Ranging target reflectivity identification method and device, identification equipment and storage medium

Abstract

The invention discloses a distance measurement target reflectivity recognition method and device, recognition equipment and a storage medium. The method comprises the steps that firstly, a black and white high-reflectivity material distance-strength table of each distance and a single black and white grey material strength-reflectivity table are obtained through off-line calibration; 2, setting the reflectivity of the black and white target, and taking a set value as a normalization reference for calculating the reflectivity; 3, carrying out real-time distance measurement, and filtering the intensity information during distance measurement to obtain filtered distance-intensity information; 4, carrying out TOF < g1 > interval judgment and white target PEAKg < 1 > calculation; and 5, calculating the relative white target intensity through a black, white and gray intensity-reflectivity table, and then outputting the white target intensity. After calibration data are obtained, reflectivity information of a measured target can be output in real time, and then a basis for judging the material of the target is provided for an upper computer or a system. And through a filtering algorithm, the identification stability is improved, and the noise interference of the target reflectivity and the influence of abnormal data fluctuation are reduced.

Description

Technical Field

[0001] The present invention relates to the field of laser sensors, and in particular to a method, a device, an identification equipment and a storage medium for identifying the reflectivity of a ranging target. Background Art

[0002] Laser ranging technology is a significant advancement in modern photoelectric detection and is widely used in a variety of fields, including industrial automation, autonomous driving, and drones. Existing laser ranging methods primarily include triangulation and time-of-flight (TOF). Triangulation determines distance by measuring the geometric relationship between the laser emission point, the receiving point, and the target object. It offers high accuracy for close-range measurements and is suitable for short-range applications requiring high precision. However, its accuracy is affected by geometric errors when measuring long distances.

[0003] On the other hand, the time-of-flight (TOF) method calculates distance by measuring the time difference between laser emission and reception, using the known value of the speed of light for conversion. This method exhibits high accuracy in long-distance measurement and is suitable for large-scale laser ranging applications. However, close-range measurement may be affected by changes in ambient light and target reflectivity.

[0004] Measuring the reflectivity of a target is often a crucial requirement in industrial and consumer laser ranging applications. However, adding additional sensors to obtain reflectivity information is often limited by structural design, platform computing power, and other factors. To address this issue, this proposal proposes a method, apparatus, device, and storage medium for identifying the reflectivity of ranging targets. Summary of the Invention

[0005] The object of the present invention is to provide a method, apparatus, identification device and storage medium for identifying the reflectivity of a ranging target, so as to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a distance measurement target reflectivity identification method, wherein the identification method utilizes the additional intensity information output by the laser distance measurement to identify the target reflectivity, and the identification method comprises the following steps:

[0007] The first step is to obtain the distance-intensity table of black and white high-reflective materials at various distances and the intensity-reflectivity table of single black, white and gray materials through offline calibration;

[0008] The second step is to set the reflectivity of the black and white targets and use the set value as the normalized benchmark for reflectivity calculation;

[0009] The third step is to perform real-time ranging and filter the intensity information during ranging to obtain filtered distance-intensity information;

[0010] Step 4: Perform TOF g1 Interval judgment and white target PEAK g1 Calculate the TOF g1 Interval judgment includes a given distance value TOFn and a threshold array {mt1, mt 2，...， mt N}, and find the interval index k such that:

[0011] (1);

[0012] like or , then no weighting is needed, and the nearest endpoint is directly taken;

[0013] The fifth step is to calculate the relative white target intensity through the black, white and gray intensity-reflectivity table and output it. The calculation formula is expressed as:

[0014] (2);

[0015] Among them, Peak represents the measured intensity, Peak g1 Indicates the calculated intensity of the white target.

[0016] Preferably, the black and white high-reflective material distance-intensity table is obtained by measuring the reflection intensity values ​​of the black target, white target and high-reflective material at different distances, and recording the measured values ​​in a table to generate a distance-intensity calibration table.

[0017] Preferably, the intensity-reflectivity table of black, white and grey materials is obtained by measuring the intensity values ​​of targets with different reflectivity at a fixed reference distance, and recording the measured values ​​in a table to generate an intensity-reflectivity calibration table.

[0018] Preferably, the filtering of the distance-intensity information is performed by processing the original intensity data using a filtering algorithm, including a median filter or a sliding average filter, and the filtered distance-intensity information is a data pair output after filtering.

[0019] Preferably, the TOF g1 Interval judgment determines the interval in which the current distance is located based on the distance range in the calibration table.

[0020] Preferably, the PEAK g1 The calculation includes the following steps:

[0021] A1: First, determine the index range and find the index idx so that the target position TOFn meets the range condition:

[0022] (3);

[0023] Where, let mt1 be a monotonically increasing position array;

[0024] A2, then extracts the offset of the adjacent position. According to the index idx, the offset of the adjacent position is obtained, which is expressed as:

[0025] (4);

[0026] Among them, let mt1 store the offset value corresponding to the position;

[0027] A3, calculate the weight coefficient, calculate the distance from TOFn to the adjacent position as the interpolation weight, expressed as:

[0028] (5);

[0029] A4 performs weighted averaging on the offset using the weight coefficient to obtain the final result:

[0030] (6).

[0031] Preferably, the content of the calculation target output includes reflectivity results and material classification.

[0032] A ranging target reflectivity recognition device is applied to the ranging target reflectivity recognition method, wherein the recognition device is composed of a calibration module, a data processing module and a dynamic calibration adaptation module;

[0033] The calibration module includes a calibration light source and target, a distance control platform and a data acquisition unit. The calibration module is used to perform an offline calibration process to obtain reflection intensity data of multiple materials and multiple distances;

[0034] The data processing module is used to process the filtered distance measurement data in real time, calculate the reflectivity and classify the material;

[0035] The dynamic calibration adaptation module is used to automatically update calibration data regularly.

[0036] A distance measuring target reflectivity recognition device is applied to a distance measuring target reflectivity recognition apparatus. The recognition device comprises a reflectivity threshold judgment unit, which judges the material type according to the calculated reflectivity value.

[0037] The ranging target reflectivity storage medium includes a calibration data storage and an algorithm code storage. The calibration data storage is used to store calibration data, and the algorithm code storage is used to store, configure and call algorithm codes.

[0038] Technical effects and advantages of the present invention:

[0039] After acquiring calibration data, the present invention can output the reflectivity information of the measured target in real time, providing a basis for the host computer or system to determine the target material. Furthermore, by applying a filtering algorithm, the stability of the recognition is improved, effectively reducing the influence of noise interference and abnormal data fluctuations in the target reflectivity, thereby improving the reliability and accuracy of the measurement results. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is an operational flow chart of the identification method of the present invention.

[0041] Figure 2 Schematic diagram of the distance-intensity curve of the present invention.

[0042] Figure 3 This is a schematic diagram of the intensity-distance curve fitting of the present invention.

[0043] Figure 4 Schematic diagram of laser ranging of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] In embodiment 1, the present invention provides Figure 1 The distance measurement target reflectivity identification method shown utilizes the additional intensity information output by the laser distance measurement to identify the target reflectivity.

[0046] It should be noted that using the intensity information from DTOF ranging, the distance and intensity information vectors are extracted and calculated to determine the target's reflectivity. Time of Flight (TOF) calculates distance based on the time it takes for a laser pulse to be emitted, reflected from the target, and then returned to the receiver. The laser rangefinder first transmits a laser pulse, which then reflects off the target. The time difference between the emission and reception of the laser pulse is then measured, and the distance (D) is calculated based on the speed of light.

[0047] The identification method includes the following steps:

[0048] The first step is to obtain the distance-intensity table of black and white high-reflective materials at various distances and the intensity-reflectivity table of single black, white and gray materials through offline calibration;

[0049] Specifically, to obtain the distance-intensity table of black and white high-reflective materials, the reflection intensity values ​​of black targets, white targets and high-reflective materials are measured at different distances, and the measured values ​​are recorded in a table to generate a distance-intensity calibration table. At different distances (100mm, 200mm, ..., 6000mm), the reflection intensity values ​​of black targets (0.3% reflectivity), white targets (90% reflectivity) and high-reflective materials are measured.

[0050] To obtain the intensity-reflectivity table of black, white and gray reflective materials, the intensity values ​​of targets with different reflectivity are measured at a fixed reference distance, and the measured values ​​are recorded in a table to generate an intensity-reflectivity calibration table. At a fixed reference distance (500mm), the intensity values ​​of targets with different reflectivity (18% and 50% gray targets) are measured.

[0051] Refer to Table 1 below;

[0052] Table 1 List of target reflectivity intensities at various distances

[0053] Material / Distance Black Target (0.3%) White target (90%) High reaction 100 0 158 787 200 14 654 2555 300 19 588 2391 500 16 460 2365 800 10 292 2242 1000 9 211 2185 1300 4 168 1377 2000 1 112 863 3000 0 73 459 4000 0 50 542 6000 0 29 342

[0054] According to the above table 1, draw the distance-intensity curve, refer to Figure 2 As shown, the green line represents high reflection, the yellow line represents white target, and the orange line represents black target.

[0055] The second step is to set the reflectivity of the black and white targets, denoted as F1 and F2 respectively, and use the set values ​​as the normalized benchmark for reflectivity calculation. The white target reflectivity benchmark value is defined as F1 = 90%, and the black target reflectivity benchmark value is defined as F2 = 0.3%.

[0056] The third step is to perform real-time ranging and filter the intensity information during ranging to obtain filtered distance-intensity information. The original intensity data is processed using a filtering algorithm (median filtering, sliding average filtering) to reduce noise interference.

[0057] Specifically, the filtering of the distance-intensity information is to use a filtering algorithm to process the original intensity data, using one of median filtering and sliding average filtering, and the filtered distance-intensity information is a data pair output after filtering.

[0058] It should be noted that the operation steps of median filtering are:

[0059] Define the filter window size and choose an odd-length sliding window (3, 5, 7). The larger the window, the stronger the filtering effect, but the more details are lost.

[0060] The sliding window traverses the data, starting from the beginning of the data sequence and sequentially covering the current data point and its adjacent points before and after the window. For example, if the window size is 5 and the current point is the i-th data point, the window range is [i-2, i+2].

[0061] Sort and take the median. Sort all the data in the window by size and take the median as the filtering result for the current point. If the data in the window is [90, 100, 95, 105, 200], after sorting it becomes [90, 95, 100, 105, 200], and the median is 100.

[0062] Boundary processing: when the window exceeds the data boundary (beginning or end), the window can be truncated or the data can be padded (mirror padding, repeated boundary values).

[0063] Example: Original data sequence: [100, 105, 90, 200, 95] (including 200 noise points);

[0064] Window size: 5;

[0065] Window data after sorting: [90, 95, 100, 105, 200];

[0066] Filtering result: The median value 100 replaces the noise point 200, and the output sequence is [100, 105, 90, 100, 95].

[0067] The operation steps of sliding average filtering are:

[0068] Define the filter window size and select the window length (3, 5, 10). The larger the window, the stronger the smoothing effect, but the real-time performance decreases.

[0069] The sliding window traverses the data, sequentially covering the current data point and several previous historical data points. When the window size is 3, the current point is the i-th data point, and the window range is [i-2, i].

[0070] Calculate the mean within the window and find the arithmetic mean of all the data within the window as the filtering result for the current point. The window data is [100, 105, 110], and the mean is 105.

[0071] Boundary processing: when the initial data is insufficient, the window can be gradually expanded or filled with default values ​​(such as 0 or the first value).

[0072] Example: Original data sequence: [100, 105, 110, 115, 120];

[0073] Window size: 3;

[0074] Filtering process: mean of points 1 to 3: (100+105+110) / 3=105;

[0075] Average of points 2 to 4: (105+110+115) / 3=110;

[0076] Average of points 3 to 5: (110+115+120) / 3=115;

[0077] Filtering result: [105,110,115] (the original data is smoothed).

[0078] Step 4: Perform TOF g1 Interval judgment and white target PEAK g1 Calculation, TOF g1 Interval judgment includes a given distance value TOFn and a threshold array {mt1, mt 2，...， mt N}, and find the interval index k such that:

[0079] (1);

[0080] like or , then no weighting is needed, just take the nearest endpoint, TOF g1 Interval judgment determines the interval where the current distance is located based on the distance range in the calibration table;

[0081] Example: If the maximum mt1 is 6000mm and the minimum mt1 is 100mm, then the TOF at 140mm is g1 The range is between 100-200.

[0082] Specifically, PEAK g1 The calculation includes the following steps:

[0083] A1: First, determine the index range and find the index idx so that the target position TOFn meets the range condition:

[0084] (2);

[0085] Where, let mt1 be a monotonically increasing position array;

[0086] A2, then extracts the offset of the adjacent position. According to the index idx, the offset of the adjacent position is obtained, which is expressed as:

[0087] (3);

[0088] Among them, let mt1 store the offset value corresponding to the position;

[0089] A3, calculate the weight coefficient, calculate the distance from TOFn to the adjacent position as the interpolation weight, expressed as:

[0090] (4);

[0091] A4 performs weighted averaging on the offset using the weight coefficient to obtain the final result:

[0092] (5).

[0093] Taking the white target (90%) as an example, the intensity value of the white target at 140mm is calculated as:

[0094] L1=140-100=40;

[0095] R1=200-140=60;

[0096] offset1=654;

[0097] offset2=158;

[0098] Get the peak intensity value of the white target at 140mm g1 =(40*654+60*158) / (40+60)=356.4.

[0099] The fifth step is to calculate the relative white target intensity through the black, white and gray intensity-reflectivity table and output it. The calculation formula is expressed as:

[0100] (6);

[0101] Among them, Peak represents the measured intensity of reflectivity, Peak g1 Indicates the calculated intensity of the white target. The output of the calculated target includes the reflectivity result and material classification. Check the intensity-reflectivity table of black, white and gray cards, as shown in Table 2 below;

[0102] Table 2 Black and white gray card intensity-reflectivity table

[0103] Material Relative Strength Reflectivity white 100% 90% Ash 60% 50% black 0% 0.3%

[0104] Perform interpolation operation. When the relative intensity is higher than 100%, the reflectivity is 90%, and when it is lower than 0, the reflectivity is 0.3%. Other intensities are checked to be within the selected threshold range. When the relative intensity is 20%, it is calculated as being between gray and black. At this time;

[0105] Offset3=50%;

[0106] Offset4=0.3%;

[0107] L2=20%-0%=20%;

[0108] R2=60%-20%=40%;

[0109] Reflectivity=(L2*Offset3+R2*Offset4) / (L2+R2)=(20%*50%+40%*0.3%) / (20%+40%)=0.1687==16.87%;

[0110] Since the calibration collects the intensity-reflectivity table of black, white and gray materials at a fixed reference distance, the calculated reflectivity is more accurate. Among them, the reflectivity exceeding 100% is considered to be a high-reflective material, and the reflectivity below 100% is considered to be an ordinary material. Therefore, the white target is an ordinary material.

[0111] refer to Figure 3 As shown, the function is used to perform strong-distance curve fitting;

[0112] First, given a set of distance and intensity data, it is expressed as:

[0113] distance x = [100, 200, 300, 500, 800, 1000, 1300, 2000, 3000, 4000, 6000];

[0114] intensity y = [158, 654, 588, 460, 292, 211, 168, 112, 73, 50, 29];

[0115] Then the relationship between intensity and distance is obtained by fitting a cubic polynomial, which is expressed as:

[0116] (7);

[0117] Among them, p=[p3,p2,p1,p0] is the polynomial coefficient calculated by the polyfit function.

[0118] It should be noted that polyfit is a function in MATLAB and NumPyP(ython) for polynomial fitting. This function can fit a set of data points to a polynomial to find trends or relationships in the data.

[0119] Then, using the polyval function, you can substitute any x value into the above polynomial to get the corresponding fitting strength value f(x).

[0120] It should be noted that polyval is a function used in MATLAB and NumPy (Python) to calculate the value of a polynomial. It is often used in conjunction with polyfit. polyfit is used to fit a polynomial and return the coefficients of the polynomial, while polyval is used to calculate the value of the polynomial for a given independent variable.

[0121] Finally, use a scatter plot and a fitted curve for visualization:

[0122] A scatter plot represents the original data points (x,y);

[0123] The fitted curve represents the polynomial f(x);

[0124] The overall visualization can be expressed as:

[0125] (8);

[0126] Where x represents the array of horizontal coordinates, containing the distance values, and y represents the array of vertical coordinates, containing the corresponding intensity values; the scatter plot plots each pair (x, y) as a point;

[0127] (9);

[0128] Where x represents the array of horizontal coordinates (distance values), f(x) represents the array of intensity values ​​calculated by the polynomial model using the polyval function, and Color='r' specifies the color of the fitted curve as red ('r' stands for red), which makes the fitted curve clearly visible in the figure.

[0129] pass Figure 3 Observe that the single function fitting error is large. Use the cubic fitting term. The origin above is the calibration data point, and the red is the fitting curve. z = 140; z1 = polyval (p, z); the white target intensity value at 140mm is 473.6, which is different from the polynomial sum of 356.4. The reason for the error is that the distance-intensity curve has an initial rise at close distance, such as Figure 3 As shown in the figure, it reaches its peak at 200mm and then slowly decays. It is difficult to restore it using a conventional single function fitting, so a piecewise function is used to restore its true model.

[0130] Example 2, reference diagram Figure 4 As shown, the ranging target reflectivity recognition device is applied to the ranging target reflectivity recognition method of embodiment 1. The recognition device is composed of a calibration module, a data processing module and a dynamic calibration adaptation module;

[0131] The calibration module includes a calibration light source and target, a distance control platform, and a data acquisition unit. The calibration module is used to perform an offline calibration process to obtain reflection intensity data for multiple materials and multiple distances.

[0132] It should be noted that the calibration light source and targets include a standard white target (90% reflectivity), a black target (0.3% reflectivity), a multi-level gray target (18%, 50%) and a target made of high-reflectivity material; the distance control platform is used to adjust the distance between the target and the laser sensor; the data acquisition unit is set to either a high-precision laser ranging sensor (TOF sensor) or a triangulation ranging device to collect distance and reflection intensity signals.

[0133] The data processing module is used to process the filtered distance measurement data in real time, calculate the reflectivity and classify the material;

[0134] It should be noted that the hardware composition of the data processing module includes a main control processor, filtering circuit / algorithm and input / output interface; the main control processor uses an embedded microcontroller (ARM Cortex-M0, M3, M4, etc.) or FPGA to run the interpolation algorithm and reflectivity calculation logic; the filtering circuit / algorithm uses a digital filtering algorithm (sliding average, median filtering); the input / output interface uses a UART / SPI / I2C interface to receive the sensor raw data and output the reflectivity results.

[0135] The dynamic calibration adaptation module is used to automatically update the calibration data regularly.

[0136] It should be noted that the dynamic calibration adaptation module compensates for sensor aging or environmental changes. Its hardware composition includes a reference target integration unit and an environmental sensor. The reference target integration unit has built-in white and black targets. The system automatically switches targets and recalibrates during periodic self-tests. The environmental sensor is a temperature and humidity sensor (such as SHT35) that corrects the impact of environmental factors on measurements.

[0137] The third embodiment is a distance measurement target reflectivity recognition device, which is applied to the distance measurement target reflectivity recognition device of the second embodiment. The recognition device includes a reflectivity threshold judgment unit, which judges the material type according to the calculated reflectivity value.

[0138] It should be noted that the implementation of the reflectivity threshold determination unit includes:

[0139] Hardware logic, comparator circuit (threshold trigger circuit), directly outputs high / low level signals.

[0140] Software logic, classification algorithm running in the microcontroller (if (Reflectivity >= 100%) → highly reflective material).

[0141] Embodiment 4, ranging target reflectivity storage medium, is applied to embodiment 1 and embodiment 2, and the storage medium includes a calibration data storage and an algorithm code storage. The calibration data storage is used to store calibration data, and the algorithm code storage is used to store, configure and call the algorithm code.

[0142] It should be noted that the calibration data storage includes: distance-intensity table (intensity values ​​of black and white targets and high-reflective materials at different distances) and intensity-reflectivity table (reflectivity-intensity mapping of different gray targets at a fixed distance); the media type is non-volatile memory (Flash, EEPROM).

[0143] The algorithm code storage content includes: interpolation calculation code (to achieve PEAKg1 interval judgment and weighted calculation), reflectivity conversion code (PEAK ref =(PEAK / PEAK g1 )*100%) and filtering algorithm code (digital filtering function); the medium type is embedded system Flash memory.

[0144] Example 5, calculation of reflectivity of P2 material;

[0145] Initialize variables: First, initialize the intermediate variable pos to 0 to determine which calibration segment the currently measured distance is in.

[0146] Determine the measurement segment: Through a loop, traverse the calibrated distance array and compare the measured distance T1 with each calibrated distance.

[0147] If T1 is less than a certain calibration distance, set pos to the current index plus one and exit the loop.

[0148] If T1 is greater than all calibration distances, pos remains 0.

[0149] Handle out-of-range situations: If pos is 0, indicating that the measured distance is greater than 6000 mm, then set pos to 12 (TOF_ATTE_COMPENSATION_COF_NUM+1).

[0150] Calculate reflectivity: If pos is 1, it means the measurement distance is less than 100mm, and directly set P2_reflectivity to the value of the corresponding intensity in the calibration array.

[0151] If pos is 12, it means the measurement distance is greater than 6000 mm, and P2_reflectivity is set to the value of the last calibrated intensity.

[0152] If pos is between 1 and 11, linear interpolation is used to calculate P2_reflectivity. Specifically, the difference between the upper and lower limit distances of the current segment and the measured distance is calculated, and then a weighted average is performed based on these differences and the corresponding intensities to obtain P2_reflectivity.

[0153] Calculate the final reflectivity: Use the formula to calculate the final reflectivity. At this time, you need to divide the result by 0.9 (because the white target reflectivity is assumed to be 90% during calibration) and multiply it by 100 to convert it to a percentage.

[0154] If the calculated result is greater than 255, it is clamped to 255 to prevent data overflow.

[0155] Cache reflectivity data;

[0156] Data Cache: Creates a loop that shifts the previously cached reflectivity data forward by one position to make room for the new reflectivity value.

[0157] Store the currently calculated P2_reflectivity into the last position of the cache.

[0158] Reflectivity value display filtering;

[0159] Filtering: Create a loop to sum the reflectivity data in the buffer and record the number of non-zero data, the minimum value, and the maximum value.

[0160] If the number of non-zero data is greater than or equal to 5, the maximum and minimum values ​​are removed and the average value of the remaining data is calculated to achieve smoothing filtering.

[0161] If there is insufficient valid data, the output result is set to 0.

[0162] Reset state:

[0163] Resets the counter, total, minimum, and maximum values ​​to their initial state in preparation for the next calculation.

[0164] Output:

[0165] Assign the filtered reflectivity value to P2_reflectivity_show as the final reflectivity value for display output.

[0166] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A ranging target reflectivity recognition method, characterized in that: The identification method utilizes the additional intensity information output by the laser ranging to identify the target reflectivity, and the identification method includes the following steps: The first step is to obtain the distance-intensity table of black and white high-reflective materials at various distances and the intensity-reflectivity table of single black, white and gray materials through offline calibration; The second step is to set the reflectivity of the black and white targets and use the set value as the normalized benchmark for reflectivity calculation; The third step is to perform real-time ranging and filter the intensity information during ranging to obtain filtered distance-intensity information; Step 4: Perform TOF g1 Interval judgment and white target PEAK g1 Calculate the TOF g1 The interval judgment includes a given distance value TOFn and an ascending threshold array {mt1, mt 2，...， mt N }, and find the interval index k such that: （1）； like or , then no weighting is needed, and the nearest endpoint is directly taken; The fifth step is to calculate the relative white target intensity through the black, white and gray intensity-reflectivity table and output it. The calculation formula is expressed as: （2）； Among them, Peak represents the measured intensity, Peak g1 Indicates the calculated intensity of the white target.

2. The distance measurement target reflectivity recognition method according to claim 1, characterized in that: The distance-intensity table of black and white high-reflective materials is obtained by measuring the reflection intensity values ​​of the black target, white target and high-reflective material at different distances, and recording the measured values ​​in a table to generate a distance-intensity calibration table.

3. The distance measurement target reflectivity recognition method according to claim 1, characterized in that: The intensity-reflectivity table of black, white and gray materials is obtained by measuring the intensity values ​​of targets with different reflectivity at a fixed reference distance, and recording the measured values ​​in a table to generate an intensity-reflectivity calibration table.

4. The distance measurement target reflectivity recognition method according to claim 1, characterized in that: The filtering of the distance-intensity information is to process the original intensity data using a filtering algorithm, including one of median filtering and sliding average filtering. The filtered distance-intensity information is a data pair output after filtering.

5. The distance measurement target reflectivity recognition method according to claim 1, characterized in that: The TOF g1 Interval judgment determines the interval in which the current distance is located based on the distance range in the calibration table.

6. The distance measurement target reflectivity recognition method according to claim 1, characterized in that: The PEAK g1 The calculation includes the following steps: A1: First, determine the index range and find the index idx so that the target position TOFn meets the range condition: （3）； Where, let mt1 be a monotonically increasing position array; A2, then extracts the offset of the adjacent position. According to the index idx, the offset of the adjacent position is obtained, which is expressed as: （4）； Among them, let mt1 store the offset value corresponding to the position; A3, calculate the weight coefficient, calculate the distance from TOFn to the adjacent position as the interpolation weight, expressed as: （5）； A4 performs weighted averaging on the offset using the weight coefficient to obtain the final result: （6）。 7. The distance measurement target reflectivity recognition method according to claim 1, characterized in that: The output of the calculation target includes reflectivity results and material classification.

8. A distance measurement target reflectivity recognition device, applied to the distance measurement target reflectivity recognition method according to any one of claims 1 to 7, characterized in that: The identification device is composed of a calibration module, a data processing module and a dynamic calibration adaptation module; The calibration module includes a calibration light source and target, a distance control platform and a data acquisition unit. The calibration module is used to perform an offline calibration process to obtain reflection intensity data of multiple materials and multiple distances; The data processing module is used to process the filtered distance measurement data in real time, calculate the reflectivity and classify the material; The dynamic calibration adaptation module is used to automatically update calibration data regularly.

9. A distance measuring target reflectivity recognition device, applied to the distance measuring target reflectivity recognition device according to claim 8, characterized in that: The recognition device includes a reflectivity threshold judgment unit, which judges the material type according to the calculated reflectivity value.

10. A storage medium for the reflectivity of a ranging target, characterized in that: The storage medium includes a calibration data memory and an algorithm code memory, wherein the calibration data memory is used for storing calibration data, and the algorithm code memory is used for storing, configuring and calling algorithm codes.

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