A temperature detection and compensation method for blood glucose meter and related device

Abstract

The embodiment of the application belongs to the field of blood glucose meters, and relates to a temperature detection and compensation method for a blood glucose meter, comprising the following steps: acquiring temperature data respectively from a detection NTC sensor, a reference NTC sensor and an environment sensor; pre-processing the temperature data, including denoising processing, smoothing processing and outlier correction processing; 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 and the detection NTC sensor, and dynamically adjusting a compensation coefficient based on the temperature difference; and calculating an actual working temperature of the blood glucose module according to the temperature estimation value and the compensation coefficient. The application also provides a temperature detection and compensation system for a blood glucose meter, a computer device and a readable storage medium. The application improves the accuracy of temperature estimation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blood glucose meters, and particularly relates to a temperature detection and compensation method for a blood glucose meter and related equipment. BACKGROUND

[0002] A blood glucose meter is an important tool for monitoring blood glucose levels, and the accuracy of the blood glucose meter 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. Changes in temperature can affect the rate of enzyme reactions, the efficiency of reagent reactions and the generation of electrochemical signals, thereby causing deviations in blood glucose measurement results.

[0003] Currently, 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) can generate a large amount of heat, resulting in inaccurate internal temperature measurement. The NTC sensor is usually located inside the module and can be affected by the heat generated by the internal components, resulting in a higher or lower measured temperature, which affects the accuracy of temperature compensation. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide 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] To solve the above technical problems, the embodiments of the present application provide a temperature detection and compensation method for a blood glucose meter, which adopts the following technical solutions:

[0006] A temperature detection and compensation method for a blood glucose meter, applied to a temperature detection and compensation system, the temperature detection and compensation system comprising a detection NTC sensor, a reference NTC sensor, an environment sensor and a blood glucose module, the method comprising the following steps:

[0007] Obtaining temperature data from the detection NTC sensor, the reference NTC sensor and the environment sensor, respectively;

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

[0009] Data fusion is performed on the temperature data to calculate a temperature estimate of the blood glucose module;

[0010] 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;

[0011] According to the temperature estimation value and the compensation coefficient, an actual working temperature of the blood glucose module is calculated.

[0012] In a possible implementation, the step of performing data fusion on the temperature data to calculate a temperature estimation value of the blood glucose module specifically comprises:

[0013] measurement models are respectively established for the detection NTC sensor, the reference NTC sensor and the environment sensor;

[0014] The temperature estimation values of the detection NTC sensor, the reference NTC sensor and the environment sensor at the time k are respectively calculated by using a linear model;

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

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

[0017] The temperature estimation values of the detection NTC sensor, the reference NTC sensor and the environment sensor are weightedly fused;

[0018] According to the fused temperature estimation value and the gain coefficient, the temperature estimation value of the blood glucose module at the time k is updated.

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

[0020] A temperature difference between the reference NTC sensor temperature and the detection NTC sensor temperature is calculated;

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

[0022] The temperature value of the detection NTC sensor is calibrated;

[0023] The temperature value of the detection NTC sensor after compensation is taken as an input value by using a Kalman filtering 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 a temperature difference between the reference NTC sensor temperature and the detection NTC sensor and dynamically adjusting a compensation coefficient based on the temperature difference, the compensation coefficient is adjusted in real time according to a working state, an environment condition and a temperature change rate.

[0025] To solve the above technical problems, the embodiment of the present application also provides a temperature detection and compensation system for a blood glucose meter, which adopts the technical scheme as follows:

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

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

[0028] A processing module is configured to pre-process the temperature data, including denoising processing, smoothing processing and outlier correction processing;

[0029] A fusion module is configured to perform data fusion on the temperature data and calculate a temperature estimation value of a blood glucose module;

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

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

[0032] To solve the above technical problems, the embodiment of the present application also provides a computer device, which adopts the technical scheme as follows:

[0033] A computer device comprises a memory and a processor, the memory stores computer readable instructions, and the processor executes the computer readable instructions to realize the steps of the temperature detection and compensation method for a blood glucose meter.

[0034] To solve the above technical problems, the embodiment of the present application also provides a computer readable storage medium, which adopts the technical scheme as follows:

[0035] A computer readable storage medium stores computer readable instructions, and the computer readable instructions are executed by a processor to realize the steps of the temperature detection and compensation method for a blood glucose meter.

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

[0037] The temperature detection and compensation method for blood glucose meter disclosed in the application comprises the following steps: acquiring temperature data from the detection NTC sensor, the reference NTC sensor and the environment sensor respectively; pre-processing the temperature data, including denoising, smoothing and outlier correction; data fusion of the temperature data to calculate the temperature estimation value of the blood glucose module; calculating the temperature difference between the reference NTC sensor and the detection NTC sensor, and dynamically adjusting the 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. By using the data of the detection NTC sensor, the reference NTC sensor and the environment sensor simultaneously, the accuracy of temperature estimation can be greatly improved, and the complementary effect of different sensors effectively reduces the error of a single sensor, especially in complex environmental conditions, ensuring the stability and reliability of the temperature estimation value. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the solutions in the application, the drawings needed in the embodiments of the application will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative effort.

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

[0040] Figure 2 is a flowchart of one embodiment of the temperature detection and compensation method for blood glucose meter according to the application;

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

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

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the application belongs; the terms used in the specification of the application are only for the purpose of describing specific embodiments and are not intended to limit the application; the specification, claims and above description of drawings of the application, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. The specification, claims and above description of drawings of the application, the terms "first", "second" and the like are used to distinguish different objects, not to describe a specific order.

[0044] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. As will be apparent to those of ordinary skill in the art, embodiments described herein can be combined with other embodiments.

[0045] In order to better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings.

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

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

[0048] The 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, and desktop computers, etc.

[0049] The server 105 can be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.

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

[0051] It should be understood that,Figure 1 The number of terminal devices, networks and servers in the system is only illustrative. Any number of terminal devices, networks and servers can be provided according to the implementation needs.

[0052] With reference to the accompanying drawings, the embodiments of the present application will be described in detail. Figure 2 Fig. 1 shows a flow chart of one embodiment of the temperature detection and compensation method for blood glucose meter according to the present application. The temperature detection and compensation method for blood glucose meter comprises the following steps:

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

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

[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 can be affected by factors such as internal heating of the module; the reference NTC sensor is used to measure the ambient temperature, which is usually used as a calibration temperature reference to help identify the deviation of the detection NTC sensor; the environment sensor is used to measure the external environment temperature, such as air temperature, to help more accurately reflect the influence of external temperature on the temperature of the device.

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

[0057] In this embodiment, the obtained sensor data is pre-processed to ensure the quality of the data. The pre-processing steps include:

[0058] Denoising: removing high-frequency noise in sensor data to ensure data smoothness and accuracy. Low-pass filtering or other denoising methods are usually used.

[0059] Smoothing: reducing temperature fluctuations or transient mutations in the data, especially in cases where the temperature changes are severe, smoothing helps to remove abnormal fluctuations.

[0060] Outlier correction: for abnormal data caused by sensor failure or external interference, the abnormal data is removed and corrected to ensure that the data used is reliable and representative of the real environment. ​

[0061] Step S203, data fusion is performed on the temperature data to calculate a 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 estimation value. 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. Sensor data fusion is usually based on their respective measurement noise and accuracy, and the system will assign different weights to different sensors. Methods such as Kalman filtering play a key role here, and the temperature estimation value can be updated over time. The fused temperature estimation value is the current environmental temperature of the device, which has taken into account the measurement error and noise of all sensors, so it can more accurately reflect the actual working environment temperature of the device.

[0063] Step S204, calculate the temperature difference between the reference NTC sensor temperature and the detection NTC sensor, and dynamically adjust 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 heat generated inside the device, its measurement value may deviate from the actual environmental 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 less affected by the heat generated by the device, so its measurement value is closer to the actual environmental temperature. The calculated temperature difference is used to adjust the compensation coefficient. Specifically, the compensation coefficient is dynamically adjusted according to the running state and internal temperature of the device. This coefficient helps to correct the temperature estimation value from the theoretical value to the actual working temperature value, thereby achieving more accurate temperature compensation.

[0065] Step S205, calculate the actual working 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 working temperature of the blood glucose module is finally calculated. The working temperature of the blood glucose module is a key factor affecting its performance and accuracy, so it is crucial to accurately measure and compensate for temperature errors. The compensated actual temperature can better reflect the true temperature of the blood glucose module in different environments, thereby improving the accuracy of temperature compensation and avoiding the impact of temperature deviation on measurement results.

[0067] The 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 in complex environmental conditions, ensuring 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 conditions and environmental conditions, and the temperature deviation caused by sensor error or equipment heating is eliminated, thereby improving the precision of the temperature compensation of the blood glucose meter.

[0068] In some optional implementations of the embodiment, the step of performing data fusion on the temperature data and calculating the temperature estimation value of the blood glucose module includes the following steps.

[0069] Respectively, the measurement model of the detection NTC sensor, the reference NTC sensor and the environmental sensor is established;

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

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

[0072] In the embodiment, the detection 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] Wherein: z NTC (k) is the temperature value measured by the detection NTC sensor at time k;

[0075] H NTC is the observation matrix of the detection NTC sensor, which can be a unit matrix in general, indicating that the temperature directly reflects the target temperature.

[0076] x(k) is the true temperature of the device (i.e. the working temperature of the blood glucose module).

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

[0078] The reference NTC sensor measures the environmental temperature, which is less affected by the internal heating of the device. Its measurement model is similar to that of the detection NTC sensor, but the error in measuring the environmental 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 time k.

[0081] H ref is the observation matrix for the reference NTC sensor, which is typically an identity matrix.

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

[0083] The ambient sensor measures the external ambient temperature, which typically reflects the temperature of the surrounding environment directly, but also has some 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 ambient sensor at time k.

[0086] H env is the observation matrix for the ambient sensor, which is typically an identity matrix.

[0087] v env (k) is the measurement noise for the ambient sensor.

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

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

[0090] State transition 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 time k (i.e., the current temperature of the device).

[0093] A is the state transition matrix, describing the change in temperature over time.

[0094] u(k) is the control input, typically including factors such as external ambient temperature or device load.

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

[0096] Observation equation (describes 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, the estimate of temperature is obtained from the measurements of these sensors through multiple iterations.

[0101] Predicted temperature estimate (based on the temperature estimate from the previous time step and the inputs at the current time step):

[0102]

[0103] where, is the predicted value based on the temperature estimate from the previous time step.

[0104] Then, the measurement residual (the difference between the actual measurement and the predicted value):

[0105]

[0106] Kalman gain (balances the contribution between the predicted temperature and the sensor measurements):

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

[0108] Updated temperature estimate (combines the predicted value, updated temperature estimate):

[0109]

[0110] where, P pred is the prediction error covariance matrix, R is the covariance matrix of measurement noise, reflecting 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 according to these estimates. By integrating the temperature estimates of each sensor and using the Kalman filtering algorithm, we finally obtain the optimized temperature estimate of the blood glucose module.

[0112] This process is similar to the previous step, as follows:

[0113] 1. Predict the temperature of the blood glucose module at the current time:

[0114]

[0115] 2. Update according to the measurement data of the sensor: combine the measurement data of the detection NTC, reference NTC and environment sensor, and finally obtain the optimized blood glucose module temperature estimate.

[0116] The present application can effectively reduce the temperature fluctuations caused by measurement noise and errors by considering the error covariance of each sensor data, thereby providing more accurate temperature estimates, especially in the fusion process of 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 the present embodiment, the step of predicting the temperature estimate of the blood glucose module at time k according to the temperature estimate and the error covariance matrix described above further comprises:

[0118] Weighted fusion of temperature estimates of detection NTC sensor, reference NTC sensor and environment sensor;

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

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

[0121] First, obtain the temperature estimate of each sensor:

[0122] The temperature estimate of the detection NTC sensor at time k.

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

[0124] The temperature estimate of the environment sensor at time k.

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

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

[0127] Refer to the error covariance of the NTC sensor.

[0128] Error covariance of environmental sensors.

[0129] Then, calculate the weighting coefficient of the sensor: According to the error covariance matrix of each sensor, the weighting coefficient can be calculated The sensor with smaller error covariance will be given a larger weight, and vice versa. The formula for calculating the weighting coefficient is:

[0130]

[0131] Similarly:

[0132]

[0133]

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

[0135] Finally, weighted fusion of temperature estimates: using the weighting coefficient to weight and fuse the temperature estimates of each sensor, to get the fused temperature estimate

[0136]

[0137] Where, 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 the gain coefficient are combined: according to the fused temperature estimate and the gain coefficient K calculated in the current Kalman filtering step k , the temperature estimate of the blood glucose module can be updated, and through the gain coefficient, the final blood glucose module temperature estimate can better fuse all sensor data to obtain a more accurate estimate.

[0139] Update formula:

[0140]

[0141] wherein:

[0142] is the updated blood glucose module temperature estimation value.

[0143] is the predicted temperature estimation value.

[0144] is the weighted fused temperature estimation value, which integrates data from the detection NTC sensor, the reference NTC sensor and the ambient sensor.

[0145] K k is the Kalman gain coefficient, which determines the combination of the predicted value and the fused temperature estimation value.

[0146] Through the above-mentioned combination of weighted fusion and gain coefficient, the final temperature estimation value will be more accurate, reducing the temperature estimation deviation caused by the error of a single sensor. The temperature estimation value will be used as the actual working temperature value of the blood glucose module for the subsequent temperature compensation step. The specific compensation coefficient will be adjusted through further algorithm (for example, by calculating the difference between the reference temperature) and finally used for the accuracy improvement of the blood glucose meter measurement result.

[0147] The present application can obtain a more accurate temperature estimation value by weighting and fusing the data of different sensors according to the weight of different sensors, and by adjusting the gain coefficient, the influence of different sensors in the final estimation value can be dynamically adjusted according to the accuracy and reliability of different sensors, so as to improve the adaptability and accuracy of the system.

[0148] In some optional implementations of the 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] calculating the temperature difference between the reference NTC sensor temperature and the detection NTC sensor temperature;

[0150] adjusting the temperature reading of the detection NTC sensor through a preset compensation algorithm based on the temperature difference;

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

[0152] renewing data fusion by taking the compensated temperature value of the detection NTC sensor as the input value through the Kalman filtering algorithm, 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 for temperature compensation, because through the temperature difference between the two sensors, the temperature deviation caused by factors such as sensor location, internal heating, etc. can be judged. After obtaining the temperature difference between the reference NTC and the detection NTC, the next step is to use a pre-set compensation algorithm 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 external environmental changes or internal heating of the sensor. The specific implementation of the compensation algorithm may be based on a linear relationship, a nonlinear relationship, or a compensation model obtained through actual device testing. The calibration step ensures that the compensated temperature value accurately reflects the current environmental temperature. Typically, the calibration process compares the sensor's measurement value with a known standard temperature value and adjusts the sensor's output. The calibration process may be completed through device standardization testing to ensure that the compensated reading is as close to the true temperature as possible. Through the Kalman filtering algorithm, data fusion is performed again, with the purpose of further optimizing the temperature estimation value of the blood glucose module. The Kalman filtering step here will take the compensated detection NTC sensor temperature value as new input data, and combine it with the data of other sensors (such as the reference NTC sensor and the environmental sensor) for weighted fusion. The process of re-fusing data combines the compensated and calibrated temperature reading (through the weighted fusion algorithm) with other information such as environmental temperature, to obtain a more accurate temperature estimation value of the blood glucose module.

[0154] The present application can dynamically adjust the compensation coefficient to optimize the temperature reading of the detection NTC sensor in real time according to the actual working state and environmental conditions of the device, 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 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 running state of the blood glucose meter in different working situations, such as whether the device is running for a long time, whether it is running in a high load mode, whether there is external interference (such as temperature change, device switching, 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 too 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. The environmental condition refers to the temperature, humidity, air pressure and other factors of the external environment where the device is located. The change of the environmental temperature will directly affect the temperature reading of the sensor, and the temperature change characteristics are different under different environmental conditions. 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 large temperature difference (such as rapid change 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 rapid change of the environment. If the device works in an environment with slow temperature change, the compensation coefficient can be relatively stable, and the adjustment range is small. The temperature change rate refers to the speed of temperature change. When the temperature change rate is high during the operation of the device, the compensation coefficient should be dynamically adjusted to cope with the rapid temperature change. When the temperature change rate is low, the adjustment range of the compensation coefficient can be small. For example, in an environment with rapid temperature rise, the temperature of the device changes rapidly, and the sensor reading also fluctuates rapidly. At this time, by monitoring the temperature change rate (such as the amount of temperature change per unit time), the adjustment intensity of the compensation coefficient can be appropriately increased to ensure that the temperature compensation can keep up with the change in real time. On the contrary, in an environment with slow temperature change, the temperature difference changes at a low rate, and the dynamic adjustment of the compensation coefficient can be more stable, with a small change range.

[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 state, environmental condition and temperature change rate. This real-time adjustment can adapt to the temperature change under different working environments, ensuring that the temperature estimation of the blood glucose meter remains high precision in complex environments.

[0158] The embodiment of the present application can acquire and process related data based on artificial intelligence technology. Artificial intelligence (AI) is the use of digital computers or digital computer-controlled machines to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0159] The basic technologies of artificial intelligence generally include technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction system, mechatronics, etc. The software technologies of artificial intelligence mainly include computer vision technology, robot technology, biometric identification technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc.

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

[0161] It should be understood that although each step in the flowchart of the accompanying drawings is displayed in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise stated herein, the execution of these steps is not strictly limited in sequence, and they can be executed in other orders. Moreover, at least part of the steps in the flowchart of the accompanying drawings can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or sub-steps or stages of other steps.

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

[0163] As shown in Figure 3 , the temperature detection and compensation system for a blood glucose meter 300 described in the 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] The acquisition module 301 is configured to acquire temperature data respectively from the detection NTC sensor, the reference NTC sensor, and the environment sensor;

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

[0166] The fusion module 303 is configured to perform data fusion on the temperature data to calculate a temperature estimation value of the blood glucose module.

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

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

[0169] The temperature detection and compensation system for the blood glucose meter provided in the present application combines the temperature estimation value and the dynamically adjusted compensation coefficient to finally calculate the actual working temperature of the blood glucose module. The working temperature of the blood glucose module is a key factor affecting its performance and accuracy, and therefore it is crucial to accurately measure and compensate 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 result.

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

[0171] establish respective measurement models for the detection NTC sensor, the reference NTC sensor, and the environment sensor;

[0172] calculate the temperature estimation values of the detection NTC sensor, the reference NTC sensor, and the environment sensor at time k respectively using a linear model;

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

[0174] The temperature detection and compensation system for the blood glucose meter provided in the present application considers the error covariance of the data of each sensor, which can effectively reduce the temperature fluctuation caused by measurement noise and error, thereby providing a more accurate temperature estimation value. In particular, in the fusion process of 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 the present embodiment, the fusion module 303 is further configured to:

[0176] weight the temperature estimation values of the detection NTC sensor, the reference NTC sensor, and the environment sensor;

[0177] The blood glucose module temperature estimation value at the k time is updated according to the fused temperature estimation value and the gain coefficient.

[0178] The temperature detection and compensation system for the blood glucose meter provided in the application can comprehensively process the data of the sensors according to the weights of the different sensors through weighted fusion, so as to obtain a more accurate temperature estimation value. Through appropriate adjustment of the gain coefficient, the influence of the different sensors on the final estimation value can be dynamically adjusted according to the accuracy and reliability of the different sensors, so as to improve the adaptability and accuracy of the system.

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

[0180] calculate a temperature difference between the reference NTC sensor temperature and the detected NTC sensor temperature;

[0181] adjust the temperature reading of the detected NTC sensor through a preset compensation algorithm based on the temperature difference;

[0182] calibrate the temperature value of the detected NTC sensor;

[0183] re-perform data fusion by taking the compensated temperature value of the detected NTC sensor as an input value through a Kalman filtering algorithm, to obtain a new temperature estimation value of the blood glucose module.

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

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

[0186] real-time adjust the compensation coefficient according to the working state, the environmental conditions and the temperature change rate.

[0187] The temperature detection and compensation system for the blood glucose meter provided in the application can real-time adjust the compensation coefficient according to the working state, the environmental conditions and the temperature change rate, so as to make the compensation process more flexible and accurate. The real-time adjustment can adapt to the temperature change under different working environments, and ensure that the temperature estimation of the blood glucose meter remains high accuracy under complex environments.

[0188] To solve the above technical problems, the embodiment of the application further provides a computer device. For details, please refer to Figure 4 , Figure 4 The basic structure block diagram of the computer device of the embodiment is shown in the following figure.

[0189] The computer device 4 includes a memory 41, a processor 42, and a network interface 43, which are communicatively connected by a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure, but it should be understood that all the shown components are not required to be implemented, and more or less components can be alternatively implemented. Among them, those skilled in the art can understand that the computer device herein is a device capable of automatically performing numerical calculation 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 (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0190] The computer device can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The computer device can interact with the user through a keyboard, a mouse, a remote controller, a touchpad, a voice control device, and the like.

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

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

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

[0194] The computer device provided in the present application combines the temperature estimation value and the dynamically adjusted compensation coefficient to finally calculate the actual working temperature of the blood glucose module. The working temperature of the blood glucose module is a key factor affecting its performance and accuracy, and therefore it is crucial to accurately measure and compensate 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, i.e., a computer readable storage medium storing computer readable instructions, which 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 in the present application combines the temperature estimation value and the dynamically adjusted compensation coefficient to finally calculate the actual working temperature of the blood glucose module. The working temperature of the blood glucose module is a key factor affecting its performance and accuracy, and therefore it is crucial to accurately measure and compensate 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.

[0197] Those skilled in the art can clearly understand the above-mentioned embodiment method can be realized by means of software and the necessary general hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application 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 plurality of instructions for making a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) execute the method described in each embodiment of the present application.

[0198] Obviously, the above-described embodiments are only some of the embodiments of the present application, not all the embodiments, and the drawings show the preferred embodiments of the present application, but do not limit the patent scope of the present application. The present application can be implemented in many different forms, and conversely, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing specific embodiments, or make equivalent replacements to some of the technical features. Any equivalent structure made by using the content of the specification and drawings, directly or indirectly applied to other related technical fields, is also within the scope of the patent protection of the present application.

Claims

1. A method for temperature estimation and compensation in a blood glucose meter, applied to a temperature estimation and compensation system, the temperature estimation and compensation system comprising a detection NTC sensor, a reference NTC sensor, an environmental sensor, and a blood glucose module; the detection NTC sensor is installed inside the blood glucose module to measure the internal temperature of the blood glucose meter, and is affected by internal factors of the blood glucose module; the reference NTC sensor is used to measure the ambient temperature as a calibration temperature reference to help identify deviations in the detection NTC sensor; the environmental sensor is used to measure the external ambient temperature to reflect the influence of the external ambient temperature on the temperature of the blood glucose meter, characterized in that... The method includes the following steps: Acquire temperature data from the detection NTC sensor, the reference NTC sensor, and the environmental sensor, respectively; The temperature data is preprocessed, including noise reduction, smoothing, and outlier correction. The temperature data is fused to calculate the estimated temperature value of the blood glucose module; Calculate the temperature difference between the reference NTC sensor temperature and the detection NTC sensor temperature, and dynamically adjust the compensation coefficient based on the temperature difference; The actual operating temperature of the blood glucose module is calculated based on the estimated temperature and the compensation coefficient. The step of performing data fusion on the temperature data to calculate the temperature estimate of the blood glucose module specifically includes: Establish separate measurement models for the detection NTC sensor, the reference NTC sensor, and the environmental sensor; The temperature estimates of the detection NTC sensor, reference NTC sensor, and environmental sensor at time k were calculated using a linear model. Based on the temperature estimate and the error covariance matrix, predict the temperature estimate of the blood glucose module at time k. Weighted fusion detection of temperature estimates from NTC sensor, reference NTC sensor and environmental sensor; The blood glucose module temperature estimate at time k is updated based on the fused temperature estimate and gain coefficient.

2. The temperature estimation 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, 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, the temperature reading of the NTC sensor is adjusted using a preset compensation algorithm; Calibrate the temperature value of the NTC sensor; The temperature value of the NTC sensor after compensation is used as the input value by the Kalman filter algorithm, and the data is fused again to obtain a new temperature estimate of the blood glucose module.

3. The temperature estimation and compensation method for a blood glucose meter according to claim 1, characterized in that, 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 the rate of temperature change.

4. A temperature estimation and compensation system for a blood glucose meter, used to perform the temperature estimation and compensation method for a blood glucose meter as described in any one of claims 1 to 3, characterized in that, The system includes: The acquisition module is used to acquire temperature data from the detection NTC sensor, the reference NTC sensor, and the environmental sensor, respectively. The processing module is used to preprocess the temperature data, including noise reduction, smoothing, and outlier correction. The fusion module is used to fuse the temperature data and calculate the temperature estimate value of the blood glucose module. The compensation module is used to calculate the temperature difference between the reference NTC sensor temperature and the detection NTC sensor, and dynamically adjust the compensation coefficient based on the temperature difference; The calculation module is used to calculate the actual operating temperature of the blood glucose module based on the temperature estimate and the compensation coefficient. The fusion module is further configured to: Establish separate measurement models for the detection NTC sensor, the reference NTC sensor, and the environmental sensor; The temperature estimates of the detection NTC sensor, reference NTC sensor, and environmental sensor at time k were calculated using a linear model. Based on the temperature estimate and the error covariance matrix, predict the temperature estimate of the blood glucose module at time k. Weighted fusion detection of temperature estimates from NTC sensor, reference NTC sensor and environmental sensor; The blood glucose module temperature estimate at time k is updated based on the fused temperature estimate and gain coefficient.

5. A computer device, characterized in that, The method includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the temperature estimation and compensation method for a blood glucose meter as described in any one of claims 1 to 3.

6. 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 estimation and compensation method for a blood glucose meter as described in any one of claims 1 to 3.

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