Temperature detection and compensation method for glucometer and related equipment

Abstract

The embodiment of the invention belongs to the field of glucometers, and relates to a temperature detection and compensation method for a glucometer, which comprises the following steps: acquiring temperature data from a detection NTC (Negative Temperature Coefficient) sensor, a reference NTC sensor and an environment sensor respectively; preprocessing the temperature data, wherein the preprocessing comprises de-noising processing, smoothing processing and abnormal value correction processing; performing data fusion on the temperature data, and calculating a temperature estimation value of the blood glucose module; calculating the temperature difference between the temperature of the reference NTC sensor and the temperature of the detection NTC sensor, and dynamically adjusting a compensation coefficient based on the temperature difference; and calculating the actual working temperature of the blood glucose module according to the temperature estimation value and the compensation coefficient. The invention further provides a temperature detection and compensation system for the glucometer, computer equipment and a readable storage medium. The accuracy of temperature estimation is improved.

Description

Technical Field

[0001] The present application relates to the technical field of blood glucose meters, and in particular to a temperature detection and compensation method and related equipment for blood glucose meters. Background Art

[0002] Blood glucose meter is an important tool for monitoring blood glucose level, and its accuracy directly affects the health management of patients. Blood glucose measurement technology relies on enzyme electrodes, optical sensors and other technologies, which are very sensitive to temperature changes. Temperature changes may affect the rate of enzyme reaction, the efficiency of reagent reaction and the generation of electrochemical signals, thus leading to deviations in blood glucose measurement results.

[0003] At present, blood glucose meters generally use a single temperature sensor (such as an NTC sensor) to monitor the working environment temperature. In some high-power working modes, the core components of the blood glucose module (such as the communication module and the processor) may generate a lot of heat, resulting in inaccurate internal temperature measurement. The NTC sensor is usually located inside the module and may be affected by the heat generated by the internal components, resulting in the measured temperature being too high or too low, affecting the accuracy of temperature compensation. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a temperature detection and compensation method for a blood glucose meter, a temperature detection and compensation system for a blood glucose meter, a computer device and a storage medium to solve the technical problem of low temperature compensation accuracy of the blood glucose meter.

[0005] In order to solve the above technical problems, the embodiment of the present application provides a temperature detection and compensation method for a blood glucose meter, which adopts the following technical solution:

[0006] A temperature detection and compensation method for a blood glucose meter is applied to a temperature detection and compensation system. The temperature detection and compensation system includes a detection NTC sensor, a reference NTC sensor, an environmental sensor, and a blood glucose module. The method includes the following steps:

[0007] Acquire temperature data from the detection NTC sensor, the reference NTC sensor, and the environmental sensor respectively;

[0008] Preprocessing the temperature data, including denoising, smoothing and outlier correction;

[0009] Performing data fusion on the temperature data to calculate a temperature estimation value of a blood glucose module;

[0010] Calculating a temperature difference between the reference NTC sensor temperature and the detection NTC sensor temperature, and dynamically adjusting a compensation coefficient based on the temperature difference;

[0011] The actual operating temperature of the blood glucose module is calculated according to the temperature estimation value and the compensation coefficient.

[0012] In a possible implementation, the step of performing data fusion on the temperature data and calculating the temperature estimation value of the blood glucose module specifically includes:

[0013] Establish respective measurement models for the detection NTC sensor, reference NTC sensor and environmental sensor;

[0014] The linear model is used to calculate the temperature estimation values ​​of the detection NTC sensor, the reference NTC sensor and the environmental sensor at time k respectively;

[0015] The temperature estimation value of the blood glucose module at time k is predicted according to the temperature estimation value and the error covariance matrix.

[0016] In a possible implementation manner, after the step of predicting the temperature estimation value of the blood glucose module at time k according to the temperature estimation value and the error covariance matrix, the step further includes:

[0017] Weighted fusion detection of temperature estimates of NTC sensors, reference NTC sensors, and ambient sensors;

[0018] Update the temperature estimate of the blood glucose module at time k according to the fused temperature estimate and gain coefficient.

[0019] In a possible implementation manner, the step of calculating the temperature difference between the reference NTC sensor temperature and the detection NTC sensor, and dynamically adjusting the compensation coefficient based on the temperature difference specifically includes:

[0020] Calculate the temperature difference between the reference NTC sensor temperature and the detection NTC sensor temperature;

[0021] Based on the temperature difference, adjusting the temperature reading of the detection NTC sensor by a preset compensation algorithm;

[0022] Calibrate the temperature value of the detection NTC sensor;

[0023] The compensated temperature value of the detected NTC sensor is used as an input value through the Kalman filter algorithm, and data fusion is performed again to obtain a new temperature estimation value of the blood glucose module.

[0024] In a possible implementation, in the step of calculating the temperature difference between the reference NTC sensor temperature and the detection NTC sensor, and dynamically adjusting the compensation coefficient based on the temperature difference, the compensation coefficient is adjusted in real time according to the working state, environmental conditions and temperature change rate.

[0025] In order to solve the above technical problems, the embodiment of the present application further provides a temperature detection and compensation system for a blood glucose meter, which adopts the following technical solution:

[0026] A temperature detection and compensation system for a blood glucose meter, comprising:

[0027] An acquisition module, used to acquire temperature data from the detection NTC sensor, the reference NTC sensor, and the environmental sensor respectively;

[0028] A processing module, used for preprocessing the temperature data, including denoising, smoothing and outlier correction;

[0029] A fusion module, used for performing data fusion on the temperature data and calculating a temperature estimation value of a blood glucose module;

[0030] A compensation module, configured to calculate a temperature difference between the reference NTC sensor temperature and the detection NTC sensor temperature, and dynamically adjust a compensation coefficient based on the temperature difference;

[0031] A calculation module is used to calculate the actual operating temperature of the blood glucose module according to the temperature estimation value and the compensation coefficient.

[0032] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0033] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the temperature detection and compensation method for a blood glucose meter as described above are implemented.

[0034] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0035] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the temperature detection and compensation method for a blood glucose meter as described above.

[0036] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0037] The temperature detection and compensation method for a blood glucose meter disclosed in the present application obtains temperature data from the detection NTC sensor, the reference NTC sensor, and the environmental sensor respectively; pre-processes the temperature data, including denoising, smoothing, and outlier correction; performs data fusion on the temperature data to calculate the temperature estimate of the blood glucose module; calculates the temperature difference between the reference NTC sensor temperature and the detection NTC sensor temperature, and dynamically adjusts the compensation coefficient based on the temperature difference; calculates the actual operating temperature of the blood glucose module according to the temperature estimate and the compensation coefficient. The present application can greatly improve the accuracy of temperature estimation by using the data of the detection NTC sensor, the reference NTC sensor, and the environmental sensor at the same time. The complementary effect of different sensors effectively reduces the error of a single sensor, especially under complex environmental conditions, ensuring the stability and reliability of the temperature estimate. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0040] Figure 2 is a flow chart of an embodiment of a temperature detection and compensation method for a blood glucose meter according to the present application;

[0041] Figure 3 is a structural schematic diagram of an embodiment of a temperature detection and compensation system for a blood glucose meter according to the present application;

[0042] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0044] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0045] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0046] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0047] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0048] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, etc.

[0049] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0050] It should be noted that the temperature detection and compensation method for the blood glucose meter provided in the embodiment of the present application is generally executed by a server, and accordingly, the temperature detection and compensation system for the blood glucose meter is generally arranged in the server.

[0051] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0052] Continue to refer Figure 2 , shows a flow chart of an embodiment of a temperature detection and compensation method for a blood glucose meter according to the present application. The temperature detection and compensation method for a blood glucose meter comprises the following steps:

[0053] Step S201, acquiring temperature data from the detection NTC sensor, the reference NTC sensor, and the environmental sensor respectively.

[0054] In this embodiment, the temperature detection and compensation method for the blood glucose meter is executed on an electronic device (eg Figure 1 The server shown in the figure) can send or receive data through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0055] In this embodiment, the detection NTC sensor is usually installed inside the blood glucose module to measure the internal temperature of the module, which may be affected by factors such as internal heat in the module; the reference NTC sensor is used to measure the ambient temperature and is usually used as a calibration temperature reference to help identify deviations of the detection NTC sensor; the environmental sensor is used to measure the external ambient temperature, such as air temperature, etc., to help more accurately reflect the impact of external temperature on device temperature.

[0056] Step S202, preprocessing the temperature data, including denoising, smoothing and outlier correction.

[0057] In this embodiment, the acquired sensor data is preprocessed to ensure the quality of the data. The preprocessing steps include:

[0058] De-noising: Remove high-frequency noise from sensor data to ensure data smoothness and accuracy. Low-pass filtering or other denoising methods are usually used.

[0059] Smoothing: Reduce temperature fluctuations or instantaneous mutations in the data. Especially when the temperature changes dramatically, smoothing helps to remove abnormal fluctuations.

[0060] Outlier correction: Abnormal data caused by sensor failure or external interference are eliminated and corrected to ensure that the data used is reliable and represents the real environment.

[0061] Step S203, performing data fusion on the temperature data to calculate the temperature estimation value of the blood glucose module.

[0062] In this embodiment, data fusion technology is used to combine temperature data from different sensors to obtain an optimized temperature estimate. Data fusion algorithms such as Kalman filtering are used here. By processing data from multiple sensors, the error of a single sensor can be effectively suppressed and the accuracy of temperature estimation can be optimized. The fusion of sensor data is usually based on their respective measurement noise and accuracy, and the system assigns different weights to different sensors. Methods such as Kalman filtering play a key role here, and the temperature estimate can be updated according to time. The fused temperature estimate is the current ambient temperature of the device, which has taken into account the measurement errors and noise of all sensors, so that it can more accurately reflect the actual working ambient temperature of the device.

[0063] Step S204, calculating the temperature difference between the reference NTC sensor temperature and the detection NTC sensor temperature, and dynamically adjusting the compensation coefficient based on the temperature difference.

[0064] In this embodiment, the temperature difference can be used as a basis for adjusting the compensation coefficient. Since the detection NTC sensor may be affected by the internal heating of the device, its measured value may deviate from the actual ambient temperature. Therefore, by comparing the temperature difference between the detection NTC sensor and the reference NTC sensor, the compensation coefficient can be dynamically adjusted. The reference NTC sensor is relatively less affected by the heating of the device, so its measured value is closer to the actual ambient temperature. The calculated temperature difference is used to adjust the compensation coefficient. Specifically, the compensation coefficient will be dynamically adjusted as the operating status and internal temperature of the device change. This coefficient helps to correct the temperature estimate from a theoretical value to an actual operating temperature value, thereby achieving more accurate temperature compensation.

[0065] Step S205, calculating the actual operating temperature of the blood glucose module according to the temperature estimation value and the compensation coefficient.

[0066] In this embodiment, by combining the temperature estimation value and the dynamically adjusted compensation coefficient, the actual operating temperature of the blood glucose module is finally calculated. The operating temperature of the blood glucose module is a key factor affecting its performance and accuracy, so it is very important to accurately measure and compensate for the temperature error. The actual temperature after compensation can better reflect the real temperature of the blood glucose module in different environments, thereby improving the accuracy of temperature compensation and avoiding the influence of temperature deviation on the measurement results.

[0067] The present application can greatly improve the accuracy of temperature estimation by simultaneously using the data of the detection NTC sensor, the reference NTC sensor and the environmental sensor. The complementary effect of different sensors effectively reduces the error of a single sensor, especially under complex environmental conditions, and ensures the stability and reliability of the temperature estimation value. By dynamically adjusting the compensation coefficient, the compensation effect can be optimized in real time according to different working states and environmental conditions, eliminating temperature deviations caused by sensor errors or equipment heating, thereby improving the accuracy of temperature compensation of the blood glucose meter.

[0068] In some optional implementations of this embodiment, the step of fusing the temperature data to calculate the temperature estimation value of the blood glucose module specifically includes:

[0069] Establish respective measurement models for the detection NTC sensor, reference NTC sensor and environmental sensor;

[0070] The linear model is used to calculate the temperature estimation values ​​of the detection NTC sensor, the reference NTC sensor and the environmental sensor at time k respectively;

[0071] The temperature estimation value of the blood glucose module at time k is predicted according to the temperature estimation value and the error covariance matrix.

[0072] In this embodiment, the NTC sensor is used to measure the temperature inside the device, which is usually affected by factors such as device heating. Its measurement model can be simplified as:

[0073] z NTC (k) = H NTC ·x(k)+v NTC (k)

[0074] Where: z NTC (k) is the temperature value measured from the detection NTC sensor at the kth moment;

[0075] H NTC It is the observation matrix for detecting the NTC sensor. Usually it can be the unit matrix, which means that the temperature directly reflects the target temperature.

[0076] x(k) is the actual temperature of the device (i.e., the operating temperature of the blood glucose module).

[0077] v NTC (k) is the measurement noise, which represents the error from the sensor or external interference.

[0078] The reference NTC sensor measures the ambient temperature and is less affected by the internal heating of the device. Its measurement model is similar to the detection NTC sensor, but the error in measuring the ambient temperature is usually smaller:

[0079] zref (k) = H ref ·x(k)+v ref (k)

[0080] Where: z ref (k) is the temperature value measured from the reference NTC sensor at the kth moment.

[0081] H ref is the observation matrix of the reference NTC sensor, which is also usually the identity matrix.

[0082] v ref (k) is the measurement noise of the reference sensor.

[0083] The environmental sensor measures the external ambient temperature, which usually directly reflects the temperature of the surrounding environment, but also has a certain amount of noise. Its model is:

[0084] z env (k) = H env ·x(k)+v env (k)

[0085] Where: z env (k) is the temperature value measured from the environmental sensor at the kth moment.

[0086] H env is the observation matrix of the environmental sensor, which is also usually the identity matrix.

[0087] v env (k) is the measurement noise of the environmental sensor.

[0088] Next, the prediction-update step in the Kalman filter algorithm is used to calculate temperature estimates for these sensors.

[0089] In the Kalman filter framework, the following spatial model is used:

[0090] State transfer equation (describing temperature change):

[0091] x(k)=A·x(k-1)+B·u(k)+w(k)

[0092] x(k) is the temperature state estimate at the kth moment (i.e., the current temperature of the device).

[0093] A is the state transition matrix, which describes the change of temperature over time.

[0094] u(k) is the control input and usually includes factors such as external ambient temperature or equipment load.

[0095] w(k) is the process noise.

[0096] Observation equation (describing the relationship between temperature and sensor data):

[0097] z NTC (k) = H NTC ·x(k)+v NTC (k)

[0098] z ref (k) = H ref ·x(k)+v ref (k)

[0099] z env (k) = H env ·x(k)+v env (k)

[0100] In the Kalman framework, an estimate of the temperature is obtained from the measurements of these sensors over multiple iterations.

[0101] Predicted temperature estimate (based on the temperature estimate at the previous moment and the input at the current moment):

[0102]

[0103] in, is a predicted value based on the temperature estimate at the previous moment.

[0104] Then, measure the residuals (the difference between the actual measurements and the predicted values):

[0105]

[0106] Kalman gain (trade-off between contribution of predicted temperature and sensor measurement):

[0107] K k =P pred ·H T ·(H·P pred ·H T +R) -1

[0108] Update temperature estimate (comprehensive prediction value, update temperature estimate):

[0109]

[0110] Among them, P pred is the prediction error covariance matrix, and R is the covariance matrix of measurement noise, which reflects the measurement accuracy of different sensors.

[0111] After obtaining the temperature estimates of each sensor, we can predict the temperature estimate of the blood glucose module based on these estimates. By combining the temperature estimates of each sensor and using the Kalman filter algorithm, we finally get the optimized temperature estimate of the blood glucose module.

[0112] The process is similar to the previous steps and is as follows:

[0113] 1. Predict the temperature of the blood sugar module at the current moment:

[0114]

[0115] 2. Update based on sensor measurement data: Combine the measurement data of the detection NTC, reference NTC and environmental sensors to finally get the optimized blood glucose module temperature estimate.

[0116] By considering the error covariance of each sensor data, the present application can effectively reduce the temperature fluctuations caused by measurement noise and errors, thereby providing a more accurate temperature estimate. Especially in the process of fusing multi-sensor data, Kalman filtering helps to reduce the deviation between sensors and improve the accuracy of temperature estimation.

[0117] In some optional implementations of this embodiment, after the step of predicting the temperature estimation value of the blood glucose module at time k according to the temperature estimation value and the error covariance matrix, the method further includes:

[0118] Weighted fusion detection of temperature estimates of NTC sensors, reference NTC sensors, and ambient sensors;

[0119] Update the temperature estimate of the blood glucose module at time k according to the fused temperature estimate and gain coefficient.

[0120] In this embodiment, the purpose of weighted fusion is to combine the measurement results from the detection NTC sensor, the reference NTC sensor and the environmental sensor to obtain a more accurate and comprehensive temperature estimate. Weighted fusion takes into account the measurement error and accuracy of each sensor, converts it into a weight, and gives priority to more reliable sensor data.

[0121] First, get a temperature estimate for each sensor:

[0122] The temperature estimation value of the NTC sensor is detected at the kth time.

[0123] The temperature estimate of the reference NTC sensor at time k.

[0124] The estimated temperature of the ambient sensor at the kth moment.

[0125] Next, calculate the error covariance of each sensor: In the Kalman filter, each sensor's temperature estimate has a corresponding error covariance matrix These covariance matrices reflect the measurement uncertainty of the individual sensors.

[0126] Detect the error covariance of the NTC sensor.

[0127] Error covariance of the reference NTC sensor.

[0128] Error covariance of environmental sensors.

[0129] Then, calculate the weighting coefficients of the sensors: Based on the error covariance matrix of each sensor, the weighting coefficients can be calculated Sensors with smaller error covariance will be given larger weights, and vice versa. The weighting coefficient is calculated as:

[0130]

[0131] Similarly:

[0132]

[0133]

[0134] This means that the weight of a sensor is inversely proportional to its measurement uncertainty (i.e. error covariance). The smaller the error, the greater its weight and contribution.

[0135] Finally, weighted fusion temperature estimate: Use the weighting coefficient to weight the temperature estimate of each sensor to obtain the fused temperature estimate

[0136]

[0137] in, It is the weighted fusion temperature estimate, which represents the comprehensive temperature estimate obtained by combining all sensor data.

[0138] The weighted fusion temperature estimate and gain coefficient are combined: according to the fused temperature estimate and the gain coefficient K calculated in the current Kalman filter step k , the temperature estimation value of the blood glucose module can be updated. Through the gain coefficient, the final temperature estimation value of the blood glucose module can better integrate all sensor data and obtain a more accurate estimation.

[0139] Update formula:

[0140]

[0141] in:

[0142] is the updated temperature estimate of the blood glucose module.

[0143] is the predicted temperature estimate.

[0144] It is the weighted fusion temperature estimate that combines the data from the detection NTC sensor, the reference NTC sensor, and the ambient sensor.

[0145] K k is the Kalman gain coefficient, which determines how the predicted value is combined with the fused temperature estimate.

[0146] Through the combination of the above-mentioned weighted fusion and gain coefficient, the final temperature estimate will be more accurate, reducing the temperature estimate deviation caused by the error of a single sensor. The temperature estimate will be used as the actual operating temperature value of the blood glucose module for the subsequent temperature compensation step. The specific compensation coefficient will be adjusted through further algorithms (for example, by calculating the difference with the reference temperature), and ultimately used to improve the accuracy of the blood glucose meter measurement results.

[0147] Through weighted fusion, the present application can comprehensively process the sensor data according to the weights of different sensors to obtain a more accurate temperature estimate. By appropriately adjusting the gain coefficient, the influence of different sensors in the final estimate can be dynamically adjusted according to their accuracy and reliability, thereby improving the adaptability and accuracy of the system.

[0148] In some optional implementations of this embodiment, the step of calculating the temperature difference between the reference NTC sensor temperature and the detection NTC sensor, and dynamically adjusting the compensation coefficient based on the temperature difference specifically includes:

[0149] Calculate the temperature difference between the reference NTC sensor temperature and the detection NTC sensor temperature;

[0150] Based on the temperature difference, adjusting the temperature reading of the detection NTC sensor by a preset compensation algorithm;

[0151] Calibrate the temperature value of the detection NTC sensor;

[0152] The compensated temperature value of the detected NTC sensor is used as an input value through the Kalman filter algorithm, and data fusion is performed again to obtain a new temperature estimation value of the blood glucose module.

[0153] In this embodiment, the core of this step is to calculate the temperature difference between the reference NTC sensor and the detection NTC sensor by comparing their temperatures, which is the basis of temperature compensation, because the temperature difference between the two sensors can be used to determine the temperature deviation caused by factors such as sensor position and internal heating. After obtaining the temperature difference between the reference NTC and the detection NTC, a preset compensation algorithm is used to adjust the temperature reading of the detection NTC sensor. The compensation algorithm dynamically adjusts the sensor temperature according to the temperature difference to eliminate errors caused by changes in the external environment or internal heating of the sensor. The specific implementation of the compensation algorithm may be based on a linear relationship, a nonlinear relationship of the temperature difference, or a compensation model obtained based on actual device testing. The calibration step ensures that the compensated temperature value accurately reflects the temperature of the current environment. Typically, the calibration process compares the measured value of the sensor with a known standard temperature value and adjusts the output of the sensor. The calibration process may be completed through device standardization testing to ensure that the compensated reading is as close to the actual temperature as possible. The data fusion is re-performed through the Kalman filter algorithm, with the aim of further optimizing the temperature estimate of the blood glucose module. The Kalman filter step here uses the compensated detected NTC sensor temperature value as the new input data and combines it with the data from other sensors (such as the reference NTC sensor and the environmental sensor) for weighted fusion. The re-data fusion process combines the compensated and calibrated temperature readings (through a weighted fusion algorithm) with other information such as the ambient temperature to obtain a more accurate estimate of the blood glucose module temperature.

[0154] This application can optimize the temperature reading of the NTC sensor in real time according to the actual working status and environmental conditions of the equipment by dynamically adjusting the compensation coefficient, thereby improving the accuracy of temperature compensation.

[0155] In some optional implementations of the present embodiment, in the step of calculating the temperature difference between the reference NTC sensor temperature and the detection NTC sensor, and dynamically adjusting the compensation coefficient based on the temperature difference, the compensation coefficient is adjusted in real time according to the working state, environmental conditions and temperature change rate.

[0156] In this embodiment, the working state refers to the operating state of the blood glucose meter in different working scenarios, such as whether the device is running for a long time, whether it is running in a high-load mode, and whether there is external interference (such as temperature changes, device switches, etc.). These states will affect the temperature change of the device, so the compensation coefficient needs to be adjusted accordingly. For example, if the blood glucose meter is in a high-load working state, the temperature value of the sensor may be high due to internal heating. Therefore, the compensation coefficient may need to be increased to adapt to the temperature change under high load. On the contrary, if the device is in an idle state, the temperature change is small, and the compensation coefficient can be appropriately reduced. Environmental conditions refer to factors such as temperature, humidity, and air pressure in the external environment where the device is located. Changes in ambient temperature will directly affect the temperature reading of the sensor, and the temperature change characteristics under different environmental conditions are different. Therefore, the compensation coefficient also needs to be adjusted in real time according to these changes. For example, if the device works in an environment with a large temperature difference (such as rapid changes between laboratory temperature and high temperature environment), the temperature change rate is fast, which may cause the sensor reading to fluctuate greatly. In this case, the compensation coefficient needs to be dynamically adjusted to cope with the rapidly changing environment. If the device works in an environment with slow temperature changes, the compensation coefficient can be relatively stable and the adjustment range is not large. The temperature change rate refers to the speed of temperature change. During the operation of the equipment, when the temperature change rate is high, the compensation coefficient should be dynamically adjusted to cope with the rapid temperature change. When the temperature change rate is low, the compensation coefficient can be adjusted less. For example, in an environment where the temperature rises rapidly, the temperature of the equipment changes rapidly, and the sensor readings will also fluctuate rapidly. At this time, by monitoring the temperature change rate in real time (such as the amount of temperature change per unit time), the adjustment of the compensation coefficient can be appropriately increased to ensure that the temperature compensation can keep up with the changes in real time. On the contrary, in an environment where the temperature changes slowly, the temperature difference changes at a lower rate, and the dynamic adjustment of the compensation coefficient can be smoother and the change range is smaller.

[0157] The present application can make the compensation process more flexible and accurate by adjusting the compensation coefficient in real time according to the working status, environmental conditions and temperature change rate. This real-time adjustment can adapt to temperature changes in different working environments and ensure that the temperature estimation of the blood glucose meter in complex environments maintains high accuracy.

[0158] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0159] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0160] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0161] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0162] Further references Figure 3 , as a response to the above Figure 2 The present application provides a temperature detection and compensation system for a blood glucose meter. The temperature detection and compensation system for a blood glucose meter is an embodiment of the system embodiment. Figure 2 Corresponding to the method embodiment shown, the system can be specifically applied to various electronic devices.

[0163] like Figure 3 As shown, the temperature detection and compensation system 300 for a blood glucose meter described in this embodiment includes: an acquisition module 301, a processing module 302, a fusion module 303, a compensation module 304 and a calculation module 305. Among them:

[0164] An acquisition module 301 is used to acquire temperature data from the detection NTC sensor, the reference NTC sensor, and the environmental sensor respectively;

[0165] The processing module 302 is used to pre-process the temperature data, including denoising, smoothing and outlier correction;

[0166] A fusion module 303, used for performing data fusion on the temperature data and calculating a temperature estimation value of a blood glucose module;

[0167] A compensation module 304, configured to calculate a temperature difference between the reference NTC sensor temperature and the detection NTC sensor temperature, and dynamically adjust a compensation coefficient based on the temperature difference;

[0168] The calculation module 305 is used to calculate the actual operating temperature of the blood glucose module according to the temperature estimation value and the compensation coefficient.

[0169] The temperature detection and compensation system for a blood glucose meter provided by the present application, by combining the temperature estimation value and the compensation coefficient adjusted dynamically, finally calculates the actual operating temperature of the blood glucose module. The operating temperature of the blood glucose module is a key factor affecting its performance and accuracy, so it is very important to accurately measure and compensate for the temperature error. The actual temperature after compensation can better reflect the real temperature of the blood glucose module in different environments, thereby improving the accuracy of temperature compensation and avoiding the influence of temperature deviation on the measurement results.

[0170] In some optional implementations of this embodiment, the fusion module 303 is further used to:

[0171] Establish respective measurement models for the detection NTC sensor, reference NTC sensor and environmental sensor;

[0172] The linear model is used to calculate the temperature estimation values ​​of the detection NTC sensor, the reference NTC sensor and the environmental sensor at time k respectively;

[0173] The temperature estimation value of the blood glucose module at time k is predicted according to the temperature estimation value and the error covariance matrix.

[0174] The temperature detection and compensation system for a blood glucose meter provided in the present application can effectively reduce temperature fluctuations caused by measurement noise and errors by considering the error covariance of each sensor data, thereby providing a more accurate temperature estimate. Especially in the process of fusing multi-sensor data, Kalman filtering helps to reduce the deviation between sensors and improve the accuracy of temperature estimation.

[0175] In some optional implementations of this embodiment, the fusion module 303 is further used to:

[0176] Weighted fusion detection of temperature estimates of NTC sensors, reference NTC sensors, and ambient sensors;

[0177] Update the temperature estimate of the blood glucose module at time k according to the fused temperature estimate and gain coefficient.

[0178] The temperature detection and compensation system for blood glucose meters provided in the present application can comprehensively process the sensor data according to the weights of different sensors through weighted fusion, so as to obtain a more accurate temperature estimation value. By appropriately adjusting the gain coefficient, the influence of different sensors in the final estimation value can be dynamically adjusted according to their accuracy and reliability, thereby improving the adaptability and accuracy of the system.

[0179] In some optional implementations of this embodiment, the compensation module 304 is further configured to:

[0180] Calculate the temperature difference between the reference NTC sensor temperature and the detection NTC sensor temperature;

[0181] Based on the temperature difference, adjusting the temperature reading of the detection NTC sensor by a preset compensation algorithm;

[0182] Calibrate the temperature value of the detection NTC sensor;

[0183] The compensated temperature value of the detected NTC sensor is used as an input value through the Kalman filter algorithm, and data fusion is performed again to obtain a new temperature estimation value of the blood glucose module.

[0184] The temperature detection and compensation system for a blood glucose meter provided in the present application can optimize the temperature reading of the NTC sensor in real time according to the actual working state and environmental conditions of the device by dynamically adjusting the compensation coefficient, thereby improving the accuracy of temperature compensation.

[0185] In some optional implementations of this embodiment, the compensation module 304 is further configured to:

[0186] The compensation coefficient is adjusted in real time according to the working status, environmental conditions and temperature change rate.

[0187] The temperature detection and compensation system for the blood glucose meter provided in the present application can make the compensation process more flexible and accurate by adjusting the compensation coefficient in real time according to the working status, environmental conditions and temperature change rate. This real-time adjustment can adapt to temperature changes in different working environments and ensure that the temperature estimation of the blood glucose meter in complex environments maintains high accuracy.

[0188] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0189] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0190] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0191] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for the temperature detection and compensation method of the blood glucose meter, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0192] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the computer-readable instructions stored in the memory 41 or process data, such as running the computer-readable instructions of the temperature detection and compensation method for the blood glucose meter.

[0193] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0194] The computer device provided by the present application, by combining the temperature estimation value and the dynamically adjusted compensation coefficient, finally calculates the actual operating temperature of the blood glucose module. The operating temperature of the blood glucose module is a key factor affecting its performance and accuracy, so it is very important to accurately measure and compensate for the temperature error. The actual temperature after compensation can better reflect the real temperature of the blood glucose module in different environments, thereby improving the accuracy of temperature compensation and avoiding the influence of temperature deviation on the measurement results.

[0195] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the temperature detection and compensation method for a blood glucose meter as described above.

[0196] The computer-readable storage medium provided by the present application, by combining the temperature estimation value and the compensation coefficient adjusted dynamically, finally calculates the actual operating temperature of the blood glucose module. The operating temperature of the blood glucose module is a key factor affecting its performance and accuracy, so it is very important to accurately measure and compensate for the temperature error. The actual temperature after compensation can better reflect the real temperature of the blood glucose module under different environments, thereby improving the accuracy of temperature compensation and avoiding the influence of temperature deviation on the measurement results.

[0197] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0198] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A temperature detection and compensation method for a blood glucose meter, applied to a temperature detection and compensation system, wherein the temperature detection and compensation system comprises a detection NTC sensor, a reference NTC sensor, an environmental sensor and a blood glucose module, characterized in that: The method comprises the following steps: Acquire temperature data from the detection NTC sensor, the reference NTC sensor, and the environmental sensor respectively; Preprocessing the temperature data, including denoising, smoothing and outlier correction; Performing data fusion on the temperature data to calculate a temperature estimation value of a blood glucose module; Calculating a temperature difference between the reference NTC sensor temperature and the detection NTC sensor temperature, and dynamically adjusting a compensation coefficient based on the temperature difference; The actual operating temperature of the blood glucose module is calculated according to the temperature estimation value and the compensation coefficient.

2. The temperature detection and compensation method for a blood glucose meter according to claim 1, characterized in that: The step of fusing the temperature data to calculate the temperature estimation value of the blood glucose module specifically includes: Establish respective measurement models for the detection NTC sensor, reference NTC sensor and environmental sensor; The linear model is used to calculate the temperature estimation values ​​of the detection NTC sensor, the reference NTC sensor and the environmental sensor at time k respectively; The temperature estimation value of the blood glucose module at time k is predicted according to the temperature estimation value and the error covariance matrix.

3. The temperature detection and compensation method for a blood glucose meter according to claim 2, characterized in that: After the step of predicting the temperature estimation value of the blood glucose module at time k according to the temperature estimation value and the error covariance matrix, the method further includes: Weighted fusion detection of temperature estimates of NTC sensors, reference NTC sensors, and ambient sensors; Update the temperature estimate of the blood glucose module at time k according to the fused temperature estimate and gain coefficient.

4. The temperature detection and compensation method for a blood glucose meter according to claim 1, characterized in that: The step of calculating the temperature difference between the reference NTC sensor temperature and the detection NTC sensor temperature, and dynamically adjusting the compensation coefficient based on the temperature difference, specifically includes: Calculate the temperature difference between the reference NTC sensor temperature and the detection NTC sensor temperature; Based on the temperature difference, adjusting the temperature reading of the detection NTC sensor by a preset compensation algorithm; Calibrate the temperature value of the detection NTC sensor; The compensated temperature value of the detected NTC sensor is used as an input value through the Kalman filter algorithm, and data fusion is performed again to obtain a new temperature estimation value of the blood glucose module.

5. The temperature detection and compensation method for a blood glucose meter according to claim 4, characterized in that: In the step of calculating the temperature difference between the reference NTC sensor temperature and the detection NTC sensor temperature, and dynamically adjusting the compensation coefficient based on the temperature difference, the compensation coefficient is adjusted in real time according to the working state, environmental conditions and temperature change rate.

6. A temperature detection and compensation system for a blood glucose meter, characterized in that: include: An acquisition module, used to acquire temperature data from the detection NTC sensor, the reference NTC sensor, and the environmental sensor respectively; A processing module, used for preprocessing the temperature data, including denoising, smoothing and outlier correction; A fusion module, used for performing data fusion on the temperature data and calculating a temperature estimation value of a blood glucose module; A compensation module, configured to calculate a temperature difference between the reference NTC sensor temperature and the detection NTC sensor temperature, and dynamically adjust a compensation coefficient based on the temperature difference; A calculation module is used to calculate the actual operating temperature of the blood glucose module according to the temperature estimation value and the compensation coefficient.

7. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the temperature detection and compensation method for a blood glucose meter as described in any one of claims 1 to 5 when executing the computer-readable instructions.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the temperature detection and compensation method for a blood glucose meter according to any one of claims 1 to 5.

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