An infrared sensing method adaptive to multi-material and multi-environment occlusion

Abstract

This application relates to the technical field of infrared sensing, and in particular to an adaptive infrared sensing method for multiple materials and multiple environmental occlusions. The method includes: acquiring real-time infrared sampling values ​​to obtain an initial illumination baseline for the current environment; identifying surface reflection characteristics from the amplitude data in the initial illumination baseline, obtaining material category information based on the surface reflection characteristics, and obtaining a corresponding material base threshold based on the material category information; acquiring the current baseline, synthesizing a real-time dynamic threshold based on the material base threshold and the current baseline, and determining whether effective gesture occlusion has occurred based on the real-time dynamic threshold; determining whether the area has entered a strong light saturation region based on the real-time infrared sampling values, and if so, locking the current baseline and generating a strong light dynamic threshold based on the material category information. This application achieves the effect of automatically adapting a single algorithm to all common lighting and material environments, ensuring the stability and reliability of infrared sensing under all operating conditions.

Description

Technical Field

[0001] This application relates to the technical field of infrared sensing, and in particular to an adaptive infrared sensing method that adapts to multiple materials and multiple environmental occlusions. Background Technology

[0002] Currently, infrared sensing control technology is widely used in smart homes, lighting control, bathroom fixtures, security sensing, and other fields, such as sensor lamps, cabinet lights, hallway motion sensor switches, sensor faucets, and smart toilet seats. These applications typically face diverse installation environments, including varying light intensities (such as direct sunlight and low-light environments) and installation surfaces with different reflective properties (such as tiles, acrylic, wood, plastic, and mirrors), requiring the sensing devices to operate stably and reliably under various conditions.

[0003] In existing technologies, traditional infrared sensing devices generally use a fixed trigger threshold combined with a fixed reference baseline to detect gesture occlusion. After the system is powered on, environmental parameters are not used as the reference baseline; instead, a fixed difference is used as the judgment standard. When the signal change detected by the infrared receiver exceeds the preset fixed threshold, a valid gesture is determined to have occurred. This solution is simple in structure, easy to implement, and can meet basic sensing requirements in a single and stable environment.

[0004] However, since the fixed trigger threshold and fixed baseline cannot be automatically adjusted with changes in the environment, when the reflective material of the mounting surface varies greatly (such as a highly reflective mirror and a low-reflective wood), or when the infrared receiver tube is close to saturation due to direct strong light, or when the baseline drifts due to slow changes in ambient light, this solution cannot guarantee accurate recognition of gestures in all scenarios, thus limiting the applicability and reliability of infrared sensing devices. Summary of the Invention

[0005] In order to achieve an algorithm that automatically adapts to all common lighting and material environments without manual intervention or firmware changes, and to ensure the stability and reliability of infrared sensing under all working conditions, this application provides an adaptive infrared sensing method for multiple materials and multiple environmental occlusions.

[0006] The above-mentioned objective of this application is achieved through the following technical solution: An adaptive infrared sensing method for multiple materials and multiple environmental occlusions, characterized in that the adaptive infrared sensing method for multiple materials and multiple environmental occlusions includes: The infrared real-time sampling value is obtained, and after filtering the infrared real-time sampling value, the initial illumination baseline of the current environment is obtained. The surface reflection characteristics are identified from the amplitude data in the initial illumination baseline, the material category information is obtained based on the surface reflection characteristics, and the corresponding material basic threshold is obtained based on the material category information. Obtain the current baseline, synthesize a real-time dynamic threshold based on the material base threshold and the current baseline, and determine whether a valid gesture occlusion has occurred based on the real-time dynamic threshold; Based on the real-time infrared sampling value, it is determined whether the area has entered the strong light saturation region. If so, the current baseline is locked, and a strong light dynamic threshold is generated based on the material category information.

[0007] Preferably, the step of filtering the real-time infrared sampled values ​​to obtain the initial illumination baseline of the current environment specifically includes: The continuously collected real-time infrared sampled values ​​are acquired to form a sampled value sequence; The sampled value sequence is subjected to mean filtering, and the average value after filtering is used as the initial illumination baseline.

[0008] Preferably, the step of identifying surface reflection characteristics from the amplitude data in the initial illumination baseline and obtaining material category information based on the surface reflection characteristics specifically includes: Multiple amplitude ranges are preset, and each amplitude range corresponds to a material category; The amplitude of the initial illumination reference baseline is compared with the plurality of amplitude intervals to determine the target amplitude interval in which the amplitude of the initial illumination reference baseline is located, and the corresponding material category information is obtained based on the target amplitude interval.

[0009] Preferably, obtaining the current baseline and synthesizing a real-time dynamic threshold based on the material base threshold and the current baseline specifically includes: The current baseline is updated in real time using a slow integral filtering algorithm, so that the current baseline smoothly follows the slow changes in ambient light. The updated current baseline is added to the material base threshold to obtain the real-time dynamic threshold.

[0010] Preferably, the step of determining whether a region has entered a strong light saturation area based on the real-time infrared sampling value, and if so, locking the current baseline and generating a strong light dynamic threshold based on the material type information, specifically includes: When the real-time infrared sampling value is greater than the preset saturation threshold, it is determined that the region has entered the strong light saturation area, and the current baseline is frozen. The corresponding lifting coefficient is obtained based on the material category information. The lifting coefficient is multiplied by the material base threshold and then added to the frozen current baseline to generate the strong light dynamic threshold.

[0011] Preferably, the method further includes a multi-channel infrared array synchronization and adaptive step, specifically including: Arrange at least two infrared transmitting and receiving channels on the equipment to form an infrared array; Control all infrared transmitting and receiving channels to run synchronously using the adaptive multi-material and multi-environment occlusion infrared sensing method to obtain the real-time gesture judgment results of each channel; The real-time gesture judgment results of each infrared transmission and reception channel are fused together, and the target operation is triggered based on the fused comprehensive judgment result.

[0012] Preferably, the method further includes a temperature compensation step, specifically comprising: Acquire the operating temperature data of the infrared transceiver; The temperature offset is calculated based on the operating temperature data, and the temperature offset is used to correct the initial illumination baseline and the material base threshold to offset the infrared signal drift caused by high and low temperature environments.

[0013] Preferably, the method further includes an ambient brightness linkage step, specifically including: Ambient natural light brightness data is collected using an external photosensor; The ambient natural light brightness data is used as the input condition for the algorithm, and linked with the adaptive multi-material and multi-environment occlusion infrared sensing method, to divide the daytime strong light mode and the nighttime weak light mode, and to configure corresponding sensitivity levels for the daytime strong light mode and the nighttime weak light mode respectively.

[0014] In summary, this application includes at least one of the following beneficial technical effects: 1. Improved the adaptability of infrared sensing devices to the reflection characteristics of different installation surfaces. Automatically identified material categories and matched differentiated thresholds through the initial baseline amplitude, reducing the need for manual debugging and firmware replacement due to changes in the installation environment. 2. Enhanced the stability of gesture recognition of infrared sensing devices in strong light environments. By using baseline freezing and differential threshold raising mechanisms, the probability of signal overload and false triggering caused by strong light saturation was reduced, ensuring the reliability of sensing under full illumination conditions. 3. Improved the infrared sensing device's ability to track slow changes in ambient light. By dynamically updating the baseline and synthesizing the threshold in real time, the sensitivity degradation caused by baseline drift over long-term use has been reduced. At the same time, the combination of multi-channel fusion, temperature compensation and ambient brightness linkage has expanded the product's applicability in complex scenarios. Attached Figure Description

[0015] Figure 1 This is a flowchart of an embodiment of the infrared sensing method for adaptive multi-material and multi-environment occlusion in this application; Figure 2 This is an example waveform diagram generated in a non-strong light environment without obstruction, according to one embodiment of this application; Figure 3 This is an example waveform diagram of unobstructed hand sweeping in a non-strong light environment, according to one embodiment of this application. Figure 4 This is an example waveform diagram of ceramic tile shading in a non-strong light environment according to one embodiment of this application; Figure 5 This is an example waveform diagram of hand sweeping under the obstruction of ceramic tiles in a non-strong light environment, according to one embodiment of this application; Figure 6 This is an example waveform diagram of a wooden board blocking light in a non-strong light environment according to one embodiment of this application; Figure 7 This is an example waveform diagram of hand sweeping under a wooden board in a non-strong light environment, according to one embodiment of this application; Figure 8 This is an example waveform diagram of mirror obstruction in a non-strong light environment according to an embodiment of this application; Figure 9 This is an example waveform diagram of hand sweeping under the obstruction of a mirror in a non-strong light environment, according to one embodiment of this application; Figure 10 This is an example waveform diagram generated in a strong light environment without obstruction, according to one embodiment of this application; Figure 11 This is an example waveform diagram of unobstructed hand sweeping under strong light conditions in one embodiment of this application; Figure 12 This is an example waveform diagram of the effect of ceramic tile shading under strong light in one embodiment of this application; Figure 13 This is an example waveform diagram of hand sweeping under strong light conditions with the tile obstructing the light, according to one embodiment of this application. Figure 14 This is an example waveform diagram of a wooden board blocking light in a strong light environment according to one embodiment of this application; Figure 15 This is an example waveform diagram of hand sweeping under strong light conditions, as shown in one embodiment of this application. Figure 16 This is an example waveform diagram of a mirror blocking a strong light environment in one embodiment of this application; Figure 17 This is an example waveform diagram of hand sweeping under strong light conditions, as shown in one embodiment of this application. Detailed Implementation

[0016] The present application will be further described in detail below with reference to the accompanying drawings.

[0017] In one embodiment, such as Figure 1As shown, this application discloses an adaptive infrared sensing method for multiple materials and multiple environmental occlusions, which specifically includes the following steps: S10: Obtain the real-time infrared sampling value, and after filtering the real-time infrared sampling value, obtain the initial illumination baseline of the current environment.

[0018] In this embodiment, the real-time infrared sampling value refers to the digital quantity obtained by the analog-to-digital converter after the infrared receiver tube converts the received infrared reflected light signal into a voltage signal during continuous operation. The initial illumination baseline refers to the environmental background signal level calculated by the system through multiple samplings and filtering after the device is powered on, under the current installation environment without any human hand or other object obstructing the view.

[0019] Specifically, taking a smart cabinet sensor light as an example, when the sensor light is first powered on and installed, the system immediately activates the continuous sampling function of the infrared receiver. Assuming the sensor light is installed on a wooden cabinet door, when the infrared light emitted by the infrared emitter illuminates the wooden surface, part of the light is absorbed, and part is reflected back to the receiver. The system continuously samples the ADC output value of the infrared receiver at a fixed sampling frequency, for example, 100 times per second, acquiring a certain number of sampled values ​​as the real-time infrared sample value. For example, continuously collecting 200 sampling points constitutes a sample value sequence. During this process, due to the uneven texture of the wooden surface or the possible presence of weak stray light interference in the environment, individual sampled values ​​may fluctuate. To eliminate the influence of these random noises on the initial baseline establishment, the system performs mean filtering on these sampled values, summing all sampled values ​​and dividing by the total number of samples to obtain a smoothed average value, which is the initial illumination baseline for the current environment. This baseline value represents the typical reflection signal level of the wooden cabinet door under the current ambient light, serving as the starting reference point for all subsequent adaptive judgments. Similarly, if a sensor light is installed on a highly reflective mirrored cabinet door, the amplitude of the initial illumination baseline will be significantly higher because the reflectivity of the mirror is much higher than that of wood. This difference can be used to automatically sense the characteristics of the installation material.

[0020] S20: Identify surface reflection characteristics from the amplitude data in the initial illumination baseline, obtain material category information based on the surface reflection characteristics, and obtain the corresponding material basic threshold based on the material category information.

[0021] In this embodiment, surface reflectivity refers to the strength of the material of the mounting surface's ability to reflect infrared light. Material category information is a classification of the mounting surface based on its reflectivity, such as high-reflectivity, medium-reflectivity, and low-reflectivity materials. The material base threshold is a preset trigger sensitivity parameter for different material categories, used for subsequent gesture recognition threshold calculations.

[0022] Specifically, refer to Figure 2-17 After acquiring the initial illumination baseline, the system extracts the amplitude data of that baseline. Taking a smart sensor switch as an example, suppose the switch might be installed in three different environments: the first is on a white tiled wall, the second is on a dark wood wall, and the third is on a metal mirrored decorative panel. The system internally presets multiple amplitude ranges. For example, the low-reflection range corresponds to a baseline value with an amplitude less than 500, the medium-reflection range corresponds to a baseline value with an amplitude between 500 and 1500, and the high-reflection range corresponds to a baseline value with an amplitude greater than 1500. When the sensor switch is installed on a white tiled wall, the amplitude of the initial illumination baseline is approximately 800. The system determines that this amplitude falls within the medium-reflection range, thus obtaining the corresponding material category information as a medium-reflection material, and automatically calling the material's basic threshold, for example, set to 50. When the switch is installed on a dark wood wall, the initial baseline amplitude is approximately 300. The system determines it to be a low-reflection material and calls the corresponding material's basic threshold, for example, set to 30. When the switch is installed on a metal mirror decorative panel, the initial baseline amplitude reaches as high as 2000, which the system identifies as a highly reflective material. It then calls the corresponding material's base threshold, for example, setting it to 80. In this way, the system can automatically identify the material characteristics of the mounting surface based on the baseline amplitude after power-on, without manual intervention, and match an appropriate base trigger sensitivity for each material, improving versatility and consistency across different installation scenarios.

[0023] S30: Obtain the current baseline, synthesize the real-time dynamic threshold based on the material base threshold and the current baseline, and determine whether a valid gesture occlusion has occurred based on the real-time dynamic threshold.

[0024] In this embodiment, the current baseline refers to the environmental background signal level updated in real time during system operation. The real-time dynamic threshold refers to the gesture recognition threshold that is dynamically adjusted according to environmental changes, and is synthesized from the current baseline and the material-based threshold. Effective gesture occlusion refers to an occlusion signal generated by a human hand or other target object within the infrared sensing range for a sufficiently long duration.

[0025] Specifically, refer to Figure 2-17Taking a corridor human body sensor panel as an example, this panel is installed on the corridor wall, above a window. During the daytime, as the angle of sunlight changes, the intensity of ambient light shining on the sensor panel changes slowly, for example, from morning to afternoon, the ambient light gradually increases. The system uses a slow integral filtering algorithm to update the current baseline in real time. For example, it collects the current infrared received signal every second and calculates a weighted average with the baseline value of the previous moment, with a weighting coefficient set to 0.95 to 0.05. This allows the baseline to follow the gradual change of ambient light at a slower speed, without drastic jumps due to instantaneous disturbances. Assuming the current baseline is updated to 600, and the system has identified the wall surface as a medium reflective material in step S20, with a corresponding material base threshold of 50, then the real-time dynamic threshold is equal to the current baseline plus the material base threshold, i.e., 650. When someone walks through the corridor and reaches close to the sensor panel, the signal detected by the infrared receiver will change significantly due to the hand blocking it, for example, rising from 600 to 700. The system compares the real-time sampled value with the real-time dynamic threshold of 650. Finding that 700 is greater than 650, it initially determines that gesture occlusion may have occurred. Simultaneously, the system also determines the duration of the gesture occlusion, requiring the occlusion signal to last for more than 300 milliseconds to be considered a valid gesture occlusion, thus filtering out brief interferences such as flying insects or flickering light spots. Only when the signal amplitude exceeds the real-time dynamic threshold and the duration meets the requirement does the system confirm a valid gesture occlusion and trigger the corresponding lighting control command.

[0026] S40: Determine whether the area has entered the strong light saturation region based on the real-time infrared sampling value. If so, lock the current baseline and generate a strong light dynamic threshold based on the material type information.

[0027] In this embodiment, the strong light saturation region refers to the state where the output signal of the infrared receiver tube approaches or reaches the maximum range of the ADC due to excessive ambient light. Locking the current baseline means pausing the real-time update of the baseline in a strong light environment and fixing it at the value before entering the strong light state. The strong light dynamic threshold is a trigger threshold specifically used for gesture recognition in a strong light environment after the lift.

[0028] Specifically, refer to Figure 2-17Taking a bathroom sensor mirror light as an example, the sensor mirror is installed above the bathroom sink, with a bathroom heater / light fixture mounted above the mirror surface. When a user turns on the bathroom heater while showering, the strong light emitted by the heater directly shines on the receiver of the infrared sensor mirror, causing the ADC sampling value to rise sharply, for example, from the normal 800 to nearly 4000, close to the full scale of the ADC. The system monitors the real-time infrared sampling value and compares it with a preset saturation threshold, for example, 3800. When the sampling value exceeds 3800, the system determines that it has entered the strong light saturation region. At this time, the system immediately performs a locking operation, freezing the current baseline at the last moment before entering the strong light state, for example, freezing the baseline to 800. Subsequently, the system obtains the corresponding lift coefficient based on the material category information identified in step S20. Assuming the mirror surface is a highly reflective material, the corresponding lift coefficient is 3.0; if the mirror surface is a common ceramic material, the corresponding lift coefficient may be 1.5. The lift coefficient is multiplied by the material base threshold and added to the frozen baseline to generate the strong light dynamic threshold. Taking a highly reflective mirror as an example, with a base threshold of 80 and an uplift coefficient of 3.0, the uplifted threshold becomes 240. The dynamic threshold for strong light is the frozen baseline of 800 plus 240, which equals 1040. This means that in strong light environments, the system no longer uses the conventional dynamic threshold but instead adopts a higher threshold of 1040. A response is only triggered when the signal change caused by a hand blocking the light exceeds this higher threshold, effectively reducing false triggers caused by direct exposure to strong light from the bathroom heater. When the user turns off the bathroom heater and the strong light dissipates, the real-time infrared sampling value falls below the saturation threshold, and the system automatically exits the strong light mode, resuming the conventional dynamic baseline following and threshold synthesis logic in step S30.

[0029] In this embodiment, by acquiring real-time infrared sampling values ​​and filtering them to generate an initial illumination baseline, preliminary quantization of the environmental background signal is achieved. Surface reflection characteristics are identified from the baseline amplitude, and material category information is obtained, enabling the system to automatically match the corresponding material base threshold based on the reflection differences of the installation surface, improving versatility across different material installation scenarios. By acquiring the current baseline and synthesizing a real-time dynamic threshold with the material base threshold, the system can dynamically adjust the judgment criteria according to environmental changes, reducing false triggers or missed detections caused by slow changes in ambient light. When the real-time infrared sampling value is detected to enter a strong light saturation region, the current baseline is locked and a strong light dynamic threshold is generated, allowing the system to maintain relatively stable gesture judgment capabilities even in strong light environments, improving sensing reliability under strong light conditions. Overall, this method achieves dual adaptation to both installation materials and ambient light, reducing the need for manual debugging and the complexity of firmware version management, and improving the stability and usability of the infrared sensing device in multiple scenarios.

[0030] In one embodiment, step S10, which involves filtering the real-time infrared sampled values ​​to obtain the initial illumination baseline of the current environment, specifically includes: S11: Acquire continuously collected real-time infrared sample values ​​to form a sample value sequence.

[0031] In this embodiment, the real-time infrared sampled value refers to the digital quantity obtained by the infrared receiver tube converting the received infrared reflected light signal into a voltage signal and then sampling it through an analog-to-digital converter during continuous operation. The sampled value sequence refers to a set of continuously sampled data arranged in chronological order.

[0032] Specifically, taking a smart cabinet sensor light as an example, once the sensor light is installed and powered on, the system immediately activates the continuous sampling mode of the infrared receiver. The infrared emitter emits infrared light pulses at a fixed frequency, and the receiver synchronously receives the reflected light signals. The microcontroller inside the system continuously reads the ADC values ​​output by the receiver at a sampling rate of 100 times per second. Each time a value is read, the system does not process it immediately but stores it in a first-in-first-out (FIFO) circular buffer. The buffer's capacity is preset to 200 sampling points. When the buffer is full, new data automatically overwrites the oldest data, always maintaining a sequence of the most recent 200 consecutive sampled values. This sequence of sampled values ​​contains real-time fluctuations in the infrared reflected signal under the current environment, such as subtle differences in reflection from the surface of the wooden cabinet door and instantaneous changes in stray light in the environment. By constructing such a continuous sequence of sampled values, the system provides sufficient data samples for subsequent filtering processing, ensuring that the initial baseline reflects the true environmental background level, rather than relying on a single instantaneous sampled value.

[0033] S12: Perform mean filtering on the sampled value sequence and use the filtered average value as the initial illumination baseline.

[0034] In this embodiment, mean filtering is a simple digital signal processing method that eliminates random noise and instantaneous fluctuations by calculating the arithmetic mean of all sampled values ​​in the sequence, resulting in a stable central trend value. The initial illumination baseline is this average value, representing the typical background signal level under unobstructed conditions in the current installation environment.

[0035] Specifically, taking a motion-sensor switch installed on a white ceramic tile wall as an example, the system has acquired 200 consecutive sample values. These values ​​may fluctuate between 480 and 520 due to the minute unevenness of the tile surface and uncontrollable light scattering in the environment. The system sums these 200 sample values ​​and then divides the sum by 200 to obtain an average value, for example, 502. This 502 is set as the initial lighting baseline for the current environment. The advantage of mean filtering is that it can effectively suppress the influence of individual abnormal sampling points, such as glitches caused by electromagnetic interference at a certain moment. Even if a sample value suddenly jumps to 600, because the number of samples involved in averaging is large, the final average value will not be excessively skewed and can still accurately reflect the true background signal. In this way, the system establishes a corresponding initial baseline after power-on, laying the foundation for subsequent material recognition and threshold configuration.

[0036] In this embodiment, by continuously acquiring real-time infrared sampling values ​​and constructing a sampling value sequence, the integrity and temporal continuity of the data source are ensured, providing a sufficient sample basis for subsequent processing. Mean filtering is employed, using the filtered average value as the initial illumination baseline. This effectively eliminates the interference of instantaneous noise and random fluctuations on baseline establishment, improving the smoothness and representativeness of the initial baseline. This allows the system to quickly obtain relatively accurate environmental background reference values ​​at the initial power-on stage, reducing the occurrence of baseline deviations from the real environment due to single sampling errors. This lays a reliable data foundation for subsequent material identification and threshold synthesis, enhancing the overall algorithm's noise resistance and initial convergence speed.

[0037] In one embodiment, step S20, namely identifying surface reflection characteristics from the amplitude data in the initial illumination baseline and obtaining material category information based on the surface reflection characteristics, specifically includes: S21: Preset multiple amplitude ranges, each amplitude range corresponds to a material category.

[0038] In this embodiment, the amplitude range refers to the numerical range of the initial illumination baseline, with each range corresponding to a specific reflection characteristic of the mounting surface. The material category is a classification of the mounting surface based on the strength of reflection, such as high-reflectivity materials, medium-reflectivity materials, and low-reflectivity materials.

[0039] Specifically, taking a general-purpose infrared sensor panel as an example, during the firmware development phase, researchers conducted extensive experiments to test the reflection characteristics of different common mounting materials. The tests revealed that when the sensor panel was installed on a matte black plastic surface, the initial baseline amplitude was typically between 100 and 300; when installed on a white latex-painted wall, the baseline amplitude was between 400 and 800; when installed on a polished tile surface, the baseline amplitude was between 900 and 1500; and when installed on mirrored stainless steel or glass mirrors, the baseline amplitude could reach over 1800 to 2500. Based on this test data, the system internally preset three amplitude ranges: a low-reflection range defined as 0 to 350, corresponding to low-reflection materials; a medium-reflection range defined as 351 to 1200, corresponding to medium-reflection materials; and a high-reflection range defined as 1201 to 4095, corresponding to high-reflection materials. Each range is bound to a material category identifier; for example, the number 1 represents low reflection, 2 represents medium reflection, and 3 represents high reflection. These preset ranges are stored in the microcontroller's non-volatile memory and are fixed at the factory, requiring no on-site adjustment. In this way, the system establishes a simple and effective set of material classification rules, making subsequent automatic identification possible.

[0040] S22: Compare the amplitude of the initial illumination reference baseline with multiple amplitude ranges to determine the target amplitude range in which the amplitude of the initial illumination reference baseline is located, and obtain the corresponding material category information based on the target amplitude range.

[0041] In this embodiment, comparison refers to comparing the calculated initial baseline amplitude with the preset interval boundaries to find the interval to which it belongs. The target amplitude interval is the interval in which the amplitude actually falls, and the corresponding material category information is the basis for the system to configure thresholds subsequently.

[0042] Specifically, taking a sensor light installed on a bathroom mirror as an example, after power-on, the system obtains an initial illumination baseline through mean filtering, assuming an amplitude of 2100. The system first compares 2100 with the upper limit of the low-reflection zone (350), finding that 2100 is greater than 350, thus excluding the low-reflection zone. Next, it compares with the upper limit of the medium-reflection zone (1200), finding that 2100 is greater than 1200, thus excluding the medium-reflection zone. Finally, it compares with the lower limit of the high-reflection zone (1201), finding that 2100 is greater than 1201, therefore determining that the amplitude falls within the high-reflection zone. The system then reads the material category identifier bound to the high-reflection zone from memory, for example, category 3, and further obtains the pre-stored material base threshold based on this identifier; for example, the base threshold for high-reflection materials is 80. The entire process is fully automated and requires no manual intervention. Even if the same sensor light is removed and installed on a surface of a different material, the system will re-execute the same comparison logic upon power-on, automatically adapting to the new installation environment. This self-identification mechanism greatly simplifies the production and installation process, allowing the same product to be applied seamlessly to various decoration scenarios.

[0043] In this embodiment, by pre-setting multiple amplitude ranges and assigning each range to a material category, a mapping relationship between amplitude and material characteristics is established, simplifying the complexity of material identification. The amplitude of the initial illumination baseline is compared with multiple amplitude ranges to determine the target amplitude range in which it falls, and material category information is obtained accordingly, achieving automatic classification of the reflectivity of the mounting surface. This method requires no additional sensors or manual annotation; material identification can be completed using only the baseline amplitude, improving the convenience and efficiency of the identification process. By using range division, the possibility of misjudgment due to small amplitude fluctuations is reduced, enhancing the robustness of material classification and providing an accurate basis for subsequent differential threshold configuration.

[0044] In one embodiment, step S30, namely obtaining the current baseline and synthesizing a real-time dynamic threshold based on the material base threshold and the current baseline, specifically includes: S31: The current baseline is updated in real time using a slow integral filtering algorithm, so that the current baseline smoothly follows the slow changes in ambient light.

[0045] In this embodiment, the slow integral filtering algorithm is a low-pass filtering method. By weighted averaging the current sampled value and the historical baseline value, the baseline update speed is slowed down, which can both follow the gradual change of ambient light and suppress transient interference. The current baseline refers to the background signal estimate value maintained in real time during system operation.

[0046] Specifically, taking a corridor motion sensor panel as an example, this panel is installed on the corridor wall, above a window. In the early morning, the natural light outside the window is weak, and the background signal measured by the infrared receiver is approximately 500. As the sun rises, the light gradually intensifies, and the background signal slowly increases. The system collects a new real-time infrared sample value every 100 milliseconds, denoted as X. Simultaneously, the system maintains a current baseline variable B. After each new value is collected, the system performs the following weighted average calculation: New baseline = 0.98 × Old baseline + 0.02 × X. Here, 0.98 and 0.02 are preset weighting coefficients, determining the baseline's sensitivity to changes. Since the weight of the new sample value is only 0.02, even if X jumps to 600 at some instant due to light spot fluctuations, the new baseline will only slowly rise from 500 to 502, without being instantly spiked. During the several hours of sunlight changes, the baseline will gradually rise from 500 to 700 at an extremely slow rate, perfectly following the gradual increase in ambient light. This slow integration mechanism effectively avoids drastic baseline fluctuations caused by brief obstructions such as flying insects or fluttering curtains, while ensuring baseline accuracy for long-term use.

[0047] S32: Add the updated current baseline to the material base threshold to obtain the real-time dynamic threshold.

[0048] In this embodiment, the real-time dynamic threshold is a real-time threshold value used to determine whether a gesture has occurred, and it consists of two parts: the current baseline and the material-based threshold. The current baseline changes slowly over time, so the real-time dynamic threshold is also dynamically adjusted accordingly.

[0049] Specifically, following the example of the corridor sensor panel, assuming the system identifies the wall surface as a medium-reflective material in step S20, with a corresponding material base threshold of 50. At startup in the morning, the current baseline is 500, so the real-time dynamic threshold is 500 plus 50, equaling 550. At noon, as ambient light increases and the current baseline slowly rises to 700, the real-time dynamic threshold automatically adjusts to 750. When someone reaches near the panel, the infrared signal will increase significantly due to hand obstruction, for example, jumping from 700 to 780. The system compares the real-time sampled value of 780 with the current real-time dynamic threshold of 750, finding that 780 is greater than 750, and initially determines that hand gesture obstruction may have occurred. Because the real-time dynamic threshold always follows the baseline change, regardless of how slowly the ambient light changes, the increment of the gesture signal relative to the background must always exceed the material base threshold of 50 to be recognized, thus ensuring consistent sensitivity. This dynamic threshold mechanism allows the system to maintain the same trigger sensitivity at different times of the day, avoiding problems such as excessively high sensitivity in the morning or excessively low sensitivity at noon.

[0050] In this embodiment, a slow integral filtering algorithm is used to update the current baseline in real time, enabling the baseline to smoothly follow the gradual changes in ambient light, such as the transition between day and night or the gradual changes caused by light source aging. This slow tracking mechanism avoids drastic changes in the baseline due to instantaneous disturbances, reducing false triggering caused by baseline drift. The updated current baseline is added to the material-based threshold to obtain a real-time dynamic threshold, allowing the judgment threshold to be adjusted synchronously with the environmental background signal, maintaining a stable relative difference between the gesture signal and the background. This method improves the system's adaptability to slow environmental changes, reduces the degradation of sensing performance caused by baseline shift during long-term use, and the dynamic threshold also enhances the accuracy and consistency of gesture judgment.

[0051] In one embodiment, in step S40, it is determined whether the area has entered a strong light saturation region based on the real-time infrared sampling value. If so, the current baseline is locked, and a strong light dynamic threshold is generated based on the material type information. Specifically, this includes: S41: When the real-time infrared sampling value is greater than the preset saturation threshold, it is determined that the region has entered the strong light saturation area and the current baseline is frozen.

[0052] In this embodiment, the saturation threshold refers to the critical value at which the output signal of the infrared receiver tube approaches the full-scale range of the ADC. When the ambient light is too strong, causing the receiver tube to enter the nonlinear region or the saturation region, the sampled value will be abnormally high. Freezing the current baseline means pausing the real-time update of the baseline and locking the current baseline at the value before entering the strong light state to prevent the baseline from being contaminated by strong light.

[0053] Specifically, taking a bathroom sensor mirror light as an example, the sensor mirror is installed above the sink, with a bathroom heater mounted above the mirror surface. When the user turns on the bathroom heater, strong infrared and visible light shine directly onto the infrared receiver, causing the ADC sampling value to spike sharply from the usual 800, quickly exceeding the preset saturation threshold. Assume the saturation threshold is set to 3800 (ADC full-scale range is 4095). The system checks the real-time infrared sampling value every 10 milliseconds. When three consecutive sampling values ​​are detected to be greater than 3800, it determines that the system has indeed entered the strong light saturation region. At this point, the system immediately performs a freeze operation, recording the last valid current baseline before entering the strong light state, for example, freezing the baseline to 820. After freezing, the system no longer performs slow integral filtering updates, and the current baseline remains unchanged at 820. The purpose of this is to prevent the strong light component from forcibly raising the baseline; otherwise, once the baseline is raised to near the saturation value, subsequent weak gesture signals will be completely unrecognizable. The freeze operation essentially saves a clean background reference for the system, providing a stable benchmark for subsequently generating the strong light dynamic threshold.

[0054] S42: Obtain the corresponding lifting coefficient based on the material category information, multiply the lifting coefficient by the material base threshold, and add it to the current baseline after freezing to generate the dynamic threshold of strong light.

[0055] In this embodiment, the lift factor is an additional threshold multiple applied to different materials under strong light conditions to compensate for the degradation of the signal-to-noise ratio caused by strong light. The strong light dynamic threshold is a gesture recognition threshold specifically designed for strong light mode, and is typically much higher than the conventional dynamic threshold.

[0056] Specifically, following the example of the bathroom sensor mirror, in step S20, the system has already identified the mirror surface as a highly reflective material, with a corresponding material base threshold of 80. After entering the strong light mode, the system retrieves the boost coefficient corresponding to the highly reflective material from the memory, for example, 3.0. The resulting threshold increment is 3.0 multiplied by 80, equaling 240. Adding the frozen baseline of 820 to 240 yields a strong light dynamic threshold of 1060. At this point, even if the strong light from the bathroom heater causes the background signal to reach over 3800, since the baseline has been frozen at 820, the strong light dynamic threshold is only 1060. The system's judgment of gestures is no longer based on the original sampled value itself, but rather on the difference between the sampled value and the frozen baseline. When the user reaches out to block the mirror, the hand will block some of the strong light, causing the received signal to drop from 3800 to approximately 1100. This drop is 2700, far exceeding the 1060 threshold, allowing the system to correctly recognize the gesture. If a conventional dynamic threshold is used, the baseline will be raised to around 3800 by strong light, and the threshold will become 3850. At this point, the gesture signal will drop by 2700 and still be below 3850, leading to missed recognition of the gesture. By freezing the baseline and raising the threshold, the system has successfully achieved stable recognition in strong light environments.

[0057] In this embodiment, when the real-time infrared sampling value exceeds a preset saturation threshold, it is determined that the system has entered a strong light saturation region and the current baseline is frozen. This prevents the strong light component from forcibly pulling the baseline, maintaining the relative stability of the baseline in strong light environments. A corresponding lifting coefficient is obtained based on the material type information. This lifting coefficient is multiplied by the material's base threshold and then added to the frozen baseline to generate a dynamic strong light threshold, achieving differentiated lifting judgment thresholds based on material grade. For highly reflective materials, the threshold is significantly raised, reducing false triggers caused by strong light reflection; for low-reflective materials, the threshold is raised slightly, preserving the recognition capability of weak gesture signals. This method improves the stability of gesture recognition in near-saturation strong light conditions, reduces signal overload or misjudgment, and automatically restores normal logic after the strong light fades, ensuring smooth mode switching.

[0058] In one embodiment, the adaptive infrared sensing method for multiple materials and multiple environmental occlusions further includes a multi-channel infrared array synchronous adaptive step, specifically including: S51: Arrange at least two infrared transmitting and receiving channels on the equipment to form an infrared array.

[0059] In this embodiment, the infrared array refers to a collection of detection units consisting of multiple independent infrared emitting and receiving tubes. Each channel has its own emitting and receiving elements, and can independently detect obstruction signals in its own direction. The arrangement position is designed according to the device shape and application requirements to cover a wider angle and area.

[0060] Specifically, taking a long, rectangular bathroom sensor mirror as an example, the mirror is 1.2 meters long and installed above the sink. To eliminate the blind spots inherent in single-channel sensors, the developers evenly arranged three sets of infrared transceiver modules along the upper edge of the mirror, located at the left, middle, and right ends respectively. Each module contains an infrared transmitter and a paired infrared receiver, spaced approximately 40 centimeters apart. These three modules together form an infrared array. In the application of smart cabinet sensor lights, an infrared transceiver module is arranged on each of the left and right sides of the cabinet, forming a dual-channel array. Each channel's transmitter emits infrared light at the same frequency and duty cycle. However, to avoid crosstalk between channels, the system uses time-division multiplexing, allowing each channel to transmit and receive in turn, ensuring that each channel only detects its own emitted signal. Through this physical layout, the detection range of the infrared array expands from the narrow fan shape of a single channel to a large-angle area covering the entire width of the mirror, effectively eliminating side blind spots.

[0061] S61: Controls all infrared transmitting and receiving channels to operate synchronously using an adaptive infrared sensing method that adapts to multiple materials and environmental occlusions, and obtains real-time gesture judgment results for each channel.

[0062] In this embodiment, synchronous operation means that each channel independently executes all the adaptive algorithm processes of claims 1 to 5, including baseline establishment, material recognition, dynamic threshold synthesis, and strong light processing. Each channel independently determines whether a gesture has occurred based on its own sampling data.

[0063] Specifically, taking a three-channel bathroom mirror as an example, the system's main control chip processes data from the left, middle, and right channels sequentially within a scheduling cycle. For the left channel, the system acquires its real-time infrared sampling value, performs mean filtering to obtain an initial baseline, identifies the installation material based on the baseline amplitude (mirrors are typically highly reflective), then initiates slow integral filtering to update the baseline, synthesizes a dynamic threshold, and determines in real time whether gesture occlusion has occurred. The middle and right channels execute the same process in parallel, but each uses its own sampling data. Due to the different installation locations of the three channels, their local environments may differ; for example, the right channel may be closer to the bathroom heater and more prone to strong light saturation. Each channel independently determines and outputs its own Boolean gesture judgment result: occlusion or no occlusion. These results are temporarily stored in memory as binary flags, awaiting the next fusion processing step. This independent operation design ensures that each channel can make optimal judgments based on its own local lighting and material conditions, unaffected by other channels.

[0064] S71: The real-time gesture judgment results of each infrared transmission and reception channel are fused together, and the target operation is triggered based on the fused comprehensive judgment result.

[0065] In this embodiment, fusion refers to combining the independent judgment results of multiple channels according to preset logical rules to obtain a final decision signal. The combined judgment result can be valid if triggered by any one channel, or it can be valid only if triggered by multiple channels simultaneously; the specific rules are set according to the application scenario.

[0066] Specifically, taking a bathroom mirror as an example, the system uses an OR logic fusion rule: as long as any one of the left, center, or right channels detects a valid gesture obstruction, it is considered that the user intends to trigger the mirror light switch. This is because in a bathroom scenario, the user may reach out from either side of the mirror. If all channels are required to trigger simultaneously, many normal operations would be ignored. When the left channel detects a hand sweeping across the left end of the mirror, its determination result is true, the system immediately sets the overall determination result to true, and sends a light-on command. In the dual-channel scenario of a smart cabinet sensor light, an AND logic fusion rule is used: only when both the left and right channels detect obstruction simultaneously is it determined to be a valid gesture. This avoids false triggers caused by accidental obstruction on one side (such as clothing touching the light). The fusion logic can be flexibly configured according to product needs, and even supports advanced strategies such as weighted voting. Through multi-channel fusion, the system not only expands the sensing range but also improves the robustness of the determination. Even if one channel temporarily malfunctions due to localized strong light, other channels can still work normally, and the overall system will not completely fail.

[0067] In this embodiment, by arranging at least two infrared transmitting and receiving channels to form an infrared array on the device, the sensing coverage is expanded, and the blind spots in the detection angle of a single channel are reduced. Controlling all channels to run synchronously using an adaptive method and acquiring the gesture judgment results of each channel ensures consistency in judgment under the same environmental conditions. The real-time gesture judgment results of each channel are fused, and the target operation is triggered based on the comprehensive judgment result, improving the accuracy of multi-directional and multi-angle gesture recognition. For example, in the application of smart cabinet sensor lights, dual channels can eliminate side blind spots; in long bathroom sensor mirrors, cross-verification of multiple signals can prevent half-side failure due to localized strong light. This method improves the coverage integrity and anti-local interference capability of the infrared sensing device, reducing missed detections caused by single-path obstruction or interference.

[0068] In one embodiment, the adaptive infrared sensing method for multiple materials and multiple environmental occlusions further includes a temperature compensation step, specifically comprising: S52: Acquire the operating temperature data of the infrared transceiver tube.

[0069] In this embodiment, the operating temperature data refers to the real-time temperature value of the environment in which the infrared emitting and receiving tubes are located. This temperature is typically obtained through a temperature sensor mounted on the circuit board or a temperature detection module integrated within the MCU. The temperature data reflects the current thermal state of the device and serves as the basis for subsequent temperature compensation.

[0070] Specifically, taking an outdoor balcony sensor switch as an example, this switch is installed on an outdoor wall and directly exposed to the natural environment. At midday in summer, the switch casing temperature under direct sunlight may exceed 60 degrees Celsius; in the early morning of winter, the temperature may drop to -10 degrees Celsius. To monitor the device temperature in real time, a digital temperature sensor, such as the DS18B20, is soldered onto the circuit board near the infrared transceiver, with a measurement accuracy of ±0.5 degrees Celsius. The main control chip reads the temperature sensor value every second via a single-bus protocol and stores it in a variable. In embedded kitchen and bathroom sensor-operated appliances, the temperature sensor is installed inside the sensing module near the water heater pipes to monitor the device temperature under long-term high-temperature conditions. The acquired temperature data is not only input to the compensation algorithm but can also be used for abnormal temperature alarms. For example, when the temperature exceeds 85 degrees Celsius, the system can actively reduce the transmission power to protect the device. Through continuous temperature monitoring, the system establishes the baseline of the device's operating thermal environment, providing a reliable basis for subsequent signal correction.

[0071] S62: Calculate the temperature offset based on the working temperature data, and use the temperature offset to correct the initial illumination baseline and material base threshold to offset the infrared signal drift caused by high and low temperature environments.

[0072] In this embodiment, temperature offset refers to the signal deviation caused by changes in the electrical characteristics of the infrared device due to temperature variations. Correction refers to subtracting or adding the offset from the original baseline or threshold to maintain consistent sensing performance of the system over a wide temperature range.

[0073] Specifically, taking an outdoor balcony sensor switch as an example, the system internally stores a temperature compensation curve, obtained through laboratory calibration, which records the changes in the dark current of the infrared receiver and the optical power of the transmitter at different temperatures. For example, at a reference temperature of 25 degrees Celsius, the baseline is 500, and the material's basic threshold is 50. When the temperature rises to 60 degrees Celsius, the dark current of the infrared receiver increases, causing the sampled value under unobstructed conditions to be about 30 units higher than at room temperature. The system reads the current temperature as 60 degrees Celsius and finds a temperature offset of +30 from the table. Therefore, the system corrects the initial illumination baseline: the corrected baseline is equal to the measured baseline minus 30, restoring it to 500. At the same time, the material's basic threshold also needs to be corrected: the optical power of the infrared transmitter decreases slightly at high temperatures, resulting in a smaller gesture signal amplitude, so the threshold should be appropriately reduced. The system finds a threshold offset of -5 from the table, and the corrected material's basic threshold is 45. The corrected baseline and threshold are then fed into the subsequent dynamic threshold synthesis process. Through this bidirectional correction, even on hot summer afternoons, the trigger sensitivity of the inductive switch remains consistent with that of spring and autumn, preventing frequent false triggers due to high temperatures. In low-temperature winter environments, the system also performs reverse correction based on the negative offset, ensuring no sensing sluggishness occurs at low temperatures. The temperature compensation algorithm effectively expands the applicable climate range of infrared sensing products.

[0074] In this embodiment, real-time monitoring of the device's thermal state is achieved by acquiring the operating temperature data of the infrared transceiver. The temperature offset is calculated based on the operating temperature data, and this offset is used to correct the initial illumination baseline and material-based threshold, thus offsetting signal drift caused by changes in the electrical characteristics of the infrared device under high or low temperature environments. For example, in an outdoor balcony sensor switch, the corrected baseline and threshold can maintain stable sensitivity under summer sun exposure or winter low temperatures, reducing frequent false triggers at high temperatures or sluggish sensing at low temperatures. This method improves the adaptability of the infrared sensing device across the entire temperature range and reduces the impact of ambient temperature changes on sensing performance, making it particularly suitable for applications with large temperature differences, such as outdoor spaces and kitchens / bathrooms.

[0075] In one embodiment, the adaptive infrared sensing method for multiple materials and multiple environmental occlusions further includes an ambient brightness linkage step, specifically including: S53: Collects ambient natural light brightness data through an external photosensor.

[0076] In this embodiment, ambient natural light intensity data refers to the visible light intensity value of the surrounding environment measured in real time by an external photosensor, typically expressed in lux or as a digital value after analog-to-digital conversion. This sensor is independent of the infrared transceiver module and is specifically designed to sense the overall level of ambient light, providing the system with additional environmental dimensional information.

[0077] Specifically, taking a bedside sensor lamp as an example, this lamp is placed on a bedside table in the bedroom. A small photoresistor or digital ambient light sensor, such as the BH1750, is embedded in the side of the lamp base. Its light-sensing window faces the room to avoid direct light from the lamp itself. The sensor collects the current ambient brightness every 100 milliseconds and transmits the data to the main control chip via an I2C interface. In daylight conditions, such as when the curtains are open, the sensor measures a brightness of approximately 500 lux; as dusk falls, the brightness gradually decreases to tens of lux; and at night, after the lights are turned off, the brightness may drop to near zero. The system stores this brightness data in a circular buffer and performs a moving average filter every second to eliminate momentary interference from bulb flickering or shadow movement. By continuously monitoring ambient brightness, the system can sense changes in light caused by day-night cycles and user actions such as turning lights on and off, providing accurate input conditions for subsequent mode switching. In the application of a corridor human body sensor panel, the photosensitive sensor is installed on the top of the panel housing, facing upwards or outwards, to collect the overall ambient brightness of the corridor area, avoiding interference from the panel's own indicator lights.

[0078] S63: Using ambient natural light brightness data as the algorithm input, it works in conjunction with an adaptive infrared sensing method that adapts to multiple materials and multiple environments to divide the daytime strong light mode and the nighttime weak light mode, and configures corresponding sensitivity levels for the daytime strong light mode and the nighttime weak light mode respectively.

[0079] In this embodiment, the daytime high-light mode and the nighttime low-light mode refer to two different sets of infrared sensing operating parameters that automatically switch according to the ambient brightness. The sensitivity level refers to the preset trigger threshold adjustment coefficient or baseline offset for each mode, used to optimize sensing performance under different lighting conditions.

[0080] Specifically, taking a bedside sensor lamp as an example, the system presets two brightness thresholds: a strong light mode switching threshold of 100 lux and a weak light mode switching threshold of 30 lux. When the ambient brightness is higher than 100 lux, the system determines it to be in daytime strong light mode and automatically activates a lower sensitivity level. At this time, the system multiplies the material base threshold obtained in step S20 by a strong light mode coefficient, such as 1.5, to raise the trigger threshold and reduce false triggers caused by sunlight reflection from outside the window or curtain swaying. At the same time, the system also appropriately increases the weight of the slow integral filter to make the baseline follow more slowly and further suppress the influence of ambient light fluctuations. When the ambient brightness drops below 30 lux, the system determines it to be in nighttime weak light mode and automatically switches to a high sensitivity level. At this time, the material base threshold is multiplied by a weak light mode coefficient, such as 0.7, to lower the trigger threshold so that the lamp can be triggered by a slight wave of the hand in a dark environment. In addition, the system also shortens the effective occlusion determination time, for example, from 333 milliseconds to 200 milliseconds, to improve the response speed. In corridor motion sensor panels, sensitivity is reduced in bright daylight mode to prevent accidental triggering due to flickering light from outside windows; sensitivity is increased in low-light nightlight mode to ensure recognition even from a distance. This mechanism, which links sensitivity to ambient brightness, allows the infrared sensor to maintain optimal performance under various lighting conditions throughout the day, saving energy and improving the user experience.

[0081] In this embodiment, ambient natural light brightness data is collected by an external photosensor, providing the system with additional information on ambient lighting. This brightness data is used as input to the algorithm in conjunction with an adaptive method to distinguish between a daytime strong light mode and a nighttime weak light mode, and corresponding sensitivity levels are configured for each mode. In the daytime strong light mode, the sensitivity is appropriately reduced to decrease false triggers caused by sunlight reflection or light flare; in the nighttime weak light mode, the sensitivity is increased so that a slight wave of the hand triggers a response. This method improves the infrared sensing device's ability to manage performance differences in day-night transition scenarios, reducing the limitation of a single sensitivity level not being able to meet all-weather requirements. Furthermore, it works in conjunction with material adaptive logic to further optimize the sensing effect under different combinations of lighting and materials.

[0082] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0083] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An adaptive infrared sensing method for multiple materials and multiple environmental occlusions, characterized in that, The adaptive infrared sensing method for multiple materials and multiple environmental occlusions includes: The infrared real-time sampling value is obtained, and after filtering the infrared real-time sampling value, the initial illumination baseline of the current environment is obtained. The surface reflection characteristics are identified from the amplitude data in the initial illumination baseline, the material category information is obtained based on the surface reflection characteristics, and the corresponding material basic threshold is obtained based on the material category information. Obtain the current baseline, synthesize a real-time dynamic threshold based on the material base threshold and the current baseline, and determine whether a valid gesture occlusion has occurred based on the real-time dynamic threshold; Based on the real-time infrared sampling value, it is determined whether the area has entered the strong light saturation region. If so, the current baseline is locked, and a strong light dynamic threshold is generated based on the material category information.

2. The adaptive infrared sensing method for multiple materials and multiple environmental occlusions according to claim 1, characterized in that, The process of filtering the real-time infrared sampled values ​​to obtain the initial illumination baseline for the current environment specifically includes: The continuously collected real-time infrared sampled values ​​are acquired to form a sampled value sequence; The sampled value sequence is subjected to mean filtering, and the average value after filtering is used as the initial illumination baseline.

3. The adaptive infrared sensing method for multiple materials and multiple environmental occlusions according to claim 2, characterized in that, The step of identifying surface reflection characteristics from the amplitude data in the initial illumination baseline and obtaining material category information based on the surface reflection characteristics specifically includes: Multiple amplitude ranges are preset, and each amplitude range corresponds to a material category; The amplitude of the initial illumination reference baseline is compared with the plurality of amplitude intervals to determine the target amplitude interval in which the amplitude of the initial illumination reference baseline is located, and the corresponding material category information is obtained based on the target amplitude interval.

4. The adaptive infrared sensing method for multiple materials and multiple environmental occlusions according to claim 1, characterized in that, The process of obtaining the current baseline and synthesizing a real-time dynamic threshold based on the material base threshold and the current baseline specifically includes: The current baseline is updated in real time using a slow integral filtering algorithm, so that the current baseline smoothly follows the slow changes in ambient light. The updated current baseline is added to the material base threshold to obtain the real-time dynamic threshold.

5. The adaptive infrared sensing method for multiple materials and multiple environmental occlusions according to claim 4, characterized in that, The step of determining whether a region has entered a strong light saturation area based on the real-time infrared sampling value, and if so, locking the current baseline and generating a strong light dynamic threshold based on the material type information, specifically includes: When the real-time infrared sampling value is greater than the preset saturation threshold, it is determined that the region has entered the strong light saturation area, and the current baseline is frozen. The corresponding lifting coefficient is obtained based on the material category information. The lifting coefficient is multiplied by the material base threshold and then added to the frozen current baseline to generate the strong light dynamic threshold.

6. The adaptive infrared sensing method for multiple materials and multiple environmental occlusions according to claim 1, characterized in that, The method further includes a multi-channel infrared array synchronization and adaptive step, specifically including: Arrange at least two infrared transmitting and receiving channels on the equipment to form an infrared array; Control all infrared transmitting and receiving channels to run synchronously using the adaptive multi-material and multi-environment occlusion infrared sensing method to obtain the real-time gesture judgment results of each channel; The real-time gesture judgment results of each infrared transmission and reception channel are fused together, and the target operation is triggered based on the fused comprehensive judgment result.

7. The adaptive infrared sensing method for multiple materials and multiple environmental occlusions according to claim 1, characterized in that, The method further includes a temperature compensation step, specifically comprising: Acquire the operating temperature data of the infrared transceiver; The temperature offset is calculated based on the operating temperature data, and the temperature offset is used to correct the initial illumination baseline and the material base threshold to offset the infrared signal drift caused by high and low temperature environments.

8. The adaptive infrared sensing method for multiple materials and multiple environmental occlusions according to claim 1, characterized in that, The method also includes an ambient brightness linkage step, specifically including: Ambient natural light brightness data is collected using an external photosensor; The ambient natural light brightness data is used as the input condition for the algorithm, and linked with the adaptive multi-material and multi-environment occlusion infrared sensing method to divide the daytime strong light mode and the nighttime weak light mode, and configure corresponding sensitivity levels for the daytime strong light mode and the nighttime weak light mode respectively.

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