A temperature control method and device for handheld ultrasonic equipment

Abstract

The present invention discloses a temperature control method and device for a handheld ultrasonic device. A first temperature prediction model and a second temperature prediction model are established according to working parameters and temperature data of the handheld ultrasonic device, so that the temperature change trend is predicted according to the first temperature prediction model and the second temperature prediction model, thereby improving the accuracy of temperature prediction, and matching a heat dissipation strategy according to the temperature change trend, thereby controlling the operation of the handheld ultrasonic device according to the heat dissipation strategy to achieve a temperature control effect. The handheld ultrasonic device is controlled in advance through active temperature prediction, so that the handheld ultrasonic device is prevented from being damaged by excessive temperature, thereby improving the heat dissipation effect of the handheld ultrasonic device.

Description

Technical Field

[0001] The present invention relates to the technical field of handheld ultrasonic equipment, and in particular to a temperature control method and device for handheld ultrasonic equipment. Background Art

[0002] Handheld ultrasound devices are widely used in the field of medical diagnosis. Their portability and real-time imaging capabilities greatly facilitate clinicians and patients. However, since handheld ultrasound devices need to continuously emit ultrasonic signals when working, the electronic components inside the device will generate heat. Especially when used for a long time, the temperature of the device may rise significantly, affecting the performance and service life of the device, and may even cause thermal damage to the user.

[0003] The temperature control of existing handheld ultrasound devices mainly relies on simple passive heat dissipation or fan cooling. Passive heat dissipation relies on the thermal conductivity of the device housing and heat sink. Under high-intensity working conditions, the heat dissipation rate is often lower than the heat generation rate, causing the device temperature to continue to rise. Although fan cooling can improve the heat dissipation efficiency, it will generate noise and affect the user experience. In addition, the energy consumption of the fan will also increase the battery consumption of the device and reduce the endurance of the device. Summary of the invention

[0004] The invention provides a handheld ultrasound equipment temperature control method and device, so as to solve the problem that the temperature control of the existing handheld ultrasound equipment belongs to passive heat dissipation, has poor heat dissipation and affects the equipment performance.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a temperature control method for a handheld ultrasound device, comprising:

[0006] Collecting temperature data and corresponding equipment operating parameters of a handheld ultrasound device, and preprocessing the temperature data;

[0007] Constructing a first temperature prediction model according to the thermodynamic model and the equipment operating parameters, and fitting the coefficients of the first temperature prediction model according to the temperature data and the corresponding equipment operating parameters to obtain an optimal first temperature prediction model;

[0008] Constructing a second temperature prediction model, extracting features of the device operating parameters according to a natural language processing algorithm to generate a feature vector; and training the second temperature prediction model according to the feature vector and the temperature data;

[0009] The temperature change trend is predicted according to the first temperature prediction model and the second temperature prediction model; a heat dissipation strategy is matched according to the temperature change trend, and the operation of the handheld ultrasound device is controlled according to the heat dissipation strategy.

[0010] The present invention establishes a first temperature prediction model and a second temperature prediction model according to the working parameters and temperature data of a handheld ultrasound device, thereby predicting the temperature change trend according to the first temperature prediction model and the second temperature prediction model, improving the accuracy of temperature prediction, and matching a heat dissipation strategy according to the temperature change trend, thereby controlling the operation of the handheld ultrasound device according to the heat dissipation strategy to achieve a temperature control effect, controlling the handheld ultrasound device in advance through active temperature prediction, avoiding damage to the handheld ultrasound device due to excessive temperature, and improving the heat dissipation effect of the handheld ultrasound device.

[0011] Further, predicting the temperature change trend according to the first temperature prediction model and the second temperature prediction model is specifically as follows:

[0012] Determine the temperature prediction mode according to the application scenario and demand. If the application scenario and demand are high-precision prediction, the temperature prediction mode is associated prediction; if the application scenario and demand are low-resource prediction, the temperature prediction mode is independent prediction;

[0013] The temperature change trend is predicted according to the prediction mode, the first temperature prediction model and the second temperature prediction model.

[0014] Further, when the temperature prediction mode is association prediction, the second temperature prediction model is trained according to the feature vector and the temperature data, specifically:

[0015] Inputting the current device operating parameters of the device into the first temperature prediction model and the second temperature prediction model respectively to obtain a first prediction result and a second prediction result;

[0016] The first prediction result and the second prediction result are merged according to a preset weight coefficient to obtain a final temperature change trend.

[0017] Further, when the temperature prediction mode is independent prediction, the second temperature prediction model is trained according to the feature vector and the temperature data, specifically:

[0018] The current working parameters are input into any one of the first temperature prediction model and the second temperature prediction model to obtain the prediction result, thereby obtaining the temperature change trend according to the prediction result.

[0019] Furthermore, matching the heat dissipation strategy according to the temperature change trend is specifically:

[0020] The temperature of the handheld ultrasound device is divided into several temperature range levels, and a heat dissipation strategy is specified for each temperature range level according to the working parameters to generate a heat dissipation strategy table;

[0021] If the temperature change trend reaches a certain temperature range level within a preset time threshold starting from the current moment, then the heat dissipation strategy matches the temperature range level at the current moment.

[0022] In a second aspect, the present invention provides a temperature control device for a handheld ultrasound device, comprising: a data acquisition module, a first temperature prediction module, a second temperature prediction module and a temperature control module;

[0023] The data acquisition module is used to collect temperature data and corresponding equipment working parameters of the handheld ultrasound device, and pre-process the temperature data;

[0024] The first temperature prediction module is used to construct a first temperature prediction model according to the thermodynamic model and the equipment operating parameters, and to fit the coefficients of the first temperature prediction model according to the temperature data and the corresponding equipment operating parameters to obtain an optimal first temperature prediction model;

[0025] The second temperature prediction module is used to construct a second temperature prediction model, extract features of the device operating parameters according to a natural language processing algorithm to generate a feature vector; and train the second temperature prediction model according to the feature vector and the temperature data;

[0026] The temperature control module is used to predict the temperature change trend according to the first temperature prediction model and the second temperature prediction model; match the heat dissipation strategy according to the temperature change trend, and control the operation of the handheld ultrasound device according to the heat dissipation strategy.

[0027] Furthermore, the temperature control module is specifically used for:

[0028] Determine the temperature prediction mode according to the application scenario and demand. If the application scenario and demand are high-precision prediction, the temperature prediction mode is associated prediction; if the application scenario and demand are low-resource prediction, the temperature prediction mode is independent prediction;

[0029] The temperature change trend is predicted according to the prediction mode, the first temperature prediction model and the second temperature prediction model.

[0030] Further, when the temperature prediction mode is association prediction, the second temperature prediction model is trained according to the feature vector and the temperature data, specifically:

[0031] Inputting the current device operating parameters of the device into the first temperature prediction model and the second temperature prediction model respectively to obtain a first prediction result and a second prediction result;

[0032] The first prediction result and the second prediction result are merged according to a preset weight coefficient to obtain a final temperature change trend.

[0033] Further, when the temperature prediction mode is independent prediction, the second temperature prediction model is trained according to the feature vector and the temperature data, specifically:

[0034] The current working parameters are input into any one of the first temperature prediction model and the second temperature prediction model to obtain the prediction result, thereby obtaining the temperature change trend according to the prediction result.

[0035] Furthermore, the temperature control module is also specifically used for:

[0036] The temperature of the handheld ultrasound device is divided into several temperature range levels, and a heat dissipation strategy is specified for each temperature range level according to the working parameters to generate a heat dissipation strategy table;

[0037] If the temperature change trend reaches a certain temperature range level within a preset time threshold starting from the current moment, then the heat dissipation strategy matches the temperature range level at the current moment. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic flow chart of a temperature control method for a handheld ultrasonic device provided by an embodiment of the present invention;

[0039] Figure 2 A schematic structural diagram of a temperature control device for a handheld ultrasonic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] 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.

[0041] Embodiment 1

[0042] Please refer to Figure 1 , Figure 1 A schematic flow chart of a temperature control method for a handheld ultrasound device provided by an embodiment of the present invention includes steps 101 to 104, which are specifically as follows:

[0043] Step 101: collecting temperature data and corresponding equipment operating parameters of a handheld ultrasound device, and preprocessing the temperature data;

[0044] In this embodiment, a temperature sensor is pre-installed at a heating part of the handheld ultrasound device, and temperature data of the handheld ultrasound device is collected according to the temperature sensor. The heating part includes modules prone to high temperatures, such as FPGA, front-end transmitting / receiving module, WI FI module, transmitting high-voltage module and battery pack.

[0045] In this embodiment, after the temperature data is acquired, the temperature data needs to be pre-processed in real time, and the real-time processing includes filtering, noise reduction and smoothing, so as to obtain an accurate temperature value.

[0046] In this embodiment, the device operating parameters are collected in real time according to the internal interface of the handheld ultrasound device, and the device operating parameters include the transmitting voltage, imaging mode, pulse repetition frequency (PRF), number of cycles and frame rate (FPS). The collected device operating parameters are stored in a cache.

[0047] Step 102: constructing a first temperature prediction model according to the thermodynamic model and the equipment operating parameters, and fitting the coefficients of the first temperature prediction model according to the temperature data and the corresponding equipment operating parameters to obtain an optimal first temperature prediction model;

[0048] In this embodiment, a first temperature prediction model is constructed according to the thermodynamic model and the equipment operating parameters, and a weight coefficient α in the first temperature prediction model is adjusted according to the historical temperature data and the equipment operating parameters. i Perform fitting to obtain the optimal parameter combination.

[0049] In this embodiment, the first temperature prediction model is specifically:

[0050]

[0051] Where T(t) is the temperature data of the handheld ultrasound device at time t, T 0 is the initial temperature data, P i (t) is the value of the i-th working parameter at time t, α i is the corresponding weight coefficient.

[0052] Step 103: construct a second temperature prediction model, extract features of the device operating parameters according to a natural language processing algorithm to generate a feature vector; and train the second temperature prediction model according to the feature vector and the temperature data;

[0053] The historical temperature data of the handheld ultrasound device and the corresponding device operating parameters are cleaned and normalized, and the characteristics of the device operating parameters are extracted according to the natural language processing algorithm (NLP) to construct a second temperature prediction model. The historical temperature data and the corresponding device operating parameters are used as training data to train the second temperature prediction model. At the same time, the model parameters are optimized through the back propagation algorithm and the gradient descent method to minimize the prediction error.

[0054] In this embodiment, the second temperature prediction model is specifically:

[0055] y t =f(x t-1 ,x t-2 ,…,x t-n ) (2)

[0056] Among them, y t is the predicted value of the device temperature at time t, x t-1 ,x t-2 ,…,x t-n is the characteristic vector of the working parameters of the first n time steps, and f is the natural language processing algorithm function.

[0057] In this embodiment, the natural language processing algorithm includes a recurrent neural network (RNN) and a long short-term memory (LSTM) network.

[0058] Step 104: predicting the temperature change trend according to the first temperature prediction model and the second temperature prediction model; matching the heat dissipation strategy according to the temperature change trend, and controlling the operation of the handheld ultrasound device according to the heat dissipation strategy.

[0059] In this embodiment, the temperature change trend is predicted according to the first temperature prediction model and the second temperature prediction model, specifically:

[0060] Determine the temperature prediction mode according to the application scenario and demand. If the application scenario and demand are high-precision prediction, the temperature prediction mode is associated prediction; if the application scenario and demand are low-resource prediction, the temperature prediction mode is independent prediction;

[0061] The temperature change trend is predicted according to the prediction mode, the first temperature prediction model and the second temperature prediction model.

[0062] In this embodiment, the first temperature prediction model and the second temperature prediction model can be independent or interrelated, depending on the application scenario and requirements. Independent prediction is suitable for resource-constrained devices, does not require highly complex calculations and fusion processes, and is easy to implement and deploy. Associated prediction is suitable for scenarios with high requirements for temperature prediction accuracy, and improves the accuracy and robustness of predictions through the collaborative work of multiple methods.

[0063] In this embodiment, when the temperature prediction mode is an associated prediction, the temperature change trend is predicted according to the first temperature prediction model and the second temperature prediction model, specifically:

[0064] Inputting the current device operating parameters of the device into the first temperature prediction model and the second temperature prediction model respectively to obtain a first prediction result and a second prediction result;

[0065] The first prediction result and the second prediction result are merged according to a preset weight coefficient to obtain a final temperature change trend.

[0066] In this embodiment, the first prediction result and the second prediction result are fused according to a fusion algorithm to obtain a final prediction result, and the fusion algorithm includes a weighted average algorithm and a voting mechanism algorithm.

[0067] As a specific example of the present invention, the fusion algorithm is specifically:

[0068] Tfinal(t)=β1·Tinitial(t)+β2·TNLP(t) (3)

[0069] Among them, Tfinal(t) is the final temperature prediction result, Tinitial(t) is the first prediction result, TNLP(t) is the second prediction result, β1 and β2 are weight coefficients, satisfying β1+β2=1.

[0070] In this embodiment, when the prediction mode is independent prediction, the temperature change trend is predicted according to the first temperature prediction model and the second temperature prediction model, specifically:

[0071] The current working parameters are input into any one of the first temperature prediction model and the second temperature prediction model to obtain the prediction result, thereby obtaining the temperature change trend according to the prediction result.

[0072] In this embodiment, the first temperature prediction module provides a benchmark temperature change trend and calculates the temperature according to the physical characteristics of the handheld ultrasound device; while the second temperature prediction model uses more historical data and complex parameter relationships to provide a more accurate temperature change trend. In actual application scenarios, the prediction mode can be divided into associated prediction and independent prediction. When high precision is required for temperature prediction, the first temperature prediction model and the second temperature prediction model are deployed, and the temperature change trend is integrated according to the first temperature prediction model and the second temperature prediction model. When the resources for temperature prediction are limited, the temperature prediction can be performed by matching one of the first temperature prediction model and the second temperature prediction model according to the current resource limitations.

[0073] In this embodiment, the independent prediction matches the second temperature prediction model by default for temperature prediction. When the computing resources of the handheld ultrasound device are insufficient to support the deployment of the second temperature prediction model, the first temperature prediction model is matched for temperature prediction.

[0074] As a specific example of an embodiment of the present invention, the handheld ultrasound device is a high-precision prediction, and the prediction mode is an associated prediction. A preliminary prediction is performed through a first temperature prediction model, and a deep prediction is performed according to a second temperature prediction model, thereby integrating the results of the preliminary prediction and the deep prediction. Specifically:

[0075] In the preliminary prediction, the first temperature prediction model is used to predict that the device temperature will reach 45°C in the high-intensity working mode. In the deep prediction, the second temperature prediction model is used to analyze the historical data and current working parameters, and predicts that the device temperature will reach 46°C under the same conditions. The weighted average method is used to fuse them to obtain the final predicted temperature.

[0076] As a specific example of an embodiment of the present invention, if β1 is 0.5 and β2 is 0.5, the fusion prediction result is:

[0077] Tfinal=0.5·45+0.5·46=45.5℃ (4)

[0078] In this embodiment, by using two prediction models in association, the system can more accurately predict the temperature change of the equipment, provide more reliable temperature control measures, and ensure the safe and stable operation of the equipment.

[0079] Please refer to Table 1, which is a heat dissipation strategy table provided by an embodiment of the present invention.

[0080]

[0081] Table 1

[0082] In this embodiment, after obtaining the temperature change trend of the device over a period of time, a corresponding heat dissipation strategy is matched according to the temperature change trend to control the operation of the handheld ultrasound device, thereby achieving the purpose of temperature control.

[0083] In this embodiment, the heat dissipation strategy is matched according to the temperature change trend, specifically:

[0084] The temperature of the handheld ultrasound device is divided into several temperature range levels, and a heat dissipation strategy is specified for each temperature range level according to the working parameters to generate a heat dissipation strategy table;

[0085] If the temperature change trend reaches a certain temperature range level within a preset time threshold starting from the current moment, then the heat dissipation strategy matches the temperature range level at the current moment.

[0086] In this embodiment, the temperature is divided into several levels according to the handheld ultrasound temperature and the power consumption of the device, and different heat dissipation strategies are specified for each temperature range level. The heat dissipation strategies include starting external heat dissipation equipment, adjusting frequency, adjusting voltage, adjusting working modules, etc., for example, adjusting fan acceleration and stopping the operation of some modules.

[0087] In this embodiment, the heat dissipation strategy includes fan acceleration, reduced power consumption mode, dynamic frequency and voltage scaling (DVFS), stopping some modules from working, workload adjustment, and passive heat dissipation enhancement.

[0088] In this embodiment, the fan acceleration includes low speed mode, medium speed mode and high speed mode. Specifically, when the temperature reaches above 35°C and the power consumption is medium, the fan is started to run at low speed. When the temperature reaches above 35°C and the power consumption is high, the fan speed is increased to medium speed mode. When the temperature reaches above 40°C, the fan enters high speed mode to maximize the heat dissipation efficiency.

[0089] In this embodiment, the power consumption reduction mode includes reducing frequency and voltage. Specifically, when the temperature exceeds 40°C and the power consumption is high, the operating frequency of the processor and GPU is reduced to reduce heat. When the temperature exceeds 45°C, the operating voltage of the processor and GPU is reduced to further reduce power consumption and heat.

[0090] In this embodiment, dynamic frequency and voltage scaling (DVFS) includes dynamically adjusting the frequency and voltage of the processor and GPU to achieve a balance between performance and heat dissipation when the temperature is high.

[0091] In this embodiment, stopping the operation of some modules includes stopping the operation of non-critical modules, such as Wi-Fi and Bluetooth modules, when the temperature reaches above 50° C., so as to reduce overall power consumption and heat generation.

[0092] In this embodiment, workload adjustment includes adjusting the workload of the device when the temperature reaches a preset threshold, such as reducing the frame rate of image processing, reducing the data transmission rate, etc., to reduce overall power consumption.

[0093] In this embodiment, the passive heat dissipation enhancement includes utilizing the thermal conductivity of the device housing and the heat sink to dissipate heat naturally when the temperature reaches a preset threshold.

[0094] Please refer to Table 2, which is a temperature threshold setting table provided in an embodiment of the present invention.

[0095] parameter Settings describe Initial temperature (T_0) 25℃ Ambient temperature when the device is started Warning threshold 43℃ Alert users to critical temperature rises Protection threshold 60℃ Critical value for automatically suspending equipment operation Recovery temperature 35℃ Temperature threshold for device restart

[0096] Table 2

[0097] In this embodiment, the device is automatically suspended when the temperature reaches a dangerous threshold. According to the safety specifications of the device, a temperature protection threshold (such as 60°C) is set, and the current temperature is compared with the protection threshold in real time. When the temperature exceeds the protection threshold, the device is automatically suspended; and the device temperature is monitored. When the temperature drops to a safe range (such as 35°C), the device is allowed to restart.

[0098] In this embodiment, when the temperature reaches the preset alarm threshold, an early warning prompt is issued to the user, including: setting a temperature early warning threshold (such as 43°C) according to the safety use specifications of the device, comparing the predicted temperature with the early warning threshold in real time, and triggering an early warning when the predicted temperature approaches or exceeds the threshold. The user is reminded that the device temperature is too high through sound, vibration or screen prompts, and the user is advised to suspend use or take cooling measures.

[0099] Please refer to Figure 2 , Figure 2 A schematic structural diagram of a temperature control device for a handheld ultrasonic device provided by an embodiment of the present invention includes: a data acquisition module 201, a first temperature prediction module 202, a second temperature prediction module 203 and a temperature control module 204;

[0100] The data acquisition module 201 is used to collect temperature data and corresponding device operating parameters of the handheld ultrasound device and pre-process the temperature data;

[0101] The first temperature prediction module 202 is used to construct a first temperature prediction model according to the thermodynamic model and the equipment operating parameters, and to fit the coefficients of the first temperature prediction model according to the temperature data and the corresponding equipment operating parameters to obtain an optimal first temperature prediction model;

[0102] The second temperature prediction module 203 is used to construct a second temperature prediction model, extract features of the device operating parameters according to a natural language processing algorithm to generate a feature vector; and train the second temperature prediction model according to the feature vector and the temperature data;

[0103] The temperature control module 204 is used to predict the temperature change trend according to the first temperature prediction model and the second temperature prediction model; match the heat dissipation strategy according to the temperature change trend, and control the operation of the handheld ultrasound device according to the heat dissipation strategy.

[0104] In this embodiment, the temperature control module is specifically used for:

[0105] Determine the temperature prediction mode according to the application scenario and demand. If the application scenario and demand are high-precision prediction, the temperature prediction mode is associated prediction; if the application scenario and demand are low-resource prediction, the temperature prediction mode is independent prediction;

[0106] The temperature change trend is predicted according to the prediction mode, the first temperature prediction model and the second temperature prediction model.

[0107] In this embodiment, when the temperature prediction mode is association prediction, the second temperature prediction model is trained according to the feature vector and the temperature data, specifically:

[0108] Inputting the current device operating parameters of the device into the first temperature prediction model and the second temperature prediction model respectively to obtain a first prediction result and a second prediction result;

[0109] The first prediction result and the second prediction result are merged according to a preset weight coefficient to obtain a final temperature change trend.

[0110] In this embodiment, when the temperature prediction mode is independent prediction, the second temperature prediction model is trained according to the feature vector and the temperature data, specifically:

[0111] The current working parameters are input into any one of the first temperature prediction model and the second temperature prediction model to obtain the prediction result, thereby obtaining the temperature change trend according to the prediction result.

[0112] In this embodiment, the temperature control module is further used for:

[0113] The temperature of the handheld ultrasound device is divided into several temperature range levels, and a heat dissipation strategy is specified for each temperature range level according to the working parameters to generate a heat dissipation strategy table;

[0114] If the temperature change trend reaches a certain temperature range level within a preset time threshold starting from the current moment, then the heat dissipation strategy matches the temperature range level at the current moment.

[0115] In this embodiment, a first temperature prediction model and a second temperature prediction model are established according to the working parameters and temperature data of the handheld ultrasound device, so that the temperature change trend is predicted according to the first temperature prediction model and the second temperature prediction model, the accuracy of temperature prediction is improved, and the heat dissipation strategy is matched according to the temperature change trend, so that the operation of the handheld ultrasound device is controlled according to the heat dissipation strategy to achieve the effect of temperature control. The handheld ultrasound device is controlled in advance through active temperature prediction to avoid damage to the handheld ultrasound device due to excessive temperature, thereby improving the heat dissipation effect of the handheld ultrasound device.

[0116] An embodiment of the present invention also provides a handheld ultrasound device temperature control system, including a temperature monitoring module, a working parameter monitoring module, a temperature prediction module, a temperature prediction module based on an NLP algorithm, a temperature warning module, an automatic protection module, a heat dissipation control module and a user interface module.

[0117] In this embodiment, the temperature monitoring module is responsible for real-time monitoring of the temperature inside the palm ultrasound device, and the working parameter monitoring module is responsible for monitoring various working parameters of the palm ultrasound device, including emission voltage, imaging mode, pulse repetition frequency (PRF), number of cycles, frame rate (FPS), etc.; the temperature prediction module predicts the temperature change trend of the device by analyzing the current temperature data and working parameters according to thermodynamic characteristics; the temperature prediction module based on the natural language processing (NLP) algorithm predicts the temperature change trend by analyzing the historical temperature data and working parameter sequence of the device; the temperature warning module reminds the user when the temperature approaches the preset threshold according to the temperature prediction result; the automatic protection module automatically suspends the operation of the device when the temperature reaches the dangerous threshold; the heat dissipation control module controls the heat dissipation system of the device when the temperature approaches the warning or protection threshold to improve the heat dissipation efficiency; the user interface module displays the real-time temperature and working status of the device through a graphical interface, displays the real-time temperature, working parameters and warning information of the device, and updates the temperature and working status information on the interface in real time to ensure that the user can understand the operating status of the device in time.

[0118] In this embodiment, the temperature change trend is predicted by real-time monitoring of the internal temperature of the handheld ultrasound device and combining the device's operating parameters. When the temperature approaches or reaches a preset threshold, the system can promptly remind the user and take measures such as automatic suspension of work when necessary to ensure the safe operation of the device and the safety of the user.

[0119] In this embodiment, a first temperature prediction model and a second temperature prediction model are established according to the working parameters and temperature data of the handheld ultrasound device, so that the temperature change trend is predicted according to the first temperature prediction model and the second temperature prediction model, the accuracy of temperature prediction is improved, and the heat dissipation strategy is matched according to the temperature change trend, so that the operation of the handheld ultrasound device is controlled according to the heat dissipation strategy to achieve the effect of temperature control. The handheld ultrasound device is controlled in advance through active temperature prediction to avoid damage to the handheld ultrasound device due to excessive temperature, thereby improving the heat dissipation effect of the handheld ultrasound device.

[0120] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A temperature control method for a handheld ultrasonic device, characterized in that: include: Collecting temperature data and corresponding equipment operating parameters of a handheld ultrasound device, and preprocessing the temperature data; Constructing a first temperature prediction model according to the thermodynamic model and the equipment operating parameters, and fitting the coefficients of the first temperature prediction model according to the temperature data and the corresponding equipment operating parameters to obtain an optimal first temperature prediction model; Constructing a second temperature prediction model, extracting features of the device operating parameters according to a natural language processing algorithm to generate a feature vector; and training the second temperature prediction model according to the feature vector and the temperature data; Predicting the temperature change trend according to the first temperature prediction model and the second temperature prediction model; specifically: determining the temperature prediction mode according to the application scenario and demand, if the application scenario and demand are high-precision predictions, the temperature prediction mode is associated prediction; if the application scenario and demand are low-resource predictions, the temperature prediction mode is independent prediction; predicting the temperature change trend according to the prediction mode, the first temperature prediction model and the second temperature prediction model; and matching the heat dissipation strategy according to the temperature change trend, and controlling the operation of the handheld ultrasound device according to the heat dissipation strategy.

2. A handheld ultrasound device temperature control method as claimed in claim 1, characterized in that: When the temperature prediction mode is association prediction: Inputting the current device operating parameters of the device into the first temperature prediction model and the second temperature prediction model respectively to obtain a first prediction result and a second prediction result; The first prediction result and the second prediction result are merged according to a preset weight coefficient to obtain a final temperature change trend.

3. A handheld ultrasound device temperature control method as claimed in claim 1, characterized in that: When the temperature prediction mode is independent prediction: The current working parameters are input into any one of the first temperature prediction model and the second temperature prediction model to obtain the prediction result, thereby obtaining the temperature change trend according to the prediction result.

4. A handheld ultrasound device temperature control method according to any one of claims 1 to 3, characterized in that: The heat dissipation strategy is matched according to the temperature change trend, specifically: The temperature of the handheld ultrasound device is divided into several temperature range levels, and a heat dissipation strategy is specified for each temperature range level according to the working parameters to generate a heat dissipation strategy table; If the temperature change trend reaches a certain temperature range level within a preset time threshold starting from the current moment, then the heat dissipation strategy matches the temperature range level at the current moment.

5. A temperature control device for a handheld ultrasonic device, characterized in that: include: A data acquisition module, a first temperature prediction module, a second temperature prediction module and a temperature control module; The data acquisition module is used to collect temperature data and corresponding equipment working parameters of the handheld ultrasound device, and pre-process the temperature data; The first temperature prediction module is used to construct a first temperature prediction model according to the thermodynamic model and the equipment operating parameters, and to fit the coefficients of the first temperature prediction model according to the temperature data and the corresponding equipment operating parameters to obtain an optimal first temperature prediction model; The second temperature prediction module is used to construct a second temperature prediction model, extract features of the device operating parameters according to a natural language processing algorithm to generate a feature vector; and train the second temperature prediction model according to the feature vector and the temperature data; The temperature control module is used to predict the temperature change trend according to the first temperature prediction model and the second temperature prediction model; specifically: determine the temperature prediction mode according to the application scenario and demand, if the application scenario and demand are high-precision prediction, the temperature prediction mode is associated prediction; if the application scenario and demand are low-resource prediction, the temperature prediction mode is independent prediction; predict the temperature change trend according to the prediction mode, the first temperature prediction model and the second temperature prediction model; and match the heat dissipation strategy according to the temperature change trend, and control the operation of the handheld ultrasound device according to the heat dissipation strategy.

6. A handheld ultrasonic equipment temperature control device as claimed in claim 5, characterized in that: When the temperature prediction mode is association prediction: Inputting the current device operating parameters of the device into the first temperature prediction model and the second temperature prediction model respectively to obtain a first prediction result and a second prediction result; The first prediction result and the second prediction result are merged according to a preset weight coefficient to obtain a final temperature change trend.

7. A handheld ultrasonic equipment temperature control device as claimed in claim 5, characterized in that: When the temperature prediction mode is independent prediction: The current working parameters are input into any one of the first temperature prediction model and the second temperature prediction model to obtain the prediction result, thereby obtaining the temperature change trend according to the prediction result.

8. A handheld ultrasonic equipment temperature control device according to any one of claims 5 to 7, characterized in that: The temperature control module is further specifically used for: The temperature of the handheld ultrasound device is divided into several temperature range levels, and a heat dissipation strategy is specified for each temperature range level according to the working parameters to generate a heat dissipation strategy table; If the temperature change trend reaches a certain temperature range level within a preset time threshold starting from the current moment, then the heat dissipation strategy matches the temperature range level at the current moment.

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