Wood detection intelligent marking system of wall detector

Abstract

The invention relates to the technical field of wood detection, and discloses a wall detector wood detection intelligent marking system, which comprises a first scanning unit, an analysis unit, a second scanning unit, a comparison unit, a calculation unit and a marking unit, a first scanning unit constructs a multi-dimensional discrimination baseline containing a dielectric distribution entropy matrix, a regional dielectric fluctuation confidence interval and an interlayer dielectric transition coefficient, the multi-dimensional discrimination baseline is adapted to a multilayer wall, an analysis unit is used for screening a suspicious region and eliminating interlayer normal interference, and a second scanning unit performs multi-angle fine scanning and calculates anisotropic speckle parameters. Capturing the structure and orientation differences of the wood structure and the repairing material; the dynamic threshold of the comparison unit is used for distinguishing material types, the dynamic safety threshold of the calculation unit is combined with the precise construction boundary of the marking unit, differential marking is carried out according to the confidence coefficient, interference of repairing materials such as cement and gypsum is effectively eliminated, precise positioning of original wood structures such as ancient house-house clear wood plugs and wood tie bars is ensured, and construction damage is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wood detection, in particular to a wall detector wood detection intelligent marking system. BACKGROUND

[0002] At present, most wall detectors use electromagnetic wave detection to identify the target by presetting the density or conductivity range of wood and common wall materials. During operation, the detector is attached to the wall surface and moved. When the sensor detects that the wood structure parameters fall within the wood threshold interval, it is determined that there is wood, and the position is prompted by light or sound. Then the staff marks it to facilitate later construction positioning.

[0003] However, for ancient houses of this type of building, the existing detection marking often has significant defects, specifically: the walls of ancient houses are mostly multi-layer composite structures, and some walls will mix cement, gypsum and other materials during later repair. The physical properties of these repair materials of different ages, especially the density, are very similar to the wooden structures (such as Ming and Qing wooden dowels and wooden tie bars) in ancient houses. During wall detector detection, due to the close density of the wooden structure and the repair material, the repair material is easily misjudged as wood, resulting in position deviation and easy damage to the original wooden structure of the ancient house. SUMMARY

[0004] In view of the defects of the prior art, the wall detector wood detection intelligent marking system is provided to solve the above problems.

[0005] The above technical purpose of the present application is realized by the following technical scheme:

[0006] The wall detector wood detection intelligent marking system comprises:

[0007] The first scanning unit is used for scanning the target area according to the first scanning mode of the wall detector, obtaining the first echo signal data of the target area, analyzing the distribution characteristics of the preprocessed first echo signal data, and obtaining the discrimination baseline. The target area is the wall surface of an ancient house.

[0008] The analysis unit is used for analyzing the first echo signal data and the discrimination baseline, screening out the area with deviated dielectric characteristics, and generating a suspicious area map.

[0009] The second scanning unit is used for scanning each suspicious point in the suspicious area map based on the second scanning mode of the wall detector, obtaining second echo signal data, and calculating the coherence difference and attenuation characteristics of the preprocessed second echo signal data to obtain anisotropic speckle parameters. The second echo signal data is multi-angle echo signal data.

[0010] A comparison unit is configured to perform cluster analysis on the anisotropic speckle parameters of the plurality of suspicious points to obtain a first dynamic threshold and a second dynamic threshold, and to compare the anisotropic speckle parameters of the suspicious points with the first dynamic threshold and the second dynamic threshold respectively to generate a baseline correction coefficient and a wood property confidence respectively;

[0011] A calculation unit is configured to calculate all the wood property confidences to generate a dynamic safety threshold of the target region;

[0012] A marking unit is configured to mark suspicious points determined to be loose fibrous structures and having a wood property confidence exceeding the dynamic safety threshold as key avoidance points, analyze spatial coordinates of all the key avoidance points to generate a construction safety boundary, and mark the construction safety boundary.

[0013] Further, distribution characteristics of the preprocessed first echo signal data are analyzed to obtain a discrimination baseline, including:

[0014] The preprocessed first echo signal data is extracted to obtain a wall material dielectric distribution entropy matrix;

[0015] Based on the wall material dielectric distribution entropy matrix, general material regions without structural abnormalities in the target region are screened, dielectric property fluctuation ranges of the general material regions are calculated, and a regional dielectric fluctuation confidence interval is obtained;

[0016] The dielectric property change law of the multi-layer structure of the target region is analyzed to obtain an interlayer dielectric transition coefficient;

[0017] The wall material dielectric distribution entropy matrix, the regional dielectric fluctuation confidence interval, and the interlayer dielectric transition coefficient are integrated to generate a multi-dimensional dielectric discrimination baseline.

[0018] Further, the first echo signal data and the discrimination baseline are analyzed to screen out regions with dielectric properties deviating from the discrimination baseline to generate a suspicious region map, including:

[0019] Based on the preprocessed first echo signal data, the number of values in which the dielectric value of each scanning point exceeds the regional dielectric fluctuation confidence interval is calculated point by point to obtain a single-point dielectric out-of-boundary amount;

[0020] According to the interlayer dielectric transition coefficient, the dielectric property change rate of adjacent scanning points is analyzed to generate a neighbor-point dielectric change residual error;

[0021] Based on the wall material dielectric distribution entropy matrix, the dielectric distribution entropy of each scanning point in a 10x10mm local region and the target region is calculated to generate a local-global entropy divergence degree;

[0022] The single-point dielectric out-of-boundary amount, the neighbor-point dielectric change residual error, and the local-global entropy divergence degree are fused to obtain a complex abnormal value;

[0023] The distribution of the composite hetero-type quantization values of all scanning points is analyzed, the average of the first 5% values is taken as a hetero-type discrimination threshold, scanning points with composite hetero-type quantization values exceeding the hetero-type discrimination threshold are screened out, and the three-dimensional space coordinates thereof are recorded to generate a suspicious point coordinate list;

[0024] The coordinates in the suspicious point coordinate list are mapped according to the actual proportion of the wall surface, and the composite hetero-type quantization value of each suspicious point is taken as a deviation degree to generate a suspicious area map.

[0025] Further, the coherence difference and attenuation characteristic of the preprocessed second echo signal data are calculated to obtain anisotropic speckle parameters, including:

[0026] The second echo signal after preprocessing is divided into signal groups according to the scanning angle, and the angle time sequence offset of each group of signals in the same propagation depth section is calculated;

[0027] The region dielectric fluctuation confidence interval and the interlayer dielectric transition coefficient are calculated to obtain a fluctuation threshold;

[0028] Based on the angle time sequence offset, the depth section signals with time sequence fluctuation exceeding the fluctuation threshold are screened out, and the depth section signals are calculated to obtain an interlayer interference stripping coefficient;

[0029] Based on the interlayer interference stripping coefficient, the second echo signal intensity is attenuated and compensated for calibration to obtain a calibrated second echo signal set;

[0030] Based on the calibrated second echo signal set, the coherence difference of different depths under the same angle and the attenuation characteristic of the same depth under different angles are analyzed to obtain a reverse coupling degree.

[0031] Further, the coherence difference and attenuation characteristic of the preprocessed second echo signal data are calculated to obtain anisotropic speckle parameters, further including:

[0032] Based on the reverse coupling degree, a dynamic analysis window is constructed, the dispersion degree of the coherence-attenuation correlation curve of each scanning point in the window is calculated to obtain a dynamic structure reference deviation value;

[0033] According to the dynamic structure reference deviation value, the direction distribution characteristics of the reverse coupling degree under different angles are analyzed to obtain a fiber orientation vector value;

[0034] The dynamic structure reference deviation value and the fiber orientation vector value are integrated to generate anisotropic speckle parameters.

[0035] Further, the anisotropic speckle parameters of a plurality of suspicious points are clustered and analyzed to obtain a first dynamic threshold and a second dynamic threshold, including:

[0036] The anisotropic speckle parameters of all suspicious points and the interlayer dielectric transition coefficients corresponding to each suspicious point are calculated to generate the interlayer penetration correction values at different wall layers;

[0037] Based on the interlayer penetration correction values, the structural uniformity correlation characteristics and fiber orientation correlation characteristics of each suspicious point are analyzed to generate a material property correlation matrix;

[0038] The material property correlation matrix is subjected to cluster analysis to obtain a cluster density gradient value;

[0039] According to the cluster density gradient value, two cluster clusters are determined, and a cluster transition adaptation coefficient of the parameters at the boundary of the two cluster clusters is calculated;

[0040] The cluster transition adaptation coefficient is calibrated according to the regional dielectric fluctuation confidence interval to obtain a calibrated adaptation coefficient;

[0041] Based on the calibrated adaptation coefficient, a first dynamic threshold and a second dynamic threshold are determined.

[0042] Further, the anisotropic speckle parameters of the suspicious point are compared with the first dynamic threshold and the second dynamic threshold, respectively, to generate a baseline correction coefficient and a wood property confidence, including:

[0043] When the comprehensive value in the anisotropic speckle parameters is ≤ the first dynamic threshold, it is determined that the internal substance of the suspicious point is a dense homogeneous body, the deviation of the dielectric properties of the suspicious point from the baseline is calculated, and a baseline correction coefficient is generated;

[0044] When the comprehensive value in the anisotropic speckle parameters is ≥ the second dynamic threshold, it is determined that the internal substance of the suspicious point is a loose fibrous structure, and the anisotropic speckle parameters, the second dynamic threshold and the second echo signal of the suspicious point are analyzed to generate a wood property confidence.

[0045] Further, all wood property confidences are calculated to generate a dynamic safety threshold of the target area, including:

[0046] The wood property confidences of all suspicious points determined to be loose fibrous structures and the interlayer dielectric transition coefficients corresponding to each suspicious point are classified and calculated according to different wall layers to obtain an interlayer confidence weighted value;

[0047] Based on the interlayer confidence weighted value, the distribution range of all interlayer confidence weighted values is determined to generate a dynamic safety threshold.

[0048] Further, the spatial coordinates of all key avoidance points are analyzed to generate a construction safety boundary, which is marked, including:

[0049] Based on the three-dimensional space coordinates of all key avoidance points and the corresponding interlayer dielectric transition coefficient, the vertical projection deviation of the coordinates in different wall layers is calculated to obtain the interlayer projection deviation value of the coordinates;

[0050] Based on the interlayer projection deviation value of the coordinates, all key avoidance point coordinates are corrected, and the boundary expansion redundancy is calculated in combination with the regional dielectric fluctuation confidence interval.

[0051] Based on the corrected key avoidance point coordinates and the boundary expansion redundancy, an initial construction safety boundary is generated.

[0052] The minimum distance from all key avoidance points in the initial construction safety boundary range to the boundary is calculated to obtain the boundary coverage check value.

[0053] Further, the spatial coordinates of all key avoidance points are analyzed to generate a construction safety boundary and are marked, and further include:

[0054] When the boundary coverage check value meets the preset requirement, a final construction safety boundary is generated.

[0055] According to the wood property confidence of the key avoidance points, all key avoidance points are differentially marked.

[0056] In summary, the present application mainly has the following beneficial effects:

[0057] The first scanning unit is used to sample and construct a wall material dielectric distribution entropy matrix with high precision, accurately capturing the subtle differences in the dielectric properties of each wall surface, and then analyzing the dielectric change law of each layer through the interlayer dielectric transition coefficient to adapt to the multi-layer wall structure. At the same time, the regional dielectric fluctuation confidence interval is established by screening the non-abnormal area, the normal characteristic range of the general material is determined, the analysis unit further integrates the single-point dielectric boundary crossing amount, the adjacent point dielectric change residual error, and the local-global entropy divergence degree to generate a composite abnormal value and screen the top 5% high abnormal points as suspicious points. This process breaks away from the limitations of traditional fixed threshold, effectively eliminates the interference of cement, gypsum and other repair materials, generates an accurate suspicious area map, and avoids misjudging repair materials as wood structures in the rough scanning stage.

[0058] The second scanning unit adopts multi-angle fixed-point scanning, divides the signal depth section according to the time delay-layer position correspondence, calculates the angle time sequence offset, determines the fluctuation threshold in combination with the regional dielectric fluctuation confidence interval and the interlayer dielectric transition coefficient, screens out abnormal signals exceeding the normal interlayer fluctuation, further compensates and calibrates the second echo signal strength through the interlayer interference stripping coefficient, eliminates the interference of different wall layers on the signal, restores the real signal characteristics of the material, further calculates the reverse coupling degree, the dynamic structure reference deviation value and extracts the fiber orientation vector value (the wood structure has clear fiber orientation, and the repair material has no directional fiber) based on the calibrated signal, and finally integrates into an anisotropic speckle parameter. The comparison unit divides the parameters into dense homogeneous bodies (repair materials) and loose fibrous structures (wood structures) through cluster analysis, quantifies the reliability of the wood structure in combination with the wood property confidence, and avoids misjudgment caused by close physical properties.

[0059] The system obtains the interlayer confidence weighted value of each layer by weighting calculation of the wood property confidence of the suspicious point of the loose fibrous structure and the interlayer dielectric transition coefficient, generates a dynamic safety threshold by subtracting 3 times the standard deviation from the mean value, and further optimizes the positioning accuracy of the marking unit: the three-dimensional coordinates of the key avoidance points are corrected through the interlayer projection deviation value, the depth positioning error caused by the interlayer dielectric difference is eliminated, the boundary expansion redundancy is calculated in combination with the regional dielectric fluctuation confidence interval, and it is ensured that the construction boundary covers all high-risk points; the verification value of the boundary coverage is verified to avoid the key avoidance points exceeding the boundary, then the key avoidance points are marked differently according to the wood property confidence, and the accurate and controllable construction boundary is ensured, which effectively avoids the damage of the original wood structure of the ancient house caused by the marking deviation, and facilitates the construction positioning in the later period. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 It is a schematic diagram of a wall detector wood detection intelligent marking system of the present application. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0062] REFERENCE Figure 1 The wall detector wood detection intelligent marking system comprises:

[0063] The first scanning unit is configured to scan a target area according to a first scanning mode of the wall detector, to obtain first echo signal data of the target area, to analyze distribution characteristics of the preprocessed first echo signal data, and to obtain a baseline for discrimination; the target area is a wall surface of an ancient house; the first scanning mode is that the wall detector moves at a constant speed to scan the target area; the electromagnetic wave has a lower transmission power and a wider beam width than those of the second scanning mode;

[0064] The analysis unit is configured to analyze the first echo signal data and the baseline for discrimination, to screen out an area with a deviated dielectric characteristic, and to generate a suspicious area map;

[0065] The second scanning unit is configured to scan each suspicious point in the suspicious area map based on a second scanning mode of the wall detector, to obtain second echo signal data; to calculate the coherence difference and the attenuation characteristics of the preprocessed second echo signal data, and to obtain an anisotropic speckle parameter; the second echo signal data is multi-angle echo signal data; the second scanning mode is that the wall detector performs point scanning on the suspicious point; the electromagnetic wave has a higher transmission power and a narrower beam width than those of the first scanning mode; and the wall detector scans the suspicious point at at least three different angles;

[0066] The comparison unit is configured to perform cluster analysis on the anisotropic speckle parameters of the plurality of suspicious points, to obtain a first dynamic threshold and a second dynamic threshold, to compare the anisotropic speckle parameter of the suspicious point with the first dynamic threshold and the second dynamic threshold respectively, and to generate a baseline correction coefficient and a wood property confidence respectively;

[0067] The calculation unit is configured to calculate all wood property confidences, and to generate a dynamic safety threshold of the target area;

[0068] The marking unit is configured to mark suspicious points determined to be loose fibrous structures and having a wood property confidence exceeding the dynamic safety threshold as key avoidance points; to analyze spatial coordinates of all key avoidance points, to generate a construction safety boundary, and to mark the construction safety boundary.

[0069] The first scanning mode wide-beam coarse scanning establishes a multi-dimensional dielectric baseline for discrimination, the interlayer dielectric transition coefficient is adapted to the multi-layer structure of the wall of the ancient house, the second scanning mode narrow-beam multi-angle scanning obtains the anisotropic speckle parameter, the cluster analysis generates the dynamic threshold, the repaired materials with similar physical characteristics to the wood structure are accurately distinguished, the key avoidance points are accurately positioned through the double screening of the wood property confidence and the dynamic safety threshold, the construction safety boundary is generated, the probability of misjudging the repaired materials as wood structures is greatly reduced, and the original wood dowel and wood tie rod structures of the ancient house are prevented from being damaged during construction.

[0070] In one case of the embodiment, the distribution characteristics of the preprocessed first echo signal data are analyzed to obtain a discrimination baseline, including:

[0071] The preprocessed first echo signal data is extracted to obtain a wall material dielectric distribution entropy matrix, specifically including: when the wall scanner moves at a constant speed to scan the wall surface of the ancient house, sampling points are set according to the horizontal (X-axis) and vertical (Y-axis) directions of the wall surface, wherein there is one sampling point per 1 cm on the X-axis and one sampling point per 1 cm on the Y-axis, and each spatial coordinate corresponds to one set of preprocessed first echo signal time domain amplitude; the time domain amplitudes of all spatial coordinates are arranged according to the spatial coordinates, i.e., a two-dimensional signal matrix is formed (the rows correspond to the X-axis, the columns correspond to the Y-axis, and the matrix elements are a sequence formed by the corresponding position time domain amplitudes); the overall amplitude range of all elements (time domain amplitudes of each spatial position) in the signal matrix is divided into 16 continuous intervals at equal intervals from the minimum value to the maximum value until all amplitudes are covered; 32 sampling points (corresponding to 32 continuous spatial coordinates of the wall surface, such as a 2x16 two-dimensional window composed of 16 points on the X-axis and 2 points on the Y-axis) are used as a sliding window, and the window is traversed with a step size of one sampling point (moving one spatial coordinate along the X-axis or Y-axis each time) in the two-dimensional space of the signal matrix, ensuring that the signals of each local area of the wall surface can be covered by the window; in each window, the signal occurrence frequency of each interval is divided by the total number of sampling points in the window to obtain the probability of each interval; then the probability of each interval is multiplied by the natural logarithm of the probability of the interval and added, and the negative value is the dielectric distribution entropy value of the center position of the window. The dielectric distribution entropy matrix of the entire region can be generated by traversing all regions.

[0072] Based on the wall material dielectric distribution entropy matrix, the general material region without structural abnormalities in the target region is screened, the dielectric characteristic fluctuation range of the general material region is calculated, and the regional dielectric fluctuation confidence interval is obtained, specifically including: calculating the variance of the 3x3 neighborhood of each dielectric distribution entropy value in the dielectric distribution entropy matrix; setting the threshold value as 30% of the global entropy value variance, and screening all neighborhood variances below the threshold value, i.e., the general material region without abnormalities; calculating the mean and standard deviation of all dielectric distribution entropy values of the general material region, adding 3 times the standard deviation to the mean to obtain the upper limit of the interval, and subtracting 3 times the standard deviation from the mean to obtain the lower limit of the interval, and the interval composed of the upper and lower limits is the regional dielectric fluctuation confidence interval;

[0073] The dielectric property variation law of the multi-layer structure of the target area is analyzed to obtain the interlayer dielectric transition coefficient, specifically including: setting signal time delay 0-1 ns corresponding to the white lime surface layer, signal time delay 1-5 ns corresponding to the mortar layer, and signal time delay 5-10 ns corresponding to the brick masonry base layer; from the dielectric distribution entropy matrix, the dielectric distribution entropy values of each wall layer (brick masonry base layer, mortar bonding layer, and white lime surface layer) are extracted in the wall depth direction (perpendicular to the wall surface) (the wall depth is obtained by multiplying the electromagnetic wave propagation speed in the medium by the echo signal time delay and then dividing by 2), to form independent entropy value sequences of each layer; in the entropy value sequence of the corresponding layer, 10 consecutive sampling points (each sampling point is spaced 1 cm) along the X axis are selected, each sampling point corresponds to a dielectric distribution entropy value, the difference value of the dielectric distribution entropy values of adjacent sampling points is calculated, then the sum of the 9 difference values (a total of 10 sampling points, the difference value of adjacent sampling points is only 9) is divided by 9 to obtain the average change amount; the average change amount is divided by 1 (representing 1 cm) to obtain the slope; the average value of the absolute values of the slopes of the two adjacent layers is divided by the average value of the dielectric distribution entropy values of the two layers to obtain the interlayer dielectric transition coefficient of the two layers;

[0074] The wall material dielectric distribution entropy matrix, the regional dielectric fluctuation confidence interval and the interlayer dielectric transition coefficient are integrated to generate a multi-dimensional dielectric discrimination baseline, specifically including: setting the wall material dielectric distribution entropy matrix weight as 0.4, the regional dielectric fluctuation confidence interval weight as 0.35, and the interlayer dielectric transition coefficient weight as 0.25; for each spatial point of the wall material dielectric distribution entropy matrix, if the dielectric distribution entropy value of the point is within the regional dielectric fluctuation confidence interval, the dielectric distribution entropy value is multiplied by 0.4 as the actual contribution value; if the dielectric distribution entropy value of the point is within the regional dielectric fluctuation confidence interval, the deviation degree of the dielectric distribution entropy value of the point is calculated: if the dielectric distribution entropy value of the point is greater than the upper limit of the interval of the regional dielectric fluctuation confidence interval, the dielectric distribution entropy value of the point is subtracted from the upper limit to obtain the absolute deviation; if the dielectric distribution entropy value of the point is less than the lower limit of the interval of the regional dielectric fluctuation confidence interval, the lower limit is subtracted from the dielectric distribution entropy value of the point to obtain the absolute deviation; then the upper limit is subtracted from the lower limit to obtain the width; the absolute deviation is divided by the width to obtain the deviation degree of the point; the deviation degree of the point is multiplied by 0.4 and then multiplied by (1-deviation degree) to obtain the actual contribution value of the point; the actual contribution value + the mean value of the regional dielectric fluctuation confidence interval multiplied by 0.35 + the interlayer dielectric transition coefficient multiplied by 0.25, i.e. the multi-dimensional dielectric value of each point can be obtained; the multi-dimensional dielectric values of all spatial points of the wall surface are arranged in order according to the positions corresponding to the X-axis rows and the Y-axis columns to form a two-dimensional matrix, which is the discrimination baseline; wherein the wall material dielectric distribution entropy matrix directly corresponds to the dielectric properties of each 1cm sampling point of the wall surface, can accurately reflect whether there is wood structure in the local area, is the core basis for abnormal positioning, and therefore the weight is relatively high, i.e. 0.4; while the regional dielectric fluctuation confidence interval represents the normal dielectric range of general materials, is the criterion for judging whether the point of the wall material dielectric distribution entropy matrix is abnormal, without which it is impossible to distinguish normal fluctuation from wood structure abnormality, and therefore the weight is relatively low, i.e. 0.35; the interlayer dielectric transition coefficient is only used for correcting the dielectric change of multi-layer walls, and the core of ancient house detection is to find wood structure rather than to identify layers, and only plays an auxiliary correction role, and therefore the weight is the lowest, i.e. 0.25.

[0075] By constructing a two-dimensional signal matrix and calculating the dielectric distribution entropy value by combining a sliding window, the subtle differences in the dielectric properties of local materials can be accurately captured, the defect that the traditional detection has weak distinguishing ability for similar density materials is overcome, the dielectric fluctuation confidence interval is determined by screening the abnormal area, the interlayer dielectric transition coefficient is introduced at the same time, the structural characteristics of the multi-layer composite wall of the ancient house are adapted, the misjudgment caused by the interlayer dielectric change is avoided, and the discrimination baseline can comprehensively reflect the local material characteristics, the overall fluctuation range and the interlayer transition law, and the distinguishing precision of the repair materials and wood structures with similar physical characteristics is improved.

[0076] By calculating the deviation degree of the point where the dielectric distribution entropy value exceeds the normal interval and dynamically correcting the contribution value, the discrimination baseline can adapt to local anomalies of the wall body, and the repair material is prevented from being misjudged due to accidental proximity to the wooden parameters.

[0077] In one case of the embodiment, the first echo signal data is analyzed and discriminated against the baseline to screen out a region with a dielectric characteristic deviating from the discrimination baseline, and a suspicious region map is generated, including:

[0078] Based on the preprocessed first echo signal data, the value of each scanning point where the dielectric value exceeds the regional dielectric fluctuation confidence interval is calculated point by point to obtain a single-point dielectric out-of-boundary quantity, specifically including: from the preprocessed first echo signal data, the dielectric distribution entropy value corresponding to each scanning point is found (i.e., the dielectric distribution entropy value corresponding to the scanning point in the wall material dielectric distribution entropy matrix); if the dielectric distribution entropy value is greater than the upper limit of the regional dielectric fluctuation confidence interval, the dielectric distribution entropy value is reduced by the upper limit of the regional dielectric fluctuation confidence interval to obtain the single-point dielectric out-of-boundary quantity; if the dielectric distribution entropy value is less than the lower limit of the regional dielectric fluctuation confidence interval, the regional dielectric fluctuation confidence interval is reduced by the dielectric distribution entropy value to obtain the single-point dielectric out-of-boundary quantity; if the lower limit of the regional dielectric fluctuation confidence interval is less than or equal to the dielectric distribution entropy value and the upper limit of the regional dielectric fluctuation confidence interval, the single-point dielectric out-of-boundary quantity is 0;

[0079] According to the interlayer dielectric transition coefficient, the dielectric characteristic change rate of adjacent scanning points is analyzed to generate a neighbor-point dielectric change residual, specifically including: for adjacent scanning points, the dielectric distribution entropy value of the latter scanning point is subtracted from the dielectric distribution entropy value of the former scanning point, and the calculation result is divided by 1 (1 representing a distance of 1 cm) to obtain an actual change rate; the absolute value of the difference between the actual change rate and the interlayer dielectric transition coefficient is calculated, and the absolute value is taken as the neighbor-point dielectric change residual;

[0080] Based on the wall material dielectric distribution entropy matrix, the dielectric distribution entropy of each scanning point in a 10*10 mm local region and a target region is calculated to generate a local-global entropy divergence degree, specifically including: the 10*10 mm local region corresponds to 1*1 sampling points (1 cm for each 1 cm on the X and Y axes, 10 mm = 1 cm), the mean value of the dielectric distribution entropy values of all sampling points in the local region is calculated, and the mean value is taken as the local average entropy; the sum of the dielectric distribution entropy values of all sampling points in the target region is divided by the total number of sampling points to obtain the global average entropy; the absolute value of the difference between the local average entropy and the global average entropy is calculated, and the absolute value is taken as the local-global entropy divergence degree;

[0081] The single-point dielectric boundary quantity, the adjacent-point dielectric change residual error and the local-global entropy divergence degree are fused to obtain a composite abnormal value, specifically including: setting the single-point dielectric boundary quantity weight as 0.4, the adjacent-point dielectric change residual error weight as 0.3 and the local-global entropy divergence degree weight as 0.3; the single-point dielectric boundary quantity, the adjacent-point dielectric change residual error and the local-global entropy divergence degree of each scanning point are multiplied by the corresponding weight and then added, so as to obtain the composite abnormal value of the scanning point; the single-point dielectric boundary quantity can directly reflect whether the entropy value of a single scanning point exceeds the confidence interval of a general material, and is the core basis for distinguishing wood structures from general materials, so the weight is relatively high, which is 0.4; the adjacent-point dielectric change residual error is used to exclude the misjudgment caused by the normal dielectric change between layers, and only plays a correction role, so the weight is relatively low, which is 0.3; the local-global entropy divergence degree verifies whether the anomaly is concentrated (wood structures are usually locally distributed) through the difference between local and global entropy, and is used as auxiliary verification, so the weight is relatively low, which is 0.3.

[0082] The distribution of the composite abnormal quantitative values of all scanning points is analyzed, the average value of the top 5% of the values is taken as an abnormality discrimination threshold, scanning points with composite abnormal quantitative values exceeding the abnormality discrimination threshold are screened out, and the three-dimensional coordinates of the scanning points are recorded to generate a suspicious point coordinate list, specifically including: the composite abnormal quantitative values of all scanning points are sorted in descending order, the composite abnormal quantitative values of the top 5% of the values are added and then divided by the number to obtain the abnormality discrimination threshold; the composite abnormal values of each scanning point are compared with the abnormality discrimination threshold one by one, and the scanning points exceeding the threshold are suspicious points; the three-dimensional coordinates (X and Y are wall coordinates, and Z is a depth coordinate, which is the depth of the wall) of all suspicious points are recorded and arranged into a suspicious point coordinate list.

[0083] The coordinates in the suspicious point coordinate list are mapped according to the actual proportions of the wall, the composite abnormal quantitative values of each suspicious point are taken as deviation degrees, and a suspicious area map is generated, specifically including: because 1 cm of the X and Y axes of the sampling points corresponds to 1 cm of the actual size of the wall, the X and Y coordinate values in the suspicious point coordinate list are directly taken as the actual X and Y coordinates of the wall to complete the mapping; the suspicious points are marked at the corresponding mapping coordinates, and the composite abnormal quantitative values of the points are taken as deviation degrees and marked beside the corresponding suspicious points to form a suspicious area map.

[0084] The single-point dielectric boundary quantity is calculated point by point to determine the scanning points with dielectric properties exceeding the normal range of general materials, and the adjacent-point dielectric change residual error is calculated in combination with the interlayer dielectric transition coefficient to effectively eliminate the interference caused by the normal dielectric change between multiple layers of the wall, so as to avoid misjudging the natural transition of the repair material between layers as an abnormal wood structure and solve the interlayer misjudgment problem caused by the close density of the two. The distinguishing accuracy of similar materials in physical properties is improved.

[0085] By accurate coordinate mapping, the marking deviation of the ancient house wood detection is reduced, and the suspicious point position is recorded by three-dimensional coordinate, which can adapt to the multi-layer composite wall of the ancient house, can determine that the suspicious point is located in the lime layer, the mortar layer or the brick base layer, can avoid mislabeling the repair materials of different layers as wood structure, can mark the composite anomaly value as the deviation degree in the atlas, can directly show the abnormal degree of the suspicious point, can reduce the invalid detection of the normal area, and finally the generated suspicious area atlas can accurately locate the high-risk point for the staff, and can avoid damaging the original wood dowel and wood tie rod structure of the ancient house due to the marking deviation.

[0086] In one case of the embodiment, the coherence difference and attenuation characteristics of the preprocessed second echo signal data are calculated to obtain anisotropic speckle parameters, including:

[0087] The preprocessed second echo signal is divided into signal groups according to the scanning angle, and the angle time offset of each group of signals in the same propagation depth section is calculated, specifically including: dividing the signal groups according to the actual scanning angle (three angles) of the second scanning mode, and each angle corresponds to one group of second echo signals; the same propagation signal depth section is set according to the time delay between layers (the signal time delay 0-1ns range corresponds to the lime surface layer, the signal time delay 1-5ns range corresponds to the mortar layer, and the signal time delay 5-10ns range corresponds to the brick masonry base layer); for each group of signals, the peak value occurrence time of the echo signal in the signal depth section is calculated; and the difference between the peak value occurrence times of the signal groups at different angles in the same depth section is calculated, which is the angle time offset of each group of signals in the same propagation depth section.

[0088] The region dielectric fluctuation confidence interval and the interlayer dielectric transition coefficient are calculated to obtain the fluctuation threshold, specifically including: subtracting the lower limit from the upper limit of the region dielectric fluctuation confidence interval to obtain the width; adding the interlayer dielectric transition coefficients of all layers and dividing by the number of transition coefficients to obtain the average transition coefficient; and multiplying the width by the average transition coefficient to obtain the fluctuation threshold.

[0089] Based on the angle time offset, the depth section signals with time fluctuation exceeding the fluctuation threshold are screened, and the depth section signals are calculated to obtain the interlayer interference stripping coefficient, specifically including: for each signal depth section, the angle time offset is compared with the fluctuation threshold one by one, if the angle time offset exceeds the fluctuation threshold, the signal depth section is retained, and if the angle time offset does not exceed the fluctuation threshold, the depth section signal is not retained; for the retained signal depth section, the average value of the signal intensity of the signal depth at different angles is calculated; then the absolute value of the difference between the signal intensity of each angle and the average value is calculated; and then the absolute value is divided by the average value to obtain the interlayer interference stripping coefficient of the angle;

[0090] The second echo signal set after calibration is obtained by performing attenuation compensation and calibration on the second echo signal strength based on the interlayer interference stripping coefficient, and specifically includes: multiplying the second echo original signal strength of each angle and depth section by (1+the interlayer interference stripping coefficient of the angle) to obtain the compensated signal strength of the angle and the depth section; and finally integrating the compensated signals of all angles and all retained signal depth sections to obtain the second echo signal set after calibration.

[0091] The reverse coupling degree is obtained by analyzing the coherence difference of different depths under the same angle and the attenuation characteristics of the same depth under different angles based on the second echo signal set after calibration, and specifically includes: dividing the calibration signals of two adjacent depth sections under the same angle into equal-length sub-sections according to 10 continuous sampling points to ensure that the sub-sections of the two adjacent depth sections correspond to the same spatial position; calculating the square sum of the amplitude difference of the corresponding positions in the signal amplitude sequence of each pair of corresponding sub-sections, and taking the reciprocal of the square sum as the local coherence value of the pair of sub-sections; adding the local coherence values of all sub-sections and dividing by the total number of sub-sections to obtain the correlation coefficient of the two depth sections; subtracting the correlation coefficient from 1 to obtain the coherence difference value of different depths under the same angle; calculating the mean value of the calibration signal strength of each angle under the same depth section, dividing the difference between the calibration signal strength of each angle and the mean value by the mean value to obtain the attenuation rate; taking the standard deviation of the attenuation rates of each angle as the attenuation characteristic value of the same depth under different angles; and multiplying the coherence difference value and the attenuation characteristic value to obtain the reverse coupling degree.

[0092] The second echo signal group is divided into three angles, the angle time sequence offset of the same depth section is calculated in combination with the corresponding relationship of time delay and layer position, the fluctuation threshold is determined based on the regional dielectric fluctuation confidence interval and the average interlayer transition coefficient, the abnormal depth section exceeding the normal interlayer fluctuation is screened out, the natural dielectric change of the multi-layer wall is avoided to be misjudged as a wooden structure signal, the signal strength is compensated by the interlayer interference stripping coefficient, the interference of different wall layers on the signal is eliminated, the real signal characteristics of the material are restored, the reverse coupling degree is calculated by the coherence difference and the attenuation characteristics, the subtle differences of the wooden structure and the repair material in signal correlation and attenuation law are accurately captured, and the accuracy of distinguishing the two is improved.

[0093] In one case of the embodiment, the coherence difference and the attenuation characteristics of the preprocessed second echo signal data are calculated to obtain anisotropic speckle parameters, and further include:

[0094] The dynamic analysis window is constructed based on the reverse coupling degree, and the dispersion degree of the coherence-attenuation correlation curve of each scanning point in the window is calculated to obtain a dynamic structure reference deviation value, specifically including: finding the range of the maximum value and the minimum value of the reverse coupling degree of all scanning points as the reverse coupling degree range; sorting the reverse coupling degrees of all scanning points, taking the 1 / 3 quantile and the 2 / 3 quantile of the sorted data as the division nodes, and dividing the reverse coupling degree range into three intervals, each interval containing an equal number of scanning points; each interval corresponds to a dynamic analysis window, and the window covers 5*5 sampling points, and in each dynamic analysis window, the coherence difference value of each scanning point is taken as the horizontal axis, and the attenuation characteristic value is taken as the vertical axis; for the coherence difference value and the attenuation characteristic value of each scanning point; calculate the mean value of all coherence difference values, which is taken as the first mean value, and calculate the mean value of the attenuation characteristic value, which is taken as the second mean value; calculate the difference between each first mean value and the first mean value, and calculate the difference between each second mean value and the second mean value; multiply the first mean value difference of each scanning point by the second mean value difference, and then add the product results of all scanning points to obtain the numerator; calculate the difference between the coherence difference value and the first mean value, and then calculate the difference between the attenuation characteristic value and the second mean value, multiply the two differences of each scanning point, and then add the product results of all scanning points to obtain the denominator; divide the numerator by the denominator to obtain the slope; subtract the product of the slope and the first mean value from the second mean value to obtain the intercept; the straight line determined by the attenuation characteristic value = slope*coherence difference value + intercept is the coherence-attenuation correlation curve; calculate the perpendicular distance of each scanning point to the coherence-attenuation correlation curve, and multiply the mean value of the perpendicular distances of all scanning points by the mean value of the reverse coupling degrees in the window to obtain the dynamic structure reference deviation value;

[0095] According to the dynamic structure reference deviation value, the direction distribution characteristics of the reverse coupling degree at different angles are analyzed to obtain a fiber orientation vector value, specifically including: dividing the reverse coupling degrees corresponding to the three scanning angles by the dynamic structure reference deviation values of the angles respectively to obtain the direction characteristic weights of the angles; taking the scanning angle as the polar angle and the direction characteristic weight as the polar radius, marking three vector points corresponding to the three angles on the polar coordinate graph according to the polar angle corresponding to the scanning angle and the polar radius corresponding to the direction characteristic weight; adding the direction characteristic weights of the three vector points to obtain a synthetic vector; the polar angle of the synthetic vector is the fiber orientation angle, and the polar radius of the synthetic vector divided by the synthetic vector is the orientation confidence, which constitutes the fiber orientation vector value (fiber orientation angle, orientation confidence);

[0096] The dynamic structure reference deviation value and the fiber orientation vector value are integrated to generate an anisotropic speckle parameter, specifically including: setting the dynamic structure reference deviation value weight as 0.5, and the orientation confidence weight in the fiber orientation vector value as 0.5; adding the dynamic structure reference deviation value and the orientation confidence after multiplying them by the corresponding weights respectively to obtain a comprehensive value of structure uniformity-orientation confidence; integrating the comprehensive value and the fiber orientation angle in the fiber orientation vector value to form a binary parameter of “comprehensive value, fiber orientation angle”, which is the anisotropic speckle parameter representing the structure uniformity and fiber orientation of the suspicious point.

[0097] A 5*5 dynamic analysis window is constructed by the reverse coupling degree, and the dynamic structure reference deviation value is calculated by the coherence-decay correlation curve dispersion degree, so as to quantize the difference between the wood loose fiber structure and the dense homogeneous structure of the repair material in signal correlation, preliminarily distinguish the two from the structure uniformity dimension, extract the fiber orientation vector value through the polar coordinate synthesis vector, lock the wood structure signal by using the fiber orientation characteristics unique to the wood structure (the repair material has no directional fiber), and further combine the orientation confidence, so as to finally integrate the structure uniformity and the orientation confidence as a binary parameter, and form the anisotropic speckle parameter which can accurately distinguish the material type from the structure+orientation two dimensions and greatly reduce the misjudgment rate.

[0098] In one case of the embodiment, the anisotropic speckle parameters of a plurality of suspicious points are subjected to cluster analysis to obtain a first dynamic threshold and a second dynamic threshold, including:

[0099] The anisotropic speckle parameters of all suspicious points and the interlayer dielectric transition coefficients corresponding to each suspicious point are calculated to generate interlayer penetration correction values in different wall layers (brick masonry base layer, mortar bonding layer, lime surface layer), specifically including: classifying the suspicious points according to the wall layers (brick masonry base layer, mortar bonding layer, lime surface layer), multiplying the comprehensive value in the anisotropic speckle parameter of each suspicious point in each class by the interlayer dielectric transition coefficient of the layer to obtain a single-point interlayer penetration correction value; adding all single-point correction values in the same layer and then dividing by the total number of suspicious points in the layer to obtain the interlayer penetration correction value of the corresponding wall layer;

[0100] Based on the interlayer penetration correction value, the structural uniformity correlation characteristics and the fiber orientation correlation characteristics of each suspicious point are analyzed to generate a material property correlation matrix, specifically including: for each suspicious point, the comprehensive value of the anisotropic speckle parameters thereof is divided by the interlayer penetration correction value of the layer where the suspicious point is located to obtain the structural uniformity correlation characteristics; the fiber orientation angle of the anisotropic speckle parameters of the suspicious point is multiplied by the interlayer penetration correction value of the layer where the suspicious point is located to obtain the fiber orientation correlation characteristics; then, the coordinates of the wall X-axis and Y-axis sampling points are sorted, each coordinate corresponds to a suspicious point, and the matrix elements are the "structural uniformity correlation characteristics and fiber orientation correlation characteristics" of the point, and the two-dimensional matrix formed is the material property correlation matrix;

[0101] The material property correlation matrix is subjected to cluster analysis to obtain a cluster density gradient value, specifically including: the difference values of the structural uniformity correlation characteristics and the fiber orientation correlation characteristics of adjacent suspicious points in the material property correlation matrix are calculated, and the average of the two difference values is calculated; for each suspicious point, the suspicious points with a difference value exceeding 2 times the average value are defined as parameter cooperativity mutation points, and the parameter cooperativity mutation points are used as initial cluster centers; taking each initial cluster center as the core, 2 sampling points are extended in the X-axis and Y-axis directions (since the sampling points are 1 cm apart, extending 2 points is 2 cm), forming a 5×5 square neighborhood; the total number of suspicious points in the neighborhood is taken as the cluster density; the difference value between the cluster density of the initial cluster center and the cluster density of the neighborhood edge point (the actual number of suspicious points in the outermost circle (5 cm×5 cm) of the neighborhood) is calculated, and the difference value is divided by the neighborhood radius (2, indicating 2 cm), which is the cluster density gradient value;

[0102] According to the cluster density gradient value, two cluster clusters are determined, and the cluster transition adaptation coefficient of the parameters at the boundary of the two cluster clusters is calculated, specifically including: the sum of the cluster density gradient values of all suspicious points is divided by the total number of cluster density gradient values to obtain a global mean; two regions where the cluster density gradient values are located in the top 20% and the gradient values of adjacent points are all higher than the global mean are screened out, and the two regions are two significant cluster clusters; the point with the maximum cluster density gradient value between the two significant cluster clusters is taken as a boundary point; the mean value of the structural uniformity correlation feature and the mean value of the fiber orientation correlation feature of the boundary point are calculated; the mean value of the structural uniformity correlation feature and the mean value of the fiber orientation correlation feature of the core region (the highest neighborhood density of 5 points) of the significant cluster cluster are calculated again; for the structural uniformity correlation feature, the mean value of the structural uniformity correlation feature of the boundary point is divided by the mean value of the structural uniformity correlation feature corresponding to the two significant cluster clusters respectively to obtain two structural uniformity correlation feature values, and the mean value of the two structural uniformity correlation feature values is calculated, which is taken as a first adaptation correlation value; for the fiber orientation correlation feature, the mean value of the fiber orientation correlation feature of the boundary point is divided by the mean value of the fiber orientation correlation feature corresponding to the two significant cluster clusters respectively to obtain two fiber orientation correlation feature values, and the mean value of the two fiber orientation correlation feature values is calculated, which is taken as a second adaptation correlation value; the mean value of the first adaptation correlation value and the second adaptation correlation value is calculated, which is the cluster transition adaptation coefficient of the parameters at the boundary of the two cluster clusters;

[0103] According to the region dielectric fluctuation confidence interval, the cluster transition adaptation coefficient is calibrated to obtain a calibrated adaptation coefficient, specifically including: for the region dielectric fluctuation confidence interval, the interval width (upper limit minus lower limit) and the interval mean value (upper limit plus lower limit divided by 2) are calculated; the cluster transition adaptation coefficient is multiplied by (the interval mean value divided by the interval width) to obtain the calibrated adaptation coefficient;

[0104] Based on the calibrated adaptation coefficient, the first dynamic threshold and the second dynamic threshold are determined, specifically including: for the two significant cluster clusters, the properties of the significant cluster cluster are: the cluster corresponding to the dense homogeneous material is the lower limit cluster, and the cluster corresponding to the loose fibrous material is the upper limit cluster; the mean value of the structural uniformity correlation feature of the core region of the two significant cluster clusters is calculated to obtain the lower limit cluster mean value and the upper limit cluster mean value respectively; the lower limit cluster mean value is multiplied by the calibrated adaptation coefficient to obtain the first dynamic threshold; the upper limit cluster mean value is multiplied by the calibrated adaptation coefficient to obtain the second dynamic threshold.

[0105] By calculating the interlayer penetration correction value by layers such as brick masonry base layer and mortar layer, the interference of different wall layers on anisotropic speckle parameters is eliminated, the accuracy of material property analysis in the same layer is ensured, the difference between the repair material and the wood structure is avoided to be covered by the dielectric difference between layers, and then the clustering analysis is used to locate the dense homogeneous (repair material and loose fibrous (wood structure) two clusters, the transition adaptive coefficient between clusters and the regional dielectric calibration threshold are combined, so that the dynamic threshold can accurately match the characteristic fluctuation of each layer of material, instead of using a unified standard. The finally determined first and second thresholds can clearly distinguish between the two types of materials, and greatly reduce the misjudgment caused by close density.

[0106] In one case of the embodiment, the anisotropic speckle parameters of the suspicious point are compared with the first dynamic threshold and the second dynamic threshold respectively, and the baseline correction coefficient and the wood property confidence are generated respectively, including:

[0107] When the comprehensive value in the anisotropic speckle parameters is less than or equal to the first dynamic threshold, it is determined that the internal substance of the suspicious point is a dense homogeneous body, the deviation degree of the dielectric property of the suspicious point from the discrimination baseline is calculated, the baseline correction coefficient is generated, and the discrimination baseline is adjusted according to the baseline correction coefficient. Specifically, when the internal substance of the suspicious point is a dense homogeneous body, the absolute value of the difference between the dielectric distribution entropy value of the suspicious point and the multi-dimensional dielectric value of the corresponding point of the discrimination baseline is divided by the multi-dimensional dielectric value of the corresponding point of the discrimination baseline to obtain the dielectric property deviation degree. Then, 1 is subtracted from the dielectric property deviation degree and multiplied by 0.6 to obtain the baseline correction coefficient. Finally, the multi-dimensional dielectric value of the corresponding position of the suspicious point in the discrimination baseline is multiplied by (1+baseline correction coefficient) to obtain the updated discrimination baseline.

[0108] When the first dynamic threshold is less than the comprehensive value in the anisotropic speckle parameters and the comprehensive value is less than the second dynamic threshold, the difference between the comprehensive value in the anisotropic speckle parameters and the first dynamic threshold and the second dynamic threshold is calculated respectively. The two difference values are compared to determine whether the internal substance of the suspicious point is a dense homogeneous body or a loose fibrous structure. Specifically, when the first dynamic threshold is less than the comprehensive value in the anisotropic speckle parameters and the comprehensive value is less than the second dynamic threshold, the comprehensive value of the anisotropic speckle parameters of the suspicious point is subtracted from the first dynamic threshold to obtain the first threshold difference value. Then, the second dynamic threshold is subtracted from the comprehensive value to obtain the second threshold difference value. Both of the two difference values are positive. For the first threshold difference value and the second threshold difference value, if the difference value of the first dynamic threshold is smaller, it is determined that the internal substance of the suspicious point is a dense homogeneous body. If the difference value of the second dynamic threshold is smaller, it is determined to be a loose fibrous structure.

[0109] When the comprehensive value in the anisotropic speckle parameter is greater than or equal to the second dynamic threshold, it is determined that the internal substance of the suspicious point is a loose fibrous structure, and the anisotropic speckle parameter, the second dynamic threshold and the second echo signal of the suspicious point are analyzed to generate a wood property confidence, specifically including: when the comprehensive value in the anisotropic speckle parameter is greater than or equal to the second dynamic threshold, the comprehensive value of the anisotropic speckle parameter of the suspicious point is subtracted from the second dynamic threshold and then divided by the second dynamic threshold to obtain a basic confidence proportion; for the interlayer interference stripping coefficient corresponding to the suspicious point, the mean value of the interlayer interference stripping coefficient at all angles is calculated; the basic confidence proportion is multiplied by 0.6 and added to the mean value of the interlayer interference stripping coefficient multiplied by 0.4 to obtain the wood property confidence.

[0110] By taking the comprehensive value of the anisotropic speckle parameter as the core, fine differentiation is realized in three intervals: below the first dynamic threshold, it is determined to be a dense homogeneous body (such as cement, gypsum and other repair materials), and a baseline correction coefficient is generated by the dielectric property deviation degree to dynamically update the discrimination baseline, avoiding repeated misjudgment of similar materials in the future; between the two thresholds, further accurate classification is realized through difference comparison; above the second dynamic threshold, the wood property confidence is calculated in combination with the mean value of the interlayer interference stripping coefficient to further verify the wood property, and to prevent high-similarity repair materials from being misjudged as wood, breaking through the limitations of single density determination, and improving the subsequent detection accuracy through dynamic correction, reducing the deviation of the mark.

[0111] In one case of the embodiment, all wood property confidences are calculated to generate a dynamic safety threshold of the target area, including:

[0112] The wood property confidences of all suspicious points determined to be loose fibrous structures and the interlayer dielectric transition coefficients corresponding to each suspicious point are classified and calculated according to different wall layers to obtain an interlayer confidence weighted value, specifically including: the suspicious points determined to be loose fibrous structures are divided into corresponding wall layers; for each wall layer, the wood property confidence of each suspicious point in the layer is multiplied by its corresponding interlayer dielectric transition coefficient and then added to obtain a weighted confidence sum in the layer; the interlayer dielectric transition coefficients of all suspicious points in the layer are summed to obtain a weight sum in the layer; the weighted confidence sum in the layer is divided by the weight sum in the layer to obtain the interlayer confidence weighted value of the layer.

[0113] Based on the interlayer confidence weighted value, the distribution range of all interlayer confidence weighted values is determined to generate a dynamic safety threshold, specifically including: the mean value and the standard deviation of the interlayer confidence weighted values corresponding to different wall layers are calculated; the mean value is subtracted by 3 times the standard deviation to obtain the reasonable lower limit of the distribution range of all interlayer confidence weighted values, which is the dynamic safety threshold of the target area.

[0114] In one case of the embodiment, the spatial coordinates of all key avoidance points are analyzed, the construction safety boundary is generated and marked, including:

[0115] Based on the three-dimensional spatial coordinates of all key avoidance points and the corresponding interlayer dielectric transition coefficient, the vertical projection deviation of the coordinates in different wall layers is calculated to obtain the interlayer coordinate projection deviation value, specifically including: according to different wall layers, the key avoidance points are divided into corresponding wall layers, and each layer of key avoidance points includes three-dimensional coordinates (X and Y are wall coordinates, and Z is depth coordinate) and corresponding interlayer dielectric transition coefficient; the mean value of the depth coordinates of all key avoidance points in each layer is calculated; then for each key avoidance point in each layer, the absolute value of the difference between the depth coordinate and the mean value is calculated; the difference absolute value is multiplied by the dielectric transition coefficient of the layer to obtain the corrected single-point deviation; the sum of the corrected single-point deviations in each layer is divided by the total number of key avoidance points in the layer to obtain the interlayer coordinate projection deviation value of the layer;

[0116] Based on the interlayer coordinate projection deviation value, all key avoidance point coordinates are corrected, and the boundary expansion redundancy is calculated in combination with the regional dielectric fluctuation confidence interval, specifically including: for each key avoidance point, the interlayer coordinate projection deviation value of the wall layer where the key avoidance point is located is added to the depth coordinate corresponding to the key avoidance point, and the X and Y wall coordinates remain unchanged to obtain the corrected three-dimensional coordinates; then the width (upper limit minus lower limit) of the regional dielectric fluctuation confidence interval is multiplied by 0.5 to obtain the boundary expansion redundancy;

[0117] Based on the corrected key avoidance point coordinates and the boundary expansion redundancy, the initial construction safety boundary is generated, specifically including: for the three-dimensional coordinates of the corrected key avoidance points in each layer, the minimum and maximum values of the X coordinates, the minimum and maximum values of the Y coordinates, and the minimum and maximum values of the Z coordinates in each layer are found; then the boundary expansion redundancy is added and subtracted from each coordinate extreme value (for example, the minimum value of the X coordinate is subtracted from the boundary expansion redundancy, and the maximum value of the X coordinate is added to the boundary expansion redundancy; the operations on the Y coordinate and the Z coordinate are the same as those on the X coordinate), and the expanded X, Y and Z ranges of each layer form the initial boundary of the layer. The integration of all layer boundaries is the overall initial construction safety boundary;

[0118] The minimum distance of all key avoidance points in the initial construction safety boundary range to the boundary is calculated to obtain the boundary coverage verification value, specifically including: for each key avoidance point in the initial construction safety boundary, the distances of the key avoidance point to the boundary in X, Y and Z directions are calculated respectively; the minimum value of the three direction distances of each key avoidance point is taken as the minimum distance of the key avoidance point to the boundary; the minimum distance of all key avoidance points is collected, and the minimum value among them is taken as the boundary coverage verification value.

[0119] In one case of the embodiment, the spatial coordinates of all key avoidance points are analyzed, the construction safety boundary is generated and marked, and the following is further included:

[0120] When the boundary coverage check value meets the preset requirement, a final construction safety boundary is generated, specifically including: the preset requirement is that the boundary coverage check value is greater than or equal to 0; when the boundary coverage check value is greater than or equal to 0, it indicates that all key avoidance points are within the initial construction safety boundary, and no key avoidance point exceeds the boundary; the overall initial construction safety boundary is directly taken as the final construction safety boundary; wherein, when the boundary coverage check value is less than 0, it indicates that there is a key avoidance point exceeding the initial construction safety boundary; all key avoidance points with a negative distance to the boundary are found, and the direction and over-limit value of the key avoidance points exceeding the initial construction safety boundary are recorded; the extreme value of the initial boundary corresponding to the over-limit direction is extended to the over-limit side, and the extension amount is the maximum over-limit value in the direction; the minimum distance of all key avoidance points to the boundary is recalculated according to the adjusted boundary, and a new boundary coverage check value is obtained; if the boundary coverage check value is still less than 0, all steps after the boundary coverage check value is less than 0 are repeated until the boundary coverage check value is greater than or equal to 0; wherein, the maximum number of repetitions is 3, and if the number of repetitions exceeds 3, an alarm is given, and manual intervention is required;

[0121] According to the wood property confidence of the key avoidance points, all key avoidance points are differentially marked, specifically including: calculating the maximum and minimum values of the wood property confidence of all key avoidance points, and taking the maximum and minimum values as the confidence distribution range; the confidence distribution range is equally divided into three confidence regions, which are: the high confidence interval corresponds to red, the medium confidence interval corresponds to blue, and the low confidence interval corresponds to yellow; for each key avoidance point, determine the interval to which the confidence belongs, and match the corresponding color; on the wall coordinate mapping diagram of the final construction safety boundary, highlight mark each key avoidance point according to the matched color, and complete the differential marking.

[0122] By classifying the loose fibrous suspicious points according to the wall layers, the interlayer confidence weighted value is calculated by taking the interlayer dielectric transition coefficient as the weight, the confidence contribution of the wood structure point is strengthened due to the difference between the transition coefficient of the repair material and the layer where the wood structure is located, the interference of the repair material is reduced, the cement of the mortar layer is avoided to be misjudged as the brick base layer wood pull tie, and based on the interlayer dielectric transition coefficient, the coordinate interlayer projection deviation value is calculated to correct the depth coordinate of the key avoidance point, so that the wood structure positioning is accurate to the specific layer, the original wall in the deep layer is avoided to be damaged for finding the shallow wood tenon, and the misdigging risk is reduced from the two dimensions of judgment and positioning.

[0123] By combining the regional dielectric fluctuation confidence interval width to calculate the boundary expansion redundancy, the corrected key avoidance point coordinate extreme value is expanded to prevent the wood structure point from being missed due to slight dielectric property fluctuation, the minimum distance of all key avoidance points to the boundary is calculated to check the boundary coverage integrity, it is ensured that no wood structure point exceeds the boundary, the generated construction safety boundary is differentially marked, the original structure such as Ming and Qing wood tenon and wood pull tie is protected from being damaged, and the construction positioning in the later period is facilitated.

[0124] While embodiments of the application have been shown and described, it is to be understood that the application is not limited to the details of the embodiments described, since numerous changes, modifications, substitutions and variations can be made thereto without departing from the spirit and scope of the application as defined by the appended claims and their equivalents.

Claims

1. A wall detection instrument wood detection intelligent marking system, characterized in that, The method comprises the following steps: A first scanning unit is used to scan a target area according to a first scanning mode of the wall detector, to obtain first echo signal data of the target area, to analyze the distribution characteristics of the preprocessed first echo signal data, and to obtain a discrimination baseline. The target area is the wall surface of an old house. An analysis unit is used to analyze the first echo signal data and the discrimination baseline, to screen out areas with deviated dielectric characteristics, and to generate a suspicious area map. A second scanning unit is used to scan each suspicious point in the suspicious area map based on a second scanning mode of the wall detector, to obtain second echo signal data, to calculate the coherence difference and attenuation characteristics of the preprocessed second echo signal data, and to obtain anisotropic speckle parameters. The second echo signal data is multi-angle echo signal data. A comparison unit is used to perform cluster analysis on the anisotropic speckle parameters of multiple suspicious points, to obtain a first dynamic threshold and a second dynamic threshold, to compare the anisotropic speckle parameters of each suspicious point with the first dynamic threshold and the second dynamic threshold, respectively, to generate a baseline correction coefficient and a wood property confidence level, respectively. A calculation unit is used to calculate all wood property confidence levels, to generate a dynamic safety threshold of the target area. A marking unit is used to mark suspicious points determined to be loose fibrous structures and having a wood property confidence level exceeding the dynamic safety threshold as key avoidance points, to analyze the spatial coordinates of all key avoidance points, to generate a construction safety boundary, and to mark the construction safety boundary.

2. The smart marker system of claim 1, wherein, The distribution characteristics of the preprocessed first echo signal data are analyzed to obtain a discrimination baseline, which comprises the following steps: The preprocessed first echo signal data is extracted to obtain a wall material dielectric distribution entropy matrix. Based on the wall material dielectric distribution entropy matrix, general material areas without structural abnormalities in the target area are screened out, and the dielectric characteristic fluctuation range of these general material areas is calculated to obtain a regional dielectric fluctuation confidence interval. The dielectric characteristic variation law of the multi-layer structure of the target area is analyzed to obtain an interlayer dielectric transition coefficient. The wall material dielectric distribution entropy matrix, the regional dielectric fluctuation confidence interval, and the interlayer dielectric transition coefficient are integrated to generate a multi-dimensional dielectric discrimination baseline.

3. The smart marker system of claim 2, wherein the smart marker system is a fence post detection system. The first echo signal data and the discrimination baseline are analyzed to screen out areas with deviated dielectric characteristics and generate a suspicious area map, which comprises the following steps: Based on the preprocessed first echo signal data, the value of the dielectric value of each scanning point exceeding the regional dielectric fluctuation confidence interval is calculated point by point to obtain a single-point dielectric out-of-boundary value. According to the interlayer dielectric transition coefficient, the dielectric characteristic variation rate of adjacent scanning points is analyzed to generate a neighboring point dielectric variation residual error. Based on the wall material dielectric distribution entropy matrix, the dielectric distribution entropy of each scanning point in a 10*10 mm local area and the target area is calculated to generate a local-global entropy divergence. The single-point dielectric out-of-boundary value, the neighboring point dielectric variation residual error, and the local-global entropy divergence are fused to obtain a composite abnormal value. The distribution of the composite abnormality quantification values of all scanning points is analyzed, the average of the first 5% values is taken as an abnormality discrimination threshold, scanning points with composite abnormality quantification values exceeding the abnormality discrimination threshold are screened out, the three-dimensional space coordinates of the scanning points are recorded, and a suspicious point coordinate list is generated; The coordinates in the suspicious point coordinate list are mapped according to the actual proportion of the wall surface, and the composite abnormality quantification value of each suspicious point is taken as a deviation degree to generate a suspicious area atlas.

4. The smart marker system of claim 2, wherein the smart marker system is a fence post detection system. The coherence difference and attenuation characteristics of the preprocessed second echo signal data are calculated to obtain anisotropic speckle parameters, including: The preprocessed second echo signal is divided into signal groups according to the scanning angle, and the angle time sequence offset of each group of signals in the same propagation depth section is calculated; The region dielectric fluctuation confidence interval and the interlayer dielectric transition coefficient are calculated to obtain a fluctuation threshold; Based on the angle time sequence offset, the depth section signals with time sequence fluctuation exceeding the fluctuation threshold are screened out, and the depth section signals are calculated to obtain an interlayer interference stripping coefficient; The second echo signal strength is attenuated and compensated for calibration based on the interlayer interference stripping coefficient to obtain a calibrated second echo signal set; Based on the calibrated second echo signal set, the coherence difference of different depths at the same angle and the attenuation characteristics of the same depth at different angles are analyzed to obtain a reverse coupling degree.

5. The smart marker system of claim 4, wherein, The coherence difference and attenuation characteristics of the preprocessed second echo signal data are calculated to obtain anisotropic speckle parameters, also including: A dynamic analysis window is constructed based on the reverse coupling degree, and the dispersion degree of the coherence-attenuation correlation curve of each scanning point in the window is calculated to obtain a dynamic structure reference deviation value; According to the dynamic structure reference deviation value, the directional distribution characteristics of the reverse coupling degree at different angles are analyzed to obtain a fiber orientation vector value; The dynamic structure reference deviation value and the fiber orientation vector value are integrated to generate anisotropic speckle parameters.

6. The smart marker system of claim 5, wherein the smart marker system is a fence post detection smart marker system. The anisotropic speckle parameters of a plurality of suspicious points are subjected to cluster analysis to obtain a first dynamic threshold and a second dynamic threshold, including: The anisotropic speckle parameters of all suspicious points and the interlayer dielectric transition coefficients corresponding to each suspicious point are calculated to generate layer interpenetration correction values at different wall layers; Based on the interlayer penetration correction value, the structural uniformity correlation characteristics and the fiber orientation correlation characteristics of each suspicious point are analyzed to generate a material property correlation matrix; The material property correlation matrix is subjected to cluster analysis to obtain a cluster density gradient value; According to the cluster density gradient value, two clusters are determined, and a cluster transition adaptation coefficient of the parameters at the boundary of the two clusters is calculated; The cluster transition adaptation coefficient is calibrated according to the region dielectric fluctuation confidence interval to obtain a calibrated adaptation coefficient; Based on the calibrated adaptation coefficient, the first dynamic threshold and the second dynamic threshold are determined.

7. The smart marker system of claim 6, wherein the smart marker system is a fence post detection smart marker system. The anisotropic speckle parameters of the suspicious point are compared with the first dynamic threshold and the second dynamic threshold respectively to generate a baseline correction coefficient and a wood property confidence degree, including: When the comprehensive value in the anisotropic speckle parameters is ≤ the first dynamic threshold, it is determined that the internal substance of the suspicious point is a dense homogeneous body, the deviation degree of the dielectric properties of the suspicious point from the discrimination baseline is calculated, and a baseline correction coefficient is generated; When the comprehensive value in the anisotropic speckle parameter is greater than or equal to the second dynamic threshold, it is determined that the internal substance of the suspicious point is a loose fibrous structure, and the anisotropic speckle parameter, the second dynamic threshold and the second echo signal of the suspicious point are analyzed to generate a wood property confidence.

8. The smart marker system of claim 7, wherein the smart marker system is a fence post detection smart marker system. All wood property confidences are calculated to generate a dynamic safety threshold of the target area, including: The wood property confidences of all suspicious points determined to be loose fibrous structures and the interlayer dielectric transition coefficients corresponding to each suspicious point are classified and calculated according to different wall layers to obtain interlayer confidence weighted values. Based on the interlayer confidence weighted values, the distribution range of all interlayer confidence weighted values is determined to generate a dynamic safety threshold.

9. The smart marker system of claim 1, wherein, The spatial coordinates of all key avoidance points are analyzed to generate a construction safety boundary and are marked, including: Based on the three-dimensional spatial coordinates of all key avoidance points and the corresponding interlayer dielectric transition coefficients, the vertical projection deviations of the coordinates in different wall layers are calculated to obtain coordinate interlayer projection deviation values. Based on the coordinate interlayer projection deviation values, all key avoidance point coordinates are corrected, and the region dielectric fluctuation confidence interval is calculated to obtain a boundary expansion redundancy. Based on the corrected key avoidance point coordinates and the boundary expansion redundancy, an initial construction safety boundary is generated. The minimum distances from all key avoidance points in the initial construction safety boundary range to the boundary are calculated to obtain boundary coverage verification values.

10. The smart marker system of claim 9, wherein the smart marker system is a fence post detection smart marker system. The spatial coordinates of all key avoidance points are analyzed to generate a construction safety boundary and are marked, and also include: When the boundary coverage verification value meets the preset requirement, a final construction safety boundary is generated. According to the wood property confidences of the key avoidance points, all key avoidance points are differentially marked.

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