A complex material self-adaptive anti-interference method and system based on a wide light spot displacement measurement system

Abstract

This invention discloses an adaptive anti-interference ranging method and system for complex materials based on a wide-spot displacement measurement system, belonging to the field of laser displacement sensor measurement technology. The method includes: acquiring single-frame raw data from a CMOS array; binding distance and intensity values ​​and removing anomalies from the raw data to obtain a valid dataset; sorting the data based on distance values; calculating the difference between adjacent distance jumps and constructing an adaptive dynamic threshold; dividing the valid data into several distance groups based on the comparison results; calculating a fusion coefficient based on the intensity fluctuation parameter within each group; when a group exhibits abnormal intensity fluctuations, performing graded processing based on the proportion of data in that group; and finally, performing weighted fusion based on the fusion coefficient of each valid group and the average distance, outputting the final distance value of the current frame. This invention effectively suppresses scattering and chromatic aberration interference from complex material surfaces by using adaptive grouping in both distance and intensity dimensions and weighted fusion based on reflection characteristics, thus improving ranging accuracy and stability.

Description

Technical Field

[0001] This invention belongs to the field of laser displacement sensor measurement and signal processing technology, specifically relating to a complex material adaptive anti-interference ranging method and system based on a wide spot displacement measurement system, which is particularly suitable for high-precision and high-stability non-contact displacement measurement on the surface of complex materials such as rock slabs and metal castings. Background Technology

[0002] Laser displacement sensors, with their non-contact and high-precision characteristics, are widely used in industrial inspection, precision manufacturing, and scientific research, becoming a core tool for measuring the position, deformation, or vibration of objects. Traditional laser displacement sensors typically use a focused, small circular spot, which offers the advantage of sub-micron level high-precision measurement. However, the measurement results are overly dependent on the quality of reflected light from a single tiny area, leading to several limitations in practical applications. The limitations of the small spot become apparent when the object being measured is made of complex materials. Complex materials include (but are not limited to) materials with rough textures, typically metal castings. These castings often have visible sand holes and uneven casting textures, easily causing large-scale fluctuations in the reflected light path. Another type of material is the composite interference of microscopic textures and color differences, typically including slabs. These slabs have particles of different colors and scattering quartz sand on their surface, generating both scattering and color difference interference. The tiny spot only collects reflected signals from a very small area, making it highly susceptible to the aforementioned material characteristics, resulting in data jumps and severely limiting the application range of laser displacement sensors.

[0003] To address the aforementioned problems of traditional small-spot laser displacement sensors, the design concept of wide-spot laser displacement sensors has emerged. Instead of simply "defocusing" the laser, it reshapes the laser spot shape and energy distribution through precise optical design and signal processing technology—spreading the laser beam into linear, rectangular, or large-diameter circular spots (up to several millimeters wide) and controlling the uniform energy distribution within the spot. This overcomes the inherent limitations of small-spot sensors and improves the measurement stability on complex material surfaces.

[0004] In real-world detection scenarios, wide-spot laser displacement sensors still suffer from multiple interferences when measuring distances from targets made of complex materials. Most complex materials exhibit surface texture undulations and regional reflectivity gradient differences, leading to chromatic aberration interference, which further amplifies the discrete errors in distance and intensity data. Current industry solutions only use an arithmetic average to smooth the raw data, offering limited anti-interference capabilities and failing to effectively suppress measurement deviations caused by scattering and chromatic aberration, leaving significant room for improvement in ranging accuracy.

[0005] Currently, when processing data acquired by area array CMOS sensors, wide-spot laser displacement sensors generally adopt a "simple averaging" approach. They lack complex grouping strategies and simply calculate the single-point distance value of the detection area by directly arithmetically averaging the distance and intensity values ​​(grayscale amplitude) acquired by the area array CMOS, attempting to offset some of the interference caused by data dispersion. The specific technical solutions and limitations are as follows: The existing core solution (simple averaging approach) is as follows: For the data acquired by the area array CMOS of the wide-spot laser displacement sensor, no grouping processing or filtering of valid and interference signals is performed. The arithmetic mean of all acquired distance values ​​is directly taken to obtain the single-point distance value of the detection area; the specific calculation method is: single-point distance mean. ,in For the first i Distance value of row CMOS, N This represents the total number of rows in the area-array CMOS sensor. The core advantages of this approach are its simple logic, low computational cost, and lack of complex parameter configuration, making it the most commonly used low-cost processing method among existing manufacturers. The main disadvantages of the above technologies include: Simple averaging methods cannot filter out clutter interference caused by scattering. When there is scattering on complex materials with special microstructures, the data collected by the area array CMOS will contain multiple abnormal distance values ​​corresponding to clutter interference. Direct averaging will mix the interference data with the valid data, resulting in increased deviation in single-point distance calculation and failing to truly reflect the actual distance of the measured object.

[0006] The simple averaging scheme does not take into account the discrete characteristics of distance difference and intensity difference, and cannot offset the interference caused by material reflection differences: the material composition difference in different regions of this type of material will cause significant fluctuations in reflectivity, which will lead to large fluctuations in distance difference acquired by the area array CMOS. Direct averaging cannot distinguish effective data in different reflectivity regions, and will flatten the distance difference in different regions, further amplifying the measurement error caused by the material reflection characteristics, especially in regions with abrupt changes in reflectivity, where the error is more obvious. Summary of the Invention

[0007] This invention addresses the aforementioned problems by providing an anti-interference grouping strategy for complex materials based on a wide-spot laser displacement sensor system. Distance data is grouped using dual thresholds for both distance and intensity. A fusion coefficient is then assigned based on the reflection characteristics of each group, fusing multiple distance data points from a single frame to calculate a single, precise distance value. This approach ensures grouping accuracy, effectively filters scattering clutter, and cancels out chromatic aberration interference, making it suitable for measurement scenarios involving complex materials such as rock slabs.

[0008] This invention provides an adaptive anti-interference method for complex materials based on a wide-spot displacement measurement system, comprising the following steps: Step S1, preprocessing of multiple data in a single frame: acquiring single-frame raw data from a CMOS array, the raw data containing distance values ​​of multiple measurement points and their corresponding intensity values, binding the distance values ​​and intensity values ​​one-to-one, and removing abnormal data from the raw data to obtain a valid dataset; Step S2, adaptive grouping: sorting the valid dataset based on distance values ​​to generate an ordered distance sequence; calculating the jump difference between adjacent distance values ​​in the ordered distance sequence and constructing an adaptive segmentation threshold; based on the jump difference... The effective dataset is divided into several distance groups based on the comparison results with the adaptive dynamic threshold; Step S3, reflection characteristic fusion coefficient allocation: based on the fluctuation parameter of the intensity value within each distance group, the fusion coefficient of each group is obtained; Step S4, abnormal group processing: when the intensity fluctuation parameter of a certain distance group exceeds the preset abnormal threshold, the group is determined to be an abnormal group, and graded processing is performed according to the proportion of the data volume of the abnormal group to the total effective data of the current frame; Step S5, single frame multiple data fusion calculation: based on the fusion coefficient of each effective group and the average distance within each group, a weighted fusion calculation is performed, and the final distance value of the current frame is output.

[0009] Preferably, in step S1, based on the distance value With corresponding intensity amplitude Constructing a binary binding dataset: , The total number of sampling points per frame; the process of removing outlier data from the original data to obtain a valid dataset includes: presetting the upper and lower limits of the sensor's physical range distance [ , [Reasonable range of basic strength] , ] Single-round traversal to remove out-of-limit extreme values: Filter out those that meet the requirements. After filtering, the valid dataset was obtained: .

[0010] Preferably, step S2 includes: step S2-1, distance and strength binding sorting: using distance as the sorting criterion, the effective dataset is rearranged in ascending order to generate an ordered sequence.

[0011] Intensity values ​​are automatically and synchronously arranged according to the corresponding distance points, forming an ordered binding sequence: ; Step S2-2, Calculate the adaptive dynamic distance difference threshold: Calculate the adjacent jump difference of the ordered distance sequence: Then calculate the global distance range: Finally, an adaptive dynamic threshold is constructed: , This is the adjustment coefficient; when When this occurs, the location is determined to be the grouping boundary; Step S2-3, Automatic Grouping Determination: Using the grouping boundary as the splitting node, the ordered binding sequence is automatically divided into... M Independent groups G 1 G 2 …G m .

[0012] Preferably, in step S3, the fluctuation parameter of the intensity value is the maximum difference between all intensity values ​​within each distance group. The process of obtaining the fusion coefficient for each group includes: calculating the maximum difference between all intensity values ​​within each group. , For the first k Within the group i Line strength value; calculate fusion coefficient , λ It is a very small positive number; the fusion coefficients of all groups are normalized to obtain the reflection characteristic fusion coefficients.

[0013] Preferably, in step S4, the abnormal group is processed in a graded manner according to the proportion of the effective data volume of the abnormal group to the total effective data volume of the current frame: if the proportion is less than or equal to a preset first proportion threshold, all sampling points of the group are directly removed; if the proportion is greater than the preset first proportion threshold and less than or equal to a preset second proportion threshold, a first-level attenuation processing is applied to the fusion weight of the abnormal group; if the proportion is greater than the preset second proportion threshold, a second-level attenuation processing is applied to the fusion weight of the abnormal group, wherein the attenuation amplitude of the second-level attenuation processing is less than the attenuation amplitude of the first-level attenuation processing.

[0014] Preferably, in step S5, the calculation process of the final distance value of the current frame includes: based on the reflection characteristic fusion coefficient of each valid group. Mean distance within groups: , Grouping k Given the large amount of data, a weighted fusion algorithm is used to merge the distance data of all valid groups to calculate the final distance value. The formula is as follows: , M This represents the number of valid groups / the number of reflective material types.

[0015] Preferably, in step S1, the abnormal data removal of the original data further includes: first removing abnormal data with excessively large distance changes within each distance group, and then removing data that exceeds the sensor's physical range and / or intensity range.

[0016] Preferably, in step S2, dividing the effective dataset into several distance groups further includes: performing clustering segmentation by combining the local data density and / or distance variance in the effective dataset to divide the distance groups.

[0017] Preferably, in step S3, the fusion coefficient is calculated based on the average intensity and intensity difference within the distance group.

[0018] This invention provides an adaptive anti-interference system for complex materials based on a wide-spot displacement measurement system, comprising: a data acquisition module for acquiring single-frame raw data acquired by a CMOS array, the raw data containing distance values ​​of multiple measurement points and their corresponding intensity amplitudes, binding the distance values ​​and intensity amplitudes one-to-one, and removing abnormal data from the raw data to obtain a valid dataset; an adaptive grouping module for sorting the valid dataset based on distance values ​​to generate an ordered distance sequence; calculating the jump difference between adjacent distance values ​​in the ordered distance sequence and constructing an adaptive segmentation threshold; and based on the jump difference and the self-interference threshold... The comparison results adapt to dynamic thresholds, dividing the effective dataset into several distance groups; a weight allocation module is used for allocating reflection characteristic fusion coefficients: based on the fluctuation parameters of intensity values ​​within each distance group, the fusion coefficients of each group are obtained; an abnormal group processing module is used to determine that a group is an abnormal group when the intensity fluctuation parameter of a certain distance group exceeds a preset abnormal threshold, and to perform hierarchical processing according to the proportion of the data volume of the abnormal group to the total effective data of the current frame; a data fusion module is used to perform weighted fusion calculation based on the fusion coefficients of each effective group and the average distance within each group, and output the final distance value of the current frame.

[0019] Technical effect Compared with existing technologies, the adaptive anti-interference method and system for complex materials based on a wide-spot displacement measurement system provided by this invention can achieve the following technical effects: This invention employs a distance-intensity sorting mechanism, which binds the distance and intensity of each set of measuring points one by one, and sorts them globally in ascending order based on the distance value. The intensity data is automatically returned to its position and rearranged synchronously with the corresponding distance, ensuring that the distance and intensity are always strictly paired, providing a reliable data foundation for subsequent distance-based clustering and intensity-based stability assessment.

[0020] This invention calculates an adaptive dynamic distance difference threshold, characterizes local morphological changes by sorting adjacent distance differences, and constructs an adaptive segmentation threshold that is dynamically updated for each frame by combining the whole frame distance range and the effective number of samples; the group boundaries and the number of groups are generated spontaneously by real-time distance data, without the need for material calibration or on-site parameter adjustment.

[0021] This invention allocates fusion coefficients based on intensity amplitude characteristics. After grouping, it retains the weight allocation logic with the intensity fluctuation amplitude within the group as the core. The more stable the intensity and the better the reflection consistency, the higher the fusion coefficient, which conforms to the actual material color difference and reflection difference rules.

[0022] This invention employs an adaptive fusion mechanism based on reflection characteristics. It automatically identifies the complexity of materials based on the number of groups and assigns a fusion coefficient to each group in combination with the strength stability of each group. Groups with abnormal reflection or severe interference are suppressed by weak coefficients or directly eliminated, breaking the limitations of traditional simple averaging.

[0023] This invention employs a differentiated abnormal grouping processing logic. For groups with abnormal intensity amplitudes and severe reflection interference, it performs elimination or attenuation suppression based on the data proportion, balancing data utilization and measurement accuracy. Attached Figure Description

[0024] Figure 1 This is a flowchart of a complex material adaptive anti-interference method based on a wide-spot displacement measurement system in an embodiment of the present invention; Figure 2 This is a schematic diagram of the data distribution of the original one-frame distance value acquired by the area array CMOS in an embodiment of the present invention; Figure 3 This is a schematic diagram of the data distribution after sorting the original distance values ​​of a single frame in an embodiment of the present invention. Detailed Implementation

[0025] To make the technical means, creative features, objectives and effects of this invention easier to understand, the following embodiments are described in detail with reference to the accompanying drawings.

[0026] This embodiment provides an adaptive anti-interference method for complex materials based on a wide-spot displacement measurement system.

[0027] Figure 1 This is a flowchart of a complex material adaptive anti-interference method based on a wide-spot displacement measurement system in an embodiment of the present invention.

[0028] like Figure 1 As shown, the adaptive anti-interference method for complex materials based on a wide-spot displacement measurement system in this embodiment includes the following steps: Step S1, preprocessing multiple data in a single frame: acquire the raw data of a single frame collected by the area array CMOS. The raw data contains the distance values ​​of multiple measurement points and their corresponding intensity values. Bind the distance values ​​and intensity values ​​one by one, and remove abnormal data from the raw data to obtain a valid dataset.

[0029] In this embodiment, multiple sets of measurement point data acquired by a single-frame area array CMOS are uniformly bound, and each set of data includes a distance value. With corresponding intensity amplitude Construct a binary binding dataset: , This represents the total number of sampling points per frame.

[0030] The process of removing abnormal data is as follows: preset the upper and lower limits of the sensor's physical measurement range [ , [Reasonable range of basic strength] , Single-round traversal to remove out-of-limit extreme values: Filter out those that meet the requirements. Obtain a valid dataset: .

[0031] Figure 2 This is a schematic diagram of the data distribution of the original one-frame distance value acquired by the area array CMOS in an embodiment of the present invention.

[0032] like Figure 2 As shown, Figure 2 As the original data, this step only removes extreme anomalies caused by optical path anomalies and scattering clutter, while fully preserving the distance distribution pattern and the original intensity characteristics.

[0033] Step S2, Adaptive Grouping: The effective dataset is sorted based on distance values ​​to generate an ordered distance sequence; the jump difference between adjacent distance values ​​in the ordered distance sequence is calculated, and an adaptive segmentation threshold is constructed; based on the comparison between the jump difference and the adaptive dynamic threshold, the effective dataset is divided into several distance groups. This step automatically clusters based entirely on the clustering characteristics of distance values, with the intensity following the distance-synchronized sorting and grouping throughout the process, without disrupting the pairing relationships. The specific process is as follows: Step S2-1, Distance and Strength Binding Sort: Using distance as the sorting criterion, the valid dataset is rearranged in ascending order to generate an ordered sequence.

[0034] Intensity values ​​are automatically and synchronously aligned with corresponding distance points, forming an ordered binding sequence: ; Figure 3This is a schematic diagram of the data distribution after sorting the original distance values ​​of a single frame in an embodiment of the present invention.

[0035] like Figure 3 As shown, after sorting, homogeneous regions that are close to each other are continuously clustered, and the corresponding intensities are synchronously and orderly arranged to ensure one-to-one correspondence without misalignment.

[0036] Step S2-2, Calculate the adaptive dynamic distance difference threshold: Calculate the adjacent jump difference of the ordered distance sequence: .

[0037] Then calculate the global distance range: .

[0038] Finally, an adaptive dynamic threshold is constructed: , As an adjustment coefficient, this coefficient is determined based on actual experimental results in this embodiment and is used to control the sensitivity of group segmentation: The values ​​are too large, the grouping and splitting conditions are lenient, and it is easy to merge similar data. Smaller values ​​and stricter segmentation conditions make it easier to divide the material into smaller groups; however, this can be adjusted based on the complexity of the material being tested. When this occurs, the location is determined to be the boundary for grouping different shapes and regions.

[0039] Step S2-3, Automatic Grouping Determination: Using the grouping boundary as the splitting node, the ordered binding sequence is automatically divided into... M Independent groups G 1 G 2 …G m The distances within the same group are highly concentrated, and each group contains multiple pairs of distance-intensity data; the number of groups and interval boundaries are generated autonomously by the distance data of the current frame, realizing fully automatic adaptive grouping.

[0040] Step S3, Reflection characteristic fusion coefficient allocation: Based on the fluctuation parameter of the intensity value within each distance group, the fusion coefficient of each group is obtained.

[0041] In this embodiment, adaptive fusion weights are assigned based on the stability characteristics of the reflection intensity of each group. The fusion weights are positively correlated with the stability of the reflection intensity of the group. The core design logic is: the smaller the fluctuation of the reflection intensity within a group and the lower the interference level, the higher the assigned fusion weight. This ensures that the final fusion result prioritizes the acquisition of distance data from signal-stable areas, effectively improving the overall ranging calculation accuracy. Specifically, the fluctuation parameter of the intensity value is the maximum difference between all intensity values ​​within each distance group. The specific implementation process is as follows: Calculate the maximum difference of all intensity values ​​within each group. , For the first k Within the group i line strength value, The smaller the value, the more stable the reflection intensity of the group and the less interference; calculate the fusion coefficient. , λ It is a very small positive number, specifically a value in the order of 0.001 to 0.01. Its purpose is to prevent the denominator from being equal to 0 when the group strength difference is 0, thus avoiding calculation errors. The fusion coefficients of all groups are normalized (ensuring that the sum of the fusion coefficients of all groups is 1) to obtain the final reflection characteristic fusion coefficients used for fusion calculation. No complex calculations are required, and it is easy to implement in engineering.

[0042] Step S4, Abnormal Group Processing: When the intensity fluctuation parameter of a certain distance group exceeds the preset abnormal threshold, the group is determined to be an abnormal group, and graded processing is performed according to the proportion of the data volume of the abnormal group to the total effective data of the current frame.

[0043] In this embodiment, the signal quality of the group is judged based on the range of reflection intensity of the groups. When the range of group intensity exceeds a preset threshold (in this embodiment, the preset threshold is determined based on the dynamic range of reflection intensity under full scale of the sensor, combined with a large number of complex material calibration experiments, and 15%-30% of the maximum reflection intensity amplitude of the sensor is taken as the reference range; this threshold is used to distinguish whether the group belongs to normal stable reflection or abnormal fluctuation caused by scattering or reflection abrupt change, and can be adaptively adjusted according to the actual sensor hardware parameters), the group signal is determined to be abnormal and the interference level is serious. Abnormal groups are processed in three gradient categories: If the effective sampling volume of the group accounts for ≤15% of the total effective data, all sampling points of the group are directly removed to completely block the interference of abnormal data on the final ranging result; If the proportion of grouped data is between 15% and 50%, the group is considered to be in a transitional state between sporadic local noise interference and the main measured area. Therefore, the group data is retained, but its fusion weight is subject to attenuation suppression. Specifically, the normalized group weight is multiplied by a preset attenuation coefficient of 0.003. This preset threshold is set according to the following logic: 15% is used to distinguish sporadic local noise interference, and abnormal groups with a proportion lower than this value are directly removed; 50% is used to distinguish whether it belongs to the main measured area covered by the light spot. Abnormal groups with a proportion higher than this value should not be removed because they contain a large amount of valid information about the main subject, and only slight attenuation processing is applied.

[0044] If the proportion of grouped data is greater than 50%, only a small attenuation coefficient is used to weaken the interference of the group, and its fusion weight is appropriately reduced. While retaining the distance information of the main measured surface, the data fluctuation is weakened, and the utilization rate of the original data and the ranging accuracy are taken into account.

[0045] Step S5, Single-frame multiple data fusion calculation: Based on the fusion coefficient of each effective group and the average distance within each group, perform weighted fusion calculation and output the final distance value of the current frame.

[0046] In this embodiment, the fusion coefficient is based on the reflection characteristics of each effective group. Mean distance within groups: , Grouping k Given the large amount of data, a weighted fusion algorithm is used to merge the distance data from all valid groups to calculate a single, accurate final distance value. The formula is as follows: , M This represents the number of valid groups / the number of reflective material types.

[0047] In an alternative implementation, in step S1, the abnormal data removal of the original data can also be carried out in the following way: the abnormal value removal method of "fixed range threshold removal" is replaced with "local distance statistical screening + fixed range removal", first removing abnormal data with excessively large distance changes within the group, and then removing extreme data that exceed the range, further improving the purity of the preprocessed data, and the calculation efficiency is not affected.

[0048] In an alternative implementation, in step S2, the effective dataset is divided into several distance groups. Alternatively, the following method can be used: replace the "dynamic threshold segmentation of adjacent distance difference" with a clustering segmentation method that combines local data density and distance variance. This multi-dimensional assistance helps determine the group boundaries and further improves the accuracy of grouping complex surfaces, without requiring manual preset of fixed intervals.

[0049] In an alternative implementation, in step S3, the calculation of the fusion coefficient of each group can also be carried out in the following way: the "calculation of fusion coefficient by inverse ratio of group strength difference" is replaced by the comprehensive calculation of fusion coefficient by "group strength mean + strength difference", and the group stability is still the core of the coefficient allocation, which does not affect the fusion accuracy and group logic.

[0050] This embodiment also provides an adaptive anti-interference ranging system, including a data acquisition module, an adaptive grouping module, a weight allocation module, an abnormal grouping processing module, and a data fusion module.

[0051] The data acquisition module is used to execute the process in step S1 of this embodiment.

[0052] The adaptive grouping module is used to execute the process in step S2 of this embodiment.

[0053] The weight allocation module is used to execute the process in step S3 of this embodiment.

[0054] The exception grouping processing module is used to execute the process in step S4 of this embodiment.

[0055] The data fusion module is used to execute the process in step S5 of this embodiment.

[0056] The above description is only used to illustrate the technical solution of the present invention and is not intended to limit it. Those skilled in the art should understand that any modifications or equivalent substitutions to the technical solution of the present invention do not depart from the spirit and scope of the technical solution of the present invention and should be considered as being within the scope of protection of the claims of the present invention.

[0057] In summary, this embodiment has the following technical effects: The intensity is automatically linked and paired according to the distance throughout the process, which not only achieves data ordering and adaptive grouping, but also fully preserves the original reflection characteristics of the intensity amplitude, providing a complete and effective basis for backend color difference suppression and anomaly detection.

[0058] An innovative adaptive fusion mechanism for reflection characteristics is designed, with the fusion coefficient deeply correlated with the stability of grouped reflection intensity. The coefficient is dynamically allocated according to the reflection characteristics, while the abnormal reflection groups are eliminated and weak coefficients are suppressed. This completely solves the problem of interference data and abnormal reflection data affecting the measurement accuracy in existing technologies. The innovation is outstanding, and the fused output is a single accurate distance value.

[0059] It eliminates the need for manual pre-setting of numerous parameters and multiple rounds of calibration, boasts strong versatility, and can be directly adapted to complex materials, reducing manual operation costs.

Claims

1. A method for adaptive anti-interference ranging of complex materials based on a wide-spot displacement measurement system, characterized in that, Includes the following steps: Step S1, preprocessing multiple data in a single frame: acquire the raw data of a single frame collected by the area array CMOS, the raw data contains the distance values ​​of multiple measurement points and their corresponding intensity values, bind the distance values ​​and intensity values ​​one by one, and remove abnormal data from the raw data to obtain a valid dataset; Step S2, Adaptive Grouping: Sort the effective dataset according to the distance value to generate an ordered distance sequence; calculate the jump difference between adjacent distance values ​​in the ordered distance sequence and construct an adaptive segmentation threshold; Based on the comparison result between the jump difference and the adaptive dynamic threshold, the effective dataset is divided into several distance groups; Step S3, Reflection characteristic fusion coefficient allocation: Based on the fluctuation parameter of the intensity value within each distance group, the fusion coefficient of each group is obtained; Step S4, abnormal group processing: When the intensity fluctuation parameter of a certain distance group exceeds the preset abnormal threshold, the group is determined to be an abnormal group, and graded processing is performed according to the proportion of the data volume of the abnormal group to the total effective data of the current frame. Step S5, Single-frame multiple data fusion calculation: Based on the fusion coefficient of each effective group and the average distance within each group, perform weighted fusion calculation and output the final distance value of the current frame.

2. The adaptive anti-interference ranging method for complex materials based on a wide-spot displacement measurement system according to claim 1, characterized in that: In step S1, based on the distance value With corresponding intensity amplitude Constructing a binary binding dataset: , This represents the total number of sampling points per frame. The process of removing outliers from the original data to obtain a valid dataset includes: Preset upper and lower limits of sensor physical range distance [ , [Reasonable range of basic strength] , Single-round traversal to remove out-of-limit extreme values: Filter out those that meet the requirements. After filtering, the valid dataset was obtained: .

3. The adaptive anti-interference ranging method for complex materials based on a wide-spot displacement measurement system according to claim 1, characterized in that: Step S2 includes: Step S2-1, Distance and Strength Binding Sort: Using the distance value as the sorting criterion, the effective dataset is rearranged in ascending order to generate an ordered sequence: ； The intensity values ​​are automatically and synchronously arranged according to the corresponding distance points, forming an ordered binding sequence: ； Step S2-2, Calculate the adaptive dynamic distance difference threshold: Calculate the adjacent jump difference of the ordered distance sequence: Then calculate the global distance range: Finally, an adaptive dynamic threshold is constructed: , This is the adjustment coefficient; when When this occurs, the location is determined to be the grouping boundary; Step S2-3, Automatic Grouping Determination: Using the grouping boundary as the dividing node, the ordered binding sequence is automatically divided into... M Independent groups G 1 G 2 …G m .

4. The adaptive anti-interference ranging method for complex materials based on a wide-spot displacement measurement system according to claim 1, characterized in that: In step S3, the fluctuation parameter of the intensity value is the maximum difference among all intensity values ​​within each distance group. The process of obtaining the fusion coefficients for each group includes: Calculate the maximum difference of all intensity values ​​within each group. , For the first k Within the group i Line strength value; calculate fusion coefficient , λ It is a very small positive number; the fusion coefficients of all groups are normalized to obtain the reflection characteristic fusion coefficients.

5. The adaptive anti-interference ranging method for complex materials based on a wide-spot displacement measurement system according to claim 1, characterized in that: In step S4, the abnormal group is processed in a hierarchical manner according to the proportion of the effective data volume to the total effective data volume of the current frame: If the proportion is less than or equal to the preset first proportion threshold, all sampling points of that group will be directly removed. If the proportion is greater than the preset first proportion threshold and less than or equal to the preset second proportion threshold, the fusion weight of the abnormal group is subjected to the first level of attenuation processing. If the proportion is greater than the preset second proportion threshold, a second-level attenuation processing is applied to the fusion weight of the abnormal group, wherein the attenuation magnitude of the second-level attenuation processing is less than the attenuation magnitude of the first-level attenuation processing.

6. The adaptive anti-interference ranging method for complex materials based on a wide-spot displacement measurement system according to claim 1, characterized in that: In step S5, the calculation process for the final distance value of the current frame includes: Based on the reflection characteristic fusion coefficient of each valid group Mean distance within groups: , Grouping k Given the large amount of data, a weighted fusion algorithm is used to merge the distance data of all valid groups to calculate the final distance value. The formula is as follows: , M This represents the number of valid groups / the number of reflective material types.

7. The adaptive anti-interference ranging method for complex materials based on a wide-spot displacement measurement system according to claim 1, characterized in that: Step S1, which involves removing abnormal data from the original data, further includes: First, remove abnormal data with excessively large distance changes within each distance group, and then remove data that exceeds the sensor's physical measurement range and / or intensity range.

8. The adaptive anti-interference ranging method for complex materials based on a wide-spot displacement measurement system according to claim 1, characterized in that: Step S2, dividing the effective dataset into several distance groups, further includes: Clustering is performed by combining the local data density and / or distance variance in the effective dataset to divide the data into distance groups.

9. The adaptive anti-interference ranging method for complex materials based on a wide-spot displacement measurement system according to claim 1, characterized in that: In step S3, the fusion coefficient is: It is calculated based on the average intensity and intensity difference within the distance group.

10. An adaptive anti-interference ranging system, characterized in that, include: The data acquisition module is used to acquire single-frame raw data acquired by the area array CMOS. The raw data includes distance values ​​of multiple measurement points and their corresponding intensity amplitudes. The distance values ​​and intensity amplitudes are bound one-to-one, and abnormal data is removed from the raw data to obtain a valid dataset. An adaptive grouping module is used to sort the effective dataset based on the distance value to generate an ordered distance sequence; calculate the jump difference between adjacent distance values ​​in the ordered distance sequence and construct an adaptive segmentation threshold; and divide the effective dataset into several distance groups based on the comparison result between the jump difference and the adaptive dynamic threshold. The weight allocation module is used for allocating the reflection characteristic fusion coefficient: based on the fluctuation parameter of the intensity value within each distance group, the fusion coefficient of each group is obtained; The abnormal group processing module is used to determine that a group is an abnormal group when the intensity fluctuation parameter of a certain distance group exceeds a preset abnormal threshold, and to perform hierarchical processing according to the proportion of the data volume of the abnormal group to the total effective data of the current frame. The data fusion module is used to perform weighted fusion calculations based on the fusion coefficients of each valid group and the average distance within each group, and output the final distance value of the current frame.

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