A multi-specification sampling and projection method for infrared ranging fluorescent flashlights

Abstract

The present invention provides a multi-specification sampling and projection method for an infrared ranging fluorescent flashlight, which relates to the field of sampling and projection technology, including: determining the relative position of the infrared ranging fluorescent flashlight and the target area, measuring the distance, and generating a distance measurement result; obtaining regional characteristic information, performing pollution risk assessment, and obtaining a pollution risk assessment coefficient; matching to obtain a first sampling area; interactively obtaining a pollution risk distribution, locating a first sampling position, and locating a first sampling area; mapping to establish a spatial coordinate system, fitting to generate a first fluorescent projection angle; performing automatic focusing to generate a first fluorescent projection focal length, and generating a first fluorescent projection area; adjusting a fluorescent projection module, and performing fluorescent projection on the first sampling area. The present invention solves the technical problem that the traditional metal specification plate sampling method requires the use of a physical specification plate, and a specification plate needs to be replaced each time sampling is performed. In large-scale sampling tasks, the operation is complicated and time-consuming, which reduces work efficiency.

Description

Technical Field

[0001] The invention relates to the technical field of sampling and projection, and in particular to a multi-specification sampling and projection method for an infrared ranging fluorescent flashlight. Background Art

[0002] The traditional metal gauge plate sampling method requires the use of a physical gauge plate, which needs to be replaced each time a sample is taken. In large-scale sampling tasks, multiple gauge plates need to be used repeatedly to sample different areas or areas. Although this method can provide a relatively standard sampling area, the operation is complicated and time-consuming. In addition, after each use, the gauge plate needs to be thoroughly disinfected and sterilized to avoid cross-contamination, which significantly increases the time and cost of subsequent processing. Especially for large-scale sampling tasks, the process of disinfecting and reusing the gauge plate becomes a bottleneck, reducing work efficiency. The traditional fluorescent flashlight is only used as a display tool to monitor whether the environment has been wiped clean. It cannot achieve automatic ranging, sampling area selection, and sampling location positioning. Operators need to rely on personal experience to judge the size of the sampling area and the detection location during use, which may lead to inconsistent results between different operators and increase sampling errors and uncertainties. Summary of the Invention

[0003] This application provides a multi-specification sampling and projection method for an infrared ranging fluorescent flashlight, aiming to solve the technical problem that the traditional metal specification plate sampling method requires the use of a physical specification plate, and a specification plate needs to be replaced each time sampling is performed. In large-scale sampling tasks, the operation is complicated and time-consuming, which reduces work efficiency.

[0004] The present application discloses a multi-specification sampling and projection method for an infrared ranging fluorescent flashlight, the method comprising: determining the relative position of the infrared ranging fluorescent flashlight and a target area, measuring the distance from the flashlight to the target area by means of the infrared ranging module of the flashlight, and generating a distance measurement result, wherein the flashlight points to the target area; performing a regional characteristic analysis on the target area to obtain regional characteristic information, performing a pollution risk assessment on the target area based on the regional characteristic information, and obtaining a pollution risk assessment coefficient; obtaining a first sampling area by matching the preset multi-specification sampling areas based on the pollution risk assessment coefficient; interactively obtaining the pollution risk distribution of the target area, and determining the pollution risk assessment coefficient. Position the first sampling position, and locate the first sampling area in combination with the first sampling area; obtain the three-dimensional coordinates of the flashlight of the flashlight, and map and establish a spatial coordinate system in combination with the first sampling position and the distance measurement result, and in the spatial coordinate system, use the three-dimensional coordinates of the flashlight as a reference to fit and generate a first fluorescence projection angle; perform automatic focusing according to the distance measurement result to generate a first fluorescence projection focal length, and generate a first fluorescence projection area according to the first sampling area mapping; adjust the fluorescence projection module of the flashlight according to the first fluorescence projection angle, the first fluorescence projection focal length, and the first fluorescence projection area to perform fluorescence projection on the first sampling area.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] The infrared ranging module automatically measures the distance between the flashlight and the target area. Combined with pre-set sampling areas with multiple specifications, the system dynamically selects an appropriate first sampling area based on the pollution risk assessment coefficient. This automated process significantly reduces manual intervention, avoids the frequent replacement of sampling tools required in traditional sampling, improves sampling efficiency, and enables efficient processing during large-scale sampling. Before sampling, the system analyzes the target area's regional characteristics, conducts a pollution risk assessment based on multiple factors, and generates a pollution risk assessment coefficient to ensure a higher sampling density in key areas, improving sampling accuracy and targeting. The system interactively obtains the pollution risk distribution of the target area and, combining infrared ranging and the flashlight's 3D coordinates, locates the optimal first sampling position, achieving precise positioning of the sampling location and area in three-dimensional space. The system automatically adjusts the fluorescence projection focal length based on the distance measurement results and generates an appropriate fluorescence projection area based on the sampling area. This automatic focus and beam area adjustment enables adaptive adjustment for surfaces of varying distances and shapes, ensuring uniform beam coverage within the sampling area. After accurately locating the sampling area through the fluorescence projection module, the system conducts fluorescence projection detection, enabling efficient detection of pollutants in the sampling area through fluorescence reactions, enhancing the sensitivity and efficiency of pollutant detection.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic flow chart of a multi-specification sampling and projection method for an infrared ranging fluorescent flashlight is provided for an embodiment of the present application;

[0009] Figure 2 An embodiment of the present application provides a flow chart of generating distance measurement results in a multi-specification sampling and projection method for an infrared ranging fluorescent flashlight. DETAILED DESCRIPTION

[0010] The embodiment of the present application solves the technical problem that the traditional metal specification plate sampling method requires the use of a physical specification plate, and a specification plate needs to be replaced each time sampling is performed. In large-scale sampling tasks, the operation is complicated and time-consuming, which reduces work efficiency.

[0011] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced in conjunction with the drawings in the specification.

[0012] like Figure 1 As shown, the embodiment of the present application provides a multi-specification sampling and projection method for an infrared ranging fluorescent flashlight, the method comprising:

[0013] The relative position of an infrared ranging fluorescent flashlight and a target area is determined, and the distance from the flashlight to the target area is measured by the infrared ranging module of the flashlight to generate a distance measurement result, wherein the flashlight is pointed at the target area.

[0014] Determine the relative positional relationship between the infrared ranging fluorescent flashlight and the target area. For example, the flashlight can have a built-in attitude sensor, such as an accelerometer and gyroscope, to detect the flashlight's three-dimensional attitude information. These sensors automatically identify the flashlight's direction and, combined with the target area's coordinates, automatically adjust the flashlight's direction to ensure alignment with the target area. The infrared ranging module includes an infrared transmitter and an infrared receiver. The transmitter emits an infrared beam toward the target area. This beam is reflected back upon encountering the target area's surface and received by the receiver. Since the reflection speed and path of infrared light are known, the receiver can measure the time difference between signal transmission and reception to determine the distance and generate a distance measurement result.

[0015] Performing a regional characteristic analysis on the target area to obtain regional characteristic information, and performing a pollution risk assessment on the target area based on the regional characteristic information to obtain a pollution risk assessment coefficient.

[0016] Analyze the target area's environmental characteristics, usage, historical data, etc. to obtain comprehensive regional characteristic information. Specifically, determine the target area's usage scenarios and functions, such as operating rooms and hospital corridors in medical environments, or different work sections in food processing workshops, such as raw material areas and processing areas. Install and use sensor equipment to continuously collect relevant data. For example, install temperature and humidity sensors to monitor the area's ambient temperature and humidity in real time, and install personnel flow monitors to monitor the frequency of personnel movement and contact in the area. Combined with the area's historical data, such as past pollution incidents, cleaning, and maintenance records, analyze the area's pollution risk characteristics. Through the above regional analysis process, regional characteristic information related to pollution risk is obtained, covering multiple dimensions such as regional environment, operating behavior, historical data, and surface materials. This characteristic information serves as the input basis for pollution risk assessment.

[0017] Based on the acquired regional characteristic information, a systematic assessment of the pollution risk in the target area is conducted. Specifically, a pollution risk assessment model is constructed based on the regional characteristic information. The core of the model is to quantify the impact of different characteristic information. For example, areas with excessively high temperature and humidity are more likely to breed microorganisms. Therefore, changes in temperature and humidity will directly affect the pollution risk assessment results. Combined with mathematical models, such as regression analysis models, pollution risks are predicted through comprehensive analysis of these characteristic information. For example, based on historical data, a probability model of pollution events under different conditions, such as temperature, humidity, and contact frequency, is established. According to the model analysis, the pollution risk assessment results are obtained, indicating the pollution risk level of the area under current conditions.

[0018] A first sampling area is obtained by matching preset multi-specification sampling areas based on the pollution risk assessment coefficient.

[0019] Before matching sampling areas, a set of different sampling areas is preset to accommodate areas of different risk levels. These area specifications can be set according to sampling standards or specific needs, dividing the pollution risk assessment coefficient into different level intervals. Different intervals correspond to different sampling areas. The interval range can be customized according to the actual application scenario. For example, 0.0-0.3 is low risk and matches the smallest sampling area; 0.3-0.7 is medium risk and matches the medium sampling area; 0.7-1.0 is high risk and matches the largest sampling area. According to the pollution risk assessment coefficient, the corresponding area is selected from the preset sampling areas as the first sampling area for the actual sampling operation.

[0020] The pollution risk distribution of the target area is interactively obtained, a first sampling position is located, and a first sampling area is located in combination with the first sampling area.

[0021] Through sensors and monitoring systems, we continuously collect data such as the ambient temperature and humidity in the area, the frequency of personnel movement, and surface contact conditions. Combined with the historical pollution data of the area, such as the frequency of pollution incidents and cleaning records, we conduct risk assessments on different locations in the area and generate a dynamic distribution map of regional pollution risks. We use a heat map to display the pollution risk level of each area, which serves as the pollution risk distribution map of the target area.

[0022] The area with the highest contamination risk in the risk distribution map is selected as the first sampling location. For example, if the entrance to a processing workshop is frequently visited by people and is displayed as a high-risk area, this location is preferred for sampling to ensure sampling effectiveness. The specific first sampling area is determined by combining the located first sampling location and the matched first sampling area. The sampling area is the actual coverage area of the sampling operation, ensuring that high-contamination risk areas are covered.

[0023] The three-dimensional coordinates of the flashlight are obtained, and a spatial coordinate system is established by mapping the first sampling position and the distance measurement result. In the spatial coordinate system, a first fluorescence projection angle is generated by fitting based on the three-dimensional coordinates of the flashlight.

[0024] The three-dimensional coordinates of a flashlight can be determined by built-in positioning sensors, such as accelerometers and gyroscopes, or external positioning devices. These sensors provide real-time feedback on the flashlight's position and attitude in three-dimensional space, generating the flashlight's three-dimensional coordinates. The three-dimensional coordinates include the flashlight's X, Y, and Z coordinates in space, as well as the flashlight's attitude relative to the world coordinate system, i.e., its angle.

[0025] In the spatial coordinate system, the position of the flashlight is set as the reference point, and the three-dimensional coordinates of the first sampling area are derived according to the first sampling position and the distance measurement result, and are mapped to the spatial coordinate system for subsequent projection angle calculation.

[0026] Based on the three-dimensional coordinates of the flashlight and the three-dimensional coordinates of the first sampling area, the flashlight's light projection angle is calculated. This process can be achieved through geometric relationships in three-dimensional space. Specifically, the three-dimensional coordinates of the flashlight (x1, y1, z1) and the three-dimensional coordinates of the first sampling area (x2, y2, z2) are determined. By calculating the direction vector of the line connecting the flashlight and the first sampling area, the first fluorescent projection angle is obtained. This angle will serve as the input to control the flashlight's light projection module to ensure that the light accurately illuminates the first sampling area.

[0027] Automatic focusing is performed according to the distance measurement result to generate a first fluorescence projection focal length, and a first fluorescence projection area is generated according to the first sampling area mapping.

[0028] The optical system of the fluorescent projection device, such as a zoom lens or liquid crystal lens, automatically adjusts its focal length based on the measured distance. If the measured distance is short, the light beam is focused on a smaller area; if the distance is long, the light beam is diffused to cover a larger surface. A first fluorescent projection focal length is generated based on the focusing result. Based on the first sampling area, the corresponding first fluorescent projection area is generated by adjusting the projected light beam. The optical system can be used to adjust the divergence angle of the light beam, or the optical lens can be used to change the size of the light beam, so that the projected light spot covers the same area as the sampling area.

[0029] According to the first fluorescence projection angle, the first fluorescence projection focal length, and the first fluorescence projection area, the fluorescence projection module of the flashlight is adjusted to perform fluorescence projection on the first sampling area.

[0030] When the angle, area, and focal length are set, the fluorescent projection module is started for actual projection. The flashlight emits an adjusted light beam and projects it onto the first sampling area for fluorescent marking. Through the previous precise adjustment, the projected fluorescent beam can completely cover the sampling area for actual fluorescent irradiation, ensuring accurate beam coverage and fluorescent response of the target sampling area.

[0031] Furthermore, if Figure 2 As shown, the method of measuring the distance from the flashlight to the target area by the infrared ranging module of the flashlight and generating a distance measurement result includes:

[0032] An image acquisition device is arranged, wherein the image acquisition device is arranged at a position adjacent to the flashlight; an image of the target area is acquired by the image acquisition device to obtain an image acquisition result; an infrared test signal is emitted by the infrared transmitter of the infrared ranging module to obtain a feedback infrared test signal; data is analyzed based on the image acquisition result and the feedback infrared test signal, and infrared emission control parameters are generated based on the data analysis result; the infrared transmitter is controlled based on the infrared emission control parameters to emit an infrared signal, receive a reflected infrared signal, perform phase difference calculation, and obtain the distance measurement result.

[0033] An image acquisition device is deployed. The image acquisition device can be a high-resolution camera or infrared imaging device that can capture clear images under different lighting conditions. The image acquisition device is deployed in an adjacent position to the flashlight to ensure that its viewing angle is consistent with the flashlight's infrared ranging module and light projection module. This ensures that the image acquisition and infrared ranging data can work together to avoid errors caused by misaligned viewing angles.

[0034] After the image acquisition device is deployed, it is aimed at the target area and continuous image frames are taken to record the appearance, size, shape and other details of the target area to generate image acquisition results.

[0035] The infrared transmitter is located inside the flashlight and is used to emit a beam of infrared light signal, usually a narrow-angle beam, to ensure that the emitted infrared signal can be focused on the target area. When the infrared test signal is emitted to the target area, part of the signal will be reflected from the surface of the target area, and the reflected infrared beam will return to the infrared receiver of the flashlight. The infrared receiver receives the reflected signal and records the time difference or phase difference between emission and reception. Based on the known speed of light, the preliminary distance calculation result between the flashlight and the target area can be calculated as the feedback infrared test signal.

[0036] The image acquisition results are combined with the feedback infrared test signal to perform multi-angle analysis. For example, the image information is used to accurately locate the boundaries of the target area, ensuring that the infrared emission direction is aligned with the center of the target identified by the image. The infrared reflectivity of the target area is analyzed by combining the material and surface characteristics of the object identified by the image. If the surface reflectivity of the target area is low, the transmission power may need to be increased. The depth or structural information in the image is used to correct the feedback signal of the infrared ranging to eliminate distance errors caused by irregular or tilted surface of the object. After the data analysis is completed, the infrared emission control parameters are generated, including emission power, frequency, timing, and angle, to precisely control the operation of the infrared emitter.

[0037] The working mode of the infrared transmitter is precisely adjusted according to the obtained infrared emission control parameters to ensure that the emitted infrared signal can match the specific conditions of the target area. When the emitted infrared signal encounters the target area, part of the signal is reflected back to the infrared receiver of the flashlight. The receiver captures these reflected signals through a photoelectric sensor and measures the phase difference between the emitted signal and the received reflected signal. Since the speed of light is known, the propagation time of the infrared light can be calculated according to the phase difference formula. Based on the propagation time and the speed of light, the distance from the flashlight to the target area is accurately calculated to obtain the distance measurement result.

[0038] Furthermore, the method for generating infrared emission control parameters according to the data analysis results includes:

[0039] Acquire basic transmitter information of the infrared transmitter; perform measurement distance interval classification based on the transmitter basic information to obtain a measurement distance interval classification result; perform measurement distance fuzzy evaluation based on the feedback infrared test signal to obtain a measurement distance fuzzy evaluation result; match the measurement distance interval classification result based on the measurement distance fuzzy evaluation result to generate a measurement distance interval classification matching result; obtain the infrared emission control parameter based on the measurement distance interval classification matching result.

[0040] Obtain basic information about the infrared transmitter, including transmit power, transmit frequency, transmit angle, and signal type. The power of the infrared transmitter determines the signal strength. The greater the transmit power, the farther the signal can travel. Different infrared ranging modules operate in different frequency ranges, and the timing and modulation method of signal transmission need to be set according to the operating frequency of the transmitter. The transmit angle of the infrared transmitter affects the signal coverage range.

[0041] Measuring distance interval classification means dividing the measuring distance into different intervals based on the performance of the transmitter, making it easier to select appropriate transmission parameters according to different distances. For example, for close distances (0-5 meters), lower transmission power and higher ranging accuracy can be used; for medium distances (5-20 meters), slightly higher transmission power is required, and the tolerance of the ranging algorithm is appropriately adjusted; for long distances (over 20 meters), maximum transmission power is required, and error correction is performed based on multiple measurements. Based on the basic information of the infrared transmitter, the total measurable distance range is divided into multiple intervals, each of which is matched with a specific ranging mode and transmission power to ensure that the system can accurately measure distance at different distances.

[0042] By analyzing the feedback infrared test signal, a fuzzy evaluation is performed to determine the accuracy and certainty of the measured distance. For example, if the feedback signal strength is below a set threshold, the currently measured distance is highly ambiguous. If the signal contains significant noise interference, the ambiguity of the ranging result is high, and it may be necessary to increase the number of measurements or adjust the transmission parameters. The resulting fuzzy evaluation result describes the accuracy of the current ranging data.

[0043] According to the fuzzy evaluation results, a suitable ranging interval is selected and the transmission control parameters are optimized. Specifically, if the fuzzy evaluation results show that the current measurement has high accuracy, it is matched with a lower ranging interval. For example, the high-precision measurement is matched with the 0-5 meter or 5-20 meter interval. This indicates that the current transmission parameters can accurately measure the distance range without significantly adjusting the transmission power or increasing the number of measurements. If the fuzzy evaluation results show that the measurement accuracy is medium, an intermediate ranging interval is selected, for example, the 5-20 meter interval. This means that the transmission power needs to be increased or the number of measurements needs to be increased to ensure reliable distance measurement results. If the measurement accuracy is low, a long-distance measurement interval is selected, or re-measurement is recommended. In this case, it is considered that the ranging error is large and parameters such as transmission power and angle need to be adjusted.

[0044] After the matching is completed, a hierarchical matching result of the measurement distance interval is generated, and the specific parameters of the infrared emission control are further determined based on this, including the emission power, emission frequency, emission angle, and number of measurements, etc., which are used as infrared emission control parameters to control the infrared emitter.

[0045] Furthermore, the method further comprises:

[0046] Image feature recognition is performed on the image acquisition result to obtain an image feature recognition result; ambient light impact evaluation is performed based on the image feature recognition result to obtain an ambient light impact evaluation coefficient; and light adaptive adjustment is performed on the infrared ranging fluorescent flashlight based on the ambient light impact evaluation coefficient.

[0047] Perform image feature recognition on the collected image acquisition results. For example, the geometric shape of the target area, such as rectangle, circle, polygon, etc., is analyzed through shape recognition algorithm. The shape information helps to identify the object category or distinguish different areas; texture feature extraction is performed to identify the surface characteristics of the object, such as roughness, smoothness, etc., and methods such as LBP (local binary pattern) can be used to analyze the surface texture.

[0048] The image feature recognition results are used to evaluate the impact of ambient light on infrared ranging. Specifically, the reflectivity of the target area is analyzed based on the surface texture characteristics of the image feature recognition results. For example, a smooth surface may cause the infrared signal to be reflected too strongly or too weakly, affecting ranging accuracy. The reflectivity is estimated based on the surface material information and light intensity. If the reflectivity is too high, it may be necessary to increase the transmit power or adjust the receiving angle. Based on this analysis, an ambient light impact evaluation coefficient is generated to quantify the impact of the current ambient light on infrared ranging.

[0049] Based on the ambient light impact evaluation coefficient, the infrared ranging fluorescent flashlight is adaptively adjusted to reduce the impact of ambient light on ranging and ensure the accuracy of the measurement results. For example, when the ambient light interference is large, the frequency of the infrared emission signal is adjusted to avoid conflict with the natural light frequency in the environment. For example, under strong sunlight, the infrared signal can be modulated to a higher frequency to reduce interference from sunlight.

[0050] Furthermore, the method of performing pollution risk assessment of the target area based on the regional characteristic information and the pollution risk assessment coefficient includes:

[0051] Obtain regional pollution risk influencing factors, wherein the regional pollution risk influencing factors include but are not limited to regional application scenarios, regional contact frequency, regional environmental temperature and humidity, regional surface material characteristics, and pollution events; perform an impact analysis on the regional characteristic information based on the regional pollution risk influencing factors to determine a set of regional pollution risk influencing factor parameters; perform an impact criticality assessment on the regional application scenarios, regional contact frequency, regional environmental temperature and humidity, regional surface material characteristics, and pollution events to obtain multiple impact criticality coefficients; perform a weighted calculation on the set of regional pollution risk influencing factor parameters based on the multiple impact criticality coefficients, and use the calculation result as the pollution risk assessment coefficient.

[0052] Specifically, factors affecting regional pollution risk are obtained, among which the regional application scenario is the purpose of the area, such as food processing areas, operating rooms, public areas, etc., and the risk assessment weights of different scenarios will be different; the regional contact frequency is the frequency with which the surface of the area is touched by people or objects, and a high contact frequency usually means a higher risk of pollution; the regional environmental temperature and humidity, that is, temperature and humidity, are important environmental conditions for the growth of microorganisms, and these data can usually be monitored by real-time sensors; the material properties of the regional surface will affect the difficulty of pollutant attachment, such as the cleaning and pollution behaviors of materials such as metal, plastic, and wood are different; pollution events such as equipment failure, cleaning failure, and sanitary accidents may greatly increase the pollution risk of local areas.

[0053] Standardize the pollution risk influencing factors for each region in the regional characteristic information to ensure comparability in subsequent calculations. For example, convert regional application scenarios into risk levels for different scenarios, such as 0.2 for low-risk scenarios, 0.8 for high-risk scenarios, and so on, normalizing all data to values between 0 and 1. Integrate the standardized regional pollution risk influencing factors to generate a parameter set of regional pollution risk influencing factors for the region.

[0054] Evaluate the relative impact of different pollution risk influencing factors on the overall pollution risk, determine the importance of each factor, and assign it corresponding weights. Specifically, through regression analysis of historical pollution data, determine the importance of each factor in the pollution risk. For example, by analyzing data such as the contact frequency and environmental conditions when the pollution incident occurs, determine the impact of these factors on the pollution incident, and calculate the weights based on this to generate multiple impact criticality coefficients for multiple factors.

[0055] The obtained criticality coefficient is used to weight the pollution risk influencing factor parameter set to obtain the final pollution risk assessment coefficient, which is used to judge the pollution risk level of the target area.

[0056] Furthermore, the interactive acquisition of the pollution risk distribution of the target area and positioning of the first sampling position includes:

[0057] The target area is gridded to obtain multiple grid areas; multiple regional pollution risk distributions of the multiple grid areas are obtained according to the pollution risk distribution; the multiple regional pollution risk distributions are evaluated as high-pollution areas based on preset risk distribution evaluation indicators, a first grid area is determined according to the evaluation results, and the first sampling position is located in the first grid area.

[0058] The target area is divided into fixed distances, such as 10x10cm grids. The specific division size is set according to the area of the target area. Multiple grid areas are obtained based on the division results, and each grid represents a sub-area of the area.

[0059] Based on the aforementioned historical data, real-time monitoring data, etc., the pollution risk distribution of each grid area is obtained, and the potential pollution risk of each grid is analyzed. The pollution risk distribution of each grid area can be expressed by a pollution risk assessment coefficient.

[0060] Different risk assessment indicators are set based on application scenarios and industry standards. These indicators can include pollution risk assessment coefficient thresholds, such as 0.7. If a grid area's risk coefficient exceeds this value, it is marked as a high-pollution area. Alternatively, they can be environmental or contact frequency thresholds, such as temperatures exceeding 30°C or human contact frequency exceeding 50 times per hour. These conditions significantly increase the risk of pollution and are considered high-risk areas. After the assessment, high-pollution areas are marked and a high-pollution area distribution map is generated, showing the risk level of each grid, with a particular emphasis on high-risk areas.

[0061] From the assessed high-pollution areas, select the grid with the highest pollution risk assessment coefficient as the first grid area. In the determined first grid area, accurately locate the first sampling position. For example, select the center position in the grid area as the sampling point to represent the average pollution risk of the entire grid and provide specific point information for subsequent sampling operations.

[0062] Furthermore, the method further comprises:

[0063] Send a fixed verification to the target user and receive the fixed verification result fed back by the target user; when the fixed verification result is negative, record the manual adjustment data of the target user; bind the manual adjustment data to the target area as the target area sampling feature, and perform subsequent sampling and projection of the target area according to the target area sampling feature.

[0064] A fixed verification request is sent to the target user to confirm whether the current fluorescent projection parameters, including angle, focal length, shape, etc., meet the user's expectations. The user can provide feedback through the touch screen, buttons or other interactive methods. The fixed verification results include yes and no.

[0065] If the user feedback result is negative, it means that the current projection effect does not meet expectations. In this case, the user is allowed to manually adjust the projection parameters, such as changing the focal length of the light, projection angle, etc. The manual adjustment can be completed through the device interface such as knobs, touch screens, and buttons. The user's manual adjustment data is recorded in real time and recorded as the user's personalized needs for use in subsequent operations to optimize the projection effect.

[0066] Bind the user's manual adjustment data to the current target area. The purpose of binding is to associate the user's adjustment preferences in a specific area. For example, if a user frequently adjusts the projection focus or angle in a specific area, these adjustments can be associated with the characteristics of that area, such as distance and reflectivity, to automatically optimize the settings under similar conditions. In future operations, when entering a sampling area or distance condition similar to a previous one is detected, the fluorescence projection parameters are automatically adjusted based on the sampling characteristics of the bound target area, thereby better adapting to individual needs and reducing the frequency of manual adjustments.

[0067] Furthermore, the method further comprises:

[0068] When the fixed verification result is yes, the first fluorescence projection angle, the first fluorescence projection focal length, and the first fluorescence projection area are bound to the target area as the target area sampling features.

[0069] When the fixed verification result is yes, it means that the user is satisfied with the current projection parameters. The current fluorescence projection parameters, including the first fluorescence projection angle, the first fluorescence projection focal length, and the first fluorescence projection area, are recorded, and the above fluorescence projection parameters are bound to the target area. In this way, when a similar target area is detected in the future, these parameters can be automatically loaded. This automation mechanism improves operational efficiency and ensures the continuity and personalization of the user experience.

[0070] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-specification sampling and projection method for an infrared ranging fluorescent flashlight, characterized in that: The method comprises: Determining the relative position of an infrared ranging fluorescent flashlight and a target area, measuring the distance from the flashlight to the target area using an infrared ranging module of the flashlight, and generating a distance measurement result, wherein the flashlight is pointing to the target area; Performing a regional characteristic analysis on the target area to obtain regional characteristic information, and performing a pollution risk assessment on the target area based on the regional characteristic information to obtain a pollution risk assessment coefficient; Based on the pollution risk assessment coefficient, a first sampling area is obtained by matching the preset multi-specification sampling areas; Interactively obtaining the pollution risk distribution of the target area, locating a first sampling position, and locating a first sampling area based on the first sampling area; Obtaining the three-dimensional coordinates of the flashlight, mapping and establishing a spatial coordinate system based on the first sampling position and the distance measurement result, and fitting and generating a first fluorescence projection angle in the spatial coordinate system based on the three-dimensional coordinates of the flashlight as a reference; Performing auto-focusing according to the distance measurement result to generate a first fluorescence projection focal length, and generating a first fluorescence projection area according to the first sampling area mapping; According to the first fluorescence projection angle, the first fluorescence projection focal length, and the first fluorescence projection area, the fluorescence projection module of the flashlight is adjusted to perform fluorescence projection on the first sampling area.

2. The multi-specification sampling and projection method of an infrared ranging fluorescent flashlight according to claim 1, characterized in that: The method of measuring the distance between the flashlight and the target area by using the infrared ranging module of the flashlight to generate a distance measurement result includes: Arranging an image acquisition device, wherein the image acquisition device is arranged adjacent to the flashlight; Capturing an image of the target area by the image acquisition device to obtain an image acquisition result; The infrared transmitter of the infrared ranging module transmits an infrared test signal to obtain a feedback infrared test signal; Performing data analysis based on the image acquisition results and the feedback infrared test signal, and generating infrared emission control parameters based on the data analysis results; The infrared transmitter is controlled according to the infrared transmission control parameter to transmit an infrared signal, and a reflected infrared signal is received to perform phase difference calculation to obtain the distance measurement result.

3. The multi-specification sampling and projection method of an infrared ranging fluorescent flashlight according to claim 2, characterized in that: The method of generating infrared emission control parameters according to the data analysis results includes: Obtaining basic transmitter information of the infrared transmitter; Performing measurement distance interval classification based on the transmitter basic information to obtain a measurement distance interval classification result; Performing a distance ambiguity evaluation on the basis of the feedback infrared test signal to obtain a distance ambiguity evaluation result; Matching the measurement distance interval classification results according to the measurement distance fuzzy evaluation results to generate a measurement distance interval classification matching result; The infrared emission control parameter is obtained according to the measurement distance interval hierarchical matching result.

4. The multi-specification sampling and projection method of an infrared ranging fluorescent flashlight according to claim 2, characterized in that: The method further comprises: Performing image feature recognition on the image acquisition result to obtain an image feature recognition result; Performing an ambient light impact evaluation based on the image feature recognition result to obtain an ambient light impact evaluation coefficient; The infrared ranging fluorescent flashlight is adaptively adjusted in light according to the environmental light impact evaluation coefficient.

5. The multi-specification sampling and projection method of an infrared ranging fluorescent flashlight according to claim 1, characterized in that: The method of performing pollution risk assessment of the target area based on the regional characteristic information and the pollution risk assessment coefficient includes: Obtaining regional pollution risk influencing factors, wherein the regional pollution risk influencing factors include but are not limited to regional application scenarios, regional contact frequency, regional environmental temperature and humidity, regional surface material characteristics, and pollution events; Performing an impact analysis on the regional characteristic information based on the regional pollution risk influencing factors to determine a parameter set of regional pollution risk influencing factors; Conduct an impact criticality assessment on the regional application scenarios, regional contact frequency, regional ambient temperature and humidity, regional surface material properties, and pollution events to obtain multiple impact criticality coefficients; A weighted calculation is performed on the parameter set of the regional pollution risk influencing factors based on the multiple influencing criticality coefficients, and the calculation result is used as the pollution risk assessment coefficient.

6. The multi-specification sampling and projection method of an infrared ranging fluorescent flashlight according to claim 1, characterized in that: The interactive acquisition of the pollution risk distribution of the target area and positioning of the first sampling position includes: Dividing the target area into grids to obtain a plurality of grid areas; Acquire a plurality of regional pollution risk distributions of the plurality of grid areas according to the pollution risk distribution; The pollution risk distribution of the multiple regions is evaluated for high pollution areas based on a preset risk distribution evaluation index, a first grid area is determined according to the evaluation result, and the first sampling position is located in the first grid area.

7. The multi-specification sampling and projection method of an infrared ranging fluorescent flashlight according to claim 1, characterized in that: The method further comprises: Sending a fixed verification to the target user and receiving the fixed verification result fed back by the target user; When the fixed verification result is no, recording the manual adjustment data of the target user; The manually adjusted data is bound to the target area as a sampling feature of the target area, and subsequent sampling and projection of the target area is performed according to the sampling feature of the target area.

8. The multi-specification sampling and projection method of an infrared ranging fluorescent flashlight according to claim 7, characterized in that: The method further comprises: When the fixed verification result is yes, the first fluorescence projection angle, the first fluorescence projection focal length, and the first fluorescence projection area are bound to the target area as the target area sampling features.

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