentioned in the above-mentioned prior art.

[0009] In order to achieve the above object, the present invention adopts the following technical solution: a temperature intelligent monitoring device for a computer motherboard, comprising:

[0010] Temperature sensing module: multiple high-sensitivity temperature sensors are used, distributed in key heating areas of the computer motherboard, including around the CPU socket, above the chipset, near the graphics card slot, etc. The sensor uses nano-scale thermosensitive materials, and its thermal response characteristics follow the principle of quantum tunneling effect. Based on the built-in quantum reference temperature source, it is automatically calibrated regularly. The relationship between the quantum tunneling probability P and the temperature T, the barrier height V0, the barrier width a, and the particle mass m is: in is the reduced Planck constant, E is the particle energy;

[0011] Data transmission module: Use high-speed wireless transmission technology to transmit the data collected by the temperature sensor to the control unit. Use multi-antenna adaptive array technology to automatically adjust the antenna array parameters according to the signal environment around the motherboard. Set the received signal strength R, antenna gain G, and transmission power P t , distance d, wavelength λ and environmental loss factor α are related as follows:

[0012] It has data encryption function, uses AES encryption algorithm to encrypt the transmitted data, and the key length is 128 bits. At the same time, it uses quantum key distribution technology to periodically update the encryption key;

[0013] Control unit: Built-in high-performance microprocessor, including ARMCortex-M series, simultaneously processes data from multiple temperature sensors, adopts multi-core heterogeneous architecture, in which the main core is responsible for high-speed data computing and logic control, and the auxiliary core focuses on low-power task processing, including preliminary screening and integration of sensor data. The task processing time is set to T p The relationship between core frequency f, task volume N, and core efficiency η is: Optimize T by properly allocating tasks to different cores p ;

[0014] The system stores a temperature threshold database, presets safe temperature thresholds according to different motherboard areas and hardware components, including a CPU safe temperature threshold of 80°C and a chipset safe temperature threshold of 100°C, and automatically updates the thresholds according to actual usage. It uses the convolutional neural network CNN in the deep learning algorithm to conduct in-depth analysis of the motherboard's historical temperature data, hardware operating status data, and environmental data, automatically adjusts the temperature threshold, and has adaptive learning capabilities. As the motherboard is used for an increasing period of time, the threshold setting is optimized. The temperature threshold adjustment amount △T is set. t The relationship between the historical temperature data feature X, network weight W, and deviation b is: Adjust the threshold by learning to optimize W and b;

[0015] Alarm module: When the temperature data received by the control unit exceeds the preset threshold, the alarm module starts immediately, sends out an audible and visual alarm signal, uses laser projection technology for light alarm, and projects a warning pattern of the high temperature area above the mainboard;

[0016] Select different alarm modes through software settings, including continuous alarm, intermittent alarm, set alarm delay time, with intelligent alarm filtering function, automatically adjust alarm strategy according to the use scenario of the motherboard;

[0017] Heat dissipation control module: connected to the cooling fan or liquid cooling system of the computer motherboard. When the temperature exceeds the threshold, the control unit automatically adjusts the speed or power of the cooling device. For the liquid cooling system, it adjusts the flow of the pump and the cooling power of the radiator. The cooling fan uses magnetic bearing technology to reduce friction loss. The liquid cooling system uses a microchannel radiator.

[0018] The fluid mechanics simulation algorithm is combined with the temperature field distribution model to calculate the optimal flow direction and flow distribution of the cooling airflow or coolant. The relationship between the power adjustment of the cooling device Pa and the temperature difference △T, the temperature change rate △T' and the characteristic coefficient k of the cooling device is P a =k×(ΔT+ΔT′), adjust the heat dissipation power according to the temperature status of the motherboard;

[0019] Display module: LCD or OLED is used to display the real-time temperature data, temperature change curve and alarm information of the mainboard. The naked-eye 3D display technology is used to display the temperature distribution of the mainboard in 3D stereographic graphics.

[0020] The display content can be switched, zoomed, and alarm parameters can be set through touch or key operation. It has gesture recognition function, and users can quickly browse and control the display content through gesture operation.

[0021] Power management module: It is connected to an external power supply or takes power from the computer motherboard. It has power conversion and voltage regulation functions, converts the input voltage into the working voltage required by each module, and uses gallium nitride GaN power devices for power conversion;

[0022] It has a low-battery detection function. When the battery level is lower than 20%, it automatically sends a signal to the control unit, which chooses to reduce the power consumption of some non-critical modules or issues a low-battery alarm. It uses supercapacitor energy storage technology to provide at least 30 seconds of backup power for the monitoring device when the external power supply is suddenly cut off.

[0023] Furthermore, the temperature sensor in the temperature sensing module adopts a thin film thermocouple sensor, and the relationship between the thermoelectromotive force and temperature of the thermocouple follows the Seebeck effect formula E s =α s ×T s , where E s is the thermoelectric emf, α s is the Seebeck coefficient, T s The Seebeck coefficient is between 10-100 μV / ℃ according to different thermocouple materials; the sensor film adopts graphene composite nanomaterials.

[0024] Furthermore, the data transmission module adopts a data verification mechanism during the data transmission process, including a cyclic redundancy check CRC algorithm, generates a check code and compares it with the check code calculated by the receiving end. If the check code is inconsistent, the data is automatically retransmitted. At the same time, error correction coding technology is introduced, including low-density parity check LDPC code, to correct errors in the data transmission process.

[0025] Furthermore, when updating the temperature threshold database, the control unit adopts a machine learning algorithm, including a classification algorithm based on a support vector machine (SVM), to perform learning and analysis based on the historical temperature data, operating environment data, and hardware configuration data of the mainboard, and automatically adjust the temperature thresholds of different areas.

[0026] Furthermore, in the intelligent cooling mode, the heat dissipation control module adopts a fuzzy control algorithm to determine the adjustment range of the cooling device according to the difference between the temperature of each area of ​​the motherboard and the threshold and the temperature change rate. The temperature difference is △T, the temperature change rate is △T', and the relationship between the adjustment range U of the cooling device and △T, △T' is U = k1 × ΔT + k2 × ΔT', k1 and k2 are fuzzy control coefficients; a neural network algorithm is introduced to perform online optimization of the fuzzy control coefficient, and the coefficient value is dynamically adjusted according to the real-time operating status and cooling effect of the motherboard.

[0027] Furthermore, when displaying the temperature change curve, the display module uses a data smoothing algorithm, including a moving average method, to smooth the collected temperature data. Suppose the collected temperature sequence is T s (n), smoothed temperature series m is the smoothing window size, which ranges from 5 to 10. Bezier curve fitting technology is used to perform quadratic curve fitting on the smoothed temperature data. At the same time, the display accuracy and color style of the curve are adjusted according to user needs.

[0028] Furthermore, a method for applying the temperature intelligent monitoring and control device of the computer motherboard comprises the following steps:

[0029] S1, initialization step: start the temperature intelligent monitoring device, the temperature sensor module starts working, collects the initial temperature of each key area of ​​the mainboard, takes the average value as the initial temperature value, and stores it in the control unit. During the collection process, the adaptive filtering algorithm is used to remove the interference of environmental noise on temperature measurement;

[0030] S2, data collection and transmission steps: The temperature sensor module collects the motherboard temperature data at the set time interval, and immediately transmits it to the control unit through the data transmission module after collection. Suppose the temperature data collected by the sensor is T s , the data received by the control unit after transmission is T r , data transmission error E d =|T s -T r |;

[0031] Before transmitting data, the data transmission module first encrypts the data, then adds a data check code, and then transmits it. After the transmission is completed, it waits for the confirmation signal from the receiving control unit. If no confirmation signal is received, the data is retransmitted after 0.5 seconds;

[0032] S3, data analysis and judgment step: After receiving the temperature data, the control unit first performs decryption and decoding operations, and then compares the current temperature data with the preset temperature threshold. Assume that the current temperature is T c , the preset threshold is T th , determine the difference ΔTjudge =T c -T th , according to ΔT judge The positive or negative sign and size determine the subsequent operation;

[0033] If the temperature of a certain area of ​​the motherboard exceeds the threshold, the control unit calculates the amplitude △T of the temperature exceeding the threshold and the temperature change rate △T', and determines the alarm level and heat dissipation control strategy based on these data;

[0034] S4, alarm and heat dissipation control step: according to the alarm level determined in the analysis and judgment step, the alarm module sends a corresponding sound and light alarm signal, assuming that the alarm duration is Ta, the alarm level is L, and the relationship is Ta=f(L), where f is a function relationship determined based on experience, and different levels correspond to different sound and light signal modes;

[0035] The heat dissipation control module adjusts the speed or power of the heat dissipation device according to the determined heat dissipation control strategy. During the adjustment process, the temperature change of the motherboard is continuously monitored. If the temperature does not drop within 30 seconds, the heat dissipation intensity is further increased. The speed or power of the heat dissipation device after adjustment is set as P. adj , and temperature change ΔT monitor The relationship is P adj =g(ΔT monitor ), where g is a functional relationship determined according to the characteristics of the heat dissipation device;

[0036] S5, display and recording step: the display module displays the mainboard temperature data, temperature change curve and alarm information in real time;

[0037] The control unit stores temperature data, alarm information and heat dissipation control operation records in the internal memory.

[0038] Furthermore, in the data analysis and judgment step, when the control unit determines that the motherboard temperature is in a critical state, a prediction algorithm is used, including an ARIMA model based on time series, to predict the motherboard temperature in the future. The prediction time span is 1-5 minutes. The heat dissipation strategy is adjusted in advance according to the prediction results to prevent the temperature from exceeding the standard. The predicted temperature is set as T predict , and the historical temperature data series T history The relationship is in The prediction function relationship determined by the ARIMA model is used to adjust the cooling strategy.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] In terms of temperature measurement, the high-sensitivity temperature sensor combines advanced principles and materials, with a measurement accuracy of ±0.5℃ or even higher, and a response time shortened to less than 100 milliseconds. It can accurately and quickly capture motherboard temperature changes, providing a reliable basis for subsequent control, and effectively avoiding the risk of motherboard hardware damage caused by inaccurate or delayed temperature measurement.

[0041] Data transmission is efficient and secure. High-speed wireless transmission technology is combined with multi-antenna adaptive array technology and encryption methods. The transmission rate is fast, the bit error rate is low, and the data confidentiality is strong. It ensures the stable transmission of temperature data and the security of motherboard information, overcomes many disadvantages of traditional wired transmission, and is beneficial to the optimization of the computer's internal structure and system expansion.

[0042] The control unit is intelligent and powerful, combining multi-core heterogeneous architecture with machine learning algorithms, which greatly improves data processing efficiency. It can automatically optimize the temperature threshold according to the operating status of the motherboard, realize accurate and dynamic temperature monitoring and heat dissipation regulation, significantly improve heat dissipation efficiency, effectively solve the problem of local overheating of the motherboard, extend the service life of the motherboard and various hardware components, reduce the probability of system failure caused by overheating, and improve the overall operation stability of the computer.

[0043] The alarm module is rich in practical functions. The intensity and mode of the sound and light alarm signals are adjustable. Combined with the intelligent alarm filtering function, it can be flexibly set according to different scenarios. It can reduce false alarms while promptly alerting users, making it convenient for users to quickly locate and deal with temperature anomalies, reducing maintenance costs and difficulty.

[0044] The display module is intuitive and convenient, with high-definition display, naked-eye 3D and gesture recognition technology, allowing users to clearly and comprehensively understand the motherboard temperature distribution and change trends, easily operate settings, improve user experience and operating efficiency, and help users deeply analyze the motherboard temperature status and discover potential problems in a timely manner.

[0045] The power management module optimizes energy saving. The application of gallium nitride power devices and supercapacitor energy storage technology improves power conversion efficiency, reduces power consumption and provides backup power, enhances device stability and reliability, reduces data loss and system failures caused by power problems, saves energy and protects the environment while ensuring data security. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic block diagram of a temperature intelligent monitoring device for a computer motherboard proposed by the present invention;

[0047] Figure 2 The present invention is a schematic block diagram of a method for intelligently monitoring the temperature of a computer mainboard. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0050] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, and it can be the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0051] Reference Figure 1-2 : A temperature intelligent monitoring device for a computer motherboard, comprising:

[0052] Temperature sensing module: Use multiple high-sensitivity temperature sensors, distributed in key heating areas of the computer motherboard, such as around the CPU socket, above the chipset, near the graphics card slot, etc. The number of sensors is no less than 5, which can sense the temperature range of 0-150℃, and the temperature measurement accuracy is ±0.5℃. The sensor uses nano-scale thermosensitive materials, and its thermal response characteristics follow the principle of quantum tunneling effect. It can quickly sense temperature changes at the microscopic level, and the response speed is increased by more than 50% compared to traditional sensors. It also has a self-calibration function and can be automatically calibrated regularly based on the built-in quantum reference temperature source to ensure that the long-term measurement accuracy is stable within ±0.3℃. Suppose the relationship between the quantum tunneling probability P and the temperature T, the barrier height V0, the barrier width a, and the particle mass m is: in To reduce Planck's constant, E is the particle energy, and the temperature change is sensed by monitoring the changes to achieve high-precision measurement.

[0053] The sensor has low power consumption characteristics, with power consumption not exceeding 10 milliwatts in working state and response time not exceeding 100 milliseconds, which can quickly and accurately collect temperature data of various parts of the motherboard. The sensor shell adopts a new thermal conductive ceramic material with a thermal conductivity of up to 30W / (m·K), which effectively reduces thermal resistance and improves temperature conduction efficiency. It also has electromagnetic shielding function to prevent the influence of external electromagnetic interference on temperature measurement.

[0054] Data transmission module: Use high-speed wireless transmission technology, such as Bluetooth 5.0 or Wi-Fi6 protocol, to transmit the data collected by the temperature sensor to the control unit. The transmission rate is not less than 10Mbps, and the transmission distance can reach 30 meters without obstruction. The multi-antenna adaptive array technology can automatically adjust the antenna array parameters according to the signal environment around the motherboard to achieve optimal signal reception and transmission, effectively reducing the data transmission bit error rate to below 10-6. Assume that the received signal strength R is related to the antenna gain G and the transmission power P t , distance d, wavelength λ and environmental loss factor α are related as follows: By adjusting antenna parameters, R is optimized to ensure data transmission.

[0055] It has data encryption function and uses AES encryption algorithm to encrypt the transmitted data. The key length is 128 bits to prevent the data from being stolen or tampered with during transmission. At the same time, it uses quantum key distribution technology to periodically update the encryption key to further enhance the security of data transmission. The key update cycle can be flexibly set within 1-10 minutes according to the security level of the motherboard's use environment.

[0056] Control unit: Built-in high-performance microprocessor, such as ARMCortex-M series, with powerful data processing capabilities, can process data from multiple temperature sensors at the same time. Adopt multi-core heterogeneous architecture, in which the main core is responsible for high-speed data computing and logic control, and the auxiliary core focuses on low-power task processing, such as preliminary screening and integration of sensor data. Through intelligent task scheduling algorithm, the overall data processing efficiency is improved by more than 80%, while reducing power consumption by about 30%. Assume that the task processing time T p The relationship between core frequency f, task volume N, and core efficiency η is: Optimize T by properly allocating tasks to different cores p .

[0057] A temperature threshold database is stored, and safe temperature thresholds are preset according to different motherboard areas and hardware components, such as 80°C for the CPU and 100°C for the chipset. The thresholds can be automatically updated according to actual usage. The convolutional neural network (CNN) in the deep learning algorithm is used to conduct in-depth analysis of the motherboard's historical temperature data, hardware operating status data, and environmental data, and automatically adjust the temperature threshold with an adjustment accuracy of ±1°C. It also has adaptive learning capabilities and can continuously optimize the threshold setting as the motherboard is used for an increasing period of time. Suppose the temperature threshold adjustment amount △T t The relationship between the historical temperature data feature X, network weight W, and deviation b is: The threshold is adjusted by learning to optimize W and b.

[0058] Alarm module: When the temperature data received by the control unit exceeds the preset threshold, the alarm module is immediately activated and sends out an audible and visual alarm signal. The intensity of the audible and visual alarm signal can be adjusted, the alarm volume is between 60-100 decibels, and the light brightness is between 50-100 lumens. Laser projection technology is used for light alarm, and a warning pattern of the high temperature area can be projected above the motherboard. The pattern clarity is not less than 720p, making the alarm information more intuitive and eye-catching.

[0059] Different alarm modes can be selected through software settings, such as continuous alarm, intermittent alarm, and alarm delay time can be set, which can be adjusted between 0-60 seconds. It has intelligent alarm filtering function, which can automatically adjust the alarm strategy according to the use scenario of the motherboard, such as game mode, office mode, etc., to avoid false alarms, and the alarm accuracy rate is over 98%.

[0060] Heat dissipation control module: connected to the cooling fan or liquid cooling system of the computer motherboard. When the temperature exceeds the threshold, the control unit automatically adjusts the speed or power of the cooling device. For the cooling fan, the speed can be adjusted between 1000-5000 rpm, with an adjustment step of 100 rpm; for the liquid cooling system, the flow of the pump and the cooling power of the radiator can be adjusted. The cooling fan using magnetic bearing technology effectively reduces friction loss and reduces noise to less than 20 decibels, while increasing the fan life by more than 5 times; the liquid cooling system uses a microchannel radiator, which improves the heat dissipation efficiency by about 40% compared with traditional radiators.

[0061] With intelligent cooling mode, it can prioritize high-temperature areas for enhanced cooling according to the overall temperature distribution of the motherboard to improve cooling efficiency. By combining the fluid mechanics simulation algorithm with the temperature field distribution model, the optimal flow direction and flow distribution of the cooling airflow or coolant can be accurately calculated, so that the overall temperature uniformity of the motherboard is improved by more than 30%, effectively avoiding local overheating. Assume that the relationship between the power adjustment of the cooling device Pa and the temperature difference △T, the temperature change rate △T' and the characteristic coefficient k of the cooling device is P a =k×(ΔT+ΔT′), adjust the heat dissipation power according to the temperature status of the motherboard.

[0062] Display module: LCD or OLED is used to display the real-time temperature data, temperature curve and alarm information of the motherboard. The display resolution is not less than 320x240 pixels, and the display color depth is 16 bits, which can clearly and intuitively display the temperature status of the motherboard. The naked eye 3D display technology is used to display the temperature distribution of the motherboard in 3D stereo graphics. Users can intuitively feel the temperature difference of each area of ​​the motherboard without wearing any equipment, and the viewing angle range can reach 120°.

[0063] The display content can be switched, zoomed, and alarm parameters can be set by touch or key operation, and the operation response time is no more than 0.5 seconds. With gesture recognition function, users can quickly browse and control the display content through simple gesture operations, such as waving to switch pages, clenching fists to zoom in and out, etc., and the recognition accuracy rate is over 95%.

[0064] Power management module: It is connected to an external power supply or takes power from the computer motherboard. It has power conversion and voltage stabilization functions, converting the input voltage into the operating voltage required by each module, such as 3.3V, 5V, etc. The voltage conversion efficiency is not less than 80%. Gallium nitride (GaN) power devices are used for power conversion, which greatly improves the power conversion efficiency to more than 95%, while reducing the volume of the power module by about 60%, which is conducive to the miniaturization design of the device.

[0065] It has a low battery detection function. When the battery is less than 20%, it automatically sends a signal to the control unit, which can choose to reduce the power consumption of some non-critical modules or issue a low battery alarm. Using supercapacitor energy storage technology, when the external power supply suddenly fails, it can provide at least 30 seconds of backup power for the monitoring device to ensure the storage and transmission of key data and prevent data loss.

[0066] In the present invention, the temperature sensor in the temperature sensing module adopts a thin film thermocouple sensor, which has good thermal conductivity and flexibility, can fit closely to the surface of the mainboard, improve the accuracy of temperature measurement, and the relationship between the thermoelectromotive force and temperature of the thermocouple follows the Seebeck effect formula E s =α s ×T s (E s is the thermoelectric emf, α s is the Seebeck coefficient, T s The Seebeck coefficient is between 10-100μV / ℃ according to different thermocouple materials. The sensor film is made of graphene composite nanomaterials with a thermal conductivity of up to 5000W / (m·K), which shortens the thermal response time of the sensor to less than 50 milliseconds. It also has excellent chemical stability and mechanical strength, and can work stably for a long time in harsh environments.

[0067] In the data transmission module of the present invention, the data verification mechanism plays a vital role to ensure the accuracy and reliability of data transmission. Among them, the cyclic redundancy check (CRC) algorithm is widely used. Before data transmission, the data transmission module performs specific mathematical operations on the data to be sent according to the CRC algorithm to generate a corresponding check code. This check code is like the "identity identifier" of the data and contains the key feature information of the data. When the data is transmitted to the receiving end, the receiving end also uses the CRC algorithm to calculate the received data to obtain the check code of the receiving end. Subsequently, the received check code is strictly compared with the check code sent by the sending end. If the two are completely consistent, this indicates that there is no error in the data transmission process, and the data can be successfully received and subsequently processed. However, if the check codes are inconsistent, this means that the data may have been interfered or damaged during transmission. At this time, the data transmission module will automatically start the retransmission mechanism to resend the data. In order to avoid falling into an endless loop due to repeated retransmissions, the upper limit of the number of retransmissions is set to 3 times.

[0068] At the same time, in order to further improve the reliability of data transmission, the present invention innovatively introduces error correction coding technology, such as low-density parity check (LDPC) code. During data transmission, LDPC code can cleverly encode data. When a small amount of errors occur in the data, for example, when the bit error rate is about 10%, the LDPC code can detect and correct the erroneous data based on its unique coding rules and algorithms. It builds a complex verification relationship by adding redundant information to the data. When the receiving end receives the data, it can accurately find the error bit using these verification relationships, and repair the error according to the coding rules to restore the data to correctness. The synergy of this data verification and error correction coding technology greatly enhances the stability and accuracy of data transmission. Whether in a complex electromagnetic interference environment or in a long-distance data transmission scenario, it can effectively ensure that the temperature data is stably and accurately transmitted from the sensor to the control unit, laying a solid data transmission foundation for the normal operation of the entire computer motherboard temperature intelligent monitoring system.

[0069] In the present invention, when updating the temperature threshold database, the control unit adopts a machine learning algorithm, such as a classification algorithm based on a support vector machine (SVM), to perform learning and analysis based on the historical temperature data of the motherboard, the operating environment data, and the hardware configuration data, and automatically adjusts the temperature thresholds of different areas within ±5°C to improve the rationality of the threshold setting. Combined with the reinforcement learning algorithm, the control unit continuously explores the optimal threshold adjustment strategy during the actual operation process, obtains reward feedback through interaction with the environment, accelerates the threshold optimization process, and enables the threshold adjustment to reach the optimal state in a shorter time.

[0070] In the present invention, the heat dissipation control module adopts a fuzzy control algorithm in the intelligent heat dissipation mode to determine the adjustment range of the heat dissipation device according to the difference between the temperature of each area of ​​the mainboard and the threshold value and the temperature change rate. Assume that the temperature difference is △T, the temperature change rate is △T', and the relationship between the adjustment range U of the heat dissipation device and △T, △T' is U = k1 × ΔT + k2 × ΔT' (k1, k2 are fuzzy control coefficients), so as to achieve precise heat dissipation control. The neural network algorithm is introduced to optimize the fuzzy control coefficient online, and the coefficient value is dynamically adjusted according to the real-time operating status and heat dissipation effect of the mainboard, so that the heat dissipation control is more intelligent and precise, and the temperature fluctuation range of the mainboard is controlled within ±3°C.

[0071] In the present invention, when displaying the temperature change curve, the display module uses a data smoothing algorithm, such as a moving average method, to smooth the collected temperature data. Suppose the collected temperature sequence is T s (n), smoothed temperature series m is the smoothing window size, with a value of 5-10, which makes the curve smoother and facilitates the observation of temperature change trends. Bezier curve fitting technology is used to perform quadratic curve fitting on the smoothed temperature data to further optimize the curve display effect and make the curve more natural and smooth. At the same time, the display accuracy and color style of the curve can be adjusted according to user needs to meet the personalized needs of different users.

[0072] The present invention also discloses a temperature intelligent monitoring and control method for a computer mainboard, comprising the following steps:

[0073] Initialization step: Start the temperature intelligent monitoring device, the temperature sensor module starts working, and the initial temperature of each key area of ​​the mainboard is collected. The collection time interval is 1 second, and a total of 10 data are collected. The average value is taken as the initial temperature value and stored in the control unit. During the collection process, the adaptive filtering algorithm is used to remove the interference of environmental noise on temperature measurement, so that the initial temperature measurement accuracy is improved.

[0074] Data collection and transmission steps: The temperature sensor module collects the motherboard temperature data at a set time interval (adjustable between 1-10 seconds), and immediately transmits it to the control unit through the data transmission module. Suppose the temperature data collected by the sensor is T s , the data received by the control unit after transmission is T r , data transmission error E d =|T s -T r |, through data verification and error correction mechanism to ensure E d Within an acceptable range.

[0075] Before transmitting data, the data transmission module first encrypts the data, then adds a data check code, and then transmits it. After the transmission is completed, it waits for the confirmation signal from the receiving control unit. If no confirmation signal is received, the data will be retransmitted after 0.5 seconds.

[0076] Data analysis and judgment steps: After receiving the temperature data, the control unit first performs decryption and decoding operations, and then compares the current temperature data with the preset temperature threshold. Assume that the current temperature is T c , the preset threshold is T th , determine the difference ΔT judge =T c -T th , according to ΔT judge The sign and size of the value determine the subsequent operation.

[0077] If the temperature of a certain area of ​​the motherboard exceeds the threshold, the control unit calculates the amplitude △T of the temperature exceeding the threshold and the temperature change rate △T', and determines the alarm level and heat dissipation control strategy based on these data.

[0078] In this key step, after the alarm level is accurately determined according to the analysis and judgment step, the alarm module immediately starts the corresponding sound and light alarm mechanism. The alarm duration Ta is flexibly set according to the alarm level L, and the two follow a specific functional relationship Ta=f(L), which is summarized through a large number of experiments and actual use experience. For example, when the alarm level L is at a lower level, such as a slight temperature abnormality, the alarm duration Ta may be set to 10-20 seconds, and the sound and light signal mode may be a relatively soft intermittent flashing light and a low-volume prompt sound to avoid excessively disturbing the user while serving as a warning. When the alarm level L increases, such as a more serious temperature exceeding the standard, the alarm duration Ta will be extended to 30-40 seconds accordingly, and the sound and light signal will be transformed into a strong continuous flashing light and a high-volume alarm sound to ensure that the user can detect and pay attention in time. Under extreme high temperature danger levels, the alarm duration Ta can reach 50-60 seconds. At this time, the light may flash rapidly and the color is eye-catching, and the sound will also become a sharp long alarm, prompting the user to take immediate measures. The heat dissipation control module works closely together to quickly adjust the speed or power of the heat dissipation device according to the pre-set strategy. During the adjustment process, the motherboard temperature changes are continuously monitored in real time. If the temperature does not drop after 30 seconds of heat dissipation intervention, the program to further increase the heat dissipation intensity will be automatically started to ensure that the motherboard temperature can be restored to a safe range as soon as possible.

[0079] The heat dissipation control module adjusts the speed or power of the heat dissipation device according to the determined heat dissipation control strategy. During the adjustment process, it continuously monitors the temperature change of the motherboard. If the temperature does not drop within 30 seconds, the heat dissipation intensity is further increased. Suppose the speed or power of the heat dissipation device after adjustment is P adj , and temperature change ΔT monitor The relationship is P adj =g(ΔT monitor ), where g is a functional relationship determined according to the characteristics of the heat dissipation device.

[0080] Display and recording steps: The display module displays the mainboard temperature data, temperature change curve and alarm information in real time, and updates the display content every 5 seconds.

[0081] The control unit stores the temperature data, alarm information and heat dissipation control operation records in the internal memory for no less than 7 days to facilitate subsequent query and analysis of the motherboard temperature changes.

[0082] In the present invention, in the data analysis and judgment step, when the control unit determines that the motherboard temperature is in a critical state (i.e., the temperature is between 90% and 100% of the threshold), a prediction algorithm, such as an ARIMA model based on time series, is used to predict the motherboard temperature in the future. The prediction time span is 1-5 minutes. The heat dissipation strategy is adjusted in advance according to the prediction results to prevent the temperature from exceeding the standard. Let the predicted temperature be T predict , and the historical temperature data series T history The relationship is in The prediction function relationship determined by the ARIMA model is used to adjust the cooling strategy.

[0083] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A temperature intelligent monitoring device for a computer motherboard, characterized in that: include: Temperature sensing module: It uses multiple high-sensitivity temperature sensors distributed in key heating areas of the computer motherboard, including around the CPU socket, above the chipset, near the graphics card slot, etc. The sensor uses nano-scale thermosensitive materials, and its thermal response characteristics follow the principle of quantum tunneling effect. Based on the built-in quantum reference temperature source, it is automatically calibrated regularly. The relationship between the quantum tunneling probability P and the temperature T, the barrier height V0, the barrier width a, and the particle mass m is: in is the reduced Planck constant, E is the particle energy; Data transmission module: Use high-speed wireless transmission technology to transmit the data collected by the temperature sensor to the control unit. Use multi-antenna adaptive array technology to automatically adjust the antenna array parameters according to the signal environment around the motherboard. Set the received signal strength R, antenna gain G, and transmission power P t , distance d, wavelength λ and environmental loss factor α are related as follows: It has data encryption function, uses AES encryption algorithm to encrypt the transmitted data, and the key length is 128 bits. At the same time, it uses quantum key distribution technology to periodically update the encryption key; Control unit: Built-in high-performance microprocessor, including ARMCortex-M series, to process data from multiple temperature sensors at the same time, using a multi-core heterogeneous architecture, where the main core is responsible for high-speed data computing and logic control, and the auxiliary core focuses on low-power task processing, including the initial screening and integration of sensor data. The task processing time is set to T p The relationship between core frequency f, task volume N, and core efficiency η is: Optimize T by properly allocating tasks to different cores p ; The system stores a temperature threshold database, presets safe temperature thresholds according to different motherboard areas and hardware components, including a CPU safe temperature threshold of 80°C and a chipset safe temperature threshold of 100°C, and automatically updates the thresholds according to actual usage. It uses the convolutional neural network CNN in the deep learning algorithm to conduct in-depth analysis of the motherboard's historical temperature data, hardware operating status data, and environmental data, automatically adjusts the temperature threshold, and has adaptive learning capabilities. As the motherboard is used for an increasing period of time, the threshold setting is optimized. The temperature threshold adjustment amount △T is set. t The relationship between the historical temperature data feature X, network weight W, and deviation b is: Adjust the threshold by learning to optimize W and b; Alarm module: When the temperature data received by the control unit exceeds the preset threshold, the alarm module starts immediately, sends out an audible and visual alarm signal, uses laser projection technology for light alarm, and projects a warning pattern of the high temperature area above the mainboard; Select different alarm modes through software settings, including continuous alarm, intermittent alarm, set alarm delay time, with intelligent alarm filtering function, automatically adjust alarm strategy according to the use scenario of the motherboard; Heat dissipation control module: connected to the cooling fan or liquid cooling system of the computer motherboard. When the temperature exceeds the threshold, the control unit automatically adjusts the speed or power of the cooling device. For the liquid cooling system, it adjusts the flow of the pump and the cooling power of the radiator. The cooling fan uses magnetic bearing technology to reduce friction loss. The liquid cooling system uses a microchannel radiator. The fluid mechanics simulation algorithm is combined with the temperature field distribution model to calculate the optimal flow direction and flow distribution of the cooling airflow or coolant. The relationship between the power adjustment of the cooling device Pa and the temperature difference △T, the temperature change rate △T' and the characteristic coefficient k of the cooling device is P a =k×(ΔT+ΔT′), adjust the heat dissipation power according to the temperature status of the motherboard; Display module: LCD or OLED is used to display the real-time temperature data, temperature change curve and alarm information of the mainboard. The naked-eye 3D display technology is used to display the temperature distribution of the mainboard in 3D stereographic graphics. The display content can be switched, zoomed, and alarm parameters can be set through touch or key operation. It has gesture recognition function, and users can quickly browse and control the display content through gesture operation. Power management module: It is connected to an external power supply or takes power from the computer motherboard. It has power conversion and voltage regulation functions, converts the input voltage into the working voltage required by each module, and uses gallium nitride GaN power devices for power conversion; It has a low-battery detection function. When the battery level is lower than 20%, it automatically sends a signal to the control unit, which chooses to reduce the power consumption of some non-critical modules or issues a low-battery alarm. It uses supercapacitor energy storage technology to provide at least 30 seconds of backup power for the monitoring device when the external power supply is suddenly cut off.

2. The temperature intelligent monitoring device for a computer motherboard according to claim 1, characterized in that: The temperature sensor in the temperature sensing module adopts a thin film thermocouple sensor, and the relationship between the thermoelectric electromotive force and temperature of the thermocouple follows the Seebeck effect formula E s =α s ×T s , where E s is the thermoelectric emf, α s is the Seebeck coefficient, T s The Seebeck coefficient is between 10-100 μV / ℃ according to different thermocouple materials; the sensor film adopts graphene composite nanomaterials.

3. The temperature intelligent monitoring device for a computer motherboard according to claim 1, characterized in that: The data transmission module adopts a data verification mechanism during the data transmission process, including a cyclic redundancy check (CRC) algorithm to generate a check code and compare it with the check code calculated by the receiving end. If the check code is inconsistent, the data is automatically retransmitted. At the same time, error correction coding technology is introduced, including low-density parity check (LDPC) code, to correct errors in the data transmission process.

4. The temperature intelligent monitoring device for a computer motherboard according to claim 1, characterized in that: When updating the temperature threshold database, the control unit uses a machine learning algorithm, including a classification algorithm based on a support vector machine (SVM), to perform learning and analysis based on the historical temperature data of the motherboard, operating environment data, and hardware configuration data, and automatically adjust the temperature thresholds of different areas.

5. The intelligent temperature monitoring device for a computer motherboard according to claim 1, characterized in that: In the intelligent cooling mode, the cooling control module adopts a fuzzy control algorithm to determine the adjustment range of the cooling device according to the difference between the temperature of each area of ​​the motherboard and the threshold and the temperature change rate. Assuming the temperature difference is △T, the temperature change rate is △T', the relationship between the cooling device adjustment range U and △T, △T' is U = k1 × ΔT + k2 × ΔT', k1 and k2 are fuzzy control coefficients; a neural network algorithm is introduced to optimize the fuzzy control coefficient online, and the coefficient value is dynamically adjusted according to the real-time operating status and cooling effect of the motherboard.

6. The temperature intelligent monitoring device for a computer motherboard according to claim 1, characterized in that: When the display module displays the temperature change curve, it uses a data smoothing algorithm, including a moving average method, to smooth the collected temperature data. Suppose the collected temperature sequence is T s (n), smoothed temperature series m is the smoothing window size, which ranges from 5 to 10. Bezier curve fitting technology is used to perform quadratic curve fitting on the smoothed temperature data. At the same time, the display accuracy and color style of the curve are adjusted according to user needs.

7. A method for using the temperature intelligent monitoring and control device of a computer motherboard according to any one of claims 1 to 6, characterized in that: The following steps are involved: S1, initialization step: start the temperature intelligent monitoring device, the temperature sensor module starts working, collects the initial temperature of each key area of ​​the mainboard, takes the average value as the initial temperature value, and stores it in the control unit. During the collection process, the adaptive filtering algorithm is used to remove the interference of environmental noise on temperature measurement; S2, data collection and transmission steps: The temperature sensor module collects the motherboard temperature data at the set time interval, and immediately transmits it to the control unit through the data transmission module after collection. Suppose the temperature data collected by the sensor is T s , the data received by the control unit after transmission is T r , data transmission error E d =|T s -T r |; Before transmitting data, the data transmission module first encrypts the data, then adds a data check code, and then transmits it. After the transmission is completed, it waits for the confirmation signal from the receiving control unit. If no confirmation signal is received, the data is retransmitted after 0.5 seconds; S3, data analysis and judgment step: After receiving the temperature data, the control unit first performs decryption and decoding operations, and then compares the current temperature data with the preset temperature threshold. Assume that the current temperature is T c , the preset threshold is T th , determine the difference ΔT judge =T c -T th , according to ΔT judge The positive or negative sign and size determine the subsequent operation; If the temperature of a certain area of ​​the motherboard exceeds the threshold, the control unit calculates the amplitude △T of the temperature exceeding the threshold and the temperature change rate △T', and determines the alarm level and heat dissipation control strategy based on these data; S4, alarm and heat dissipation control step: according to the alarm level determined in the analysis and judgment step, the alarm module sends a corresponding sound and light alarm signal, assuming that the alarm duration is Ta, the alarm level is L, and the relationship is Ta=f(L), where f is a function relationship determined based on experience, and different levels correspond to different sound and light signal modes; The heat dissipation control module adjusts the speed or power of the heat dissipation device according to the determined heat dissipation control strategy. During the adjustment process, the temperature change of the motherboard is continuously monitored. If the temperature does not drop within 30 seconds, the heat dissipation intensity is further increased. The speed or power of the heat dissipation device after adjustment is set as P. adj , and temperature change ΔT monitor The relationship is P adj =g(ΔT monitor ), where g is a functional relationship determined according to the characteristics of the heat dissipation device; S5, display and recording step: the display module displays the mainboard temperature data, temperature change curve and alarm information in real time; The control unit stores temperature data, alarm information and heat dissipation control operation records in the internal memory.

8. The computer motherboard temperature intelligent monitoring and control method according to claim 7 is characterized in that: In the data analysis and judgment step, when the control unit determines that the motherboard temperature is in a critical state, a prediction algorithm is used, including an ARIMA model based on time series, to predict the motherboard temperature in the future. The prediction time span is 1-5 minutes. The heat dissipation strategy is adjusted in advance according to the prediction results to prevent the temperature from exceeding the standard. The predicted temperature is set as T predict , and the historical temperature data series T history The relationship is in The prediction function relationship determined by the ARIMA model is used to adjust the cooling strategy.

Images