Target spot automatic locking method and system for non-contact temperature measurement and related device

Abstract

The invention discloses an automatic target spot locking method and system for non-contact temperature measurement and a related device, which are used for improving the operation reliability, the measurement precision and the robustness of non-contact temperature measurement. The method comprises the following steps: constructing a space thermal field distribution model taking relative displacement as an independent variable in combination with temperature time sequence data; according to the scanning speed in the scanning process and a preset thermal response time parameter, performing deconvolution compensation processing on the space thermal field distribution model; dividing the scanning path into a background area, an edge change area and an effective hot window area according to the amplitude and the change form of the space temperature gradient; estimating a target measurement distance between the infrared thermopile and the measured target based on a mapping relation between the sharpness characteristic and the measurement distance; and when the spatial span meets a preset minimum window length condition and the temperature variance is lower than a stability threshold, generating a target spot locking signal, and performing distance compensation on the temperature data in the effective hot window region based on the target measurement distance to determine a target measurement temperature.

Description

Technical Field

[0001] This application relates to the field of infrared temperature measurement technology, and in particular to a non-contact temperature measurement method, system and related device for automatic target locking. Background Technology

[0002] Infrared thermometry technology is widely used in medical screening, public health and epidemic prevention, and home health monitoring due to its advantages such as non-contact operation, fast response, and avoidance of cross-infection. Common infrared thermometry devices, such as infrared forehead thermometers or ear thermometers, work by receiving the infrared radiation energy emitted by the target object and, in conjunction with ambient temperature compensation, converting it into the surface temperature of the target object.

[0003] Existing infrared temperature measurement devices typically employ two working modes: one is the point measurement mode, where the user estimates the distance visually, aligns the device with the measurement area, and presses a button to collect single-point data; the other is the scanning mode, where the user presses a button and sweeps horizontally across the measurement area, and the device internally records the highest temperature value during the scanning process using a comparison algorithm, and outputs it as the final measurement result.

[0004] However, these temperature measurement methods all rely on the user actively aligning the measurement with the target area and manually triggering the measurement. The measurement process is highly dependent on the user's operating experience and stability, and the measurement results depend to some extent on the state conditions at the time of measurement. When there are changes in position or fluctuations in state during the measurement process, it is often difficult to distinguish between valid human body thermal signals and spurious signals caused by improper operation (such as moving too fast or too far) or environmental interference. This results in poor stability of the final output temperature reading and a high risk of missed or incorrect measurements. Summary of the Invention

[0005] This application provides a method, system, and related device for automatic target locking in non-contact temperature measurement, which improves the operational reliability, measurement accuracy, and robustness of non-contact temperature measurement.

[0006] The first aspect of this application provides a non-contact temperature measurement method for automatic target locking, comprising:

[0007] Acquire temperature timing data collected by the infrared thermopile and motion timing data collected by the inertial measurement unit, and perform synchronization and alignment processing; The relative displacement during the scanning process is calculated based on the motion time series data, and a spatial thermal field distribution model with the relative displacement as the independent variable is constructed in combination with the temperature time series data. Based on the scanning speed and preset thermal response time parameters during the scanning process, deconvolution compensation processing is performed on the spatial thermal field distribution model. The spatial temperature gradient is calculated based on the processed spatial thermal field distribution model, and the scanning path is divided into background area, edge change area and effective thermal window area according to the amplitude and change pattern of the spatial temperature gradient. Extract the sharpness features corresponding to the edge change region, and estimate the target measurement distance between the infrared thermopile and the target based on the mapping relationship between the sharpness features and the measurement distance; Monitor the spatial span and temperature variance of the effective thermal window area. When the spatial span meets the preset minimum window length condition and the temperature variance is lower than the stability threshold, generate a target locking signal. In response to the target locking signal, the temperature data within the effective thermal window is compensated for distance based on the target measurement distance to determine the target measurement temperature.

[0008] Optionally, the step of calculating the relative displacement during the scanning process based on the motion time-series data and constructing a spatial thermal field distribution model with the relative displacement as the independent variable in combination with the temperature time-series data includes: Gravity component filtering is performed on the acceleration signal in the motion time series data to separate the linear acceleration component characterizing the scanning motion from the motion time series data; The scanning velocity during the scanning process is obtained by performing time integration on the linear acceleration component, and the cumulative integration is performed along the scanning direction based on the scanning velocity to obtain the cumulative relative displacement sequence corresponding to the scanning process. Using the cumulative relative displacement sequence as a spatial independent variable, the temperature time series data synchronized with the motion time series data is mapped to the corresponding relative displacement position, and the temperature data between adjacent relative displacement positions is interpolated to generate a spatial thermal field distribution model that changes continuously along the scanning direction.

[0009] Optionally, the step of performing deconvolution compensation processing on the spatial thermal field distribution model based on the scanning speed and preset thermal response time parameters during the scanning process includes: A spatial response model characterizing the thermal response hysteresis of the infrared thermopile during scanning is constructed based on the scanning speed and the preset thermal response time parameter. Based on the spatial response model, deconvolution compensation processing is performed on the spatial thermal field distribution model.

[0010] Optionally, extracting the sharpness features corresponding to the edge change region and estimating the target measurement distance between the infrared thermopile and the target based on the mapping relationship between the sharpness features and the measurement distance includes: An extreme value search operation is performed on the spatial temperature gradient along the scanning direction within the edge change region to extract the maximum amplitude of the spatial temperature gradient, and the temperature amplitude increment between the start and end positions of the edge change region is calculated. The ratio between the maximum amplitude of the spatial temperature gradient and the temperature amplitude increment is calculated to generate a normalized sharpness factor that characterizes the steepness of the edge transition. The pre-established field-of-view optical transfer function model is invoked, and the normalized sharpness factor is substituted into the field-of-view optical transfer function model to calculate the corresponding physical distance value. The physical distance value is then subjected to amplitude limiting processing to output the target measurement distance. The field-of-view optical transfer function model is used to describe the fuzzy response characteristics of the infrared thermopile to the edge of space temperature under different measurement distance conditions.

[0011] Optionally, the step of responding to the target locking signal by performing distance compensation on the temperature data within the effective thermal window based on the target measurement distance to determine the target measurement temperature includes: The attenuation coefficient of infrared radiation energy during air transmission is calculated based on the target measurement distance and the preset atmospheric radiation transmission model. The average temperature value of the preset core area within the effective thermal window is calculated as the original measurement temperature. The original measured temperature is compensated for by inverse gain using the attenuation coefficient, and the influence component of the current ambient temperature is added to calculate the true surface temperature of the target as the target measured temperature.

[0012] Optionally, after extracting the sharpness features corresponding to the edge change region and before generating the target point locking signal, the method further includes: Within the effective thermal window, the transient response curve of temperature time series data as a function of relative displacement is extracted based on the spatial thermal field distribution model, and the rate of temperature change and the fluctuation amplitude of the transient response curve within the preset displacement disturbance range are calculated. The temperature change rate and its fluctuation amplitude are matched with a preset set of transient thermal response characteristics of biological media to generate a biological confidence factor characterizing the properties of the medium corresponding to the effective thermal window area, and it is determined whether the biological confidence factor is higher than a preset biological confidence threshold. When the spatial span meets the preset minimum window length condition and the temperature variance is lower than the stability threshold, a target locking signal is generated, including: When the spatial span meets the preset minimum window length condition, the temperature variance is lower than the stability threshold, and the biological confidence factor is higher than the preset biological confidence threshold, a target locking signal is generated.

[0013] Optionally, the step of calculating the spatial temperature gradient based on the processed spatial thermal field distribution model, and dividing the scanning path into a background area, an edge change area, and an effective thermal window area according to the amplitude and variation pattern of the spatial temperature gradient, includes: The first spatial derivative of the processed spatial thermal field distribution model is calculated to obtain the spatial temperature gradient sequence. Identify the first segment in the spatial temperature gradient sequence that continuously exceeds a preset positive gradient threshold, and mark the segment as an edge change region, and record the starting displacement point and ending displacement point of the edge change region; After the edge change zone, identify the segment where the absolute value of the spatial temperature gradient is continuously lower than a preset stability threshold, and mark the segment as an effective thermal window zone; The segments located before the edge change zone and the segments located after the effective thermal window zone where the spatial temperature gradient shows a negative abrupt change are marked as background zones.

[0014] A second aspect of this application provides a non-contact temperature measurement automatic target locking system, comprising: The acquisition unit is used to acquire temperature timing data collected by the infrared thermopile and motion timing data collected by the inertial measurement unit, and to perform synchronization and alignment processing. The construction unit is used to calculate the relative displacement during the scanning process based on the motion time series data, and to construct a spatial thermal field distribution model with the relative displacement as the independent variable in combination with the temperature time series data. The processing unit is used to perform deconvolution compensation processing on the spatial thermal field distribution model according to the scanning speed and preset thermal response time parameters during the scanning process. The calculation unit is used to calculate the spatial temperature gradient based on the processed spatial thermal field distribution model, and to divide the scanning path into a background area, an edge change area and an effective thermal window area according to the amplitude and change pattern of the spatial temperature gradient. An estimation unit is used to extract the sharpness features corresponding to the edge change area, and estimate the target measurement distance between the infrared thermopile and the target under test based on the mapping relationship between the sharpness features and the measurement distance. The locking unit is used to monitor the spatial span and temperature variance of the effective thermal window area. When the spatial span meets the preset minimum window length condition and the temperature variance is lower than the stability threshold, a target locking signal is generated. A determining unit is configured to, in response to the target locking signal, perform distance compensation on the temperature data within the effective thermal window based on the target measurement distance, so as to determine the target measurement temperature.

[0015] A third aspect of this application provides a non-contact temperature measurement target automatic locking device, the device comprising: Processor, memory, input / output units, and bus; The processor is connected to the memory, the input / output unit, and the bus; The memory stores a program, which the processor calls to execute the first aspect and any optional non-contact temperature measurement target automatic locking method in the first aspect.

[0016] The fourth aspect of this application provides a computer-readable storage medium storing a program that, when executed on a computer, performs the first aspect and any optional non-contact temperature measurement target automatic locking method of the first aspect.

[0017] As can be seen from the above technical solutions, this application has the following advantages: By aligning the time-series temperature data collected by the infrared thermopile with the motion time-series data collected by the inertial measurement unit during temperature measurement, a one-dimensional spatial thermal field distribution model with relative displacement as the independent variable is constructed. In this spatial thermal field model, deconvolution compensation is performed by combining the scanning speed and the thermal response parameters of the infrared thermopile to eliminate the influence of sensor thermal inertia and scanning speed changes on the thermal field topology, ensuring that the subsequently calculated spatial temperature gradient accurately reflects the temperature change characteristics of the target area. Based on the gradient magnitude and change pattern of the spatial temperature gradient, the scanning path is divided into a background area, an edge change area, and an effective thermal window area, thus distinguishing the target heat source from background interference. After obtaining the spatial range of the effective thermal window area, the spatial span and temperature variance of this range are monitored. When the spatial span meets the preset minimum window length condition and the temperature variance is below the stability threshold, a target locking signal is generated to ensure that the temperature data of the locked area has sufficient stability and spatial continuity. Subsequently, the spatial temperature gradient sharpness characteristics of the edge change area are extracted, and the measurement distance between the sensor and the target is estimated based on the mapping relationship between gradient sharpness and measurement distance. After generating the target lock signal, distance compensation is performed on the temperature data within the effective thermal window based on the estimated measurement distance to determine the final measurement temperature.

[0018] The entire temperature measurement process requires no subjective judgment from the user. Even with changes in scanning speed, different target distances, or the presence of local cold spot interference, it can still accurately identify the effective temperature measurement area and lock the temperature measurement target point. At the same time, it eliminates sensor response lag and distance measurement effects, thereby significantly improving the operational reliability, measurement accuracy, and robustness of non-contact temperature measurement. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A schematic flowchart of an embodiment of the non-contact temperature measurement automatic target locking method provided in this application; Figure 2 A schematic diagram of an embodiment of the automatic target locking method for non-contact temperature measurement provided in this application, which involves constructing a spatial thermal field distribution model and performing deconvolution compensation processing; Figure 3 A schematic flowchart of an embodiment of the automatic target locking method for non-contact temperature measurement provided in this application for estimating the target measurement distance between an infrared thermopile and the target being measured; Figure 4 A schematic flowchart of an embodiment of the automatic target locking method for non-contact temperature measurement provided in this application for determining the target measurement temperature; Figure 5 A schematic flowchart of an embodiment of the target automatic locking method for non-contact temperature measurement provided in this application for verifying medium properties; Figure 6 A schematic diagram of an embodiment of the non-contact temperature measurement automatic target locking system provided in this application; Figure 7 This is a schematic diagram of an embodiment of the non-contact temperature measurement target automatic locking device provided in this application. Detailed Implementation

[0021] This application provides a method, system, and related device for automatic target locking in non-contact temperature measurement, which improves the operational reliability, measurement accuracy, and robustness of non-contact temperature measurement.

[0022] It should be noted that the non-contact temperature measurement target automatic locking method provided in this application can be executed by various electronic devices with data processing and sensor signal acquisition capabilities, or it can be applied to a distributed architecture where terminal data acquisition is combined with server processing. Specifically, it can be applied to handheld infrared temperature measurement devices, such as medical forehead thermometers and industrial thermometers, or to general-purpose mobile terminals integrating infrared thermopile sensors and inertial measurement units, including but not limited to smartphones, tablets, smartwatches, smart bracelets, and augmented reality head-mounted displays. Furthermore, it can be applied to intelligent robots (such as medical care robots and power inspection robots) or automatic guidance devices equipped with temperature measurement modules. The server can be an independent physical server, a server cluster consisting of multiple physical servers, or a distributed cloud computing platform, used for backend processing and model reconstruction of time-series data uploaded by the terminal. For ease of explanation and understanding, the specific embodiments of this application use devices as the executing entity of the method for illustrative purposes, but this does not constitute a limitation on the scope of protection of this application.

[0023] Please see Figure 1 , Figure 1 An embodiment of the non-contact temperature measurement target automatic locking method provided in this application includes: 101. Acquire the temperature timing data collected by the infrared thermopile and the motion timing data collected by the inertial measurement unit, and perform synchronization and alignment processing; In practical applications, such as when a user uses a handheld infrared thermometer to scan their forehead horizontally, the infrared thermopile continuously collects temperature values ​​at different locations, while the inertial measurement unit (IMU) simultaneously records the device's motion information during the scanning process. Since these two types of data typically originate from different sensor modules, the IMU usually has a high refresh rate, while the infrared thermopile, limited by its physical thermal response characteristics, often has a lower effective sampling frequency. Directly processing this data can easily lead to a mismatch between temperature changes and the actual scanning position, affecting the accuracy of spatial thermal field modeling and target identification. Therefore, during the data acquisition phase, it is necessary to synchronize and align the temperature time-series data with the motion time-series data. This can be achieved through methods such as unified timestamps, interpolation resampling, or buffer alignment, ensuring that temperature data at the same time point accurately corresponds to the motion state at the same moment.

[0024] Temperature time-series data refers to the sequence of temperature measurement results continuously output by the infrared thermopile during the scanning process according to the preset sampling frequency. This sequence can reflect the temperature changes of the measured scene in the time dimension. Motion time-series data refers to the data sequence of acceleration, angular velocity or attitude changes, etc., synchronously output by the inertial measurement unit during the same scanning process, which reflects the motion state of the terminal and is used to describe the motion process of the infrared thermopile relative to the measured target.

[0025] 102. Calculate the relative displacement during the scanning process based on motion time series data, and construct a spatial thermal field distribution model with relative displacement as the independent variable by combining temperature time series data; It should be noted that relative displacement refers to the change in position of the infrared thermopile relative to the target object along the preset scanning direction during the scanning process. This displacement does not depend on absolute spatial coordinates but reflects the relative relationship between the scanning start point and the current sampling position. In practical applications, for example, when a user performs a linear scan of the forehead area using a relatively natural handheld method, the motion time-series data collected by the inertial measurement unit can reflect the changes in the device's motion state during the scanning process. By analyzing this motion state, the displacement trend of the infrared thermopile along the scanning direction can be obtained. Subsequently, the temperature time-series data synchronized with this displacement change is correlated, so that each temperature sample value can be mapped to the corresponding relative displacement position, thereby transforming the temperature sequence, which was originally arranged only by time, into a temperature distribution relationship arranged by spatial position. Based on the above mapping relationship, a spatial thermal field distribution model can be constructed with relative displacement as the independent variable and temperature as the dependent variable. This model can intuitively reflect the temperature changes at different spatial positions along the scanning path.

[0026] 103. Based on the scanning speed and preset thermal response time parameters during the scanning process, perform deconvolution compensation processing on the spatial thermal field distribution model; Due to the thermal inertia of infrared thermopile, at high scanning speeds, the temperature sensor output may lag behind the actual temperature change of the target object, leading to distortion in the spatial thermal field distribution model. To eliminate this spatial distribution deviation caused by the sensor's dynamic characteristics, this step quantifies the speed of movement at each relative displacement position using scanning speed information and employs a deconvolution algorithm to compensate for the spatial thermal field model using the preset thermal response time parameter of the infrared thermopile, thereby restoring the true temperature change topology. This preset thermal response time parameter refers to the time required for the infrared thermopile's output response to reach a stable value after receiving a temperature change signal; it represents the sensor's dynamic response characteristic.

[0027] For example, when a user quickly scans a region of their forehead with a handheld thermometer, the uncompensated spatial thermal field may show an insufficiently steep temperature rise or a shift in peak position. Deconvolution compensation can correct this lag, allowing the temperature gradient to more accurately reflect the actual heat source distribution. In practical implementation, this thermal response time parameter can be obtained through a step-temperature excitation experiment on an infrared thermopile. Specifically, under controlled conditions, the radiation temperature within the sensor's field of view undergoes a sudden change of known amplitude over a short period. The change in the sensor output over time is recorded, and a time constant parameter describing its dynamic response characteristics is fitted based on this change. This thermal response time parameter describes the response speed required for the sensor output to reach a steady state, serving as a fundamental parameter for constructing a spatial response model.

[0028] The deconvolution compensation process here is based on the spatial response model to construct a constrained deconvolution operation. Specifically, it can be achieved by introducing regularization terms or band-limited constraints into the deconvolution algorithm. This restores the steepness of the temperature edges while suppressing the non-physical amplification of high-frequency noise, thus achieving a balance between edge restoration accuracy and signal stability. In this way, even with fluctuations in scanning speed or environmental noise interference, physically plausible spatial thermal field distribution results can still be obtained.

[0029] 104. Calculate the spatial temperature gradient based on the processed spatial thermal field distribution model, and divide the scanning path into background area, edge change area and effective thermal window area according to the amplitude and change pattern of the spatial temperature gradient. After obtaining the spatial thermal field distribution model after deconvolution compensation correction, the topological morphology of the thermal field distribution needs to be analyzed in order to accurately identify the effective temperature measurement area from continuous scanning data. In actual operation, when the infrared thermopile moves from the air background to the human body, the temperature data does not directly and abruptly change to a single value in space, but rather exhibits a specific pattern of change. Therefore, by calculating the spatial temperature gradient, that is, by determining the rate of change of temperature value relative to relative displacement, the magnitude and pattern of the spatial temperature gradient can reflect the thermal distribution characteristics of different regions. The background area is usually characterized by a small and stable temperature gradient, corresponding to the non-target heat source or environmental background part on the scanning path; the edge change area is characterized by a rapidly increasing or decreasing temperature gradient, reflecting the edge of the measured target or the transition position of the heat source; the effective thermal window area is characterized by a low temperature gradient amplitude and a relatively stable temperature, corresponding to the core measurement area required for physiological thermometry.

[0030] In some specific embodiments, the first spatial derivative of the processed spatial thermal field distribution model is first calculated to obtain a spatial temperature gradient sequence. This sequence reflects the rate of temperature change along the scanning direction and is the basis for determining the characteristics of different scanning regions. Subsequently, within the spatial temperature gradient sequence, the first segment continuously exceeding a preset positive gradient threshold is identified. This segment corresponds to a transition region where the temperature rises rapidly, and it is marked as an edge change region. The start and end displacement points of the edge change region are recorded to define the spatial range of this transition region. Afterward, segments following the edge change region where the absolute value of the spatial temperature gradient is continuously below a preset stability threshold are identified. These segments correspond to regions with stable temperature changes and small gradient amplitudes, and are marked as effective thermal windows for subsequent target locking and temperature measurement. Finally, segments before the edge change region and segments after the effective thermal window region where the spatial temperature gradient shows a negative abrupt change are marked as background regions to eliminate the influence of non-target regions or interfering heat sources in the scanning path. In practical applications, such as when a handheld device scans the forehead, the background area corresponds to the environment outside the air or skin, the edge change area corresponds to the transition zone between the forehead and the hairline or sweat-covered area, and the effective thermal window area corresponds to the core blood supply area of ​​the forehead, which can provide stable and reliable heat source information for subsequent temperature locking.

[0031] 105. Extract the sharpness features corresponding to the edge change area, and estimate the target measurement distance between the infrared thermopile and the target based on the mapping relationship between the sharpness features and the measurement distance; After identifying the edge change zone in the scanning path, high-precision temperature compensation requires obtaining the physical distance between the device and the target at the current moment. In practical non-contact measurement scenarios, due to the inherent field of view (FOV) characteristics of infrared thermopile, the sensor's ability to resolve the edge of the heat source is directly related to the detection distance. When the user holds the device close to their forehead for scanning, the edge transition in the spatial thermal field distribution model exhibits a steep characteristic, specifically manifested as a significant gradient peak. Conversely, when the measurement distance is far, the sensor's field of view covers both the background and the target over a longer displacement distance, causing the received radiant energy to accumulate slowly, resulting in a smoother and more blurred edge transition curve. Based on this optical physical phenomenon, a sharpness feature that quantifies the steepness is extracted from the edge change zone and substituted into a pre-defined algorithm model for reverse calculation, thereby estimating the current actual measurement distance without the need for additional ranging hardware. This process not only reduces hardware costs but also avoids the problem of deviation between the ranging and temperature measurement points caused by the misalignment of optical axes of different types of sensors.

[0032] Among them, the sharpness feature refers to the numerical index used to describe the steepness of the temperature distribution curve at the rising edge in space within the edge change zone. Its physical meaning corresponds to the spatial frequency of the thermal field edge. The mapping relationship is a functional relationship or lookup table formed through experimental calibration, which can reliably invert the sharpness feature value into the target physical distance.

[0033] 106. Monitor the spatial span and temperature variance of the effective thermal window area. When the spatial span meets the preset minimum window length condition and the temperature variance is lower than the stability threshold, generate a target locking signal. After identifying the effective thermal window area in the scanning path, it is necessary to further determine whether the current scanning state meets the stable and reliable temperature measurement conditions. This is to avoid prematurely outputting measurement results before the target has fully entered the field of view or while the measurement process is still in a dynamic transition phase. In practical scenarios, such as when a user holds a device to scan the forehead horizontally or in an arc, the temperature signal received by the infrared thermopile may fluctuate in both time and space due to factors such as uneven hand movements, changes in scanning speed, or short pauses. It is difficult to determine whether the effective temperature measurement area has been aligned based on a single instantaneous temperature value.

[0034] Based on this, two key indicators can be continuously monitored within the effective thermal window area: first, the spatial span of the effective thermal window area in the scanning direction, which characterizes the effective length occupied by the target area in the sensor's field of view during the current scanning process; and second, the temperature variance of the temperature data within the effective thermal window area, which quantifies the stability of the temperature distribution within that area. When the scanning path has just entered or is about to leave the target area, although the local temperature value may be close to the human body temperature range, the spatial span of the effective thermal window area is small, and the temperature data changes significantly with displacement, resulting in a large temperature variance. At this time, it is not suitable to trigger a measurement. As the scanning process continues, the coverage area of ​​the target area in the field of view gradually increases. When the spatial span reaches the preset minimum window length condition, it indicates that the infrared thermopile has stably covered the target within a sufficiently long scanning segment. At the same time, when the temperature variance is lower than the preset stability threshold, it indicates that the temperature distribution within the effective thermal window area tends to be stable, and the jitter and transient interference during the scanning process have been significantly reduced.

[0035] The device will generate a target lock signal only when both of the above conditions are met, which indicates that the current scanning state has met the criteria for reliable measurement, significantly improving the robustness and consistency of non-contact temperature measurement in dynamic scanning scenarios.

[0036] 107. In response to the target lock signal, perform distance compensation on the temperature data within the effective thermal window based on the target measurement distance to determine the target measurement temperature.

[0037] After the target lock signal is generated, it indicates that the target is stably covered within the field of view of the infrared thermopile in the current scanning state, and the temperature measurement conditions meet the reliability requirements. However, in non-contact temperature measurement scenarios, even if the target is locked, different measurement distances still significantly affect the intensity of the infrared radiation signal. This is because infrared radiation experiences energy attenuation during air propagation, and the spatial averaging effect caused by the sensor's field of view also changes with distance. If the temperature data collected within the effective thermal window is directly used as the final output, systematic deviations can easily occur under near-distance and far-distance measurement conditions, thus affecting measurement consistency. Therefore, after responding to the target lock signal, distance compensation processing with the target measurement distance as the core parameter can be introduced to uniformly correct the temperature data within the effective thermal window.

[0038] In actual operation, the system first selects representative temperature data within the effective thermal window as the base measurement value. For example, it selects the core section with the most stable temperature distribution within this area to reduce the impact of edge effects on the compensation results. Subsequently, based on the target measurement distance and a preset physical or empirical model, it compensates for factors such as radiation attenuation and field of view expansion introduced by changes in measurement distance. This ensures that the compensated temperature result can equivalently reflect the true thermal radiation level of the target under standard measurement distance conditions. This allows for more consistent target measurement temperature results under different user habits and scanning distances, avoiding problems such as lower temperatures at greater distances or reading jumps due to unstable distances.

[0039] In this embodiment, the temperature time-series data collected by the infrared thermopile and the motion time-series data collected by the inertial measurement unit are uniformly aligned during the temperature measurement process, and a one-dimensional spatial thermal field distribution model with relative displacement as the independent variable is constructed. In this spatial thermal field model, deconvolution compensation is performed in combination with the scanning speed and the thermal response parameters of the infrared thermopile to eliminate the influence of sensor thermal inertia and scanning speed changes on the thermal field topology, so that the subsequently calculated spatial temperature gradient can truly reflect the temperature change characteristics of the target area. Based on the gradient magnitude and change pattern of the spatial temperature gradient, the scanning path is divided into a background area, an edge change area, and an effective thermal window area, thereby distinguishing the target heat source from background interference. After obtaining the spatial range of the effective thermal window area, the spatial span and temperature variance of the range are monitored. When the spatial span meets the preset minimum window length condition and the temperature variance is lower than the stability threshold, a target locking signal is generated to ensure that the temperature data of the locked area has sufficient stability and spatial continuity. Subsequently, the spatial temperature gradient sharpness features of the edge change area are extracted, and the measurement distance between the sensor and the target is estimated based on the mapping relationship between gradient sharpness and measurement distance. After generating the target lock signal, distance compensation is performed on the temperature data within the effective thermal window based on the estimated measurement distance to determine the final measurement temperature.

[0040] The entire temperature measurement process requires no subjective judgment from the user. Even with changes in scanning speed, different target distances, or the presence of local cold spot interference, it can still accurately identify the effective temperature measurement area and lock the temperature measurement target point. At the same time, it eliminates sensor response lag and distance measurement effects, thereby significantly improving the operational reliability, measurement accuracy, and robustness of non-contact temperature measurement.

[0041] Please see Figure 2 According to some embodiments of the present invention, the construction of a spatial thermal field distribution model in step 102 and the execution of deconvolution compensation processing in step 103 may specifically include, but are not limited to, the following: 201. Perform gravity component filtering on the acceleration signal in the motion time series data to separate the linear acceleration component characterizing the scanning motion from the motion time series data; After synchronizing the motion time series data with the temperature time series data, in order to transform the temperature sampling results that change over time during the non-contact temperature measurement process into thermal distribution information with spatial significance, this embodiment further processes the motion time series data to calculate the relative displacement during the scanning process and construct a spatial thermal field distribution model accordingly.

[0042] Specifically, the acceleration signal in the motion time series data first undergoes gravity component filtering to eliminate the constant or slowly changing components introduced by gravity projection under different device postures. Only the linear acceleration component generated by the user's handheld scanning motion is retained, ensuring that the motion state reflected in subsequent integration calculations is not affected by changes in device posture such as tilt or rotation, and can truly correspond to the scanning action itself. The obtained linear acceleration component refers to the acceleration vector that reflects the velocity change of an object due to external forces (such as hand pushing) after removing the influence of gravity, and is the physical basis for calculating the object's trajectory. Gravity component filtering refers to the data cleaning process that identifies and subtracts the influence of gravity acceleration through posture fusion algorithms.

[0043] 202. Perform time integration on the linear acceleration components to obtain the scanning velocity during the scanning process, and perform cumulative integration along the scanning direction based on the scanning velocity to obtain the cumulative relative displacement sequence corresponding to the scanning process; After obtaining the linear acceleration component, it is integrated over time to obtain the scanning speed change during the scanning process. Since non-contact temperature measurement typically involves the user holding the device and performing a short, approximately linear sweep across the target surface, this process includes a complete motion cycle of acceleration start-up-uniform / variable speed scanning-deceleration stop. This embodiment further performs cumulative integration on the scanning speed along a preset scanning direction to calculate the physical distance relative to the scanning start point at each moment, obtaining the cumulative relative displacement sequence corresponding to the scanning process. This cumulative relative displacement sequence is used to describe the spatial movement of the infrared thermopile relative to the measured target surface during the scanning process, enabling subsequent temperature data to be converted from the time domain to the spatial domain for analysis.

[0044] 203. Using the cumulative relative displacement sequence as the spatial independent variable, the temperature time series data synchronized with the motion time series data is mapped to the corresponding relative displacement position, and the temperature data between adjacent relative displacement positions is interpolated to generate a spatial thermal field distribution model that changes continuously along the scanning direction. Based on this, using the cumulative relative displacement sequence as the spatial independent variable, the temperature time series data synchronized with the motion time series data is mapped to the corresponding relative displacement position, thereby establishing the correspondence between temperature sampling values ​​and spatial positions. Since the scanning speed may fluctuate slightly during the actual scanning process, resulting in non-uniform spatial distribution of adjacent temperature sampling points, further interpolation processing can be performed on the temperature data between adjacent relative displacement positions to compensate for gaps or jumps caused by uneven spatial sampling, making the generated temperature distribution exhibit continuous spatial variation. Through the above processing, a spatial thermal field distribution model that continuously varies along the scanning direction and eliminates the influence of speed fluctuations is finally constructed, intuitively reflecting the topological morphology of the surface temperature distribution of the measured target with spatial position.

[0045] 204. Construct a spatial response model characterizing the thermal response hysteresis of the infrared thermopile during the scanning process based on the scanning speed and preset thermal response time parameters; After obtaining a continuously varying spatial thermal field distribution model along the scanning direction, this embodiment needs to quantitatively model the thermal response hysteresis effect generated by the infrared thermopile during scanning in order to further eliminate measurement distortion introduced by the sensor's own physical characteristics under dynamic measurement conditions. As a type of thermal sensor, the output of an infrared thermopile is not an instantaneous response to incident radiation energy, but rather, limited by the thermal capacity and heat conduction path of the thermal element, it exhibits a response characteristic that gradually approaches a steady state over time. This characteristic is usually characterized by a pre-calibrated thermal response time parameter. Under static measurement conditions, this thermal response time parameter mainly affects the waiting time required for measurement stabilization; however, under scanning measurement conditions, because the infrared thermopile continuously undergoes spatial displacement relative to the target being measured, this temporal response hysteresis is mapped to a spatial response extension by the scanning speed, causing the temperature signal acquired by the sensor at a certain spatial location to be mixed with the residual thermal response from adjacent preceding locations.

[0046] Based on this, this embodiment combines the scanning speed calculated in the preceding steps with the preset thermal response time parameter to convert the time-domain thermal response characteristics of the infrared thermopile into spatial-domain response characteristics, constructing a spatial response model to describe the thermal response hysteresis effect during scanning. This spatial response model is used to characterize the spatial smoothing and tailing effects produced by the real spatial temperature distribution after passing through the sensor system under a given scanning speed condition. Its essence can be understood as a spatial filtering model that varies with the scanning speed.

[0047] 205. Perform deconvolution compensation processing on the spatial thermal field distribution model based on the spatial response model.

[0048] After constructing the spatial response model, this embodiment further performs deconvolution compensation processing on the spatial thermal field distribution model, which includes thermal response hysteresis distortion, based on the spatial response model. During the scanning temperature measurement process, the actual acquired spatial thermal field distribution model can be regarded as the result of convolution operation between the real spatial temperature distribution and the spatial response model. This convolution effect will cause the temperature peaks in the spatial thermal field distribution to be suppressed, and the edges of temperature changes will be widened and shifted in position in the scanning direction, thereby affecting the accuracy of subsequent spatial gradient calculation and target point determination.

[0049] Deconvolution compensation processing restores the acquired spatial thermal field distribution model by introducing the inverse operation relationship of the spatial response model, thereby offsetting the spatial ambiguity effect caused by thermal response hysteresis. After deconvolution compensation processing, the temperature change edges smoothed by thermal inertia in the spatial thermal field distribution model are restored, the temperature peak amplitude is closer to the true radiation level of the measured target, and its spatial position is also corrected accordingly, thus significantly improving the realism of the spatial thermal field structure.

[0050] This embodiment enables the acquisition of a spatial thermal field model that closely matches the actual surface temperature distribution of a target, even when the user's scanning speed varies. This provides a reliable data foundation for subsequent region division and target locking based on spatial temperature gradients.

[0051] Please see Figure 3 According to some embodiments of the present invention, in step 105, the sharpness features corresponding to the edge change region are extracted, and the target measurement distance between the infrared thermopile and the target under test is estimated based on the mapping relationship between the sharpness features and the measurement distance. Specifically, this may include, but is not limited to, the following: 301. Perform extreme value search operation on the spatial temperature gradient along the scanning direction within the edge change region, extract the maximum amplitude of the spatial temperature gradient, and calculate the temperature amplitude increment between the start and end positions of the edge change region. After completing the region division of the scanning path and determining the edge change zone, this embodiment further utilizes the spatial temperature structure characteristics within the edge change zone to estimate the target measurement distance between the infrared thermopile and the target being measured. Specifically, an extreme value search operation is performed on the spatial temperature gradient along the scanning direction within the edge change zone to extract the maximum amplitude of the spatial temperature gradient. This maximum amplitude is used to characterize the steepest temperature change at the rising edge in space, reflecting the clarity of the thermal field edge in the spatial domain. Simultaneously, to avoid being affected by differences in the absolute temperature level of the target due to relying solely on the gradient amplitude, this embodiment also calculates the temperature amplitude increment between the starting and ending positions of the edge change zone to characterize the overall magnitude of temperature change within that edge region.

[0052] 302. Calculate the ratio between the maximum amplitude of the spatial temperature gradient and the temperature amplitude increment to generate a normalized sharpness factor to characterize the steepness of the edge transition. After obtaining the maximum amplitude of the spatial temperature gradient and the corresponding temperature amplitude increment, the ratio of these two values ​​is calculated to generate a normalized sharpness factor characterizing the steepness of the edge transition. In practical applications, the surface temperature of the target being measured is uncertain, and the ambient temperature also varies. If only the absolute value of the gradient is used to judge the steepness of the edge, it will be directly affected by the magnitude of the target temperature difference; a larger temperature difference naturally results in a larger gradient, but this does not necessarily mean a closer distance. To eliminate the influence of the target's absolute temperature on the ranging logic, normalization processing is required to effectively eliminate the direct influence of absolute temperature differences under different target conditions or measurement conditions on the gradient amplitude. Specifically, the maximum amplitude of the gradient (slope) is divided by the temperature amplitude increment (slope height), resulting in a dimensionless parameter that is independent of the temperature difference and only related to the edge geometry. The resulting sharpness factor mainly reflects the relative steepness of the thermal field edge in space, thus possessing better stability and comparability. This normalized sharpness factor physically corresponds to the strength of the high-frequency characteristics of the edge transition in the spatial thermal field and can directly reflect the resolution capability of the infrared thermopile for the target edge under the current measurement distance conditions.

[0053] 303. Call the pre-established field-of-view optical transfer function model, substitute the normalized sharpness factor into the field-of-view optical transfer function model to obtain the corresponding physical distance value, and perform amplitude limiting processing on the physical distance value to output the target measurement distance. The field-of-view optical transfer function model is used to describe the fuzzy response characteristics of the infrared thermopile to the edge of space temperature under different measurement distance conditions.

[0054] Specifically, this field-of-view optical transfer function model can be represented by a function model or lookup table structure describing the relationship between the normalized sharpness factor and the actual measurement distance. Its physical basis stems from the spatial integration effect under the condition of a fixed field of view of the infrared thermopile: the infrared thermopile has a fixed field of view, and its detection area expands conically with increasing distance. When the sensor is close to the target, the detection spot is small, and the temperature changes drastically when sweeping across the target edge, resulting in a high sharpness factor. When the distance is far, the detection spot is large, and a longer half-background, half-target mixing integration process occurs when sweeping across the edge, leading to a gradual temperature change and a low sharpness factor. This field-of-view optical transfer function model can be pre-established through calibration experiments. During calibration, the infrared thermopile is kept at different known distances from a standard heat source with a clearly defined edge structure. Spatial thermal field data is collected under the same scanning speed and environmental conditions, and the corresponding normalized sharpness factor is calculated in a manner consistent with the actual measurement process. By statistically fitting the sharpness factors at different distances, the mapping relationship between the sharpness factor and distance can be established.

[0055] During the runtime phase, a pre-established field-of-view optical transfer function (FOV) model is invoked. The normalized sharpness factor is substituted into the FOV model for calculation to obtain the corresponding physical distance value. This FOV model describes the fuzzy response characteristics of an infrared thermopile to the edge of spatial temperature caused by changes in the field-of-view coverage under different measurement distance conditions. This model can be pre-established through standard heat source calibration experiments, recording the correspondence between the normalized sharpness factor and the actual measurement distance. To ensure the physical rationality of the output results and the stability of the system, the calculated physical distance value also needs to be limited to a preset effective measurement distance range. Excessive distance estimates, such as negative or extremely long distances, are discarded, and the final output is the target measurement distance.

[0056] In this embodiment, reliable estimation of the measurement distance can be achieved based on the spatial thermal field structure characteristics without the need to introduce additional ranging hardware, which greatly reduces hardware costs and improves the system integration.

[0057] It is important to distinguish between the spatial response model and the field-of-view optical transfer function model, which have different physical mechanisms and applications. Specifically, the spatial response model primarily reflects the temperature response lag and spatial tailing effect caused by the sensor's thermal inertia during dynamic scanning of the infrared thermopile. This model is a mathematical function constructed based on scanning speed and preset thermal response time parameters. It is used to perform deconvolution compensation on the acquired spatial thermal field distribution model, eliminating signal amplitude attenuation and edge smoothing caused by thermal inertia, thereby restoring the sharp characteristics of the true temperature edges. In short, the spatial response model mainly corrects the dynamic delay of the temperature signal in time and space; its input is the spatial thermal field model, and its output is the thermally compensated temperature curve. The field-of-view optical transfer function model, on the other hand, describes the optical imaging characteristics of the infrared thermopile itself, reflecting the sensor's natural blurring response to spatial temperature edges at different measurement distances. This model does not involve thermal inertia; instead, it is based on a sharpness-distance mapping relationship established through pre-calibration experiments, using a normalized sharpness factor as input and outputting the target measurement distance. In short, the role of the field-of-view optical transfer function model is to convert physical optical imaging characteristics into distance estimation parameters, and solve the problem of edge widening or flattening caused by the limited field of view of infrared sensors.

[0058] Therefore, although both models affect the steepness of the edge curve, their physical sources are different: the spatial response model originates from the dynamic response characteristics (thermal inertia) of the thermal sensor and is used for temperature signal reconstruction; the field-angle optical transfer function model originates from the physical characteristics of optical imaging (field-angle effect) and is used for distance inversion. The device first completes thermal inertia compensation through the spatial response model to restore the true shape of the temperature edge, and then uses the field-angle optical transfer function model to map sharpness to distance, achieving high-precision ranging without additional ranging hardware.

[0059] Please see Figure 4 According to some embodiments of the present invention, in step 107, in response to the target locking signal, distance compensation is performed on the temperature data within the effective thermal window based on the target measurement distance to determine the target measurement temperature. Specifically, this may include, but is not limited to, the following: 401. Calculate the attenuation coefficient of infrared radiation energy during air transmission based on the target measurement distance and the preset atmospheric radiation transmission model; In the physical process of non-contact temperature measurement, infrared radiation does not propagate in a vacuum but must pass through an air medium of a certain thickness. Components in the air, such as water vapor and carbon dioxide, absorb and scatter infrared radiation in specific wavelengths, causing the radiation energy received by the sensor to exhibit a non-linear attenuation trend with increasing measurement distance. Ignoring this physical phenomenon and relying solely on factory calibration for calculations will result in lower readings at long distances. This step utilizes the previously estimated real-time target measurement distance to invoke a preset atmospheric radiation transfer model. This model simulates the energy loss characteristics of infrared light propagating through the air and can calculate a specific attenuation coefficient based on the current distance value, providing a correction basis for subsequently reconstructing the emitted energy of the target surface.

[0060] The atmospheric radiative transfer model is a mathematical model based on the Beer-Lambert law, used to describe the attenuation of infrared radiation intensity as it changes with transmission distance and atmospheric medium properties. The attenuation coefficient is a dimensionless value less than 1, which characterizes the proportion of radiative energy remaining after transmission through the air.

[0061] 402. Calculate the average temperature value of the preset core area within the effective thermal window as the original measurement temperature; Although the preceding steps have defined a relatively stable effective thermal window, noise data may still exist at the beginning and end of this window due to incomplete field of view stabilization or minor edge jitter. To further improve the confidence of the data, this step does not directly use the average value of the entire thermal window, but instead extracts data from a predetermined core area within the effective thermal window, such as the 50% area in the center of the thermal window. This core area typically corresponds to the stage where the scanning process is most stable and the sensor's field of view is most fully filled by the target. The device performs a statistical averaging operation on the temperature data within this core area, filtering out random thermal noise, and uses the calculation result as the raw, uncorrected temperature measurement.

[0062] 403. The original measured temperature is compensated by reverse gain using the attenuation coefficient, and the influence component of the current ambient temperature is superimposed to calculate the true surface temperature of the target as the target measured temperature.

[0063] Since infrared thermopile essentially detects the radiative temperature difference between the target and the sensor itself (ambient temperature), and this radiative signal undergoes air attenuation during transmission, the directly measured raw temperature is actually the temperature difference after distance attenuation. To restore the true surface temperature of the target, the original measured temperature is subjected to inverse gain compensation processing using the attenuation coefficient calculated above, to counteract the energy attenuation effect of infrared radiation during air transmission; the compensated temperature difference value is then superimposed with the current ambient temperature to finally calculate the true surface temperature of the target, which is used as the target measured temperature.

[0064] In this embodiment, temperature reading drift caused by changes in measurement distance can be effectively eliminated, so that users can obtain consistent and accurate body temperature readings even when scanning at different distances, achieving true distance-adaptive measurement.

[0065] Please see Figure 5 According to some embodiments of the present invention, after extracting the sharpness features corresponding to the edge change area in step 105, in order to further improve the anti-interference capability of the measurement and prevent false triggering due to scanning non-biological constant-temperature heat sources (such as a cup containing hot water, the heat dissipation surface of working electronic devices, etc.), a medium property verification step can be added before generating the target point locking signal in step 106. This step specifically includes the following steps: 501. Within the effective thermal window, extract the transient response curve of temperature time series data as a function of relative displacement based on the spatial thermal field distribution model, and calculate the rate of temperature change and the fluctuation amplitude of the transient response curve within the preset displacement disturbance range. After identifying a potential effective thermal window, the system does not immediately lock onto the temperature of that area. Instead, it further analyzes the microscopic thermal response characteristics within that area. This is because, in real-world physical scenarios, human skin is not an ideal, uniform thermal radiator. Instead, it is a multi-layered, complex biological medium composed of the epidermis, dermis, subcutaneous fat layer, and capillary network. Its heat conduction, heat capacity distribution, and radiation characteristics exhibit significant non-uniformity at the microscopic spatial scale. When a user holds the device and scans along the forehead surface, slight hand tremors or continuous slippage are inevitable. This minute displacement constitutes a pre-defined displacement perturbation condition. Under this perturbation, the radiation signal received by the infrared thermopile does not change completely smoothly in the spatial dimension but reflects the subtle texture features of the thermal field on the surface of the target. This step, based on the established spatial thermal field distribution model, extracts the transient response curve formed by temperature data changing with relative displacement within the effective thermal window and calculates the rate of temperature change and its fluctuation amplitude within the minute displacement range.

[0066] Among them, the transient response curve is used to characterize the response behavior of the temperature signal to small spatial disturbances during the scanning process, reflecting the spatial thermal texture characteristics of the surface of the target being measured; the temperature change rate is used to characterize the overall gradient characteristics of temperature change with displacement; the fluctuation amplitude is the oscillation intensity of the temperature signal in the local spatial scale after removing the overall change trend, used to describe the micro-roughness of the thermal field.

[0067] For uniform non-biological heat sources such as metals, ceramics, or hot liquids, the surface heat distribution is highly smooth, and the transient response curve usually shows an approximately linear change under small displacement perturbations, with a low rate of temperature change and fluctuation amplitude close to zero. However, for human skin, due to the non-uniformity of subcutaneous blood perfusion and the presence of skin surface microstructures, its transient response curve exhibits non-random characteristics with a certain slope and superimposed with specific frequency fluctuations at the microscale. This characteristic can be regarded as a biological thermal response signal with statistical regularity.

[0068] 502. Match the rate of temperature change and its fluctuation amplitude with the preset set of transient thermal response characteristics of biological media to generate a biological confidence factor characterizing the properties of the media corresponding to the effective thermal window area, and determine whether the biological confidence factor is higher than the preset biological confidence threshold. After obtaining characteristic parameters such as the rate of temperature change and the amplitude of temperature fluctuation, these parameters are matched with a pre-defined set of transient thermal response characteristics for biological media. This set of characteristics is obtained by collecting thermal response data from a large number of human skin samples and various common non-biological heat sources under dynamic scanning conditions. It describes the typical thermal response behavior of living biological surfaces under conditions of minute displacement disturbances. Its characteristic parameters include at least the statistical distribution range of the rate of temperature change, the mean and variance characteristics of the temperature fluctuation amplitude, and the correlation between the two within a pre-defined displacement scale. This set of transient thermal response characteristics for biological media can be obtained by dynamically scanning multiple subjects under different environmental conditions, while simultaneously collecting scanning data from several common non-biological isothermal or quasi-isothermal heat sources as control samples. After feature extraction and statistical analysis of the collected data, a parameter set is formed to distinguish between biological media and non-biological heat sources.

[0069] In actual operation, feature matching or similarity evaluation algorithms are used to compare and analyze the thermal response characteristics corresponding to the current effective thermal window with the set of transient thermal response characteristics of biological media, quantitatively assess their matching degree, and generate a normalized biological confidence factor. The biological confidence factor is used to quantitatively characterize the credibility of the current target belonging to a living organism. The higher the value, the more the thermal response behavior of the target is consistent with the typical thermal characteristics of human skin.

[0070] Subsequently, the biological confidence factor is compared with the preset biological confidence threshold. When the biological confidence factor is lower than the threshold, the system determines that the currently scanned heat source may be a non-biological pseudo-heat source, thereby avoiding the false locking of environmental interference heat sources.

[0071] 503. When the spatial span meets the preset minimum window length condition, the temperature variance is lower than the stability threshold, and the biological confidence factor is higher than the preset biological confidence threshold, a target locking signal is generated.

[0072] During the comprehensive judgment phase, the system employs a multi-condition joint decision mechanism to determine whether a target locking signal has been generated. Specifically, firstly, the spatial span of the effective thermal window area in the scanning direction must meet a preset minimum window length condition to ensure that the acquired target surface area is complete and representative; secondly, the temperature variance within the effective thermal window area must be lower than a preset stability threshold to ensure the temporal and spatial stability of the thermal signal during acquisition; and thirdly, the biological confidence factor must be higher than a preset biological confidence threshold to confirm that the current measurement object has biological media properties.

[0073] Only when the aforementioned spatial continuity condition, thermal stability condition, and biological attribute condition are simultaneously satisfied is the current scanning process considered to be a valid, stable measurement operation targeting a real human target, and a target locking signal is generated accordingly to trigger the subsequent distance compensation and target measurement temperature calculation process.

[0074] It should be noted that, The following provides a detailed description of the non-contact temperature measurement automatic target locking system provided in this application. Please refer to [link / reference]. Figure 6 , Figure 6 Another embodiment of the non-contact temperature measurement target automatic locking system provided in this application, the system includes: The acquisition unit 601 is used to acquire temperature timing data collected by the infrared thermopile and motion timing data collected by the inertial measurement unit, and to perform synchronization alignment processing. The building unit 602 is used to calculate the relative displacement during the scanning process based on motion time series data, and to build a spatial thermal field distribution model with relative displacement as the independent variable by combining temperature time series data; The processing unit 603 is used to perform deconvolution compensation processing on the spatial thermal field distribution model according to the scanning speed and preset thermal response time parameters during the scanning process. The calculation unit 604 is used to calculate the spatial temperature gradient based on the processed spatial thermal field distribution model, and divide the scanning path into background area, edge change area and effective thermal window area according to the amplitude and change pattern of the spatial temperature gradient. The estimation unit 605 is used to extract the sharpness features corresponding to the edge change area and estimate the target measurement distance between the infrared thermopile and the target under test based on the mapping relationship between the sharpness features and the measurement distance. The locking unit 606 is used to monitor the spatial span and temperature variance of the effective thermal window area. When the spatial span meets the preset minimum window length condition and the temperature variance is lower than the stability threshold, a target locking signal is generated. The determination unit 607 is used to determine the target measurement temperature by performing distance compensation on the temperature data within the effective thermal window based on the target measurement distance in response to the target locking signal.

[0075] In this embodiment, the functions of each unit are the same as described above. Figures 1 to 5 The steps in the method embodiments shown correspond to those in the examples, and will not be repeated here.

[0076] This application also provides a non-contact temperature measurement automatic target locking device; please refer to [link to relevant documentation]. Figure 7 , Figure 7 One embodiment of the non-contact temperature measurement target automatic locking device provided in this application includes: Processor 701, memory 702, input / output unit 703, bus 704; The processor 701 is connected to the memory 702, the input / output unit 703, and the bus 704; The memory 702 stores a program, and the processor 701 calls the program to execute any of the above-mentioned non-contact temperature measurement target automatic locking methods.

[0077] This application also relates to a computer-readable storage medium storing a program that, when run on a computer, causes the computer to perform any of the above-described non-contact temperature measurement target automatic locking methods.

[0078] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0080] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0081] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0082] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A non-contact temperature measurement method for automatic target locking, characterized in that, The method includes: Acquire temperature timing data collected by the infrared thermopile and motion timing data collected by the inertial measurement unit, and perform synchronization and alignment processing; The relative displacement during the scanning process is calculated based on the motion time series data, and a spatial thermal field distribution model with the relative displacement as the independent variable is constructed in combination with the temperature time series data. Based on the scanning speed and preset thermal response time parameters during the scanning process, deconvolution compensation processing is performed on the spatial thermal field distribution model. The spatial temperature gradient is calculated based on the processed spatial thermal field distribution model, and the scanning path is divided into background area, edge change area and effective thermal window area according to the amplitude and change pattern of the spatial temperature gradient. Extract the sharpness features corresponding to the edge change region, and estimate the target measurement distance between the infrared thermopile and the target based on the mapping relationship between the sharpness features and the measurement distance; Monitor the spatial span and temperature variance of the effective thermal window area. When the spatial span meets the preset minimum window length condition and the temperature variance is lower than the stability threshold, generate a target locking signal. In response to the target locking signal, the temperature data within the effective thermal window is compensated for distance based on the target measurement distance to determine the target measurement temperature.

2. The automatic target locking method according to claim 1, characterized in that, The step of calculating the relative displacement during the scanning process based on the motion time-series data, and constructing a spatial thermal field distribution model with the relative displacement as the independent variable in conjunction with the temperature time-series data, includes: Gravity component filtering is performed on the acceleration signal in the motion time series data to separate the linear acceleration component characterizing the scanning motion from the motion time series data; The scanning velocity during the scanning process is obtained by performing time integration on the linear acceleration component, and the cumulative integration is performed along the scanning direction based on the scanning velocity to obtain the cumulative relative displacement sequence corresponding to the scanning process. Using the cumulative relative displacement sequence as a spatial independent variable, the temperature time series data synchronized with the motion time series data is mapped to the corresponding relative displacement position, and the temperature data between adjacent relative displacement positions is interpolated to generate a spatial thermal field distribution model that changes continuously along the scanning direction.

3. The automatic target locking method according to claim 2, characterized in that, The step of performing deconvolution compensation processing on the spatial thermal field distribution model based on the scanning speed and preset thermal response time parameters during the scanning process includes: A spatial response model characterizing the thermal response hysteresis of the infrared thermopile during scanning is constructed based on the scanning speed and the preset thermal response time parameter. Based on the spatial response model, deconvolution compensation processing is performed on the spatial thermal field distribution model.

4. The automatic target locking method according to claim 1, characterized in that, The step of extracting the sharpness features corresponding to the edge change region and estimating the target measurement distance between the infrared thermopile and the target based on the mapping relationship between the sharpness features and the measurement distance includes: An extreme value search operation is performed on the spatial temperature gradient along the scanning direction within the edge change region to extract the maximum amplitude of the spatial temperature gradient, and the temperature amplitude increment between the start and end positions of the edge change region is calculated. The ratio between the maximum amplitude of the spatial temperature gradient and the temperature amplitude increment is calculated to generate a normalized sharpness factor that characterizes the steepness of the edge transition. The pre-established field-of-view optical transfer function model is invoked, and the normalized sharpness factor is substituted into the field-of-view optical transfer function model to calculate the corresponding physical distance value. The physical distance value is then subjected to amplitude limiting processing to output the target measurement distance. The field-of-view optical transfer function model is used to describe the fuzzy response characteristics of the infrared thermopile to the edge of space temperature under different measurement distance conditions.

5. The automatic target locking method according to claim 1, characterized in that, The step of responding to the target lock signal by performing distance compensation on the temperature data within the effective thermal window based on the target measurement distance to determine the target measurement temperature includes: The attenuation coefficient of infrared radiation energy during air transmission is calculated based on the target measurement distance and the preset atmospheric radiation transmission model. The average temperature value of the preset core area within the effective thermal window is calculated as the original measurement temperature. The original measured temperature is compensated for by inverse gain using the attenuation coefficient, and the influence component of the current ambient temperature is added to calculate the true surface temperature of the target as the target measured temperature.

6. The automatic target locking method according to claim 1, characterized in that, After extracting the sharpness features corresponding to the edge change region and before generating the target point locking signal, the method further includes: Within the effective thermal window, the transient response curve of temperature time series data as a function of relative displacement is extracted based on the spatial thermal field distribution model, and the rate of temperature change and the fluctuation amplitude of the transient response curve within the preset displacement disturbance range are calculated. The temperature change rate and its fluctuation amplitude are matched with a preset set of transient thermal response characteristics of biological media to generate a biological confidence factor characterizing the properties of the medium corresponding to the effective thermal window area, and it is determined whether the biological confidence factor is higher than a preset biological confidence threshold. When the spatial span meets the preset minimum window length condition and the temperature variance is lower than the stability threshold, a target locking signal is generated, including: When the spatial span meets the preset minimum window length condition, the temperature variance is lower than the stability threshold, and the biological confidence factor is higher than the preset biological confidence threshold, a target locking signal is generated.

7. The automatic target locking method according to any one of claims 1 to 6, characterized in that, The spatial temperature gradient is calculated based on the processed spatial thermal field distribution model, and the scanning path is divided into a background area, an edge change area, and an effective thermal window area according to the amplitude and variation pattern of the spatial temperature gradient, including: The first spatial derivative of the processed spatial thermal field distribution model is calculated to obtain the spatial temperature gradient sequence. Identify the first segment in the spatial temperature gradient sequence that continuously exceeds a preset positive gradient threshold, and mark the segment as an edge change region, and record the starting displacement point and ending displacement point of the edge change region; After the edge change zone, identify the segment where the absolute value of the spatial temperature gradient is continuously lower than a preset stability threshold, and mark the segment as an effective thermal window zone; The segments located before the edge change zone and the segments located after the effective thermal window zone where the spatial temperature gradient shows a negative abrupt change are marked as background zones.

8. A non-contact temperature measurement target automatic locking system, characterized in that, The system includes: The acquisition unit is used to acquire temperature timing data collected by the infrared thermopile and motion timing data collected by the inertial measurement unit, and to perform synchronization and alignment processing. The construction unit is used to calculate the relative displacement during the scanning process based on the motion time series data, and to construct a spatial thermal field distribution model with the relative displacement as the independent variable in combination with the temperature time series data. The processing unit is used to perform deconvolution compensation processing on the spatial thermal field distribution model according to the scanning speed and preset thermal response time parameters during the scanning process. The calculation unit is used to calculate the spatial temperature gradient based on the processed spatial thermal field distribution model, and to divide the scanning path into a background area, an edge change area and an effective thermal window area according to the amplitude and change pattern of the spatial temperature gradient. An estimation unit is used to extract the sharpness features corresponding to the edge change area, and estimate the target measurement distance between the infrared thermopile and the target under test based on the mapping relationship between the sharpness features and the measurement distance. The locking unit is used to monitor the spatial span and temperature variance of the effective thermal window area. When the spatial span meets the preset minimum window length condition and the temperature variance is lower than the stability threshold, a target locking signal is generated. A determining unit is configured to, in response to the target locking signal, perform distance compensation on the temperature data within the effective thermal window based on the target measurement distance, so as to determine the target measurement temperature.

9. A non-contact temperature measurement target automatic locking device, characterized in that, The device includes: Processor, memory, input / output units, and bus; The processor is connected to the memory, the input / output unit, and the bus; The memory stores a program, which the processor invokes to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains a program that, when executed on a computer, performs the method as described in any one of claims 1 to 7.

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